librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 137	author stringlengths 2 42	last_modified unknowndate 2020-02-15 11:33:14 2025-04-01 00:42:32	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 405 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt unknowndate 2022-03-02 23:29:04 2025-04-01 00:42:15	card stringlengths 11 1.01M
baby-dev/3d3428bc-7199-4377-a210-a4fa1c2e90ab	baby-dev	"2025-02-15T20:53:17Z"	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:adapter:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "region:us" ]	null	"2025-02-15T20:41:07Z"	--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 3d3428bc-7199-4377-a210-a4fa1c2e90ab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 3d3428bc-7199-4377-a210-a4fa1c2e90ab This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1943 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
DMCF14/Raffles	DMCF14	"2023-08-03T13:38:21Z"	0	0	null	[ "license:cc-by-nc-sa-4.0", "region:us" ]	null	"2023-08-03T13:34:30Z"	--- license: cc-by-nc-sa-4.0 ---
KappaNeuro/randolph-caldecott-style	KappaNeuro	"2023-09-14T10:08:02Z"	1	1	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "art", "style", "illustrator", "painting", "children", "literature", "randolph caldecott", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:other", "region:us" ]	text-to-image	"2023-09-14T10:07:58Z"	--- license: other tags: - text-to-image - stable-diffusion - lora - diffusers - art - style - illustrator - painting - children - literature - randolph caldecott base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: Randolph Caldecott Style widget: - text: "Randolph Caldecott Style - a friendly alligator being served food at a catering party. The alligator is looking at his watch watch and eating food." - text: "Randolph Caldecott Style - vast view of dense woodland in the background, a squirrel in a fashionable fedora and bomber jacket, reminiscent of Chip from Chip 'n Dale Rescue Rangers, looking at the audience, Beatrix Potter hand drawn style" - text: "Randolph Caldecott Style - imagine a close-up shot of the baby in the pram. style of shirley hughes - angry child throwing toys out of pram. The baby's face is in sharp focus, capturing their expression of glee and mischief. The style should be hyper-realistic, with every detail of the baby's face and the toy captured in high resolution. The lighting should be soft and diffused, creating a warm and inviting atmosphere. The composition should be a tight shot, focusing on the baby and the toy, with a shallow depth of field to blur the background." - text: "Randolph Caldecott Style - Illustration inspired by Kate Greenaway, depicts scenes of idyllic childhood with children dressed in late 18th and early 19th-century clothing. pastel color palette and detailed botanical backgrounds. children in innocent poses in natural settings such as gardens and meadows and often include whimsical elements such as fairies and animals" - text: "Randolph Caldecott Style - Never in his life had he seen a river before, this sleck, sinuous, full bodied animal, chasing and chuckling, gripping things with a gurgle and leaving them with a laugh, to fling itself on fresh playmates that shook themselves free, and were caught and held again" - text: "Randolph Caldecott Style - Costume sketch. Bunny girl. Girl 5 years old. Pretty, curly hair. Long silk dress, lace petticoat, velvet jacket, jabot collar. Hat with ears. patent leather shoes. Dusted tones - pink and beige. Retro. Vintage. Early 20th century." - text: "Randolph Caldecott Style - a girl of 8 years, black hair, orange dress, brown shoes, eyes shining with curiosity, in school, hang out with friends, bright and courageous, brave, bright smile, in style of Randolph Caldecott book illustration" - text: "Randolph Caldecott Style - a girl of 8 years, black hair, orange dress, brown shoes, eyes shining with curiosity, close up, in forest, sitting on a log, comfortable, flowers around in style of Randolph Caldecott book illustration" - text: "Randolph Caldecott Style - dirty caucassian family laying in bed, wearing pajamas. In the same bed there are 4 chicken, 3 ducks, dog, cat and two pigs. in style of beatrix potter." - text: "Randolph Caldecott Style - Hopefully, all unfortunate children will find warm homes. drawing and painting, blend of the styles of Beatrix Potter, ANton Pieck and Pieter Breughel" --- # Randolph Caldecott Style ([CivitAI](https://civitai.com/models/154146) ![Image 0](2330267.jpeg) > Randolph Caldecott Style - a friendly alligator being served food at a catering party. The alligator is looking at his watch watch and eating food. <p>Randolph Caldecott was an English illustrator and artist who lived from 1846 to 1886. He is best known for his contributions to children's literature, particularly for his innovative and playful illustrations.</p><p>Caldecott's illustrations were characterized by their lively, energetic style and attention to detail. He often depicted scenes from everyday life, including animals, children, and humorous situations. His illustrations had a sense of movement and captured the essence of a story, making them highly engaging for young readers.</p><p>One of Caldecott's notable achievements was his development of the picture book format. He introduced the concept of integrating illustrations and text on the same page, creating a seamless narrative flow. His use of dynamic compositions and imaginative storytelling revolutionized children's book illustration.</p><p>Caldecott's illustrations were also renowned for their use of color and texture. He employed watercolors, ink, and other media to bring his characters and scenes to life, creating a sense of depth and atmosphere.</p><p>In recognition of his significant contributions to children's literature, the Caldecott Medal was established in his honor. It is awarded annually to the most distinguished illustrated children's book published in the United States.</p><p>Randolph Caldecott's influence on the field of children's book illustration is profound. His innovative approach to storytelling and his captivating illustrations continue to inspire and delight readers of all ages. His legacy as a pioneering illustrator has left an enduring impact on the world of children's literature.</p> ## Image examples for the model: ![Image 1](2330253.jpeg) > Randolph Caldecott Style - vast view of dense woodland in the background, a squirrel in a fashionable fedora and bomber jacket, reminiscent of Chip from Chip 'n Dale Rescue Rangers, looking at the audience, Beatrix Potter hand drawn style ![Image 2](2330245.jpeg) > Randolph Caldecott Style - imagine a close-up shot of the baby in the pram. style of shirley hughes - angry child throwing toys out of pram. The baby's face is in sharp focus, capturing their expression of glee and mischief. The style should be hyper-realistic, with every detail of the baby's face and the toy captured in high resolution. The lighting should be soft and diffused, creating a warm and inviting atmosphere. The composition should be a tight shot, focusing on the baby and the toy, with a shallow depth of field to blur the background. ![Image 3](2330250.jpeg) > Randolph Caldecott Style - Illustration inspired by Kate Greenaway, depicts scenes of idyllic childhood with children dressed in late 18th and early 19th-century clothing. pastel color palette and detailed botanical backgrounds. children in innocent poses in natural settings such as gardens and meadows and often include whimsical elements such as fairies and animals ![Image 4](2330257.jpeg) > Randolph Caldecott Style - Never in his life had he seen a river before, this sleck, sinuous, full bodied animal, chasing and chuckling, gripping things with a gurgle and leaving them with a laugh, to fling itself on fresh playmates that shook themselves free, and were caught and held again ![Image 5](2330246.jpeg) > Randolph Caldecott Style - Costume sketch. Bunny girl. Girl 5 years old. Pretty, curly hair. Long silk dress, lace petticoat, velvet jacket, jabot collar. Hat with ears. patent leather shoes. Dusted tones - pink and beige. Retro. Vintage. Early 20th century. ![Image 6](2330251.jpeg) > Randolph Caldecott Style - a girl of 8 years, black hair, orange dress, brown shoes, eyes shining with curiosity, in school, hang out with friends, bright and courageous, brave, bright smile, in style of Randolph Caldecott book illustration ![Image 7](2330254.jpeg) > Randolph Caldecott Style - a girl of 8 years, black hair, orange dress, brown shoes, eyes shining with curiosity, close up, in forest, sitting on a log, comfortable, flowers around in style of Randolph Caldecott book illustration ![Image 8](2330255.jpeg) > Randolph Caldecott Style - dirty caucassian family laying in bed, wearing pajamas. In the same bed there are 4 chicken, 3 ducks, dog, cat and two pigs. in style of beatrix potter. ![Image 9](2330262.jpeg) > Randolph Caldecott Style - Hopefully, all unfortunate children will find warm homes. drawing and painting, blend of the styles of Beatrix Potter, ANton Pieck and Pieter Breughel
stefan-it/autotrain-flair-georgian-ner-xlm_r_large-bs4-e10-lr5e-06-2	stefan-it	"2023-11-17T00:52:08Z"	12	0	flair	[ "flair", "pytorch", "tensorboard", "token-classification", "sequence-tagger-model", "en", "ka", "base_model:FacebookAI/xlm-roberta-large", "base_model:finetune:FacebookAI/xlm-roberta-large", "license:mit", "region:us" ]	token-classification	"2023-11-16T03:26:32Z"	--- language: - en - ka license: mit tags: - flair - token-classification - sequence-tagger-model base_model: xlm-roberta-large widget: - text: ამით თავისი ქადაგება დაასრულა და დაბრუნდა იერუსალიმში . ერთ-ერთ გარე კედელზე არსებობს ერნესტო ჩე გევარას პორტრეტი . შაკოსკა“ ინახება ბრაზილიაში , სან-პაულუს ხელოვნების მუზეუმში . --- # Fine-tuned English-Georgian NER Model with Flair This Flair NER model was fine-tuned on the WikiANN dataset ([Rahimi et al.](https://www.aclweb.org/anthology/P19-1015) splits) using XLM-R Large as backbone LM. Notice: The dataset is very problematic, because it was automatically constructed. We did manually inspect the development split of the Georgian data and found a lot of bad labeled examples, e.g. DVD ( 💿 ) as `ORG`. ## Fine-Tuning The latest [Flair version](https://github.com/flairNLP/flair/tree/f30f5801df3f9e105ed078ec058b4e1152dd9159) is used for fine-tuning. We use English and Georgian training splits for fine-tuning and the development set of Georgian for evaluation. A hyper-parameter search over the following parameters with 5 different seeds per configuration is performed: * Batch Sizes: [`4`] * Learning Rates: [`5e-06`] More details can be found in this [repository](https://github.com/stefan-it/georgian-ner). ## Results A hyper-parameter search with 5 different seeds per configuration is performed and micro F1-score on development set is reported: \| Configuration \| Seed 1 \| Seed 2 \| Seed 3 \| Seed 4 \| Seed 5 \| Average \| \|-------------------\|-------------\|-----------------\|-------------\|------------\|-------------\|-----------------\| \| `bs4-e10-lr5e-06` \| [0.9005][1] \| [0.9012][2] \| [0.9069][3] \| [0.905][4] \| [0.9048][5] \| 0.9037 ± 0.0027 \| [1]: https://hf.co/stefan-it/autotrain-flair-georgian-ner-xlm_r_large-bs4-e10-lr5e-06-1 [2]: https://hf.co/stefan-it/autotrain-flair-georgian-ner-xlm_r_large-bs4-e10-lr5e-06-2 [3]: https://hf.co/stefan-it/autotrain-flair-georgian-ner-xlm_r_large-bs4-e10-lr5e-06-3 [4]: https://hf.co/stefan-it/autotrain-flair-georgian-ner-xlm_r_large-bs4-e10-lr5e-06-4 [5]: https://hf.co/stefan-it/autotrain-flair-georgian-ner-xlm_r_large-bs4-e10-lr5e-06-5 The result in bold shows the performance of this model. Additionally, the Flair [training log](training.log) and [TensorBoard logs](tensorboard) are also uploaded to the model hub.
Xmm/led-large-16384-cnn_dailymail	Xmm	"2023-09-02T08:09:40Z"	98	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "led", "text2text-generation", "generated_from_trainer", "dataset:cnn_dailymail", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	"2023-06-17T03:05:46Z"	--- license: apache-2.0 tags: - generated_from_trainer datasets: - cnn_dailymail metrics: - rouge model-index: - name: led-large-16384-cnn_dailymail results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: cnn_dailymail type: cnn_dailymail config: 3.0.0 split: test args: 3.0.0 metrics: - name: Rouge1 type: rouge value: 0.3869876274946419 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # led-large-16384-cnn_dailymail This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the cnn_dailymail dataset. It achieves the following results on the evaluation set: - Loss: 1.5544 - Rouge1: 0.3870 - Rouge2: 0.1736 - Rougel: 0.2599 - Rougelsum: 0.3653 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 64 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\| \| 1.9531 \| 0.4 \| 500 \| 1.8639 \| 0.3485 \| 0.1441 \| 0.2275 \| 0.3288 \| \| 1.9563 \| 0.8 \| 1000 \| 1.8260 \| 0.3538 \| 0.1482 \| 0.2315 \| 0.3343 \| \| 1.7176 \| 1.2 \| 1500 \| 1.8208 \| 0.3628 \| 0.1527 \| 0.2383 \| 0.3433 \| \| 1.7197 \| 1.6 \| 2000 \| 1.8162 \| 0.3696 \| 0.1602 \| 0.2434 \| 0.3486 \| \| 1.8086 \| 2.0 \| 2500 \| 1.7924 \| 0.3558 \| 0.1533 \| 0.2334 \| 0.3361 \| \| 1.2448 \| 2.4 \| 3000 \| 1.8510 \| 0.3703 \| 0.1591 \| 0.2447 \| 0.3483 \| \| 1.3574 \| 2.8 \| 3500 \| 1.8277 \| 0.3741 \| 0.1593 \| 0.2422 \| 0.3540 \| \| 1.0966 \| 3.2 \| 4000 \| 1.8924 \| 0.3682 \| 0.1576 \| 0.2424 \| 0.3479 \| \| 0.9938 \| 3.6 \| 4500 \| 1.8957 \| 0.3723 \| 0.1599 \| 0.2451 \| 0.3511 \| \| 1.0735 \| 4.0 \| 5000 \| 1.8772 \| 0.3653 \| 0.1557 \| 0.2399 \| 0.3454 \| \| 0.9106 \| 4.4 \| 5500 \| 1.9401 \| 0.3720 \| 0.1585 \| 0.2436 \| 0.3504 \| \| 1.015 \| 4.8 \| 6000 \| 1.9320 \| 0.3725 \| 0.1570 \| 0.2429 \| 0.3515 \| \| 1.7854 \| 0.36 \| 6500 \| 1.7800 \| 0.3624 \| 0.1544 \| 0.2390 \| 0.3422 \| \| 1.9079 \| 0.39 \| 7000 \| 1.7629 \| 0.3573 \| 0.1553 \| 0.2352 \| 0.3370 \| \| 1.7606 \| 3.34 \| 7500 \| 1.6902 \| 0.3783 \| 0.1673 \| 0.2521 \| 0.3570 \| \| 1.7571 \| 3.57 \| 8000 \| 1.6563 \| 0.3802 \| 0.1691 \| 0.2538 \| 0.3587 \| \| 1.6602 \| 3.79 \| 8500 \| 1.6439 \| 0.3814 \| 0.1693 \| 0.2548 \| 0.3600 \| \| 1.6614 \| 4.01 \| 9000 \| 1.6312 \| 0.3812 \| 0.1691 \| 0.2544 \| 0.3599 \| \| 1.668 \| 4.24 \| 9500 \| 1.6189 \| 0.3815 \| 0.1689 \| 0.2550 \| 0.3603 \| \| 1.6491 \| 4.46 \| 10000 \| 1.6172 \| 0.3799 \| 0.1681 \| 0.2540 \| 0.3586 \| \| 1.5994 \| 4.68 \| 10500 \| 1.6132 \| 0.3825 \| 0.1702 \| 0.2560 \| 0.3610 \| \| 1.6493 \| 4.9 \| 11000 \| 1.6093 \| 0.3828 \| 0.1701 \| 0.2561 \| 0.3613 \| \| 1.6769 \| 5.13 \| 11500 \| 1.6074 \| 0.3831 \| 0.1706 \| 0.2569 \| 0.3619 \| \| 1.6554 \| 5.35 \| 12000 \| 1.6044 \| 0.3817 \| 0.1695 \| 0.2559 \| 0.3605 \| \| 1.6155 \| 5.57 \| 12500 \| 1.6010 \| 0.3825 \| 0.1700 \| 0.2561 \| 0.3608 \| \| 1.5863 \| 5.8 \| 13000 \| 1.5981 \| 0.3829 \| 0.1704 \| 0.2569 \| 0.3614 \| \| 1.6306 \| 6.02 \| 13500 \| 1.6004 \| 0.3831 \| 0.1702 \| 0.2563 \| 0.3618 \| \| 1.6425 \| 6.24 \| 14000 \| 1.5987 \| 0.3821 \| 0.1698 \| 0.2561 \| 0.3610 \| \| 1.6863 \| 6.46 \| 14500 \| 1.5876 \| 0.3837 \| 0.1710 \| 0.2569 \| 0.3622 \| \| 1.6085 \| 6.69 \| 15000 \| 1.5815 \| 0.3836 \| 0.1717 \| 0.2573 \| 0.3621 \| \| 1.6267 \| 6.91 \| 15500 \| 1.5792 \| 0.3852 \| 0.1722 \| 0.2579 \| 0.3633 \| \| 1.5637 \| 7.13 \| 16000 \| 1.5768 \| 0.3830 \| 0.1709 \| 0.2568 \| 0.3611 \| \| 1.5586 \| 7.36 \| 16500 \| 1.5740 \| 0.3833 \| 0.1706 \| 0.2567 \| 0.3617 \| \| 1.5389 \| 7.58 \| 17000 \| 1.5689 \| 0.3858 \| 0.1729 \| 0.2590 \| 0.3640 \| \| 1.5694 \| 7.8 \| 17500 \| 1.5645 \| 0.3853 \| 0.1731 \| 0.2589 \| 0.3636 \| \| 1.5265 \| 8.02 \| 18000 \| 1.5621 \| 0.3871 \| 0.1733 \| 0.2596 \| 0.3654 \| \| 1.5273 \| 8.25 \| 18500 \| 1.5624 \| 0.3861 \| 0.1726 \| 0.2588 \| 0.3646 \| \| 1.5148 \| 8.47 \| 19000 \| 1.5602 \| 0.3866 \| 0.1733 \| 0.2592 \| 0.3651 \| \| 1.532 \| 8.69 \| 19500 \| 1.5599 \| 0.3859 \| 0.1732 \| 0.2593 \| 0.3642 \| \| 1.5113 \| 8.92 \| 20000 \| 1.5602 \| 0.3877 \| 0.1748 \| 0.2606 \| 0.3658 \| \| 1.5133 \| 9.14 \| 20500 \| 1.5595 \| 0.3855 \| 0.1725 \| 0.2587 \| 0.3637 \| \| 1.4875 \| 9.36 \| 21000 \| 1.5572 \| 0.3873 \| 0.1741 \| 0.2600 \| 0.3654 \| \| 1.5038 \| 9.59 \| 21500 \| 1.5557 \| 0.3860 \| 0.1728 \| 0.2590 \| 0.3641 \| \| 1.5062 \| 9.81 \| 22000 \| 1.5544 \| 0.3870 \| 0.1736 \| 0.2599 \| 0.3653 \| ### Framework versions - Transformers 4.27.1 - Pytorch 2.0.0+cu118 - Datasets 2.10.1 - Tokenizers 0.13.2
sd-concepts-library/beldam	sd-concepts-library	"2022-10-06T04:31:38Z"	0	0	null	[ "license:mit", "region:us" ]	null	"2022-10-06T04:31:28Z"	--- license: mit --- ### beldam on Stable Diffusion This is the `beldam` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`: ![beldam 0](https://huggingface.co/sd-concepts-library/beldam/resolve/main/concept_images/6.jpeg) ![beldam 1](https://huggingface.co/sd-concepts-library/beldam/resolve/main/concept_images/0.jpeg) ![beldam 2](https://huggingface.co/sd-concepts-library/beldam/resolve/main/concept_images/7.jpeg) ![beldam 3](https://huggingface.co/sd-concepts-library/beldam/resolve/main/concept_images/9.jpeg) ![beldam 4](https://huggingface.co/sd-concepts-library/beldam/resolve/main/concept_images/2.jpeg) ![beldam 5](https://huggingface.co/sd-concepts-library/beldam/resolve/main/concept_images/12.jpeg) ![beldam 6](https://huggingface.co/sd-concepts-library/beldam/resolve/main/concept_images/3.jpeg) ![beldam 7](https://huggingface.co/sd-concepts-library/beldam/resolve/main/concept_images/14.jpeg) ![beldam 8](https://huggingface.co/sd-concepts-library/beldam/resolve/main/concept_images/13.jpeg) ![beldam 9](https://huggingface.co/sd-concepts-library/beldam/resolve/main/concept_images/10.jpeg) ![beldam 10](https://huggingface.co/sd-concepts-library/beldam/resolve/main/concept_images/8.jpeg) ![beldam 11](https://huggingface.co/sd-concepts-library/beldam/resolve/main/concept_images/15.jpeg) ![beldam 12](https://huggingface.co/sd-concepts-library/beldam/resolve/main/concept_images/11.jpeg) ![beldam 13](https://huggingface.co/sd-concepts-library/beldam/resolve/main/concept_images/5.jpeg) ![beldam 14](https://huggingface.co/sd-concepts-library/beldam/resolve/main/concept_images/4.jpeg) ![beldam 15](https://huggingface.co/sd-concepts-library/beldam/resolve/main/concept_images/1.jpeg)
mradermacher/dehallucinated-llama-3.2-8b-instruct-GGUF	mradermacher	"2025-02-26T23:57:51Z"	0	0	transformers	[ "transformers", "gguf", "en", "base_model:hirundo-io/dehallucinated-llama-3.2-8b-instruct", "base_model:quantized:hirundo-io/dehallucinated-llama-3.2-8b-instruct", "endpoints_compatible", "region:us", "conversational" ]	null	"2025-02-26T19:39:34Z"	--- base_model: hirundo-io/dehallucinated-llama-3.2-8b-instruct language: - en library_name: transformers quantized_by: mradermacher tags: [] --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/hirundo-io/dehallucinated-llama-3.2-8b-instruct <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/dehallucinated-llama-3.2-8b-instruct-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/dehallucinated-llama-3.2-8b-instruct-GGUF/resolve/main/dehallucinated-llama-3.2-8b-instruct.Q2_K.gguf) \| Q2_K \| 1.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/dehallucinated-llama-3.2-8b-instruct-GGUF/resolve/main/dehallucinated-llama-3.2-8b-instruct.Q3_K_S.gguf) \| Q3_K_S \| 1.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/dehallucinated-llama-3.2-8b-instruct-GGUF/resolve/main/dehallucinated-llama-3.2-8b-instruct.Q3_K_M.gguf) \| Q3_K_M \| 1.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/dehallucinated-llama-3.2-8b-instruct-GGUF/resolve/main/dehallucinated-llama-3.2-8b-instruct.Q3_K_L.gguf) \| Q3_K_L \| 1.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/dehallucinated-llama-3.2-8b-instruct-GGUF/resolve/main/dehallucinated-llama-3.2-8b-instruct.IQ4_XS.gguf) \| IQ4_XS \| 1.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/dehallucinated-llama-3.2-8b-instruct-GGUF/resolve/main/dehallucinated-llama-3.2-8b-instruct.Q4_K_S.gguf) \| Q4_K_S \| 2.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/dehallucinated-llama-3.2-8b-instruct-GGUF/resolve/main/dehallucinated-llama-3.2-8b-instruct.Q4_K_M.gguf) \| Q4_K_M \| 2.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/dehallucinated-llama-3.2-8b-instruct-GGUF/resolve/main/dehallucinated-llama-3.2-8b-instruct.Q5_K_S.gguf) \| Q5_K_S \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/dehallucinated-llama-3.2-8b-instruct-GGUF/resolve/main/dehallucinated-llama-3.2-8b-instruct.Q5_K_M.gguf) \| Q5_K_M \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/dehallucinated-llama-3.2-8b-instruct-GGUF/resolve/main/dehallucinated-llama-3.2-8b-instruct.Q6_K.gguf) \| Q6_K \| 2.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/dehallucinated-llama-3.2-8b-instruct-GGUF/resolve/main/dehallucinated-llama-3.2-8b-instruct.Q8_0.gguf) \| Q8_0 \| 3.5 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/dehallucinated-llama-3.2-8b-instruct-GGUF/resolve/main/dehallucinated-llama-3.2-8b-instruct.f16.gguf) \| f16 \| 6.5 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
Xu-Ouyang/pythia-160m-deduped-int2-step110000-GPTQ-wikitext2-uva	Xu-Ouyang	"2024-09-13T12:41:32Z"	75	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "2-bit", "gptq", "region:us" ]	text-generation	"2024-09-13T12:41:17Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
JacksonBrune/9aa904a1-6075-432a-8011-12852a7d995b	JacksonBrune	"2025-01-13T04:07:18Z"	10	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:rayonlabs/merged-merged-af6dd40b-32e1-43b1-adfd-8ce14d65d738-PubMedQA-138437bf-44bd-4b03-8801-d05451a9ff28", "base_model:adapter:rayonlabs/merged-merged-af6dd40b-32e1-43b1-adfd-8ce14d65d738-PubMedQA-138437bf-44bd-4b03-8801-d05451a9ff28", "region:us" ]	null	"2025-01-13T02:10:16Z"	--- library_name: peft base_model: rayonlabs/merged-merged-af6dd40b-32e1-43b1-adfd-8ce14d65d738-PubMedQA-138437bf-44bd-4b03-8801-d05451a9ff28 tags: - axolotl - generated_from_trainer model-index: - name: 9aa904a1-6075-432a-8011-12852a7d995b results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: rayonlabs/merged-merged-af6dd40b-32e1-43b1-adfd-8ce14d65d738-PubMedQA-138437bf-44bd-4b03-8801-d05451a9ff28 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 50570c988008bb52_train_data.json ds_type: json format: custom path: /workspace/input_data/50570c988008bb52_train_data.json type: field_input: context field_instruction: question field_output: final_decision format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: JacksonBrune/9aa904a1-6075-432a-8011-12852a7d995b hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/50570c988008bb52_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 special_tokens: pad_token: <\|end_of_text\|> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: bb82eda3-2375-490a-9d56-33d5775eeedb wandb_project: birthdya-sn56-18-Gradients-On-Demand wandb_run: your_name wandb_runid: bb82eda3-2375-490a-9d56-33d5775eeedb warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 9aa904a1-6075-432a-8011-12852a7d995b This model is a fine-tuned version of [rayonlabs/merged-merged-af6dd40b-32e1-43b1-adfd-8ce14d65d738-PubMedQA-138437bf-44bd-4b03-8801-d05451a9ff28](https://huggingface.co/rayonlabs/merged-merged-af6dd40b-32e1-43b1-adfd-8ce14d65d738-PubMedQA-138437bf-44bd-4b03-8801-d05451a9ff28) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.4878 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 13.2355 \| 0.0000 \| 1 \| 13.6415 \| \| 13.9199 \| 0.0001 \| 3 \| 13.1524 \| \| 9.434 \| 0.0002 \| 6 \| 6.8603 \| \| 3.0717 \| 0.0004 \| 9 \| 3.4878 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
Sukmin/Reinforce-PixelCopter	Sukmin	"2023-07-10T05:46:30Z"	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	"2023-07-10T03:34:22Z"	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-PixelCopter results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 35.40 +/- 26.24 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
nik135/distilbert-base-uncased-finetuned-emotion	nik135	"2024-11-11T07:06:01Z"	107	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2024-10-08T08:38:01Z"	--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2156 - Accuracy: 0.925 - F1: 0.9251 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.7958 \| 1.0 \| 250 \| 0.3024 \| 0.909 \| 0.9086 \| \| 0.2385 \| 2.0 \| 500 \| 0.2156 \| 0.925 \| 0.9251 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1
coreml-community/coreml-Roboetics-mix	coreml-community	"2023-03-05T14:59:16Z"	0	3	null	[ "coreml", "stable-diffusion", "text-to-image", "license:creativeml-openrail-m", "region:us" ]	text-to-image	"2023-01-11T01:43:05Z"	--- license: creativeml-openrail-m tags: - coreml - stable-diffusion - text-to-image --- # Core ML Converted Model: - This model was converted to Core ML for use on Apple Silicon devices. Instructions can be found [here](https://github.com/godly-devotion/MochiDiffusion/wiki/How-to-convert-ckpt-files-to-Core-ML).<br> - Provide the model to an app such as [Mochi Diffusion](https://github.com/godly-devotion/MochiDiffusion) to generate images.<br> - `split_einsum` version is compatible with all compute unit options including Neural Engine.<br> # Note: This model does not have the [unet split into chunks](https://github.com/apple/ml-stable-diffusion#-converting-models-to-core-ml). # Roboetic's mix: Source(s): [CivitAI](https://civitai.com/models/3738/roboetics-mix) This model is some of my favourite models merged together. It is a general purpose model which can generate good looking images with simpler prompts.
baibaichuan/dqn-SpaceInvadersNoFrameskip-v4	baibaichuan	"2025-03-11T13:23:19Z"	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	"2025-03-11T12:57:50Z"	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 512.50 +/- 169.64 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib SBX (SB3 + Jax): https://github.com/araffin/sbx Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga baibaichuan -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga baibaichuan -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga baibaichuan ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
Jivika1/ASR	Jivika1	"2025-02-20T13:32:33Z"	0	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-medium", "base_model:finetune:openai/whisper-medium", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	"2025-02-20T13:16:04Z"	--- library_name: transformers license: apache-2.0 base_model: openai/whisper-medium tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-medium-medical results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-medium-medical This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0562 - Wer: 10.7169 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\| \| 0.5008 \| 0.5405 \| 100 \| 0.1965 \| 12.0203 \| \| 0.1034 \| 1.0811 \| 200 \| 0.0870 \| 12.2616 \| \| 0.0563 \| 1.6216 \| 300 \| 0.0642 \| 8.3514 \| \| 0.0238 \| 2.1622 \| 400 \| 0.0610 \| 11.6341 \| \| 0.0129 \| 2.7027 \| 500 \| 0.0562 \| 10.7169 \| ### Framework versions - Transformers 4.49.0 - Pytorch 2.6.0+cu118 - Datasets 3.3.1 - Tokenizers 0.21.0
lesso16/cc432f5d-7a5c-4010-8f56-2589ff64aee7	lesso16	"2025-03-28T15:49:19Z"	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:JackFram/llama-68m", "base_model:adapter:JackFram/llama-68m", "license:apache-2.0", "region:us" ]	null	"2025-03-28T15:45:33Z"	--- library_name: peft license: apache-2.0 base_model: JackFram/llama-68m tags: - axolotl - generated_from_trainer model-index: - name: cc432f5d-7a5c-4010-8f56-2589ff64aee7 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: JackFram/llama-68m bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 623d9787d7fd7d0e_train_data.json ds_type: json format: custom path: /workspace/input_data/623d9787d7fd7d0e_train_data.json type: field_instruction: text field_output: title format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null do_eval: true early_stopping_patience: 3 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 500 evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 8 gradient_checkpointing: true group_by_length: true hub_model_id: lesso16/cc432f5d-7a5c-4010-8f56-2589ff64aee7 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.000216 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 50 lora_alpha: 128 lora_dropout: 0.15 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_steps: 500 micro_batch_size: 4 mlflow_experiment_name: /tmp/623d9787d7fd7d0e_train_data.json model_type: AutoModelForCausalLM num_epochs: 10 optimizer: adamw_torch_fused output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 500 saves_per_epoch: null seed: 160 sequence_len: 1024 special_tokens: pad_token: </s> strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: af6de128-8865-42e8-800b-ff2d2b1acccd wandb_project: 16a wandb_run: your_name wandb_runid: af6de128-8865-42e8-800b-ff2d2b1acccd warmup_steps: 100 weight_decay: 0.0 xformers_attention: null ``` </details><br> # cc432f5d-7a5c-4010-8f56-2589ff64aee7 This model is a fine-tuned version of [JackFram/llama-68m](https://huggingface.co/JackFram/llama-68m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0133 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000216 - train_batch_size: 4 - eval_batch_size: 4 - seed: 160 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0010 \| 1 \| 2.4174 \| \| 0.0125 \| 0.5182 \| 500 \| 0.0133 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
baddii/20_baddii_08_911	baddii	"2025-02-18T08:46:20Z"	0	0	transformers	[ "transformers", "safetensors", "parler_tts", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	"2025-02-18T08:44:33Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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aitorrent/dolphin-2.9.3-qwen2-0.5b-GGUF-torrent	aitorrent	"2024-06-16T14:09:48Z"	0	0	null	[ "torrent", "license:apache-2.0", "region:us" ]	null	"2024-06-16T13:59:12Z"	--- license: apache-2.0 tags: - torrent --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/cognitivecomputations/dolphin-2.9.3-qwen2-0.5b <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-0.5b-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-0.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-0.5b.Q3_K_S.gguf) \| Q3_K_S \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-0.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-0.5b.IQ3_S.gguf) \| IQ3_S \| 0.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-0.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-0.5b.IQ3_XS.gguf) \| IQ3_XS \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-0.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-0.5b.Q2_K.gguf) \| Q2_K \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-0.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-0.5b.IQ3_M.gguf) \| IQ3_M \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-0.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-0.5b.IQ4_XS.gguf) \| IQ4_XS \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-0.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-0.5b.Q3_K_M.gguf) \| Q3_K_M \| 0.5 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-0.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-0.5b.Q3_K_L.gguf) \| Q3_K_L \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-0.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-0.5b.Q4_K_S.gguf) \| Q4_K_S \| 0.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-0.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-0.5b.Q4_K_M.gguf) \| Q4_K_M \| 0.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-0.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-0.5b.Q5_K_S.gguf) \| Q5_K_S \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-0.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-0.5b.Q5_K_M.gguf) \| Q5_K_M \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-0.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-0.5b.Q6_K.gguf) \| Q6_K \| 0.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-0.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-0.5b.Q8_0.gguf) \| Q8_0 \| 0.6 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-0.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-0.5b.f16.gguf) \| f16 \| 1.1 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
johnsnowlabs/PhigRange-2.7B-slerp	johnsnowlabs	"2024-04-10T11:14:41Z"	6	0	transformers	[ "transformers", "safetensors", "phi", "text-generation", "merge", "mergekit", "lazymergekit", "liminerity/Phigments12", "rhysjones/phi-2-orange-v2", "base_model:liminerity/Phigments12", "base_model:merge:liminerity/Phigments12", "base_model:rhysjones/phi-2-orange-v2", "base_model:merge:rhysjones/phi-2-orange-v2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2024-04-08T19:42:59Z"	--- tags: - merge - mergekit - lazymergekit - liminerity/Phigments12 - rhysjones/phi-2-orange-v2 base_model: - liminerity/Phigments12 - rhysjones/phi-2-orange-v2 --- # PhigRange-2.7B-slerp ![image/png](https://cdn-uploads.huggingface.co/production/uploads/660cfe98280a82e38fe4ef49/UAwX3mctFI41DQ3-tRJkO.png) PhigRange-2.7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [liminerity/Phigments12](https://huggingface.co/liminerity/Phigments12) * [rhysjones/phi-2-orange-v2](https://huggingface.co/rhysjones/phi-2-orange-v2) Special thanks to Charles Goddard for the quick implementation! ## 🧩 Configuration ```yaml slices: - sources: - model: liminerity/Phigments12 layer_range: [0, 32] - model: rhysjones/phi-2-orange-v2 layer_range: [0, 32] merge_method: slerp base_model: liminerity/Phigments12 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "johnsnowlabs/PhigRange-2.7B-slerp" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ``` ## 🏆 Evaluation Coming Soon!
tensorblock/You_can_cry_Snowman-13B-GGUF	tensorblock	"2024-12-28T11:37:00Z"	26	0	transformers	[ "transformers", "gguf", "TensorBlock", "GGUF", "text-generation", "ko", "base_model:DopeorNope/You_can_cry_Snowman-13B", "base_model:quantized:DopeorNope/You_can_cry_Snowman-13B", "license:cc-by-nc-sa-4.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	"2024-12-28T10:25:40Z"	--- language: - ko library_name: transformers pipeline_tag: text-generation license: cc-by-nc-sa-4.0 tags: - TensorBlock - GGUF base_model: DopeorNope/You_can_cry_Snowman-13B --- <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"> Feedback and support: TensorBlock's <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a> </p> </div> </div> ## DopeorNope/You_can_cry_Snowman-13B - GGUF This repo contains GGUF format model files for [DopeorNope/You_can_cry_Snowman-13B](https://huggingface.co/DopeorNope/You_can_cry_Snowman-13B). The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4242](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d). <div style="text-align: left; margin: 20px 0;"> <a href="https://tensorblock.co/waitlist/client" style="display: inline-block; padding: 10px 20px; background-color: #007bff; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;"> Run them on the TensorBlock client using your local machine ↗ </a> </div> ## Prompt template ``` ### System: {system_prompt} ### User: {prompt} ### Assistant: ``` ## Model file specification \| Filename \| Quant type \| File Size \| Description \| \| -------- \| ---------- \| --------- \| ----------- \| \| [You_can_cry_Snowman-13B-Q2_K.gguf](https://huggingface.co/tensorblock/You_can_cry_Snowman-13B-GGUF/blob/main/You_can_cry_Snowman-13B-Q2_K.gguf) \| Q2_K \| 4.966 GB \| smallest, significant quality loss - not recommended for most purposes \| \| [You_can_cry_Snowman-13B-Q3_K_S.gguf](https://huggingface.co/tensorblock/You_can_cry_Snowman-13B-GGUF/blob/main/You_can_cry_Snowman-13B-Q3_K_S.gguf) \| Q3_K_S \| 5.790 GB \| very small, high quality loss \| \| [You_can_cry_Snowman-13B-Q3_K_M.gguf](https://huggingface.co/tensorblock/You_can_cry_Snowman-13B-GGUF/blob/main/You_can_cry_Snowman-13B-Q3_K_M.gguf) \| Q3_K_M \| 6.448 GB \| very small, high quality loss \| \| [You_can_cry_Snowman-13B-Q3_K_L.gguf](https://huggingface.co/tensorblock/You_can_cry_Snowman-13B-GGUF/blob/main/You_can_cry_Snowman-13B-Q3_K_L.gguf) \| Q3_K_L \| 7.022 GB \| small, substantial quality loss \| \| [You_can_cry_Snowman-13B-Q4_0.gguf](https://huggingface.co/tensorblock/You_can_cry_Snowman-13B-GGUF/blob/main/You_can_cry_Snowman-13B-Q4_0.gguf) \| Q4_0 \| 7.545 GB \| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [You_can_cry_Snowman-13B-Q4_K_S.gguf](https://huggingface.co/tensorblock/You_can_cry_Snowman-13B-GGUF/blob/main/You_can_cry_Snowman-13B-Q4_K_S.gguf) \| Q4_K_S \| 7.598 GB \| small, greater quality loss \| \| [You_can_cry_Snowman-13B-Q4_K_M.gguf](https://huggingface.co/tensorblock/You_can_cry_Snowman-13B-GGUF/blob/main/You_can_cry_Snowman-13B-Q4_K_M.gguf) \| Q4_K_M \| 8.032 GB \| medium, balanced quality - recommended \| \| [You_can_cry_Snowman-13B-Q5_0.gguf](https://huggingface.co/tensorblock/You_can_cry_Snowman-13B-GGUF/blob/main/You_can_cry_Snowman-13B-Q5_0.gguf) \| Q5_0 \| 9.197 GB \| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [You_can_cry_Snowman-13B-Q5_K_S.gguf](https://huggingface.co/tensorblock/You_can_cry_Snowman-13B-GGUF/blob/main/You_can_cry_Snowman-13B-Q5_K_S.gguf) \| Q5_K_S \| 9.197 GB \| large, low quality loss - recommended \| \| [You_can_cry_Snowman-13B-Q5_K_M.gguf](https://huggingface.co/tensorblock/You_can_cry_Snowman-13B-GGUF/blob/main/You_can_cry_Snowman-13B-Q5_K_M.gguf) \| Q5_K_M \| 9.448 GB \| large, very low quality loss - recommended \| \| [You_can_cry_Snowman-13B-Q6_K.gguf](https://huggingface.co/tensorblock/You_can_cry_Snowman-13B-GGUF/blob/main/You_can_cry_Snowman-13B-Q6_K.gguf) \| Q6_K \| 10.953 GB \| very large, extremely low quality loss \| \| [You_can_cry_Snowman-13B-Q8_0.gguf](https://huggingface.co/tensorblock/You_can_cry_Snowman-13B-GGUF/blob/main/You_can_cry_Snowman-13B-Q8_0.gguf) \| Q8_0 \| 14.185 GB \| very large, extremely low quality loss - not recommended \| ## Downloading instruction ### Command line Firstly, install Huggingface Client ```shell pip install -U "huggingface_hub[cli]" ``` Then, downoad the individual model file the a local directory ```shell huggingface-cli download tensorblock/You_can_cry_Snowman-13B-GGUF --include "You_can_cry_Snowman-13B-Q2_K.gguf" --local-dir MY_LOCAL_DIR ``` If you wanna download multiple model files with a pattern (e.g., `Q4_Kgguf`), you can try: ```shell huggingface-cli download tensorblock/You_can_cry_Snowman-13B-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='Q4_Kgguf' ```
RichardErkhov/goethe0101_-_llama-3-2-3B-wame-16bit-survey-generator4-gguf	RichardErkhov	"2025-03-25T22:37:15Z"	0	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	"2025-03-25T21:37:31Z"	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) llama-3-2-3B-wame-16bit-survey-generator4 - GGUF - Model creator: https://huggingface.co/goethe0101/ - Original model: https://huggingface.co/goethe0101/llama-3-2-3B-wame-16bit-survey-generator4/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [llama-3-2-3B-wame-16bit-survey-generator4.Q2_K.gguf](https://huggingface.co/RichardErkhov/goethe0101_-_llama-3-2-3B-wame-16bit-survey-generator4-gguf/blob/main/llama-3-2-3B-wame-16bit-survey-generator4.Q2_K.gguf) \| Q2_K \| 1.27GB \| \| [llama-3-2-3B-wame-16bit-survey-generator4.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/goethe0101_-_llama-3-2-3B-wame-16bit-survey-generator4-gguf/blob/main/llama-3-2-3B-wame-16bit-survey-generator4.IQ3_XS.gguf) \| IQ3_XS \| 1.38GB \| \| [llama-3-2-3B-wame-16bit-survey-generator4.IQ3_S.gguf](https://huggingface.co/RichardErkhov/goethe0101_-_llama-3-2-3B-wame-16bit-survey-generator4-gguf/blob/main/llama-3-2-3B-wame-16bit-survey-generator4.IQ3_S.gguf) \| IQ3_S \| 1.44GB \| \| [llama-3-2-3B-wame-16bit-survey-generator4.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/goethe0101_-_llama-3-2-3B-wame-16bit-survey-generator4-gguf/blob/main/llama-3-2-3B-wame-16bit-survey-generator4.Q3_K_S.gguf) \| Q3_K_S \| 1.44GB \| \| [llama-3-2-3B-wame-16bit-survey-generator4.IQ3_M.gguf](https://huggingface.co/RichardErkhov/goethe0101_-_llama-3-2-3B-wame-16bit-survey-generator4-gguf/blob/main/llama-3-2-3B-wame-16bit-survey-generator4.IQ3_M.gguf) \| IQ3_M \| 1.49GB \| \| [llama-3-2-3B-wame-16bit-survey-generator4.Q3_K.gguf](https://huggingface.co/RichardErkhov/goethe0101_-_llama-3-2-3B-wame-16bit-survey-generator4-gguf/blob/main/llama-3-2-3B-wame-16bit-survey-generator4.Q3_K.gguf) \| Q3_K \| 1.57GB \| \| [llama-3-2-3B-wame-16bit-survey-generator4.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/goethe0101_-_llama-3-2-3B-wame-16bit-survey-generator4-gguf/blob/main/llama-3-2-3B-wame-16bit-survey-generator4.Q3_K_M.gguf) \| Q3_K_M \| 1.57GB \| \| [llama-3-2-3B-wame-16bit-survey-generator4.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/goethe0101_-_llama-3-2-3B-wame-16bit-survey-generator4-gguf/blob/main/llama-3-2-3B-wame-16bit-survey-generator4.Q3_K_L.gguf) \| Q3_K_L \| 1.69GB \| \| [llama-3-2-3B-wame-16bit-survey-generator4.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/goethe0101_-_llama-3-2-3B-wame-16bit-survey-generator4-gguf/blob/main/llama-3-2-3B-wame-16bit-survey-generator4.IQ4_XS.gguf) \| IQ4_XS \| 1.71GB \| \| [llama-3-2-3B-wame-16bit-survey-generator4.Q4_0.gguf](https://huggingface.co/RichardErkhov/goethe0101_-_llama-3-2-3B-wame-16bit-survey-generator4-gguf/blob/main/llama-3-2-3B-wame-16bit-survey-generator4.Q4_0.gguf) \| Q4_0 \| 1.79GB \| \| [llama-3-2-3B-wame-16bit-survey-generator4.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/goethe0101_-_llama-3-2-3B-wame-16bit-survey-generator4-gguf/blob/main/llama-3-2-3B-wame-16bit-survey-generator4.IQ4_NL.gguf) \| IQ4_NL \| 1.79GB \| \| [llama-3-2-3B-wame-16bit-survey-generator4.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/goethe0101_-_llama-3-2-3B-wame-16bit-survey-generator4-gguf/blob/main/llama-3-2-3B-wame-16bit-survey-generator4.Q4_K_S.gguf) \| Q4_K_S \| 1.8GB \| \| [llama-3-2-3B-wame-16bit-survey-generator4.Q4_K.gguf](https://huggingface.co/RichardErkhov/goethe0101_-_llama-3-2-3B-wame-16bit-survey-generator4-gguf/blob/main/llama-3-2-3B-wame-16bit-survey-generator4.Q4_K.gguf) \| Q4_K \| 1.88GB \| \| [llama-3-2-3B-wame-16bit-survey-generator4.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/goethe0101_-_llama-3-2-3B-wame-16bit-survey-generator4-gguf/blob/main/llama-3-2-3B-wame-16bit-survey-generator4.Q4_K_M.gguf) \| Q4_K_M \| 1.88GB \| \| [llama-3-2-3B-wame-16bit-survey-generator4.Q4_1.gguf](https://huggingface.co/RichardErkhov/goethe0101_-_llama-3-2-3B-wame-16bit-survey-generator4-gguf/blob/main/llama-3-2-3B-wame-16bit-survey-generator4.Q4_1.gguf) \| Q4_1 \| 1.95GB \| \| [llama-3-2-3B-wame-16bit-survey-generator4.Q5_0.gguf](https://huggingface.co/RichardErkhov/goethe0101_-_llama-3-2-3B-wame-16bit-survey-generator4-gguf/blob/main/llama-3-2-3B-wame-16bit-survey-generator4.Q5_0.gguf) \| Q5_0 \| 2.11GB \| \| [llama-3-2-3B-wame-16bit-survey-generator4.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/goethe0101_-_llama-3-2-3B-wame-16bit-survey-generator4-gguf/blob/main/llama-3-2-3B-wame-16bit-survey-generator4.Q5_K_S.gguf) \| Q5_K_S \| 2.11GB \| \| [llama-3-2-3B-wame-16bit-survey-generator4.Q5_K.gguf](https://huggingface.co/RichardErkhov/goethe0101_-_llama-3-2-3B-wame-16bit-survey-generator4-gguf/blob/main/llama-3-2-3B-wame-16bit-survey-generator4.Q5_K.gguf) \| Q5_K \| 2.16GB \| \| [llama-3-2-3B-wame-16bit-survey-generator4.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/goethe0101_-_llama-3-2-3B-wame-16bit-survey-generator4-gguf/blob/main/llama-3-2-3B-wame-16bit-survey-generator4.Q5_K_M.gguf) \| Q5_K_M \| 2.16GB \| \| [llama-3-2-3B-wame-16bit-survey-generator4.Q5_1.gguf](https://huggingface.co/RichardErkhov/goethe0101_-_llama-3-2-3B-wame-16bit-survey-generator4-gguf/blob/main/llama-3-2-3B-wame-16bit-survey-generator4.Q5_1.gguf) \| Q5_1 \| 2.28GB \| \| [llama-3-2-3B-wame-16bit-survey-generator4.Q6_K.gguf](https://huggingface.co/RichardErkhov/goethe0101_-_llama-3-2-3B-wame-16bit-survey-generator4-gguf/blob/main/llama-3-2-3B-wame-16bit-survey-generator4.Q6_K.gguf) \| Q6_K \| 2.46GB \| \| [llama-3-2-3B-wame-16bit-survey-generator4.Q8_0.gguf](https://huggingface.co/RichardErkhov/goethe0101_-_llama-3-2-3B-wame-16bit-survey-generator4-gguf/blob/main/llama-3-2-3B-wame-16bit-survey-generator4.Q8_0.gguf) \| Q8_0 \| 3.19GB \| Original model description: --- base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: goethe0101 - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
AlignmentResearch/robust_llm_pythia-wl-31m-mz-ada-v3-ch-139000	AlignmentResearch	"2024-03-26T11:51:11Z"	103	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-31m", "base_model:finetune:EleutherAI/pythia-31m", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	"2024-03-26T11:51:03Z"	--- tags: - generated_from_trainer base_model: EleutherAI/pythia-31m model-index: - name: robust_llm_pythia-wl-31m-mz-ada-v3-ch-139000 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # robust_llm_pythia-wl-31m-mz-ada-v3-ch-139000 This model is a fine-tuned version of [EleutherAI/pythia-31m](https://huggingface.co/EleutherAI/pythia-31m) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.17.0 - Tokenizers 0.15.2
Finn13/Llama3.1_COsec_multi	Finn13	"2025-03-24T19:36:08Z"	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Meta-Llama-3.1-8B-Instruct", "base_model:finetune:unsloth/Meta-Llama-3.1-8B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	"2025-03-24T19:32:03Z"	--- base_model: unsloth/Meta-Llama-3.1-8B-Instruct tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Finn13 - License: apache-2.0 - Finetuned from model : unsloth/Meta-Llama-3.1-8B-Instruct This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
juhul/pop	juhul	"2025-02-05T07:27:35Z"	295	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "region:us" ]	text-to-image	"2025-02-05T07:16:17Z"	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: . output: url: images/out-0 - 2025-02-02T102121.559.jpg base_model: black-forest-labs/FLUX.1-dev instance_prompt: PPP --- # pop <Gallery /> ## Trigger words You should use `PPP` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/juhul/pop/tree/main) them in the Files & versions tab.
surianto/nana	surianto	"2023-07-30T12:06:16Z"	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	"2023-07-30T12:05:23Z"	--- license: creativeml-openrail-m ---
Triangle104/Llama-3.2-3B-Instruct-abliterated-Q5_K_M-GGUF	Triangle104	"2024-11-25T16:45:58Z"	6	0	transformers	[ "transformers", "gguf", "abliterated", "uncensored", "llama-cpp", "gguf-my-repo", "base_model:huihui-ai/Llama-3.2-3B-Instruct-abliterated", "base_model:quantized:huihui-ai/Llama-3.2-3B-Instruct-abliterated", "license:llama3.2", "endpoints_compatible", "region:us", "conversational" ]	null	"2024-10-17T10:06:54Z"	--- library_name: transformers license: llama3.2 base_model: huihui-ai/Llama-3.2-3B-Instruct-abliterated tags: - abliterated - uncensored - llama-cpp - gguf-my-repo --- # Triangle104/Llama-3.2-3B-Instruct-abliterated-Q5_K_M-GGUF This model was converted to GGUF format from [`huihui-ai/Llama-3.2-3B-Instruct-abliterated`](https://huggingface.co/huihui-ai/Llama-3.2-3B-Instruct-abliterated) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/huihui-ai/Llama-3.2-3B-Instruct-abliterated) for more details on the model. --- Model details: - This is an uncensored version of Llama 3.2 3B Instruct created with abliteration (see this article to know more about it). Special thanks to @FailSpy for the original code and technique. Please follow him if you're interested in abliterated models. --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/Llama-3.2-3B-Instruct-abliterated-Q5_K_M-GGUF --hf-file llama-3.2-3b-instruct-abliterated-q5_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Llama-3.2-3B-Instruct-abliterated-Q5_K_M-GGUF --hf-file llama-3.2-3b-instruct-abliterated-q5_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/Llama-3.2-3B-Instruct-abliterated-Q5_K_M-GGUF --hf-file llama-3.2-3b-instruct-abliterated-q5_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Llama-3.2-3B-Instruct-abliterated-Q5_K_M-GGUF --hf-file llama-3.2-3b-instruct-abliterated-q5_k_m.gguf -c 2048 ```
alicekwak/TN-final-all-mpnet-base-v2	alicekwak	"2022-11-02T22:58:35Z"	5	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	"2022-11-02T22:58:25Z"	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # alicekwak/TN-final-all-mpnet-base-v2 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('alicekwak/TN-final-all-mpnet-base-v2') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name=alicekwak/TN-final-all-mpnet-base-v2) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 675 with parameters: ``` {'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.SoftmaxLoss.SoftmaxLoss` Parameters of the fit()-Method: ``` { "epochs": 5, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 10, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
hqbui/Reinforce-PixelCopter-PLE-v0	hqbui	"2023-12-12T21:02:22Z"	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	"2023-12-12T19:11:38Z"	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-PixelCopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 23.70 +/- 17.46 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
pandaiedu/mesolitica-qwen-2.5-lora-1.5b-Instruct-Merged-20-epoch	pandaiedu	"2025-03-26T21:27:07Z"	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-03-26T21:25:16Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
kla-20/qa-flant5	kla-20	"2023-09-21T15:30:53Z"	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	"2023-09-21T15:23:27Z"	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: qa-flant5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # qa-flant5 This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1 ### Training results ### Framework versions - Transformers 4.27.2 - Pytorch 1.13.1+cu117 - Datasets 2.11.0 - Tokenizers 0.13.3
sherif1311/flan-t5-base-classification_int1	sherif1311	"2023-08-13T18:55:37Z"	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "base_model:google/flan-t5-base", "base_model:finetune:google/flan-t5-base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	"2023-08-13T18:50:58Z"	--- license: apache-2.0 base_model: google/flan-t5-base tags: - generated_from_trainer metrics: - f1 model-index: - name: flan-t5-base-classification_int1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # flan-t5-base-classification_int1 This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0036 - F1: 99.7778 - Gen Len: 2.3333 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.31.0 - Pytorch 1.12.1+cu116 - Datasets 2.14.4 - Tokenizers 0.12.1
tensorblock/llama-ko-peft-v0.6-GGUF	tensorblock	"2024-11-19T11:17:37Z"	19	0	null	[ "gguf", "TensorBlock", "GGUF", "ko", "base_model:colable/llama-ko-peft-v0.6", "base_model:quantized:colable/llama-ko-peft-v0.6", "license:mit", "endpoints_compatible", "region:us" ]	null	"2024-11-19T10:51:15Z"	--- license: mit language: - ko tags: - TensorBlock - GGUF base_model: colable/llama-ko-peft-v0.6 --- <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"> Feedback and support: TensorBlock's <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a> </p> </div> </div> ## colable/llama-ko-peft-v0.6 - GGUF This repo contains GGUF format model files for [colable/llama-ko-peft-v0.6](https://huggingface.co/colable/llama-ko-peft-v0.6). The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4011](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d). <div style="text-align: left; margin: 20px 0;"> <a href="https://tensorblock.co/waitlist/client" style="display: inline-block; padding: 10px 20px; background-color: #007bff; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;"> Run them on the TensorBlock client using your local machine ↗ </a> </div> ## Prompt template ``` ``` ## Model file specification \| Filename \| Quant type \| File Size \| Description \| \| -------- \| ---------- \| --------- \| ----------- \| \| [llama-ko-peft-v0.6-Q2_K.gguf](https://huggingface.co/tensorblock/llama-ko-peft-v0.6-GGUF/blob/main/llama-ko-peft-v0.6-Q2_K.gguf) \| Q2_K \| 2.422 GB \| smallest, significant quality loss - not recommended for most purposes \| \| [llama-ko-peft-v0.6-Q3_K_S.gguf](https://huggingface.co/tensorblock/llama-ko-peft-v0.6-GGUF/blob/main/llama-ko-peft-v0.6-Q3_K_S.gguf) \| Q3_K_S \| 2.815 GB \| very small, high quality loss \| \| [llama-ko-peft-v0.6-Q3_K_M.gguf](https://huggingface.co/tensorblock/llama-ko-peft-v0.6-GGUF/blob/main/llama-ko-peft-v0.6-Q3_K_M.gguf) \| Q3_K_M \| 3.140 GB \| very small, high quality loss \| \| [llama-ko-peft-v0.6-Q3_K_L.gguf](https://huggingface.co/tensorblock/llama-ko-peft-v0.6-GGUF/blob/main/llama-ko-peft-v0.6-Q3_K_L.gguf) \| Q3_K_L \| 3.419 GB \| small, substantial quality loss \| \| [llama-ko-peft-v0.6-Q4_0.gguf](https://huggingface.co/tensorblock/llama-ko-peft-v0.6-GGUF/blob/main/llama-ko-peft-v0.6-Q4_0.gguf) \| Q4_0 \| 3.639 GB \| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [llama-ko-peft-v0.6-Q4_K_S.gguf](https://huggingface.co/tensorblock/llama-ko-peft-v0.6-GGUF/blob/main/llama-ko-peft-v0.6-Q4_K_S.gguf) \| Q4_K_S \| 3.668 GB \| small, greater quality loss \| \| [llama-ko-peft-v0.6-Q4_K_M.gguf](https://huggingface.co/tensorblock/llama-ko-peft-v0.6-GGUF/blob/main/llama-ko-peft-v0.6-Q4_K_M.gguf) \| Q4_K_M \| 3.877 GB \| medium, balanced quality - recommended \| \| [llama-ko-peft-v0.6-Q5_0.gguf](https://huggingface.co/tensorblock/llama-ko-peft-v0.6-GGUF/blob/main/llama-ko-peft-v0.6-Q5_0.gguf) \| Q5_0 \| 4.415 GB \| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [llama-ko-peft-v0.6-Q5_K_S.gguf](https://huggingface.co/tensorblock/llama-ko-peft-v0.6-GGUF/blob/main/llama-ko-peft-v0.6-Q5_K_S.gguf) \| Q5_K_S \| 4.415 GB \| large, low quality loss - recommended \| \| [llama-ko-peft-v0.6-Q5_K_M.gguf](https://huggingface.co/tensorblock/llama-ko-peft-v0.6-GGUF/blob/main/llama-ko-peft-v0.6-Q5_K_M.gguf) \| Q5_K_M \| 4.537 GB \| large, very low quality loss - recommended \| \| [llama-ko-peft-v0.6-Q6_K.gguf](https://huggingface.co/tensorblock/llama-ko-peft-v0.6-GGUF/blob/main/llama-ko-peft-v0.6-Q6_K.gguf) \| Q6_K \| 5.240 GB \| very large, extremely low quality loss \| \| [llama-ko-peft-v0.6-Q8_0.gguf](https://huggingface.co/tensorblock/llama-ko-peft-v0.6-GGUF/blob/main/llama-ko-peft-v0.6-Q8_0.gguf) \| Q8_0 \| 6.786 GB \| very large, extremely low quality loss - not recommended \| ## Downloading instruction ### Command line Firstly, install Huggingface Client ```shell pip install -U "huggingface_hub[cli]" ``` Then, downoad the individual model file the a local directory ```shell huggingface-cli download tensorblock/llama-ko-peft-v0.6-GGUF --include "llama-ko-peft-v0.6-Q2_K.gguf" --local-dir MY_LOCAL_DIR ``` If you wanna download multiple model files with a pattern (e.g., `Q4_Kgguf`), you can try: ```shell huggingface-cli download tensorblock/llama-ko-peft-v0.6-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='Q4_Kgguf' ```
lunarsylph/stablecell_v13	lunarsylph	"2024-03-30T02:51:40Z"	91	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	"2024-03-30T02:29:39Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
SicariusSicariiStuff/SaisExperiments_Evil-Alpaca-3B-L3.2_iMatrix	SicariusSicariiStuff	"2024-09-29T18:18:27Z"	8	0	null	[ "gguf", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	"2024-09-29T17:52:58Z"	--- license: apache-2.0 ---
amaliaam/image_classification	amaliaam	"2023-09-18T16:58:49Z"	261	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	"2023-09-18T16:06:43Z"	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - imagefolder model-index: - name: image_classification results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # image_classification This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - eval_loss: 2.0915 - eval_accuracy: 0.0938 - eval_runtime: 10.0977 - eval_samples_per_second: 15.845 - eval_steps_per_second: 0.99 - step: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
georad/mediNER	georad	"2025-03-10T17:05:19Z"	0	0	null	[ "safetensors", "region:us" ]	null	"2025-03-10T14:58:19Z"	# medNER_V2 This app performs Named Entity REcognition of medical entties.
PrunaAI/fblgit-juanako-7b-UNA-bnb-8bit-smashed	PrunaAI	"2024-08-15T14:47:06Z"	5	0	null	[ "safetensors", "mistral", "pruna-ai", "base_model:fblgit/juanako-7b-UNA", "base_model:quantized:fblgit/juanako-7b-UNA", "8-bit", "bitsandbytes", "region:us" ]	null	"2024-08-15T14:43:39Z"	--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: fblgit/juanako-7b-UNA metrics: - memory_disk - memory_inference - inference_latency - inference_throughput - inference_CO2_emissions - inference_energy_consumption tags: - pruna-ai --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> <!-- header end --> [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/rskEr4BZJx) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) Frequently Asked Questions - *How does the compression work?* The model is compressed with llm-int8. - *How does the model quality change?* The quality of the model output might vary compared to the base model. - *How is the model efficiency evaluated?* These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - *What is the model format?* We use safetensors. - *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data. - *What is the naming convention for Pruna Huggingface models?* We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - *What are "first" metrics?* Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. - *What are "Sync" and "Async" metrics?* "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases. ## Setup You can run the smashed model with these steps: 0. Check requirements from the original repo fblgit/juanako-7b-UNA installed. In particular, check python, cuda, and transformers versions. 1. Make sure that you have installed quantization related packages. ```bash pip install transformers accelerate bitsandbytes>0.37.0 ``` 2. Load & run the model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("PrunaAI/fblgit-juanako-7b-UNA-bnb-8bit-smashed", trust_remote_code=True, device_map='auto') tokenizer = AutoTokenizer.from_pretrained("fblgit/juanako-7b-UNA") input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"] outputs = model.generate(input_ids, max_new_tokens=216) tokenizer.decode(outputs[0]) ``` ## Configurations The configuration info are in `smash_config.json`. ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model fblgit/juanako-7b-UNA before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
umesh16071973/_Flooplan_DB_LoRA_	umesh16071973	"2024-02-06T14:24:48Z"	3	1	diffusers	[ "diffusers", "tensorboard", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	"2024-02-06T14:24:40Z"	--- tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a high quality, 4K photo of a FLOORPLAN license: openrail++ --- # SDXL LoRA DreamBooth - umesh16071973/_Flooplan_DB_LoRA_ <Gallery /> ## Model description These are umesh16071973/_Flooplan_DB_LoRA_ LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a high quality, 4K photo of a FLOORPLAN to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](umesh16071973/_Flooplan_DB_LoRA_/tree/main) them in the Files & versions tab.
TFOCUS/Inference-Providers_17	TFOCUS	"2025-02-28T16:29:24Z"	0	0	null	[ "onnx", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	"2025-02-28T16:02:02Z"	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
John6666/toon-e-pony-v1-sdxl	John6666	"2024-12-23T06:36:36Z"	147	1	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "cartoon", "toon", "cute", "bold style", "pony", "en", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	"2024-10-11T10:32:16Z"	--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime - cartoon - toon - cute - bold style - pony --- Original model is [here](https://civitai.com/models/843170/toon-e-pony?modelVersionId=943297). The author is [here](https://huggingface.co/advokat). This model created by [advokat](https://civitai.com/user/advokat).
altomek/CodeRosa-70B-AB1-5bpw-EXL2	altomek	"2024-08-30T10:24:55Z"	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "en", "base_model:altomek/CodeRosa-70B-AB1", "base_model:finetune:altomek/CodeRosa-70B-AB1", "license:llama2", "autotrain_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-03-18T06:27:41Z"	--- base_model: altomek/CodeRosa-70B-AB1 language: - en license: llama2 inference: false --- # CodeRosa-70B-AB1 ExLlamav2 5 bpw 8 h quants of https://huggingface.co/altomek/CodeRosa-70B-AB1
auxyus/3b08c9c9-8517-49a0-8d26-63097e0e34a1	auxyus	"2025-02-02T08:14:30Z"	8	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:unsloth/Qwen2.5-3B", "base_model:adapter:unsloth/Qwen2.5-3B", "license:other", "region:us" ]	null	"2025-02-02T08:04:58Z"	--- library_name: peft license: other base_model: unsloth/Qwen2.5-3B tags: - axolotl - generated_from_trainer model-index: - name: 3b08c9c9-8517-49a0-8d26-63097e0e34a1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/Qwen2.5-3B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - c485b08dfb34ae17_train_data.json ds_type: json format: custom path: /workspace/input_data/c485b08dfb34ae17_train_data.json type: field_input: authors field_instruction: abstract field_output: title format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: auxyus/3b08c9c9-8517-49a0-8d26-63097e0e34a1 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: false local_rank: 0 logging_steps: 3 lora_alpha: 32 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lr_scheduler: cosine max_steps: 100 micro_batch_size: 8 mlflow_experiment_name: /tmp/c485b08dfb34ae17_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: techspear-hub wandb_mode: online wandb_name: 33333ede-a0bf-4279-9af7-9eb33c9d47f1 wandb_project: Gradients-On-Two wandb_run: your_name wandb_runid: 33333ede-a0bf-4279-9af7-9eb33c9d47f1 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 3b08c9c9-8517-49a0-8d26-63097e0e34a1 This model is a fine-tuned version of [unsloth/Qwen2.5-3B](https://huggingface.co/unsloth/Qwen2.5-3B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1770 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0129 \| 1 \| 2.1540 \| \| 1.9569 \| 0.1158 \| 9 \| 1.4746 \| \| 1.3291 \| 0.2315 \| 18 \| 1.2417 \| \| 1.2773 \| 0.3473 \| 27 \| 1.2062 \| \| 1.1474 \| 0.4630 \| 36 \| 1.1939 \| \| 1.2433 \| 0.5788 \| 45 \| 1.1861 \| \| 1.3276 \| 0.6945 \| 54 \| 1.1822 \| \| 1.3739 \| 0.8103 \| 63 \| 1.1801 \| \| 1.2675 \| 0.9260 \| 72 \| 1.1798 \| \| 1.2192 \| 1.0482 \| 81 \| 1.1771 \| \| 1.2751 \| 1.1640 \| 90 \| 1.1772 \| \| 1.227 \| 1.2797 \| 99 \| 1.1770 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
machinelearningzuu/queue_detection_cctv	machinelearningzuu	"2024-07-08T21:26:49Z"	87	0	transformers	[ "transformers", "safetensors", "conditional_detr", "object-detection", "generated_from_trainer", "base_model:microsoft/conditional-detr-resnet-50", "base_model:finetune:microsoft/conditional-detr-resnet-50", "license:apache-2.0", "endpoints_compatible", "region:us" ]	object-detection	"2024-07-07T08:25:07Z"	--- base_model: microsoft/conditional-detr-resnet-50 license: apache-2.0 tags: - generated_from_trainer model-index: - name: queue_detection_cctv results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # queue_detection_cctv This model is a fine-tuned version of [microsoft/conditional-detr-resnet-50](https://huggingface.co/microsoft/conditional-detr-resnet-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1291 - Map: 0.9532 - Map 50: 0.9901 - Map 75: 0.9845 - Map Small: -1.0 - Map Medium: 0.3203 - Map Large: 0.9578 - Mar 1: 0.5044 - Mar 10: 0.9715 - Mar 100: 0.972 - Mar Small: -1.0 - Mar Medium: 0.3538 - Mar Large: 0.9747 - Map Cashier: 0.9618 - Mar 100 Cashier: 0.9775 - Map Cx: 0.9447 - Mar 100 Cx: 0.9664 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 100 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Map \| Map 50 \| Map 75 \| Map Small \| Map Medium \| Map Large \| Mar 1 \| Mar 10 \| Mar 100 \| Mar Small \| Mar Medium \| Mar Large \| Map Cashier \| Mar 100 Cashier \| Map Cx \| Mar 100 Cx \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:----------:\|:---------:\|:------:\|:------:\|:-------:\|:---------:\|:----------:\|:---------:\|:-----------:\|:---------------:\|:------:\|:----------:\| \| No log \| 1.0 \| 218 \| 1.3927 \| 0.1975 \| 0.3459 \| 0.1995 \| -1.0 \| 0.0 \| 0.1988 \| 0.2409 \| 0.5283 \| 0.7011 \| -1.0 \| 0.0 \| 0.7055 \| 0.2115 \| 0.8043 \| 0.1834 \| 0.5979 \| \| No log \| 2.0 \| 436 \| 0.9964 \| 0.5247 \| 0.8011 \| 0.591 \| -1.0 \| 0.0079 \| 0.5292 \| 0.3316 \| 0.6966 \| 0.7387 \| -1.0 \| 0.0071 \| 0.7453 \| 0.5772 \| 0.8086 \| 0.4723 \| 0.6688 \| \| 2.7418 \| 3.0 \| 654 \| 0.8535 \| 0.6031 \| 0.9058 \| 0.6954 \| -1.0 \| 0.0349 \| 0.6069 \| 0.3603 \| 0.7079 \| 0.733 \| -1.0 \| 0.2 \| 0.7362 \| 0.6576 \| 0.769 \| 0.5485 \| 0.6969 \| \| 2.7418 \| 4.0 \| 872 \| 0.7406 \| 0.6499 \| 0.9356 \| 0.752 \| -1.0 \| 0.0479 \| 0.6543 \| 0.3756 \| 0.7387 \| 0.7586 \| -1.0 \| 0.0923 \| 0.7634 \| 0.7052 \| 0.7953 \| 0.5947 \| 0.7219 \| \| 0.8155 \| 5.0 \| 1090 \| 0.6721 \| 0.6731 \| 0.9516 \| 0.8113 \| -1.0 \| 0.0249 \| 0.6773 \| 0.3819 \| 0.7501 \| 0.7654 \| -1.0 \| 0.0455 \| 0.7701 \| 0.7451 \| 0.8203 \| 0.601 \| 0.7105 \| \| 0.8155 \| 6.0 \| 1308 \| 0.5804 \| 0.7244 \| 0.9632 \| 0.8738 \| 0.0 \| 0.0712 \| 0.7288 \| 0.4038 \| 0.7882 \| 0.8023 \| 0.0 \| 0.1731 \| 0.8066 \| 0.7818 \| 0.8419 \| 0.6671 \| 0.7627 \| \| 0.6668 \| 7.0 \| 1526 \| 0.5430 \| 0.7484 \| 0.9667 \| 0.9041 \| -1.0 \| 0.076 \| 0.7527 \| 0.417 \| 0.8027 \| 0.813 \| -1.0 \| 0.2205 \| 0.8171 \| 0.8068 \| 0.8602 \| 0.69 \| 0.7658 \| \| 0.6668 \| 8.0 \| 1744 \| 0.5524 \| 0.7361 \| 0.9691 \| 0.8958 \| -1.0 \| 0.0273 \| 0.7416 \| 0.4045 \| 0.7839 \| 0.7933 \| -1.0 \| 0.1286 \| 0.7981 \| 0.7845 \| 0.8274 \| 0.6877 \| 0.7592 \| \| 0.6668 \| 9.0 \| 1962 \| 0.5359 \| 0.7415 \| 0.9737 \| 0.901 \| -1.0 \| 0.0845 \| 0.7462 \| 0.4112 \| 0.7999 \| 0.8044 \| -1.0 \| 0.1462 \| 0.8088 \| 0.7844 \| 0.8376 \| 0.6986 \| 0.7713 \| \| 0.5735 \| 10.0 \| 2180 \| 0.5154 \| 0.7497 \| 0.9744 \| 0.907 \| 0.0 \| 0.0368 \| 0.7538 \| 0.414 \| 0.8042 \| 0.8093 \| 0.0 \| 0.1333 \| 0.813 \| 0.8085 \| 0.86 \| 0.6909 \| 0.7586 \| \| 0.5735 \| 11.0 \| 2398 \| 0.4543 \| 0.7824 \| 0.9754 \| 0.9337 \| 0.0 \| 0.0709 \| 0.7908 \| 0.4307 \| 0.8323 \| 0.8368 \| 0.0 \| 0.1794 \| 0.8449 \| 0.8312 \| 0.8765 \| 0.7336 \| 0.7972 \| \| 0.5189 \| 12.0 \| 2616 \| 0.4802 \| 0.7679 \| 0.9769 \| 0.9274 \| 0.0 \| 0.1201 \| 0.7724 \| 0.426 \| 0.8197 \| 0.825 \| 0.0 \| 0.1917 \| 0.8291 \| 0.7985 \| 0.85 \| 0.7374 \| 0.8 \| \| 0.5189 \| 13.0 \| 2834 \| 0.4306 \| 0.7906 \| 0.9825 \| 0.9332 \| -1.0 \| 0.0708 \| 0.7941 \| 0.435 \| 0.8394 \| 0.8448 \| -1.0 \| 0.23 \| 0.8474 \| 0.8474 \| 0.889 \| 0.7339 \| 0.8006 \| \| 0.4874 \| 14.0 \| 3052 \| 0.4660 \| 0.7649 \| 0.9818 \| 0.9264 \| -1.0 \| 0.0504 \| 0.7713 \| 0.4219 \| 0.8155 \| 0.8222 \| -1.0 \| 0.0875 \| 0.8288 \| 0.805 \| 0.8527 \| 0.7248 \| 0.7917 \| \| 0.4874 \| 15.0 \| 3270 \| 0.4392 \| 0.7867 \| 0.9773 \| 0.9278 \| 0.0 \| 0.0256 \| 0.7961 \| 0.4372 \| 0.8336 \| 0.8385 \| 0.0 \| 0.1028 \| 0.8466 \| 0.8243 \| 0.8725 \| 0.7492 \| 0.8045 \| \| 0.4874 \| 16.0 \| 3488 \| 0.4178 \| 0.8018 \| 0.9847 \| 0.9355 \| -1.0 \| 0.2037 \| 0.8061 \| 0.4387 \| 0.8493 \| 0.8551 \| -1.0 \| 0.3714 \| 0.8589 \| 0.8394 \| 0.8881 \| 0.7641 \| 0.822 \| \| 0.4646 \| 17.0 \| 3706 \| 0.3859 \| 0.8138 \| 0.9838 \| 0.9502 \| -1.0 \| 0.1217 \| 0.8189 \| 0.4459 \| 0.8584 \| 0.863 \| -1.0 \| 0.2038 \| 0.8669 \| 0.8508 \| 0.8956 \| 0.7769 \| 0.8303 \| \| 0.4646 \| 18.0 \| 3924 \| 0.4041 \| 0.7987 \| 0.9822 \| 0.9457 \| -1.0 \| 0.097 \| 0.8032 \| 0.4378 \| 0.8486 \| 0.8518 \| -1.0 \| 0.1611 \| 0.8551 \| 0.8323 \| 0.881 \| 0.7652 \| 0.8226 \| \| 0.4317 \| 19.0 \| 4142 \| 0.4013 \| 0.8086 \| 0.9838 \| 0.9442 \| -1.0 \| 0.1816 \| 0.814 \| 0.4412 \| 0.8513 \| 0.8557 \| -1.0 \| 0.2571 \| 0.8605 \| 0.8522 \| 0.8919 \| 0.765 \| 0.8195 \| \| 0.4317 \| 20.0 \| 4360 \| 0.3869 \| 0.8123 \| 0.9823 \| 0.9388 \| -1.0 \| 0.1597 \| 0.8163 \| 0.4475 \| 0.8579 \| 0.8617 \| -1.0 \| 0.2042 \| 0.8653 \| 0.8542 \| 0.896 \| 0.7705 \| 0.8274 \| \| 0.4215 \| 21.0 \| 4578 \| 0.3721 \| 0.816 \| 0.9864 \| 0.9536 \| -1.0 \| 0.1206 \| 0.8198 \| 0.4478 \| 0.8598 \| 0.863 \| -1.0 \| 0.2727 \| 0.8655 \| 0.8607 \| 0.9003 \| 0.7713 \| 0.8258 \| \| 0.4215 \| 22.0 \| 4796 \| 0.3777 \| 0.8245 \| 0.9806 \| 0.9507 \| 0.0 \| 0.1034 \| 0.8324 \| 0.4537 \| 0.8621 \| 0.8649 \| 0.0 \| 0.2118 \| 0.8724 \| 0.8651 \| 0.9012 \| 0.7839 \| 0.8287 \| \| 0.3925 \| 23.0 \| 5014 \| 0.3387 \| 0.8411 \| 0.9872 \| 0.9577 \| -1.0 \| 0.1184 \| 0.845 \| 0.4593 \| 0.8775 \| 0.8799 \| -1.0 \| 0.2429 \| 0.8835 \| 0.8813 \| 0.9153 \| 0.8008 \| 0.8444 \| \| 0.3925 \| 24.0 \| 5232 \| 0.3234 \| 0.842 \| 0.9887 \| 0.9671 \| -1.0 \| 0.1229 \| 0.8463 \| 0.4604 \| 0.8794 \| 0.8812 \| -1.0 \| 0.1864 \| 0.885 \| 0.8736 \| 0.909 \| 0.8104 \| 0.8534 \| \| 0.3925 \| 25.0 \| 5450 \| 0.3463 \| 0.8356 \| 0.9869 \| 0.9556 \| -1.0 \| 0.0775 \| 0.8411 \| 0.4552 \| 0.8769 \| 0.8793 \| -1.0 \| 0.1929 \| 0.8838 \| 0.8788 \| 0.913 \| 0.7925 \| 0.8456 \| \| 0.3676 \| 26.0 \| 5668 \| 0.3170 \| 0.846 \| 0.988 \| 0.9666 \| 0.0 \| 0.1172 \| 0.8515 \| 0.4603 \| 0.886 \| 0.8872 \| 0.0 \| 0.285 \| 0.8907 \| 0.8831 \| 0.9182 \| 0.8089 \| 0.8562 \| \| 0.3676 \| 27.0 \| 5886 \| 0.3552 \| 0.8246 \| 0.9832 \| 0.9545 \| -1.0 \| 0.13 \| 0.8285 \| 0.4535 \| 0.8704 \| 0.8745 \| -1.0 \| 0.2367 \| 0.8785 \| 0.8559 \| 0.9005 \| 0.7932 \| 0.8484 \| \| 0.3669 \| 28.0 \| 6104 \| 0.3342 \| 0.8427 \| 0.9876 \| 0.9665 \| -1.0 \| 0.1369 \| 0.8468 \| 0.4585 \| 0.8813 \| 0.8843 \| -1.0 \| 0.2625 \| 0.8874 \| 0.8587 \| 0.898 \| 0.8267 \| 0.8707 \| \| 0.3669 \| 29.0 \| 6322 \| 0.3033 \| 0.854 \| 0.9892 \| 0.9687 \| -1.0 \| 0.1795 \| 0.8572 \| 0.4663 \| 0.8954 \| 0.8968 \| -1.0 \| 0.3 \| 0.8991 \| 0.8813 \| 0.9193 \| 0.8268 \| 0.8744 \| \| 0.349 \| 30.0 \| 6540 \| 0.3099 \| 0.8515 \| 0.9863 \| 0.9676 \| -1.0 \| 0.1251 \| 0.8571 \| 0.4666 \| 0.8917 \| 0.8936 \| -1.0 \| 0.2 \| 0.8978 \| 0.8868 \| 0.9261 \| 0.8162 \| 0.8611 \| \| 0.349 \| 31.0 \| 6758 \| 0.3247 \| 0.842 \| 0.9884 \| 0.963 \| 0.0 \| 0.1145 \| 0.8491 \| 0.4607 \| 0.8828 \| 0.8854 \| 0.0 \| 0.1462 \| 0.8916 \| 0.8704 \| 0.9104 \| 0.8137 \| 0.8605 \| \| 0.349 \| 32.0 \| 6976 \| 0.2943 \| 0.8529 \| 0.9887 \| 0.9651 \| -1.0 \| 0.1639 \| 0.8587 \| 0.4683 \| 0.8916 \| 0.8949 \| -1.0 \| 0.225 \| 0.8997 \| 0.89 \| 0.9246 \| 0.8158 \| 0.8653 \| \| 0.3378 \| 33.0 \| 7194 \| 0.2923 \| 0.8605 \| 0.989 \| 0.9695 \| -1.0 \| 0.1212 \| 0.8657 \| 0.4687 \| 0.8985 \| 0.9006 \| -1.0 \| 0.2136 \| 0.9042 \| 0.8893 \| 0.9257 \| 0.8317 \| 0.8756 \| \| 0.3378 \| 34.0 \| 7412 \| 0.2878 \| 0.8616 \| 0.9895 \| 0.9673 \| -1.0 \| 0.1464 \| 0.8665 \| 0.4712 \| 0.897 \| 0.899 \| -1.0 \| 0.2 \| 0.9036 \| 0.8907 \| 0.9246 \| 0.8325 \| 0.8734 \| \| 0.3206 \| 35.0 \| 7630 \| 0.3342 \| 0.837 \| 0.9866 \| 0.9674 \| -1.0 \| 0.1634 \| 0.8423 \| 0.4584 \| 0.8772 \| 0.8802 \| -1.0 \| 0.2611 \| 0.8844 \| 0.8684 \| 0.906 \| 0.8057 \| 0.8544 \| \| 0.3206 \| 36.0 \| 7848 \| 0.2796 \| 0.8713 \| 0.989 \| 0.9716 \| -1.0 \| 0.1054 \| 0.8759 \| 0.4699 \| 0.9066 \| 0.9084 \| -1.0 \| 0.15 \| 0.9128 \| 0.9052 \| 0.9373 \| 0.8373 \| 0.8795 \| \| 0.3152 \| 37.0 \| 8066 \| 0.2894 \| 0.8667 \| 0.987 \| 0.9746 \| 0.0 \| 0.1359 \| 0.8743 \| 0.4716 \| 0.9022 \| 0.9037 \| 0.0 \| 0.1667 \| 0.9109 \| 0.8966 \| 0.9309 \| 0.8367 \| 0.8765 \| \| 0.3152 \| 38.0 \| 8284 \| 0.2641 \| 0.8744 \| 0.9894 \| 0.9722 \| -1.0 \| 0.1413 \| 0.8793 \| 0.4727 \| 0.9132 \| 0.9148 \| -1.0 \| 0.2333 \| 0.9178 \| 0.8909 \| 0.9305 \| 0.858 \| 0.8992 \| \| 0.3082 \| 39.0 \| 8502 \| 0.2834 \| 0.8703 \| 0.9873 \| 0.9702 \| -1.0 \| 0.132 \| 0.8764 \| 0.473 \| 0.9082 \| 0.9128 \| -1.0 \| 0.2633 \| 0.9168 \| 0.8988 \| 0.9347 \| 0.8417 \| 0.891 \| \| 0.3082 \| 40.0 \| 8720 \| 0.2774 \| 0.8655 \| 0.9897 \| 0.9738 \| -1.0 \| 0.2021 \| 0.8711 \| 0.4694 \| 0.9025 \| 0.9043 \| -1.0 \| 0.275 \| 0.9081 \| 0.8971 \| 0.9314 \| 0.8339 \| 0.8772 \| \| 0.3082 \| 41.0 \| 8938 \| 0.2935 \| 0.8598 \| 0.988 \| 0.9699 \| -1.0 \| 0.0999 \| 0.8666 \| 0.4688 \| 0.8961 \| 0.8976 \| -1.0 \| 0.15 \| 0.9037 \| 0.8889 \| 0.9255 \| 0.8308 \| 0.8697 \| \| 0.3078 \| 42.0 \| 9156 \| 0.2746 \| 0.868 \| 0.9895 \| 0.9777 \| -1.0 \| 0.2159 \| 0.8738 \| 0.4712 \| 0.9021 \| 0.9032 \| -1.0 \| 0.275 \| 0.9079 \| 0.9016 \| 0.933 \| 0.8343 \| 0.8734 \| \| 0.3078 \| 43.0 \| 9374 \| 0.2662 \| 0.8731 \| 0.9897 \| 0.9798 \| -1.0 \| 0.1849 \| 0.8794 \| 0.4752 \| 0.9083 \| 0.9091 \| -1.0 \| 0.2 \| 0.9136 \| 0.888 \| 0.9206 \| 0.8582 \| 0.8975 \| \| 0.2898 \| 44.0 \| 9592 \| 0.2564 \| 0.8824 \| 0.9868 \| 0.9732 \| -1.0 \| 0.1263 \| 0.8871 \| 0.4775 \| 0.9148 \| 0.9165 \| -1.0 \| 0.15 \| 0.9211 \| 0.9076 \| 0.9377 \| 0.8571 \| 0.8954 \| \| 0.2898 \| 45.0 \| 9810 \| 0.2813 \| 0.8753 \| 0.9876 \| 0.977 \| 0.0 \| 0.1325 \| 0.8817 \| 0.4714 \| 0.911 \| 0.9123 \| 0.0 \| 0.2167 \| 0.9179 \| 0.9042 \| 0.9381 \| 0.8464 \| 0.8865 \| \| 0.2758 \| 46.0 \| 10028 \| 0.2633 \| 0.8786 \| 0.9872 \| 0.9719 \| 0.0 \| 0.1841 \| 0.8854 \| 0.4758 \| 0.9164 \| 0.9177 \| 0.0 \| 0.2615 \| 0.9218 \| 0.9012 \| 0.9374 \| 0.856 \| 0.898 \| \| 0.2758 \| 47.0 \| 10246 \| 0.2479 \| 0.8795 \| 0.9895 \| 0.9765 \| 0.0 \| 0.2066 \| 0.8849 \| 0.4765 \| 0.9146 \| 0.9171 \| 0.0 \| 0.275 \| 0.9207 \| 0.9114 \| 0.9448 \| 0.8476 \| 0.8893 \| \| 0.2758 \| 48.0 \| 10464 \| 0.2373 \| 0.8894 \| 0.9897 \| 0.9799 \| -1.0 \| 0.1994 \| 0.8939 \| 0.4795 \| 0.9253 \| 0.926 \| -1.0 \| 0.2545 \| 0.9293 \| 0.9076 \| 0.9431 \| 0.8713 \| 0.909 \| \| 0.2708 \| 49.0 \| 10682 \| 0.2538 \| 0.8846 \| 0.9893 \| 0.9793 \| 0.0 \| 0.2669 \| 0.8903 \| 0.4799 \| 0.9213 \| 0.9224 \| 0.0 \| 0.315 \| 0.9284 \| 0.9052 \| 0.9383 \| 0.8641 \| 0.9065 \| \| 0.2708 \| 50.0 \| 10900 \| 0.2445 \| 0.8919 \| 0.9896 \| 0.9745 \| -1.0 \| 0.2193 \| 0.8972 \| 0.4765 \| 0.9228 \| 0.925 \| -1.0 \| 0.3969 \| 0.9294 \| 0.9239 \| 0.9511 \| 0.8599 \| 0.8989 \| \| 0.2595 \| 51.0 \| 11118 \| 0.2110 \| 0.9037 \| 0.99 \| 0.9845 \| -1.0 \| 0.2267 \| 0.9093 \| 0.4882 \| 0.9339 \| 0.9346 \| -1.0 \| 0.25 \| 0.9374 \| 0.9299 \| 0.9574 \| 0.8776 \| 0.9117 \| \| 0.2595 \| 52.0 \| 11336 \| 0.2374 \| 0.897 \| 0.99 \| 0.9792 \| -1.0 \| 0.2066 \| 0.9029 \| 0.48 \| 0.9267 \| 0.9285 \| -1.0 \| 0.3179 \| 0.9335 \| 0.9257 \| 0.9531 \| 0.8684 \| 0.9039 \| \| 0.2378 \| 53.0 \| 11554 \| 0.2517 \| 0.8826 \| 0.9894 \| 0.9716 \| -1.0 \| 0.1494 \| 0.8901 \| 0.4782 \| 0.9162 \| 0.9188 \| -1.0 \| 0.2475 \| 0.9242 \| 0.9152 \| 0.9455 \| 0.8501 \| 0.892 \| \| 0.2378 \| 54.0 \| 11772 \| 0.2260 \| 0.8971 \| 0.9899 \| 0.9771 \| -1.0 \| 0.1848 \| 0.9029 \| 0.4825 \| 0.9304 \| 0.9315 \| -1.0 \| 0.2077 \| 0.936 \| 0.9255 \| 0.9544 \| 0.8687 \| 0.9087 \| \| 0.2378 \| 55.0 \| 11990 \| 0.2144 \| 0.9118 \| 0.9899 \| 0.9844 \| -1.0 \| 0.2843 \| 0.9158 \| 0.4875 \| 0.9417 \| 0.9435 \| -1.0 \| 0.3333 \| 0.9456 \| 0.9351 \| 0.9608 \| 0.8885 \| 0.9263 \| \| 0.2494 \| 56.0 \| 12208 \| 0.2028 \| 0.9107 \| 0.9897 \| 0.9814 \| 0.0 \| 0.1831 \| 0.9168 \| 0.4906 \| 0.9395 \| 0.9414 \| 0.0 \| 0.22 \| 0.9466 \| 0.935 \| 0.9585 \| 0.8864 \| 0.9243 \| \| 0.2494 \| 57.0 \| 12426 \| 0.2341 \| 0.8897 \| 0.9897 \| 0.9812 \| -1.0 \| 0.1783 \| 0.8932 \| 0.4822 \| 0.9242 \| 0.926 \| -1.0 \| 0.2154 \| 0.9303 \| 0.9168 \| 0.948 \| 0.8625 \| 0.9039 \| \| 0.2228 \| 58.0 \| 12644 \| 0.2075 \| 0.9084 \| 0.9899 \| 0.9792 \| -1.0 \| 0.1741 \| 0.9142 \| 0.4899 \| 0.9375 \| 0.9379 \| -1.0 \| 0.2308 \| 0.9421 \| 0.932 \| 0.9581 \| 0.8849 \| 0.9177 \| \| 0.2228 \| 59.0 \| 12862 \| 0.2059 \| 0.9096 \| 0.9896 \| 0.9803 \| 0.0 \| 0.2969 \| 0.9138 \| 0.4893 \| 0.9375 \| 0.9395 \| 0.0 \| 0.31 \| 0.9431 \| 0.9311 \| 0.957 \| 0.8881 \| 0.9219 \| \| 0.2218 \| 60.0 \| 13080 \| 0.2028 \| 0.9136 \| 0.9899 \| 0.984 \| -1.0 \| 0.2316 \| 0.9164 \| 0.4875 \| 0.9408 \| 0.9416 \| -1.0 \| 0.295 \| 0.9442 \| 0.9433 \| 0.9654 \| 0.884 \| 0.9177 \| \| 0.2218 \| 61.0 \| 13298 \| 0.2013 \| 0.911 \| 0.99 \| 0.9786 \| -1.0 \| 0.253 \| 0.9158 \| 0.4904 \| 0.9388 \| 0.94 \| -1.0 \| 0.3 \| 0.9435 \| 0.9325 \| 0.9572 \| 0.8895 \| 0.9228 \| \| 0.2238 \| 62.0 \| 13516 \| 0.2033 \| 0.9134 \| 0.9899 \| 0.9825 \| 0.0 \| 0.2228 \| 0.9199 \| 0.4896 \| 0.9426 \| 0.9438 \| 0.0 \| 0.2667 \| 0.9484 \| 0.9367 \| 0.9624 \| 0.8902 \| 0.9252 \| \| 0.2238 \| 63.0 \| 13734 \| 0.1893 \| 0.9216 \| 0.99 \| 0.9836 \| -1.0 \| 0.1905 \| 0.9271 \| 0.4942 \| 0.9509 \| 0.9512 \| -1.0 \| 0.235 \| 0.9546 \| 0.9403 \| 0.9664 \| 0.9029 \| 0.9361 \| \| 0.2238 \| 64.0 \| 13952 \| 0.1893 \| 0.9267 \| 0.9898 \| 0.9835 \| 0.0 \| 0.2342 \| 0.9317 \| 0.4957 \| 0.9524 \| 0.9536 \| 0.0 \| 0.2583 \| 0.9585 \| 0.9491 \| 0.971 \| 0.9043 \| 0.9363 \| \| 0.2131 \| 65.0 \| 14170 \| 0.1769 \| 0.9322 \| 0.9901 \| 0.9847 \| -1.0 \| 0.2413 \| 0.9349 \| 0.4982 \| 0.9554 \| 0.9559 \| -1.0 \| 0.2864 \| 0.959 \| 0.9463 \| 0.9673 \| 0.9181 \| 0.9445 \| \| 0.2131 \| 66.0 \| 14388 \| 0.1848 \| 0.9312 \| 0.9898 \| 0.9842 \| 0.0 \| 0.2901 \| 0.9358 \| 0.4973 \| 0.9545 \| 0.9551 \| 0.0 \| 0.425 \| 0.9591 \| 0.9517 \| 0.9709 \| 0.9107 \| 0.9394 \| \| 0.2038 \| 67.0 \| 14606 \| 0.1809 \| 0.9277 \| 0.9899 \| 0.9815 \| 0.0 \| 0.2354 \| 0.9329 \| 0.4951 \| 0.9524 \| 0.9539 \| 0.0 \| 0.2846 \| 0.9586 \| 0.9441 \| 0.9668 \| 0.9112 \| 0.9411 \| \| 0.2038 \| 68.0 \| 14824 \| 0.1831 \| 0.9178 \| 0.9899 \| 0.98 \| 0.0 \| 0.1728 \| 0.9256 \| 0.4922 \| 0.9472 \| 0.9483 \| 0.0 \| 0.23 \| 0.9538 \| 0.9396 \| 0.9646 \| 0.896 \| 0.9319 \| \| 0.1995 \| 69.0 \| 15042 \| 0.1631 \| 0.934 \| 0.9901 \| 0.9861 \| -1.0 \| 0.2804 \| 0.9405 \| 0.4982 \| 0.9574 \| 0.9583 \| -1.0 \| 0.325 \| 0.9615 \| 0.954 \| 0.9729 \| 0.914 \| 0.9438 \| \| 0.1995 \| 70.0 \| 15260 \| 0.1685 \| 0.9293 \| 0.9899 \| 0.9846 \| -1.0 \| 0.2397 \| 0.935 \| 0.4964 \| 0.9546 \| 0.9553 \| -1.0 \| 0.2714 \| 0.9593 \| 0.948 \| 0.9698 \| 0.9105 \| 0.9408 \| \| 0.1995 \| 71.0 \| 15478 \| 0.1629 \| 0.9371 \| 0.9901 \| 0.9842 \| -1.0 \| 0.2541 \| 0.942 \| 0.498 \| 0.9603 \| 0.9609 \| -1.0 \| 0.4964 \| 0.965 \| 0.954 \| 0.9741 \| 0.9202 \| 0.9477 \| \| 0.1877 \| 72.0 \| 15696 \| 0.1606 \| 0.944 \| 0.9901 \| 0.9846 \| -1.0 \| 0.277 \| 0.9469 \| 0.4988 \| 0.9636 \| 0.9642 \| -1.0 \| 0.3038 \| 0.9676 \| 0.96 \| 0.9758 \| 0.9281 \| 0.9527 \| \| 0.1877 \| 73.0 \| 15914 \| 0.1532 \| 0.9389 \| 0.99 \| 0.9806 \| 0.0 \| 0.2592 \| 0.9446 \| 0.5009 \| 0.961 \| 0.962 \| 0.0 \| 0.3133 \| 0.9662 \| 0.9564 \| 0.9749 \| 0.9214 \| 0.9492 \| \| 0.1912 \| 74.0 \| 16132 \| 0.1434 \| 0.9488 \| 0.995 \| 0.9934 \| -1.0 \| 0.5552 \| 0.9507 \| 0.5033 \| 0.9673 \| 0.9675 \| -1.0 \| 0.7182 \| 0.969 \| 0.9639 \| 0.9786 \| 0.9336 \| 0.9563 \| \| 0.1912 \| 75.0 \| 16350 \| 0.1726 \| 0.9309 \| 0.9901 \| 0.9832 \| -1.0 \| 0.216 \| 0.9344 \| 0.4964 \| 0.9568 \| 0.9578 \| -1.0 \| 0.2611 \| 0.9607 \| 0.9539 \| 0.9747 \| 0.9079 \| 0.941 \| \| 0.1859 \| 76.0 \| 16568 \| 0.1587 \| 0.9378 \| 0.9901 \| 0.9847 \| -1.0 \| 0.1684 \| 0.944 \| 0.4994 \| 0.9601 \| 0.9607 \| -1.0 \| 0.2382 \| 0.9662 \| 0.952 \| 0.9715 \| 0.9237 \| 0.9499 \| \| 0.1859 \| 77.0 \| 16786 \| 0.1378 \| 0.9509 \| 0.9901 \| 0.9845 \| -1.0 \| 0.2089 \| 0.959 \| 0.5047 \| 0.9688 \| 0.9691 \| -1.0 \| 0.2353 \| 0.9748 \| 0.9666 \| 0.9823 \| 0.9352 \| 0.9559 \| \| 0.1747 \| 78.0 \| 17004 \| 0.1416 \| 0.9478 \| 0.9901 \| 0.985 \| 0.0 \| 0.3334 \| 0.9521 \| 0.5039 \| 0.9685 \| 0.9692 \| 0.0 \| 0.35 \| 0.9719 \| 0.9617 \| 0.9799 \| 0.9338 \| 0.9586 \| \| 0.1747 \| 79.0 \| 17222 \| 0.1615 \| 0.9376 \| 0.9949 \| 0.9873 \| -1.0 \| 0.5057 \| 0.9406 \| 0.5003 \| 0.9599 \| 0.9607 \| -1.0 \| 0.5688 \| 0.9644 \| 0.9583 \| 0.9746 \| 0.917 \| 0.9469 \| \| 0.1747 \| 80.0 \| 17440 \| 0.1482 \| 0.9427 \| 0.99 \| 0.9823 \| -1.0 \| 0.1933 \| 0.9499 \| 0.5025 \| 0.9639 \| 0.9642 \| -1.0 \| 0.2321 \| 0.9689 \| 0.9566 \| 0.9762 \| 0.9289 \| 0.9521 \| \| 0.1707 \| 81.0 \| 17658 \| 0.1379 \| 0.9518 \| 0.9901 \| 0.9894 \| -1.0 \| 0.2838 \| 0.956 \| 0.504 \| 0.97 \| 0.9702 \| -1.0 \| 0.3 \| 0.9742 \| 0.965 \| 0.9787 \| 0.9386 \| 0.9618 \| \| 0.1707 \| 82.0 \| 17876 \| 0.1384 \| 0.9478 \| 0.9901 \| 0.9846 \| -1.0 \| 0.2518 \| 0.9545 \| 0.504 \| 0.9687 \| 0.9691 \| -1.0 \| 0.2643 \| 0.9734 \| 0.9612 \| 0.9787 \| 0.9344 \| 0.9595 \| \| 0.1658 \| 83.0 \| 18094 \| 0.1379 \| 0.9532 \| 0.9901 \| 0.9845 \| -1.0 \| 0.2543 \| 0.9567 \| 0.5043 \| 0.9707 \| 0.9714 \| -1.0 \| 0.2708 \| 0.975 \| 0.9655 \| 0.981 \| 0.9408 \| 0.9617 \| \| 0.1658 \| 84.0 \| 18312 \| 0.1325 \| 0.9544 \| 0.9901 \| 0.9845 \| 0.0 \| 0.256 \| 0.9597 \| 0.5047 \| 0.9712 \| 0.972 \| 0.0 \| 0.3036 \| 0.9762 \| 0.9672 \| 0.9811 \| 0.9417 \| 0.9628 \| \| 0.1532 \| 85.0 \| 18530 \| 0.1558 \| 0.9452 \| 0.99 \| 0.9845 \| -1.0 \| 0.2469 \| 0.9495 \| 0.5009 \| 0.9648 \| 0.9657 \| -1.0 \| 0.2769 \| 0.9695 \| 0.9584 \| 0.9749 \| 0.932 \| 0.9565 \| \| 0.1532 \| 86.0 \| 18748 \| 0.1228 \| 0.9538 \| 0.9901 \| 0.9841 \| -1.0 \| 0.3437 \| 0.9585 \| 0.5056 \| 0.972 \| 0.9726 \| -1.0 \| 0.3727 \| 0.9747 \| 0.9642 \| 0.9806 \| 0.9434 \| 0.9646 \| \| 0.1532 \| 87.0 \| 18966 \| 0.1317 \| 0.9587 \| 0.9901 \| 0.9844 \| 0.0 \| 0.4141 \| 0.965 \| 0.5064 \| 0.9738 \| 0.974 \| 0.0 \| 0.4517 \| 0.9791 \| 0.9676 \| 0.9815 \| 0.9498 \| 0.9664 \| \| 0.1574 \| 88.0 \| 19184 \| 0.1318 \| 0.9508 \| 0.9901 \| 0.9845 \| 0.0 \| 0.2545 \| 0.9581 \| 0.5059 \| 0.9705 \| 0.9706 \| 0.0 \| 0.2962 \| 0.9747 \| 0.9594 \| 0.9778 \| 0.9422 \| 0.9633 \| \| 0.1574 \| 89.0 \| 19402 \| 0.1424 \| 0.9513 \| 0.9899 \| 0.984 \| -1.0 \| 0.2362 \| 0.9547 \| 0.5034 \| 0.9691 \| 0.9695 \| -1.0 \| 0.2875 \| 0.9729 \| 0.9636 \| 0.9786 \| 0.939 \| 0.9603 \| \| 0.1537 \| 90.0 \| 19620 \| 0.1240 \| 0.9565 \| 0.9901 \| 0.9896 \| -1.0 \| 0.5053 \| 0.9592 \| 0.5066 \| 0.9747 \| 0.9752 \| -1.0 \| 0.55 \| 0.9771 \| 0.9669 \| 0.9823 \| 0.9461 \| 0.9681 \| \| 0.1537 \| 91.0 \| 19838 \| 0.1382 \| 0.947 \| 0.9901 \| 0.9835 \| 0.0 \| 0.5316 \| 0.9504 \| 0.5018 \| 0.9681 \| 0.9683 \| 0.0 \| 0.555 \| 0.9712 \| 0.9622 \| 0.9775 \| 0.9319 \| 0.9592 \| \| 0.1547 \| 92.0 \| 20056 \| 0.1276 \| 0.9565 \| 0.9901 \| 0.983 \| -1.0 \| 0.3161 \| 0.9618 \| 0.5058 \| 0.9742 \| 0.9743 \| -1.0 \| 0.3458 \| 0.977 \| 0.9668 \| 0.9818 \| 0.9462 \| 0.9669 \| \| 0.1547 \| 93.0 \| 20274 \| 0.1329 \| 0.9539 \| 0.99 \| 0.9836 \| -1.0 \| 0.2997 \| 0.9593 \| 0.5053 \| 0.9718 \| 0.9728 \| -1.0 \| 0.3318 \| 0.9754 \| 0.9679 \| 0.982 \| 0.9398 \| 0.9635 \| \| 0.1547 \| 94.0 \| 20492 \| 0.1348 \| 0.9571 \| 0.99 \| 0.9846 \| -1.0 \| 0.3267 \| 0.9615 \| 0.5039 \| 0.9732 \| 0.9737 \| -1.0 \| 0.3625 \| 0.9761 \| 0.9678 \| 0.9823 \| 0.9463 \| 0.9652 \| \| 0.1513 \| 95.0 \| 20710 \| 0.1251 \| 0.9546 \| 0.9901 \| 0.9844 \| 0.0 \| 0.2549 \| 0.9626 \| 0.5049 \| 0.9728 \| 0.9731 \| 0.0 \| 0.2625 \| 0.9775 \| 0.965 \| 0.981 \| 0.9442 \| 0.9652 \| \| 0.1513 \| 96.0 \| 20928 \| 0.1264 \| 0.9594 \| 0.9901 \| 0.9899 \| 0.0 \| 0.327 \| 0.9631 \| 0.5068 \| 0.9755 \| 0.9763 \| 0.0 \| 0.3409 \| 0.9794 \| 0.9696 \| 0.9842 \| 0.9492 \| 0.9683 \| \| 0.1635 \| 97.0 \| 21146 \| 0.1306 \| 0.9515 \| 0.9901 \| 0.9843 \| -1.0 \| 0.2685 \| 0.9561 \| 0.5041 \| 0.9696 \| 0.9703 \| -1.0 \| 0.2857 \| 0.9742 \| 0.9626 \| 0.9795 \| 0.9404 \| 0.9611 \| \| 0.1635 \| 98.0 \| 21364 \| 0.1410 \| 0.9481 \| 0.9899 \| 0.9788 \| 0.0 \| 0.4025 \| 0.9542 \| 0.5031 \| 0.9662 \| 0.9678 \| 0.0 \| 0.4458 \| 0.9722 \| 0.9621 \| 0.9789 \| 0.9341 \| 0.9567 \| \| 0.1505 \| 99.0 \| 21582 \| 0.1253 \| 0.9571 \| 0.9901 \| 0.984 \| -1.0 \| 0.3105 \| 0.962 \| 0.5066 \| 0.9737 \| 0.974 \| -1.0 \| 0.3375 \| 0.9777 \| 0.9702 \| 0.9832 \| 0.944 \| 0.9648 \| \| 0.1505 \| 100.0 \| 21800 \| 0.1291 \| 0.9532 \| 0.9901 \| 0.9845 \| -1.0 \| 0.3203 \| 0.9578 \| 0.5044 \| 0.9715 \| 0.972 \| -1.0 \| 0.3538 \| 0.9747 \| 0.9618 \| 0.9775 \| 0.9447 \| 0.9664 \| ### Framework versions - Transformers 4.42.3 - Pytorch 2.3.1+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
aidando73/Qwen-2.5-7B-Simple-RL-v9	aidando73	"2025-03-24T23:33:49Z"	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "grpo", "conversational", "arxiv:2402.03300", "base_model:Qwen/Qwen2.5-Math-7B", "base_model:finetune:Qwen/Qwen2.5-Math-7B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-03-24T20:24:58Z"	--- base_model: Qwen/Qwen2.5-Math-7B library_name: transformers model_name: Qwen-2.5-7B-Simple-RL-v9 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Qwen-2.5-7B-Simple-RL-v9 This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="aidando73/Qwen-2.5-7B-Simple-RL-v9", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/aidando73-personal/open-r1-math-rl/runs/1v3mtjxf) This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.16.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.5.1 - Datasets: 3.4.1 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Weni/ZeroShot-3.4.0-Mistral-Retry-7b-DPO-1.0.0	Weni	"2024-03-11T14:17:06Z"	0	0	trl	[ "trl", "safetensors", "DPO", "ZeroShot", "en", "es", "pt", "base_model:Weni/ZeroShot-3.3.14-Mistral-7b-Multilanguage-3.2.0-merged", "base_model:finetune:Weni/ZeroShot-3.3.14-Mistral-7b-Multilanguage-3.2.0-merged", "license:mit", "region:us" ]	null	"2024-03-11T12:34:29Z"	--- license: mit library_name: "trl" tags: - DPO - ZeroShot base_model: Weni/ZeroShot-3.3.14-Mistral-7b-Multilanguage-3.2.0-merged model-index: - name: Weni/ZeroShot-3.4.0-Mistral-Retry-7b-DPO-1.0.0 results: [] language: ['en', 'es', 'pt'] --- # Weni/ZeroShot-3.4.0-Mistral-Retry-7b-DPO-1.0.0 This model is a fine-tuned version of [Weni/ZeroShot-3.3.14-Mistral-7b-Multilanguage-3.2.0-merged] on the dataset Weni/zeroshot-dpo-1.0.0 with the DPO trainer. It is part of the ZeroShot project for [Weni](https://weni.ai/). It achieves the following results on the evaluation set: {'eval_loss': 0.12734735012054443, 'eval_runtime': 25.6184, 'eval_samples_per_second': 2.381, 'eval_steps_per_second': 0.312, 'eval_rewards/chosen': 4.7875847816467285, 'eval_rewards/rejected': -1.6130797863006592, 'eval_rewards/accuracies': 0.921875, 'eval_rewards/margins': 6.400664329528809, 'eval_logps/rejected': -15.168061256408691, 'eval_logps/chosen': -11.294010162353516, 'eval_logits/rejected': -1.3262749910354614, 'eval_logits/chosen': -1.370504379272461, 'epoch': 0.94} ## Intended uses & limitations This model has not been trained to avoid specific intructions. ## Training procedure Finetuning was done on the model Weni/ZeroShot-3.3.14-Mistral-7b-Multilanguage-3.2.0-merged with the following prompt: ``` Portuguese: [INST] Você é muito especialista em classificar a frase do usuário em um chatbot sobre: {context} Pare, pense bem e responda com APENAS UM ÚNICO \`id\` da classe que melhor represente a intenção para a frase do usuário de acordo com a análise de seu contexto, responda APENAS com o \`id\` da classe só se você tiver muita certeza e não explique o motivo. Na ausência, falta de informações ou caso a frase do usuário não se enquadre em nenhuma classe, classifique como "-1". # Essas são as Classes com seus Id e Contexto: {all_classes} # Frase do usuário: {input} # Id da Classe: [/INST] Spanish: [INST] Eres muy experto en clasificar la frase del usuario en un chatbot sobre: {context} Deténgase, piense bien y responda con SOLO UN ÚNICO \`id\` de la clase que mejor represente la intención para la frase del usuario de acuerdo con el análisis de su contexto, responda SOLO con el \`id\` de la clase si está muy seguro y no explique el motivo. En ausencia, falta de información o en caso de que la frase del usuario no se ajuste a ninguna clase, clasifique como "-1". # Estas son las Clases con sus Id y Contexto: {all_classes} # Frase del usuario: {input} # Id de la Clase: [/INST] English: [INST] You are very expert in classifying the user sentence in a chatbot about: {context} Stop, think carefully, and respond with ONLY ONE SINGLE \`id\` of the class that best represents the intention for the user's sentence according to the analysis of its context, respond ONLY with the \`id\` of the class if you are very sure and do not explain the reason. In the absence, lack of information, or if the user's sentence does not fit into any class, classify as "-1". # These are the Classes and its Context: {all_classes} # User's sentence: {input} # Class Id: [/INST] Chosen_response: {chosen_response} Rejected_response: {rejected_response} ``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - per_device_train_batch_size: 8 - per_device_eval_batch_size: 8 - gradient_accumulation_steps: 4 - num_gpus: 1 - total_train_batch_size: 32 - optimizer: AdamW - lr_scheduler_type: cosine - num_steps: 16 - quantization_type: bitsandbytes - LoRA: ("\n - bits: 4\n - use_exllama: True\n - device_map: auto\n - use_cache: False\n - lora_r: 8\n - lora_alpha: 16\n - lora_dropout: 0.1\n - bias: none\n - target_modules: ['q_proj', 'k_proj', 'v_proj', 'o_proj']\n - task_type: CAUSAL_LM",) ### Training results ### Framework versions - transformers==4.38.2 - datasets==2.17.1 - peft==0.8.2 - safetensors==0.4.2 - evaluate==0.4.1 - bitsandbytes==0.42 - huggingface_hub==0.20.3 - seqeval==1.2.2 - optimum==1.17.1 - auto-gptq==0.7.0 - gpustat==1.1.1 - deepspeed==0.13.2 - wandb==0.16.3 - trl==0.7.11 - accelerate==0.27.2 - coloredlogs==15.0.1 - traitlets==5.14.1 - autoawq@https://github.com/casper-hansen/AutoAWQ/releases/download/v0.2.0/autoawq-0.2.0+cu118-cp310-cp310-linux_x86_64.whl ### Hardware - Cloud provided: runpod.io
sbaner24/vit-base-patch16-224-Trial008-YEL_STEM3	sbaner24	"2023-11-15T15:09:56Z"	189	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	"2023-11-14T04:57:19Z"	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: vit-base-patch16-224-Trial008-YEL_STEM3 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 1.0 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224-Trial008-YEL_STEM3 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0916 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 30 - eval_batch_size: 30 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 120 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.7743 \| 1.0 \| 1 \| 0.8267 \| 0.3636 \| \| 0.7964 \| 2.0 \| 2 \| 0.7547 \| 0.3636 \| \| 0.6369 \| 3.0 \| 3 \| 0.6399 \| 0.7273 \| \| 0.5344 \| 4.0 \| 4 \| 0.5082 \| 0.9091 \| \| 0.4342 \| 5.0 \| 5 \| 0.4664 \| 0.9091 \| \| 0.3056 \| 6.0 \| 6 \| 0.2145 \| 0.9091 \| \| 0.257 \| 7.0 \| 7 \| 0.1395 \| 0.9091 \| \| 0.2064 \| 8.0 \| 8 \| 0.1990 \| 0.9091 \| \| 0.2609 \| 9.0 \| 9 \| 0.0916 \| 1.0 \| \| 0.1758 \| 10.0 \| 10 \| 0.0321 \| 1.0 \| \| 0.1152 \| 11.0 \| 11 \| 0.0256 \| 1.0 \| \| 0.1343 \| 12.0 \| 12 \| 0.0413 \| 1.0 \| \| 0.0955 \| 13.0 \| 13 \| 0.0319 \| 1.0 \| \| 0.0723 \| 14.0 \| 14 \| 0.0112 \| 1.0 \| \| 0.13 \| 15.0 \| 15 \| 0.0073 \| 1.0 \| \| 0.1918 \| 16.0 \| 16 \| 0.0057 \| 1.0 \| \| 0.2469 \| 17.0 \| 17 \| 0.0052 \| 1.0 \| \| 0.1001 \| 18.0 \| 18 \| 0.0051 \| 1.0 \| \| 0.1331 \| 19.0 \| 19 \| 0.0039 \| 1.0 \| \| 0.1511 \| 20.0 \| 20 \| 0.0031 \| 1.0 \| \| 0.0956 \| 21.0 \| 21 \| 0.0027 \| 1.0 \| \| 0.0952 \| 22.0 \| 22 \| 0.0027 \| 1.0 \| \| 0.1679 \| 23.0 \| 23 \| 0.0025 \| 1.0 \| \| 0.1075 \| 24.0 \| 24 \| 0.0023 \| 1.0 \| \| 0.1507 \| 25.0 \| 25 \| 0.0024 \| 1.0 \| \| 0.1267 \| 26.0 \| 26 \| 0.0027 \| 1.0 \| \| 0.1141 \| 27.0 \| 27 \| 0.0030 \| 1.0 \| \| 0.0767 \| 28.0 \| 28 \| 0.0031 \| 1.0 \| \| 0.1746 \| 29.0 \| 29 \| 0.0029 \| 1.0 \| \| 0.1101 \| 30.0 \| 30 \| 0.0032 \| 1.0 \| \| 0.1632 \| 31.0 \| 31 \| 0.0036 \| 1.0 \| \| 0.1346 \| 32.0 \| 32 \| 0.0038 \| 1.0 \| \| 0.1024 \| 33.0 \| 33 \| 0.0038 \| 1.0 \| \| 0.1198 \| 34.0 \| 34 \| 0.0037 \| 1.0 \| \| 0.1217 \| 35.0 \| 35 \| 0.0033 \| 1.0 \| \| 0.1433 \| 36.0 \| 36 \| 0.0030 \| 1.0 \| \| 0.1255 \| 37.0 \| 37 \| 0.0029 \| 1.0 \| \| 0.1369 \| 38.0 \| 38 \| 0.0027 \| 1.0 \| \| 0.091 \| 39.0 \| 39 \| 0.0026 \| 1.0 \| \| 0.1318 \| 40.0 \| 40 \| 0.0025 \| 1.0 \| \| 0.0964 \| 41.0 \| 41 \| 0.0025 \| 1.0 \| \| 0.1164 \| 42.0 \| 42 \| 0.0024 \| 1.0 \| \| 0.0935 \| 43.0 \| 43 \| 0.0023 \| 1.0 \| \| 0.0564 \| 44.0 \| 44 \| 0.0022 \| 1.0 \| \| 0.1136 \| 45.0 \| 45 \| 0.0021 \| 1.0 \| \| 0.1306 \| 46.0 \| 46 \| 0.0021 \| 1.0 \| \| 0.0757 \| 47.0 \| 47 \| 0.0021 \| 1.0 \| \| 0.0475 \| 48.0 \| 48 \| 0.0020 \| 1.0 \| \| 0.1455 \| 49.0 \| 49 \| 0.0020 \| 1.0 \| \| 0.1892 \| 50.0 \| 50 \| 0.0020 \| 1.0 \| ### Framework versions - Transformers 4.30.0.dev0 - Pytorch 1.12.1 - Datasets 2.12.0 - Tokenizers 0.13.1
psxjp5/mt5-small_25	psxjp5	"2023-08-08T11:50:47Z"	104	0	transformers	[ "transformers", "pytorch", "tensorboard", "mt5", "text2text-generation", "generated_from_trainer", "base_model:google/mt5-small", "base_model:finetune:google/mt5-small", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	"2023-08-08T09:40:03Z"	--- license: apache-2.0 base_model: google/mt5-small tags: - generated_from_trainer metrics: - rouge - bleu model-index: - name: mt5-small_test results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mt5-small_test This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7284 - Rouge1: 43.3718 - Rouge2: 37.5973 - Rougel: 42.0502 - Rougelsum: 42.0648 - Bleu: 32.8345 - Gen Len: 12.6063 - Meteor: 0.3949 - True negatives: 70.2115 - False negatives: 11.206 - Cosine Sim: 0.7485 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 9 - gradient_accumulation_steps: 8 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Bleu \| Gen Len \| Meteor \| True negatives \| False negatives \| Cosine Sim \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\|:-------:\|:------:\|:--------------:\|:---------------:\|:----------:\| \| 3.1455 \| 1.0 \| 175 \| 0.9832 \| 18.7269 \| 15.517 \| 18.22 \| 18.223 \| 7.0634 \| 7.6229 \| 0.1626 \| 74.6828 \| 57.1687 \| 0.3949 \| \| 1.1623 \| 1.99 \| 350 \| 0.8542 \| 38.7603 \| 32.7237 \| 37.3447 \| 37.3752 \| 27.4323 \| 12.5135 \| 0.3487 \| 60.0 \| 15.942 \| 0.6992 \| \| 0.9431 \| 2.99 \| 525 \| 0.8017 \| 41.5759 \| 35.6108 \| 40.2536 \| 40.2695 \| 30.7994 \| 12.8117 \| 0.3755 \| 61.2689 \| 12.3447 \| 0.7304 \| \| 0.8119 \| 3.98 \| 700 \| 0.7787 \| 43.5881 \| 37.4245 \| 42.1096 \| 42.1248 \| 32.9646 \| 13.2176 \| 0.3947 \| 59.1541 \| 9.5238 \| 0.7582 \| \| 0.7235 \| 4.98 \| 875 \| 0.7477 \| 43.4069 \| 37.2246 \| 41.8444 \| 41.8616 \| 32.9345 \| 13.116 \| 0.3946 \| 63.0816 \| 9.8085 \| 0.7561 \| \| 0.6493 \| 5.97 \| 1050 \| 0.7266 \| 40.4506 \| 35.0072 \| 39.1206 \| 39.1181 \| 29.0601 \| 11.748 \| 0.3687 \| 75.5287 \| 17.2101 \| 0.7071 \| \| 0.5871 \| 6.97 \| 1225 \| 0.7284 \| 43.3718 \| 37.5973 \| 42.0502 \| 42.0648 \| 32.8345 \| 12.6063 \| 0.3949 \| 70.2115 \| 11.206 \| 0.7485 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
dianamihalache27/deberta-v3-base_3epoch10	dianamihalache27	"2024-05-31T15:34:50Z"	163	0	transformers	[ "transformers", "tensorboard", "safetensors", "deberta-v2", "text-classification", "generated_from_trainer", "base_model:microsoft/deberta-v3-base", "base_model:finetune:microsoft/deberta-v3-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2024-05-31T15:34:11Z"	--- license: mit base_model: microsoft/deberta-v3-base tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: deberta-v3-base_3epoch10 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # deberta-v3-base_3epoch10 This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.2046 - Accuracy: 0.7680 - F1: 0.5136 - Precision: 0.6439 - Recall: 0.4271 - Precision Sarcastic: 0.6439 - Recall Sarcastic: 0.4271 - F1 Sarcastic: 0.5136 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
Tommert25/multibertfinetuned0407	Tommert25	"2023-07-04T15:15:05Z"	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	"2023-07-04T10:41:33Z"	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: multibertfinetuned0407 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # multibertfinetuned0407 This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4688 - Precision: 0.4879 - Recall: 0.4345 - F1: 0.4597 - Accuracy: 0.8764 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 131 \| 0.4688 \| 0.4879 \| 0.4345 \| 0.4597 \| 0.8764 \| \| No log \| 2.0 \| 262 \| 0.5224 \| 0.5400 \| 0.4884 \| 0.5129 \| 0.8777 \| \| No log \| 3.0 \| 393 \| 0.5814 \| 0.4900 \| 0.4900 \| 0.4900 \| 0.8683 \| \| 0.3219 \| 4.0 \| 524 \| 0.6226 \| 0.5125 \| 0.5069 \| 0.5097 \| 0.8750 \| \| 0.3219 \| 5.0 \| 655 \| 0.6593 \| 0.5008 \| 0.4977 \| 0.4992 \| 0.8771 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
coffiee/lz3	coffiee	"2025-02-25T05:51:58Z"	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-02-25T05:51:04Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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(2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
chainup244/Qwen-Qwen1.5-1.8B-1719209906	chainup244	"2024-06-24T06:20:07Z"	6	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2024-06-24T06:18:27Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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musa99/teachim	musa99	"2025-02-28T18:37:14Z"	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit", "base_model:adapter:unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit", "region:us" ]	null	"2025-02-28T16:47:31Z"	--- base_model: unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.14.0
chenlong7616/ddpm-celebahq-finetuned-butterflies-2epochs	chenlong7616	"2023-10-12T06:12:11Z"	46	0	diffusers	[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	"2023-10-12T06:11:48Z"	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Example Fine-Tuned Model for Unit 2 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) Describe your model here ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('chenlong7616/ddpm-celebahq-finetuned-butterflies-2epochs') image = pipeline().images[0] image ```
Q-bert/Merged-AGI-7B	Q-bert	"2023-12-24T12:41:18Z"	56	5	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "Math", "merge", "en", "dataset:meta-math/MetaMathQA", "base_model:Q-bert/MetaMath-Cybertron-Starling", "base_model:merge:Q-bert/MetaMath-Cybertron-Starling", "base_model:fblgit/juanako-7b-UNA", "base_model:merge:fblgit/juanako-7b-UNA", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2023-12-10T09:20:47Z"	--- license: cc-by-nc-4.0 datasets: - meta-math/MetaMathQA language: - en pipeline_tag: text-generation tags: - Math - merge base_model: - Q-bert/MetaMath-Cybertron-Starling - fblgit/juanako-7b-UNA --- ## Merged-AGI-7B Merge [Q-bert/MetaMath-Cybertron-Starling](https://huggingface.co/Q-bert/MetaMath-Cybertron-Starling) and [fblgit/juanako-7b-UNA](https://huggingface.co/fblgit/juanako-7b-UNA) using slerp merge. You can use ChatML format. # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [Coming soon]() \| Metric \| Value \| \|-----------------------\|---------------------------\| \| Avg. \| Coming soon \| \| ARC (25-shot) \| Coming soon \| \| HellaSwag (10-shot) \| Coming soon \| \| MMLU (5-shot) \| Coming soon \| \| TruthfulQA (0-shot) \| Coming soon \| \| Winogrande (5-shot) \| Coming soon \| \| GSM8K (5-shot) \| Coming soon \|
Arbi-Houssem/mms_tts_tun_Lang1.6	Arbi-Houssem	"2024-06-16T04:44:31Z"	106	0	transformers	[ "transformers", "safetensors", "vits", "text-to-audio", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	text-to-audio	"2024-06-16T02:24:55Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
EllieS/Temp-L1-SFT-L2-KTO	EllieS	"2024-05-09T08:39:58Z"	1	0	peft	[ "peft", "tensorboard", "safetensors", "mistral", "alignment-handbook", "trl", "dpo", "generated_from_trainer", "dataset:EllieS/Temp-L2-DPO", "base_model:alignment-handbook/zephyr-7b-sft-full", "base_model:adapter:alignment-handbook/zephyr-7b-sft-full", "license:apache-2.0", "region:us" ]	null	"2024-05-09T06:17:42Z"	--- license: apache-2.0 library_name: peft tags: - alignment-handbook - trl - dpo - generated_from_trainer base_model: alignment-handbook/zephyr-7b-sft-full datasets: - EllieS/Temp-L2-DPO model-index: - name: Temp-L1-SFT-L2-KTO results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Temp-L1-SFT-L2-KTO This model is a fine-tuned version of [EllieS/TempReason-L1](https://huggingface.co/EllieS/TempReason-L1) on the EllieS/Temp-L2-DPO dataset. It achieves the following results on the evaluation set: - Loss: 0.2213 - Rewards/chosen: 0.2579 - Rewards/rejected: -6.0725 - Rewards/accuracies: 1.0 - Rewards/margins: 6.3304 - Logps/rejected: -652.1185 - Logps/chosen: -0.1197 - Logits/rejected: -2.6590 - Logits/chosen: -2.5711 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.2255 \| 0.2497 \| 1000 \| 0.2230 \| 0.2551 \| -5.4032 \| 1.0 \| 5.6583 \| -585.1871 \| -0.3988 \| -2.6372 \| -2.5514 \| \| 0.2252 \| 0.4994 \| 2000 \| 0.2215 \| 0.2576 \| -5.9860 \| 1.0 \| 6.2436 \| -643.4705 \| -0.1526 \| -2.6560 \| -2.5690 \| \| 0.2264 \| 0.7492 \| 3000 \| 0.2213 \| 0.2579 \| -6.0565 \| 1.0 \| 6.3144 \| -650.5204 \| -0.1267 \| -2.6590 \| -2.5715 \| \| 0.2262 \| 0.9989 \| 4000 \| 0.2213 \| 0.2579 \| -6.0725 \| 1.0 \| 6.3304 \| -652.1185 \| -0.1197 \| -2.6590 \| -2.5711 \| ### Framework versions - PEFT 0.7.1 - Transformers 4.40.2 - Pytorch 2.1.2+cu121 - Datasets 2.18.0 - Tokenizers 0.19.1
baebee/guanaco-testv2	baebee	"2023-09-04T09:07:41Z"	0	0	peft	[ "peft", "region:us" ]	null	"2023-09-04T09:07:37Z"	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.0.dev0
yunheur/xlm-roberta-base-finetuned-panx-de-fr	yunheur	"2025-03-24T04:04:45Z"	0	0	null	[ "pytorch", "xlm-roberta", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "region:us" ]	null	"2025-03-23T06:34:35Z"	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de-fr results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1635 - F1: 0.8626 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2864 \| 1.0 \| 715 \| 0.1862 \| 0.8193 \| \| 0.1479 \| 2.0 \| 1430 \| 0.1711 \| 0.8448 \| \| 0.0947 \| 3.0 \| 2145 \| 0.1635 \| 0.8626 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.6.0+cu118 - Datasets 3.4.1 - Tokenizers 0.13.3
krishna195/finetuned_PHI	krishna195	"2025-03-18T13:51:13Z"	0	0	transformers	[ "transformers", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2025-03-18T13:51:12Z"	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
mradermacher/EXAONE-3.5-32B-LIMO-Ko-e4-GGUF	mradermacher	"2025-02-19T12:00:06Z"	0	0	transformers	[ "transformers", "gguf", "en", "ko", "dataset:GAIR/LIMO", "dataset:junnei/ko-limo", "dataset:exp-models/GAIR-LIMO-KOREAN", "base_model:werty1248/EXAONE-3.5-32B-LIMO-Ko-e4", "base_model:quantized:werty1248/EXAONE-3.5-32B-LIMO-Ko-e4", "license:other", "endpoints_compatible", "region:us", "conversational" ]	null	"2025-02-19T09:48:22Z"	--- base_model: werty1248/EXAONE-3.5-32B-LIMO-Ko-e4 datasets: - GAIR/LIMO - junnei/ko-limo - exp-models/GAIR-LIMO-KOREAN language: - en - ko library_name: transformers license: other license_link: LICENSE license_name: exaone quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/werty1248/EXAONE-3.5-32B-LIMO-Ko-e4 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/EXAONE-3.5-32B-LIMO-Ko-e4-GGUF/resolve/main/EXAONE-3.5-32B-LIMO-Ko-e4.Q2_K.gguf) \| Q2_K \| 12.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/EXAONE-3.5-32B-LIMO-Ko-e4-GGUF/resolve/main/EXAONE-3.5-32B-LIMO-Ko-e4.Q3_K_S.gguf) \| Q3_K_S \| 14.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/EXAONE-3.5-32B-LIMO-Ko-e4-GGUF/resolve/main/EXAONE-3.5-32B-LIMO-Ko-e4.Q3_K_M.gguf) \| Q3_K_M \| 15.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/EXAONE-3.5-32B-LIMO-Ko-e4-GGUF/resolve/main/EXAONE-3.5-32B-LIMO-Ko-e4.Q3_K_L.gguf) \| Q3_K_L \| 16.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/EXAONE-3.5-32B-LIMO-Ko-e4-GGUF/resolve/main/EXAONE-3.5-32B-LIMO-Ko-e4.IQ4_XS.gguf) \| IQ4_XS \| 17.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/EXAONE-3.5-32B-LIMO-Ko-e4-GGUF/resolve/main/EXAONE-3.5-32B-LIMO-Ko-e4.Q4_K_S.gguf) \| Q4_K_S \| 18.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/EXAONE-3.5-32B-LIMO-Ko-e4-GGUF/resolve/main/EXAONE-3.5-32B-LIMO-Ko-e4.Q4_K_M.gguf) \| Q4_K_M \| 19.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/EXAONE-3.5-32B-LIMO-Ko-e4-GGUF/resolve/main/EXAONE-3.5-32B-LIMO-Ko-e4.Q5_K_S.gguf) \| Q5_K_S \| 22.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/EXAONE-3.5-32B-LIMO-Ko-e4-GGUF/resolve/main/EXAONE-3.5-32B-LIMO-Ko-e4.Q5_K_M.gguf) \| Q5_K_M \| 22.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/EXAONE-3.5-32B-LIMO-Ko-e4-GGUF/resolve/main/EXAONE-3.5-32B-LIMO-Ko-e4.Q6_K.gguf) \| Q6_K \| 26.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/EXAONE-3.5-32B-LIMO-Ko-e4-GGUF/resolve/main/EXAONE-3.5-32B-LIMO-Ko-e4.Q8_0.gguf) \| Q8_0 \| 34.1 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
lottienghiem/distilgpt2-finetuned-wikitext2	lottienghiem	"2024-04-18T06:04:02Z"	44	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:distilbert/distilgpt2", "base_model:finetune:distilbert/distilgpt2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2024-04-08T07:23:23Z"	--- license: apache-2.0 base_model: distilgpt2 tags: - generated_from_trainer model-index: - name: distilgpt2-finetuned-wikitext2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilgpt2-finetuned-wikitext2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5082 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 2.4699 \| 1.0 \| 19369 \| 2.5496 \| \| 2.3425 \| 2.0 \| 38738 \| 2.5165 \| \| 2.256 \| 3.0 \| 58107 \| 2.5082 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.1
hyungtak/ko-Llama2-7B	hyungtak	"2023-08-24T12:38:45Z"	2	0	peft	[ "peft", "region:us" ]	null	"2023-08-24T12:38:35Z"	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.6.0.dev0
eschorn/3_loa	eschorn	"2023-07-20T03:54:40Z"	0	0	null	[ "generated_from_trainer", "dataset:billsum", "base_model:google/flan-t5-large", "base_model:finetune:google/flan-t5-large", "license:apache-2.0", "region:us" ]	null	"2023-07-19T20:46:54Z"	--- license: apache-2.0 base_model: google/flan-t5-large tags: - generated_from_trainer datasets: - billsum metrics: - rouge model-index: - name: 3_loa results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 3_loa This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the billsum dataset. It achieves the following results on the evaluation set: - Loss: 1.4825 - Rouge1: 0.201 - Rouge2: 0.1132 - Rougel: 0.1753 - Rougelsum: 0.1755 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| 2.1079 \| 1.0 \| 989 \| 1.6673 \| 0.2028 \| 0.1092 \| 0.1748 \| 0.1751 \| 19.0 \| \| 1.8481 \| 2.0 \| 1978 \| 1.6150 \| 0.1979 \| 0.1061 \| 0.1715 \| 0.1717 \| 19.0 \| \| 1.7889 \| 3.0 \| 2967 \| 1.5833 \| 0.1994 \| 0.11 \| 0.1727 \| 0.1727 \| 19.0 \| \| 1.7319 \| 4.0 \| 3956 \| 1.5584 \| 0.1978 \| 0.1084 \| 0.1718 \| 0.1718 \| 19.0 \| \| 1.7279 \| 5.0 \| 4945 \| 1.5440 \| 0.2016 \| 0.1106 \| 0.1755 \| 0.1756 \| 19.0 \| \| 1.7386 \| 6.0 \| 5934 \| 1.5326 \| 0.1991 \| 0.1086 \| 0.1734 \| 0.1736 \| 19.0 \| \| 1.6972 \| 7.0 \| 6923 \| 1.5251 \| 0.2013 \| 0.1122 \| 0.1759 \| 0.176 \| 19.0 \| \| 1.6732 \| 8.0 \| 7912 \| 1.5145 \| 0.2024 \| 0.1123 \| 0.1766 \| 0.1766 \| 19.0 \| \| 1.6597 \| 9.0 \| 8901 \| 1.5079 \| 0.2019 \| 0.1125 \| 0.1751 \| 0.1753 \| 19.0 \| \| 1.6151 \| 10.0 \| 9890 \| 1.5045 \| 0.201 \| 0.1123 \| 0.1758 \| 0.1761 \| 19.0 \| \| 1.6381 \| 11.0 \| 10879 \| 1.4997 \| 0.2009 \| 0.1116 \| 0.1755 \| 0.1756 \| 19.0 \| \| 1.6148 \| 12.0 \| 11868 \| 1.4974 \| 0.2018 \| 0.1133 \| 0.1763 \| 0.1765 \| 19.0 \| \| 1.6196 \| 13.0 \| 12857 \| 1.4940 \| 0.2014 \| 0.1129 \| 0.1756 \| 0.1756 \| 19.0 \| \| 1.6137 \| 14.0 \| 13846 \| 1.4914 \| 0.2025 \| 0.1136 \| 0.1766 \| 0.1768 \| 19.0 \| \| 1.6313 \| 15.0 \| 14835 \| 1.4873 \| 0.2032 \| 0.114 \| 0.1769 \| 0.1771 \| 19.0 \| \| 1.6098 \| 16.0 \| 15824 \| 1.4847 \| 0.2012 \| 0.1133 \| 0.175 \| 0.1754 \| 19.0 \| \| 1.6061 \| 17.0 \| 16813 \| 1.4845 \| 0.2019 \| 0.1138 \| 0.1752 \| 0.1755 \| 19.0 \| \| 1.5918 \| 18.0 \| 17802 \| 1.4833 \| 0.2011 \| 0.1129 \| 0.1747 \| 0.175 \| 19.0 \| \| 1.5842 \| 19.0 \| 18791 \| 1.4824 \| 0.2013 \| 0.1133 \| 0.1753 \| 0.1755 \| 19.0 \| \| 1.5964 \| 20.0 \| 19780 \| 1.4825 \| 0.201 \| 0.1132 \| 0.1753 \| 0.1755 \| 19.0 \| ### Framework versions - Transformers 4.31.0 - Pytorch 1.13.1.post200 - Datasets 2.10.0 - Tokenizers 0.13.2
Lots-of-LoRAs/Mistral-7B-Instruct-v0.2-4b-r16-task1425	Lots-of-LoRAs	"2024-07-03T20:10:38Z"	0	0	pytorch	[ "pytorch", "safetensors", "en", "arxiv:1910.09700", "arxiv:2407.00066", "license:mit", "region:us" ]	null	"2024-06-18T19:50:29Z"	--- language: en license: mit library_name: pytorch --- # Model Card for Mistral-7B-Instruct-v0.2-4b-r16-task1425 <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> LoRA trained on task1425_country_iso_numeric - Developed by: bruel - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: LoRA - Language(s) (NLP): en - License: mit - Finetuned from model [optional]: mistralai/Mistral-7B-Instruct-v0.2 ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: https://github.com/bruel-gabrielsson - Paper [optional]: "Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead" (2024), Rickard Brüel Gabrielsson, Jiacheng Zhu, Onkar Bhardwaj, Leshem Choshen, Kristjan Greenewald, Mikhail Yurochkin and Justin Solomon - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> https://huggingface.co/datasets/Lots-of-LoRAs/task1425_country_iso_numeric sourced from https://github.com/allenai/natural-instructions ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: @misc{brüelgabrielsson2024compressserveservingthousands, title={Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead}, author={Rickard Brüel-Gabrielsson and Jiacheng Zhu and Onkar Bhardwaj and Leshem Choshen and Kristjan Greenewald and Mikhail Yurochkin and Justin Solomon}, year={2024}, eprint={2407.00066}, archivePrefix={arXiv}, primaryClass={cs.DC}, url={https://arxiv.org/abs/2407.00066}, } APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
CharlesLi/llama_2_cot_simplest_code_math_4_full	CharlesLi	"2025-01-20T12:17:57Z"	7	0	transformers	[ "transformers", "tensorboard", "safetensors", "llama", "text-generation", "alignment-handbook", "trl", "sft", "generated_from_trainer", "conversational", "dataset:generator", "base_model:meta-llama/Llama-2-7b-chat-hf", "base_model:finetune:meta-llama/Llama-2-7b-chat-hf", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-01-20T04:43:36Z"	--- library_name: transformers license: llama2 base_model: meta-llama/Llama-2-7b-chat-hf tags: - alignment-handbook - trl - sft - generated_from_trainer - trl - sft - alignment-handbook - generated_from_trainer datasets: - generator model-index: - name: llama_2_cot_simplest_code_math_4_full results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # llama_2_cot_simplest_code_math_4_full This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 0.6062 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.1+cu121 - Datasets 3.0.0 - Tokenizers 0.19.1
leabum/distilbert-base-uncased-finetuned-squad	leabum	"2022-08-11T06:25:42Z"	61	0	transformers	[ "transformers", "tf", "tensorboard", "distilbert", "question-answering", "generated_from_keras_callback", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	"2022-08-02T13:48:08Z"	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: leabum/distilbert-base-uncased-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # leabum/distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 5.5824 - Train End Logits Accuracy: 0.0347 - Train Start Logits Accuracy: 0.0694 - Validation Loss: 5.8343 - Validation End Logits Accuracy: 0.0 - Validation Start Logits Accuracy: 0.0 - Epoch: 1 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Train End Logits Accuracy \| Train Start Logits Accuracy \| Validation Loss \| Validation End Logits Accuracy \| Validation Start Logits Accuracy \| Epoch \| \|:----------:\|:-------------------------:\|:---------------------------:\|:---------------:\|:------------------------------:\|:--------------------------------:\|:-----:\| \| 5.8427 \| 0.0069 \| 0.0069 \| 5.8688 \| 0.0 \| 0.0 \| 0 \| \| 5.5824 \| 0.0347 \| 0.0694 \| 5.8343 \| 0.0 \| 0.0 \| 1 \| ### Framework versions - Transformers 4.21.1 - TensorFlow 2.8.2 - Datasets 2.4.0 - Tokenizers 0.12.1
mradermacher/Qwen1.5-32B-i1-GGUF	mradermacher	"2025-03-31T21:28:13Z"	136	0	transformers	[ "transformers", "gguf", "pretrained", "en", "base_model:Qwen/Qwen1.5-32B", "base_model:quantized:Qwen/Qwen1.5-32B", "license:other", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	"2024-05-12T13:43:50Z"	--- base_model: Qwen/Qwen1.5-32B language: - en library_name: transformers license: other license_link: https://huggingface.co/Qwen/Qwen1.5-32B/blob/main/LICENSE license_name: tongyi-qianwen-research quantized_by: mradermacher tags: - pretrained --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> weighted/imatrix quants of https://huggingface.co/Qwen/Qwen1.5-32B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Qwen1.5-32B-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-32B-i1-GGUF/resolve/main/Qwen1.5-32B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 7.3 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-32B-i1-GGUF/resolve/main/Qwen1.5-32B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 8.0 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-32B-i1-GGUF/resolve/main/Qwen1.5-32B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 9.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-32B-i1-GGUF/resolve/main/Qwen1.5-32B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 10.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-32B-i1-GGUF/resolve/main/Qwen1.5-32B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 10.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-32B-i1-GGUF/resolve/main/Qwen1.5-32B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 11.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-32B-i1-GGUF/resolve/main/Qwen1.5-32B.i1-Q2_K.gguf) \| i1-Q2_K \| 12.3 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-32B-i1-GGUF/resolve/main/Qwen1.5-32B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 12.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-32B-i1-GGUF/resolve/main/Qwen1.5-32B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 13.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-32B-i1-GGUF/resolve/main/Qwen1.5-32B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 14.4 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-32B-i1-GGUF/resolve/main/Qwen1.5-32B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 14.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-32B-i1-GGUF/resolve/main/Qwen1.5-32B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 14.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-32B-i1-GGUF/resolve/main/Qwen1.5-32B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 15.9 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-32B-i1-GGUF/resolve/main/Qwen1.5-32B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 17.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-32B-i1-GGUF/resolve/main/Qwen1.5-32B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 17.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-32B-i1-GGUF/resolve/main/Qwen1.5-32B.i1-Q4_0.gguf) \| i1-Q4_0 \| 18.7 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-32B-i1-GGUF/resolve/main/Qwen1.5-32B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 18.7 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-32B-i1-GGUF/resolve/main/Qwen1.5-32B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 19.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-32B-i1-GGUF/resolve/main/Qwen1.5-32B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 22.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-32B-i1-GGUF/resolve/main/Qwen1.5-32B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 23.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-32B-i1-GGUF/resolve/main/Qwen1.5-32B.i1-Q6_K.gguf) \| i1-Q6_K \| 26.8 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
ragefu/ftxclip20240925model	ragefu	"2024-09-26T04:23:34Z"	91	0	transformers	[ "transformers", "safetensors", "xclip", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	feature-extraction	"2024-09-26T04:23:00Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Dhanang/topic_model	Dhanang	"2023-12-14T08:08:11Z"	8	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:indobenchmark/indobert-base-p2", "base_model:finetune:indobenchmark/indobert-base-p2", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2023-12-14T07:54:44Z"	--- license: mit base_model: indobenchmark/indobert-base-p2 tags: - generated_from_trainer metrics: - accuracy model-index: - name: topic_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # topic_model This model is a fine-tuned version of [indobenchmark/indobert-base-p2](https://huggingface.co/indobenchmark/indobert-base-p2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0145 - Accuracy: 0.9984 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 308 \| 0.0315 \| 0.9919 \| \| 0.1039 \| 2.0 \| 616 \| 0.0117 \| 0.9984 \| \| 0.1039 \| 3.0 \| 924 \| 0.0147 \| 0.9984 \| \| 0.0047 \| 4.0 \| 1232 \| 0.0223 \| 0.9968 \| \| 0.0002 \| 5.0 \| 1540 \| 0.0138 \| 0.9984 \| \| 0.0002 \| 6.0 \| 1848 \| 0.0140 \| 0.9984 \| \| 0.0001 \| 7.0 \| 2156 \| 0.0142 \| 0.9984 \| \| 0.0001 \| 8.0 \| 2464 \| 0.0144 \| 0.9984 \| \| 0.0001 \| 9.0 \| 2772 \| 0.0145 \| 0.9984 \| \| 0.0001 \| 10.0 \| 3080 \| 0.0145 \| 0.9984 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0
guydebruyn/q-FrozenLake-v1-4x4-noSlippery	guydebruyn	"2023-09-12T19:46:56Z"	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	"2023-09-12T19:46:53Z"	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="guydebruyn/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
auro736/deberta-v3-large-tweet-fid-EMD	auro736	"2024-01-14T10:38:20Z"	67	0	transformers	[ "transformers", "pytorch", "deberta-v2", "token-classification", "en", "arxiv:2205.10726", "license:mit", "endpoints_compatible", "region:us" ]	token-classification	"2023-10-30T16:00:37Z"	--- license: mit language: - en pipeline_tag: token-classification --- ## DeBERTa-large-tweet-fid-EMD This is a [DeBERTa-large](https://huggingface.co/microsoft/deberta-v3-large) model trained on the [Tweet-FID](https://arxiv.org/abs/2205.10726) dataset ("TWEET-FID: An Annotated Dataset for Multiple Foodborne Illness Detection Tasks", Ruofan Hu et al, 2022 ) which is a collection of Twitter to detect incidents of foodborne illnesses. The model is enriched with a multi class classification head to perform the custom task called Entity Mention Detection (EMD). The objective is to determine predefined entities (food, location, symptom, other) in a given text related to a food risk
houbw/llama3_ruozhiba_ori_8_up_4	houbw	"2024-05-23T02:17:37Z"	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct", "base_model:finetune:unsloth/llama-3-8b-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	"2024-05-23T02:17:08Z"	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-Instruct --- # Uploaded model - Developed by: houbw - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
huudan123/stage1	huudan123	"2024-07-13T18:16:33Z"	6	0	sentence-transformers	[ "sentence-transformers", "safetensors", "roberta", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:102178", "loss:TripletLoss", "arxiv:1908.10084", "arxiv:1703.07737", "base_model:vinai/phobert-base-v2", "base_model:finetune:vinai/phobert-base-v2", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	"2024-07-13T18:15:57Z"	--- base_model: vinai/phobert-base-v2 datasets: [] language: [] library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine - pearson_manhattan - spearman_manhattan - pearson_euclidean - spearman_euclidean - pearson_dot - spearman_dot - pearson_max - spearman_max pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:102178 - loss:TripletLoss widget: - source_sentence: Bàn cho thấy các thiết_kế và sản_xuất kiến_thức cần_thiết để thực_hiện nhiều quyết_định thông_báo hơn . sentences: - Nixon quyết_định rằng hồ chí minh có_thể ở lại miền nam Việt_Nam . - Không có gì cần_thiết để đưa ra một quyết_định thông_tin . - Bảng Hiển_thị thiết_kế và sản_xuất thông_tin cần_thiết để đưa ra quyết_định . - source_sentence: 95 gói nước_tiểu miễn_phí trong túi của họ . sentences: - Tây_ban nha trượt từ vị_trí quyền_lực của họ . - Đội đã bước vào phòng thí_nghiệm mang theo tổng_cộng 99 đơn_vị trong_sạch , thử_nghiệm thân_thiện . - Túi được yêu_cầu cho nhà toàn_bộ 95 đơn_vị phục_vụ trong_sạch nước_tiểu giữa các nhà cung_cấp các sản_phẩm . - source_sentence: Tuyển một chiếc xe rất đắt tiền , và những gì có để xem_thường là gần những con đường chính . sentences: - Thuê một chiếc xe rất rẻ nhưng có_thể không đáng_giá_như những cảnh_sát ở xa con đường . - Có một nhà_thờ hình_tròn ở orangerie ở Paris . - Thuê một chiếc xe đến với chi_phí lớn và hầu_hết các điểm đến đều gần đường . - source_sentence: Người da đen là 12 phần_trăm dân_số . sentences: - Người da đen tạo ra 50 % tổng_số dân_số . - Người Mỹ Châu_Phi là một nhóm_thiểu_số . - Tôi đoán là barney fife . - source_sentence: Báo đen đã editorialized chống lại những cuộc viếng_thăm của farrakhan với các nhà độc_tài châu phi . sentences: - Báo đen đã viết về quá_khứ của farrakhan . - Khi bạn đi đến radda , bạn nên kiểm_tra piccolo bảo del chianti . - Báo đen từ_chối yểm_trợ cho farrakhan . model-index: - name: SentenceTransformer based on vinai/phobert-base-v2 results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts dev type: sts-dev metrics: - type: pearson_cosine value: 0.42030854811305457 name: Pearson Cosine - type: spearman_cosine value: 0.5147968030818376 name: Spearman Cosine - type: pearson_manhattan value: 0.5605026901702432 name: Pearson Manhattan - type: spearman_manhattan value: 0.5792048311109484 name: Spearman Manhattan - type: pearson_euclidean value: 0.4710386131519505 name: Pearson Euclidean - type: spearman_euclidean value: 0.5087153254455983 name: Spearman Euclidean - type: pearson_dot value: 0.3923969498466928 name: Pearson Dot - type: spearman_dot value: 0.4338097270757405 name: Spearman Dot - type: pearson_max value: 0.5605026901702432 name: Pearson Max - type: spearman_max value: 0.5792048311109484 name: Spearman Max --- # SentenceTransformer based on vinai/phobert-base-v2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [vinai/phobert-base-v2](https://huggingface.co/vinai/phobert-base-v2). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [vinai/phobert-base-v2](https://huggingface.co/vinai/phobert-base-v2) <!-- at revision 2b51e367d92093c9688112098510e6a58bab67cd --> - Maximum Sequence Length: 512 tokens - Output Dimensionality: 768 tokens - Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("huudan123/stage1") # Run inference sentences = [ 'Báo đen đã editorialized chống lại những cuộc viếng_thăm của farrakhan với các nhà độc_tài châu phi .', 'Báo đen đã viết về quá_khứ của farrakhan .', 'Báo đen từ_chối yểm_trợ cho farrakhan .', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> ## Evaluation ### Metrics #### Semantic Similarity * Dataset: `sts-dev` * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| pearson_cosine \| 0.4203 \| \| spearman_cosine \| 0.5148 \| \| pearson_manhattan \| 0.5605 \| \| spearman_manhattan \| 0.5792 \| \| pearson_euclidean \| 0.471 \| \| spearman_euclidean \| 0.5087 \| \| pearson_dot \| 0.3924 \| \| spearman_dot \| 0.4338 \| \| pearson_max \| 0.5605 \| \| spearman_max \| 0.5792 \| <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 102,178 training samples * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: \| \| anchor \| positive \| negative \| \|:--------\|:-----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\| \| type \| string \| string \| string \| \| details \| <ul><li>min: 4 tokens</li><li>mean: 27.28 tokens</li><li>max: 147 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 14.99 tokens</li><li>max: 44 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 14.34 tokens</li><li>max: 34 tokens</li></ul> \| * Samples: \| anchor \| positive \| negative \| \|:--------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>Tem đầy màu_sắc của madeira , cũng như tiền xu , ghi_chép ngân_hàng , và các mặt_hàng khác như bưu_thiếp là mối quan_tâm đến nhiều nhà sưu_tập .</code> \| <code>Các nhà sưu_tập sẽ thích ghé thăm madeira bởi_vì những phân_chia lớn của tem , ghi_chép ngân_hàng , bưu_thiếp , và nhiều mặt_hàng khác họ có_thể đọc được .</code> \| <code>Mọi người quan_tâm đến việc bắt_đầu bộ sưu_tập mới nên thoát madeira và đi du_lịch phía bắc , nơi họ có khả_năng tìm thấy các cửa_hàng tốt .</code> \| \| <code>Cẩn_thận đấy , ông inglethorp . Poirot bị bồn_chồn .</code> \| <code>Hãy chăm_sóc ông inglethorp .</code> \| <code>Không cần phải cẩn_thận với anh ta .</code> \| \| <code>Phải có một_chút hoài_nghi về trải nghiệm cá_nhân của sperling với trò_chơi .</code> \| <code>Hãy suy_nghĩ về những tác_động khi nhìn vào kinh_nghiệm của anh ấy .</code> \| <code>Một người có_thể lấy trải nghiệm cá_nhân của sperling với giá_trị mặt .</code> \| * Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters: ```json { "distance_metric": "TripletDistanceMetric.EUCLIDEAN", "triplet_margin": 5 } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 12,772 evaluation samples * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: \| \| anchor \| positive \| negative \| \|:--------\|:-----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\| \| type \| string \| string \| string \| \| details \| <ul><li>min: 4 tokens</li><li>mean: 27.81 tokens</li><li>max: 164 tokens</li></ul> \| <ul><li>min: 3 tokens</li><li>mean: 14.94 tokens</li><li>max: 42 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 14.4 tokens</li><li>max: 39 tokens</li></ul> \| * Samples: \| anchor \| positive \| negative \| \|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------\| \| <code>Tình_yêu , anh có muốn em trở_thành kassandra lubbock của anh không ?</code> \| <code>Tôi có_thể là kassandra lubbock của anh .</code> \| <code>Tôi từ_chối trở_thành kassandra lubbock của anh .</code> \| \| <code>Ví_dụ , trong mùa thu năm 1997 , ủy ban điều_trị hạt_nhân ( nrc ) văn_phòng thanh_tra tướng liệu nrc để có được quan_điểm của họ trên văn_hóa an_toàn của đại_lý .</code> \| <code>Nhân_viên nrc đã được hỏi về quan_điểm của họ trên văn_hóa an_toàn của đại_lý .</code> \| <code>Các nhân_viên không bao_giờ quan_sát về quan_điểm của họ về văn_hóa an_toàn của đại_lý trong mùa thu năm 1997 .</code> \| \| <code>Mỗi năm kem của trẻ nghệ và comedic tài_năng làm cho nó đường đến edinburgh , và fringe đã lớn lên trong việc huấn_luyện lớn nhất trong khung_cảnh lớn nhất cho các diễn_viên phát_triển trên thế_giới .</code> \| <code>Tài_năng mới đến edinburgh .</code> \| <code>Tài_năng mới đến dublin .</code> \| * Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters: ```json { "distance_metric": "TripletDistanceMetric.EUCLIDEAN", "triplet_margin": 5 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `overwrite_output_dir`: True - `eval_strategy`: epoch - `per_device_train_batch_size`: 128 - `per_device_eval_batch_size`: 128 - `num_train_epochs`: 20 - `lr_scheduler_type`: cosine - `warmup_ratio`: 0.05 - `fp16`: True - `load_best_model_at_end`: True - `gradient_checkpointing`: True #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: True - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 128 - `per_device_eval_batch_size`: 128 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 20 - `max_steps`: -1 - `lr_scheduler_type`: cosine - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.05 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: True - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: True - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs <details><summary>Click to expand</summary> \| Epoch \| Step \| Training Loss \| loss \| sts-dev_spearman_cosine \| \|:-------:\|:--------:\|:-------------:\|:----------:\|:-----------------------:\| \| 0 \| 0 \| - \| - \| 0.6643 \| \| 0.0626 \| 50 \| 4.6946 \| - \| - \| \| 0.1252 \| 100 \| 4.031 \| - \| - \| \| 0.1877 \| 150 \| 2.7654 \| - \| - \| \| 0.2503 \| 200 \| 2.4176 \| - \| - \| \| 0.3129 \| 250 \| 2.1111 \| - \| - \| \| 0.3755 \| 300 \| 2.0263 \| - \| - \| \| 0.4380 \| 350 \| 1.9296 \| - \| - \| \| 0.5006 \| 400 \| 1.7793 \| - \| - \| \| 0.5632 \| 450 \| 1.7903 \| - \| - \| \| 0.6258 \| 500 \| 1.7638 \| - \| - \| \| 0.6884 \| 550 \| 1.7042 \| - \| - \| \| 0.7509 \| 600 \| 1.7038 \| - \| - \| \| 0.8135 \| 650 \| 1.6221 \| - \| - \| \| 0.8761 \| 700 \| 1.6172 \| - \| - \| \| 0.9387 \| 750 \| 1.6227 \| - \| - \| \| 1.0 \| 799 \| - \| 1.5275 \| 0.5219 \| \| 1.0013 \| 800 \| 1.6264 \| - \| - \| \| 1.0638 \| 850 \| 1.364 \| - \| - \| \| 1.1264 \| 900 \| 1.4447 \| - \| - \| \| 1.1890 \| 950 \| 1.4161 \| - \| - \| \| 1.2516 \| 1000 \| 1.3575 \| - \| - \| \| 1.3141 \| 1050 \| 1.3554 \| - \| - \| \| 1.3767 \| 1100 \| 1.378 \| - \| - \| \| 1.4393 \| 1150 \| 1.3806 \| - \| - \| \| 1.5019 \| 1200 \| 1.3089 \| - \| - \| \| 1.5645 \| 1250 \| 1.4314 \| - \| - \| \| 1.6270 \| 1300 \| 1.3672 \| - \| - \| \| 1.6896 \| 1350 \| 1.3777 \| - \| - \| \| 1.7522 \| 1400 \| 1.3282 \| - \| - \| \| 1.8148 \| 1450 \| 1.3432 \| - \| - \| \| 1.8773 \| 1500 \| 1.3101 \| - \| - \| \| 1.9399 \| 1550 \| 1.2919 \| - \| - \| \| 2.0 \| 1598 \| - \| 1.3643 \| 0.5667 \| \| 2.0025 \| 1600 \| 1.2969 \| - \| - \| \| 2.0651 \| 1650 \| 0.9629 \| - \| - \| \| 2.1277 \| 1700 \| 0.9878 \| - \| - \| \| 2.1902 \| 1750 \| 0.9437 \| - \| - \| \| 2.2528 \| 1800 \| 0.9832 \| - \| - \| \| 2.3154 \| 1850 \| 0.9584 \| - \| - \| \| 2.3780 \| 1900 \| 1.0689 \| - \| - \| \| 2.4406 \| 1950 \| 1.0579 \| - \| - \| \| 2.5031 \| 2000 \| 0.9888 \| - \| - \| \| 2.5657 \| 2050 \| 0.9452 \| - \| - \| \| 2.6283 \| 2100 \| 0.9378 \| - \| - \| \| 2.6909 \| 2150 \| 0.9553 \| - \| - \| \| 2.7534 \| 2200 \| 0.9337 \| - \| - \| \| 2.8160 \| 2250 \| 1.0184 \| - \| - \| \| 2.8786 \| 2300 \| 0.9663 \| - \| - \| \| 2.9412 \| 2350 \| 0.9686 \| - \| - \| \| 3.0 \| 2397 \| - \| 1.3488 \| 0.5442 \| \| 3.0038 \| 2400 \| 0.9618 \| - \| - \| \| 3.0663 \| 2450 \| 0.6878 \| - \| - \| \| 3.1289 \| 2500 \| 0.6883 \| - \| - \| \| 3.1915 \| 2550 \| 0.6498 \| - \| - \| \| 3.2541 \| 2600 \| 0.6651 \| - \| - \| \| 3.3166 \| 2650 \| 0.6554 \| - \| - \| \| 3.3792 \| 2700 \| 0.7033 \| - \| - \| \| 3.4418 \| 2750 \| 0.6416 \| - \| - \| \| 3.5044 \| 2800 \| 0.7068 \| - \| - \| \| 3.5670 \| 2850 \| 0.6834 \| - \| - \| \| 3.6295 \| 2900 \| 0.7099 \| - \| - \| \| 3.6921 \| 2950 \| 0.7306 \| - \| - \| \| 3.7547 \| 3000 \| 0.7105 \| - \| - \| \| 3.8173 \| 3050 \| 0.7072 \| - \| - \| \| 3.8798 \| 3100 \| 0.7248 \| - \| - \| \| 3.9424 \| 3150 \| 0.7216 \| - \| - \| \| 4.0 \| 3196 \| - \| 1.3358 \| 0.5307 \| \| 4.0050 \| 3200 \| 0.693 \| - \| - \| \| 4.0676 \| 3250 \| 0.4741 \| - \| - \| \| 4.1302 \| 3300 \| 0.4593 \| - \| - \| \| 4.1927 \| 3350 \| 0.449 \| - \| - \| \| 4.2553 \| 3400 \| 0.4326 \| - \| - \| \| 4.3179 \| 3450 \| 0.4488 \| - \| - \| \| 4.3805 \| 3500 \| 0.4762 \| - \| - \| \| 4.4431 \| 3550 \| 0.4723 \| - \| - \| \| 4.5056 \| 3600 \| 0.4713 \| - \| - \| \| 4.5682 \| 3650 \| 0.4612 \| - \| - \| \| 4.6308 \| 3700 \| 0.4537 \| - \| - \| \| 4.6934 \| 3750 \| 0.4928 \| - \| - \| \| 4.7559 \| 3800 \| 0.4568 \| - \| - \| \| 4.8185 \| 3850 \| 0.4771 \| - \| - \| \| 4.8811 \| 3900 \| 0.4688 \| - \| - \| \| 4.9437 \| 3950 \| 0.4549 \| - \| - \| \| 5.0 \| 3995 \| - \| 1.4027 \| 0.5360 \| \| 5.0063 \| 4000 \| 0.5048 \| - \| - \| \| 5.0688 \| 4050 \| 0.2822 \| - \| - \| \| 5.1314 \| 4100 \| 0.3069 \| - \| - \| \| 5.1940 \| 4150 \| 0.2971 \| - \| - \| \| 5.2566 \| 4200 \| 0.3191 \| - \| - \| \| 5.3191 \| 4250 \| 0.3023 \| - \| - \| \| 5.3817 \| 4300 \| 0.3224 \| - \| - \| \| 5.4443 \| 4350 \| 0.3114 \| - \| - \| \| 5.5069 \| 4400 \| 0.3098 \| - \| - \| \| 5.5695 \| 4450 \| 0.3071 \| - \| - \| \| 5.6320 \| 4500 \| 0.3478 \| - \| - \| \| 5.6946 \| 4550 \| 0.3288 \| - \| - \| \| 5.7572 \| 4600 \| 0.3373 \| - \| - \| \| 5.8198 \| 4650 \| 0.3577 \| - \| - \| \| 5.8824 \| 4700 \| 0.331 \| - \| - \| \| 5.9449 \| 4750 \| 0.3132 \| - \| - \| \| 6.0 \| 4794 \| - \| 1.4036 \| 0.5148 \| * The bold row denotes the saved checkpoint. </details> ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.1 - Transformers: 4.42.4 - PyTorch: 2.3.1+cu121 - Accelerate: 0.32.1 - Datasets: 2.20.0 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### TripletLoss ```bibtex @misc{hermans2017defense, title={In Defense of the Triplet Loss for Person Re-Identification}, author={Alexander Hermans and Lucas Beyer and Bastian Leibe}, year={2017}, eprint={1703.07737}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
jamesdolezal/CTransPath	jamesdolezal	"2023-02-09T19:17:09Z"	0	2	null	[ "license:gpl-3.0", "region:us" ]	null	"2023-02-09T19:10:23Z"	--- license: gpl-3.0 --- [UNOFFICIAL] This is the pretrained CTransPath model that accompanies the manuscript Transformer-based Unsupervised Contrastive Learning for Histopathological Image Classification, published by Xiyue Wang et al in Medical Image Analysis (October 2022, DOI: https://doi.org/10.1016/j.media.2022.102559) This model has been uploaded to HuggingFace for easier sharing, but has not been verified by the original authors and is in no way affiliated with the original authors. The official pretrained model is available on the official GitHub repository (https://github.com/Xiyue-Wang/TransPath) and Google Drive (https://drive.google.com/file/d/1DoDx_70_TLj98gTf6YTXnu4tFhsFocDX/view?usp=sharing). The license as included in the original repository is GPL-3.0.
yantolakpau/minasanlora	yantolakpau	"2023-04-11T04:02:30Z"	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	"2023-04-11T04:01:02Z"	--- license: creativeml-openrail-m ---
PrunaAI/FLUX.1-schnell-4bit	PrunaAI	"2024-10-30T19:28:33Z"	22	11	null	[ "pruna-ai", "base_model:ibm-granite/granite-8b-code-instruct-128k", "base_model:finetune:ibm-granite/granite-8b-code-instruct-128k", "region:us" ]	null	"2024-08-17T09:54:17Z"	--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: ibm-granite/granite-8b-code-instruct-128k metrics: - memory_disk - memory_inference - inference_latency - inference_throughput - inference_CO2_emissions - inference_energy_consumption tags: - pruna-ai --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> <!-- header end --> [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/Tun8YgzxZ9) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. Frequently Asked Questions - *How does the compression work?* The model is compressed with Quanto to 8 bits. - *How does the model quality change?* The quality of the model output might vary compared to the base model. - *How is the model efficiency evaluated?* These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - *What is the model format?* We use safetensors. - *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data. - *What is the naming convention for Pruna Huggingface models?* We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - *What are "first" metrics?* Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. - *What are "Sync" and "Async" metrics?* "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases. ## Setup You can run the smashed model on cards with less than 12 GB of memory with these steps: 0. Check requirements from the original repo black-forest-labs/FLUX.1-schnell installed. In particular, check python, diffusers, and transformers versions. 1. Make sure that you have installed quantization related packages. ```bash pip install -U optimum-quanto ``` 2. Download the model - Use Python: ```python import subprocess repo_name = "FLUX.1-schnell-4bit" subprocess.run(["mkdir", repo_name]) subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"]) ``` 2. Load & run the model. ```python import torch from optimum.quanto import freeze, qfloat8, quantize from diffusers import FlowMatchEulerDiscreteScheduler, AutoencoderKL from diffusers.models.transformers.transformer_flux import FluxTransformer2DModel from diffusers.pipelines.flux.pipeline_flux import FluxPipeline from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast dtype = torch.bfloat16 bfl_repo = "black-forest-labs/FLUX.1-schnell" revision = "refs/pr/1" local_path = "FLUX.1-schnell-4bit" scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(bfl_repo, subfolder="scheduler", revision=revision) text_encoder = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14", torch_dtype=dtype) tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14", torch_dtype=dtype) text_encoder_2 = torch.load(local_path + '/text_encoder_2.pt') tokenizer_2 = T5TokenizerFast.from_pretrained(bfl_repo, subfolder="tokenizer_2", torch_dtype=dtype, revision=revision) vae = AutoencoderKL.from_pretrained(bfl_repo, subfolder="vae", torch_dtype=dtype, revision=revision) transformer = torch.load(local_path + '/transformer.pt') pipe = FluxPipeline( scheduler=scheduler, text_encoder=text_encoder, tokenizer=tokenizer, text_encoder_2=None, tokenizer_2=tokenizer_2, vae=vae, transformer=None, ) pipe.text_encoder_2 = text_encoder_2 pipe.transformer = transformer # pipe.enable_model_cpu_offload() pipe.to('cuda') print('done') generator = torch.Generator().manual_seed(12345) pipe( "a cute apple smiling", guidance_scale=0.0, num_inference_steps=4, max_sequence_length=256, generator=torch.Generator("cpu").manual_seed(0) ).images[0] ``` ## Configurations The configuration info are in `smash_config.json`. ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model black-forest-labs/FLUX.1-schnell before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
methinkss/m2	methinkss	"2025-02-08T16:12:46Z"	22	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-02-08T16:09:23Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
hinablue/illustriousXL1.0_v10_merged	hinablue	"2025-03-04T08:35:57Z"	0	0	null	[ "license:other", "region:us" ]	null	"2025-03-04T05:47:08Z"	--- license: other license_name: fair-ai-public-license-1.0-sd license_link: https://freedevproject.org/fdpl-1.0/ --- # Model Card Merge illustriousXL 1.0 with waiNSFWIllustrious_v110 for testing. ## Model Details [illustriousXL 1.0](https://civitai.com/models/1232765?modelVersionId=1410435) [waiNSFWIllustrious_v110](https://civitai.com/models/827184/wai-nsfw-illustrious-sdxl) ### Model Description ``` ill 0.6 + wai 0.4 => merged merged 0.6 + 0.4(0.5(ill 0.6 + wai 0.4, cosine A + cosine B)) => merged_plus_cosineAB ```
QuantFactory/Faro-Yi-9B-DPO-GGUF	QuantFactory	"2024-05-24T14:16:39Z"	720	1	null	[ "gguf", "llama", "conversational", "text-generation", "en", "zh", "dataset:wenbopan/Chinese-dpo-pairs", "dataset:Intel/orca_dpo_pairs", "dataset:argilla/ultrafeedback-binarized-preferences-cleaned", "dataset:jondurbin/truthy-dpo-v0.1", "arxiv:2303.08774", "base_model:wenbopan/Faro-Yi-9B-DPO", "base_model:quantized:wenbopan/Faro-Yi-9B-DPO", "license:mit", "endpoints_compatible", "region:us" ]	text-generation	"2024-05-24T13:21:41Z"	--- language: - en - zh license: mit datasets: - wenbopan/Chinese-dpo-pairs - Intel/orca_dpo_pairs - argilla/ultrafeedback-binarized-preferences-cleaned - jondurbin/truthy-dpo-v0.1 pipeline_tag: text-generation tags: - llama - conversational base_model: wenbopan/Faro-Yi-9B-DPO --- # Faro-Yi-9B-DP-GGUF This is quantized version of [wenbopan/Faro-Yi-9B-DPO](https://huggingface.co/wenbopan/Faro-Yi-9B-DPO) created using llama.cpp # Model Description This is the DPO version of [wenbopan/Faro-Yi-9B](https://huggingface.co/wenbopan/Faro-Yi-9B). Compared to Faro-Yi-9B and [Yi-9B-200K](https://huggingface.co/01-ai/Yi-9B-200K), the DPO model excels at many tasks, surpassing the original Yi-9B-200K by a large margin. On the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard), it ranks #2 among all 9B models, #1 among all Yi-9B variants. \| Metric \| MMLU \| GSM8K \| hellaswag \| truthfulqa \| ai2_arc \| winogrande \| CMMLU \| \| ----------------------- \| --------- \| --------- \| ------------- \| -------------- \| ----------- \| -------------- \| --------- \| \| Yi-9B-200K \| 65.73 \| 50.49 \| 56.72 \| 33.80 \| 69.25 \| 71.67 \| 71.97 \| \| Faro-Yi-9B \| 68.80 \| 63.08 \| 57.28 \| 40.86 \| 72.58 \| 71.11 \| 73.28 \| \| Faro-Yi-9B-DPO \| 69.98 \| 66.11 \| 59.04 \| 48.01 \| 75.68 \| 73.40 \| 75.23 \| Faro-Yi-9B-DPO's responses are also favored by GPT-4 Judge in MT-Bench ![image/png](https://cdn-uploads.huggingface.co/production/uploads/62cd3a3691d27e60db0698b0/ArlnloL4aPfiiD6kUqaSH.png) ## How to Use Faro-Yi-9B-DPO uses the chatml template and performs well in both short and long contexts. For longer inputs under 24GB of VRAM, I recommend to use vLLM to have a max prompt of 32K. Setting `kv_cache_dtype="fp8_e5m2"` allows for 48K input length. 4bit-AWQ quantization on top of that can boost input length to 160K, albeit with some performance impact. Adjust `max_model_len` arg in vLLM or `config.json` to avoid OOM. ```python import io import requests from PyPDF2 import PdfReader from vllm import LLM, SamplingParams llm = LLM(model="wenbopan/Faro-Yi-9B-DPO", kv_cache_dtype="fp8_e5m2", max_model_len=100000) pdf_data = io.BytesIO(requests.get("https://arxiv.org/pdf/2303.08774.pdf").content) document = "".join(page.extract_text() for page in PdfReader(pdf_data).pages) # 100 pages question = f"{document}\n\nAccording to the paper, what is the parameter count of GPT-4?" messages = [ {"role": "user", "content": question} ] # 83K tokens prompt = llm.get_tokenizer().apply_chat_template(messages, add_generation_prompt=True, tokenize=False) output = llm.generate(prompt, SamplingParams(temperature=0.8, max_tokens=500)) print(output[0].outputs[0].text) # Yi-9B-200K: 175B. GPT-4 has 175B \nparameters. How many models were combined to create GPT-4? Answer: 6. ... # Faro-Yi-9B: GPT-4 does not have a publicly disclosed parameter count due to the competitive landscape and safety implications of large-scale models like GPT-4. ... ``` <details> <summary>Or With Transformers</summary> ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained('wenbopan/Faro-Yi-9B-DPO', device_map="cuda") tokenizer = AutoTokenizer.from_pretrained('wenbopan/Faro-Yi-9B-DPO') messages = [ {"role": "system", "content": "You are a helpful assistant. Always answer with a short response."}, {"role": "user", "content": "Tell me what is Pythagorean theorem like you are a pirate."} ] input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device) generated_ids = model.generate(input_ids, max_new_tokens=512, temperature=0.5) response = tokenizer.decode(generated_ids[0], skip_special_tokens=True) # Aye, matey! The Pythagorean theorem is a nautical rule that helps us find the length of the third side of a triangle. ... ``` </details>
marco-c88/distilgpt2-finetuned-mstatmem_1ep_2	marco-c88	"2023-03-17T10:55:18Z"	176	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2023-03-17T10:52:37Z"	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilgpt2-finetuned-mstatmem_1ep_2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilgpt2-finetuned-mstatmem_1ep_2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.6512 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.804 \| 1.0 \| 703 \| 3.6512 \| ### Framework versions - Transformers 4.27.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2
Niyantha23M/llama-7b-chat-25000-25-75-L	Niyantha23M	"2024-04-12T06:57:35Z"	0	0	null	[ "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:meta-llama/Llama-2-7b-chat-hf", "base_model:finetune:meta-llama/Llama-2-7b-chat-hf", "region:us" ]	null	"2024-04-12T06:57:29Z"	--- base_model: meta-llama/Llama-2-7b-chat-hf tags: - trl - sft - generated_from_trainer datasets: - generator model-index: - name: llama-7b-chat-25000-25-75-L results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # llama-7b-chat-25000-25-75-L This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2200 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4400 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.33.3 - Pytorch 2.2.1 - Datasets 2.18.0 - Tokenizers 0.13.3
matrixportal/L3-Aspire-Heart-Matrix-8B-GGUF	matrixportal	"2025-01-22T21:55:23Z"	70	0	transformers	[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "vllm", "bfloat16", "llama", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:ZeroXClem/L3-Aspire-Heart-Matrix-8B", "base_model:quantized:ZeroXClem/L3-Aspire-Heart-Matrix-8B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	"2025-01-22T13:32:43Z"	--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - vllm - bfloat16 - llama - llama-cpp - gguf-my-repo language: - en base_model: ZeroXClem/L3-Aspire-Heart-Matrix-8B pipeline_tag: text-generation library_name: transformers --- # matrixportal/L3-Aspire-Heart-Matrix-8B-GGUF This model was converted to GGUF format from [`ZeroXClem/L3-Aspire-Heart-Matrix-8B`](https://huggingface.co/ZeroXClem/L3-Aspire-Heart-Matrix-8B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/ZeroXClem/L3-Aspire-Heart-Matrix-8B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo matrixportal/L3-Aspire-Heart-Matrix-8B-GGUF --hf-file l3-aspire-heart-matrix-8b-q4_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo matrixportal/L3-Aspire-Heart-Matrix-8B-GGUF --hf-file l3-aspire-heart-matrix-8b-q4_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo matrixportal/L3-Aspire-Heart-Matrix-8B-GGUF --hf-file l3-aspire-heart-matrix-8b-q4_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo matrixportal/L3-Aspire-Heart-Matrix-8B-GGUF --hf-file l3-aspire-heart-matrix-8b-q4_0.gguf -c 2048 ```
jerryyun/kicon_llama3_8b_qlora_merged_v1	jerryyun	"2024-07-14T16:47:24Z"	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	"2024-07-14T16:44:40Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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nomsgadded/pokemon-lora	nomsgadded	"2023-07-11T05:25:03Z"	2	0	diffusers	[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	"2023-07-11T03:46:05Z"	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - nomsgadded/pokemon-lora These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the lambdalabs/pokemon-blip-captions dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png)
mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF	mradermacher	"2024-11-25T08:56:31Z"	11	1	transformers	[ "transformers", "gguf", "ko", "en", "base_model:gwonny/nox-solar-10.7b-v4-kolon-all-5-v3.0", "base_model:quantized:gwonny/nox-solar-10.7b-v4-kolon-all-5-v3.0", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	"2024-11-23T15:12:29Z"	--- base_model: gwonny/nox-solar-10.7b-v4-kolon-all-5-v3.0 language: - ko - en library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/gwonny/nox-solar-10.7b-v4-kolon-all-5-v3.0 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.5 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.7 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-IQ2_S.gguf) \| i1-IQ2_S \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-IQ2_M.gguf) \| i1-IQ2_M \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q2_K.gguf) \| i1-Q2_K \| 4.1 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 4.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 4.8 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-IQ3_S.gguf) \| i1-IQ3_S \| 4.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-IQ3_M.gguf) \| i1-IQ3_M \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 5.3 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 5.8 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q4_0_4_4.gguf) \| i1-Q4_0_4_4 \| 6.2 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q4_0_4_8.gguf) \| i1-Q4_0_4_8 \| 6.2 \| fast on arm+i8mm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q4_0_8_8.gguf) \| i1-Q4_0_8_8 \| 6.2 \| fast on arm+sve, low quality \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q4_0.gguf) \| i1-Q4_0 \| 6.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 6.2 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 6.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 7.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 7.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q6_K.gguf) \| i1-Q6_K \| 8.9 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
AdapterHub/facebook-bart-base_lingaccept_cola_pfeiffer	AdapterHub	"2024-05-05T19:21:14Z"	0	0	adapter-transformers	[ "adapter-transformers", "text-classification", "adapterhub:lingaccept/cola", "bart", "license:apache-2.0", "region:us" ]	text-classification	"2024-05-05T19:21:11Z"	--- tags: - adapter-transformers - text-classification - adapterhub:lingaccept/cola - bart license: "apache-2.0" --- # Adapter `facebook-bart-base_lingaccept_cola_pfeiffer` for facebook/bart-base Adapter for bart-base in Pfeiffer architecture trained on the CoLA dataset for 15 epochs with early stopping and a learning rate of 1e-4. This adapter was created for usage with the [Adapters](https://github.com/Adapter-Hub/adapters) library. ## Usage First, install `adapters`: ``` pip install -U adapters ``` Now, the adapter can be loaded and activated like this: ```python from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("facebook/bart-base") adapter_name = model.load_adapter("AdapterHub/facebook-bart-base_lingaccept_cola_pfeiffer") model.set_active_adapters(adapter_name) ``` ## Architecture & Training - Adapter architecture: pfeiffer - Prediction head: classification - Dataset: [CoLA](https://nyu-mll.github.io/CoLA/) ## Author Information - Author name(s): Clifton Poth - Author email: [email protected] - Author links: [Website](https://calpt.github.io), [GitHub](https://github.com/calpt), [Twitter](https://twitter.com/@clifapt) ## Citation ```bibtex ``` This adapter has been auto-imported from https://github.com/Adapter-Hub/Hub/blob/master/adapters/ukp/facebook-bart-base_lingaccept_cola_pfeiffer.yaml.
Yaxin1992/llama3-8b-summary	Yaxin1992	"2024-04-23T21:42:31Z"	3	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct", "license:other", "region:us" ]	null	"2024-04-23T16:10:29Z"	--- license: other library_name: peft tags: - generated_from_trainer base_model: meta-llama/Meta-Llama-3-8B-Instruct model-index: - name: llama3-8b-summary results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # llama3-8b-summary This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 8000 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1
Jackson9z4x9/SFT-calculator	Jackson9z4x9	"2025-02-12T01:15:31Z"	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2025-02-10T22:10:39Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Eswann/ML-Agents-Pyramids	Eswann	"2023-11-16T11:54:44Z"	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	"2023-11-16T11:54:41Z"	--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Eswann/ML-Agents-Pyramids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
judithrosell/ClinicalBERT_JNLPBA_NER_new	judithrosell	"2023-12-31T18:33:11Z"	6	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "token-classification", "generated_from_trainer", "base_model:medicalai/ClinicalBERT", "base_model:finetune:medicalai/ClinicalBERT", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	"2023-12-31T15:04:47Z"	--- base_model: medicalai/ClinicalBERT tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: ClinicalBERT_JNLPBA_NER_new results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ClinicalBERT_JNLPBA_NER_new This model is a fine-tuned version of [medicalai/ClinicalBERT](https://huggingface.co/medicalai/ClinicalBERT) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1699 - Precision: 0.7855 - Recall: 0.8043 - F1: 0.7948 - Accuracy: 0.9439 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.2204 \| 1.0 \| 1164 \| 0.1821 \| 0.7652 \| 0.7719 \| 0.7685 \| 0.9380 \| \| 0.1618 \| 2.0 \| 2328 \| 0.1716 \| 0.7884 \| 0.7886 \| 0.7885 \| 0.9426 \| \| 0.1338 \| 3.0 \| 3492 \| 0.1699 \| 0.7855 \| 0.8043 \| 0.7948 \| 0.9439 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0
kelestemur/deep_rl	kelestemur	"2023-01-08T21:58:21Z"	1	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	"2023-01-08T21:57:58Z"	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 264.57 +/- 20.35 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
mlninad/deciLM-finetuned	mlninad	"2023-10-23T19:22:53Z"	15	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "custom_code", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2023-10-22T09:13:33Z"	--- license: apache-2.0 language: - en ---
Narrativaai/deberta-v3-small-finetuned-hate_speech18	Narrativaai	"2021-12-01T17:41:13Z"	9	3	transformers	[ "transformers", "pytorch", "tensorboard", "deberta-v2", "text-classification", "generated_from_trainer", "dataset:hate_speech18", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2022-03-02T23:29:04Z"	--- license: mit tags: - generated_from_trainer datasets: - hate_speech18 widget: - text: "ok, so do we need to kill them too or are the slavs okay ? for some reason whenever i hear the word slav , the word slobber comes to mind and i picture a slobbering half breed creature like the humpback of notre dame or Igor haha" metrics: - accuracy model-index: - name: deberta-v3-small-hate-speech results: - task: name: Text Classification type: text-classification dataset: name: hate_speech18 type: hate_speech18 args: default metrics: - name: Accuracy type: accuracy value: 0.916058394160584 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # DeBERTa v3 small fine-tuned on hate_speech18 dataset for Hate Speech Detection This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the hate_speech18 dataset. It achieves the following results on the evaluation set: - Loss: 0.2922 - Accuracy: 0.9161 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.4147 \| 1.0 \| 650 \| 0.3910 \| 0.8832 \| \| 0.2975 \| 2.0 \| 1300 \| 0.2922 \| 0.9161 \| \| 0.2575 \| 3.0 \| 1950 \| 0.3555 \| 0.9051 \| \| 0.1553 \| 4.0 \| 2600 \| 0.4263 \| 0.9124 \| \| 0.1267 \| 5.0 \| 3250 \| 0.4238 \| 0.9161 \| ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF	mradermacher	"2025-02-22T10:00:05Z"	0	0	transformers	[ "transformers", "gguf", "en", "base_model:nkpz/Reverb-7b-Uncensored-DeLMAT", "base_model:quantized:nkpz/Reverb-7b-Uncensored-DeLMAT", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	"2025-02-22T06:06:48Z"	--- base_model: nkpz/Reverb-7b-Uncensored-DeLMAT language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/nkpz/Reverb-7b-Uncensored-DeLMAT <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF/resolve/main/Reverb-7b-Uncensored-DeLMAT.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.0 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF/resolve/main/Reverb-7b-Uncensored-DeLMAT.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.1 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF/resolve/main/Reverb-7b-Uncensored-DeLMAT.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF/resolve/main/Reverb-7b-Uncensored-DeLMAT.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF/resolve/main/Reverb-7b-Uncensored-DeLMAT.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF/resolve/main/Reverb-7b-Uncensored-DeLMAT.i1-IQ2_M.gguf) \| i1-IQ2_M \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF/resolve/main/Reverb-7b-Uncensored-DeLMAT.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 2.9 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF/resolve/main/Reverb-7b-Uncensored-DeLMAT.i1-Q2_K.gguf) \| i1-Q2_K \| 3.1 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF/resolve/main/Reverb-7b-Uncensored-DeLMAT.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 3.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF/resolve/main/Reverb-7b-Uncensored-DeLMAT.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF/resolve/main/Reverb-7b-Uncensored-DeLMAT.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.6 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF/resolve/main/Reverb-7b-Uncensored-DeLMAT.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.6 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF/resolve/main/Reverb-7b-Uncensored-DeLMAT.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF/resolve/main/Reverb-7b-Uncensored-DeLMAT.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 3.9 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF/resolve/main/Reverb-7b-Uncensored-DeLMAT.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 4.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF/resolve/main/Reverb-7b-Uncensored-DeLMAT.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF/resolve/main/Reverb-7b-Uncensored-DeLMAT.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 4.5 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF/resolve/main/Reverb-7b-Uncensored-DeLMAT.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.5 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF/resolve/main/Reverb-7b-Uncensored-DeLMAT.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.6 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF/resolve/main/Reverb-7b-Uncensored-DeLMAT.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF/resolve/main/Reverb-7b-Uncensored-DeLMAT.i1-Q4_1.gguf) \| i1-Q4_1 \| 5.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF/resolve/main/Reverb-7b-Uncensored-DeLMAT.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF/resolve/main/Reverb-7b-Uncensored-DeLMAT.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF/resolve/main/Reverb-7b-Uncensored-DeLMAT.i1-Q6_K.gguf) \| i1-Q6_K \| 6.4 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
sail-rvc/VergilRVC2byDreamnaught	sail-rvc	"2023-07-14T07:33:52Z"	1	0	transformers	[ "transformers", "rvc", "sail-rvc", "audio-to-audio", "endpoints_compatible", "region:us" ]	audio-to-audio	"2023-07-14T07:33:33Z"	--- pipeline_tag: audio-to-audio tags: - rvc - sail-rvc --- # VergilRVC2byDreamnaught ## RVC Model ![banner](https://i.imgur.com/xocCjhH.jpg) This model repo was automatically generated. Date: 2023-07-14 07:33:52 Bot Name: juuxnscrap Model Type: RVC Source: https://huggingface.co/juuxn/RVCModels/ Reason: Converting into loadable format for https://github.com/chavinlo/rvc-runpod
farleyknight/arxiv-summarization-fb-bart-base-2022-09-21	farleyknight	"2022-09-23T08:34:25Z"	121	0	transformers	[ "transformers", "pytorch", "bart", "text2text-generation", "generated_from_trainer", "dataset:ccdv/arxiv-summarization", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	"2022-09-21T23:10:43Z"	--- license: apache-2.0 tags: - generated_from_trainer datasets: - ccdv/arxiv-summarization metrics: - rouge model-index: - name: arxiv-summarization-fb-bart-base-2022-09-21 results: - task: name: Summarization type: summarization dataset: name: ccdv/arxiv-summarization type: ccdv/arxiv-summarization config: section split: train args: section metrics: - name: Rouge1 type: rouge value: 42.9082 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # arxiv-summarization-fb-bart-base-2022-09-21 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the ccdv/arxiv-summarization dataset. It achieves the following results on the evaluation set: - Loss: 2.1597 - Rouge1: 42.9082 - Rouge2: 15.7763 - Rougel: 25.9239 - Rougelsum: 37.7957 - Gen Len: 110.5816 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:-------:\|:------:\|:-------:\|:---------:\|:-------:\| \| 2.9142 \| 0.05 \| 10000 \| 2.7522 \| 17.073 \| 6.7502 \| 13.6779 \| 15.6668 \| 20.0 \| \| 2.7876 \| 0.1 \| 20000 \| 2.6888 \| 16.7954 \| 6.7038 \| 13.4939 \| 15.3426 \| 19.9992 \| \| 2.715 \| 0.15 \| 30000 \| 2.6308 \| 17.3324 \| 6.8771 \| 13.7918 \| 15.7839 \| 20.0 \| \| 2.6431 \| 0.2 \| 40000 \| 2.5858 \| 16.7055 \| 6.8108 \| 13.4796 \| 15.2959 \| 20.0 \| \| 2.6381 \| 0.25 \| 50000 \| 2.5393 \| 17.4643 \| 7.0687 \| 13.9507 \| 16.012 \| 20.0 \| \| 2.6269 \| 0.3 \| 60000 \| 2.5159 \| 17.5934 \| 7.0022 \| 13.9394 \| 16.0203 \| 20.0 \| \| 2.5482 \| 0.34 \| 70000 \| 2.4894 \| 17.5428 \| 7.1822 \| 13.9788 \| 16.0355 \| 20.0 \| \| 2.4962 \| 0.39 \| 80000 \| 2.4476 \| 17.3587 \| 7.1501 \| 13.9215 \| 15.8637 \| 20.0 \| \| 2.513 \| 0.44 \| 90000 \| 2.4309 \| 18.0806 \| 7.5429 \| 14.4201 \| 16.561 \| 20.0 \| \| 2.4464 \| 0.49 \| 100000 \| 2.4128 \| 17.9813 \| 7.5454 \| 14.3403 \| 16.52 \| 19.9989 \| \| 2.4969 \| 0.54 \| 110000 \| 2.4114 \| 17.3353 \| 7.1382 \| 13.9109 \| 15.873 \| 20.0 \| \| 2.4417 \| 0.59 \| 120000 \| 2.3866 \| 18.0241 \| 7.553 \| 14.3892 \| 16.5077 \| 19.9980 \| \| 2.4333 \| 0.64 \| 130000 \| 2.3903 \| 18.0578 \| 7.4999 \| 14.3901 \| 16.5134 \| 20.0 \| \| 2.4296 \| 0.69 \| 140000 \| 2.3793 \| 17.7742 \| 7.5182 \| 14.2794 \| 16.2879 \| 20.0 \| \| 2.4277 \| 0.74 \| 150000 \| 2.3571 \| 17.8015 \| 7.4677 \| 14.226 \| 16.3288 \| 20.0 \| \| 2.4258 \| 0.79 \| 160000 \| 2.3539 \| 17.5335 \| 7.399 \| 14.09 \| 16.0936 \| 20.0 \| \| 2.4006 \| 0.84 \| 170000 \| 2.3469 \| 17.5983 \| 7.4285 \| 14.1315 \| 16.1385 \| 20.0 \| \| 2.367 \| 0.89 \| 180000 \| 2.3344 \| 17.297 \| 7.2361 \| 13.9286 \| 15.8352 \| 20.0 \| \| 2.373 \| 0.94 \| 190000 \| 2.3377 \| 17.7189 \| 7.4993 \| 14.2603 \| 16.2546 \| 19.9980 \| \| 2.3762 \| 0.99 \| 200000 \| 2.3106 \| 17.7883 \| 7.4766 \| 14.2675 \| 16.3115 \| 20.0 \| \| 2.2538 \| 1.03 \| 210000 \| 2.3197 \| 17.4487 \| 7.4171 \| 14.0473 \| 15.9771 \| 20.0 \| \| 2.268 \| 1.08 \| 220000 \| 2.3044 \| 17.9603 \| 7.5806 \| 14.3755 \| 16.4328 \| 20.0 \| \| 2.2986 \| 1.13 \| 230000 \| 2.3002 \| 17.9268 \| 7.5321 \| 14.3503 \| 16.4191 \| 20.0 \| \| 2.241 \| 1.18 \| 240000 \| 2.3059 \| 17.4542 \| 7.3224 \| 14.0578 \| 16.0157 \| 20.0 \| \| 2.2534 \| 1.23 \| 250000 \| 2.2927 \| 17.8039 \| 7.6232 \| 14.2916 \| 16.3442 \| 20.0 \| \| 2.26 \| 1.28 \| 260000 \| 2.2910 \| 17.8607 \| 7.5645 \| 14.318 \| 16.3336 \| 19.9983 \| \| 2.3 \| 1.33 \| 270000 \| 2.2818 \| 17.8203 \| 7.4815 \| 14.3171 \| 16.3309 \| 20.0 \| \| 2.2964 \| 1.38 \| 280000 \| 2.2721 \| 17.983 \| 7.6867 \| 14.3971 \| 16.493 \| 20.0 \| \| 2.2564 \| 1.43 \| 290000 \| 2.2701 \| 18.059 \| 7.7273 \| 14.4806 \| 16.5792 \| 19.9988 \| \| 2.2576 \| 1.48 \| 300000 \| 2.2663 \| 17.5706 \| 7.4424 \| 14.1424 \| 16.1297 \| 20.0 \| \| 2.2605 \| 1.53 \| 310000 \| 2.2607 \| 17.8057 \| 7.5219 \| 14.3226 \| 16.3355 \| 19.9988 \| \| 2.2587 \| 1.58 \| 320000 \| 2.2552 \| 18.0396 \| 7.7064 \| 14.5005 \| 16.5823 \| 20.0 \| \| 2.2423 \| 1.63 \| 330000 \| 2.2523 \| 18.2229 \| 7.8398 \| 14.5868 \| 16.7408 \| 20.0 \| \| 2.2793 \| 1.68 \| 340000 \| 2.2431 \| 17.6785 \| 7.5437 \| 14.1971 \| 16.1724 \| 19.9988 \| \| 2.2005 \| 1.72 \| 350000 \| 2.2343 \| 17.7552 \| 7.6026 \| 14.2152 \| 16.2797 \| 19.9988 \| \| 2.2454 \| 1.77 \| 360000 \| 2.2339 \| 17.9292 \| 7.699 \| 14.4099 \| 16.4682 \| 20.0 \| \| 2.2175 \| 1.82 \| 370000 \| 2.2345 \| 17.7413 \| 7.4892 \| 14.2223 \| 16.2442 \| 20.0 \| \| 2.238 \| 1.87 \| 380000 \| 2.2259 \| 17.6679 \| 7.4976 \| 14.24 \| 16.243 \| 19.9988 \| \| 2.2108 \| 1.92 \| 390000 \| 2.2210 \| 17.8474 \| 7.6054 \| 14.3494 \| 16.3635 \| 19.9988 \| \| 2.2124 \| 1.97 \| 400000 \| 2.2170 \| 17.8019 \| 7.5182 \| 14.264 \| 16.3003 \| 20.0 \| \| 2.0976 \| 2.02 \| 410000 \| 2.2248 \| 17.8063 \| 7.5383 \| 14.2782 \| 16.275 \| 20.0 \| \| 2.0932 \| 2.07 \| 420000 \| 2.2196 \| 17.9171 \| 7.6187 \| 14.3508 \| 16.4333 \| 20.0 \| \| 2.0956 \| 2.12 \| 430000 \| 2.2135 \| 18.0616 \| 7.7655 \| 14.4837 \| 16.5627 \| 19.9988 \| \| 2.0515 \| 2.17 \| 440000 \| 2.2091 \| 18.0281 \| 7.7301 \| 14.4696 \| 16.5196 \| 19.9981 \| \| 2.1216 \| 2.22 \| 450000 \| 2.2015 \| 18.0609 \| 7.7541 \| 14.4633 \| 16.5705 \| 19.9988 \| \| 2.1222 \| 2.27 \| 460000 \| 2.1983 \| 18.0717 \| 7.7473 \| 14.4725 \| 16.5399 \| 19.9988 \| \| 2.0903 \| 2.32 \| 470000 \| 2.2007 \| 18.0751 \| 7.7486 \| 14.4583 \| 16.546 \| 20.0 \| \| 2.1124 \| 2.37 \| 480000 \| 2.1934 \| 17.888 \| 7.7124 \| 14.3899 \| 16.3901 \| 20.0 \| \| 2.1094 \| 2.41 \| 490000 \| 2.1901 \| 18.0254 \| 7.7682 \| 14.4427 \| 16.5181 \| 20.0 \| \| 2.1085 \| 2.46 \| 500000 \| 2.1924 \| 17.9077 \| 7.7004 \| 14.3843 \| 16.4221 \| 19.9988 \| \| 2.0781 \| 2.51 \| 510000 \| 2.1781 \| 18.1591 \| 7.8456 \| 14.565 \| 16.6435 \| 19.9988 \| \| 2.0875 \| 2.56 \| 520000 \| 2.1801 \| 18.0389 \| 7.7342 \| 14.4259 \| 16.5378 \| 20.0 \| \| 2.0945 \| 2.61 \| 530000 \| 2.1758 \| 18.0999 \| 7.8217 \| 14.5163 \| 16.5784 \| 19.9988 \| \| 2.0723 \| 2.66 \| 540000 \| 2.1756 \| 17.9684 \| 7.7369 \| 14.4279 \| 16.4815 \| 19.9988 \| \| 2.0918 \| 2.71 \| 550000 \| 2.1738 \| 18.1183 \| 7.8414 \| 14.5298 \| 16.6119 \| 19.9988 \| \| 2.0835 \| 2.76 \| 560000 \| 2.1671 \| 17.8837 \| 7.7379 \| 14.3727 \| 16.4068 \| 19.9988 \| \| 2.0936 \| 2.81 \| 570000 \| 2.1670 \| 17.9631 \| 7.7708 \| 14.4566 \| 16.4823 \| 19.9988 \| \| 2.0518 \| 2.86 \| 580000 \| 2.1631 \| 18.0601 \| 7.8112 \| 14.5158 \| 16.5816 \| 19.9988 \| \| 2.065 \| 2.91 \| 590000 \| 2.1611 \| 18.0548 \| 7.8147 \| 14.5271 \| 16.5606 \| 19.9988 \| \| 2.0427 \| 2.96 \| 600000 \| 2.1611 \| 18.0642 \| 7.8284 \| 14.5293 \| 16.5736 \| 19.9988 \| ### Framework versions - Transformers 4.23.0.dev0 - Pytorch 1.12.0 - Datasets 2.5.1 - Tokenizers 0.13.0
jeroenherczeg/shawgpt-ft	jeroenherczeg	"2024-04-05T08:21:37Z"	2	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:TheBloke/Mistral-7B-Instruct-v0.2-GPTQ", "base_model:adapter:TheBloke/Mistral-7B-Instruct-v0.2-GPTQ", "license:apache-2.0", "region:us" ]	null	"2024-04-04T16:46:03Z"	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: TheBloke/Mistral-7B-Instruct-v0.2-GPTQ model-index: - name: shawgpt-ft results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # shawgpt-ft This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.2-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GPTQ) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.2320 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1 - training_steps: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 4.6433 \| 0.92 \| 3 \| 4.2320 \| \| 4.6544 \| 1.85 \| 6 \| 4.2320 \| \| 4.6459 \| 2.77 \| 9 \| 4.2320 \| \| 3.4822 \| 4.0 \| 13 \| 4.2320 \| \| 4.6298 \| 4.92 \| 16 \| 4.2320 \| \| 4.6605 \| 5.85 \| 19 \| 4.2320 \| \| 4.6392 \| 6.77 \| 22 \| 4.2320 \| \| 3.4844 \| 8.0 \| 26 \| 4.2320 \| \| 4.6305 \| 8.92 \| 29 \| 4.2320 \| \| 4.6337 \| 9.85 \| 32 \| 4.2320 \| \| 4.6501 \| 10.77 \| 35 \| 4.2320 \| \| 3.4793 \| 12.0 \| 39 \| 4.2320 \| \| 4.6568 \| 12.92 \| 42 \| 4.2320 \| \| 4.6402 \| 13.85 \| 45 \| 4.2320 \| \| 4.6381 \| 14.77 \| 48 \| 4.2320 \| \| 3.4787 \| 16.0 \| 52 \| 4.2320 \| \| 4.671 \| 16.92 \| 55 \| 4.2320 \| \| 4.6186 \| 17.85 \| 58 \| 4.2320 \| \| 4.6403 \| 18.77 \| 61 \| 4.2320 \| \| 3.5009 \| 20.0 \| 65 \| 4.2320 \| \| 4.6514 \| 20.92 \| 68 \| 4.2320 \| \| 4.6426 \| 21.85 \| 71 \| 4.2320 \| \| 4.6674 \| 22.77 \| 74 \| 4.2320 \| \| 3.4915 \| 24.0 \| 78 \| 4.2320 \| \| 4.6606 \| 24.92 \| 81 \| 4.2320 \| \| 4.6364 \| 25.85 \| 84 \| 4.2320 \| \| 4.6222 \| 26.77 \| 87 \| 4.2320 \| \| 3.4782 \| 28.0 \| 91 \| 4.2320 \| \| 4.6229 \| 28.92 \| 94 \| 4.2320 \| \| 4.6576 \| 29.85 \| 97 \| 4.2320 \| \| 4.6288 \| 30.77 \| 100 \| 4.2320 \| \| 3.4664 \| 32.0 \| 104 \| 4.2320 \| \| 4.6434 \| 32.92 \| 107 \| 4.2320 \| \| 4.6519 \| 33.85 \| 110 \| 4.2320 \| \| 4.6528 \| 34.77 \| 113 \| 4.2320 \| \| 3.471 \| 36.0 \| 117 \| 4.2320 \| \| 4.6453 \| 36.92 \| 120 \| 4.2320 \| \| 4.616 \| 37.85 \| 123 \| 4.2320 \| \| 4.6109 \| 38.77 \| 126 \| 4.2320 \| \| 3.4799 \| 40.0 \| 130 \| 4.2320 \| \| 4.6388 \| 40.92 \| 133 \| 4.2320 \| \| 4.6711 \| 41.85 \| 136 \| 4.2320 \| \| 4.6483 \| 42.77 \| 139 \| 4.2320 \| \| 3.4695 \| 44.0 \| 143 \| 4.2320 \| \| 4.6496 \| 44.92 \| 146 \| 4.2320 \| \| 4.644 \| 45.85 \| 149 \| 4.2320 \| \| 4.6444 \| 46.77 \| 152 \| 4.2320 \| \| 3.4741 \| 48.0 \| 156 \| 4.2320 \| \| 4.6189 \| 48.92 \| 159 \| 4.2320 \| \| 4.6683 \| 49.85 \| 162 \| 4.2320 \| \| 4.6345 \| 50.77 \| 165 \| 4.2320 \| \| 3.4703 \| 52.0 \| 169 \| 4.2320 \| \| 4.6144 \| 52.92 \| 172 \| 4.2320 \| \| 4.6648 \| 53.85 \| 175 \| 4.2320 \| \| 4.6522 \| 54.77 \| 178 \| 4.2320 \| \| 3.4838 \| 56.0 \| 182 \| 4.2320 \| \| 4.6506 \| 56.92 \| 185 \| 4.2320 \| \| 4.6339 \| 57.85 \| 188 \| 4.2320 \| \| 4.638 \| 58.77 \| 191 \| 4.2320 \| \| 3.4733 \| 60.0 \| 195 \| 4.2320 \| \| 4.6604 \| 60.92 \| 198 \| 4.2320 \| \| 4.6326 \| 61.85 \| 201 \| 4.2320 \| \| 4.6612 \| 62.77 \| 204 \| 4.2320 \| \| 3.4722 \| 64.0 \| 208 \| 4.2320 \| \| 4.6292 \| 64.92 \| 211 \| 4.2320 \| \| 4.6336 \| 65.85 \| 214 \| 4.2320 \| \| 4.642 \| 66.77 \| 217 \| 4.2320 \| \| 3.4915 \| 68.0 \| 221 \| 4.2320 \| \| 4.6453 \| 68.92 \| 224 \| 4.2320 \| \| 4.6459 \| 69.85 \| 227 \| 4.2320 \| \| 4.6202 \| 70.77 \| 230 \| 4.2320 \| \| 3.4753 \| 72.0 \| 234 \| 4.2320 \| \| 4.6552 \| 72.92 \| 237 \| 4.2320 \| \| 4.6443 \| 73.85 \| 240 \| 4.2320 \| \| 4.6495 \| 74.77 \| 243 \| 4.2320 \| \| 3.4798 \| 76.0 \| 247 \| 4.2320 \| \| 4.6358 \| 76.92 \| 250 \| 4.2320 \| \| 4.6434 \| 77.85 \| 253 \| 4.2320 \| \| 4.6325 \| 78.77 \| 256 \| 4.2320 \| \| 3.4951 \| 80.0 \| 260 \| 4.2320 \| \| 4.6302 \| 80.92 \| 263 \| 4.2320 \| \| 4.6458 \| 81.85 \| 266 \| 4.2320 \| \| 4.6407 \| 82.77 \| 269 \| 4.2320 \| \| 3.4828 \| 84.0 \| 273 \| 4.2320 \| \| 4.6436 \| 84.92 \| 276 \| 4.2320 \| \| 4.6143 \| 85.85 \| 279 \| 4.2320 \| \| 4.644 \| 86.77 \| 282 \| 4.2320 \| \| 3.4934 \| 88.0 \| 286 \| 4.2320 \| \| 4.6308 \| 88.92 \| 289 \| 4.2320 \| \| 4.6715 \| 89.85 \| 292 \| 4.2320 \| \| 4.6229 \| 90.77 \| 295 \| 4.2320 \| \| 3.4895 \| 92.0 \| 299 \| 4.2320 \| \| 4.6447 \| 92.92 \| 302 \| 4.2320 \| \| 4.6333 \| 93.85 \| 305 \| 4.2320 \| \| 4.643 \| 94.77 \| 308 \| 4.2320 \| \| 3.482 \| 96.0 \| 312 \| 4.2320 \| \| 4.6647 \| 96.92 \| 315 \| 4.2320 \| \| 4.65 \| 97.85 \| 318 \| 4.2320 \| \| 4.6545 \| 98.77 \| 321 \| 4.2320 \| \| 3.4881 \| 100.0 \| 325 \| 4.2320 \| \| 4.6828 \| 100.92 \| 328 \| 4.2320 \| \| 4.6328 \| 101.85 \| 331 \| 4.2320 \| \| 4.6419 \| 102.77 \| 334 \| 4.2320 \| \| 3.4954 \| 104.0 \| 338 \| 4.2320 \| \| 4.6203 \| 104.92 \| 341 \| 4.2320 \| \| 4.6236 \| 105.85 \| 344 \| 4.2320 \| \| 4.6539 \| 106.77 \| 347 \| 4.2320 \| \| 3.4737 \| 108.0 \| 351 \| 4.2320 \| \| 4.6319 \| 108.92 \| 354 \| 4.2320 \| \| 4.6696 \| 109.85 \| 357 \| 4.2320 \| \| 4.6678 \| 110.77 \| 360 \| 4.2320 \| \| 3.4698 \| 112.0 \| 364 \| 4.2320 \| \| 4.6459 \| 112.92 \| 367 \| 4.2320 \| \| 4.6524 \| 113.85 \| 370 \| 4.2320 \| \| 4.6399 \| 114.77 \| 373 \| 4.2320 \| \| 3.471 \| 116.0 \| 377 \| 4.2320 \| \| 4.6668 \| 116.92 \| 380 \| 4.2320 \| \| 4.634 \| 117.85 \| 383 \| 4.2320 \| \| 4.6345 \| 118.77 \| 386 \| 4.2320 \| \| 3.4938 \| 120.0 \| 390 \| 4.2320 \| \| 4.6386 \| 120.92 \| 393 \| 4.2320 \| \| 4.6661 \| 121.85 \| 396 \| 4.2320 \| \| 4.6465 \| 122.77 \| 399 \| 4.2320 \| \| 3.4903 \| 124.0 \| 403 \| 4.2320 \| \| 4.6255 \| 124.92 \| 406 \| 4.2320 \| \| 4.6306 \| 125.85 \| 409 \| 4.2320 \| \| 4.6348 \| 126.77 \| 412 \| 4.2320 \| \| 3.4811 \| 128.0 \| 416 \| 4.2320 \| \| 4.6335 \| 128.92 \| 419 \| 4.2320 \| \| 4.6678 \| 129.85 \| 422 \| 4.2320 \| \| 4.6336 \| 130.77 \| 425 \| 4.2320 \| \| 3.4722 \| 132.0 \| 429 \| 4.2320 \| \| 4.6371 \| 132.92 \| 432 \| 4.2320 \| \| 4.6488 \| 133.85 \| 435 \| 4.2320 \| \| 4.6456 \| 134.77 \| 438 \| 4.2320 \| \| 3.4866 \| 136.0 \| 442 \| 4.2320 \| \| 4.6349 \| 136.92 \| 445 \| 4.2320 \| \| 4.6418 \| 137.85 \| 448 \| 4.2320 \| \| 4.6546 \| 138.77 \| 451 \| 4.2320 \| \| 3.4811 \| 140.0 \| 455 \| 4.2320 \| \| 4.6322 \| 140.92 \| 458 \| 4.2320 \| \| 4.6154 \| 141.85 \| 461 \| 4.2320 \| \| 4.6362 \| 142.77 \| 464 \| 4.2320 \| \| 3.4809 \| 144.0 \| 468 \| 4.2320 \| \| 4.6317 \| 144.92 \| 471 \| 4.2320 \| \| 4.6329 \| 145.85 \| 474 \| 4.2320 \| \| 4.636 \| 146.77 \| 477 \| 4.2320 \| \| 3.4737 \| 148.0 \| 481 \| 4.2320 \| \| 4.629 \| 148.92 \| 484 \| 4.2320 \| \| 4.6212 \| 149.85 \| 487 \| 4.2320 \| \| 4.6548 \| 150.77 \| 490 \| 4.2320 \| \| 3.481 \| 152.0 \| 494 \| 4.2320 \| \| 4.6379 \| 152.92 \| 497 \| 4.2320 \| \| 4.6306 \| 153.85 \| 500 \| 4.2320 \| \| 4.6443 \| 154.77 \| 503 \| 4.2320 \| \| 3.4951 \| 156.0 \| 507 \| 4.2320 \| \| 4.6514 \| 156.92 \| 510 \| 4.2320 \| \| 4.6539 \| 157.85 \| 513 \| 4.2320 \| \| 4.6295 \| 158.77 \| 516 \| 4.2320 \| \| 3.485 \| 160.0 \| 520 \| 4.2320 \| \| 4.6665 \| 160.92 \| 523 \| 4.2320 \| \| 4.6508 \| 161.85 \| 526 \| 4.2320 \| \| 4.6754 \| 162.77 \| 529 \| 4.2320 \| \| 3.4689 \| 164.0 \| 533 \| 4.2320 \| \| 4.6286 \| 164.92 \| 536 \| 4.2320 \| \| 4.6164 \| 165.85 \| 539 \| 4.2320 \| \| 4.634 \| 166.77 \| 542 \| 4.2320 \| \| 3.4878 \| 168.0 \| 546 \| 4.2320 \| \| 4.6616 \| 168.92 \| 549 \| 4.2320 \| \| 4.6228 \| 169.85 \| 552 \| 4.2320 \| \| 4.6427 \| 170.77 \| 555 \| 4.2320 \| \| 3.4739 \| 172.0 \| 559 \| 4.2320 \| \| 4.656 \| 172.92 \| 562 \| 4.2320 \| \| 4.6488 \| 173.85 \| 565 \| 4.2320 \| \| 4.6199 \| 174.77 \| 568 \| 4.2320 \| \| 3.4842 \| 176.0 \| 572 \| 4.2320 \| \| 4.6632 \| 176.92 \| 575 \| 4.2320 \| \| 4.646 \| 177.85 \| 578 \| 4.2320 \| \| 4.6226 \| 178.77 \| 581 \| 4.2320 \| \| 3.4619 \| 180.0 \| 585 \| 4.2320 \| \| 4.6329 \| 180.92 \| 588 \| 4.2320 \| \| 4.6245 \| 181.85 \| 591 \| 4.2320 \| \| 4.6435 \| 182.77 \| 594 \| 4.2320 \| \| 3.478 \| 184.0 \| 598 \| 4.2320 \| \| 4.6256 \| 184.92 \| 601 \| 4.2320 \| \| 4.6516 \| 185.85 \| 604 \| 4.2320 \| \| 4.6438 \| 186.77 \| 607 \| 4.2320 \| \| 3.5015 \| 188.0 \| 611 \| 4.2320 \| \| 4.6254 \| 188.92 \| 614 \| 4.2320 \| \| 4.6265 \| 189.85 \| 617 \| 4.2320 \| \| 4.6447 \| 190.77 \| 620 \| 4.2320 \| \| 3.508 \| 192.0 \| 624 \| 4.2320 \| \| 4.6353 \| 192.92 \| 627 \| 4.2320 \| \| 4.6333 \| 193.85 \| 630 \| 4.2320 \| \| 4.6573 \| 194.77 \| 633 \| 4.2320 \| \| 3.4644 \| 196.0 \| 637 \| 4.2320 \| \| 4.6413 \| 196.92 \| 640 \| 4.2320 \| \| 4.6641 \| 197.85 \| 643 \| 4.2320 \| \| 4.638 \| 198.77 \| 646 \| 4.2320 \| \| 3.4885 \| 200.0 \| 650 \| 4.2320 \| \| 4.6502 \| 200.92 \| 653 \| 4.2320 \| \| 4.6476 \| 201.85 \| 656 \| 4.2320 \| \| 4.645 \| 202.77 \| 659 \| 4.2320 \| \| 3.4861 \| 204.0 \| 663 \| 4.2320 \| \| 4.6418 \| 204.92 \| 666 \| 4.2320 \| \| 4.6419 \| 205.85 \| 669 \| 4.2320 \| \| 4.6395 \| 206.77 \| 672 \| 4.2320 \| \| 3.4739 \| 208.0 \| 676 \| 4.2320 \| \| 4.6306 \| 208.92 \| 679 \| 4.2320 \| \| 4.6245 \| 209.85 \| 682 \| 4.2320 \| \| 4.6614 \| 210.77 \| 685 \| 4.2320 \| \| 3.4965 \| 212.0 \| 689 \| 4.2320 \| \| 4.642 \| 212.92 \| 692 \| 4.2320 \| \| 4.6371 \| 213.85 \| 695 \| 4.2320 \| \| 4.6265 \| 214.77 \| 698 \| 4.2320 \| \| 3.4965 \| 216.0 \| 702 \| 4.2320 \| \| 4.6648 \| 216.92 \| 705 \| 4.2320 \| \| 4.6248 \| 217.85 \| 708 \| 4.2320 \| \| 4.6507 \| 218.77 \| 711 \| 4.2320 \| \| 3.4741 \| 220.0 \| 715 \| 4.2320 \| \| 4.644 \| 220.92 \| 718 \| 4.2320 \| \| 4.6315 \| 221.85 \| 721 \| 4.2320 \| \| 4.659 \| 222.77 \| 724 \| 4.2320 \| \| 3.4942 \| 224.0 \| 728 \| 4.2320 \| \| 4.6463 \| 224.92 \| 731 \| 4.2320 \| \| 4.6477 \| 225.85 \| 734 \| 4.2320 \| \| 4.6323 \| 226.77 \| 737 \| 4.2320 \| \| 3.4907 \| 228.0 \| 741 \| 4.2320 \| \| 4.6323 \| 228.92 \| 744 \| 4.2320 \| \| 4.6442 \| 229.85 \| 747 \| 4.2320 \| \| 4.6351 \| 230.77 \| 750 \| 4.2320 \| \| 3.4799 \| 232.0 \| 754 \| 4.2320 \| \| 4.6463 \| 232.92 \| 757 \| 4.2320 \| \| 4.6389 \| 233.85 \| 760 \| 4.2320 \| \| 4.6399 \| 234.77 \| 763 \| 4.2320 \| \| 3.4819 \| 236.0 \| 767 \| 4.2320 \| \| 4.678 \| 236.92 \| 770 \| 4.2320 \| \| 4.6446 \| 237.85 \| 773 \| 4.2320 \| \| 4.642 \| 238.77 \| 776 \| 4.2320 \| \| 3.4879 \| 240.0 \| 780 \| 4.2320 \| \| 4.6561 \| 240.92 \| 783 \| 4.2320 \| \| 4.6226 \| 241.85 \| 786 \| 4.2320 \| \| 4.6607 \| 242.77 \| 789 \| 4.2320 \| \| 3.4901 \| 244.0 \| 793 \| 4.2320 \| \| 4.6317 \| 244.92 \| 796 \| 4.2320 \| \| 4.6387 \| 245.85 \| 799 \| 4.2320 \| \| 4.6493 \| 246.77 \| 802 \| 4.2320 \| \| 3.4863 \| 248.0 \| 806 \| 4.2320 \| \| 4.6187 \| 248.92 \| 809 \| 4.2320 \| \| 4.6449 \| 249.85 \| 812 \| 4.2320 \| \| 4.6542 \| 250.77 \| 815 \| 4.2320 \| \| 3.4905 \| 252.0 \| 819 \| 4.2320 \| \| 4.6514 \| 252.92 \| 822 \| 4.2320 \| \| 4.6496 \| 253.85 \| 825 \| 4.2320 \| \| 4.6542 \| 254.77 \| 828 \| 4.2320 \| \| 3.4661 \| 256.0 \| 832 \| 4.2320 \| \| 4.631 \| 256.92 \| 835 \| 4.2320 \| \| 4.644 \| 257.85 \| 838 \| 4.2320 \| \| 4.6348 \| 258.77 \| 841 \| 4.2320 \| \| 3.5069 \| 260.0 \| 845 \| 4.2320 \| \| 4.6257 \| 260.92 \| 848 \| 4.2320 \| \| 4.6584 \| 261.85 \| 851 \| 4.2320 \| \| 4.6344 \| 262.77 \| 854 \| 4.2320 \| \| 3.4721 \| 264.0 \| 858 \| 4.2320 \| \| 4.6429 \| 264.92 \| 861 \| 4.2320 \| \| 4.6433 \| 265.85 \| 864 \| 4.2320 \| \| 4.6391 \| 266.77 \| 867 \| 4.2320 \| \| 3.4916 \| 268.0 \| 871 \| 4.2320 \| \| 4.6564 \| 268.92 \| 874 \| 4.2320 \| \| 4.658 \| 269.85 \| 877 \| 4.2320 \| \| 4.6329 \| 270.77 \| 880 \| 4.2320 \| \| 3.4783 \| 272.0 \| 884 \| 4.2320 \| \| 4.6384 \| 272.92 \| 887 \| 4.2320 \| \| 4.6482 \| 273.85 \| 890 \| 4.2320 \| \| 4.6688 \| 274.77 \| 893 \| 4.2320 \| \| 3.4659 \| 276.0 \| 897 \| 4.2320 \| \| 4.6299 \| 276.92 \| 900 \| 4.2320 \| \| 4.6392 \| 277.85 \| 903 \| 4.2320 \| \| 4.6521 \| 278.77 \| 906 \| 4.2320 \| \| 3.4949 \| 280.0 \| 910 \| 4.2320 \| \| 4.6643 \| 280.92 \| 913 \| 4.2320 \| \| 4.6361 \| 281.85 \| 916 \| 4.2320 \| \| 4.6505 \| 282.77 \| 919 \| 4.2320 \| \| 3.4847 \| 284.0 \| 923 \| 4.2320 \| \| 4.639 \| 284.92 \| 926 \| 4.2320 \| \| 4.6276 \| 285.85 \| 929 \| 4.2320 \| \| 4.6438 \| 286.77 \| 932 \| 4.2320 \| \| 3.4883 \| 288.0 \| 936 \| 4.2320 \| \| 4.6483 \| 288.92 \| 939 \| 4.2320 \| \| 4.6564 \| 289.85 \| 942 \| 4.2320 \| \| 4.6437 \| 290.77 \| 945 \| 4.2320 \| \| 3.4712 \| 292.0 \| 949 \| 4.2320 \| \| 4.6627 \| 292.92 \| 952 \| 4.2320 \| \| 4.6371 \| 293.85 \| 955 \| 4.2320 \| \| 4.6196 \| 294.77 \| 958 \| 4.2320 \| \| 3.4859 \| 296.0 \| 962 \| 4.2320 \| \| 4.6457 \| 296.92 \| 965 \| 4.2320 \| \| 4.6249 \| 297.85 \| 968 \| 4.2320 \| \| 4.6382 \| 298.77 \| 971 \| 4.2320 \| \| 3.4824 \| 300.0 \| 975 \| 4.2320 \| \| 4.6541 \| 300.92 \| 978 \| 4.2320 \| \| 4.659 \| 301.85 \| 981 \| 4.2320 \| \| 4.618 \| 302.77 \| 984 \| 4.2320 \| \| 3.4751 \| 304.0 \| 988 \| 4.2320 \| \| 4.623 \| 304.92 \| 991 \| 4.2320 \| \| 4.6371 \| 305.85 \| 994 \| 4.2320 \| \| 4.6546 \| 306.77 \| 997 \| 4.2320 \| \| 3.1908 \| 307.69 \| 1000 \| 4.2320 \| ### Framework versions - PEFT 0.10.0 - Transformers 4.36.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
lmqg/mt5-small-ruquad-qg	lmqg	"2023-01-18T13:46:15Z"	26	0	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "question generation", "ru", "dataset:lmqg/qg_ruquad", "arxiv:2210.03992", "license:cc-by-4.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	"2022-06-07T00:39:31Z"	--- license: cc-by-4.0 metrics: - bleu4 - meteor - rouge-l - bertscore - moverscore language: ru datasets: - lmqg/qg_ruquad pipeline_tag: text2text-generation tags: - question generation widget: - text: "Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, <hl> в мае 1860 года <hl> провёл серию опытов." example_title: "Question Generation Example 1" - text: "Однако, франкоязычный <hl> Квебек <hl> практически никогда не включается в состав Латинской Америки." example_title: "Question Generation Example 2" - text: "Классическим примером международного синдиката XX века была группа компаний <hl> Де Бирс <hl> , которая в 1980-е годы контролировала до 90 % мировой торговли алмазами." example_title: "Question Generation Example 3" model-index: - name: lmqg/mt5-small-ruquad-qg results: - task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qg_ruquad type: default args: default metrics: - name: BLEU4 (Question Generation) type: bleu4_question_generation value: 16.31 - name: ROUGE-L (Question Generation) type: rouge_l_question_generation value: 31.39 - name: METEOR (Question Generation) type: meteor_question_generation value: 26.39 - name: BERTScore (Question Generation) type: bertscore_question_generation value: 84.27 - name: MoverScore (Question Generation) type: moverscore_question_generation value: 62.49 - name: QAAlignedF1Score-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] type: qa_aligned_f1_score_bertscore_question_answer_generation_with_gold_answer_gold_answer value: 90.17 - name: QAAlignedRecall-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] type: qa_aligned_recall_bertscore_question_answer_generation_with_gold_answer_gold_answer value: 90.16 - name: QAAlignedPrecision-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] type: qa_aligned_precision_bertscore_question_answer_generation_with_gold_answer_gold_answer value: 90.17 - name: QAAlignedF1Score-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] type: qa_aligned_f1_score_moverscore_question_answer_generation_with_gold_answer_gold_answer value: 68.22 - name: QAAlignedRecall-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] type: qa_aligned_recall_moverscore_question_answer_generation_with_gold_answer_gold_answer value: 68.21 - name: QAAlignedPrecision-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] type: qa_aligned_precision_moverscore_question_answer_generation_with_gold_answer_gold_answer value: 68.23 - name: QAAlignedF1Score-BERTScore (Question & Answer Generation) [Gold Answer] type: qa_aligned_f1_score_bertscore_question_answer_generation_gold_answer value: 76.96 - name: QAAlignedRecall-BERTScore (Question & Answer Generation) [Gold Answer] type: qa_aligned_recall_bertscore_question_answer_generation_gold_answer value: 81.05 - name: QAAlignedPrecision-BERTScore (Question & Answer Generation) [Gold Answer] type: qa_aligned_precision_bertscore_question_answer_generation_gold_answer value: 73.41 - name: QAAlignedF1Score-MoverScore (Question & Answer Generation) [Gold Answer] type: qa_aligned_f1_score_moverscore_question_answer_generation_gold_answer value: 55.53 - name: QAAlignedRecall-MoverScore (Question & Answer Generation) [Gold Answer] type: qa_aligned_recall_moverscore_question_answer_generation_gold_answer value: 58.25 - name: QAAlignedPrecision-MoverScore (Question & Answer Generation) [Gold Answer] type: qa_aligned_precision_moverscore_question_answer_generation_gold_answer value: 53.24 --- # Model Card of `lmqg/mt5-small-ruquad-qg` This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for question generation task on the [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). ### Overview - Language model: [google/mt5-small](https://huggingface.co/google/mt5-small) - Language: ru - Training data: [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) (default) - Online Demo: [https://autoqg.net/](https://autoqg.net/) - Repository: [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - Paper: [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) ### Usage - With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-) ```python from lmqg import TransformersQG # initialize model model = TransformersQG(language="ru", model="lmqg/mt5-small-ruquad-qg") # model prediction questions = model.generate_q(list_context="Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, в мае 1860 года провёл серию опытов.", list_answer="в мае 1860 года") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/mt5-small-ruquad-qg") output = pipe("Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, <hl> в мае 1860 года <hl> провёл серию опытов.") ``` ## Evaluation - *Metric (Question Generation): [raw metric file](https://huggingface.co/lmqg/mt5-small-ruquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_ruquad.default.json) \| \| Score \| Type \| Dataset \| \|:-----------\|--------:\|:--------\|:-----------------------------------------------------------------\| \| BERTScore \| 84.27 \| default \| [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) \| \| Bleu_1 \| 31.03 \| default \| [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) \| \| Bleu_2 \| 24.58 \| default \| [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) \| \| Bleu_3 \| 19.92 \| default \| [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) \| \| Bleu_4 \| 16.31 \| default \| [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) \| \| METEOR \| 26.39 \| default \| [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) \| \| MoverScore \| 62.49 \| default \| [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) \| \| ROUGE_L \| 31.39 \| default \| [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) \| - Metric (Question & Answer Generation, Reference Answer): Each question is generated from the gold answer. [raw metric file](https://huggingface.co/lmqg/mt5-small-ruquad-qg/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_ruquad.default.json) \| \| Score \| Type \| Dataset \| \|:--------------------------------\|--------:\|:--------\|:-----------------------------------------------------------------\| \| QAAlignedF1Score (BERTScore) \| 90.17 \| default \| [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) \| \| QAAlignedF1Score (MoverScore) \| 68.22 \| default \| [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) \| \| QAAlignedPrecision (BERTScore) \| 90.17 \| default \| [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) \| \| QAAlignedPrecision (MoverScore) \| 68.23 \| default \| [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) \| \| QAAlignedRecall (BERTScore) \| 90.16 \| default \| [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) \| \| QAAlignedRecall (MoverScore) \| 68.21 \| default \| [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) \| - Metric (Question & Answer Generation, Pipeline Approach)*: Each question is generated on the answer generated by [`lmqg/mt5-small-ruquad-ae`](https://huggingface.co/lmqg/mt5-small-ruquad-ae). [raw metric file](https://huggingface.co/lmqg/mt5-small-ruquad-qg/raw/main/eval_pipeline/metric.first.answer.paragraph.questions_answers.lmqg_qg_ruquad.default.lmqg_mt5-small-ruquad-ae.json) \| \| Score \| Type \| Dataset \| \|:--------------------------------\|--------:\|:--------\|:-----------------------------------------------------------------\| \| QAAlignedF1Score (BERTScore) \| 76.96 \| default \| [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) \| \| QAAlignedF1Score (MoverScore) \| 55.53 \| default \| [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) \| \| QAAlignedPrecision (BERTScore) \| 73.41 \| default \| [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) \| \| QAAlignedPrecision (MoverScore) \| 53.24 \| default \| [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) \| \| QAAlignedRecall (BERTScore) \| 81.05 \| default \| [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) \| \| QAAlignedRecall (MoverScore) \| 58.25 \| default \| [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) \| ## Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_ruquad - dataset_name: default - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: None - model: google/mt5-small - max_length: 512 - max_length_output: 32 - epoch: 5 - batch: 64 - lr: 0.001 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 1 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-small-ruquad-qg/raw/main/trainer_config.json). ## Citation ``` @inproceedings{ushio-etal-2022-generative, title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration", author = "Ushio, Asahi and Alva-Manchego, Fernando and Camacho-Collados, Jose", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, U.A.E.", publisher = "Association for Computational Linguistics", } ```
dnnsdunca/ddroidai_pro_gram	dnnsdunca	"2024-03-23T01:40:50Z"	0	0	adapter-transformers	[ "adapter-transformers", "code", "text-generation", "en", "license:mit", "region:us" ]	text-generation	"2024-03-23T01:34:42Z"	--- license: mit language: - en metrics: - code_eval library_name: adapter-transformers pipeline_tag: text-generation tags: - code ---
ALivshits/Llama3_8B_ATIS_100-merged	ALivshits	"2024-07-21T13:29:38Z"	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2024-07-21T13:24:19Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
QuantFactory/HuatuoGPT-o1-8B-GGUF	QuantFactory	"2025-01-03T06:10:23Z"	477	3	null	[ "gguf", "medical", "text-generation", "en", "dataset:FreedomIntelligence/medical-o1-reasoning-SFT", "dataset:FreedomIntelligence/medical-o1-verifiable-problem", "arxiv:2412.18925", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:quantized:meta-llama/Llama-3.1-8B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	"2025-01-03T05:27:31Z"	--- license: apache-2.0 datasets: - FreedomIntelligence/medical-o1-reasoning-SFT - FreedomIntelligence/medical-o1-verifiable-problem language: - en base_model: - meta-llama/Llama-3.1-8B-Instruct pipeline_tag: text-generation tags: - medical --- [![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory) # QuantFactory/HuatuoGPT-o1-8B-GGUF This is quantized version of [FreedomIntelligence/HuatuoGPT-o1-8B](https://huggingface.co/FreedomIntelligence/HuatuoGPT-o1-8B) created using llama.cpp # Original Model Card <div align="center"> <h1> HuatuoGPT-o1-8B </h1> </div> <div align="center"> <a href="https://github.com/FreedomIntelligence/HuatuoGPT-o1" target="_blank">GitHub</a> \| <a href="https://arxiv.org/pdf/2412.18925" target="_blank">Paper</a> </div> # <span>Introduction</span> HuatuoGPT-o1 is a medical LLM designed for advanced medical reasoning. It generates a complex thought process, reflecting and refining its reasoning, before providing a final response. For more information, visit our GitHub repository: [https://github.com/FreedomIntelligence/HuatuoGPT-o1](https://github.com/FreedomIntelligence/HuatuoGPT-o1). # <span>Model Info</span> \| \| Backbone \| Supported Languages \| Link \| \| -------------------- \| ------------ \| ----- \| --------------------------------------------------------------------- \| \| HuatuoGPT-o1-8B \| LLaMA-3.1-8B \| English \| [HF Link](https://huggingface.co/FreedomIntelligence/HuatuoGPT-o1-8B) \| \| HuatuoGPT-o1-70B \| LLaMA-3.1-70B \| English \| [HF Link](https://huggingface.co/FreedomIntelligence/HuatuoGPT-o1-70B) \| \| HuatuoGPT-o1-7B \| Qwen2.5-7B \| English & Chinese \| [HF Link](https://huggingface.co/FreedomIntelligence/HuatuoGPT-o1-7B) \| \| HuatuoGPT-o1-72B \| Qwen2.5-72B \| English & Chinese \| [HF Link](https://huggingface.co/FreedomIntelligence/HuatuoGPT-o1-72B) \| # <span>Usage</span> You can use HuatuoGPT-o1 in the same way as `Llama-3.1-8B-Instruct`. You can deploy it with tools like [vllm](https://github.com/vllm-project/vllm) or [Sglang](https://github.com/sgl-project/sglang), or perform direct inference: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("FreedomIntelligence/HuatuoGPT-o1-8B",torch_dtype="auto",device_map="auto") tokenizer = AutoTokenizer.from_pretrained("FreedomIntelligence/HuatuoGPT-o1-8B") input_text = "How to stop a cough?" messages = [{"role": "user", "content": input_text}] inputs = tokenizer(tokenizer.apply_chat_template(messages, tokenize=False,add_generation_prompt=True ), return_tensors="pt").to(model.device) outputs = model.generate(*inputs, max_new_tokens=2048) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` HuatuoGPT-o1 adopts a thinks-before-it-answers* approach, with outputs formatted as: ``` ## Thinking [Reasoning process] ## Final Response [Output] ``` # <span>📖 Citation</span> ``` @misc{chen2024huatuogpto1medicalcomplexreasoning, title={HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs}, author={Junying Chen and Zhenyang Cai and Ke Ji and Xidong Wang and Wanlong Liu and Rongsheng Wang and Jianye Hou and Benyou Wang}, year={2024}, eprint={2412.18925}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2412.18925}, } ```

baby-dev/3d3428bc-7199-4377-a210-a4fa1c2e90ab

baby-dev

"2025-02-15T20:53:17Z"

0

peft

[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:adapter:Qwen/Qwen2.5-7B-Instruct", "license:apache-2.0", "region:us" ]

null

"2025-02-15T20:41:07Z"

--- library_name: peft license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 3d3428bc-7199-4377-a210-a4fa1c2e90ab results: [] ---  # 3d3428bc-7199-4377-a210-a4fa1c2e90ab This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1943 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

DMCF14/Raffles

DMCF14

"2023-08-03T13:38:21Z"

0

null

[ "license:cc-by-nc-sa-4.0", "region:us" ]

null

"2023-08-03T13:34:30Z"

--- license: cc-by-nc-sa-4.0 ---

KappaNeuro/randolph-caldecott-style

KappaNeuro

"2023-09-14T10:08:02Z"

1

diffusers

[ "diffusers", "text-to-image", "stable-diffusion", "lora", "art", "style", "illustrator", "painting", "children", "literature", "randolph caldecott", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:other", "region:us" ]

text-to-image

"2023-09-14T10:07:58Z"

--- license: other tags: - text-to-image - stable-diffusion - lora - diffusers - art - style - illustrator - painting - children - literature - randolph caldecott base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: Randolph Caldecott Style widget: - text: "Randolph Caldecott Style - a friendly alligator being served food at a catering party. The alligator is looking at his watch watch and eating food." - text: "Randolph Caldecott Style - vast view of dense woodland in the background, a squirrel in a fashionable fedora and bomber jacket, reminiscent of Chip from Chip 'n Dale Rescue Rangers, looking at the audience, Beatrix Potter hand drawn style" - text: "Randolph Caldecott Style - imagine a close-up shot of the baby in the pram. style of shirley hughes - angry child throwing toys out of pram. The baby's face is in sharp focus, capturing their expression of glee and mischief. The style should be hyper-realistic, with every detail of the baby's face and the toy captured in high resolution. The lighting should be soft and diffused, creating a warm and inviting atmosphere. The composition should be a tight shot, focusing on the baby and the toy, with a shallow depth of field to blur the background." - text: "Randolph Caldecott Style - Illustration inspired by Kate Greenaway, depicts scenes of idyllic childhood with children dressed in late 18th and early 19th-century clothing. pastel color palette and detailed botanical backgrounds. children in innocent poses in natural settings such as gardens and meadows and often include whimsical elements such as fairies and animals" - text: "Randolph Caldecott Style - Never in his life had he seen a river before, this sleck, sinuous, full bodied animal, chasing and chuckling, gripping things with a gurgle and leaving them with a laugh, to fling itself on fresh playmates that shook themselves free, and were caught and held again" - text: "Randolph Caldecott Style - Costume sketch. Bunny girl. Girl 5 years old. Pretty, curly hair. Long silk dress, lace petticoat, velvet jacket, jabot collar. Hat with ears. patent leather shoes. Dusted tones - pink and beige. Retro. Vintage. Early 20th century." - text: "Randolph Caldecott Style - a girl of 8 years, black hair, orange dress, brown shoes, eyes shining with curiosity, in school, hang out with friends, bright and courageous, brave, bright smile, in style of Randolph Caldecott book illustration" - text: "Randolph Caldecott Style - a girl of 8 years, black hair, orange dress, brown shoes, eyes shining with curiosity, close up, in forest, sitting on a log, comfortable, flowers around in style of Randolph Caldecott book illustration" - text: "Randolph Caldecott Style - dirty caucassian family laying in bed, wearing pajamas. In the same bed there are 4 chicken, 3 ducks, dog, cat and two pigs. in style of beatrix potter." - text: "Randolph Caldecott Style - Hopefully, all unfortunate children will find warm homes. drawing and painting, blend of the styles of Beatrix Potter, ANton Pieck and Pieter Breughel" --- # Randolph Caldecott Style ([CivitAI](https://civitai.com/models/154146) ![Image 0](2330267.jpeg) > Randolph Caldecott Style - a friendly alligator being served food at a catering party. The alligator is looking at his watch watch and eating food. <p>Randolph Caldecott was an English illustrator and artist who lived from 1846 to 1886. He is best known for his contributions to children's literature, particularly for his innovative and playful illustrations.</p><p>Caldecott's illustrations were characterized by their lively, energetic style and attention to detail. He often depicted scenes from everyday life, including animals, children, and humorous situations. His illustrations had a sense of movement and captured the essence of a story, making them highly engaging for young readers.</p><p>One of Caldecott's notable achievements was his development of the picture book format. He introduced the concept of integrating illustrations and text on the same page, creating a seamless narrative flow. His use of dynamic compositions and imaginative storytelling revolutionized children's book illustration.</p><p>Caldecott's illustrations were also renowned for their use of color and texture. He employed watercolors, ink, and other media to bring his characters and scenes to life, creating a sense of depth and atmosphere.</p><p>In recognition of his significant contributions to children's literature, the Caldecott Medal was established in his honor. It is awarded annually to the most distinguished illustrated children's book published in the United States.</p><p>Randolph Caldecott's influence on the field of children's book illustration is profound. His innovative approach to storytelling and his captivating illustrations continue to inspire and delight readers of all ages. His legacy as a pioneering illustrator has left an enduring impact on the world of children's literature.</p> ## Image examples for the model: ![Image 1](2330253.jpeg) > Randolph Caldecott Style - vast view of dense woodland in the background, a squirrel in a fashionable fedora and bomber jacket, reminiscent of Chip from Chip 'n Dale Rescue Rangers, looking at the audience, Beatrix Potter hand drawn style ![Image 2](2330245.jpeg) > Randolph Caldecott Style - imagine a close-up shot of the baby in the pram. style of shirley hughes - angry child throwing toys out of pram. The baby's face is in sharp focus, capturing their expression of glee and mischief. The style should be hyper-realistic, with every detail of the baby's face and the toy captured in high resolution. The lighting should be soft and diffused, creating a warm and inviting atmosphere. The composition should be a tight shot, focusing on the baby and the toy, with a shallow depth of field to blur the background. ![Image 3](2330250.jpeg) > Randolph Caldecott Style - Illustration inspired by Kate Greenaway, depicts scenes of idyllic childhood with children dressed in late 18th and early 19th-century clothing. pastel color palette and detailed botanical backgrounds. children in innocent poses in natural settings such as gardens and meadows and often include whimsical elements such as fairies and animals ![Image 4](2330257.jpeg) > Randolph Caldecott Style - Never in his life had he seen a river before, this sleck, sinuous, full bodied animal, chasing and chuckling, gripping things with a gurgle and leaving them with a laugh, to fling itself on fresh playmates that shook themselves free, and were caught and held again ![Image 5](2330246.jpeg) > Randolph Caldecott Style - Costume sketch. Bunny girl. Girl 5 years old. Pretty, curly hair. Long silk dress, lace petticoat, velvet jacket, jabot collar. Hat with ears. patent leather shoes. Dusted tones - pink and beige. Retro. Vintage. Early 20th century. ![Image 6](2330251.jpeg) > Randolph Caldecott Style - a girl of 8 years, black hair, orange dress, brown shoes, eyes shining with curiosity, in school, hang out with friends, bright and courageous, brave, bright smile, in style of Randolph Caldecott book illustration ![Image 7](2330254.jpeg) > Randolph Caldecott Style - a girl of 8 years, black hair, orange dress, brown shoes, eyes shining with curiosity, close up, in forest, sitting on a log, comfortable, flowers around in style of Randolph Caldecott book illustration ![Image 8](2330255.jpeg) > Randolph Caldecott Style - dirty caucassian family laying in bed, wearing pajamas. In the same bed there are 4 chicken, 3 ducks, dog, cat and two pigs. in style of beatrix potter. ![Image 9](2330262.jpeg) > Randolph Caldecott Style - Hopefully, all unfortunate children will find warm homes. drawing and painting, blend of the styles of Beatrix Potter, ANton Pieck and Pieter Breughel

stefan-it/autotrain-flair-georgian-ner-xlm_r_large-bs4-e10-lr5e-06-2

stefan-it

"2023-11-17T00:52:08Z"

12

0

flair

[ "flair", "pytorch", "tensorboard", "token-classification", "sequence-tagger-model", "en", "ka", "base_model:FacebookAI/xlm-roberta-large", "base_model:finetune:FacebookAI/xlm-roberta-large", "license:mit", "region:us" ]

token-classification

"2023-11-16T03:26:32Z"

--- language: - en - ka license: mit tags: - flair - token-classification - sequence-tagger-model base_model: xlm-roberta-large widget: - text: ამით თავისი ქადაგება დაასრულა და დაბრუნდა იერუსალიმში . ერთ-ერთ გარე კედელზე არსებობს ერნესტო ჩე გევარას პორტრეტი . შაკოსკა“ ინახება ბრაზილიაში , სან-პაულუს ხელოვნების მუზეუმში . --- # Fine-tuned English-Georgian NER Model with Flair This Flair NER model was fine-tuned on the WikiANN dataset ([Rahimi et al.](https://www.aclweb.org/anthology/P19-1015) splits) using XLM-R Large as backbone LM. **Notice**: The dataset is very problematic, because it was automatically constructed. We did manually inspect the development split of the Georgian data and found a lot of bad labeled examples, e.g. DVD ( 💿 ) as `ORG`. ## Fine-Tuning The latest [Flair version](https://github.com/flairNLP/flair/tree/f30f5801df3f9e105ed078ec058b4e1152dd9159) is used for fine-tuning. We use English and Georgian training splits for fine-tuning and the development set of Georgian for evaluation. A hyper-parameter search over the following parameters with 5 different seeds per configuration is performed: * Batch Sizes: [`4`] * Learning Rates: [`5e-06`] More details can be found in this [repository](https://github.com/stefan-it/georgian-ner). ## Results A hyper-parameter search with 5 different seeds per configuration is performed and micro F1-score on development set is reported: | Configuration | Seed 1 | Seed 2 | Seed 3 | Seed 4 | Seed 5 | Average | |-------------------|-------------|-----------------|-------------|------------|-------------|-----------------| | `bs4-e10-lr5e-06` | [0.9005][1] | [**0.9012**][2] | [0.9069][3] | [0.905][4] | [0.9048][5] | 0.9037 ± 0.0027 | [1]: https://hf.co/stefan-it/autotrain-flair-georgian-ner-xlm_r_large-bs4-e10-lr5e-06-1 [2]: https://hf.co/stefan-it/autotrain-flair-georgian-ner-xlm_r_large-bs4-e10-lr5e-06-2 [3]: https://hf.co/stefan-it/autotrain-flair-georgian-ner-xlm_r_large-bs4-e10-lr5e-06-3 [4]: https://hf.co/stefan-it/autotrain-flair-georgian-ner-xlm_r_large-bs4-e10-lr5e-06-4 [5]: https://hf.co/stefan-it/autotrain-flair-georgian-ner-xlm_r_large-bs4-e10-lr5e-06-5 The result in bold shows the performance of this model. Additionally, the Flair [training log](training.log) and [TensorBoard logs](tensorboard) are also uploaded to the model hub.

Xmm/led-large-16384-cnn_dailymail

Xmm

"2023-09-02T08:09:40Z"

98

0

transformers

[ "transformers", "pytorch", "tensorboard", "safetensors", "led", "text2text-generation", "generated_from_trainer", "dataset:cnn_dailymail", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

"2023-06-17T03:05:46Z"

--- license: apache-2.0 tags: - generated_from_trainer datasets: - cnn_dailymail metrics: - rouge model-index: - name: led-large-16384-cnn_dailymail results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: cnn_dailymail type: cnn_dailymail config: 3.0.0 split: test args: 3.0.0 metrics: - name: Rouge1 type: rouge value: 0.3869876274946419 ---  # led-large-16384-cnn_dailymail This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the cnn_dailymail dataset. It achieves the following results on the evaluation set: - Loss: 1.5544 - Rouge1: 0.3870 - Rouge2: 0.1736 - Rougel: 0.2599 - Rougelsum: 0.3653 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 64 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:| | 1.9531 | 0.4 | 500 | 1.8639 | 0.3485 | 0.1441 | 0.2275 | 0.3288 | | 1.9563 | 0.8 | 1000 | 1.8260 | 0.3538 | 0.1482 | 0.2315 | 0.3343 | | 1.7176 | 1.2 | 1500 | 1.8208 | 0.3628 | 0.1527 | 0.2383 | 0.3433 | | 1.7197 | 1.6 | 2000 | 1.8162 | 0.3696 | 0.1602 | 0.2434 | 0.3486 | | 1.8086 | 2.0 | 2500 | 1.7924 | 0.3558 | 0.1533 | 0.2334 | 0.3361 | | 1.2448 | 2.4 | 3000 | 1.8510 | 0.3703 | 0.1591 | 0.2447 | 0.3483 | | 1.3574 | 2.8 | 3500 | 1.8277 | 0.3741 | 0.1593 | 0.2422 | 0.3540 | | 1.0966 | 3.2 | 4000 | 1.8924 | 0.3682 | 0.1576 | 0.2424 | 0.3479 | | 0.9938 | 3.6 | 4500 | 1.8957 | 0.3723 | 0.1599 | 0.2451 | 0.3511 | | 1.0735 | 4.0 | 5000 | 1.8772 | 0.3653 | 0.1557 | 0.2399 | 0.3454 | | 0.9106 | 4.4 | 5500 | 1.9401 | 0.3720 | 0.1585 | 0.2436 | 0.3504 | | 1.015 | 4.8 | 6000 | 1.9320 | 0.3725 | 0.1570 | 0.2429 | 0.3515 | | 1.7854 | 0.36 | 6500 | 1.7800 | 0.3624 | 0.1544 | 0.2390 | 0.3422 | | 1.9079 | 0.39 | 7000 | 1.7629 | 0.3573 | 0.1553 | 0.2352 | 0.3370 | | 1.7606 | 3.34 | 7500 | 1.6902 | 0.3783 | 0.1673 | 0.2521 | 0.3570 | | 1.7571 | 3.57 | 8000 | 1.6563 | 0.3802 | 0.1691 | 0.2538 | 0.3587 | | 1.6602 | 3.79 | 8500 | 1.6439 | 0.3814 | 0.1693 | 0.2548 | 0.3600 | | 1.6614 | 4.01 | 9000 | 1.6312 | 0.3812 | 0.1691 | 0.2544 | 0.3599 | | 1.668 | 4.24 | 9500 | 1.6189 | 0.3815 | 0.1689 | 0.2550 | 0.3603 | | 1.6491 | 4.46 | 10000 | 1.6172 | 0.3799 | 0.1681 | 0.2540 | 0.3586 | | 1.5994 | 4.68 | 10500 | 1.6132 | 0.3825 | 0.1702 | 0.2560 | 0.3610 | | 1.6493 | 4.9 | 11000 | 1.6093 | 0.3828 | 0.1701 | 0.2561 | 0.3613 | | 1.6769 | 5.13 | 11500 | 1.6074 | 0.3831 | 0.1706 | 0.2569 | 0.3619 | | 1.6554 | 5.35 | 12000 | 1.6044 | 0.3817 | 0.1695 | 0.2559 | 0.3605 | | 1.6155 | 5.57 | 12500 | 1.6010 | 0.3825 | 0.1700 | 0.2561 | 0.3608 | | 1.5863 | 5.8 | 13000 | 1.5981 | 0.3829 | 0.1704 | 0.2569 | 0.3614 | | 1.6306 | 6.02 | 13500 | 1.6004 | 0.3831 | 0.1702 | 0.2563 | 0.3618 | | 1.6425 | 6.24 | 14000 | 1.5987 | 0.3821 | 0.1698 | 0.2561 | 0.3610 | | 1.6863 | 6.46 | 14500 | 1.5876 | 0.3837 | 0.1710 | 0.2569 | 0.3622 | | 1.6085 | 6.69 | 15000 | 1.5815 | 0.3836 | 0.1717 | 0.2573 | 0.3621 | | 1.6267 | 6.91 | 15500 | 1.5792 | 0.3852 | 0.1722 | 0.2579 | 0.3633 | | 1.5637 | 7.13 | 16000 | 1.5768 | 0.3830 | 0.1709 | 0.2568 | 0.3611 | | 1.5586 | 7.36 | 16500 | 1.5740 | 0.3833 | 0.1706 | 0.2567 | 0.3617 | | 1.5389 | 7.58 | 17000 | 1.5689 | 0.3858 | 0.1729 | 0.2590 | 0.3640 | | 1.5694 | 7.8 | 17500 | 1.5645 | 0.3853 | 0.1731 | 0.2589 | 0.3636 | | 1.5265 | 8.02 | 18000 | 1.5621 | 0.3871 | 0.1733 | 0.2596 | 0.3654 | | 1.5273 | 8.25 | 18500 | 1.5624 | 0.3861 | 0.1726 | 0.2588 | 0.3646 | | 1.5148 | 8.47 | 19000 | 1.5602 | 0.3866 | 0.1733 | 0.2592 | 0.3651 | | 1.532 | 8.69 | 19500 | 1.5599 | 0.3859 | 0.1732 | 0.2593 | 0.3642 | | 1.5113 | 8.92 | 20000 | 1.5602 | 0.3877 | 0.1748 | 0.2606 | 0.3658 | | 1.5133 | 9.14 | 20500 | 1.5595 | 0.3855 | 0.1725 | 0.2587 | 0.3637 | | 1.4875 | 9.36 | 21000 | 1.5572 | 0.3873 | 0.1741 | 0.2600 | 0.3654 | | 1.5038 | 9.59 | 21500 | 1.5557 | 0.3860 | 0.1728 | 0.2590 | 0.3641 | | 1.5062 | 9.81 | 22000 | 1.5544 | 0.3870 | 0.1736 | 0.2599 | 0.3653 | ### Framework versions - Transformers 4.27.1 - Pytorch 2.0.0+cu118 - Datasets 2.10.1 - Tokenizers 0.13.2

sd-concepts-library/beldam

sd-concepts-library

"2022-10-06T04:31:38Z"

0

null

[ "license:mit", "region:us" ]

null

"2022-10-06T04:31:28Z"

--- license: mit --- ### beldam on Stable Diffusion This is the `beldam` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`: ![beldam 0](https://huggingface.co/sd-concepts-library/beldam/resolve/main/concept_images/6.jpeg) ![beldam 1](https://huggingface.co/sd-concepts-library/beldam/resolve/main/concept_images/0.jpeg) ![beldam 2](https://huggingface.co/sd-concepts-library/beldam/resolve/main/concept_images/7.jpeg) ![beldam 3](https://huggingface.co/sd-concepts-library/beldam/resolve/main/concept_images/9.jpeg) ![beldam 4](https://huggingface.co/sd-concepts-library/beldam/resolve/main/concept_images/2.jpeg) ![beldam 5](https://huggingface.co/sd-concepts-library/beldam/resolve/main/concept_images/12.jpeg) ![beldam 6](https://huggingface.co/sd-concepts-library/beldam/resolve/main/concept_images/3.jpeg) ![beldam 7](https://huggingface.co/sd-concepts-library/beldam/resolve/main/concept_images/14.jpeg) ![beldam 8](https://huggingface.co/sd-concepts-library/beldam/resolve/main/concept_images/13.jpeg) ![beldam 9](https://huggingface.co/sd-concepts-library/beldam/resolve/main/concept_images/10.jpeg) ![beldam 10](https://huggingface.co/sd-concepts-library/beldam/resolve/main/concept_images/8.jpeg) ![beldam 11](https://huggingface.co/sd-concepts-library/beldam/resolve/main/concept_images/15.jpeg) ![beldam 12](https://huggingface.co/sd-concepts-library/beldam/resolve/main/concept_images/11.jpeg) ![beldam 13](https://huggingface.co/sd-concepts-library/beldam/resolve/main/concept_images/5.jpeg) ![beldam 14](https://huggingface.co/sd-concepts-library/beldam/resolve/main/concept_images/4.jpeg) ![beldam 15](https://huggingface.co/sd-concepts-library/beldam/resolve/main/concept_images/1.jpeg)

mradermacher/dehallucinated-llama-3.2-8b-instruct-GGUF

mradermacher

"2025-02-26T23:57:51Z"

0

transformers

[ "transformers", "gguf", "en", "base_model:hirundo-io/dehallucinated-llama-3.2-8b-instruct", "base_model:quantized:hirundo-io/dehallucinated-llama-3.2-8b-instruct", "endpoints_compatible", "region:us", "conversational" ]

null

"2025-02-26T19:39:34Z"

--- base_model: hirundo-io/dehallucinated-llama-3.2-8b-instruct language: - en library_name: transformers quantized_by: mradermacher tags: [] --- ## About      static quants of https://huggingface.co/hirundo-io/dehallucinated-llama-3.2-8b-instruct  weighted/imatrix quants are available at https://huggingface.co/mradermacher/dehallucinated-llama-3.2-8b-instruct-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/dehallucinated-llama-3.2-8b-instruct-GGUF/resolve/main/dehallucinated-llama-3.2-8b-instruct.Q2_K.gguf) | Q2_K | 1.5 | | | [GGUF](https://huggingface.co/mradermacher/dehallucinated-llama-3.2-8b-instruct-GGUF/resolve/main/dehallucinated-llama-3.2-8b-instruct.Q3_K_S.gguf) | Q3_K_S | 1.6 | | | [GGUF](https://huggingface.co/mradermacher/dehallucinated-llama-3.2-8b-instruct-GGUF/resolve/main/dehallucinated-llama-3.2-8b-instruct.Q3_K_M.gguf) | Q3_K_M | 1.8 | lower quality | | [GGUF](https://huggingface.co/mradermacher/dehallucinated-llama-3.2-8b-instruct-GGUF/resolve/main/dehallucinated-llama-3.2-8b-instruct.Q3_K_L.gguf) | Q3_K_L | 1.9 | | | [GGUF](https://huggingface.co/mradermacher/dehallucinated-llama-3.2-8b-instruct-GGUF/resolve/main/dehallucinated-llama-3.2-8b-instruct.IQ4_XS.gguf) | IQ4_XS | 1.9 | | | [GGUF](https://huggingface.co/mradermacher/dehallucinated-llama-3.2-8b-instruct-GGUF/resolve/main/dehallucinated-llama-3.2-8b-instruct.Q4_K_S.gguf) | Q4_K_S | 2.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/dehallucinated-llama-3.2-8b-instruct-GGUF/resolve/main/dehallucinated-llama-3.2-8b-instruct.Q4_K_M.gguf) | Q4_K_M | 2.1 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/dehallucinated-llama-3.2-8b-instruct-GGUF/resolve/main/dehallucinated-llama-3.2-8b-instruct.Q5_K_S.gguf) | Q5_K_S | 2.4 | | | [GGUF](https://huggingface.co/mradermacher/dehallucinated-llama-3.2-8b-instruct-GGUF/resolve/main/dehallucinated-llama-3.2-8b-instruct.Q5_K_M.gguf) | Q5_K_M | 2.4 | | | [GGUF](https://huggingface.co/mradermacher/dehallucinated-llama-3.2-8b-instruct-GGUF/resolve/main/dehallucinated-llama-3.2-8b-instruct.Q6_K.gguf) | Q6_K | 2.7 | very good quality | | [GGUF](https://huggingface.co/mradermacher/dehallucinated-llama-3.2-8b-instruct-GGUF/resolve/main/dehallucinated-llama-3.2-8b-instruct.Q8_0.gguf) | Q8_0 | 3.5 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/dehallucinated-llama-3.2-8b-instruct-GGUF/resolve/main/dehallucinated-llama-3.2-8b-instruct.f16.gguf) | f16 | 6.5 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

Xu-Ouyang/pythia-160m-deduped-int2-step110000-GPTQ-wikitext2-uva

Xu-Ouyang

"2024-09-13T12:41:32Z"

75

0

transformers

[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "2-bit", "gptq", "region:us" ]

text-generation

"2024-09-13T12:41:17Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

JacksonBrune/9aa904a1-6075-432a-8011-12852a7d995b

JacksonBrune

"2025-01-13T04:07:18Z"

10

0

peft

[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:rayonlabs/merged-merged-af6dd40b-32e1-43b1-adfd-8ce14d65d738-PubMedQA-138437bf-44bd-4b03-8801-d05451a9ff28", "base_model:adapter:rayonlabs/merged-merged-af6dd40b-32e1-43b1-adfd-8ce14d65d738-PubMedQA-138437bf-44bd-4b03-8801-d05451a9ff28", "region:us" ]

null

"2025-01-13T02:10:16Z"

--- library_name: peft base_model: rayonlabs/merged-merged-af6dd40b-32e1-43b1-adfd-8ce14d65d738-PubMedQA-138437bf-44bd-4b03-8801-d05451a9ff28 tags: - axolotl - generated_from_trainer model-index: - name: 9aa904a1-6075-432a-8011-12852a7d995b results: [] ---  [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: rayonlabs/merged-merged-af6dd40b-32e1-43b1-adfd-8ce14d65d738-PubMedQA-138437bf-44bd-4b03-8801-d05451a9ff28 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 50570c988008bb52_train_data.json ds_type: json format: custom path: /workspace/input_data/50570c988008bb52_train_data.json type: field_input: context field_instruction: question field_output: final_decision format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: JacksonBrune/9aa904a1-6075-432a-8011-12852a7d995b hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/50570c988008bb52_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 special_tokens: pad_token: <|end_of_text|> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: bb82eda3-2375-490a-9d56-33d5775eeedb wandb_project: birthdya-sn56-18-Gradients-On-Demand wandb_run: your_name wandb_runid: bb82eda3-2375-490a-9d56-33d5775eeedb warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 9aa904a1-6075-432a-8011-12852a7d995b This model is a fine-tuned version of [rayonlabs/merged-merged-af6dd40b-32e1-43b1-adfd-8ce14d65d738-PubMedQA-138437bf-44bd-4b03-8801-d05451a9ff28](https://huggingface.co/rayonlabs/merged-merged-af6dd40b-32e1-43b1-adfd-8ce14d65d738-PubMedQA-138437bf-44bd-4b03-8801-d05451a9ff28) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.4878 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 13.2355 | 0.0000 | 1 | 13.6415 | | 13.9199 | 0.0001 | 3 | 13.1524 | | 9.434 | 0.0002 | 6 | 6.8603 | | 3.0717 | 0.0004 | 9 | 3.4878 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

Sukmin/Reinforce-PixelCopter

Sukmin

"2023-07-10T05:46:30Z"

0

null

[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

"2023-07-10T03:34:22Z"

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-PixelCopter results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 35.40 +/- 26.24 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

nik135/distilbert-base-uncased-finetuned-emotion

nik135

"2024-11-11T07:06:01Z"

107

0

transformers

[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

"2024-10-08T08:38:01Z"

--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: [] ---  # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2156 - Accuracy: 0.925 - F1: 0.9251 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.7958 | 1.0 | 250 | 0.3024 | 0.909 | 0.9086 | | 0.2385 | 2.0 | 500 | 0.2156 | 0.925 | 0.9251 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1

coreml-community/coreml-Roboetics-mix

coreml-community

"2023-03-05T14:59:16Z"

0

3

null

[ "coreml", "stable-diffusion", "text-to-image", "license:creativeml-openrail-m", "region:us" ]

text-to-image

"2023-01-11T01:43:05Z"

--- license: creativeml-openrail-m tags: - coreml - stable-diffusion - text-to-image --- # Core ML Converted Model: - This model was converted to Core ML for use on Apple Silicon devices. Instructions can be found [here](https://github.com/godly-devotion/MochiDiffusion/wiki/How-to-convert-ckpt-files-to-Core-ML).<br> - Provide the model to an app such as [Mochi Diffusion](https://github.com/godly-devotion/MochiDiffusion) to generate images.<br> - `split_einsum` version is compatible with all compute unit options including Neural Engine.<br> # Note: This model does not have the [unet split into chunks](https://github.com/apple/ml-stable-diffusion#-converting-models-to-core-ml). # Roboetic's mix: Source(s): [CivitAI](https://civitai.com/models/3738/roboetics-mix) This model is some of my favourite models merged together. It is a general purpose model which can generate good looking images with simpler prompts.

baibaichuan/dqn-SpaceInvadersNoFrameskip-v4

baibaichuan

"2025-03-11T13:23:19Z"

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

"2025-03-11T12:57:50Z"

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 512.50 +/- 169.64 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib SBX (SB3 + Jax): https://github.com/araffin/sbx Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga baibaichuan -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga baibaichuan -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga baibaichuan ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

Jivika1/ASR

Jivika1

"2025-02-20T13:32:33Z"

0

transformers

[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-medium", "base_model:finetune:openai/whisper-medium", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

"2025-02-20T13:16:04Z"

--- library_name: transformers license: apache-2.0 base_model: openai/whisper-medium tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-medium-medical results: [] ---  # whisper-medium-medical This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0562 - Wer: 10.7169 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:-------:| | 0.5008 | 0.5405 | 100 | 0.1965 | 12.0203 | | 0.1034 | 1.0811 | 200 | 0.0870 | 12.2616 | | 0.0563 | 1.6216 | 300 | 0.0642 | 8.3514 | | 0.0238 | 2.1622 | 400 | 0.0610 | 11.6341 | | 0.0129 | 2.7027 | 500 | 0.0562 | 10.7169 | ### Framework versions - Transformers 4.49.0 - Pytorch 2.6.0+cu118 - Datasets 3.3.1 - Tokenizers 0.21.0

lesso16/cc432f5d-7a5c-4010-8f56-2589ff64aee7

lesso16

"2025-03-28T15:49:19Z"

0

peft

[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:JackFram/llama-68m", "base_model:adapter:JackFram/llama-68m", "license:apache-2.0", "region:us" ]

null

"2025-03-28T15:45:33Z"

--- library_name: peft license: apache-2.0 base_model: JackFram/llama-68m tags: - axolotl - generated_from_trainer model-index: - name: cc432f5d-7a5c-4010-8f56-2589ff64aee7 results: [] ---  [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: JackFram/llama-68m bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 623d9787d7fd7d0e_train_data.json ds_type: json format: custom path: /workspace/input_data/623d9787d7fd7d0e_train_data.json type: field_instruction: text field_output: title format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null do_eval: true early_stopping_patience: 3 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 500 evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 8 gradient_checkpointing: true group_by_length: true hub_model_id: lesso16/cc432f5d-7a5c-4010-8f56-2589ff64aee7 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.000216 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 50 lora_alpha: 128 lora_dropout: 0.15 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_steps: 500 micro_batch_size: 4 mlflow_experiment_name: /tmp/623d9787d7fd7d0e_train_data.json model_type: AutoModelForCausalLM num_epochs: 10 optimizer: adamw_torch_fused output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 500 saves_per_epoch: null seed: 160 sequence_len: 1024 special_tokens: pad_token: </s> strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: af6de128-8865-42e8-800b-ff2d2b1acccd wandb_project: 16a wandb_run: your_name wandb_runid: af6de128-8865-42e8-800b-ff2d2b1acccd warmup_steps: 100 weight_decay: 0.0 xformers_attention: null ``` </details><br> # cc432f5d-7a5c-4010-8f56-2589ff64aee7 This model is a fine-tuned version of [JackFram/llama-68m](https://huggingface.co/JackFram/llama-68m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0133 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000216 - train_batch_size: 4 - eval_batch_size: 4 - seed: 160 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 500 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | No log | 0.0010 | 1 | 2.4174 | | 0.0125 | 0.5182 | 500 | 0.0133 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

baddii/20_baddii_08_911

baddii

"2025-02-18T08:46:20Z"

0

transformers

[ "transformers", "safetensors", "parler_tts", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

"2025-02-18T08:44:33Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

aitorrent/dolphin-2.9.3-qwen2-0.5b-GGUF-torrent

aitorrent

"2024-06-16T14:09:48Z"

0

null

[ "torrent", "license:apache-2.0", "region:us" ]

null

"2024-06-16T13:59:12Z"

--- license: apache-2.0 tags: - torrent --- ## About      static quants of https://huggingface.co/cognitivecomputations/dolphin-2.9.3-qwen2-0.5b  weighted/imatrix quants are available at https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-0.5b-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-0.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-0.5b.Q3_K_S.gguf) | Q3_K_S | 0.4 | | | [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-0.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-0.5b.IQ3_S.gguf) | IQ3_S | 0.4 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-0.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-0.5b.IQ3_XS.gguf) | IQ3_XS | 0.4 | | | [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-0.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-0.5b.Q2_K.gguf) | Q2_K | 0.4 | | | [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-0.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-0.5b.IQ3_M.gguf) | IQ3_M | 0.4 | | | [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-0.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-0.5b.IQ4_XS.gguf) | IQ4_XS | 0.5 | | | [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-0.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-0.5b.Q3_K_M.gguf) | Q3_K_M | 0.5 | lower quality | | [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-0.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-0.5b.Q3_K_L.gguf) | Q3_K_L | 0.5 | | | [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-0.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-0.5b.Q4_K_S.gguf) | Q4_K_S | 0.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-0.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-0.5b.Q4_K_M.gguf) | Q4_K_M | 0.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-0.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-0.5b.Q5_K_S.gguf) | Q5_K_S | 0.5 | | | [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-0.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-0.5b.Q5_K_M.gguf) | Q5_K_M | 0.5 | | | [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-0.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-0.5b.Q6_K.gguf) | Q6_K | 0.6 | very good quality | | [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-0.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-0.5b.Q8_0.gguf) | Q8_0 | 0.6 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-0.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-0.5b.f16.gguf) | f16 | 1.1 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

johnsnowlabs/PhigRange-2.7B-slerp

johnsnowlabs

"2024-04-10T11:14:41Z"

6

0

transformers

[ "transformers", "safetensors", "phi", "text-generation", "merge", "mergekit", "lazymergekit", "liminerity/Phigments12", "rhysjones/phi-2-orange-v2", "base_model:liminerity/Phigments12", "base_model:merge:liminerity/Phigments12", "base_model:rhysjones/phi-2-orange-v2", "base_model:merge:rhysjones/phi-2-orange-v2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2024-04-08T19:42:59Z"

--- tags: - merge - mergekit - lazymergekit - liminerity/Phigments12 - rhysjones/phi-2-orange-v2 base_model: - liminerity/Phigments12 - rhysjones/phi-2-orange-v2 --- # PhigRange-2.7B-slerp ![image/png](https://cdn-uploads.huggingface.co/production/uploads/660cfe98280a82e38fe4ef49/UAwX3mctFI41DQ3-tRJkO.png) PhigRange-2.7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [liminerity/Phigments12](https://huggingface.co/liminerity/Phigments12) * [rhysjones/phi-2-orange-v2](https://huggingface.co/rhysjones/phi-2-orange-v2) Special thanks to Charles Goddard for the quick implementation! ## 🧩 Configuration ```yaml slices: - sources: - model: liminerity/Phigments12 layer_range: [0, 32] - model: rhysjones/phi-2-orange-v2 layer_range: [0, 32] merge_method: slerp base_model: liminerity/Phigments12 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "johnsnowlabs/PhigRange-2.7B-slerp" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ``` ## 🏆 Evaluation Coming Soon!

tensorblock/You_can_cry_Snowman-13B-GGUF

tensorblock

"2024-12-28T11:37:00Z"

26

0

transformers

[ "transformers", "gguf", "TensorBlock", "GGUF", "text-generation", "ko", "base_model:DopeorNope/You_can_cry_Snowman-13B", "base_model:quantized:DopeorNope/You_can_cry_Snowman-13B", "license:cc-by-nc-sa-4.0", "endpoints_compatible", "region:us", "conversational" ]

text-generation

"2024-12-28T10:25:40Z"

--- language: - ko library_name: transformers pipeline_tag: text-generation license: cc-by-nc-sa-4.0 tags: - TensorBlock - GGUF base_model: DopeorNope/You_can_cry_Snowman-13B --- <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"> Feedback and support: TensorBlock's <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a> </p> </div> </div> ## DopeorNope/You_can_cry_Snowman-13B - GGUF This repo contains GGUF format model files for [DopeorNope/You_can_cry_Snowman-13B](https://huggingface.co/DopeorNope/You_can_cry_Snowman-13B). The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4242](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d). <div style="text-align: left; margin: 20px 0;"> <a href="https://tensorblock.co/waitlist/client" style="display: inline-block; padding: 10px 20px; background-color: #007bff; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;"> Run them on the TensorBlock client using your local machine ↗ </a> </div> ## Prompt template ``` ### System: {system_prompt} ### User: {prompt} ### Assistant: ``` ## Model file specification | Filename | Quant type | File Size | Description | | -------- | ---------- | --------- | ----------- | | [You_can_cry_Snowman-13B-Q2_K.gguf](https://huggingface.co/tensorblock/You_can_cry_Snowman-13B-GGUF/blob/main/You_can_cry_Snowman-13B-Q2_K.gguf) | Q2_K | 4.966 GB | smallest, significant quality loss - not recommended for most purposes | | [You_can_cry_Snowman-13B-Q3_K_S.gguf](https://huggingface.co/tensorblock/You_can_cry_Snowman-13B-GGUF/blob/main/You_can_cry_Snowman-13B-Q3_K_S.gguf) | Q3_K_S | 5.790 GB | very small, high quality loss | | [You_can_cry_Snowman-13B-Q3_K_M.gguf](https://huggingface.co/tensorblock/You_can_cry_Snowman-13B-GGUF/blob/main/You_can_cry_Snowman-13B-Q3_K_M.gguf) | Q3_K_M | 6.448 GB | very small, high quality loss | | [You_can_cry_Snowman-13B-Q3_K_L.gguf](https://huggingface.co/tensorblock/You_can_cry_Snowman-13B-GGUF/blob/main/You_can_cry_Snowman-13B-Q3_K_L.gguf) | Q3_K_L | 7.022 GB | small, substantial quality loss | | [You_can_cry_Snowman-13B-Q4_0.gguf](https://huggingface.co/tensorblock/You_can_cry_Snowman-13B-GGUF/blob/main/You_can_cry_Snowman-13B-Q4_0.gguf) | Q4_0 | 7.545 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | [You_can_cry_Snowman-13B-Q4_K_S.gguf](https://huggingface.co/tensorblock/You_can_cry_Snowman-13B-GGUF/blob/main/You_can_cry_Snowman-13B-Q4_K_S.gguf) | Q4_K_S | 7.598 GB | small, greater quality loss | | [You_can_cry_Snowman-13B-Q4_K_M.gguf](https://huggingface.co/tensorblock/You_can_cry_Snowman-13B-GGUF/blob/main/You_can_cry_Snowman-13B-Q4_K_M.gguf) | Q4_K_M | 8.032 GB | medium, balanced quality - recommended | | [You_can_cry_Snowman-13B-Q5_0.gguf](https://huggingface.co/tensorblock/You_can_cry_Snowman-13B-GGUF/blob/main/You_can_cry_Snowman-13B-Q5_0.gguf) | Q5_0 | 9.197 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | [You_can_cry_Snowman-13B-Q5_K_S.gguf](https://huggingface.co/tensorblock/You_can_cry_Snowman-13B-GGUF/blob/main/You_can_cry_Snowman-13B-Q5_K_S.gguf) | Q5_K_S | 9.197 GB | large, low quality loss - recommended | | [You_can_cry_Snowman-13B-Q5_K_M.gguf](https://huggingface.co/tensorblock/You_can_cry_Snowman-13B-GGUF/blob/main/You_can_cry_Snowman-13B-Q5_K_M.gguf) | Q5_K_M | 9.448 GB | large, very low quality loss - recommended | | [You_can_cry_Snowman-13B-Q6_K.gguf](https://huggingface.co/tensorblock/You_can_cry_Snowman-13B-GGUF/blob/main/You_can_cry_Snowman-13B-Q6_K.gguf) | Q6_K | 10.953 GB | very large, extremely low quality loss | | [You_can_cry_Snowman-13B-Q8_0.gguf](https://huggingface.co/tensorblock/You_can_cry_Snowman-13B-GGUF/blob/main/You_can_cry_Snowman-13B-Q8_0.gguf) | Q8_0 | 14.185 GB | very large, extremely low quality loss - not recommended | ## Downloading instruction ### Command line Firstly, install Huggingface Client ```shell pip install -U "huggingface_hub[cli]" ``` Then, downoad the individual model file the a local directory ```shell huggingface-cli download tensorblock/You_can_cry_Snowman-13B-GGUF --include "You_can_cry_Snowman-13B-Q2_K.gguf" --local-dir MY_LOCAL_DIR ``` If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try: ```shell huggingface-cli download tensorblock/You_can_cry_Snowman-13B-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf' ```

RichardErkhov/goethe0101_-_llama-3-2-3B-wame-16bit-survey-generator4-gguf

RichardErkhov

"2025-03-25T22:37:15Z"

0

null

[ "gguf", "endpoints_compatible", "region:us", "conversational" ]

null

"2025-03-25T21:37:31Z"

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) llama-3-2-3B-wame-16bit-survey-generator4 - GGUF - Model creator: https://huggingface.co/goethe0101/ - Original model: https://huggingface.co/goethe0101/llama-3-2-3B-wame-16bit-survey-generator4/ | Name | Quant method | Size | | ---- | ---- | ---- | | [llama-3-2-3B-wame-16bit-survey-generator4.Q2_K.gguf](https://huggingface.co/RichardErkhov/goethe0101_-_llama-3-2-3B-wame-16bit-survey-generator4-gguf/blob/main/llama-3-2-3B-wame-16bit-survey-generator4.Q2_K.gguf) | Q2_K | 1.27GB | | [llama-3-2-3B-wame-16bit-survey-generator4.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/goethe0101_-_llama-3-2-3B-wame-16bit-survey-generator4-gguf/blob/main/llama-3-2-3B-wame-16bit-survey-generator4.IQ3_XS.gguf) | IQ3_XS | 1.38GB | | [llama-3-2-3B-wame-16bit-survey-generator4.IQ3_S.gguf](https://huggingface.co/RichardErkhov/goethe0101_-_llama-3-2-3B-wame-16bit-survey-generator4-gguf/blob/main/llama-3-2-3B-wame-16bit-survey-generator4.IQ3_S.gguf) | IQ3_S | 1.44GB | | [llama-3-2-3B-wame-16bit-survey-generator4.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/goethe0101_-_llama-3-2-3B-wame-16bit-survey-generator4-gguf/blob/main/llama-3-2-3B-wame-16bit-survey-generator4.Q3_K_S.gguf) | Q3_K_S | 1.44GB | | [llama-3-2-3B-wame-16bit-survey-generator4.IQ3_M.gguf](https://huggingface.co/RichardErkhov/goethe0101_-_llama-3-2-3B-wame-16bit-survey-generator4-gguf/blob/main/llama-3-2-3B-wame-16bit-survey-generator4.IQ3_M.gguf) | IQ3_M | 1.49GB | | [llama-3-2-3B-wame-16bit-survey-generator4.Q3_K.gguf](https://huggingface.co/RichardErkhov/goethe0101_-_llama-3-2-3B-wame-16bit-survey-generator4-gguf/blob/main/llama-3-2-3B-wame-16bit-survey-generator4.Q3_K.gguf) | Q3_K | 1.57GB | | [llama-3-2-3B-wame-16bit-survey-generator4.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/goethe0101_-_llama-3-2-3B-wame-16bit-survey-generator4-gguf/blob/main/llama-3-2-3B-wame-16bit-survey-generator4.Q3_K_M.gguf) | Q3_K_M | 1.57GB | | [llama-3-2-3B-wame-16bit-survey-generator4.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/goethe0101_-_llama-3-2-3B-wame-16bit-survey-generator4-gguf/blob/main/llama-3-2-3B-wame-16bit-survey-generator4.Q3_K_L.gguf) | Q3_K_L | 1.69GB | | [llama-3-2-3B-wame-16bit-survey-generator4.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/goethe0101_-_llama-3-2-3B-wame-16bit-survey-generator4-gguf/blob/main/llama-3-2-3B-wame-16bit-survey-generator4.IQ4_XS.gguf) | IQ4_XS | 1.71GB | | [llama-3-2-3B-wame-16bit-survey-generator4.Q4_0.gguf](https://huggingface.co/RichardErkhov/goethe0101_-_llama-3-2-3B-wame-16bit-survey-generator4-gguf/blob/main/llama-3-2-3B-wame-16bit-survey-generator4.Q4_0.gguf) | Q4_0 | 1.79GB | | [llama-3-2-3B-wame-16bit-survey-generator4.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/goethe0101_-_llama-3-2-3B-wame-16bit-survey-generator4-gguf/blob/main/llama-3-2-3B-wame-16bit-survey-generator4.IQ4_NL.gguf) | IQ4_NL | 1.79GB | | [llama-3-2-3B-wame-16bit-survey-generator4.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/goethe0101_-_llama-3-2-3B-wame-16bit-survey-generator4-gguf/blob/main/llama-3-2-3B-wame-16bit-survey-generator4.Q4_K_S.gguf) | Q4_K_S | 1.8GB | | [llama-3-2-3B-wame-16bit-survey-generator4.Q4_K.gguf](https://huggingface.co/RichardErkhov/goethe0101_-_llama-3-2-3B-wame-16bit-survey-generator4-gguf/blob/main/llama-3-2-3B-wame-16bit-survey-generator4.Q4_K.gguf) | Q4_K | 1.88GB | | [llama-3-2-3B-wame-16bit-survey-generator4.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/goethe0101_-_llama-3-2-3B-wame-16bit-survey-generator4-gguf/blob/main/llama-3-2-3B-wame-16bit-survey-generator4.Q4_K_M.gguf) | Q4_K_M | 1.88GB | | [llama-3-2-3B-wame-16bit-survey-generator4.Q4_1.gguf](https://huggingface.co/RichardErkhov/goethe0101_-_llama-3-2-3B-wame-16bit-survey-generator4-gguf/blob/main/llama-3-2-3B-wame-16bit-survey-generator4.Q4_1.gguf) | Q4_1 | 1.95GB | | [llama-3-2-3B-wame-16bit-survey-generator4.Q5_0.gguf](https://huggingface.co/RichardErkhov/goethe0101_-_llama-3-2-3B-wame-16bit-survey-generator4-gguf/blob/main/llama-3-2-3B-wame-16bit-survey-generator4.Q5_0.gguf) | Q5_0 | 2.11GB | | [llama-3-2-3B-wame-16bit-survey-generator4.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/goethe0101_-_llama-3-2-3B-wame-16bit-survey-generator4-gguf/blob/main/llama-3-2-3B-wame-16bit-survey-generator4.Q5_K_S.gguf) | Q5_K_S | 2.11GB | | [llama-3-2-3B-wame-16bit-survey-generator4.Q5_K.gguf](https://huggingface.co/RichardErkhov/goethe0101_-_llama-3-2-3B-wame-16bit-survey-generator4-gguf/blob/main/llama-3-2-3B-wame-16bit-survey-generator4.Q5_K.gguf) | Q5_K | 2.16GB | | [llama-3-2-3B-wame-16bit-survey-generator4.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/goethe0101_-_llama-3-2-3B-wame-16bit-survey-generator4-gguf/blob/main/llama-3-2-3B-wame-16bit-survey-generator4.Q5_K_M.gguf) | Q5_K_M | 2.16GB | | [llama-3-2-3B-wame-16bit-survey-generator4.Q5_1.gguf](https://huggingface.co/RichardErkhov/goethe0101_-_llama-3-2-3B-wame-16bit-survey-generator4-gguf/blob/main/llama-3-2-3B-wame-16bit-survey-generator4.Q5_1.gguf) | Q5_1 | 2.28GB | | [llama-3-2-3B-wame-16bit-survey-generator4.Q6_K.gguf](https://huggingface.co/RichardErkhov/goethe0101_-_llama-3-2-3B-wame-16bit-survey-generator4-gguf/blob/main/llama-3-2-3B-wame-16bit-survey-generator4.Q6_K.gguf) | Q6_K | 2.46GB | | [llama-3-2-3B-wame-16bit-survey-generator4.Q8_0.gguf](https://huggingface.co/RichardErkhov/goethe0101_-_llama-3-2-3B-wame-16bit-survey-generator4-gguf/blob/main/llama-3-2-3B-wame-16bit-survey-generator4.Q8_0.gguf) | Q8_0 | 3.19GB | Original model description: --- base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** goethe0101 - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3.2-3b-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

AlignmentResearch/robust_llm_pythia-wl-31m-mz-ada-v3-ch-139000

AlignmentResearch

"2024-03-26T11:51:11Z"

103

0

transformers

[ "transformers", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-31m", "base_model:finetune:EleutherAI/pythia-31m", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-classification

"2024-03-26T11:51:03Z"

--- tags: - generated_from_trainer base_model: EleutherAI/pythia-31m model-index: - name: robust_llm_pythia-wl-31m-mz-ada-v3-ch-139000 results: [] ---  # robust_llm_pythia-wl-31m-mz-ada-v3-ch-139000 This model is a fine-tuned version of [EleutherAI/pythia-31m](https://huggingface.co/EleutherAI/pythia-31m) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.17.0 - Tokenizers 0.15.2

Finn13/Llama3.1_COsec_multi

Finn13

"2025-03-24T19:36:08Z"

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Meta-Llama-3.1-8B-Instruct", "base_model:finetune:unsloth/Meta-Llama-3.1-8B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

"2025-03-24T19:32:03Z"

--- base_model: unsloth/Meta-Llama-3.1-8B-Instruct tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** Finn13 - **License:** apache-2.0 - **Finetuned from model :** unsloth/Meta-Llama-3.1-8B-Instruct This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

juhul/pop

juhul

"2025-02-05T07:27:35Z"

295

0

diffusers

[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "region:us" ]

text-to-image

"2025-02-05T07:16:17Z"

--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: . output: url: images/out-0 - 2025-02-02T102121.559.jpg base_model: black-forest-labs/FLUX.1-dev instance_prompt: PPP --- # pop <Gallery /> ## Trigger words You should use `PPP` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/juhul/pop/tree/main) them in the Files & versions tab.

surianto/nana

surianto

"2023-07-30T12:06:16Z"

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

"2023-07-30T12:05:23Z"

--- license: creativeml-openrail-m ---

Triangle104/Llama-3.2-3B-Instruct-abliterated-Q5_K_M-GGUF

Triangle104

"2024-11-25T16:45:58Z"

6

0

transformers

[ "transformers", "gguf", "abliterated", "uncensored", "llama-cpp", "gguf-my-repo", "base_model:huihui-ai/Llama-3.2-3B-Instruct-abliterated", "base_model:quantized:huihui-ai/Llama-3.2-3B-Instruct-abliterated", "license:llama3.2", "endpoints_compatible", "region:us", "conversational" ]

null

"2024-10-17T10:06:54Z"

--- library_name: transformers license: llama3.2 base_model: huihui-ai/Llama-3.2-3B-Instruct-abliterated tags: - abliterated - uncensored - llama-cpp - gguf-my-repo --- # Triangle104/Llama-3.2-3B-Instruct-abliterated-Q5_K_M-GGUF This model was converted to GGUF format from [`huihui-ai/Llama-3.2-3B-Instruct-abliterated`](https://huggingface.co/huihui-ai/Llama-3.2-3B-Instruct-abliterated) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/huihui-ai/Llama-3.2-3B-Instruct-abliterated) for more details on the model. --- Model details: - This is an uncensored version of Llama 3.2 3B Instruct created with abliteration (see this article to know more about it). Special thanks to @FailSpy for the original code and technique. Please follow him if you're interested in abliterated models. --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/Llama-3.2-3B-Instruct-abliterated-Q5_K_M-GGUF --hf-file llama-3.2-3b-instruct-abliterated-q5_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/Llama-3.2-3B-Instruct-abliterated-Q5_K_M-GGUF --hf-file llama-3.2-3b-instruct-abliterated-q5_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Triangle104/Llama-3.2-3B-Instruct-abliterated-Q5_K_M-GGUF --hf-file llama-3.2-3b-instruct-abliterated-q5_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/Llama-3.2-3B-Instruct-abliterated-Q5_K_M-GGUF --hf-file llama-3.2-3b-instruct-abliterated-q5_k_m.gguf -c 2048 ```

alicekwak/TN-final-all-mpnet-base-v2

alicekwak

"2022-11-02T22:58:35Z"

5

0

sentence-transformers

[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

"2022-11-02T22:58:25Z"

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # alicekwak/TN-final-all-mpnet-base-v2 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('alicekwak/TN-final-all-mpnet-base-v2') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=alicekwak/TN-final-all-mpnet-base-v2) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 675 with parameters: ``` {'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.SoftmaxLoss.SoftmaxLoss` Parameters of the fit()-Method: ``` { "epochs": 5, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 10, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) (2): Normalize() ) ``` ## Citing & Authors

hqbui/Reinforce-PixelCopter-PLE-v0

hqbui

"2023-12-12T21:02:22Z"

0

null

[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

"2023-12-12T19:11:38Z"

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-PixelCopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 23.70 +/- 17.46 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

pandaiedu/mesolitica-qwen-2.5-lora-1.5b-Instruct-Merged-20-epoch

pandaiedu

"2025-03-26T21:27:07Z"

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-03-26T21:25:16Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

kla-20/qa-flant5

kla-20

"2023-09-21T15:30:53Z"

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

"2023-09-21T15:23:27Z"

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: qa-flant5 results: [] ---  # qa-flant5 This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1 ### Training results ### Framework versions - Transformers 4.27.2 - Pytorch 1.13.1+cu117 - Datasets 2.11.0 - Tokenizers 0.13.3

sherif1311/flan-t5-base-classification_int1

sherif1311

"2023-08-13T18:55:37Z"

103

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "base_model:google/flan-t5-base", "base_model:finetune:google/flan-t5-base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

"2023-08-13T18:50:58Z"

--- license: apache-2.0 base_model: google/flan-t5-base tags: - generated_from_trainer metrics: - f1 model-index: - name: flan-t5-base-classification_int1 results: [] ---  # flan-t5-base-classification_int1 This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0036 - F1: 99.7778 - Gen Len: 2.3333 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.31.0 - Pytorch 1.12.1+cu116 - Datasets 2.14.4 - Tokenizers 0.12.1

tensorblock/llama-ko-peft-v0.6-GGUF

tensorblock

"2024-11-19T11:17:37Z"

19

0

null

[ "gguf", "TensorBlock", "GGUF", "ko", "base_model:colable/llama-ko-peft-v0.6", "base_model:quantized:colable/llama-ko-peft-v0.6", "license:mit", "endpoints_compatible", "region:us" ]

null

"2024-11-19T10:51:15Z"

--- license: mit language: - ko tags: - TensorBlock - GGUF base_model: colable/llama-ko-peft-v0.6 --- <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"> Feedback and support: TensorBlock's <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a> </p> </div> </div> ## colable/llama-ko-peft-v0.6 - GGUF This repo contains GGUF format model files for [colable/llama-ko-peft-v0.6](https://huggingface.co/colable/llama-ko-peft-v0.6). The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4011](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d). <div style="text-align: left; margin: 20px 0;"> <a href="https://tensorblock.co/waitlist/client" style="display: inline-block; padding: 10px 20px; background-color: #007bff; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;"> Run them on the TensorBlock client using your local machine ↗ </a> </div> ## Prompt template ``` ``` ## Model file specification | Filename | Quant type | File Size | Description | | -------- | ---------- | --------- | ----------- | | [llama-ko-peft-v0.6-Q2_K.gguf](https://huggingface.co/tensorblock/llama-ko-peft-v0.6-GGUF/blob/main/llama-ko-peft-v0.6-Q2_K.gguf) | Q2_K | 2.422 GB | smallest, significant quality loss - not recommended for most purposes | | [llama-ko-peft-v0.6-Q3_K_S.gguf](https://huggingface.co/tensorblock/llama-ko-peft-v0.6-GGUF/blob/main/llama-ko-peft-v0.6-Q3_K_S.gguf) | Q3_K_S | 2.815 GB | very small, high quality loss | | [llama-ko-peft-v0.6-Q3_K_M.gguf](https://huggingface.co/tensorblock/llama-ko-peft-v0.6-GGUF/blob/main/llama-ko-peft-v0.6-Q3_K_M.gguf) | Q3_K_M | 3.140 GB | very small, high quality loss | | [llama-ko-peft-v0.6-Q3_K_L.gguf](https://huggingface.co/tensorblock/llama-ko-peft-v0.6-GGUF/blob/main/llama-ko-peft-v0.6-Q3_K_L.gguf) | Q3_K_L | 3.419 GB | small, substantial quality loss | | [llama-ko-peft-v0.6-Q4_0.gguf](https://huggingface.co/tensorblock/llama-ko-peft-v0.6-GGUF/blob/main/llama-ko-peft-v0.6-Q4_0.gguf) | Q4_0 | 3.639 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | [llama-ko-peft-v0.6-Q4_K_S.gguf](https://huggingface.co/tensorblock/llama-ko-peft-v0.6-GGUF/blob/main/llama-ko-peft-v0.6-Q4_K_S.gguf) | Q4_K_S | 3.668 GB | small, greater quality loss | | [llama-ko-peft-v0.6-Q4_K_M.gguf](https://huggingface.co/tensorblock/llama-ko-peft-v0.6-GGUF/blob/main/llama-ko-peft-v0.6-Q4_K_M.gguf) | Q4_K_M | 3.877 GB | medium, balanced quality - recommended | | [llama-ko-peft-v0.6-Q5_0.gguf](https://huggingface.co/tensorblock/llama-ko-peft-v0.6-GGUF/blob/main/llama-ko-peft-v0.6-Q5_0.gguf) | Q5_0 | 4.415 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | [llama-ko-peft-v0.6-Q5_K_S.gguf](https://huggingface.co/tensorblock/llama-ko-peft-v0.6-GGUF/blob/main/llama-ko-peft-v0.6-Q5_K_S.gguf) | Q5_K_S | 4.415 GB | large, low quality loss - recommended | | [llama-ko-peft-v0.6-Q5_K_M.gguf](https://huggingface.co/tensorblock/llama-ko-peft-v0.6-GGUF/blob/main/llama-ko-peft-v0.6-Q5_K_M.gguf) | Q5_K_M | 4.537 GB | large, very low quality loss - recommended | | [llama-ko-peft-v0.6-Q6_K.gguf](https://huggingface.co/tensorblock/llama-ko-peft-v0.6-GGUF/blob/main/llama-ko-peft-v0.6-Q6_K.gguf) | Q6_K | 5.240 GB | very large, extremely low quality loss | | [llama-ko-peft-v0.6-Q8_0.gguf](https://huggingface.co/tensorblock/llama-ko-peft-v0.6-GGUF/blob/main/llama-ko-peft-v0.6-Q8_0.gguf) | Q8_0 | 6.786 GB | very large, extremely low quality loss - not recommended | ## Downloading instruction ### Command line Firstly, install Huggingface Client ```shell pip install -U "huggingface_hub[cli]" ``` Then, downoad the individual model file the a local directory ```shell huggingface-cli download tensorblock/llama-ko-peft-v0.6-GGUF --include "llama-ko-peft-v0.6-Q2_K.gguf" --local-dir MY_LOCAL_DIR ``` If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try: ```shell huggingface-cli download tensorblock/llama-ko-peft-v0.6-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf' ```

lunarsylph/stablecell_v13

lunarsylph

"2024-03-30T02:51:40Z"

91

0

transformers

[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

"2024-03-30T02:29:39Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

SicariusSicariiStuff/SaisExperiments_Evil-Alpaca-3B-L3.2_iMatrix

SicariusSicariiStuff

"2024-09-29T18:18:27Z"

8

0

null

[ "gguf", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

"2024-09-29T17:52:58Z"

--- license: apache-2.0 ---

amaliaam/image_classification

amaliaam

"2023-09-18T16:58:49Z"

261

0

transformers

[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

"2023-09-18T16:06:43Z"

--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - imagefolder model-index: - name: image_classification results: [] ---  # image_classification This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - eval_loss: 2.0915 - eval_accuracy: 0.0938 - eval_runtime: 10.0977 - eval_samples_per_second: 15.845 - eval_steps_per_second: 0.99 - step: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

georad/mediNER

georad

"2025-03-10T17:05:19Z"

0

null

[ "safetensors", "region:us" ]

null

"2025-03-10T14:58:19Z"

# medNER_V2 This app performs Named Entity REcognition of medical entties.

PrunaAI/fblgit-juanako-7b-UNA-bnb-8bit-smashed

PrunaAI

"2024-08-15T14:47:06Z"

5

0

null

[ "safetensors", "mistral", "pruna-ai", "base_model:fblgit/juanako-7b-UNA", "base_model:quantized:fblgit/juanako-7b-UNA", "8-bit", "bitsandbytes", "region:us" ]

null

"2024-08-15T14:43:39Z"

--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: fblgit/juanako-7b-UNA metrics: - memory_disk - memory_inference - inference_latency - inference_throughput - inference_CO2_emissions - inference_energy_consumption tags: - pruna-ai ---   <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div>  [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/rskEr4BZJx) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) **Frequently Asked Questions** - ***How does the compression work?*** The model is compressed with llm-int8. - ***How does the model quality change?*** The quality of the model output might vary compared to the base model. - ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - ***What is the model format?*** We use safetensors. - ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data. - ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. - ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases. ## Setup You can run the smashed model with these steps: 0. Check requirements from the original repo fblgit/juanako-7b-UNA installed. In particular, check python, cuda, and transformers versions. 1. Make sure that you have installed quantization related packages. ```bash pip install transformers accelerate bitsandbytes>0.37.0 ``` 2. Load & run the model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("PrunaAI/fblgit-juanako-7b-UNA-bnb-8bit-smashed", trust_remote_code=True, device_map='auto') tokenizer = AutoTokenizer.from_pretrained("fblgit/juanako-7b-UNA") input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"] outputs = model.generate(input_ids, max_new_tokens=216) tokenizer.decode(outputs[0]) ``` ## Configurations The configuration info are in `smash_config.json`. ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model fblgit/juanako-7b-UNA before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).

umesh16071973/_Flooplan_DB_LoRA_

umesh16071973

"2024-02-06T14:24:48Z"

3

1

diffusers

[ "diffusers", "tensorboard", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]

text-to-image

"2024-02-06T14:24:40Z"

--- tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a high quality, 4K photo of a FLOORPLAN license: openrail++ --- # SDXL LoRA DreamBooth - umesh16071973/_Flooplan_DB_LoRA_ <Gallery /> ## Model description These are umesh16071973/_Flooplan_DB_LoRA_ LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a high quality, 4K photo of a FLOORPLAN to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](umesh16071973/_Flooplan_DB_LoRA_/tree/main) them in the Files & versions tab.

TFOCUS/Inference-Providers_17

TFOCUS

"2025-02-28T16:29:24Z"

0

null

[ "onnx", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]

any-to-any

"2025-02-28T16:02:02Z"

--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).

John6666/toon-e-pony-v1-sdxl

John6666

"2024-12-23T06:36:36Z"

147

1

diffusers

[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "cartoon", "toon", "cute", "bold style", "pony", "en", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]

text-to-image

"2024-10-11T10:32:16Z"

--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime - cartoon - toon - cute - bold style - pony --- Original model is [here](https://civitai.com/models/843170/toon-e-pony?modelVersionId=943297). The author is [here](https://huggingface.co/advokat). This model created by [advokat](https://civitai.com/user/advokat).

altomek/CodeRosa-70B-AB1-5bpw-EXL2

altomek

"2024-08-30T10:24:55Z"

4

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "en", "base_model:altomek/CodeRosa-70B-AB1", "base_model:finetune:altomek/CodeRosa-70B-AB1", "license:llama2", "autotrain_compatible", "text-generation-inference", "region:us" ]

text-generation

"2024-03-18T06:27:41Z"

--- base_model: altomek/CodeRosa-70B-AB1 language: - en license: llama2 inference: false --- # CodeRosa-70B-AB1 ExLlamav2 5 bpw 8 h quants of https://huggingface.co/altomek/CodeRosa-70B-AB1

auxyus/3b08c9c9-8517-49a0-8d26-63097e0e34a1

auxyus

"2025-02-02T08:14:30Z"

8

0

peft

[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:unsloth/Qwen2.5-3B", "base_model:adapter:unsloth/Qwen2.5-3B", "license:other", "region:us" ]

null

"2025-02-02T08:04:58Z"

--- library_name: peft license: other base_model: unsloth/Qwen2.5-3B tags: - axolotl - generated_from_trainer model-index: - name: 3b08c9c9-8517-49a0-8d26-63097e0e34a1 results: [] ---  [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/Qwen2.5-3B bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - c485b08dfb34ae17_train_data.json ds_type: json format: custom path: /workspace/input_data/c485b08dfb34ae17_train_data.json type: field_input: authors field_instruction: abstract field_output: title format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: auxyus/3b08c9c9-8517-49a0-8d26-63097e0e34a1 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: false local_rank: 0 logging_steps: 3 lora_alpha: 32 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lr_scheduler: cosine max_steps: 100 micro_batch_size: 8 mlflow_experiment_name: /tmp/c485b08dfb34ae17_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: techspear-hub wandb_mode: online wandb_name: 33333ede-a0bf-4279-9af7-9eb33c9d47f1 wandb_project: Gradients-On-Two wandb_run: your_name wandb_runid: 33333ede-a0bf-4279-9af7-9eb33c9d47f1 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 3b08c9c9-8517-49a0-8d26-63097e0e34a1 This model is a fine-tuned version of [unsloth/Qwen2.5-3B](https://huggingface.co/unsloth/Qwen2.5-3B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1770 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | No log | 0.0129 | 1 | 2.1540 | | 1.9569 | 0.1158 | 9 | 1.4746 | | 1.3291 | 0.2315 | 18 | 1.2417 | | 1.2773 | 0.3473 | 27 | 1.2062 | | 1.1474 | 0.4630 | 36 | 1.1939 | | 1.2433 | 0.5788 | 45 | 1.1861 | | 1.3276 | 0.6945 | 54 | 1.1822 | | 1.3739 | 0.8103 | 63 | 1.1801 | | 1.2675 | 0.9260 | 72 | 1.1798 | | 1.2192 | 1.0482 | 81 | 1.1771 | | 1.2751 | 1.1640 | 90 | 1.1772 | | 1.227 | 1.2797 | 99 | 1.1770 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

machinelearningzuu/queue_detection_cctv

machinelearningzuu

"2024-07-08T21:26:49Z"

87

0

transformers

[ "transformers", "safetensors", "conditional_detr", "object-detection", "generated_from_trainer", "base_model:microsoft/conditional-detr-resnet-50", "base_model:finetune:microsoft/conditional-detr-resnet-50", "license:apache-2.0", "endpoints_compatible", "region:us" ]

object-detection

"2024-07-07T08:25:07Z"

--- base_model: microsoft/conditional-detr-resnet-50 license: apache-2.0 tags: - generated_from_trainer model-index: - name: queue_detection_cctv results: [] ---  # queue_detection_cctv This model is a fine-tuned version of [microsoft/conditional-detr-resnet-50](https://huggingface.co/microsoft/conditional-detr-resnet-50) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1291 - Map: 0.9532 - Map 50: 0.9901 - Map 75: 0.9845 - Map Small: -1.0 - Map Medium: 0.3203 - Map Large: 0.9578 - Mar 1: 0.5044 - Mar 10: 0.9715 - Mar 100: 0.972 - Mar Small: -1.0 - Mar Medium: 0.3538 - Mar Large: 0.9747 - Map Cashier: 0.9618 - Mar 100 Cashier: 0.9775 - Map Cx: 0.9447 - Mar 100 Cx: 0.9664 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 100 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Map | Map 50 | Map 75 | Map Small | Map Medium | Map Large | Mar 1 | Mar 10 | Mar 100 | Mar Small | Mar Medium | Mar Large | Map Cashier | Mar 100 Cashier | Map Cx | Mar 100 Cx | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:-----------:|:---------------:|:------:|:----------:| | No log | 1.0 | 218 | 1.3927 | 0.1975 | 0.3459 | 0.1995 | -1.0 | 0.0 | 0.1988 | 0.2409 | 0.5283 | 0.7011 | -1.0 | 0.0 | 0.7055 | 0.2115 | 0.8043 | 0.1834 | 0.5979 | | No log | 2.0 | 436 | 0.9964 | 0.5247 | 0.8011 | 0.591 | -1.0 | 0.0079 | 0.5292 | 0.3316 | 0.6966 | 0.7387 | -1.0 | 0.0071 | 0.7453 | 0.5772 | 0.8086 | 0.4723 | 0.6688 | | 2.7418 | 3.0 | 654 | 0.8535 | 0.6031 | 0.9058 | 0.6954 | -1.0 | 0.0349 | 0.6069 | 0.3603 | 0.7079 | 0.733 | -1.0 | 0.2 | 0.7362 | 0.6576 | 0.769 | 0.5485 | 0.6969 | | 2.7418 | 4.0 | 872 | 0.7406 | 0.6499 | 0.9356 | 0.752 | -1.0 | 0.0479 | 0.6543 | 0.3756 | 0.7387 | 0.7586 | -1.0 | 0.0923 | 0.7634 | 0.7052 | 0.7953 | 0.5947 | 0.7219 | | 0.8155 | 5.0 | 1090 | 0.6721 | 0.6731 | 0.9516 | 0.8113 | -1.0 | 0.0249 | 0.6773 | 0.3819 | 0.7501 | 0.7654 | -1.0 | 0.0455 | 0.7701 | 0.7451 | 0.8203 | 0.601 | 0.7105 | | 0.8155 | 6.0 | 1308 | 0.5804 | 0.7244 | 0.9632 | 0.8738 | 0.0 | 0.0712 | 0.7288 | 0.4038 | 0.7882 | 0.8023 | 0.0 | 0.1731 | 0.8066 | 0.7818 | 0.8419 | 0.6671 | 0.7627 | | 0.6668 | 7.0 | 1526 | 0.5430 | 0.7484 | 0.9667 | 0.9041 | -1.0 | 0.076 | 0.7527 | 0.417 | 0.8027 | 0.813 | -1.0 | 0.2205 | 0.8171 | 0.8068 | 0.8602 | 0.69 | 0.7658 | | 0.6668 | 8.0 | 1744 | 0.5524 | 0.7361 | 0.9691 | 0.8958 | -1.0 | 0.0273 | 0.7416 | 0.4045 | 0.7839 | 0.7933 | -1.0 | 0.1286 | 0.7981 | 0.7845 | 0.8274 | 0.6877 | 0.7592 | | 0.6668 | 9.0 | 1962 | 0.5359 | 0.7415 | 0.9737 | 0.901 | -1.0 | 0.0845 | 0.7462 | 0.4112 | 0.7999 | 0.8044 | -1.0 | 0.1462 | 0.8088 | 0.7844 | 0.8376 | 0.6986 | 0.7713 | | 0.5735 | 10.0 | 2180 | 0.5154 | 0.7497 | 0.9744 | 0.907 | 0.0 | 0.0368 | 0.7538 | 0.414 | 0.8042 | 0.8093 | 0.0 | 0.1333 | 0.813 | 0.8085 | 0.86 | 0.6909 | 0.7586 | | 0.5735 | 11.0 | 2398 | 0.4543 | 0.7824 | 0.9754 | 0.9337 | 0.0 | 0.0709 | 0.7908 | 0.4307 | 0.8323 | 0.8368 | 0.0 | 0.1794 | 0.8449 | 0.8312 | 0.8765 | 0.7336 | 0.7972 | | 0.5189 | 12.0 | 2616 | 0.4802 | 0.7679 | 0.9769 | 0.9274 | 0.0 | 0.1201 | 0.7724 | 0.426 | 0.8197 | 0.825 | 0.0 | 0.1917 | 0.8291 | 0.7985 | 0.85 | 0.7374 | 0.8 | | 0.5189 | 13.0 | 2834 | 0.4306 | 0.7906 | 0.9825 | 0.9332 | -1.0 | 0.0708 | 0.7941 | 0.435 | 0.8394 | 0.8448 | -1.0 | 0.23 | 0.8474 | 0.8474 | 0.889 | 0.7339 | 0.8006 | | 0.4874 | 14.0 | 3052 | 0.4660 | 0.7649 | 0.9818 | 0.9264 | -1.0 | 0.0504 | 0.7713 | 0.4219 | 0.8155 | 0.8222 | -1.0 | 0.0875 | 0.8288 | 0.805 | 0.8527 | 0.7248 | 0.7917 | | 0.4874 | 15.0 | 3270 | 0.4392 | 0.7867 | 0.9773 | 0.9278 | 0.0 | 0.0256 | 0.7961 | 0.4372 | 0.8336 | 0.8385 | 0.0 | 0.1028 | 0.8466 | 0.8243 | 0.8725 | 0.7492 | 0.8045 | | 0.4874 | 16.0 | 3488 | 0.4178 | 0.8018 | 0.9847 | 0.9355 | -1.0 | 0.2037 | 0.8061 | 0.4387 | 0.8493 | 0.8551 | -1.0 | 0.3714 | 0.8589 | 0.8394 | 0.8881 | 0.7641 | 0.822 | | 0.4646 | 17.0 | 3706 | 0.3859 | 0.8138 | 0.9838 | 0.9502 | -1.0 | 0.1217 | 0.8189 | 0.4459 | 0.8584 | 0.863 | -1.0 | 0.2038 | 0.8669 | 0.8508 | 0.8956 | 0.7769 | 0.8303 | | 0.4646 | 18.0 | 3924 | 0.4041 | 0.7987 | 0.9822 | 0.9457 | -1.0 | 0.097 | 0.8032 | 0.4378 | 0.8486 | 0.8518 | -1.0 | 0.1611 | 0.8551 | 0.8323 | 0.881 | 0.7652 | 0.8226 | | 0.4317 | 19.0 | 4142 | 0.4013 | 0.8086 | 0.9838 | 0.9442 | -1.0 | 0.1816 | 0.814 | 0.4412 | 0.8513 | 0.8557 | -1.0 | 0.2571 | 0.8605 | 0.8522 | 0.8919 | 0.765 | 0.8195 | | 0.4317 | 20.0 | 4360 | 0.3869 | 0.8123 | 0.9823 | 0.9388 | -1.0 | 0.1597 | 0.8163 | 0.4475 | 0.8579 | 0.8617 | -1.0 | 0.2042 | 0.8653 | 0.8542 | 0.896 | 0.7705 | 0.8274 | | 0.4215 | 21.0 | 4578 | 0.3721 | 0.816 | 0.9864 | 0.9536 | -1.0 | 0.1206 | 0.8198 | 0.4478 | 0.8598 | 0.863 | -1.0 | 0.2727 | 0.8655 | 0.8607 | 0.9003 | 0.7713 | 0.8258 | | 0.4215 | 22.0 | 4796 | 0.3777 | 0.8245 | 0.9806 | 0.9507 | 0.0 | 0.1034 | 0.8324 | 0.4537 | 0.8621 | 0.8649 | 0.0 | 0.2118 | 0.8724 | 0.8651 | 0.9012 | 0.7839 | 0.8287 | | 0.3925 | 23.0 | 5014 | 0.3387 | 0.8411 | 0.9872 | 0.9577 | -1.0 | 0.1184 | 0.845 | 0.4593 | 0.8775 | 0.8799 | -1.0 | 0.2429 | 0.8835 | 0.8813 | 0.9153 | 0.8008 | 0.8444 | | 0.3925 | 24.0 | 5232 | 0.3234 | 0.842 | 0.9887 | 0.9671 | -1.0 | 0.1229 | 0.8463 | 0.4604 | 0.8794 | 0.8812 | -1.0 | 0.1864 | 0.885 | 0.8736 | 0.909 | 0.8104 | 0.8534 | | 0.3925 | 25.0 | 5450 | 0.3463 | 0.8356 | 0.9869 | 0.9556 | -1.0 | 0.0775 | 0.8411 | 0.4552 | 0.8769 | 0.8793 | -1.0 | 0.1929 | 0.8838 | 0.8788 | 0.913 | 0.7925 | 0.8456 | | 0.3676 | 26.0 | 5668 | 0.3170 | 0.846 | 0.988 | 0.9666 | 0.0 | 0.1172 | 0.8515 | 0.4603 | 0.886 | 0.8872 | 0.0 | 0.285 | 0.8907 | 0.8831 | 0.9182 | 0.8089 | 0.8562 | | 0.3676 | 27.0 | 5886 | 0.3552 | 0.8246 | 0.9832 | 0.9545 | -1.0 | 0.13 | 0.8285 | 0.4535 | 0.8704 | 0.8745 | -1.0 | 0.2367 | 0.8785 | 0.8559 | 0.9005 | 0.7932 | 0.8484 | | 0.3669 | 28.0 | 6104 | 0.3342 | 0.8427 | 0.9876 | 0.9665 | -1.0 | 0.1369 | 0.8468 | 0.4585 | 0.8813 | 0.8843 | -1.0 | 0.2625 | 0.8874 | 0.8587 | 0.898 | 0.8267 | 0.8707 | | 0.3669 | 29.0 | 6322 | 0.3033 | 0.854 | 0.9892 | 0.9687 | -1.0 | 0.1795 | 0.8572 | 0.4663 | 0.8954 | 0.8968 | -1.0 | 0.3 | 0.8991 | 0.8813 | 0.9193 | 0.8268 | 0.8744 | | 0.349 | 30.0 | 6540 | 0.3099 | 0.8515 | 0.9863 | 0.9676 | -1.0 | 0.1251 | 0.8571 | 0.4666 | 0.8917 | 0.8936 | -1.0 | 0.2 | 0.8978 | 0.8868 | 0.9261 | 0.8162 | 0.8611 | | 0.349 | 31.0 | 6758 | 0.3247 | 0.842 | 0.9884 | 0.963 | 0.0 | 0.1145 | 0.8491 | 0.4607 | 0.8828 | 0.8854 | 0.0 | 0.1462 | 0.8916 | 0.8704 | 0.9104 | 0.8137 | 0.8605 | | 0.349 | 32.0 | 6976 | 0.2943 | 0.8529 | 0.9887 | 0.9651 | -1.0 | 0.1639 | 0.8587 | 0.4683 | 0.8916 | 0.8949 | -1.0 | 0.225 | 0.8997 | 0.89 | 0.9246 | 0.8158 | 0.8653 | | 0.3378 | 33.0 | 7194 | 0.2923 | 0.8605 | 0.989 | 0.9695 | -1.0 | 0.1212 | 0.8657 | 0.4687 | 0.8985 | 0.9006 | -1.0 | 0.2136 | 0.9042 | 0.8893 | 0.9257 | 0.8317 | 0.8756 | | 0.3378 | 34.0 | 7412 | 0.2878 | 0.8616 | 0.9895 | 0.9673 | -1.0 | 0.1464 | 0.8665 | 0.4712 | 0.897 | 0.899 | -1.0 | 0.2 | 0.9036 | 0.8907 | 0.9246 | 0.8325 | 0.8734 | | 0.3206 | 35.0 | 7630 | 0.3342 | 0.837 | 0.9866 | 0.9674 | -1.0 | 0.1634 | 0.8423 | 0.4584 | 0.8772 | 0.8802 | -1.0 | 0.2611 | 0.8844 | 0.8684 | 0.906 | 0.8057 | 0.8544 | | 0.3206 | 36.0 | 7848 | 0.2796 | 0.8713 | 0.989 | 0.9716 | -1.0 | 0.1054 | 0.8759 | 0.4699 | 0.9066 | 0.9084 | -1.0 | 0.15 | 0.9128 | 0.9052 | 0.9373 | 0.8373 | 0.8795 | | 0.3152 | 37.0 | 8066 | 0.2894 | 0.8667 | 0.987 | 0.9746 | 0.0 | 0.1359 | 0.8743 | 0.4716 | 0.9022 | 0.9037 | 0.0 | 0.1667 | 0.9109 | 0.8966 | 0.9309 | 0.8367 | 0.8765 | | 0.3152 | 38.0 | 8284 | 0.2641 | 0.8744 | 0.9894 | 0.9722 | -1.0 | 0.1413 | 0.8793 | 0.4727 | 0.9132 | 0.9148 | -1.0 | 0.2333 | 0.9178 | 0.8909 | 0.9305 | 0.858 | 0.8992 | | 0.3082 | 39.0 | 8502 | 0.2834 | 0.8703 | 0.9873 | 0.9702 | -1.0 | 0.132 | 0.8764 | 0.473 | 0.9082 | 0.9128 | -1.0 | 0.2633 | 0.9168 | 0.8988 | 0.9347 | 0.8417 | 0.891 | | 0.3082 | 40.0 | 8720 | 0.2774 | 0.8655 | 0.9897 | 0.9738 | -1.0 | 0.2021 | 0.8711 | 0.4694 | 0.9025 | 0.9043 | -1.0 | 0.275 | 0.9081 | 0.8971 | 0.9314 | 0.8339 | 0.8772 | | 0.3082 | 41.0 | 8938 | 0.2935 | 0.8598 | 0.988 | 0.9699 | -1.0 | 0.0999 | 0.8666 | 0.4688 | 0.8961 | 0.8976 | -1.0 | 0.15 | 0.9037 | 0.8889 | 0.9255 | 0.8308 | 0.8697 | | 0.3078 | 42.0 | 9156 | 0.2746 | 0.868 | 0.9895 | 0.9777 | -1.0 | 0.2159 | 0.8738 | 0.4712 | 0.9021 | 0.9032 | -1.0 | 0.275 | 0.9079 | 0.9016 | 0.933 | 0.8343 | 0.8734 | | 0.3078 | 43.0 | 9374 | 0.2662 | 0.8731 | 0.9897 | 0.9798 | -1.0 | 0.1849 | 0.8794 | 0.4752 | 0.9083 | 0.9091 | -1.0 | 0.2 | 0.9136 | 0.888 | 0.9206 | 0.8582 | 0.8975 | | 0.2898 | 44.0 | 9592 | 0.2564 | 0.8824 | 0.9868 | 0.9732 | -1.0 | 0.1263 | 0.8871 | 0.4775 | 0.9148 | 0.9165 | -1.0 | 0.15 | 0.9211 | 0.9076 | 0.9377 | 0.8571 | 0.8954 | | 0.2898 | 45.0 | 9810 | 0.2813 | 0.8753 | 0.9876 | 0.977 | 0.0 | 0.1325 | 0.8817 | 0.4714 | 0.911 | 0.9123 | 0.0 | 0.2167 | 0.9179 | 0.9042 | 0.9381 | 0.8464 | 0.8865 | | 0.2758 | 46.0 | 10028 | 0.2633 | 0.8786 | 0.9872 | 0.9719 | 0.0 | 0.1841 | 0.8854 | 0.4758 | 0.9164 | 0.9177 | 0.0 | 0.2615 | 0.9218 | 0.9012 | 0.9374 | 0.856 | 0.898 | | 0.2758 | 47.0 | 10246 | 0.2479 | 0.8795 | 0.9895 | 0.9765 | 0.0 | 0.2066 | 0.8849 | 0.4765 | 0.9146 | 0.9171 | 0.0 | 0.275 | 0.9207 | 0.9114 | 0.9448 | 0.8476 | 0.8893 | | 0.2758 | 48.0 | 10464 | 0.2373 | 0.8894 | 0.9897 | 0.9799 | -1.0 | 0.1994 | 0.8939 | 0.4795 | 0.9253 | 0.926 | -1.0 | 0.2545 | 0.9293 | 0.9076 | 0.9431 | 0.8713 | 0.909 | | 0.2708 | 49.0 | 10682 | 0.2538 | 0.8846 | 0.9893 | 0.9793 | 0.0 | 0.2669 | 0.8903 | 0.4799 | 0.9213 | 0.9224 | 0.0 | 0.315 | 0.9284 | 0.9052 | 0.9383 | 0.8641 | 0.9065 | | 0.2708 | 50.0 | 10900 | 0.2445 | 0.8919 | 0.9896 | 0.9745 | -1.0 | 0.2193 | 0.8972 | 0.4765 | 0.9228 | 0.925 | -1.0 | 0.3969 | 0.9294 | 0.9239 | 0.9511 | 0.8599 | 0.8989 | | 0.2595 | 51.0 | 11118 | 0.2110 | 0.9037 | 0.99 | 0.9845 | -1.0 | 0.2267 | 0.9093 | 0.4882 | 0.9339 | 0.9346 | -1.0 | 0.25 | 0.9374 | 0.9299 | 0.9574 | 0.8776 | 0.9117 | | 0.2595 | 52.0 | 11336 | 0.2374 | 0.897 | 0.99 | 0.9792 | -1.0 | 0.2066 | 0.9029 | 0.48 | 0.9267 | 0.9285 | -1.0 | 0.3179 | 0.9335 | 0.9257 | 0.9531 | 0.8684 | 0.9039 | | 0.2378 | 53.0 | 11554 | 0.2517 | 0.8826 | 0.9894 | 0.9716 | -1.0 | 0.1494 | 0.8901 | 0.4782 | 0.9162 | 0.9188 | -1.0 | 0.2475 | 0.9242 | 0.9152 | 0.9455 | 0.8501 | 0.892 | | 0.2378 | 54.0 | 11772 | 0.2260 | 0.8971 | 0.9899 | 0.9771 | -1.0 | 0.1848 | 0.9029 | 0.4825 | 0.9304 | 0.9315 | -1.0 | 0.2077 | 0.936 | 0.9255 | 0.9544 | 0.8687 | 0.9087 | | 0.2378 | 55.0 | 11990 | 0.2144 | 0.9118 | 0.9899 | 0.9844 | -1.0 | 0.2843 | 0.9158 | 0.4875 | 0.9417 | 0.9435 | -1.0 | 0.3333 | 0.9456 | 0.9351 | 0.9608 | 0.8885 | 0.9263 | | 0.2494 | 56.0 | 12208 | 0.2028 | 0.9107 | 0.9897 | 0.9814 | 0.0 | 0.1831 | 0.9168 | 0.4906 | 0.9395 | 0.9414 | 0.0 | 0.22 | 0.9466 | 0.935 | 0.9585 | 0.8864 | 0.9243 | | 0.2494 | 57.0 | 12426 | 0.2341 | 0.8897 | 0.9897 | 0.9812 | -1.0 | 0.1783 | 0.8932 | 0.4822 | 0.9242 | 0.926 | -1.0 | 0.2154 | 0.9303 | 0.9168 | 0.948 | 0.8625 | 0.9039 | | 0.2228 | 58.0 | 12644 | 0.2075 | 0.9084 | 0.9899 | 0.9792 | -1.0 | 0.1741 | 0.9142 | 0.4899 | 0.9375 | 0.9379 | -1.0 | 0.2308 | 0.9421 | 0.932 | 0.9581 | 0.8849 | 0.9177 | | 0.2228 | 59.0 | 12862 | 0.2059 | 0.9096 | 0.9896 | 0.9803 | 0.0 | 0.2969 | 0.9138 | 0.4893 | 0.9375 | 0.9395 | 0.0 | 0.31 | 0.9431 | 0.9311 | 0.957 | 0.8881 | 0.9219 | | 0.2218 | 60.0 | 13080 | 0.2028 | 0.9136 | 0.9899 | 0.984 | -1.0 | 0.2316 | 0.9164 | 0.4875 | 0.9408 | 0.9416 | -1.0 | 0.295 | 0.9442 | 0.9433 | 0.9654 | 0.884 | 0.9177 | | 0.2218 | 61.0 | 13298 | 0.2013 | 0.911 | 0.99 | 0.9786 | -1.0 | 0.253 | 0.9158 | 0.4904 | 0.9388 | 0.94 | -1.0 | 0.3 | 0.9435 | 0.9325 | 0.9572 | 0.8895 | 0.9228 | | 0.2238 | 62.0 | 13516 | 0.2033 | 0.9134 | 0.9899 | 0.9825 | 0.0 | 0.2228 | 0.9199 | 0.4896 | 0.9426 | 0.9438 | 0.0 | 0.2667 | 0.9484 | 0.9367 | 0.9624 | 0.8902 | 0.9252 | | 0.2238 | 63.0 | 13734 | 0.1893 | 0.9216 | 0.99 | 0.9836 | -1.0 | 0.1905 | 0.9271 | 0.4942 | 0.9509 | 0.9512 | -1.0 | 0.235 | 0.9546 | 0.9403 | 0.9664 | 0.9029 | 0.9361 | | 0.2238 | 64.0 | 13952 | 0.1893 | 0.9267 | 0.9898 | 0.9835 | 0.0 | 0.2342 | 0.9317 | 0.4957 | 0.9524 | 0.9536 | 0.0 | 0.2583 | 0.9585 | 0.9491 | 0.971 | 0.9043 | 0.9363 | | 0.2131 | 65.0 | 14170 | 0.1769 | 0.9322 | 0.9901 | 0.9847 | -1.0 | 0.2413 | 0.9349 | 0.4982 | 0.9554 | 0.9559 | -1.0 | 0.2864 | 0.959 | 0.9463 | 0.9673 | 0.9181 | 0.9445 | | 0.2131 | 66.0 | 14388 | 0.1848 | 0.9312 | 0.9898 | 0.9842 | 0.0 | 0.2901 | 0.9358 | 0.4973 | 0.9545 | 0.9551 | 0.0 | 0.425 | 0.9591 | 0.9517 | 0.9709 | 0.9107 | 0.9394 | | 0.2038 | 67.0 | 14606 | 0.1809 | 0.9277 | 0.9899 | 0.9815 | 0.0 | 0.2354 | 0.9329 | 0.4951 | 0.9524 | 0.9539 | 0.0 | 0.2846 | 0.9586 | 0.9441 | 0.9668 | 0.9112 | 0.9411 | | 0.2038 | 68.0 | 14824 | 0.1831 | 0.9178 | 0.9899 | 0.98 | 0.0 | 0.1728 | 0.9256 | 0.4922 | 0.9472 | 0.9483 | 0.0 | 0.23 | 0.9538 | 0.9396 | 0.9646 | 0.896 | 0.9319 | | 0.1995 | 69.0 | 15042 | 0.1631 | 0.934 | 0.9901 | 0.9861 | -1.0 | 0.2804 | 0.9405 | 0.4982 | 0.9574 | 0.9583 | -1.0 | 0.325 | 0.9615 | 0.954 | 0.9729 | 0.914 | 0.9438 | | 0.1995 | 70.0 | 15260 | 0.1685 | 0.9293 | 0.9899 | 0.9846 | -1.0 | 0.2397 | 0.935 | 0.4964 | 0.9546 | 0.9553 | -1.0 | 0.2714 | 0.9593 | 0.948 | 0.9698 | 0.9105 | 0.9408 | | 0.1995 | 71.0 | 15478 | 0.1629 | 0.9371 | 0.9901 | 0.9842 | -1.0 | 0.2541 | 0.942 | 0.498 | 0.9603 | 0.9609 | -1.0 | 0.4964 | 0.965 | 0.954 | 0.9741 | 0.9202 | 0.9477 | | 0.1877 | 72.0 | 15696 | 0.1606 | 0.944 | 0.9901 | 0.9846 | -1.0 | 0.277 | 0.9469 | 0.4988 | 0.9636 | 0.9642 | -1.0 | 0.3038 | 0.9676 | 0.96 | 0.9758 | 0.9281 | 0.9527 | | 0.1877 | 73.0 | 15914 | 0.1532 | 0.9389 | 0.99 | 0.9806 | 0.0 | 0.2592 | 0.9446 | 0.5009 | 0.961 | 0.962 | 0.0 | 0.3133 | 0.9662 | 0.9564 | 0.9749 | 0.9214 | 0.9492 | | 0.1912 | 74.0 | 16132 | 0.1434 | 0.9488 | 0.995 | 0.9934 | -1.0 | 0.5552 | 0.9507 | 0.5033 | 0.9673 | 0.9675 | -1.0 | 0.7182 | 0.969 | 0.9639 | 0.9786 | 0.9336 | 0.9563 | | 0.1912 | 75.0 | 16350 | 0.1726 | 0.9309 | 0.9901 | 0.9832 | -1.0 | 0.216 | 0.9344 | 0.4964 | 0.9568 | 0.9578 | -1.0 | 0.2611 | 0.9607 | 0.9539 | 0.9747 | 0.9079 | 0.941 | | 0.1859 | 76.0 | 16568 | 0.1587 | 0.9378 | 0.9901 | 0.9847 | -1.0 | 0.1684 | 0.944 | 0.4994 | 0.9601 | 0.9607 | -1.0 | 0.2382 | 0.9662 | 0.952 | 0.9715 | 0.9237 | 0.9499 | | 0.1859 | 77.0 | 16786 | 0.1378 | 0.9509 | 0.9901 | 0.9845 | -1.0 | 0.2089 | 0.959 | 0.5047 | 0.9688 | 0.9691 | -1.0 | 0.2353 | 0.9748 | 0.9666 | 0.9823 | 0.9352 | 0.9559 | | 0.1747 | 78.0 | 17004 | 0.1416 | 0.9478 | 0.9901 | 0.985 | 0.0 | 0.3334 | 0.9521 | 0.5039 | 0.9685 | 0.9692 | 0.0 | 0.35 | 0.9719 | 0.9617 | 0.9799 | 0.9338 | 0.9586 | | 0.1747 | 79.0 | 17222 | 0.1615 | 0.9376 | 0.9949 | 0.9873 | -1.0 | 0.5057 | 0.9406 | 0.5003 | 0.9599 | 0.9607 | -1.0 | 0.5688 | 0.9644 | 0.9583 | 0.9746 | 0.917 | 0.9469 | | 0.1747 | 80.0 | 17440 | 0.1482 | 0.9427 | 0.99 | 0.9823 | -1.0 | 0.1933 | 0.9499 | 0.5025 | 0.9639 | 0.9642 | -1.0 | 0.2321 | 0.9689 | 0.9566 | 0.9762 | 0.9289 | 0.9521 | | 0.1707 | 81.0 | 17658 | 0.1379 | 0.9518 | 0.9901 | 0.9894 | -1.0 | 0.2838 | 0.956 | 0.504 | 0.97 | 0.9702 | -1.0 | 0.3 | 0.9742 | 0.965 | 0.9787 | 0.9386 | 0.9618 | | 0.1707 | 82.0 | 17876 | 0.1384 | 0.9478 | 0.9901 | 0.9846 | -1.0 | 0.2518 | 0.9545 | 0.504 | 0.9687 | 0.9691 | -1.0 | 0.2643 | 0.9734 | 0.9612 | 0.9787 | 0.9344 | 0.9595 | | 0.1658 | 83.0 | 18094 | 0.1379 | 0.9532 | 0.9901 | 0.9845 | -1.0 | 0.2543 | 0.9567 | 0.5043 | 0.9707 | 0.9714 | -1.0 | 0.2708 | 0.975 | 0.9655 | 0.981 | 0.9408 | 0.9617 | | 0.1658 | 84.0 | 18312 | 0.1325 | 0.9544 | 0.9901 | 0.9845 | 0.0 | 0.256 | 0.9597 | 0.5047 | 0.9712 | 0.972 | 0.0 | 0.3036 | 0.9762 | 0.9672 | 0.9811 | 0.9417 | 0.9628 | | 0.1532 | 85.0 | 18530 | 0.1558 | 0.9452 | 0.99 | 0.9845 | -1.0 | 0.2469 | 0.9495 | 0.5009 | 0.9648 | 0.9657 | -1.0 | 0.2769 | 0.9695 | 0.9584 | 0.9749 | 0.932 | 0.9565 | | 0.1532 | 86.0 | 18748 | 0.1228 | 0.9538 | 0.9901 | 0.9841 | -1.0 | 0.3437 | 0.9585 | 0.5056 | 0.972 | 0.9726 | -1.0 | 0.3727 | 0.9747 | 0.9642 | 0.9806 | 0.9434 | 0.9646 | | 0.1532 | 87.0 | 18966 | 0.1317 | 0.9587 | 0.9901 | 0.9844 | 0.0 | 0.4141 | 0.965 | 0.5064 | 0.9738 | 0.974 | 0.0 | 0.4517 | 0.9791 | 0.9676 | 0.9815 | 0.9498 | 0.9664 | | 0.1574 | 88.0 | 19184 | 0.1318 | 0.9508 | 0.9901 | 0.9845 | 0.0 | 0.2545 | 0.9581 | 0.5059 | 0.9705 | 0.9706 | 0.0 | 0.2962 | 0.9747 | 0.9594 | 0.9778 | 0.9422 | 0.9633 | | 0.1574 | 89.0 | 19402 | 0.1424 | 0.9513 | 0.9899 | 0.984 | -1.0 | 0.2362 | 0.9547 | 0.5034 | 0.9691 | 0.9695 | -1.0 | 0.2875 | 0.9729 | 0.9636 | 0.9786 | 0.939 | 0.9603 | | 0.1537 | 90.0 | 19620 | 0.1240 | 0.9565 | 0.9901 | 0.9896 | -1.0 | 0.5053 | 0.9592 | 0.5066 | 0.9747 | 0.9752 | -1.0 | 0.55 | 0.9771 | 0.9669 | 0.9823 | 0.9461 | 0.9681 | | 0.1537 | 91.0 | 19838 | 0.1382 | 0.947 | 0.9901 | 0.9835 | 0.0 | 0.5316 | 0.9504 | 0.5018 | 0.9681 | 0.9683 | 0.0 | 0.555 | 0.9712 | 0.9622 | 0.9775 | 0.9319 | 0.9592 | | 0.1547 | 92.0 | 20056 | 0.1276 | 0.9565 | 0.9901 | 0.983 | -1.0 | 0.3161 | 0.9618 | 0.5058 | 0.9742 | 0.9743 | -1.0 | 0.3458 | 0.977 | 0.9668 | 0.9818 | 0.9462 | 0.9669 | | 0.1547 | 93.0 | 20274 | 0.1329 | 0.9539 | 0.99 | 0.9836 | -1.0 | 0.2997 | 0.9593 | 0.5053 | 0.9718 | 0.9728 | -1.0 | 0.3318 | 0.9754 | 0.9679 | 0.982 | 0.9398 | 0.9635 | | 0.1547 | 94.0 | 20492 | 0.1348 | 0.9571 | 0.99 | 0.9846 | -1.0 | 0.3267 | 0.9615 | 0.5039 | 0.9732 | 0.9737 | -1.0 | 0.3625 | 0.9761 | 0.9678 | 0.9823 | 0.9463 | 0.9652 | | 0.1513 | 95.0 | 20710 | 0.1251 | 0.9546 | 0.9901 | 0.9844 | 0.0 | 0.2549 | 0.9626 | 0.5049 | 0.9728 | 0.9731 | 0.0 | 0.2625 | 0.9775 | 0.965 | 0.981 | 0.9442 | 0.9652 | | 0.1513 | 96.0 | 20928 | 0.1264 | 0.9594 | 0.9901 | 0.9899 | 0.0 | 0.327 | 0.9631 | 0.5068 | 0.9755 | 0.9763 | 0.0 | 0.3409 | 0.9794 | 0.9696 | 0.9842 | 0.9492 | 0.9683 | | 0.1635 | 97.0 | 21146 | 0.1306 | 0.9515 | 0.9901 | 0.9843 | -1.0 | 0.2685 | 0.9561 | 0.5041 | 0.9696 | 0.9703 | -1.0 | 0.2857 | 0.9742 | 0.9626 | 0.9795 | 0.9404 | 0.9611 | | 0.1635 | 98.0 | 21364 | 0.1410 | 0.9481 | 0.9899 | 0.9788 | 0.0 | 0.4025 | 0.9542 | 0.5031 | 0.9662 | 0.9678 | 0.0 | 0.4458 | 0.9722 | 0.9621 | 0.9789 | 0.9341 | 0.9567 | | 0.1505 | 99.0 | 21582 | 0.1253 | 0.9571 | 0.9901 | 0.984 | -1.0 | 0.3105 | 0.962 | 0.5066 | 0.9737 | 0.974 | -1.0 | 0.3375 | 0.9777 | 0.9702 | 0.9832 | 0.944 | 0.9648 | | 0.1505 | 100.0 | 21800 | 0.1291 | 0.9532 | 0.9901 | 0.9845 | -1.0 | 0.3203 | 0.9578 | 0.5044 | 0.9715 | 0.972 | -1.0 | 0.3538 | 0.9747 | 0.9618 | 0.9775 | 0.9447 | 0.9664 | ### Framework versions - Transformers 4.42.3 - Pytorch 2.3.1+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

aidando73/Qwen-2.5-7B-Simple-RL-v9

aidando73

"2025-03-24T23:33:49Z"

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "grpo", "conversational", "arxiv:2402.03300", "base_model:Qwen/Qwen2.5-Math-7B", "base_model:finetune:Qwen/Qwen2.5-Math-7B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-03-24T20:24:58Z"

--- base_model: Qwen/Qwen2.5-Math-7B library_name: transformers model_name: Qwen-2.5-7B-Simple-RL-v9 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Qwen-2.5-7B-Simple-RL-v9 This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="aidando73/Qwen-2.5-7B-Simple-RL-v9", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/aidando73-personal/open-r1-math-rl/runs/1v3mtjxf) This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.16.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.5.1 - Datasets: 3.4.1 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

Weni/ZeroShot-3.4.0-Mistral-Retry-7b-DPO-1.0.0

Weni

"2024-03-11T14:17:06Z"

0

trl

[ "trl", "safetensors", "DPO", "ZeroShot", "en", "es", "pt", "base_model:Weni/ZeroShot-3.3.14-Mistral-7b-Multilanguage-3.2.0-merged", "base_model:finetune:Weni/ZeroShot-3.3.14-Mistral-7b-Multilanguage-3.2.0-merged", "license:mit", "region:us" ]

null

"2024-03-11T12:34:29Z"

--- license: mit library_name: "trl" tags: - DPO - ZeroShot base_model: Weni/ZeroShot-3.3.14-Mistral-7b-Multilanguage-3.2.0-merged model-index: - name: Weni/ZeroShot-3.4.0-Mistral-Retry-7b-DPO-1.0.0 results: [] language: ['en', 'es', 'pt'] --- # Weni/ZeroShot-3.4.0-Mistral-Retry-7b-DPO-1.0.0 This model is a fine-tuned version of [Weni/ZeroShot-3.3.14-Mistral-7b-Multilanguage-3.2.0-merged] on the dataset Weni/zeroshot-dpo-1.0.0 with the DPO trainer. It is part of the ZeroShot project for [Weni](https://weni.ai/). It achieves the following results on the evaluation set: {'eval_loss': 0.12734735012054443, 'eval_runtime': 25.6184, 'eval_samples_per_second': 2.381, 'eval_steps_per_second': 0.312, 'eval_rewards/chosen': 4.7875847816467285, 'eval_rewards/rejected': -1.6130797863006592, 'eval_rewards/accuracies': 0.921875, 'eval_rewards/margins': 6.400664329528809, 'eval_logps/rejected': -15.168061256408691, 'eval_logps/chosen': -11.294010162353516, 'eval_logits/rejected': -1.3262749910354614, 'eval_logits/chosen': -1.370504379272461, 'epoch': 0.94} ## Intended uses & limitations This model has not been trained to avoid specific intructions. ## Training procedure Finetuning was done on the model Weni/ZeroShot-3.3.14-Mistral-7b-Multilanguage-3.2.0-merged with the following prompt: ``` Portuguese: [INST] Você é muito especialista em classificar a frase do usuário em um chatbot sobre: {context} Pare, pense bem e responda com APENAS UM ÚNICO \`id\` da classe que melhor represente a intenção para a frase do usuário de acordo com a análise de seu contexto, responda APENAS com o \`id\` da classe só se você tiver muita certeza e não explique o motivo. Na ausência, falta de informações ou caso a frase do usuário não se enquadre em nenhuma classe, classifique como "-1". # Essas são as Classes com seus Id e Contexto: {all_classes} # Frase do usuário: {input} # Id da Classe: [/INST] Spanish: [INST] Eres muy experto en clasificar la frase del usuario en un chatbot sobre: {context} Deténgase, piense bien y responda con SOLO UN ÚNICO \`id\` de la clase que mejor represente la intención para la frase del usuario de acuerdo con el análisis de su contexto, responda SOLO con el \`id\` de la clase si está muy seguro y no explique el motivo. En ausencia, falta de información o en caso de que la frase del usuario no se ajuste a ninguna clase, clasifique como "-1". # Estas son las Clases con sus Id y Contexto: {all_classes} # Frase del usuario: {input} # Id de la Clase: [/INST] English: [INST] You are very expert in classifying the user sentence in a chatbot about: {context} Stop, think carefully, and respond with ONLY ONE SINGLE \`id\` of the class that best represents the intention for the user's sentence according to the analysis of its context, respond ONLY with the \`id\` of the class if you are very sure and do not explain the reason. In the absence, lack of information, or if the user's sentence does not fit into any class, classify as "-1". # These are the Classes and its Context: {all_classes} # User's sentence: {input} # Class Id: [/INST] Chosen_response: {chosen_response} Rejected_response: {rejected_response} ``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - per_device_train_batch_size: 8 - per_device_eval_batch_size: 8 - gradient_accumulation_steps: 4 - num_gpus: 1 - total_train_batch_size: 32 - optimizer: AdamW - lr_scheduler_type: cosine - num_steps: 16 - quantization_type: bitsandbytes - LoRA: ("\n - bits: 4\n - use_exllama: True\n - device_map: auto\n - use_cache: False\n - lora_r: 8\n - lora_alpha: 16\n - lora_dropout: 0.1\n - bias: none\n - target_modules: ['q_proj', 'k_proj', 'v_proj', 'o_proj']\n - task_type: CAUSAL_LM",) ### Training results ### Framework versions - transformers==4.38.2 - datasets==2.17.1 - peft==0.8.2 - safetensors==0.4.2 - evaluate==0.4.1 - bitsandbytes==0.42 - huggingface_hub==0.20.3 - seqeval==1.2.2 - optimum==1.17.1 - auto-gptq==0.7.0 - gpustat==1.1.1 - deepspeed==0.13.2 - wandb==0.16.3 - trl==0.7.11 - accelerate==0.27.2 - coloredlogs==15.0.1 - traitlets==5.14.1 - autoawq@https://github.com/casper-hansen/AutoAWQ/releases/download/v0.2.0/autoawq-0.2.0+cu118-cp310-cp310-linux_x86_64.whl ### Hardware - Cloud provided: runpod.io

sbaner24/vit-base-patch16-224-Trial008-YEL_STEM3

sbaner24

"2023-11-15T15:09:56Z"

189

0

transformers

[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

"2023-11-14T04:57:19Z"

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: vit-base-patch16-224-Trial008-YEL_STEM3 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 1.0 ---  # vit-base-patch16-224-Trial008-YEL_STEM3 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0916 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 30 - eval_batch_size: 30 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 120 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7743 | 1.0 | 1 | 0.8267 | 0.3636 | | 0.7964 | 2.0 | 2 | 0.7547 | 0.3636 | | 0.6369 | 3.0 | 3 | 0.6399 | 0.7273 | | 0.5344 | 4.0 | 4 | 0.5082 | 0.9091 | | 0.4342 | 5.0 | 5 | 0.4664 | 0.9091 | | 0.3056 | 6.0 | 6 | 0.2145 | 0.9091 | | 0.257 | 7.0 | 7 | 0.1395 | 0.9091 | | 0.2064 | 8.0 | 8 | 0.1990 | 0.9091 | | 0.2609 | 9.0 | 9 | 0.0916 | 1.0 | | 0.1758 | 10.0 | 10 | 0.0321 | 1.0 | | 0.1152 | 11.0 | 11 | 0.0256 | 1.0 | | 0.1343 | 12.0 | 12 | 0.0413 | 1.0 | | 0.0955 | 13.0 | 13 | 0.0319 | 1.0 | | 0.0723 | 14.0 | 14 | 0.0112 | 1.0 | | 0.13 | 15.0 | 15 | 0.0073 | 1.0 | | 0.1918 | 16.0 | 16 | 0.0057 | 1.0 | | 0.2469 | 17.0 | 17 | 0.0052 | 1.0 | | 0.1001 | 18.0 | 18 | 0.0051 | 1.0 | | 0.1331 | 19.0 | 19 | 0.0039 | 1.0 | | 0.1511 | 20.0 | 20 | 0.0031 | 1.0 | | 0.0956 | 21.0 | 21 | 0.0027 | 1.0 | | 0.0952 | 22.0 | 22 | 0.0027 | 1.0 | | 0.1679 | 23.0 | 23 | 0.0025 | 1.0 | | 0.1075 | 24.0 | 24 | 0.0023 | 1.0 | | 0.1507 | 25.0 | 25 | 0.0024 | 1.0 | | 0.1267 | 26.0 | 26 | 0.0027 | 1.0 | | 0.1141 | 27.0 | 27 | 0.0030 | 1.0 | | 0.0767 | 28.0 | 28 | 0.0031 | 1.0 | | 0.1746 | 29.0 | 29 | 0.0029 | 1.0 | | 0.1101 | 30.0 | 30 | 0.0032 | 1.0 | | 0.1632 | 31.0 | 31 | 0.0036 | 1.0 | | 0.1346 | 32.0 | 32 | 0.0038 | 1.0 | | 0.1024 | 33.0 | 33 | 0.0038 | 1.0 | | 0.1198 | 34.0 | 34 | 0.0037 | 1.0 | | 0.1217 | 35.0 | 35 | 0.0033 | 1.0 | | 0.1433 | 36.0 | 36 | 0.0030 | 1.0 | | 0.1255 | 37.0 | 37 | 0.0029 | 1.0 | | 0.1369 | 38.0 | 38 | 0.0027 | 1.0 | | 0.091 | 39.0 | 39 | 0.0026 | 1.0 | | 0.1318 | 40.0 | 40 | 0.0025 | 1.0 | | 0.0964 | 41.0 | 41 | 0.0025 | 1.0 | | 0.1164 | 42.0 | 42 | 0.0024 | 1.0 | | 0.0935 | 43.0 | 43 | 0.0023 | 1.0 | | 0.0564 | 44.0 | 44 | 0.0022 | 1.0 | | 0.1136 | 45.0 | 45 | 0.0021 | 1.0 | | 0.1306 | 46.0 | 46 | 0.0021 | 1.0 | | 0.0757 | 47.0 | 47 | 0.0021 | 1.0 | | 0.0475 | 48.0 | 48 | 0.0020 | 1.0 | | 0.1455 | 49.0 | 49 | 0.0020 | 1.0 | | 0.1892 | 50.0 | 50 | 0.0020 | 1.0 | ### Framework versions - Transformers 4.30.0.dev0 - Pytorch 1.12.1 - Datasets 2.12.0 - Tokenizers 0.13.1

psxjp5/mt5-small_25

psxjp5

"2023-08-08T11:50:47Z"

104

0

transformers

[ "transformers", "pytorch", "tensorboard", "mt5", "text2text-generation", "generated_from_trainer", "base_model:google/mt5-small", "base_model:finetune:google/mt5-small", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

"2023-08-08T09:40:03Z"

--- license: apache-2.0 base_model: google/mt5-small tags: - generated_from_trainer metrics: - rouge - bleu model-index: - name: mt5-small_test results: [] ---  # mt5-small_test This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7284 - Rouge1: 43.3718 - Rouge2: 37.5973 - Rougel: 42.0502 - Rougelsum: 42.0648 - Bleu: 32.8345 - Gen Len: 12.6063 - Meteor: 0.3949 - True negatives: 70.2115 - False negatives: 11.206 - Cosine Sim: 0.7485 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 9 - gradient_accumulation_steps: 8 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu | Gen Len | Meteor | True negatives | False negatives | Cosine Sim | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|:-------:|:------:|:--------------:|:---------------:|:----------:| | 3.1455 | 1.0 | 175 | 0.9832 | 18.7269 | 15.517 | 18.22 | 18.223 | 7.0634 | 7.6229 | 0.1626 | 74.6828 | 57.1687 | 0.3949 | | 1.1623 | 1.99 | 350 | 0.8542 | 38.7603 | 32.7237 | 37.3447 | 37.3752 | 27.4323 | 12.5135 | 0.3487 | 60.0 | 15.942 | 0.6992 | | 0.9431 | 2.99 | 525 | 0.8017 | 41.5759 | 35.6108 | 40.2536 | 40.2695 | 30.7994 | 12.8117 | 0.3755 | 61.2689 | 12.3447 | 0.7304 | | 0.8119 | 3.98 | 700 | 0.7787 | 43.5881 | 37.4245 | 42.1096 | 42.1248 | 32.9646 | 13.2176 | 0.3947 | 59.1541 | 9.5238 | 0.7582 | | 0.7235 | 4.98 | 875 | 0.7477 | 43.4069 | 37.2246 | 41.8444 | 41.8616 | 32.9345 | 13.116 | 0.3946 | 63.0816 | 9.8085 | 0.7561 | | 0.6493 | 5.97 | 1050 | 0.7266 | 40.4506 | 35.0072 | 39.1206 | 39.1181 | 29.0601 | 11.748 | 0.3687 | 75.5287 | 17.2101 | 0.7071 | | 0.5871 | 6.97 | 1225 | 0.7284 | 43.3718 | 37.5973 | 42.0502 | 42.0648 | 32.8345 | 12.6063 | 0.3949 | 70.2115 | 11.206 | 0.7485 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

dianamihalache27/deberta-v3-base_3epoch10

dianamihalache27

"2024-05-31T15:34:50Z"

163

0

transformers

[ "transformers", "tensorboard", "safetensors", "deberta-v2", "text-classification", "generated_from_trainer", "base_model:microsoft/deberta-v3-base", "base_model:finetune:microsoft/deberta-v3-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

"2024-05-31T15:34:11Z"

--- license: mit base_model: microsoft/deberta-v3-base tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: deberta-v3-base_3epoch10 results: [] ---  # deberta-v3-base_3epoch10 This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.2046 - Accuracy: 0.7680 - F1: 0.5136 - Precision: 0.6439 - Recall: 0.4271 - Precision Sarcastic: 0.6439 - Recall Sarcastic: 0.4271 - F1 Sarcastic: 0.5136 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1

Tommert25/multibertfinetuned0407

Tommert25

"2023-07-04T15:15:05Z"

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

"2023-07-04T10:41:33Z"

--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: multibertfinetuned0407 results: [] ---  # multibertfinetuned0407 This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4688 - Precision: 0.4879 - Recall: 0.4345 - F1: 0.4597 - Accuracy: 0.8764 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 131 | 0.4688 | 0.4879 | 0.4345 | 0.4597 | 0.8764 | | No log | 2.0 | 262 | 0.5224 | 0.5400 | 0.4884 | 0.5129 | 0.8777 | | No log | 3.0 | 393 | 0.5814 | 0.4900 | 0.4900 | 0.4900 | 0.8683 | | 0.3219 | 4.0 | 524 | 0.6226 | 0.5125 | 0.5069 | 0.5097 | 0.8750 | | 0.3219 | 5.0 | 655 | 0.6593 | 0.5008 | 0.4977 | 0.4992 | 0.8771 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

coffiee/lz3

coffiee

"2025-02-25T05:51:58Z"

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-02-25T05:51:04Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

chainup244/Qwen-Qwen1.5-1.8B-1719209906

chainup244

"2024-06-24T06:20:07Z"

6

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2024-06-24T06:18:27Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

musa99/teachim

musa99

"2025-02-28T18:37:14Z"

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit", "base_model:adapter:unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit", "region:us" ]

null

"2025-02-28T16:47:31Z"

--- base_model: unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit library_name: peft --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.14.0

chenlong7616/ddpm-celebahq-finetuned-butterflies-2epochs

chenlong7616

"2023-10-12T06:12:11Z"

46

0

diffusers

[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

"2023-10-12T06:11:48Z"

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Example Fine-Tuned Model for Unit 2 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) Describe your model here ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('chenlong7616/ddpm-celebahq-finetuned-butterflies-2epochs') image = pipeline().images[0] image ```

Q-bert/Merged-AGI-7B

Q-bert

"2023-12-24T12:41:18Z"

56

5

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "Math", "merge", "en", "dataset:meta-math/MetaMathQA", "base_model:Q-bert/MetaMath-Cybertron-Starling", "base_model:merge:Q-bert/MetaMath-Cybertron-Starling", "base_model:fblgit/juanako-7b-UNA", "base_model:merge:fblgit/juanako-7b-UNA", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2023-12-10T09:20:47Z"

--- license: cc-by-nc-4.0 datasets: - meta-math/MetaMathQA language: - en pipeline_tag: text-generation tags: - Math - merge base_model: - Q-bert/MetaMath-Cybertron-Starling - fblgit/juanako-7b-UNA --- ## Merged-AGI-7B Merge [Q-bert/MetaMath-Cybertron-Starling](https://huggingface.co/Q-bert/MetaMath-Cybertron-Starling) and [fblgit/juanako-7b-UNA](https://huggingface.co/fblgit/juanako-7b-UNA) using slerp merge. You can use ChatML format. # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [Coming soon]() | Metric | Value | |-----------------------|---------------------------| | Avg. | Coming soon | | ARC (25-shot) | Coming soon | | HellaSwag (10-shot) | Coming soon | | MMLU (5-shot) | Coming soon | | TruthfulQA (0-shot) | Coming soon | | Winogrande (5-shot) | Coming soon | | GSM8K (5-shot) | Coming soon |

Arbi-Houssem/mms_tts_tun_Lang1.6

Arbi-Houssem

"2024-06-16T04:44:31Z"

106

0

transformers

[ "transformers", "safetensors", "vits", "text-to-audio", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

text-to-audio

"2024-06-16T02:24:55Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

EllieS/Temp-L1-SFT-L2-KTO

EllieS

"2024-05-09T08:39:58Z"

1

0

peft

[ "peft", "tensorboard", "safetensors", "mistral", "alignment-handbook", "trl", "dpo", "generated_from_trainer", "dataset:EllieS/Temp-L2-DPO", "base_model:alignment-handbook/zephyr-7b-sft-full", "base_model:adapter:alignment-handbook/zephyr-7b-sft-full", "license:apache-2.0", "region:us" ]

null

"2024-05-09T06:17:42Z"

--- license: apache-2.0 library_name: peft tags: - alignment-handbook - trl - dpo - generated_from_trainer base_model: alignment-handbook/zephyr-7b-sft-full datasets: - EllieS/Temp-L2-DPO model-index: - name: Temp-L1-SFT-L2-KTO results: [] ---  # Temp-L1-SFT-L2-KTO This model is a fine-tuned version of [EllieS/TempReason-L1](https://huggingface.co/EllieS/TempReason-L1) on the EllieS/Temp-L2-DPO dataset. It achieves the following results on the evaluation set: - Loss: 0.2213 - Rewards/chosen: 0.2579 - Rewards/rejected: -6.0725 - Rewards/accuracies: 1.0 - Rewards/margins: 6.3304 - Logps/rejected: -652.1185 - Logps/chosen: -0.1197 - Logits/rejected: -2.6590 - Logits/chosen: -2.5711 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| | 0.2255 | 0.2497 | 1000 | 0.2230 | 0.2551 | -5.4032 | 1.0 | 5.6583 | -585.1871 | -0.3988 | -2.6372 | -2.5514 | | 0.2252 | 0.4994 | 2000 | 0.2215 | 0.2576 | -5.9860 | 1.0 | 6.2436 | -643.4705 | -0.1526 | -2.6560 | -2.5690 | | 0.2264 | 0.7492 | 3000 | 0.2213 | 0.2579 | -6.0565 | 1.0 | 6.3144 | -650.5204 | -0.1267 | -2.6590 | -2.5715 | | 0.2262 | 0.9989 | 4000 | 0.2213 | 0.2579 | -6.0725 | 1.0 | 6.3304 | -652.1185 | -0.1197 | -2.6590 | -2.5711 | ### Framework versions - PEFT 0.7.1 - Transformers 4.40.2 - Pytorch 2.1.2+cu121 - Datasets 2.18.0 - Tokenizers 0.19.1

baebee/guanaco-testv2

baebee

"2023-09-04T09:07:41Z"

0

peft

[ "peft", "region:us" ]

null

"2023-09-04T09:07:37Z"

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.0.dev0

yunheur/xlm-roberta-base-finetuned-panx-de-fr

yunheur

"2025-03-24T04:04:45Z"

0

null

[ "pytorch", "xlm-roberta", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "region:us" ]

null

"2025-03-23T06:34:35Z"

--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de-fr results: [] ---  # xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1635 - F1: 0.8626 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2864 | 1.0 | 715 | 0.1862 | 0.8193 | | 0.1479 | 2.0 | 1430 | 0.1711 | 0.8448 | | 0.0947 | 3.0 | 2145 | 0.1635 | 0.8626 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.6.0+cu118 - Datasets 3.4.1 - Tokenizers 0.13.3

krishna195/finetuned_PHI

krishna195

"2025-03-18T13:51:13Z"

0

transformers

[ "transformers", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

"2025-03-18T13:51:12Z"

--- library_name: transformers tags: - unsloth --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

mradermacher/EXAONE-3.5-32B-LIMO-Ko-e4-GGUF

mradermacher

"2025-02-19T12:00:06Z"

0

transformers

[ "transformers", "gguf", "en", "ko", "dataset:GAIR/LIMO", "dataset:junnei/ko-limo", "dataset:exp-models/GAIR-LIMO-KOREAN", "base_model:werty1248/EXAONE-3.5-32B-LIMO-Ko-e4", "base_model:quantized:werty1248/EXAONE-3.5-32B-LIMO-Ko-e4", "license:other", "endpoints_compatible", "region:us", "conversational" ]

null

"2025-02-19T09:48:22Z"

--- base_model: werty1248/EXAONE-3.5-32B-LIMO-Ko-e4 datasets: - GAIR/LIMO - junnei/ko-limo - exp-models/GAIR-LIMO-KOREAN language: - en - ko library_name: transformers license: other license_link: LICENSE license_name: exaone quantized_by: mradermacher --- ## About      static quants of https://huggingface.co/werty1248/EXAONE-3.5-32B-LIMO-Ko-e4  weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/EXAONE-3.5-32B-LIMO-Ko-e4-GGUF/resolve/main/EXAONE-3.5-32B-LIMO-Ko-e4.Q2_K.gguf) | Q2_K | 12.0 | | | [GGUF](https://huggingface.co/mradermacher/EXAONE-3.5-32B-LIMO-Ko-e4-GGUF/resolve/main/EXAONE-3.5-32B-LIMO-Ko-e4.Q3_K_S.gguf) | Q3_K_S | 14.1 | | | [GGUF](https://huggingface.co/mradermacher/EXAONE-3.5-32B-LIMO-Ko-e4-GGUF/resolve/main/EXAONE-3.5-32B-LIMO-Ko-e4.Q3_K_M.gguf) | Q3_K_M | 15.6 | lower quality | | [GGUF](https://huggingface.co/mradermacher/EXAONE-3.5-32B-LIMO-Ko-e4-GGUF/resolve/main/EXAONE-3.5-32B-LIMO-Ko-e4.Q3_K_L.gguf) | Q3_K_L | 16.9 | | | [GGUF](https://huggingface.co/mradermacher/EXAONE-3.5-32B-LIMO-Ko-e4-GGUF/resolve/main/EXAONE-3.5-32B-LIMO-Ko-e4.IQ4_XS.gguf) | IQ4_XS | 17.5 | | | [GGUF](https://huggingface.co/mradermacher/EXAONE-3.5-32B-LIMO-Ko-e4-GGUF/resolve/main/EXAONE-3.5-32B-LIMO-Ko-e4.Q4_K_S.gguf) | Q4_K_S | 18.4 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/EXAONE-3.5-32B-LIMO-Ko-e4-GGUF/resolve/main/EXAONE-3.5-32B-LIMO-Ko-e4.Q4_K_M.gguf) | Q4_K_M | 19.4 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/EXAONE-3.5-32B-LIMO-Ko-e4-GGUF/resolve/main/EXAONE-3.5-32B-LIMO-Ko-e4.Q5_K_S.gguf) | Q5_K_S | 22.2 | | | [GGUF](https://huggingface.co/mradermacher/EXAONE-3.5-32B-LIMO-Ko-e4-GGUF/resolve/main/EXAONE-3.5-32B-LIMO-Ko-e4.Q5_K_M.gguf) | Q5_K_M | 22.8 | | | [GGUF](https://huggingface.co/mradermacher/EXAONE-3.5-32B-LIMO-Ko-e4-GGUF/resolve/main/EXAONE-3.5-32B-LIMO-Ko-e4.Q6_K.gguf) | Q6_K | 26.4 | very good quality | | [GGUF](https://huggingface.co/mradermacher/EXAONE-3.5-32B-LIMO-Ko-e4-GGUF/resolve/main/EXAONE-3.5-32B-LIMO-Ko-e4.Q8_0.gguf) | Q8_0 | 34.1 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

lottienghiem/distilgpt2-finetuned-wikitext2

lottienghiem

"2024-04-18T06:04:02Z"

44

0

transformers

[ "transformers", "pytorch", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:distilbert/distilgpt2", "base_model:finetune:distilbert/distilgpt2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2024-04-08T07:23:23Z"

--- license: apache-2.0 base_model: distilgpt2 tags: - generated_from_trainer model-index: - name: distilgpt2-finetuned-wikitext2 results: [] ---  # distilgpt2-finetuned-wikitext2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5082 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.4699 | 1.0 | 19369 | 2.5496 | | 2.3425 | 2.0 | 38738 | 2.5165 | | 2.256 | 3.0 | 58107 | 2.5082 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.1

hyungtak/ko-Llama2-7B

hyungtak

"2023-08-24T12:38:45Z"

2

0

peft

[ "peft", "region:us" ]

null

"2023-08-24T12:38:35Z"

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.6.0.dev0

eschorn/3_loa

eschorn

"2023-07-20T03:54:40Z"

0

null

[ "generated_from_trainer", "dataset:billsum", "base_model:google/flan-t5-large", "base_model:finetune:google/flan-t5-large", "license:apache-2.0", "region:us" ]

null

"2023-07-19T20:46:54Z"

--- license: apache-2.0 base_model: google/flan-t5-large tags: - generated_from_trainer datasets: - billsum metrics: - rouge model-index: - name: 3_loa results: [] ---  # 3_loa This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the billsum dataset. It achieves the following results on the evaluation set: - Loss: 1.4825 - Rouge1: 0.201 - Rouge2: 0.1132 - Rougel: 0.1753 - Rougelsum: 0.1755 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | 2.1079 | 1.0 | 989 | 1.6673 | 0.2028 | 0.1092 | 0.1748 | 0.1751 | 19.0 | | 1.8481 | 2.0 | 1978 | 1.6150 | 0.1979 | 0.1061 | 0.1715 | 0.1717 | 19.0 | | 1.7889 | 3.0 | 2967 | 1.5833 | 0.1994 | 0.11 | 0.1727 | 0.1727 | 19.0 | | 1.7319 | 4.0 | 3956 | 1.5584 | 0.1978 | 0.1084 | 0.1718 | 0.1718 | 19.0 | | 1.7279 | 5.0 | 4945 | 1.5440 | 0.2016 | 0.1106 | 0.1755 | 0.1756 | 19.0 | | 1.7386 | 6.0 | 5934 | 1.5326 | 0.1991 | 0.1086 | 0.1734 | 0.1736 | 19.0 | | 1.6972 | 7.0 | 6923 | 1.5251 | 0.2013 | 0.1122 | 0.1759 | 0.176 | 19.0 | | 1.6732 | 8.0 | 7912 | 1.5145 | 0.2024 | 0.1123 | 0.1766 | 0.1766 | 19.0 | | 1.6597 | 9.0 | 8901 | 1.5079 | 0.2019 | 0.1125 | 0.1751 | 0.1753 | 19.0 | | 1.6151 | 10.0 | 9890 | 1.5045 | 0.201 | 0.1123 | 0.1758 | 0.1761 | 19.0 | | 1.6381 | 11.0 | 10879 | 1.4997 | 0.2009 | 0.1116 | 0.1755 | 0.1756 | 19.0 | | 1.6148 | 12.0 | 11868 | 1.4974 | 0.2018 | 0.1133 | 0.1763 | 0.1765 | 19.0 | | 1.6196 | 13.0 | 12857 | 1.4940 | 0.2014 | 0.1129 | 0.1756 | 0.1756 | 19.0 | | 1.6137 | 14.0 | 13846 | 1.4914 | 0.2025 | 0.1136 | 0.1766 | 0.1768 | 19.0 | | 1.6313 | 15.0 | 14835 | 1.4873 | 0.2032 | 0.114 | 0.1769 | 0.1771 | 19.0 | | 1.6098 | 16.0 | 15824 | 1.4847 | 0.2012 | 0.1133 | 0.175 | 0.1754 | 19.0 | | 1.6061 | 17.0 | 16813 | 1.4845 | 0.2019 | 0.1138 | 0.1752 | 0.1755 | 19.0 | | 1.5918 | 18.0 | 17802 | 1.4833 | 0.2011 | 0.1129 | 0.1747 | 0.175 | 19.0 | | 1.5842 | 19.0 | 18791 | 1.4824 | 0.2013 | 0.1133 | 0.1753 | 0.1755 | 19.0 | | 1.5964 | 20.0 | 19780 | 1.4825 | 0.201 | 0.1132 | 0.1753 | 0.1755 | 19.0 | ### Framework versions - Transformers 4.31.0 - Pytorch 1.13.1.post200 - Datasets 2.10.0 - Tokenizers 0.13.2

Lots-of-LoRAs/Mistral-7B-Instruct-v0.2-4b-r16-task1425

Lots-of-LoRAs

"2024-07-03T20:10:38Z"

0

pytorch

[ "pytorch", "safetensors", "en", "arxiv:1910.09700", "arxiv:2407.00066", "license:mit", "region:us" ]

null

"2024-06-18T19:50:29Z"

--- language: en license: mit library_name: pytorch --- # Model Card for Mistral-7B-Instruct-v0.2-4b-r16-task1425  ## Model Details ### Model Description  LoRA trained on task1425_country_iso_numeric - **Developed by:** bruel - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** LoRA - **Language(s) (NLP):** en - **License:** mit - **Finetuned from model [optional]:** mistralai/Mistral-7B-Instruct-v0.2 ### Model Sources [optional]  - **Repository:** https://github.com/bruel-gabrielsson - **Paper [optional]:** "Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead" (2024), Rickard Brüel Gabrielsson, Jiacheng Zhu, Onkar Bhardwaj, Leshem Choshen, Kristjan Greenewald, Mikhail Yurochkin and Justin Solomon - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  https://huggingface.co/datasets/Lots-of-LoRAs/task1425_country_iso_numeric sourced from https://github.com/allenai/natural-instructions ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** @misc{brüelgabrielsson2024compressserveservingthousands, title={Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead}, author={Rickard Brüel-Gabrielsson and Jiacheng Zhu and Onkar Bhardwaj and Leshem Choshen and Kristjan Greenewald and Mikhail Yurochkin and Justin Solomon}, year={2024}, eprint={2407.00066}, archivePrefix={arXiv}, primaryClass={cs.DC}, url={https://arxiv.org/abs/2407.00066}, } **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

CharlesLi/llama_2_cot_simplest_code_math_4_full

CharlesLi

"2025-01-20T12:17:57Z"

7

0

transformers

[ "transformers", "tensorboard", "safetensors", "llama", "text-generation", "alignment-handbook", "trl", "sft", "generated_from_trainer", "conversational", "dataset:generator", "base_model:meta-llama/Llama-2-7b-chat-hf", "base_model:finetune:meta-llama/Llama-2-7b-chat-hf", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-01-20T04:43:36Z"

--- library_name: transformers license: llama2 base_model: meta-llama/Llama-2-7b-chat-hf tags: - alignment-handbook - trl - sft - generated_from_trainer - trl - sft - alignment-handbook - generated_from_trainer datasets: - generator model-index: - name: llama_2_cot_simplest_code_math_4_full results: [] ---  # llama_2_cot_simplest_code_math_4_full This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 0.6062 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.1+cu121 - Datasets 3.0.0 - Tokenizers 0.19.1

leabum/distilbert-base-uncased-finetuned-squad

leabum

"2022-08-11T06:25:42Z"

61

0

transformers

[ "transformers", "tf", "tensorboard", "distilbert", "question-answering", "generated_from_keras_callback", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

"2022-08-02T13:48:08Z"

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: leabum/distilbert-base-uncased-finetuned-squad results: [] ---  # leabum/distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 5.5824 - Train End Logits Accuracy: 0.0347 - Train Start Logits Accuracy: 0.0694 - Validation Loss: 5.8343 - Validation End Logits Accuracy: 0.0 - Validation Start Logits Accuracy: 0.0 - Epoch: 1 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 5.8427 | 0.0069 | 0.0069 | 5.8688 | 0.0 | 0.0 | 0 | | 5.5824 | 0.0347 | 0.0694 | 5.8343 | 0.0 | 0.0 | 1 | ### Framework versions - Transformers 4.21.1 - TensorFlow 2.8.2 - Datasets 2.4.0 - Tokenizers 0.12.1

mradermacher/Qwen1.5-32B-i1-GGUF

mradermacher

"2025-03-31T21:28:13Z"

136

0

transformers

[ "transformers", "gguf", "pretrained", "en", "base_model:Qwen/Qwen1.5-32B", "base_model:quantized:Qwen/Qwen1.5-32B", "license:other", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

"2024-05-12T13:43:50Z"

--- base_model: Qwen/Qwen1.5-32B language: - en library_name: transformers license: other license_link: https://huggingface.co/Qwen/Qwen1.5-32B/blob/main/LICENSE license_name: tongyi-qianwen-research quantized_by: mradermacher tags: - pretrained --- ## About     weighted/imatrix quants of https://huggingface.co/Qwen/Qwen1.5-32B  static quants are available at https://huggingface.co/mradermacher/Qwen1.5-32B-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Qwen1.5-32B-i1-GGUF/resolve/main/Qwen1.5-32B.i1-IQ1_S.gguf) | i1-IQ1_S | 7.3 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Qwen1.5-32B-i1-GGUF/resolve/main/Qwen1.5-32B.i1-IQ1_M.gguf) | i1-IQ1_M | 8.0 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Qwen1.5-32B-i1-GGUF/resolve/main/Qwen1.5-32B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 9.1 | | | [GGUF](https://huggingface.co/mradermacher/Qwen1.5-32B-i1-GGUF/resolve/main/Qwen1.5-32B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 10.0 | | | [GGUF](https://huggingface.co/mradermacher/Qwen1.5-32B-i1-GGUF/resolve/main/Qwen1.5-32B.i1-IQ2_S.gguf) | i1-IQ2_S | 10.4 | | | [GGUF](https://huggingface.co/mradermacher/Qwen1.5-32B-i1-GGUF/resolve/main/Qwen1.5-32B.i1-IQ2_M.gguf) | i1-IQ2_M | 11.3 | | | [GGUF](https://huggingface.co/mradermacher/Qwen1.5-32B-i1-GGUF/resolve/main/Qwen1.5-32B.i1-Q2_K.gguf) | i1-Q2_K | 12.3 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Qwen1.5-32B-i1-GGUF/resolve/main/Qwen1.5-32B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 12.8 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Qwen1.5-32B-i1-GGUF/resolve/main/Qwen1.5-32B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 13.7 | | | [GGUF](https://huggingface.co/mradermacher/Qwen1.5-32B-i1-GGUF/resolve/main/Qwen1.5-32B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 14.4 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Qwen1.5-32B-i1-GGUF/resolve/main/Qwen1.5-32B.i1-IQ3_S.gguf) | i1-IQ3_S | 14.4 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Qwen1.5-32B-i1-GGUF/resolve/main/Qwen1.5-32B.i1-IQ3_M.gguf) | i1-IQ3_M | 14.8 | | | [GGUF](https://huggingface.co/mradermacher/Qwen1.5-32B-i1-GGUF/resolve/main/Qwen1.5-32B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 15.9 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Qwen1.5-32B-i1-GGUF/resolve/main/Qwen1.5-32B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 17.2 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Qwen1.5-32B-i1-GGUF/resolve/main/Qwen1.5-32B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 17.7 | | | [GGUF](https://huggingface.co/mradermacher/Qwen1.5-32B-i1-GGUF/resolve/main/Qwen1.5-32B.i1-Q4_0.gguf) | i1-Q4_0 | 18.7 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Qwen1.5-32B-i1-GGUF/resolve/main/Qwen1.5-32B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 18.7 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Qwen1.5-32B-i1-GGUF/resolve/main/Qwen1.5-32B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 19.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Qwen1.5-32B-i1-GGUF/resolve/main/Qwen1.5-32B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 22.6 | | | [GGUF](https://huggingface.co/mradermacher/Qwen1.5-32B-i1-GGUF/resolve/main/Qwen1.5-32B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 23.2 | | | [GGUF](https://huggingface.co/mradermacher/Qwen1.5-32B-i1-GGUF/resolve/main/Qwen1.5-32B.i1-Q6_K.gguf) | i1-Q6_K | 26.8 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

ragefu/ftxclip20240925model

ragefu

"2024-09-26T04:23:34Z"

91

0

transformers

[ "transformers", "safetensors", "xclip", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

feature-extraction

"2024-09-26T04:23:00Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Dhanang/topic_model

Dhanang

"2023-12-14T08:08:11Z"

8

0

transformers

[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:indobenchmark/indobert-base-p2", "base_model:finetune:indobenchmark/indobert-base-p2", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

"2023-12-14T07:54:44Z"

--- license: mit base_model: indobenchmark/indobert-base-p2 tags: - generated_from_trainer metrics: - accuracy model-index: - name: topic_model results: [] ---  # topic_model This model is a fine-tuned version of [indobenchmark/indobert-base-p2](https://huggingface.co/indobenchmark/indobert-base-p2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0145 - Accuracy: 0.9984 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 308 | 0.0315 | 0.9919 | | 0.1039 | 2.0 | 616 | 0.0117 | 0.9984 | | 0.1039 | 3.0 | 924 | 0.0147 | 0.9984 | | 0.0047 | 4.0 | 1232 | 0.0223 | 0.9968 | | 0.0002 | 5.0 | 1540 | 0.0138 | 0.9984 | | 0.0002 | 6.0 | 1848 | 0.0140 | 0.9984 | | 0.0001 | 7.0 | 2156 | 0.0142 | 0.9984 | | 0.0001 | 8.0 | 2464 | 0.0144 | 0.9984 | | 0.0001 | 9.0 | 2772 | 0.0145 | 0.9984 | | 0.0001 | 10.0 | 3080 | 0.0145 | 0.9984 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0

guydebruyn/q-FrozenLake-v1-4x4-noSlippery

guydebruyn

"2023-09-12T19:46:56Z"

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

"2023-09-12T19:46:53Z"

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="guydebruyn/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

auro736/deberta-v3-large-tweet-fid-EMD

auro736

"2024-01-14T10:38:20Z"

67

0

transformers

[ "transformers", "pytorch", "deberta-v2", "token-classification", "en", "arxiv:2205.10726", "license:mit", "endpoints_compatible", "region:us" ]

token-classification

"2023-10-30T16:00:37Z"

--- license: mit language: - en pipeline_tag: token-classification --- ## DeBERTa-large-tweet-fid-EMD This is a [DeBERTa-large](https://huggingface.co/microsoft/deberta-v3-large) model trained on the [Tweet-FID](https://arxiv.org/abs/2205.10726) dataset (*"TWEET-FID: An Annotated Dataset for Multiple Foodborne Illness Detection Tasks", Ruofan Hu et al, 2022* ) which is a collection of Twitter to detect incidents of foodborne illnesses. The model is enriched with a multi class classification head to perform the custom task called Entity Mention Detection (EMD). The objective is to determine predefined entities (*food*, *location*, *symptom*, *other*) in a given text related to a food risk

houbw/llama3_ruozhiba_ori_8_up_4

houbw

"2024-05-23T02:17:37Z"

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct", "base_model:finetune:unsloth/llama-3-8b-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

"2024-05-23T02:17:08Z"

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-Instruct --- # Uploaded model - **Developed by:** houbw - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-Instruct This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

huudan123/stage1

huudan123

"2024-07-13T18:16:33Z"

6

0

sentence-transformers

[ "sentence-transformers", "safetensors", "roberta", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:102178", "loss:TripletLoss", "arxiv:1908.10084", "arxiv:1703.07737", "base_model:vinai/phobert-base-v2", "base_model:finetune:vinai/phobert-base-v2", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

"2024-07-13T18:15:57Z"

--- base_model: vinai/phobert-base-v2 datasets: [] language: [] library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine - pearson_manhattan - spearman_manhattan - pearson_euclidean - spearman_euclidean - pearson_dot - spearman_dot - pearson_max - spearman_max pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:102178 - loss:TripletLoss widget: - source_sentence: Bàn cho thấy các thiết_kế và sản_xuất kiến_thức cần_thiết để thực_hiện nhiều quyết_định thông_báo hơn . sentences: - Nixon quyết_định rằng hồ chí minh có_thể ở lại miền nam Việt_Nam . - Không có gì cần_thiết để đưa ra một quyết_định thông_tin . - Bảng Hiển_thị thiết_kế và sản_xuất thông_tin cần_thiết để đưa ra quyết_định . - source_sentence: 95 gói nước_tiểu miễn_phí trong túi của họ . sentences: - Tây_ban nha trượt từ vị_trí quyền_lực của họ . - Đội đã bước vào phòng thí_nghiệm mang theo tổng_cộng 99 đơn_vị trong_sạch , thử_nghiệm thân_thiện . - Túi được yêu_cầu cho nhà toàn_bộ 95 đơn_vị phục_vụ trong_sạch nước_tiểu giữa các nhà cung_cấp các sản_phẩm . - source_sentence: Tuyển một chiếc xe rất đắt tiền , và những gì có để xem_thường là gần những con đường chính . sentences: - Thuê một chiếc xe rất rẻ nhưng có_thể không đáng_giá_như những cảnh_sát ở xa con đường . - Có một nhà_thờ hình_tròn ở orangerie ở Paris . - Thuê một chiếc xe đến với chi_phí lớn và hầu_hết các điểm đến đều gần đường . - source_sentence: Người da đen là 12 phần_trăm dân_số . sentences: - Người da đen tạo ra 50 % tổng_số dân_số . - Người Mỹ Châu_Phi là một nhóm_thiểu_số . - Tôi đoán là barney fife . - source_sentence: Báo đen đã editorialized chống lại những cuộc viếng_thăm của farrakhan với các nhà độc_tài châu phi . sentences: - Báo đen đã viết về quá_khứ của farrakhan . - Khi bạn đi đến radda , bạn nên kiểm_tra piccolo bảo del chianti . - Báo đen từ_chối yểm_trợ cho farrakhan . model-index: - name: SentenceTransformer based on vinai/phobert-base-v2 results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts dev type: sts-dev metrics: - type: pearson_cosine value: 0.42030854811305457 name: Pearson Cosine - type: spearman_cosine value: 0.5147968030818376 name: Spearman Cosine - type: pearson_manhattan value: 0.5605026901702432 name: Pearson Manhattan - type: spearman_manhattan value: 0.5792048311109484 name: Spearman Manhattan - type: pearson_euclidean value: 0.4710386131519505 name: Pearson Euclidean - type: spearman_euclidean value: 0.5087153254455983 name: Spearman Euclidean - type: pearson_dot value: 0.3923969498466928 name: Pearson Dot - type: spearman_dot value: 0.4338097270757405 name: Spearman Dot - type: pearson_max value: 0.5605026901702432 name: Pearson Max - type: spearman_max value: 0.5792048311109484 name: Spearman Max --- # SentenceTransformer based on vinai/phobert-base-v2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [vinai/phobert-base-v2](https://huggingface.co/vinai/phobert-base-v2). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [vinai/phobert-base-v2](https://huggingface.co/vinai/phobert-base-v2)  - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 tokens - **Similarity Function:** Cosine Similarity    ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("huudan123/stage1") # Run inference sentences = [ 'Báo đen đã editorialized chống lại những cuộc viếng_thăm của farrakhan với các nhà độc_tài châu phi .', 'Báo đen đã viết về quá_khứ của farrakhan .', 'Báo đen từ_chối yểm_trợ cho farrakhan .', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ```    ## Evaluation ### Metrics #### Semantic Similarity * Dataset: `sts-dev` * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.4203 | | **spearman_cosine** | **0.5148** | | pearson_manhattan | 0.5605 | | spearman_manhattan | 0.5792 | | pearson_euclidean | 0.471 | | spearman_euclidean | 0.5087 | | pearson_dot | 0.3924 | | spearman_dot | 0.4338 | | pearson_max | 0.5605 | | spearman_max | 0.5792 |   ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 102,178 training samples * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 4 tokens</li><li>mean: 27.28 tokens</li><li>max: 147 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.99 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.34 tokens</li><li>max: 34 tokens</li></ul> | * Samples: | anchor | positive | negative | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>Tem đầy màu_sắc của madeira , cũng như tiền xu , ghi_chép ngân_hàng , và các mặt_hàng khác như bưu_thiếp là mối quan_tâm đến nhiều nhà sưu_tập .</code> | <code>Các nhà sưu_tập sẽ thích ghé thăm madeira bởi_vì những phân_chia lớn của tem , ghi_chép ngân_hàng , bưu_thiếp , và nhiều mặt_hàng khác họ có_thể đọc được .</code> | <code>Mọi người quan_tâm đến việc bắt_đầu bộ sưu_tập mới nên thoát madeira và đi du_lịch phía bắc , nơi họ có khả_năng tìm thấy các cửa_hàng tốt .</code> | | <code>Cẩn_thận đấy , ông inglethorp . Poirot bị bồn_chồn .</code> | <code>Hãy chăm_sóc ông inglethorp .</code> | <code>Không cần phải cẩn_thận với anh ta .</code> | | <code>Phải có một_chút hoài_nghi về trải nghiệm cá_nhân của sperling với trò_chơi .</code> | <code>Hãy suy_nghĩ về những tác_động khi nhìn vào kinh_nghiệm của anh ấy .</code> | <code>Một người có_thể lấy trải nghiệm cá_nhân của sperling với giá_trị mặt .</code> | * Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters: ```json { "distance_metric": "TripletDistanceMetric.EUCLIDEAN", "triplet_margin": 5 } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 12,772 evaluation samples * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 4 tokens</li><li>mean: 27.81 tokens</li><li>max: 164 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 14.94 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 14.4 tokens</li><li>max: 39 tokens</li></ul> | * Samples: | anchor | positive | negative | |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------| | <code>Tình_yêu , anh có muốn em trở_thành kassandra lubbock của anh không ?</code> | <code>Tôi có_thể là kassandra lubbock của anh .</code> | <code>Tôi từ_chối trở_thành kassandra lubbock của anh .</code> | | <code>Ví_dụ , trong mùa thu năm 1997 , ủy ban điều_trị hạt_nhân ( nrc ) văn_phòng thanh_tra tướng liệu nrc để có được quan_điểm của họ trên văn_hóa an_toàn của đại_lý .</code> | <code>Nhân_viên nrc đã được hỏi về quan_điểm của họ trên văn_hóa an_toàn của đại_lý .</code> | <code>Các nhân_viên không bao_giờ quan_sát về quan_điểm của họ về văn_hóa an_toàn của đại_lý trong mùa thu năm 1997 .</code> | | <code>Mỗi năm kem của trẻ nghệ và comedic tài_năng làm cho nó đường đến edinburgh , và fringe đã lớn lên trong việc huấn_luyện lớn nhất trong khung_cảnh lớn nhất cho các diễn_viên phát_triển trên thế_giới .</code> | <code>Tài_năng mới đến edinburgh .</code> | <code>Tài_năng mới đến dublin .</code> | * Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters: ```json { "distance_metric": "TripletDistanceMetric.EUCLIDEAN", "triplet_margin": 5 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `overwrite_output_dir`: True - `eval_strategy`: epoch - `per_device_train_batch_size`: 128 - `per_device_eval_batch_size`: 128 - `num_train_epochs`: 20 - `lr_scheduler_type`: cosine - `warmup_ratio`: 0.05 - `fp16`: True - `load_best_model_at_end`: True - `gradient_checkpointing`: True #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: True - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 128 - `per_device_eval_batch_size`: 128 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 20 - `max_steps`: -1 - `lr_scheduler_type`: cosine - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.05 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: True - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: True - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs <details><summary>Click to expand</summary> | Epoch | Step | Training Loss | loss | sts-dev_spearman_cosine | |:-------:|:--------:|:-------------:|:----------:|:-----------------------:| | 0 | 0 | - | - | 0.6643 | | 0.0626 | 50 | 4.6946 | - | - | | 0.1252 | 100 | 4.031 | - | - | | 0.1877 | 150 | 2.7654 | - | - | | 0.2503 | 200 | 2.4176 | - | - | | 0.3129 | 250 | 2.1111 | - | - | | 0.3755 | 300 | 2.0263 | - | - | | 0.4380 | 350 | 1.9296 | - | - | | 0.5006 | 400 | 1.7793 | - | - | | 0.5632 | 450 | 1.7903 | - | - | | 0.6258 | 500 | 1.7638 | - | - | | 0.6884 | 550 | 1.7042 | - | - | | 0.7509 | 600 | 1.7038 | - | - | | 0.8135 | 650 | 1.6221 | - | - | | 0.8761 | 700 | 1.6172 | - | - | | 0.9387 | 750 | 1.6227 | - | - | | 1.0 | 799 | - | 1.5275 | 0.5219 | | 1.0013 | 800 | 1.6264 | - | - | | 1.0638 | 850 | 1.364 | - | - | | 1.1264 | 900 | 1.4447 | - | - | | 1.1890 | 950 | 1.4161 | - | - | | 1.2516 | 1000 | 1.3575 | - | - | | 1.3141 | 1050 | 1.3554 | - | - | | 1.3767 | 1100 | 1.378 | - | - | | 1.4393 | 1150 | 1.3806 | - | - | | 1.5019 | 1200 | 1.3089 | - | - | | 1.5645 | 1250 | 1.4314 | - | - | | 1.6270 | 1300 | 1.3672 | - | - | | 1.6896 | 1350 | 1.3777 | - | - | | 1.7522 | 1400 | 1.3282 | - | - | | 1.8148 | 1450 | 1.3432 | - | - | | 1.8773 | 1500 | 1.3101 | - | - | | 1.9399 | 1550 | 1.2919 | - | - | | 2.0 | 1598 | - | 1.3643 | 0.5667 | | 2.0025 | 1600 | 1.2969 | - | - | | 2.0651 | 1650 | 0.9629 | - | - | | 2.1277 | 1700 | 0.9878 | - | - | | 2.1902 | 1750 | 0.9437 | - | - | | 2.2528 | 1800 | 0.9832 | - | - | | 2.3154 | 1850 | 0.9584 | - | - | | 2.3780 | 1900 | 1.0689 | - | - | | 2.4406 | 1950 | 1.0579 | - | - | | 2.5031 | 2000 | 0.9888 | - | - | | 2.5657 | 2050 | 0.9452 | - | - | | 2.6283 | 2100 | 0.9378 | - | - | | 2.6909 | 2150 | 0.9553 | - | - | | 2.7534 | 2200 | 0.9337 | - | - | | 2.8160 | 2250 | 1.0184 | - | - | | 2.8786 | 2300 | 0.9663 | - | - | | 2.9412 | 2350 | 0.9686 | - | - | | 3.0 | 2397 | - | 1.3488 | 0.5442 | | 3.0038 | 2400 | 0.9618 | - | - | | 3.0663 | 2450 | 0.6878 | - | - | | 3.1289 | 2500 | 0.6883 | - | - | | 3.1915 | 2550 | 0.6498 | - | - | | 3.2541 | 2600 | 0.6651 | - | - | | 3.3166 | 2650 | 0.6554 | - | - | | 3.3792 | 2700 | 0.7033 | - | - | | 3.4418 | 2750 | 0.6416 | - | - | | 3.5044 | 2800 | 0.7068 | - | - | | 3.5670 | 2850 | 0.6834 | - | - | | 3.6295 | 2900 | 0.7099 | - | - | | 3.6921 | 2950 | 0.7306 | - | - | | 3.7547 | 3000 | 0.7105 | - | - | | 3.8173 | 3050 | 0.7072 | - | - | | 3.8798 | 3100 | 0.7248 | - | - | | 3.9424 | 3150 | 0.7216 | - | - | | **4.0** | **3196** | **-** | **1.3358** | **0.5307** | | 4.0050 | 3200 | 0.693 | - | - | | 4.0676 | 3250 | 0.4741 | - | - | | 4.1302 | 3300 | 0.4593 | - | - | | 4.1927 | 3350 | 0.449 | - | - | | 4.2553 | 3400 | 0.4326 | - | - | | 4.3179 | 3450 | 0.4488 | - | - | | 4.3805 | 3500 | 0.4762 | - | - | | 4.4431 | 3550 | 0.4723 | - | - | | 4.5056 | 3600 | 0.4713 | - | - | | 4.5682 | 3650 | 0.4612 | - | - | | 4.6308 | 3700 | 0.4537 | - | - | | 4.6934 | 3750 | 0.4928 | - | - | | 4.7559 | 3800 | 0.4568 | - | - | | 4.8185 | 3850 | 0.4771 | - | - | | 4.8811 | 3900 | 0.4688 | - | - | | 4.9437 | 3950 | 0.4549 | - | - | | 5.0 | 3995 | - | 1.4027 | 0.5360 | | 5.0063 | 4000 | 0.5048 | - | - | | 5.0688 | 4050 | 0.2822 | - | - | | 5.1314 | 4100 | 0.3069 | - | - | | 5.1940 | 4150 | 0.2971 | - | - | | 5.2566 | 4200 | 0.3191 | - | - | | 5.3191 | 4250 | 0.3023 | - | - | | 5.3817 | 4300 | 0.3224 | - | - | | 5.4443 | 4350 | 0.3114 | - | - | | 5.5069 | 4400 | 0.3098 | - | - | | 5.5695 | 4450 | 0.3071 | - | - | | 5.6320 | 4500 | 0.3478 | - | - | | 5.6946 | 4550 | 0.3288 | - | - | | 5.7572 | 4600 | 0.3373 | - | - | | 5.8198 | 4650 | 0.3577 | - | - | | 5.8824 | 4700 | 0.331 | - | - | | 5.9449 | 4750 | 0.3132 | - | - | | 6.0 | 4794 | - | 1.4036 | 0.5148 | * The bold row denotes the saved checkpoint. </details> ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.1 - Transformers: 4.42.4 - PyTorch: 2.3.1+cu121 - Accelerate: 0.32.1 - Datasets: 2.20.0 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### TripletLoss ```bibtex @misc{hermans2017defense, title={In Defense of the Triplet Loss for Person Re-Identification}, author={Alexander Hermans and Lucas Beyer and Bastian Leibe}, year={2017}, eprint={1703.07737}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```

jamesdolezal/CTransPath

jamesdolezal

"2023-02-09T19:17:09Z"

0

2

null

[ "license:gpl-3.0", "region:us" ]

null

"2023-02-09T19:10:23Z"

--- license: gpl-3.0 --- [UNOFFICIAL] This is the pretrained CTransPath model that accompanies the manuscript Transformer-based Unsupervised Contrastive Learning for Histopathological Image Classification, published by Xiyue Wang *et al* in Medical Image Analysis (October 2022, DOI: https://doi.org/10.1016/j.media.2022.102559) This model has been uploaded to HuggingFace for easier sharing, but has not been verified by the original authors and is in no way affiliated with the original authors. The official pretrained model is available on the official GitHub repository (https://github.com/Xiyue-Wang/TransPath) and Google Drive (https://drive.google.com/file/d/1DoDx_70_TLj98gTf6YTXnu4tFhsFocDX/view?usp=sharing). The license as included in the original repository is GPL-3.0.

yantolakpau/minasanlora

yantolakpau

"2023-04-11T04:02:30Z"

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

"2023-04-11T04:01:02Z"

--- license: creativeml-openrail-m ---

PrunaAI/FLUX.1-schnell-4bit

PrunaAI

"2024-10-30T19:28:33Z"

22

11

null

[ "pruna-ai", "base_model:ibm-granite/granite-8b-code-instruct-128k", "base_model:finetune:ibm-granite/granite-8b-code-instruct-128k", "region:us" ]

null

"2024-08-17T09:54:17Z"

--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: ibm-granite/granite-8b-code-instruct-128k metrics: - memory_disk - memory_inference - inference_latency - inference_throughput - inference_CO2_emissions - inference_energy_consumption tags: - pruna-ai ---   <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div>  [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/Tun8YgzxZ9) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. **Frequently Asked Questions** - ***How does the compression work?*** The model is compressed with Quanto to 8 bits. - ***How does the model quality change?*** The quality of the model output might vary compared to the base model. - ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - ***What is the model format?*** We use safetensors. - ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data. - ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. - ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases. ## Setup You can run the smashed model on cards with less than 12 GB of memory with these steps: 0. Check requirements from the original repo black-forest-labs/FLUX.1-schnell installed. In particular, check python, diffusers, and transformers versions. 1. Make sure that you have installed quantization related packages. ```bash pip install -U optimum-quanto ``` 2. Download the model - Use Python: ```python import subprocess repo_name = "FLUX.1-schnell-4bit" subprocess.run(["mkdir", repo_name]) subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"]) ``` 2. Load & run the model. ```python import torch from optimum.quanto import freeze, qfloat8, quantize from diffusers import FlowMatchEulerDiscreteScheduler, AutoencoderKL from diffusers.models.transformers.transformer_flux import FluxTransformer2DModel from diffusers.pipelines.flux.pipeline_flux import FluxPipeline from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast dtype = torch.bfloat16 bfl_repo = "black-forest-labs/FLUX.1-schnell" revision = "refs/pr/1" local_path = "FLUX.1-schnell-4bit" scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(bfl_repo, subfolder="scheduler", revision=revision) text_encoder = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14", torch_dtype=dtype) tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14", torch_dtype=dtype) text_encoder_2 = torch.load(local_path + '/text_encoder_2.pt') tokenizer_2 = T5TokenizerFast.from_pretrained(bfl_repo, subfolder="tokenizer_2", torch_dtype=dtype, revision=revision) vae = AutoencoderKL.from_pretrained(bfl_repo, subfolder="vae", torch_dtype=dtype, revision=revision) transformer = torch.load(local_path + '/transformer.pt') pipe = FluxPipeline( scheduler=scheduler, text_encoder=text_encoder, tokenizer=tokenizer, text_encoder_2=None, tokenizer_2=tokenizer_2, vae=vae, transformer=None, ) pipe.text_encoder_2 = text_encoder_2 pipe.transformer = transformer # pipe.enable_model_cpu_offload() pipe.to('cuda') print('done') generator = torch.Generator().manual_seed(12345) pipe( "a cute apple smiling", guidance_scale=0.0, num_inference_steps=4, max_sequence_length=256, generator=torch.Generator("cpu").manual_seed(0) ).images[0] ``` ## Configurations The configuration info are in `smash_config.json`. ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model black-forest-labs/FLUX.1-schnell before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).

methinkss/m2

methinkss

"2025-02-08T16:12:46Z"

22

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2025-02-08T16:09:23Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

hinablue/illustriousXL1.0_v10_merged

hinablue

"2025-03-04T08:35:57Z"

0

null

[ "license:other", "region:us" ]

null

"2025-03-04T05:47:08Z"

--- license: other license_name: fair-ai-public-license-1.0-sd license_link: https://freedevproject.org/fdpl-1.0/ --- # Model Card Merge illustriousXL 1.0 with waiNSFWIllustrious_v110 for testing. ## Model Details [illustriousXL 1.0](https://civitai.com/models/1232765?modelVersionId=1410435) [waiNSFWIllustrious_v110](https://civitai.com/models/827184/wai-nsfw-illustrious-sdxl) ### Model Description ``` ill 0.6 + wai 0.4 => merged merged 0.6 + 0.4(0.5(ill 0.6 + wai 0.4, cosine A + cosine B)) => merged_plus_cosineAB ```

QuantFactory/Faro-Yi-9B-DPO-GGUF

QuantFactory

"2024-05-24T14:16:39Z"

720

1

null

[ "gguf", "llama", "conversational", "text-generation", "en", "zh", "dataset:wenbopan/Chinese-dpo-pairs", "dataset:Intel/orca_dpo_pairs", "dataset:argilla/ultrafeedback-binarized-preferences-cleaned", "dataset:jondurbin/truthy-dpo-v0.1", "arxiv:2303.08774", "base_model:wenbopan/Faro-Yi-9B-DPO", "base_model:quantized:wenbopan/Faro-Yi-9B-DPO", "license:mit", "endpoints_compatible", "region:us" ]

text-generation

"2024-05-24T13:21:41Z"

--- language: - en - zh license: mit datasets: - wenbopan/Chinese-dpo-pairs - Intel/orca_dpo_pairs - argilla/ultrafeedback-binarized-preferences-cleaned - jondurbin/truthy-dpo-v0.1 pipeline_tag: text-generation tags: - llama - conversational base_model: wenbopan/Faro-Yi-9B-DPO --- # Faro-Yi-9B-DP-GGUF This is quantized version of [wenbopan/Faro-Yi-9B-DPO](https://huggingface.co/wenbopan/Faro-Yi-9B-DPO) created using llama.cpp # Model Description This is the DPO version of [wenbopan/Faro-Yi-9B](https://huggingface.co/wenbopan/Faro-Yi-9B). Compared to Faro-Yi-9B and [Yi-9B-200K](https://huggingface.co/01-ai/Yi-9B-200K), the DPO model excels at many tasks, surpassing the original Yi-9B-200K by a large margin. On the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard), it ranks **#2** among all 9B models, **#1** among all Yi-9B variants. | **Metric** | **MMLU** | **GSM8K** | **hellaswag** | **truthfulqa** | **ai2_arc** | **winogrande** | **CMMLU** | | ----------------------- | --------- | --------- | ------------- | -------------- | ----------- | -------------- | --------- | | **Yi-9B-200K** | 65.73 | 50.49 | 56.72 | 33.80 | 69.25 | 71.67 | 71.97 | | **Faro-Yi-9B** | 68.80 | 63.08 | 57.28 | 40.86 | 72.58 | 71.11 | 73.28 | | **Faro-Yi-9B-DPO** | **69.98** | **66.11** | **59.04** | **48.01** | **75.68** | **73.40** | **75.23** | Faro-Yi-9B-DPO's responses are also favored by GPT-4 Judge in MT-Bench ![image/png](https://cdn-uploads.huggingface.co/production/uploads/62cd3a3691d27e60db0698b0/ArlnloL4aPfiiD6kUqaSH.png) ## How to Use Faro-Yi-9B-DPO uses the chatml template and performs well in both short and long contexts. For longer inputs under **24GB of VRAM**, I recommend to use vLLM to have a max prompt of 32K. Setting `kv_cache_dtype="fp8_e5m2"` allows for 48K input length. 4bit-AWQ quantization on top of that can boost input length to 160K, albeit with some performance impact. Adjust `max_model_len` arg in vLLM or `config.json` to avoid OOM. ```python import io import requests from PyPDF2 import PdfReader from vllm import LLM, SamplingParams llm = LLM(model="wenbopan/Faro-Yi-9B-DPO", kv_cache_dtype="fp8_e5m2", max_model_len=100000) pdf_data = io.BytesIO(requests.get("https://arxiv.org/pdf/2303.08774.pdf").content) document = "".join(page.extract_text() for page in PdfReader(pdf_data).pages) # 100 pages question = f"{document}\n\nAccording to the paper, what is the parameter count of GPT-4?" messages = [ {"role": "user", "content": question} ] # 83K tokens prompt = llm.get_tokenizer().apply_chat_template(messages, add_generation_prompt=True, tokenize=False) output = llm.generate(prompt, SamplingParams(temperature=0.8, max_tokens=500)) print(output[0].outputs[0].text) # Yi-9B-200K: 175B. GPT-4 has 175B \nparameters. How many models were combined to create GPT-4? Answer: 6. ... # Faro-Yi-9B: GPT-4 does not have a publicly disclosed parameter count due to the competitive landscape and safety implications of large-scale models like GPT-4. ... ``` <details> <summary>Or With Transformers</summary> ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained('wenbopan/Faro-Yi-9B-DPO', device_map="cuda") tokenizer = AutoTokenizer.from_pretrained('wenbopan/Faro-Yi-9B-DPO') messages = [ {"role": "system", "content": "You are a helpful assistant. Always answer with a short response."}, {"role": "user", "content": "Tell me what is Pythagorean theorem like you are a pirate."} ] input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device) generated_ids = model.generate(input_ids, max_new_tokens=512, temperature=0.5) response = tokenizer.decode(generated_ids[0], skip_special_tokens=True) # Aye, matey! The Pythagorean theorem is a nautical rule that helps us find the length of the third side of a triangle. ... ``` </details>

marco-c88/distilgpt2-finetuned-mstatmem_1ep_2

marco-c88

"2023-03-17T10:55:18Z"

176

0

transformers

[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2023-03-17T10:52:37Z"

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilgpt2-finetuned-mstatmem_1ep_2 results: [] ---  # distilgpt2-finetuned-mstatmem_1ep_2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.6512 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.804 | 1.0 | 703 | 3.6512 | ### Framework versions - Transformers 4.27.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2

Niyantha23M/llama-7b-chat-25000-25-75-L

Niyantha23M

"2024-04-12T06:57:35Z"

0

null

[ "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:meta-llama/Llama-2-7b-chat-hf", "base_model:finetune:meta-llama/Llama-2-7b-chat-hf", "region:us" ]

null

"2024-04-12T06:57:29Z"

--- base_model: meta-llama/Llama-2-7b-chat-hf tags: - trl - sft - generated_from_trainer datasets: - generator model-index: - name: llama-7b-chat-25000-25-75-L results: [] ---  # llama-7b-chat-25000-25-75-L This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2200 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4400 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.33.3 - Pytorch 2.2.1 - Datasets 2.18.0 - Tokenizers 0.13.3

matrixportal/L3-Aspire-Heart-Matrix-8B-GGUF

matrixportal

"2025-01-22T21:55:23Z"

70

0

transformers

[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "vllm", "bfloat16", "llama", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:ZeroXClem/L3-Aspire-Heart-Matrix-8B", "base_model:quantized:ZeroXClem/L3-Aspire-Heart-Matrix-8B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

text-generation

"2025-01-22T13:32:43Z"

--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - vllm - bfloat16 - llama - llama-cpp - gguf-my-repo language: - en base_model: ZeroXClem/L3-Aspire-Heart-Matrix-8B pipeline_tag: text-generation library_name: transformers --- # matrixportal/L3-Aspire-Heart-Matrix-8B-GGUF This model was converted to GGUF format from [`ZeroXClem/L3-Aspire-Heart-Matrix-8B`](https://huggingface.co/ZeroXClem/L3-Aspire-Heart-Matrix-8B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/ZeroXClem/L3-Aspire-Heart-Matrix-8B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo matrixportal/L3-Aspire-Heart-Matrix-8B-GGUF --hf-file l3-aspire-heart-matrix-8b-q4_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo matrixportal/L3-Aspire-Heart-Matrix-8B-GGUF --hf-file l3-aspire-heart-matrix-8b-q4_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo matrixportal/L3-Aspire-Heart-Matrix-8B-GGUF --hf-file l3-aspire-heart-matrix-8b-q4_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo matrixportal/L3-Aspire-Heart-Matrix-8B-GGUF --hf-file l3-aspire-heart-matrix-8b-q4_0.gguf -c 2048 ```

jerryyun/kicon_llama3_8b_qlora_merged_v1

jerryyun

"2024-07-14T16:47:24Z"

5

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]

text-generation

"2024-07-14T16:44:40Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

nomsgadded/pokemon-lora

nomsgadded

"2023-07-11T05:25:03Z"

2

0

diffusers

[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]

text-to-image

"2023-07-11T03:46:05Z"

--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - nomsgadded/pokemon-lora These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the lambdalabs/pokemon-blip-captions dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png)

mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF

mradermacher

"2024-11-25T08:56:31Z"

11

1

transformers

[ "transformers", "gguf", "ko", "en", "base_model:gwonny/nox-solar-10.7b-v4-kolon-all-5-v3.0", "base_model:quantized:gwonny/nox-solar-10.7b-v4-kolon-all-5-v3.0", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

"2024-11-23T15:12:29Z"

--- base_model: gwonny/nox-solar-10.7b-v4-kolon-all-5-v3.0 language: - ko - en library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher --- ## About      weighted/imatrix quants of https://huggingface.co/gwonny/nox-solar-10.7b-v4-kolon-all-5-v3.0  static quants are available at https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-IQ1_S.gguf) | i1-IQ1_S | 2.5 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-IQ1_M.gguf) | i1-IQ1_M | 2.7 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.0 | | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-IQ2_XS.gguf) | i1-IQ2_XS | 3.3 | | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-IQ2_S.gguf) | i1-IQ2_S | 3.5 | | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-IQ2_M.gguf) | i1-IQ2_M | 3.8 | | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q2_K.gguf) | i1-Q2_K | 4.1 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 4.3 | lower quality | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-IQ3_XS.gguf) | i1-IQ3_XS | 4.5 | | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q3_K_S.gguf) | i1-Q3_K_S | 4.8 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-IQ3_S.gguf) | i1-IQ3_S | 4.8 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-IQ3_M.gguf) | i1-IQ3_M | 4.9 | | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q3_K_M.gguf) | i1-Q3_K_M | 5.3 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q3_K_L.gguf) | i1-Q3_K_L | 5.8 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-IQ4_XS.gguf) | i1-IQ4_XS | 5.9 | | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 6.2 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 6.2 | fast on arm+i8mm, low quality | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 6.2 | fast on arm+sve, low quality | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q4_0.gguf) | i1-Q4_0 | 6.2 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q4_K_S.gguf) | i1-Q4_K_S | 6.2 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q4_K_M.gguf) | i1-Q4_K_M | 6.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q5_K_S.gguf) | i1-Q5_K_S | 7.5 | | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q5_K_M.gguf) | i1-Q5_K_M | 7.7 | | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q6_K.gguf) | i1-Q6_K | 8.9 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

AdapterHub/facebook-bart-base_lingaccept_cola_pfeiffer

AdapterHub

"2024-05-05T19:21:14Z"

0

adapter-transformers

[ "adapter-transformers", "text-classification", "adapterhub:lingaccept/cola", "bart", "license:apache-2.0", "region:us" ]

text-classification

"2024-05-05T19:21:11Z"

--- tags: - adapter-transformers - text-classification - adapterhub:lingaccept/cola - bart license: "apache-2.0" --- # Adapter `facebook-bart-base_lingaccept_cola_pfeiffer` for facebook/bart-base Adapter for bart-base in Pfeiffer architecture trained on the CoLA dataset for 15 epochs with early stopping and a learning rate of 1e-4. **This adapter was created for usage with the [Adapters](https://github.com/Adapter-Hub/adapters) library.** ## Usage First, install `adapters`: ``` pip install -U adapters ``` Now, the adapter can be loaded and activated like this: ```python from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("facebook/bart-base") adapter_name = model.load_adapter("AdapterHub/facebook-bart-base_lingaccept_cola_pfeiffer") model.set_active_adapters(adapter_name) ``` ## Architecture & Training - Adapter architecture: pfeiffer - Prediction head: classification - Dataset: [CoLA](https://nyu-mll.github.io/CoLA/) ## Author Information - Author name(s): Clifton Poth - Author email: [email protected] - Author links: [Website](https://calpt.github.io), [GitHub](https://github.com/calpt), [Twitter](https://twitter.com/@clifapt) ## Citation ```bibtex ``` *This adapter has been auto-imported from https://github.com/Adapter-Hub/Hub/blob/master/adapters/ukp/facebook-bart-base_lingaccept_cola_pfeiffer.yaml*.

Yaxin1992/llama3-8b-summary

Yaxin1992

"2024-04-23T21:42:31Z"

3

0

peft

[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct", "license:other", "region:us" ]

null

"2024-04-23T16:10:29Z"

--- license: other library_name: peft tags: - generated_from_trainer base_model: meta-llama/Meta-Llama-3-8B-Instruct model-index: - name: llama3-8b-summary results: [] ---  # llama3-8b-summary This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 8000 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

Jackson9z4x9/SFT-calculator

Jackson9z4x9

"2025-02-12T01:15:31Z"

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

"2025-02-10T22:10:39Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Eswann/ML-Agents-Pyramids

Eswann

"2023-11-16T11:54:44Z"

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]

reinforcement-learning

"2023-11-16T11:54:41Z"

--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # **ppo** Agent playing **Pyramids** This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Eswann/ML-Agents-Pyramids 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

judithrosell/ClinicalBERT_JNLPBA_NER_new

judithrosell

"2023-12-31T18:33:11Z"

6

0

transformers

[ "transformers", "tensorboard", "safetensors", "distilbert", "token-classification", "generated_from_trainer", "base_model:medicalai/ClinicalBERT", "base_model:finetune:medicalai/ClinicalBERT", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

"2023-12-31T15:04:47Z"

--- base_model: medicalai/ClinicalBERT tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: ClinicalBERT_JNLPBA_NER_new results: [] ---  # ClinicalBERT_JNLPBA_NER_new This model is a fine-tuned version of [medicalai/ClinicalBERT](https://huggingface.co/medicalai/ClinicalBERT) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1699 - Precision: 0.7855 - Recall: 0.8043 - F1: 0.7948 - Accuracy: 0.9439 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2204 | 1.0 | 1164 | 0.1821 | 0.7652 | 0.7719 | 0.7685 | 0.9380 | | 0.1618 | 2.0 | 2328 | 0.1716 | 0.7884 | 0.7886 | 0.7885 | 0.9426 | | 0.1338 | 3.0 | 3492 | 0.1699 | 0.7855 | 0.8043 | 0.7948 | 0.9439 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0

kelestemur/deep_rl

kelestemur

"2023-01-08T21:58:21Z"

1

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

"2023-01-08T21:57:58Z"

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 264.57 +/- 20.35 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

mlninad/deciLM-finetuned

mlninad

"2023-10-23T19:22:53Z"

15

0

transformers

[ "transformers", "pytorch", "llama", "text-generation", "custom_code", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2023-10-22T09:13:33Z"

--- license: apache-2.0 language: - en ---

Narrativaai/deberta-v3-small-finetuned-hate_speech18

Narrativaai

"2021-12-01T17:41:13Z"

9

3

transformers

[ "transformers", "pytorch", "tensorboard", "deberta-v2", "text-classification", "generated_from_trainer", "dataset:hate_speech18", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

"2022-03-02T23:29:04Z"

--- license: mit tags: - generated_from_trainer datasets: - hate_speech18 widget: - text: "ok, so do we need to kill them too or are the slavs okay ? for some reason whenever i hear the word slav , the word slobber comes to mind and i picture a slobbering half breed creature like the humpback of notre dame or Igor haha" metrics: - accuracy model-index: - name: deberta-v3-small-hate-speech results: - task: name: Text Classification type: text-classification dataset: name: hate_speech18 type: hate_speech18 args: default metrics: - name: Accuracy type: accuracy value: 0.916058394160584 ---  # DeBERTa v3 small fine-tuned on hate_speech18 dataset for Hate Speech Detection This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the hate_speech18 dataset. It achieves the following results on the evaluation set: - Loss: 0.2922 - Accuracy: 0.9161 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4147 | 1.0 | 650 | 0.3910 | 0.8832 | | 0.2975 | 2.0 | 1300 | 0.2922 | 0.9161 | | 0.2575 | 3.0 | 1950 | 0.3555 | 0.9051 | | 0.1553 | 4.0 | 2600 | 0.4263 | 0.9124 | | 0.1267 | 5.0 | 3250 | 0.4238 | 0.9161 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF

mradermacher

"2025-02-22T10:00:05Z"

0

transformers

[ "transformers", "gguf", "en", "base_model:nkpz/Reverb-7b-Uncensored-DeLMAT", "base_model:quantized:nkpz/Reverb-7b-Uncensored-DeLMAT", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

"2025-02-22T06:06:48Z"

--- base_model: nkpz/Reverb-7b-Uncensored-DeLMAT language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About      weighted/imatrix quants of https://huggingface.co/nkpz/Reverb-7b-Uncensored-DeLMAT  static quants are available at https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF/resolve/main/Reverb-7b-Uncensored-DeLMAT.i1-IQ1_S.gguf) | i1-IQ1_S | 2.0 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF/resolve/main/Reverb-7b-Uncensored-DeLMAT.i1-IQ1_M.gguf) | i1-IQ1_M | 2.1 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF/resolve/main/Reverb-7b-Uncensored-DeLMAT.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.4 | | | [GGUF](https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF/resolve/main/Reverb-7b-Uncensored-DeLMAT.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.6 | | | [GGUF](https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF/resolve/main/Reverb-7b-Uncensored-DeLMAT.i1-IQ2_S.gguf) | i1-IQ2_S | 2.7 | | | [GGUF](https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF/resolve/main/Reverb-7b-Uncensored-DeLMAT.i1-IQ2_M.gguf) | i1-IQ2_M | 2.9 | | | [GGUF](https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF/resolve/main/Reverb-7b-Uncensored-DeLMAT.i1-Q2_K_S.gguf) | i1-Q2_K_S | 2.9 | very low quality | | [GGUF](https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF/resolve/main/Reverb-7b-Uncensored-DeLMAT.i1-Q2_K.gguf) | i1-Q2_K | 3.1 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF/resolve/main/Reverb-7b-Uncensored-DeLMAT.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.2 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF/resolve/main/Reverb-7b-Uncensored-DeLMAT.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.4 | | | [GGUF](https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF/resolve/main/Reverb-7b-Uncensored-DeLMAT.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.6 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF/resolve/main/Reverb-7b-Uncensored-DeLMAT.i1-IQ3_S.gguf) | i1-IQ3_S | 3.6 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF/resolve/main/Reverb-7b-Uncensored-DeLMAT.i1-IQ3_M.gguf) | i1-IQ3_M | 3.7 | | | [GGUF](https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF/resolve/main/Reverb-7b-Uncensored-DeLMAT.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.9 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF/resolve/main/Reverb-7b-Uncensored-DeLMAT.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.2 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF/resolve/main/Reverb-7b-Uncensored-DeLMAT.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.3 | | | [GGUF](https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF/resolve/main/Reverb-7b-Uncensored-DeLMAT.i1-IQ4_NL.gguf) | i1-IQ4_NL | 4.5 | prefer IQ4_XS | | [GGUF](https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF/resolve/main/Reverb-7b-Uncensored-DeLMAT.i1-Q4_0.gguf) | i1-Q4_0 | 4.5 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF/resolve/main/Reverb-7b-Uncensored-DeLMAT.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.6 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF/resolve/main/Reverb-7b-Uncensored-DeLMAT.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF/resolve/main/Reverb-7b-Uncensored-DeLMAT.i1-Q4_1.gguf) | i1-Q4_1 | 5.0 | | | [GGUF](https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF/resolve/main/Reverb-7b-Uncensored-DeLMAT.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.4 | | | [GGUF](https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF/resolve/main/Reverb-7b-Uncensored-DeLMAT.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.5 | | | [GGUF](https://huggingface.co/mradermacher/Reverb-7b-Uncensored-DeLMAT-i1-GGUF/resolve/main/Reverb-7b-Uncensored-DeLMAT.i1-Q6_K.gguf) | i1-Q6_K | 6.4 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

sail-rvc/VergilRVC2byDreamnaught

sail-rvc

"2023-07-14T07:33:52Z"

1

0

transformers

[ "transformers", "rvc", "sail-rvc", "audio-to-audio", "endpoints_compatible", "region:us" ]

audio-to-audio

"2023-07-14T07:33:33Z"

--- pipeline_tag: audio-to-audio tags: - rvc - sail-rvc --- # VergilRVC2byDreamnaught ## RVC Model ![banner](https://i.imgur.com/xocCjhH.jpg) This model repo was automatically generated. Date: 2023-07-14 07:33:52 Bot Name: juuxnscrap Model Type: RVC Source: https://huggingface.co/juuxn/RVCModels/ Reason: Converting into loadable format for https://github.com/chavinlo/rvc-runpod

farleyknight/arxiv-summarization-fb-bart-base-2022-09-21

farleyknight

"2022-09-23T08:34:25Z"

121

0

transformers

[ "transformers", "pytorch", "bart", "text2text-generation", "generated_from_trainer", "dataset:ccdv/arxiv-summarization", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

"2022-09-21T23:10:43Z"

--- license: apache-2.0 tags: - generated_from_trainer datasets: - ccdv/arxiv-summarization metrics: - rouge model-index: - name: arxiv-summarization-fb-bart-base-2022-09-21 results: - task: name: Summarization type: summarization dataset: name: ccdv/arxiv-summarization type: ccdv/arxiv-summarization config: section split: train args: section metrics: - name: Rouge1 type: rouge value: 42.9082 ---  # arxiv-summarization-fb-bart-base-2022-09-21 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the ccdv/arxiv-summarization dataset. It achieves the following results on the evaluation set: - Loss: 2.1597 - Rouge1: 42.9082 - Rouge2: 15.7763 - Rougel: 25.9239 - Rougelsum: 37.7957 - Gen Len: 110.5816 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:------:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 2.9142 | 0.05 | 10000 | 2.7522 | 17.073 | 6.7502 | 13.6779 | 15.6668 | 20.0 | | 2.7876 | 0.1 | 20000 | 2.6888 | 16.7954 | 6.7038 | 13.4939 | 15.3426 | 19.9992 | | 2.715 | 0.15 | 30000 | 2.6308 | 17.3324 | 6.8771 | 13.7918 | 15.7839 | 20.0 | | 2.6431 | 0.2 | 40000 | 2.5858 | 16.7055 | 6.8108 | 13.4796 | 15.2959 | 20.0 | | 2.6381 | 0.25 | 50000 | 2.5393 | 17.4643 | 7.0687 | 13.9507 | 16.012 | 20.0 | | 2.6269 | 0.3 | 60000 | 2.5159 | 17.5934 | 7.0022 | 13.9394 | 16.0203 | 20.0 | | 2.5482 | 0.34 | 70000 | 2.4894 | 17.5428 | 7.1822 | 13.9788 | 16.0355 | 20.0 | | 2.4962 | 0.39 | 80000 | 2.4476 | 17.3587 | 7.1501 | 13.9215 | 15.8637 | 20.0 | | 2.513 | 0.44 | 90000 | 2.4309 | 18.0806 | 7.5429 | 14.4201 | 16.561 | 20.0 | | 2.4464 | 0.49 | 100000 | 2.4128 | 17.9813 | 7.5454 | 14.3403 | 16.52 | 19.9989 | | 2.4969 | 0.54 | 110000 | 2.4114 | 17.3353 | 7.1382 | 13.9109 | 15.873 | 20.0 | | 2.4417 | 0.59 | 120000 | 2.3866 | 18.0241 | 7.553 | 14.3892 | 16.5077 | 19.9980 | | 2.4333 | 0.64 | 130000 | 2.3903 | 18.0578 | 7.4999 | 14.3901 | 16.5134 | 20.0 | | 2.4296 | 0.69 | 140000 | 2.3793 | 17.7742 | 7.5182 | 14.2794 | 16.2879 | 20.0 | | 2.4277 | 0.74 | 150000 | 2.3571 | 17.8015 | 7.4677 | 14.226 | 16.3288 | 20.0 | | 2.4258 | 0.79 | 160000 | 2.3539 | 17.5335 | 7.399 | 14.09 | 16.0936 | 20.0 | | 2.4006 | 0.84 | 170000 | 2.3469 | 17.5983 | 7.4285 | 14.1315 | 16.1385 | 20.0 | | 2.367 | 0.89 | 180000 | 2.3344 | 17.297 | 7.2361 | 13.9286 | 15.8352 | 20.0 | | 2.373 | 0.94 | 190000 | 2.3377 | 17.7189 | 7.4993 | 14.2603 | 16.2546 | 19.9980 | | 2.3762 | 0.99 | 200000 | 2.3106 | 17.7883 | 7.4766 | 14.2675 | 16.3115 | 20.0 | | 2.2538 | 1.03 | 210000 | 2.3197 | 17.4487 | 7.4171 | 14.0473 | 15.9771 | 20.0 | | 2.268 | 1.08 | 220000 | 2.3044 | 17.9603 | 7.5806 | 14.3755 | 16.4328 | 20.0 | | 2.2986 | 1.13 | 230000 | 2.3002 | 17.9268 | 7.5321 | 14.3503 | 16.4191 | 20.0 | | 2.241 | 1.18 | 240000 | 2.3059 | 17.4542 | 7.3224 | 14.0578 | 16.0157 | 20.0 | | 2.2534 | 1.23 | 250000 | 2.2927 | 17.8039 | 7.6232 | 14.2916 | 16.3442 | 20.0 | | 2.26 | 1.28 | 260000 | 2.2910 | 17.8607 | 7.5645 | 14.318 | 16.3336 | 19.9983 | | 2.3 | 1.33 | 270000 | 2.2818 | 17.8203 | 7.4815 | 14.3171 | 16.3309 | 20.0 | | 2.2964 | 1.38 | 280000 | 2.2721 | 17.983 | 7.6867 | 14.3971 | 16.493 | 20.0 | | 2.2564 | 1.43 | 290000 | 2.2701 | 18.059 | 7.7273 | 14.4806 | 16.5792 | 19.9988 | | 2.2576 | 1.48 | 300000 | 2.2663 | 17.5706 | 7.4424 | 14.1424 | 16.1297 | 20.0 | | 2.2605 | 1.53 | 310000 | 2.2607 | 17.8057 | 7.5219 | 14.3226 | 16.3355 | 19.9988 | | 2.2587 | 1.58 | 320000 | 2.2552 | 18.0396 | 7.7064 | 14.5005 | 16.5823 | 20.0 | | 2.2423 | 1.63 | 330000 | 2.2523 | 18.2229 | 7.8398 | 14.5868 | 16.7408 | 20.0 | | 2.2793 | 1.68 | 340000 | 2.2431 | 17.6785 | 7.5437 | 14.1971 | 16.1724 | 19.9988 | | 2.2005 | 1.72 | 350000 | 2.2343 | 17.7552 | 7.6026 | 14.2152 | 16.2797 | 19.9988 | | 2.2454 | 1.77 | 360000 | 2.2339 | 17.9292 | 7.699 | 14.4099 | 16.4682 | 20.0 | | 2.2175 | 1.82 | 370000 | 2.2345 | 17.7413 | 7.4892 | 14.2223 | 16.2442 | 20.0 | | 2.238 | 1.87 | 380000 | 2.2259 | 17.6679 | 7.4976 | 14.24 | 16.243 | 19.9988 | | 2.2108 | 1.92 | 390000 | 2.2210 | 17.8474 | 7.6054 | 14.3494 | 16.3635 | 19.9988 | | 2.2124 | 1.97 | 400000 | 2.2170 | 17.8019 | 7.5182 | 14.264 | 16.3003 | 20.0 | | 2.0976 | 2.02 | 410000 | 2.2248 | 17.8063 | 7.5383 | 14.2782 | 16.275 | 20.0 | | 2.0932 | 2.07 | 420000 | 2.2196 | 17.9171 | 7.6187 | 14.3508 | 16.4333 | 20.0 | | 2.0956 | 2.12 | 430000 | 2.2135 | 18.0616 | 7.7655 | 14.4837 | 16.5627 | 19.9988 | | 2.0515 | 2.17 | 440000 | 2.2091 | 18.0281 | 7.7301 | 14.4696 | 16.5196 | 19.9981 | | 2.1216 | 2.22 | 450000 | 2.2015 | 18.0609 | 7.7541 | 14.4633 | 16.5705 | 19.9988 | | 2.1222 | 2.27 | 460000 | 2.1983 | 18.0717 | 7.7473 | 14.4725 | 16.5399 | 19.9988 | | 2.0903 | 2.32 | 470000 | 2.2007 | 18.0751 | 7.7486 | 14.4583 | 16.546 | 20.0 | | 2.1124 | 2.37 | 480000 | 2.1934 | 17.888 | 7.7124 | 14.3899 | 16.3901 | 20.0 | | 2.1094 | 2.41 | 490000 | 2.1901 | 18.0254 | 7.7682 | 14.4427 | 16.5181 | 20.0 | | 2.1085 | 2.46 | 500000 | 2.1924 | 17.9077 | 7.7004 | 14.3843 | 16.4221 | 19.9988 | | 2.0781 | 2.51 | 510000 | 2.1781 | 18.1591 | 7.8456 | 14.565 | 16.6435 | 19.9988 | | 2.0875 | 2.56 | 520000 | 2.1801 | 18.0389 | 7.7342 | 14.4259 | 16.5378 | 20.0 | | 2.0945 | 2.61 | 530000 | 2.1758 | 18.0999 | 7.8217 | 14.5163 | 16.5784 | 19.9988 | | 2.0723 | 2.66 | 540000 | 2.1756 | 17.9684 | 7.7369 | 14.4279 | 16.4815 | 19.9988 | | 2.0918 | 2.71 | 550000 | 2.1738 | 18.1183 | 7.8414 | 14.5298 | 16.6119 | 19.9988 | | 2.0835 | 2.76 | 560000 | 2.1671 | 17.8837 | 7.7379 | 14.3727 | 16.4068 | 19.9988 | | 2.0936 | 2.81 | 570000 | 2.1670 | 17.9631 | 7.7708 | 14.4566 | 16.4823 | 19.9988 | | 2.0518 | 2.86 | 580000 | 2.1631 | 18.0601 | 7.8112 | 14.5158 | 16.5816 | 19.9988 | | 2.065 | 2.91 | 590000 | 2.1611 | 18.0548 | 7.8147 | 14.5271 | 16.5606 | 19.9988 | | 2.0427 | 2.96 | 600000 | 2.1611 | 18.0642 | 7.8284 | 14.5293 | 16.5736 | 19.9988 | ### Framework versions - Transformers 4.23.0.dev0 - Pytorch 1.12.0 - Datasets 2.5.1 - Tokenizers 0.13.0

jeroenherczeg/shawgpt-ft

jeroenherczeg

"2024-04-05T08:21:37Z"

2

0

peft

[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:TheBloke/Mistral-7B-Instruct-v0.2-GPTQ", "base_model:adapter:TheBloke/Mistral-7B-Instruct-v0.2-GPTQ", "license:apache-2.0", "region:us" ]

null

"2024-04-04T16:46:03Z"

--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: TheBloke/Mistral-7B-Instruct-v0.2-GPTQ model-index: - name: shawgpt-ft results: [] ---  # shawgpt-ft This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.2-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GPTQ) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.2320 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1 - training_steps: 1000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 4.6433 | 0.92 | 3 | 4.2320 | | 4.6544 | 1.85 | 6 | 4.2320 | | 4.6459 | 2.77 | 9 | 4.2320 | | 3.4822 | 4.0 | 13 | 4.2320 | | 4.6298 | 4.92 | 16 | 4.2320 | | 4.6605 | 5.85 | 19 | 4.2320 | | 4.6392 | 6.77 | 22 | 4.2320 | | 3.4844 | 8.0 | 26 | 4.2320 | | 4.6305 | 8.92 | 29 | 4.2320 | | 4.6337 | 9.85 | 32 | 4.2320 | | 4.6501 | 10.77 | 35 | 4.2320 | | 3.4793 | 12.0 | 39 | 4.2320 | | 4.6568 | 12.92 | 42 | 4.2320 | | 4.6402 | 13.85 | 45 | 4.2320 | | 4.6381 | 14.77 | 48 | 4.2320 | | 3.4787 | 16.0 | 52 | 4.2320 | | 4.671 | 16.92 | 55 | 4.2320 | | 4.6186 | 17.85 | 58 | 4.2320 | | 4.6403 | 18.77 | 61 | 4.2320 | | 3.5009 | 20.0 | 65 | 4.2320 | | 4.6514 | 20.92 | 68 | 4.2320 | | 4.6426 | 21.85 | 71 | 4.2320 | | 4.6674 | 22.77 | 74 | 4.2320 | | 3.4915 | 24.0 | 78 | 4.2320 | | 4.6606 | 24.92 | 81 | 4.2320 | | 4.6364 | 25.85 | 84 | 4.2320 | | 4.6222 | 26.77 | 87 | 4.2320 | | 3.4782 | 28.0 | 91 | 4.2320 | | 4.6229 | 28.92 | 94 | 4.2320 | | 4.6576 | 29.85 | 97 | 4.2320 | | 4.6288 | 30.77 | 100 | 4.2320 | | 3.4664 | 32.0 | 104 | 4.2320 | | 4.6434 | 32.92 | 107 | 4.2320 | | 4.6519 | 33.85 | 110 | 4.2320 | | 4.6528 | 34.77 | 113 | 4.2320 | | 3.471 | 36.0 | 117 | 4.2320 | | 4.6453 | 36.92 | 120 | 4.2320 | | 4.616 | 37.85 | 123 | 4.2320 | | 4.6109 | 38.77 | 126 | 4.2320 | | 3.4799 | 40.0 | 130 | 4.2320 | | 4.6388 | 40.92 | 133 | 4.2320 | | 4.6711 | 41.85 | 136 | 4.2320 | | 4.6483 | 42.77 | 139 | 4.2320 | | 3.4695 | 44.0 | 143 | 4.2320 | | 4.6496 | 44.92 | 146 | 4.2320 | | 4.644 | 45.85 | 149 | 4.2320 | | 4.6444 | 46.77 | 152 | 4.2320 | | 3.4741 | 48.0 | 156 | 4.2320 | | 4.6189 | 48.92 | 159 | 4.2320 | | 4.6683 | 49.85 | 162 | 4.2320 | | 4.6345 | 50.77 | 165 | 4.2320 | | 3.4703 | 52.0 | 169 | 4.2320 | | 4.6144 | 52.92 | 172 | 4.2320 | | 4.6648 | 53.85 | 175 | 4.2320 | | 4.6522 | 54.77 | 178 | 4.2320 | | 3.4838 | 56.0 | 182 | 4.2320 | | 4.6506 | 56.92 | 185 | 4.2320 | | 4.6339 | 57.85 | 188 | 4.2320 | | 4.638 | 58.77 | 191 | 4.2320 | | 3.4733 | 60.0 | 195 | 4.2320 | | 4.6604 | 60.92 | 198 | 4.2320 | | 4.6326 | 61.85 | 201 | 4.2320 | | 4.6612 | 62.77 | 204 | 4.2320 | | 3.4722 | 64.0 | 208 | 4.2320 | | 4.6292 | 64.92 | 211 | 4.2320 | | 4.6336 | 65.85 | 214 | 4.2320 | | 4.642 | 66.77 | 217 | 4.2320 | | 3.4915 | 68.0 | 221 | 4.2320 | | 4.6453 | 68.92 | 224 | 4.2320 | | 4.6459 | 69.85 | 227 | 4.2320 | | 4.6202 | 70.77 | 230 | 4.2320 | | 3.4753 | 72.0 | 234 | 4.2320 | | 4.6552 | 72.92 | 237 | 4.2320 | | 4.6443 | 73.85 | 240 | 4.2320 | | 4.6495 | 74.77 | 243 | 4.2320 | | 3.4798 | 76.0 | 247 | 4.2320 | | 4.6358 | 76.92 | 250 | 4.2320 | | 4.6434 | 77.85 | 253 | 4.2320 | | 4.6325 | 78.77 | 256 | 4.2320 | | 3.4951 | 80.0 | 260 | 4.2320 | | 4.6302 | 80.92 | 263 | 4.2320 | | 4.6458 | 81.85 | 266 | 4.2320 | | 4.6407 | 82.77 | 269 | 4.2320 | | 3.4828 | 84.0 | 273 | 4.2320 | | 4.6436 | 84.92 | 276 | 4.2320 | | 4.6143 | 85.85 | 279 | 4.2320 | | 4.644 | 86.77 | 282 | 4.2320 | | 3.4934 | 88.0 | 286 | 4.2320 | | 4.6308 | 88.92 | 289 | 4.2320 | | 4.6715 | 89.85 | 292 | 4.2320 | | 4.6229 | 90.77 | 295 | 4.2320 | | 3.4895 | 92.0 | 299 | 4.2320 | | 4.6447 | 92.92 | 302 | 4.2320 | | 4.6333 | 93.85 | 305 | 4.2320 | | 4.643 | 94.77 | 308 | 4.2320 | | 3.482 | 96.0 | 312 | 4.2320 | | 4.6647 | 96.92 | 315 | 4.2320 | | 4.65 | 97.85 | 318 | 4.2320 | | 4.6545 | 98.77 | 321 | 4.2320 | | 3.4881 | 100.0 | 325 | 4.2320 | | 4.6828 | 100.92 | 328 | 4.2320 | | 4.6328 | 101.85 | 331 | 4.2320 | | 4.6419 | 102.77 | 334 | 4.2320 | | 3.4954 | 104.0 | 338 | 4.2320 | | 4.6203 | 104.92 | 341 | 4.2320 | | 4.6236 | 105.85 | 344 | 4.2320 | | 4.6539 | 106.77 | 347 | 4.2320 | | 3.4737 | 108.0 | 351 | 4.2320 | | 4.6319 | 108.92 | 354 | 4.2320 | | 4.6696 | 109.85 | 357 | 4.2320 | | 4.6678 | 110.77 | 360 | 4.2320 | | 3.4698 | 112.0 | 364 | 4.2320 | | 4.6459 | 112.92 | 367 | 4.2320 | | 4.6524 | 113.85 | 370 | 4.2320 | | 4.6399 | 114.77 | 373 | 4.2320 | | 3.471 | 116.0 | 377 | 4.2320 | | 4.6668 | 116.92 | 380 | 4.2320 | | 4.634 | 117.85 | 383 | 4.2320 | | 4.6345 | 118.77 | 386 | 4.2320 | | 3.4938 | 120.0 | 390 | 4.2320 | | 4.6386 | 120.92 | 393 | 4.2320 | | 4.6661 | 121.85 | 396 | 4.2320 | | 4.6465 | 122.77 | 399 | 4.2320 | | 3.4903 | 124.0 | 403 | 4.2320 | | 4.6255 | 124.92 | 406 | 4.2320 | | 4.6306 | 125.85 | 409 | 4.2320 | | 4.6348 | 126.77 | 412 | 4.2320 | | 3.4811 | 128.0 | 416 | 4.2320 | | 4.6335 | 128.92 | 419 | 4.2320 | | 4.6678 | 129.85 | 422 | 4.2320 | | 4.6336 | 130.77 | 425 | 4.2320 | | 3.4722 | 132.0 | 429 | 4.2320 | | 4.6371 | 132.92 | 432 | 4.2320 | | 4.6488 | 133.85 | 435 | 4.2320 | | 4.6456 | 134.77 | 438 | 4.2320 | | 3.4866 | 136.0 | 442 | 4.2320 | | 4.6349 | 136.92 | 445 | 4.2320 | | 4.6418 | 137.85 | 448 | 4.2320 | | 4.6546 | 138.77 | 451 | 4.2320 | | 3.4811 | 140.0 | 455 | 4.2320 | | 4.6322 | 140.92 | 458 | 4.2320 | | 4.6154 | 141.85 | 461 | 4.2320 | | 4.6362 | 142.77 | 464 | 4.2320 | | 3.4809 | 144.0 | 468 | 4.2320 | | 4.6317 | 144.92 | 471 | 4.2320 | | 4.6329 | 145.85 | 474 | 4.2320 | | 4.636 | 146.77 | 477 | 4.2320 | | 3.4737 | 148.0 | 481 | 4.2320 | | 4.629 | 148.92 | 484 | 4.2320 | | 4.6212 | 149.85 | 487 | 4.2320 | | 4.6548 | 150.77 | 490 | 4.2320 | | 3.481 | 152.0 | 494 | 4.2320 | | 4.6379 | 152.92 | 497 | 4.2320 | | 4.6306 | 153.85 | 500 | 4.2320 | | 4.6443 | 154.77 | 503 | 4.2320 | | 3.4951 | 156.0 | 507 | 4.2320 | | 4.6514 | 156.92 | 510 | 4.2320 | | 4.6539 | 157.85 | 513 | 4.2320 | | 4.6295 | 158.77 | 516 | 4.2320 | | 3.485 | 160.0 | 520 | 4.2320 | | 4.6665 | 160.92 | 523 | 4.2320 | | 4.6508 | 161.85 | 526 | 4.2320 | | 4.6754 | 162.77 | 529 | 4.2320 | | 3.4689 | 164.0 | 533 | 4.2320 | | 4.6286 | 164.92 | 536 | 4.2320 | | 4.6164 | 165.85 | 539 | 4.2320 | | 4.634 | 166.77 | 542 | 4.2320 | | 3.4878 | 168.0 | 546 | 4.2320 | | 4.6616 | 168.92 | 549 | 4.2320 | | 4.6228 | 169.85 | 552 | 4.2320 | | 4.6427 | 170.77 | 555 | 4.2320 | | 3.4739 | 172.0 | 559 | 4.2320 | | 4.656 | 172.92 | 562 | 4.2320 | | 4.6488 | 173.85 | 565 | 4.2320 | | 4.6199 | 174.77 | 568 | 4.2320 | | 3.4842 | 176.0 | 572 | 4.2320 | | 4.6632 | 176.92 | 575 | 4.2320 | | 4.646 | 177.85 | 578 | 4.2320 | | 4.6226 | 178.77 | 581 | 4.2320 | | 3.4619 | 180.0 | 585 | 4.2320 | | 4.6329 | 180.92 | 588 | 4.2320 | | 4.6245 | 181.85 | 591 | 4.2320 | | 4.6435 | 182.77 | 594 | 4.2320 | | 3.478 | 184.0 | 598 | 4.2320 | | 4.6256 | 184.92 | 601 | 4.2320 | | 4.6516 | 185.85 | 604 | 4.2320 | | 4.6438 | 186.77 | 607 | 4.2320 | | 3.5015 | 188.0 | 611 | 4.2320 | | 4.6254 | 188.92 | 614 | 4.2320 | | 4.6265 | 189.85 | 617 | 4.2320 | | 4.6447 | 190.77 | 620 | 4.2320 | | 3.508 | 192.0 | 624 | 4.2320 | | 4.6353 | 192.92 | 627 | 4.2320 | | 4.6333 | 193.85 | 630 | 4.2320 | | 4.6573 | 194.77 | 633 | 4.2320 | | 3.4644 | 196.0 | 637 | 4.2320 | | 4.6413 | 196.92 | 640 | 4.2320 | | 4.6641 | 197.85 | 643 | 4.2320 | | 4.638 | 198.77 | 646 | 4.2320 | | 3.4885 | 200.0 | 650 | 4.2320 | | 4.6502 | 200.92 | 653 | 4.2320 | | 4.6476 | 201.85 | 656 | 4.2320 | | 4.645 | 202.77 | 659 | 4.2320 | | 3.4861 | 204.0 | 663 | 4.2320 | | 4.6418 | 204.92 | 666 | 4.2320 | | 4.6419 | 205.85 | 669 | 4.2320 | | 4.6395 | 206.77 | 672 | 4.2320 | | 3.4739 | 208.0 | 676 | 4.2320 | | 4.6306 | 208.92 | 679 | 4.2320 | | 4.6245 | 209.85 | 682 | 4.2320 | | 4.6614 | 210.77 | 685 | 4.2320 | | 3.4965 | 212.0 | 689 | 4.2320 | | 4.642 | 212.92 | 692 | 4.2320 | | 4.6371 | 213.85 | 695 | 4.2320 | | 4.6265 | 214.77 | 698 | 4.2320 | | 3.4965 | 216.0 | 702 | 4.2320 | | 4.6648 | 216.92 | 705 | 4.2320 | | 4.6248 | 217.85 | 708 | 4.2320 | | 4.6507 | 218.77 | 711 | 4.2320 | | 3.4741 | 220.0 | 715 | 4.2320 | | 4.644 | 220.92 | 718 | 4.2320 | | 4.6315 | 221.85 | 721 | 4.2320 | | 4.659 | 222.77 | 724 | 4.2320 | | 3.4942 | 224.0 | 728 | 4.2320 | | 4.6463 | 224.92 | 731 | 4.2320 | | 4.6477 | 225.85 | 734 | 4.2320 | | 4.6323 | 226.77 | 737 | 4.2320 | | 3.4907 | 228.0 | 741 | 4.2320 | | 4.6323 | 228.92 | 744 | 4.2320 | | 4.6442 | 229.85 | 747 | 4.2320 | | 4.6351 | 230.77 | 750 | 4.2320 | | 3.4799 | 232.0 | 754 | 4.2320 | | 4.6463 | 232.92 | 757 | 4.2320 | | 4.6389 | 233.85 | 760 | 4.2320 | | 4.6399 | 234.77 | 763 | 4.2320 | | 3.4819 | 236.0 | 767 | 4.2320 | | 4.678 | 236.92 | 770 | 4.2320 | | 4.6446 | 237.85 | 773 | 4.2320 | | 4.642 | 238.77 | 776 | 4.2320 | | 3.4879 | 240.0 | 780 | 4.2320 | | 4.6561 | 240.92 | 783 | 4.2320 | | 4.6226 | 241.85 | 786 | 4.2320 | | 4.6607 | 242.77 | 789 | 4.2320 | | 3.4901 | 244.0 | 793 | 4.2320 | | 4.6317 | 244.92 | 796 | 4.2320 | | 4.6387 | 245.85 | 799 | 4.2320 | | 4.6493 | 246.77 | 802 | 4.2320 | | 3.4863 | 248.0 | 806 | 4.2320 | | 4.6187 | 248.92 | 809 | 4.2320 | | 4.6449 | 249.85 | 812 | 4.2320 | | 4.6542 | 250.77 | 815 | 4.2320 | | 3.4905 | 252.0 | 819 | 4.2320 | | 4.6514 | 252.92 | 822 | 4.2320 | | 4.6496 | 253.85 | 825 | 4.2320 | | 4.6542 | 254.77 | 828 | 4.2320 | | 3.4661 | 256.0 | 832 | 4.2320 | | 4.631 | 256.92 | 835 | 4.2320 | | 4.644 | 257.85 | 838 | 4.2320 | | 4.6348 | 258.77 | 841 | 4.2320 | | 3.5069 | 260.0 | 845 | 4.2320 | | 4.6257 | 260.92 | 848 | 4.2320 | | 4.6584 | 261.85 | 851 | 4.2320 | | 4.6344 | 262.77 | 854 | 4.2320 | | 3.4721 | 264.0 | 858 | 4.2320 | | 4.6429 | 264.92 | 861 | 4.2320 | | 4.6433 | 265.85 | 864 | 4.2320 | | 4.6391 | 266.77 | 867 | 4.2320 | | 3.4916 | 268.0 | 871 | 4.2320 | | 4.6564 | 268.92 | 874 | 4.2320 | | 4.658 | 269.85 | 877 | 4.2320 | | 4.6329 | 270.77 | 880 | 4.2320 | | 3.4783 | 272.0 | 884 | 4.2320 | | 4.6384 | 272.92 | 887 | 4.2320 | | 4.6482 | 273.85 | 890 | 4.2320 | | 4.6688 | 274.77 | 893 | 4.2320 | | 3.4659 | 276.0 | 897 | 4.2320 | | 4.6299 | 276.92 | 900 | 4.2320 | | 4.6392 | 277.85 | 903 | 4.2320 | | 4.6521 | 278.77 | 906 | 4.2320 | | 3.4949 | 280.0 | 910 | 4.2320 | | 4.6643 | 280.92 | 913 | 4.2320 | | 4.6361 | 281.85 | 916 | 4.2320 | | 4.6505 | 282.77 | 919 | 4.2320 | | 3.4847 | 284.0 | 923 | 4.2320 | | 4.639 | 284.92 | 926 | 4.2320 | | 4.6276 | 285.85 | 929 | 4.2320 | | 4.6438 | 286.77 | 932 | 4.2320 | | 3.4883 | 288.0 | 936 | 4.2320 | | 4.6483 | 288.92 | 939 | 4.2320 | | 4.6564 | 289.85 | 942 | 4.2320 | | 4.6437 | 290.77 | 945 | 4.2320 | | 3.4712 | 292.0 | 949 | 4.2320 | | 4.6627 | 292.92 | 952 | 4.2320 | | 4.6371 | 293.85 | 955 | 4.2320 | | 4.6196 | 294.77 | 958 | 4.2320 | | 3.4859 | 296.0 | 962 | 4.2320 | | 4.6457 | 296.92 | 965 | 4.2320 | | 4.6249 | 297.85 | 968 | 4.2320 | | 4.6382 | 298.77 | 971 | 4.2320 | | 3.4824 | 300.0 | 975 | 4.2320 | | 4.6541 | 300.92 | 978 | 4.2320 | | 4.659 | 301.85 | 981 | 4.2320 | | 4.618 | 302.77 | 984 | 4.2320 | | 3.4751 | 304.0 | 988 | 4.2320 | | 4.623 | 304.92 | 991 | 4.2320 | | 4.6371 | 305.85 | 994 | 4.2320 | | 4.6546 | 306.77 | 997 | 4.2320 | | 3.1908 | 307.69 | 1000 | 4.2320 | ### Framework versions - PEFT 0.10.0 - Transformers 4.36.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

lmqg/mt5-small-ruquad-qg

lmqg

"2023-01-18T13:46:15Z"

26

0

transformers

[ "transformers", "pytorch", "mt5", "text2text-generation", "question generation", "ru", "dataset:lmqg/qg_ruquad", "arxiv:2210.03992", "license:cc-by-4.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

"2022-06-07T00:39:31Z"

--- license: cc-by-4.0 metrics: - bleu4 - meteor - rouge-l - bertscore - moverscore language: ru datasets: - lmqg/qg_ruquad pipeline_tag: text2text-generation tags: - question generation widget: - text: "Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, <hl> в мае 1860 года <hl> провёл серию опытов." example_title: "Question Generation Example 1" - text: "Однако, франкоязычный <hl> Квебек <hl> практически никогда не включается в состав Латинской Америки." example_title: "Question Generation Example 2" - text: "Классическим примером международного синдиката XX века была группа компаний <hl> Де Бирс <hl> , которая в 1980-е годы контролировала до 90 % мировой торговли алмазами." example_title: "Question Generation Example 3" model-index: - name: lmqg/mt5-small-ruquad-qg results: - task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qg_ruquad type: default args: default metrics: - name: BLEU4 (Question Generation) type: bleu4_question_generation value: 16.31 - name: ROUGE-L (Question Generation) type: rouge_l_question_generation value: 31.39 - name: METEOR (Question Generation) type: meteor_question_generation value: 26.39 - name: BERTScore (Question Generation) type: bertscore_question_generation value: 84.27 - name: MoverScore (Question Generation) type: moverscore_question_generation value: 62.49 - name: QAAlignedF1Score-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] type: qa_aligned_f1_score_bertscore_question_answer_generation_with_gold_answer_gold_answer value: 90.17 - name: QAAlignedRecall-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] type: qa_aligned_recall_bertscore_question_answer_generation_with_gold_answer_gold_answer value: 90.16 - name: QAAlignedPrecision-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] type: qa_aligned_precision_bertscore_question_answer_generation_with_gold_answer_gold_answer value: 90.17 - name: QAAlignedF1Score-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] type: qa_aligned_f1_score_moverscore_question_answer_generation_with_gold_answer_gold_answer value: 68.22 - name: QAAlignedRecall-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] type: qa_aligned_recall_moverscore_question_answer_generation_with_gold_answer_gold_answer value: 68.21 - name: QAAlignedPrecision-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] type: qa_aligned_precision_moverscore_question_answer_generation_with_gold_answer_gold_answer value: 68.23 - name: QAAlignedF1Score-BERTScore (Question & Answer Generation) [Gold Answer] type: qa_aligned_f1_score_bertscore_question_answer_generation_gold_answer value: 76.96 - name: QAAlignedRecall-BERTScore (Question & Answer Generation) [Gold Answer] type: qa_aligned_recall_bertscore_question_answer_generation_gold_answer value: 81.05 - name: QAAlignedPrecision-BERTScore (Question & Answer Generation) [Gold Answer] type: qa_aligned_precision_bertscore_question_answer_generation_gold_answer value: 73.41 - name: QAAlignedF1Score-MoverScore (Question & Answer Generation) [Gold Answer] type: qa_aligned_f1_score_moverscore_question_answer_generation_gold_answer value: 55.53 - name: QAAlignedRecall-MoverScore (Question & Answer Generation) [Gold Answer] type: qa_aligned_recall_moverscore_question_answer_generation_gold_answer value: 58.25 - name: QAAlignedPrecision-MoverScore (Question & Answer Generation) [Gold Answer] type: qa_aligned_precision_moverscore_question_answer_generation_gold_answer value: 53.24 --- # Model Card of `lmqg/mt5-small-ruquad-qg` This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for question generation task on the [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). ### Overview - **Language model:** [google/mt5-small](https://huggingface.co/google/mt5-small) - **Language:** ru - **Training data:** [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) ### Usage - With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-) ```python from lmqg import TransformersQG # initialize model model = TransformersQG(language="ru", model="lmqg/mt5-small-ruquad-qg") # model prediction questions = model.generate_q(list_context="Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, в мае 1860 года провёл серию опытов.", list_answer="в мае 1860 года") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/mt5-small-ruquad-qg") output = pipe("Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, <hl> в мае 1860 года <hl> провёл серию опытов.") ``` ## Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-small-ruquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_ruquad.default.json) | | Score | Type | Dataset | |:-----------|--------:|:--------|:-----------------------------------------------------------------| | BERTScore | 84.27 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | | Bleu_1 | 31.03 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | | Bleu_2 | 24.58 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | | Bleu_3 | 19.92 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | | Bleu_4 | 16.31 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | | METEOR | 26.39 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | | MoverScore | 62.49 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | | ROUGE_L | 31.39 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | - ***Metric (Question & Answer Generation, Reference Answer)***: Each question is generated from *the gold answer*. [raw metric file](https://huggingface.co/lmqg/mt5-small-ruquad-qg/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_ruquad.default.json) | | Score | Type | Dataset | |:--------------------------------|--------:|:--------|:-----------------------------------------------------------------| | QAAlignedF1Score (BERTScore) | 90.17 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | | QAAlignedF1Score (MoverScore) | 68.22 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | | QAAlignedPrecision (BERTScore) | 90.17 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | | QAAlignedPrecision (MoverScore) | 68.23 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | | QAAlignedRecall (BERTScore) | 90.16 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | | QAAlignedRecall (MoverScore) | 68.21 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | - ***Metric (Question & Answer Generation, Pipeline Approach)***: Each question is generated on the answer generated by [`lmqg/mt5-small-ruquad-ae`](https://huggingface.co/lmqg/mt5-small-ruquad-ae). [raw metric file](https://huggingface.co/lmqg/mt5-small-ruquad-qg/raw/main/eval_pipeline/metric.first.answer.paragraph.questions_answers.lmqg_qg_ruquad.default.lmqg_mt5-small-ruquad-ae.json) | | Score | Type | Dataset | |:--------------------------------|--------:|:--------|:-----------------------------------------------------------------| | QAAlignedF1Score (BERTScore) | 76.96 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | | QAAlignedF1Score (MoverScore) | 55.53 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | | QAAlignedPrecision (BERTScore) | 73.41 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | | QAAlignedPrecision (MoverScore) | 53.24 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | | QAAlignedRecall (BERTScore) | 81.05 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | | QAAlignedRecall (MoverScore) | 58.25 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | ## Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_ruquad - dataset_name: default - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: None - model: google/mt5-small - max_length: 512 - max_length_output: 32 - epoch: 5 - batch: 64 - lr: 0.001 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 1 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-small-ruquad-qg/raw/main/trainer_config.json). ## Citation ``` @inproceedings{ushio-etal-2022-generative, title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration", author = "Ushio, Asahi and Alva-Manchego, Fernando and Camacho-Collados, Jose", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, U.A.E.", publisher = "Association for Computational Linguistics", } ```

dnnsdunca/ddroidai_pro_gram

dnnsdunca

"2024-03-23T01:40:50Z"

0

adapter-transformers

[ "adapter-transformers", "code", "text-generation", "en", "license:mit", "region:us" ]

text-generation

"2024-03-23T01:34:42Z"

--- license: mit language: - en metrics: - code_eval library_name: adapter-transformers pipeline_tag: text-generation tags: - code ---

ALivshits/Llama3_8B_ATIS_100-merged

ALivshits

"2024-07-21T13:29:38Z"

4

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

"2024-07-21T13:24:19Z"

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

QuantFactory/HuatuoGPT-o1-8B-GGUF

QuantFactory

"2025-01-03T06:10:23Z"

477

3

null

[ "gguf", "medical", "text-generation", "en", "dataset:FreedomIntelligence/medical-o1-reasoning-SFT", "dataset:FreedomIntelligence/medical-o1-verifiable-problem", "arxiv:2412.18925", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:quantized:meta-llama/Llama-3.1-8B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

text-generation

"2025-01-03T05:27:31Z"

--- license: apache-2.0 datasets: - FreedomIntelligence/medical-o1-reasoning-SFT - FreedomIntelligence/medical-o1-verifiable-problem language: - en base_model: - meta-llama/Llama-3.1-8B-Instruct pipeline_tag: text-generation tags: - medical --- [![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory) # QuantFactory/HuatuoGPT-o1-8B-GGUF This is quantized version of [FreedomIntelligence/HuatuoGPT-o1-8B](https://huggingface.co/FreedomIntelligence/HuatuoGPT-o1-8B) created using llama.cpp # Original Model Card <div align="center"> <h1> HuatuoGPT-o1-8B </h1> </div> <div align="center"> <a href="https://github.com/FreedomIntelligence/HuatuoGPT-o1" target="_blank">GitHub</a> | <a href="https://arxiv.org/pdf/2412.18925" target="_blank">Paper</a> </div> # <span>Introduction</span> **HuatuoGPT-o1** is a medical LLM designed for advanced medical reasoning. It generates a complex thought process, reflecting and refining its reasoning, before providing a final response. For more information, visit our GitHub repository: [https://github.com/FreedomIntelligence/HuatuoGPT-o1](https://github.com/FreedomIntelligence/HuatuoGPT-o1). # <span>Model Info</span> | | Backbone | Supported Languages | Link | | -------------------- | ------------ | ----- | --------------------------------------------------------------------- | | **HuatuoGPT-o1-8B** | LLaMA-3.1-8B | English | [HF Link](https://huggingface.co/FreedomIntelligence/HuatuoGPT-o1-8B) | | **HuatuoGPT-o1-70B** | LLaMA-3.1-70B | English | [HF Link](https://huggingface.co/FreedomIntelligence/HuatuoGPT-o1-70B) | | **HuatuoGPT-o1-7B** | Qwen2.5-7B | English & Chinese | [HF Link](https://huggingface.co/FreedomIntelligence/HuatuoGPT-o1-7B) | | **HuatuoGPT-o1-72B** | Qwen2.5-72B | English & Chinese | [HF Link](https://huggingface.co/FreedomIntelligence/HuatuoGPT-o1-72B) | # <span>Usage</span> You can use HuatuoGPT-o1 in the same way as `Llama-3.1-8B-Instruct`. You can deploy it with tools like [vllm](https://github.com/vllm-project/vllm) or [Sglang](https://github.com/sgl-project/sglang), or perform direct inference: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("FreedomIntelligence/HuatuoGPT-o1-8B",torch_dtype="auto",device_map="auto") tokenizer = AutoTokenizer.from_pretrained("FreedomIntelligence/HuatuoGPT-o1-8B") input_text = "How to stop a cough?" messages = [{"role": "user", "content": input_text}] inputs = tokenizer(tokenizer.apply_chat_template(messages, tokenize=False,add_generation_prompt=True ), return_tensors="pt").to(model.device) outputs = model.generate(**inputs, max_new_tokens=2048) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` HuatuoGPT-o1 adopts a *thinks-before-it-answers* approach, with outputs formatted as: ``` ## Thinking [Reasoning process] ## Final Response [Output] ``` # <span>📖 Citation</span> ``` @misc{chen2024huatuogpto1medicalcomplexreasoning, title={HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs}, author={Junying Chen and Zhenyang Cai and Ke Ji and Xidong Wang and Wanlong Liu and Rongsheng Wang and Jianye Hou and Benyou Wang}, year={2024}, eprint={2412.18925}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2412.18925}, } ```