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train · 880k rows

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-07-13 18:27:38	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 518 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-07-13 18:27:10	card stringlengths 11 1.01M
RogerB/afro-xlmr-large-kinyarwanda-finetuned-kintweets-finetuned	RogerB	2023-09-28T17:48:00Z	103	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "fill-mask", "generated_from_trainer", "base_model:RogerB/afro-xlmr-large-kinyarwanda-finetuned", "base_model:finetune:RogerB/afro-xlmr-large-kinyarwanda-finetuned", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-09-28T17:31:53Z	--- license: mit base_model: RogerB/afro-xlmr-large-kinyarwanda-finetuned tags: - generated_from_trainer model-index: - name: afro-xlmr-large-kinyarwanda-finetuned-kintweets-finetuned 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. --> # afro-xlmr-large-kinyarwanda-finetuned-kintweets-finetuned This model is a fine-tuned version of [RogerB/afro-xlmr-large-kinyarwanda-finetuned](https://huggingface.co/RogerB/afro-xlmr-large-kinyarwanda-finetuned) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9862 ## 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: 9 - eval_batch_size: 9 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.1616 \| 1.0 \| 334 \| 2.0138 \| \| 2.0785 \| 2.0 \| 668 \| 1.9996 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
R136a1/MythoMax-L2-13B-exl2	R136a1	2023-09-28T17:47:41Z	108	6	transformers	[ "transformers", "llama", "text-generation", "en", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-09-21T16:27:43Z	--- license: other language: - en --- [EXL2](https://github.com/turboderp/exllamav2/tree/master#exllamav2) Quantization of [Gryphe's MythoMax L2 13B](https://huggingface.co/Gryphe/MythoMax-L2-13b). Other quantized models are available from TheBloke: [GGML](https://huggingface.co/TheBloke/MythoMax-L2-13B-GGML) - [GPTQ](https://huggingface.co/TheBloke/MythoMax-L2-13B-GPTQ) - [GGUF](https://huggingface.co/TheBloke/MythoMax-L2-13B-GGUF) - [AWQ](https://huggingface.co/TheBloke/MythoMax-L2-13B-AWQ) ## Model details Base Perplexity : 5.7447 \| Branch \| bits \| Perplexity \| Description \| \|----------------------------------------------------------------------\|----------\|----------------\|-------------------------------------------------------------\| \| [3bit](https://huggingface.co/R136a1/MythoMax-L2-13B-exl2/tree/3bit) \| 3.73 \| 5.8251 \| Low bits quant while still good \| \| [4bit](https://huggingface.co/R136a1/MythoMax-L2-13B-exl2/tree/4bit) \| 4.33 \| 5.7784 \| can go 6K context on T4 GPU \| \| [main](https://huggingface.co/R136a1/MythoMax-L2-13B-exl2/tree/main) \| 5.33 \| 5.7427 \| 4k Context on T4 GPU (recommended if you use Google Colab) \| \| [6bit](https://huggingface.co/R136a1/MythoMax-L2-13B-exl2/tree/6bit) \| 6.13 \| 5.7347 \| For those who want better quality and capable of running it \| ## Prompt Format Alpaca format: ``` ### Instruction: ### Response: ```
Yntec/nuipenimix2	Yntec	2023-09-28T17:39:27Z	12,747	4	diffusers	[ "diffusers", "safetensors", "Anime", "Cute", "Animals", "McSionnaigh", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-09-28T16:58:20Z	--- license: creativeml-openrail-m library_name: diffusers pipeline_tag: text-to-image tags: - Anime - Cute - Animals - McSionnaigh - stable-diffusion - stable-diffusion-diffusers - diffusers - text-to-image --- # nuipenimix 2 fp16 no-ema version of this model. Original page: https://civitai.com/models/81937?modelVersionId=139841 Sample and prompt: ![Sample](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/SoiwQEhZqvAW9JdXn5Abu.png) ![Sample](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/MIbzralOKww2sWggu9lFW.png) icon of cute little red panda, round frame, blue glow, purple background
gokuls/HBERTv1_emb_compress_48_L12_H64_A2	gokuls	2023-09-28T17:39:14Z	46	0	transformers	[ "transformers", "pytorch", "hybridbert", "fill-mask", "generated_from_trainer", "dataset:gokuls/wiki_book_corpus_complete_processed_bert_dataset", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-09-27T07:12:38Z	--- tags: - generated_from_trainer datasets: - gokuls/wiki_book_corpus_complete_processed_bert_dataset metrics: - accuracy model-index: - name: HBERTv1_emb_compress_48_L12_H64_A2 results: - task: name: Masked Language Modeling type: fill-mask dataset: name: gokuls/wiki_book_corpus_complete_processed_bert_dataset type: gokuls/wiki_book_corpus_complete_processed_bert_dataset metrics: - name: Accuracy type: accuracy value: 0.12850906143802152 --- <!-- 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. --> # HBERTv1_emb_compress_48_L12_H64_A2 This model is a fine-tuned version of [](https://huggingface.co/) on the gokuls/wiki_book_corpus_complete_processed_bert_dataset dataset. It achieves the following results on the evaluation set: - Loss: 6.4079 - Accuracy: 0.1285 ## 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: 96 - eval_batch_size: 96 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10000 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:--------:\| \| 8.6554 \| 0.16 \| 10000 \| 8.5846 \| 0.0483 \| \| 7.2331 \| 0.33 \| 20000 \| 7.2280 \| 0.0542 \| \| 7.0014 \| 0.49 \| 30000 \| 6.9927 \| 0.0677 \| \| 6.8699 \| 0.66 \| 40000 \| 6.8637 \| 0.0856 \| \| 6.7777 \| 0.82 \| 50000 \| 6.7726 \| 0.0922 \| \| 6.7091 \| 0.98 \| 60000 \| 6.7101 \| 0.0974 \| \| 6.6626 \| 1.15 \| 70000 \| 6.6620 \| 0.1015 \| \| 6.6279 \| 1.31 \| 80000 \| 6.6255 \| 0.1040 \| \| 6.5917 \| 1.47 \| 90000 \| 6.5948 \| 0.1068 \| \| 6.5691 \| 1.64 \| 100000 \| 6.5695 \| 0.1094 \| \| 6.5486 \| 1.8 \| 110000 \| 6.5460 \| 0.1122 \| \| 6.5246 \| 1.97 \| 120000 \| 6.5275 \| 0.1144 \| \| 6.5069 \| 2.13 \| 130000 \| 6.5115 \| 0.1162 \| \| 6.5001 \| 2.29 \| 140000 \| 6.4962 \| 0.1180 \| \| 6.4785 \| 2.46 \| 150000 \| 6.4822 \| 0.1197 \| \| 6.4706 \| 2.62 \| 160000 \| 6.4714 \| 0.1212 \| \| 6.4612 \| 2.79 \| 170000 \| 6.4610 \| 0.1225 \| \| 6.4485 \| 2.95 \| 180000 \| 6.4530 \| 0.1233 \| \| 6.4477 \| 3.11 \| 190000 \| 6.4441 \| 0.1243 \| \| 6.4373 \| 3.28 \| 200000 \| 6.4395 \| 0.1251 \| \| 6.4351 \| 3.44 \| 210000 \| 6.4322 \| 0.1259 \| \| 6.4273 \| 3.6 \| 220000 \| 6.4264 \| 0.1262 \| \| 6.4153 \| 3.77 \| 230000 \| 6.4219 \| 0.1269 \| \| 6.4188 \| 3.93 \| 240000 \| 6.4182 \| 0.1274 \| \| 6.4128 \| 4.1 \| 250000 \| 6.4150 \| 0.1278 \| \| 6.4189 \| 4.26 \| 260000 \| 6.4121 \| 0.1280 \| \| 6.4102 \| 4.42 \| 270000 \| 6.4112 \| 0.1282 \| \| 6.4105 \| 4.59 \| 280000 \| 6.4087 \| 0.1285 \| \| 6.4065 \| 4.75 \| 290000 \| 6.4067 \| 0.1287 \| \| 6.4082 \| 4.92 \| 300000 \| 6.4070 \| 0.1285 \| ### Framework versions - Transformers 4.33.2 - Pytorch 1.14.0a0+410ce96 - Datasets 2.14.5 - Tokenizers 0.13.3
arnaucas/wildfire-classifier	arnaucas	2023-09-28T17:32:08Z	249	0	transformers	[ "transformers", "pytorch", "safetensors", "vit", "image-classification", "generated_from_trainer", "climate", "biology", "base_model:google/vit-base-patch16-384", "base_model:finetune:google/vit-base-patch16-384", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-09-18T16:49:26Z	--- license: apache-2.0 base_model: google/vit-base-patch16-384 tags: - generated_from_trainer - climate - biology metrics: - accuracy model-index: - name: wildfire-classifier results: [] widget: - src: https://news.erau.edu/-/media/images/news/headlines/january-2023/wildfire-overhead-drone-shot.jpg?h=749&w=1000&hash=13476D2A9BBA829375B2EB7E83588E18 example_title: Drone-shot - src: https://www.ecuadorforestofclouds.org/uploads/7/4/1/4/74143387/2015367_orig.jpg example_title: Cloudy forest --- <!-- 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. --> # Wildfire classifier This model is a fine-tuned version of [google/vit-base-patch16-384](https://huggingface.co/google/vit-base-patch16-384) on the [Kaggle Wildfire Dataset](https://www.kaggle.com/datasets/elmadafri/the-wildfire-dataset). It achieves the following results on the evaluation set: - Loss: 0.2329 - Accuracy: 0.9202 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.1208 \| 1.28 \| 100 \| 0.2329 \| 0.9202 \| \| 0.0261 \| 2.56 \| 200 \| 0.2469 \| 0.9316 \| \| 0.0007 \| 3.85 \| 300 \| 0.2358 \| 0.9392 \| ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3 ### Aditional resources [Fine-tuning tutorial](https://huggingface.co/blog/fine-tune-vit)
CyberHarem/taniguchi_harumi_citrus	CyberHarem	2023-09-28T17:23:37Z	0	0	null	[ "art", "text-to-image", "dataset:CyberHarem/taniguchi_harumi_citrus", "license:mit", "region:us" ]	text-to-image	2023-09-28T17:12:22Z	--- license: mit datasets: - CyberHarem/taniguchi_harumi_citrus pipeline_tag: text-to-image tags: - art --- # Lora of taniguchi_harumi_citrus This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 4080, you need to download `4080/taniguchi_harumi_citrus.pt` as the embedding and `4080/taniguchi_harumi_citrus.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. The best step we recommend is 4080, with the score of 0.918. The trigger words are: 1. `taniguchi_harumi_citrus` 2. `long_hair, brown_eyes, bangs, brown_hair, blunt_bangs, yellow_eyes` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: \| Steps \| Score \| Download \| pattern_1 \| pattern_2 \| pattern_3 \| pattern_4 \| bikini \| bondage \| free \| maid \| miko \| nude \| nude2 \| suit \| yukata \| \|:---------\|:----------\|:-------------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------\|:--------------------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-----------------------------------------------\|:------------------------------------------------\|:-------------------------------------\|:-----------------------------------------\| \| 5100 \| 0.884 \| [Download](5100/taniguchi_harumi_citrus.zip) \| ![pattern_1-5100](5100/previews/pattern_1.png) \| ![pattern_2-5100](5100/previews/pattern_2.png) \| ![pattern_3-5100](5100/previews/pattern_3.png) \| ![pattern_4-5100](5100/previews/pattern_4.png) \| ![bikini-5100](5100/previews/bikini.png) \| [<NSFW, click to see>](5100/previews/bondage.png) \| ![free-5100](5100/previews/free.png) \| ![maid-5100](5100/previews/maid.png) \| ![miko-5100](5100/previews/miko.png) \| [<NSFW, click to see>](5100/previews/nude.png) \| [<NSFW, click to see>](5100/previews/nude2.png) \| ![suit-5100](5100/previews/suit.png) \| ![yukata-5100](5100/previews/yukata.png) \| \| 4760 \| 0.907 \| [Download](4760/taniguchi_harumi_citrus.zip) \| ![pattern_1-4760](4760/previews/pattern_1.png) \| ![pattern_2-4760](4760/previews/pattern_2.png) \| ![pattern_3-4760](4760/previews/pattern_3.png) \| ![pattern_4-4760](4760/previews/pattern_4.png) \| ![bikini-4760](4760/previews/bikini.png) \| [<NSFW, click to see>](4760/previews/bondage.png) \| ![free-4760](4760/previews/free.png) \| ![maid-4760](4760/previews/maid.png) \| ![miko-4760](4760/previews/miko.png) \| [<NSFW, click to see>](4760/previews/nude.png) \| [<NSFW, click to see>](4760/previews/nude2.png) \| ![suit-4760](4760/previews/suit.png) \| ![yukata-4760](4760/previews/yukata.png) \| \| 4420 \| 0.856 \| [Download](4420/taniguchi_harumi_citrus.zip) \| ![pattern_1-4420](4420/previews/pattern_1.png) \| ![pattern_2-4420](4420/previews/pattern_2.png) \| ![pattern_3-4420](4420/previews/pattern_3.png) \| ![pattern_4-4420](4420/previews/pattern_4.png) \| ![bikini-4420](4420/previews/bikini.png) \| [<NSFW, click to see>](4420/previews/bondage.png) \| ![free-4420](4420/previews/free.png) \| ![maid-4420](4420/previews/maid.png) \| ![miko-4420](4420/previews/miko.png) \| [<NSFW, click to see>](4420/previews/nude.png) \| [<NSFW, click to see>](4420/previews/nude2.png) \| ![suit-4420](4420/previews/suit.png) \| ![yukata-4420](4420/previews/yukata.png) \| \| 4080 \| 0.918 \| [Download](4080/taniguchi_harumi_citrus.zip) \| ![pattern_1-4080](4080/previews/pattern_1.png) \| ![pattern_2-4080](4080/previews/pattern_2.png) \| ![pattern_3-4080](4080/previews/pattern_3.png) \| ![pattern_4-4080](4080/previews/pattern_4.png) \| ![bikini-4080](4080/previews/bikini.png) \| [<NSFW, click to see>](4080/previews/bondage.png) \| ![free-4080](4080/previews/free.png) \| ![maid-4080](4080/previews/maid.png) \| ![miko-4080](4080/previews/miko.png) \| [<NSFW, click to see>](4080/previews/nude.png) \| [<NSFW, click to see>](4080/previews/nude2.png) \| ![suit-4080](4080/previews/suit.png) \| ![yukata-4080](4080/previews/yukata.png) \| \| 3740 \| 0.913 \| [Download](3740/taniguchi_harumi_citrus.zip) \| ![pattern_1-3740](3740/previews/pattern_1.png) \| ![pattern_2-3740](3740/previews/pattern_2.png) \| ![pattern_3-3740](3740/previews/pattern_3.png) \| ![pattern_4-3740](3740/previews/pattern_4.png) \| ![bikini-3740](3740/previews/bikini.png) \| [<NSFW, click to see>](3740/previews/bondage.png) \| ![free-3740](3740/previews/free.png) \| ![maid-3740](3740/previews/maid.png) \| ![miko-3740](3740/previews/miko.png) \| [<NSFW, click to see>](3740/previews/nude.png) \| [<NSFW, click to see>](3740/previews/nude2.png) \| ![suit-3740](3740/previews/suit.png) \| ![yukata-3740](3740/previews/yukata.png) \| \| 3400 \| 0.852 \| [Download](3400/taniguchi_harumi_citrus.zip) \| ![pattern_1-3400](3400/previews/pattern_1.png) \| ![pattern_2-3400](3400/previews/pattern_2.png) \| ![pattern_3-3400](3400/previews/pattern_3.png) \| ![pattern_4-3400](3400/previews/pattern_4.png) \| ![bikini-3400](3400/previews/bikini.png) \| [<NSFW, click to see>](3400/previews/bondage.png) \| ![free-3400](3400/previews/free.png) \| ![maid-3400](3400/previews/maid.png) \| ![miko-3400](3400/previews/miko.png) \| [<NSFW, click to see>](3400/previews/nude.png) \| [<NSFW, click to see>](3400/previews/nude2.png) \| ![suit-3400](3400/previews/suit.png) \| ![yukata-3400](3400/previews/yukata.png) \| \| 3060 \| 0.867 \| [Download](3060/taniguchi_harumi_citrus.zip) \| ![pattern_1-3060](3060/previews/pattern_1.png) \| ![pattern_2-3060](3060/previews/pattern_2.png) \| ![pattern_3-3060](3060/previews/pattern_3.png) \| ![pattern_4-3060](3060/previews/pattern_4.png) \| ![bikini-3060](3060/previews/bikini.png) \| [<NSFW, click to see>](3060/previews/bondage.png) \| ![free-3060](3060/previews/free.png) \| ![maid-3060](3060/previews/maid.png) \| ![miko-3060](3060/previews/miko.png) \| [<NSFW, click to see>](3060/previews/nude.png) \| [<NSFW, click to see>](3060/previews/nude2.png) \| ![suit-3060](3060/previews/suit.png) \| ![yukata-3060](3060/previews/yukata.png) \| \| 2720 \| 0.891 \| [Download](2720/taniguchi_harumi_citrus.zip) \| ![pattern_1-2720](2720/previews/pattern_1.png) \| ![pattern_2-2720](2720/previews/pattern_2.png) \| ![pattern_3-2720](2720/previews/pattern_3.png) \| ![pattern_4-2720](2720/previews/pattern_4.png) \| ![bikini-2720](2720/previews/bikini.png) \| [<NSFW, click to see>](2720/previews/bondage.png) \| ![free-2720](2720/previews/free.png) \| ![maid-2720](2720/previews/maid.png) \| ![miko-2720](2720/previews/miko.png) \| [<NSFW, click to see>](2720/previews/nude.png) \| [<NSFW, click to see>](2720/previews/nude2.png) \| ![suit-2720](2720/previews/suit.png) \| ![yukata-2720](2720/previews/yukata.png) \| \| 2380 \| 0.893 \| [Download](2380/taniguchi_harumi_citrus.zip) \| ![pattern_1-2380](2380/previews/pattern_1.png) \| ![pattern_2-2380](2380/previews/pattern_2.png) \| ![pattern_3-2380](2380/previews/pattern_3.png) \| ![pattern_4-2380](2380/previews/pattern_4.png) \| ![bikini-2380](2380/previews/bikini.png) \| [<NSFW, click to see>](2380/previews/bondage.png) \| ![free-2380](2380/previews/free.png) \| ![maid-2380](2380/previews/maid.png) \| ![miko-2380](2380/previews/miko.png) \| [<NSFW, click to see>](2380/previews/nude.png) \| [<NSFW, click to see>](2380/previews/nude2.png) \| ![suit-2380](2380/previews/suit.png) \| ![yukata-2380](2380/previews/yukata.png) \| \| 2040 \| 0.850 \| [Download](2040/taniguchi_harumi_citrus.zip) \| ![pattern_1-2040](2040/previews/pattern_1.png) \| ![pattern_2-2040](2040/previews/pattern_2.png) \| ![pattern_3-2040](2040/previews/pattern_3.png) \| ![pattern_4-2040](2040/previews/pattern_4.png) \| ![bikini-2040](2040/previews/bikini.png) \| [<NSFW, click to see>](2040/previews/bondage.png) \| ![free-2040](2040/previews/free.png) \| ![maid-2040](2040/previews/maid.png) \| ![miko-2040](2040/previews/miko.png) \| [<NSFW, click to see>](2040/previews/nude.png) \| [<NSFW, click to see>](2040/previews/nude2.png) \| ![suit-2040](2040/previews/suit.png) \| ![yukata-2040](2040/previews/yukata.png) \| \| 1700 \| 0.846 \| [Download](1700/taniguchi_harumi_citrus.zip) \| ![pattern_1-1700](1700/previews/pattern_1.png) \| ![pattern_2-1700](1700/previews/pattern_2.png) \| ![pattern_3-1700](1700/previews/pattern_3.png) \| ![pattern_4-1700](1700/previews/pattern_4.png) \| ![bikini-1700](1700/previews/bikini.png) \| [<NSFW, click to see>](1700/previews/bondage.png) \| ![free-1700](1700/previews/free.png) \| ![maid-1700](1700/previews/maid.png) \| ![miko-1700](1700/previews/miko.png) \| [<NSFW, click to see>](1700/previews/nude.png) \| [<NSFW, click to see>](1700/previews/nude2.png) \| ![suit-1700](1700/previews/suit.png) \| ![yukata-1700](1700/previews/yukata.png) \| \| 1360 \| 0.844 \| [Download](1360/taniguchi_harumi_citrus.zip) \| ![pattern_1-1360](1360/previews/pattern_1.png) \| ![pattern_2-1360](1360/previews/pattern_2.png) \| ![pattern_3-1360](1360/previews/pattern_3.png) \| ![pattern_4-1360](1360/previews/pattern_4.png) \| ![bikini-1360](1360/previews/bikini.png) \| [<NSFW, click to see>](1360/previews/bondage.png) \| ![free-1360](1360/previews/free.png) \| ![maid-1360](1360/previews/maid.png) \| ![miko-1360](1360/previews/miko.png) \| [<NSFW, click to see>](1360/previews/nude.png) \| [<NSFW, click to see>](1360/previews/nude2.png) \| ![suit-1360](1360/previews/suit.png) \| ![yukata-1360](1360/previews/yukata.png) \| \| 1020 \| 0.768 \| [Download](1020/taniguchi_harumi_citrus.zip) \| ![pattern_1-1020](1020/previews/pattern_1.png) \| ![pattern_2-1020](1020/previews/pattern_2.png) \| ![pattern_3-1020](1020/previews/pattern_3.png) \| ![pattern_4-1020](1020/previews/pattern_4.png) \| ![bikini-1020](1020/previews/bikini.png) \| [<NSFW, click to see>](1020/previews/bondage.png) \| ![free-1020](1020/previews/free.png) \| ![maid-1020](1020/previews/maid.png) \| ![miko-1020](1020/previews/miko.png) \| [<NSFW, click to see>](1020/previews/nude.png) \| [<NSFW, click to see>](1020/previews/nude2.png) \| ![suit-1020](1020/previews/suit.png) \| ![yukata-1020](1020/previews/yukata.png) \| \| 680 \| 0.677 \| [Download](680/taniguchi_harumi_citrus.zip) \| ![pattern_1-680](680/previews/pattern_1.png) \| ![pattern_2-680](680/previews/pattern_2.png) \| ![pattern_3-680](680/previews/pattern_3.png) \| ![pattern_4-680](680/previews/pattern_4.png) \| ![bikini-680](680/previews/bikini.png) \| [<NSFW, click to see>](680/previews/bondage.png) \| ![free-680](680/previews/free.png) \| ![maid-680](680/previews/maid.png) \| ![miko-680](680/previews/miko.png) \| [<NSFW, click to see>](680/previews/nude.png) \| [<NSFW, click to see>](680/previews/nude2.png) \| ![suit-680](680/previews/suit.png) \| ![yukata-680](680/previews/yukata.png) \| \| 340 \| 0.531 \| [Download](340/taniguchi_harumi_citrus.zip) \| ![pattern_1-340](340/previews/pattern_1.png) \| ![pattern_2-340](340/previews/pattern_2.png) \| ![pattern_3-340](340/previews/pattern_3.png) \| ![pattern_4-340](340/previews/pattern_4.png) \| ![bikini-340](340/previews/bikini.png) \| [<NSFW, click to see>](340/previews/bondage.png) \| ![free-340](340/previews/free.png) \| ![maid-340](340/previews/maid.png) \| ![miko-340](340/previews/miko.png) \| [<NSFW, click to see>](340/previews/nude.png) \| [<NSFW, click to see>](340/previews/nude2.png) \| ![suit-340](340/previews/suit.png) \| ![yukata-340](340/previews/yukata.png) \|
thewiz/kd-bertBase-bertTiny	thewiz	2023-09-28T17:13:49Z	120	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "dataset:glue", "base_model:google/bert_uncased_L-2_H-128_A-2", "base_model:finetune:google/bert_uncased_L-2_H-128_A-2", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-09-28T16:36:52Z	--- license: apache-2.0 base_model: google/bert_uncased_L-2_H-128_A-2 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: kd-bertBase-bertTiny results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: sst2 split: validation args: sst2 metrics: - name: Accuracy type: accuracy value: 0.8268348623853211 --- <!-- 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. --> # kd-bertBase-bertTiny This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 1.0591 - Accuracy: 0.8268 ## 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: 6e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 33 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 7 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.4776 \| 1.0 \| 527 \| 1.1240 \| 0.8062 \| \| 0.8442 \| 2.0 \| 1054 \| 1.0475 \| 0.8165 \| \| 0.6568 \| 3.0 \| 1581 \| 1.0529 \| 0.8131 \| \| 0.5623 \| 4.0 \| 2108 \| 1.0503 \| 0.8188 \| \| 0.5066 \| 5.0 \| 2635 \| 1.0471 \| 0.8303 \| \| 0.4736 \| 6.0 \| 3162 \| 1.0711 \| 0.8280 \| \| 0.4603 \| 7.0 \| 3689 \| 1.0591 \| 0.8268 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
davidramos/lora-flan-t5-small	davidramos	2023-09-28T17:02:57Z	0	0	null	[ "generated_from_trainer", "license:apache-2.0", "region:us" ]	null	2023-09-27T14:10:45Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: lora-flan-t5-small 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. --> # lora-flan-t5-small This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the None 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 - lr_scheduler_warmup_ratio: 0.04 - training_steps: 15000 ### Training results ### Framework versions - Transformers 4.30.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
ezzini/marian-finetuned-mcwc-en-to-es	ezzini	2023-09-28T17:00:33Z	60	0	transformers	[ "transformers", "tf", "marian", "text2text-generation", "generated_from_keras_callback", "base_model:Helsinki-NLP/opus-mt-en-es", "base_model:finetune:Helsinki-NLP/opus-mt-en-es", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-09-28T16:06:09Z	--- license: apache-2.0 base_model: Helsinki-NLP/opus-mt-en-es tags: - generated_from_keras_callback model-index: - name: ezzini/marian-finetuned-mcwc-en-to-es 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. --> # ezzini/marian-finetuned-mcwc-en-to-es This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-es](https://huggingface.co/Helsinki-NLP/opus-mt-en-es) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.8292 - Validation Loss: 0.9642 - Epoch: 2 ## 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': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 3666, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 1.1192 \| 1.0032 \| 0 \| \| 0.9233 \| 0.9729 \| 1 \| \| 0.8292 \| 0.9642 \| 2 \| ### Framework versions - Transformers 4.33.3 - TensorFlow 2.13.0 - Datasets 2.14.5 - Tokenizers 0.13.3
afaranda/my_awesome_billsum_model	afaranda	2023-09-28T16:58:13Z	103	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "dataset:billsum", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-09-28T14:03:18Z	--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer datasets: - billsum metrics: - rouge model-index: - name: my_awesome_billsum_model results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: billsum type: billsum config: default split: ca_test args: default metrics: - name: Rouge1 type: rouge value: 0.1398 --- <!-- 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. --> # my_awesome_billsum_model This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset. It achieves the following results on the evaluation set: - Loss: 2.4899 - Rouge1: 0.1398 - Rouge2: 0.0512 - Rougel: 0.117 - Rougelsum: 0.117 - 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: 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: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 62 \| 2.7815 \| 0.1245 \| 0.0363 \| 0.1044 \| 0.1044 \| 19.0 \| \| No log \| 2.0 \| 124 \| 2.5728 \| 0.133 \| 0.0452 \| 0.1112 \| 0.1114 \| 19.0 \| \| No log \| 3.0 \| 186 \| 2.5064 \| 0.1382 \| 0.0514 \| 0.1163 \| 0.1164 \| 19.0 \| \| No log \| 4.0 \| 248 \| 2.4899 \| 0.1398 \| 0.0512 \| 0.117 \| 0.117 \| 19.0 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
vineetsharma/databricks-dolly-15k-pythia-70m-deduped-v1	vineetsharma	2023-09-28T16:55:16Z	156	0	transformers	[ "transformers", "pytorch", "gpt_neox", "text-generation", "generated_from_trainer", "base_model:EleutherAI/pythia-70m-deduped", "base_model:finetune:EleutherAI/pythia-70m-deduped", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-09-24T17:01:45Z	--- license: apache-2.0 base_model: EleutherAI/pythia-70m-deduped tags: - generated_from_trainer model-index: - name: databricks-dolly-15k-pythia-70m-deduped-v1 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. --> # databricks-dolly-15k-pythia-70m-deduped-v1 This model is a fine-tuned version of [EleutherAI/pythia-70m-deduped](https://huggingface.co/EleutherAI/pythia-70m-deduped) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.3063 ## 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: 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 5.8656 \| 1.0 \| 1501 \| 4.6547 \| \| 4.609 \| 2.0 \| 3002 \| 4.4454 \| \| 4.1706 \| 3.0 \| 4503 \| 4.3141 \| \| 3.8124 \| 4.0 \| 6004 \| 4.3063 \| \| 3.4925 \| 5.0 \| 7505 \| 4.3213 \| \| 3.1927 \| 6.0 \| 9006 \| 4.3806 \| \| 2.9257 \| 7.0 \| 10507 \| 4.4425 \| \| 2.6838 \| 8.0 \| 12008 \| 4.5654 \| \| 2.4619 \| 9.0 \| 13509 \| 4.7032 \| \| 2.2826 \| 10.0 \| 15010 \| 4.8537 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
Crataco/AI-Dungeon-2-Classic-GGML	Crataco	2023-09-28T16:50:44Z	0	6	null	[ "ggml", "causal-lm", "gpt2", "text-generation", "en", "license:mit", "region:us" ]	text-generation	2023-09-23T19:06:30Z	--- language: - en tags: - ggml - causal-lm - gpt2 - text-generation license: mit --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6251b9851842c08ef3111c4f/NtCV4dSUaHb7XnMYrm1iF.png) ### This repository contains quantized conversions of the AI Dungeon 2 checkpoint, "model_v5". For use with frontends that support GGML quantized GPT-2 models. This model works best with KoboldCpp's "Adventure" mode. Last updated on 2023-09-23. Model \| RAM usage (KoboldCpp) \| RAM usage (Oobabooga) :--:\|:--:\|:--: aid2classic-ggml-q4_0.bin \| 984.1 MiB \| 1.4 GiB aid2classic-ggml-q4_1.bin \| 1.1 GiB \| 1.5 GiB aid2classic-ggml-q5_0.bin \| 1.2 GiB \| 1.6 GiB aid2classic-ggml-q5_1.bin \| 1.2 GiB \| 1.7 GiB aid2classic-ggml-q8_0.bin \| 1.7 GiB \| 2.2 GiB aid2classic-ggml-f16.bin \| 3.2 GiB \| 3.6 GiB Description: - 2019 AI Dungeon users may recognize this model as the same one that powered [the open-source AI Dungeon 2 project](https://github.com/Latitude-Archives/AIDungeon) and its various forks. This was before AI Dungeon 2 moved to its own website and consequently rebranded to "AI Dungeon". - 2020-2021 AI Dungeon users may recognize this model as "Classic", the free tier below Griffin (free, but later used "energy") and Dragon (subscription). - If you want a better model trained on the same dataset at the cost of higher hardware requirements, check out [Spring Dragon 13B](https://huggingface.co/TheBloke/Spring-Dragon-GGUF), intended to replicate 2020 AI Dungeon's "Dragon" experience on local hardware. - The motivation behind these quantizations was that [Henk717/ai-dungeon2-classic-ggml](https://huggingface.co/Henk717/ai-dungeon2-classic-ggml) was older and lacked other quantization formats. The workflow for this quantization was also different: henk717's mentions being converted to a 16-bit Pytorch checkpoint before being converted to GGML. This one was converted straight from Tensorflow to 16-bit GGML before being quantized. Notes: - KoboldCpp [[bfc696f]](https://github.com/LostRuins/koboldcpp/tree/bfc696fcc452975dbe8967c39301ba856d04a030) was tested without OpenBLAS. - Oobabooga [[895ec9d]](https://github.com/oobabooga/text-generation-webui/tree/895ec9dadb96120e8202a83052bf9032ca3245ae) was tested with with the `--model <model> --loader ctransformers --model_type gpt2` launch arguments. - ggerganov/ggml [[8ca2c19]](https://github.com/ggerganov/ggml/tree/8ca2c19a3bb8622954d858fbf6383522684eaf34)'s gpt-2 conversion script was used for conversion and quantization. - The original model was found in the `generator/gpt2/models/model_v5` directory of [AI Dungeon 2 Unleashed](https://henk.tech/aid/).
erkam/sg2im-256-bs-16x2	erkam	2023-09-28T16:50:24Z	2	0	diffusers	[ "diffusers", "sg-to-image", "scene-graph", "stable-diffusion", "stable-diffusion-diffusers", "lora", "base_model:stabilityai/stable-diffusion-2", "base_model:adapter:stabilityai/stable-diffusion-2", "license:creativeml-openrail-m", "region:us" ]	null	2023-09-23T01:13:28Z	--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2 tags: - sg-to-image - scene-graph - stable-diffusion - stable-diffusion-diffusers - diffusers - lora inference: true --- # LoRA text2image fine-tuning - erkam/sg2im-256-bs-16x2 These are LoRA adaption weights for stabilityai/stable-diffusion-2. The weights were fine-tuned on the vg dataset. You can find some example images in the following.
AmirH98/Reinforce-CartPole-v1	AmirH98	2023-09-28T16:48:12Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-09-28T16:48:03Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . 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
facebook/mbart-large-50-many-to-many-mmt	facebook	2023-09-28T16:42:59Z	295,669	336	transformers	[ "transformers", "pytorch", "tf", "jax", "rust", "safetensors", "mbart", "text2text-generation", "mbart-50", "translation", "multilingual", "ar", "cs", "de", "en", "es", "et", "fi", "fr", "gu", "hi", "it", "ja", "kk", "ko", "lt", "lv", "my", "ne", "nl", "ro", "ru", "si", "tr", "vi", "zh", "af", "az", "bn", "fa", "he", "hr", "id", "ka", "km", "mk", "ml", "mn", "mr", "pl", "ps", "pt", "sv", "sw", "ta", "te", "th", "tl", "uk", "ur", "xh", "gl", "sl", "arxiv:2008.00401", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2022-03-02T23:29:05Z	--- language: - multilingual - ar - cs - de - en - es - et - fi - fr - gu - hi - it - ja - kk - ko - lt - lv - my - ne - nl - ro - ru - si - tr - vi - zh - af - az - bn - fa - he - hr - id - ka - km - mk - ml - mn - mr - pl - ps - pt - sv - sw - ta - te - th - tl - uk - ur - xh - gl - sl tags: - mbart-50 pipeline_tag: translation --- # mBART-50 many to many multilingual machine translation This model is a fine-tuned checkpoint of [mBART-large-50](https://huggingface.co/facebook/mbart-large-50). `mbart-large-50-many-to-many-mmt` is fine-tuned for multilingual machine translation. It was introduced in [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://arxiv.org/abs/2008.00401) paper. The model can translate directly between any pair of 50 languages. To translate into a target language, the target language id is forced as the first generated token. To force the target language id as the first generated token, pass the `forced_bos_token_id` parameter to the `generate` method. ```python from transformers import MBartForConditionalGeneration, MBart50TokenizerFast article_hi = "संयुक्त राष्ट्र के प्रमुख का कहना है कि सीरिया में कोई सैन्य समाधान नहीं है" article_ar = "الأمين العام للأمم المتحدة يقول إنه لا يوجد حل عسكري في سوريا." model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt") tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt") # translate Hindi to French tokenizer.src_lang = "hi_IN" encoded_hi = tokenizer(article_hi, return_tensors="pt") generated_tokens = model.generate( encoded_hi, forced_bos_token_id=tokenizer.lang_code_to_id["fr_XX"] ) tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) # => "Le chef de l 'ONU affirme qu 'il n 'y a pas de solution militaire dans la Syrie." # translate Arabic to English tokenizer.src_lang = "ar_AR" encoded_ar = tokenizer(article_ar, return_tensors="pt") generated_tokens = model.generate( encoded_ar, forced_bos_token_id=tokenizer.lang_code_to_id["en_XX"] ) tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) # => "The Secretary-General of the United Nations says there is no military solution in Syria." ``` See the [model hub](https://huggingface.co/models?filter=mbart-50) to look for more fine-tuned versions. ## Languages covered Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI) ## BibTeX entry and citation info ``` @article{tang2020multilingual, title={Multilingual Translation with Extensible Multilingual Pretraining and Finetuning}, author={Yuqing Tang and Chau Tran and Xian Li and Peng-Jen Chen and Naman Goyal and Vishrav Chaudhary and Jiatao Gu and Angela Fan}, year={2020}, eprint={2008.00401}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
CyberHarem/aihara_mei_citrus	CyberHarem	2023-09-28T16:39:48Z	0	1	null	[ "art", "text-to-image", "dataset:CyberHarem/aihara_mei_citrus", "license:mit", "region:us" ]	text-to-image	2023-09-28T16:27:11Z	--- license: mit datasets: - CyberHarem/aihara_mei_citrus pipeline_tag: text-to-image tags: - art --- # Lora of aihara_mei_citrus This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 6900, you need to download `6900/aihara_mei_citrus.pt` as the embedding and `6900/aihara_mei_citrus.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. The best step we recommend is 6900, with the score of 0.945. The trigger words are: 1. `aihara_mei_citrus` 2. `black_hair, long_hair, purple_eyes, hair_between_eyes` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: \| Steps \| Score \| Download \| pattern_1 \| pattern_2 \| pattern_3 \| pattern_4 \| pattern_5 \| pattern_6 \| bikini \| bondage \| free \| maid \| miko \| nude \| nude2 \| suit \| yukata \| \|:---------\|:----------\|:-------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-------------------------------------------------\|:--------------------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-----------------------------------------------\|:------------------------------------------------\|:-------------------------------------\|:-----------------------------------------\| \| 6900 \| 0.945 \| [Download](6900/aihara_mei_citrus.zip) \| ![pattern_1-6900](6900/previews/pattern_1.png) \| ![pattern_2-6900](6900/previews/pattern_2.png) \| ![pattern_3-6900](6900/previews/pattern_3.png) \| ![pattern_4-6900](6900/previews/pattern_4.png) \| ![pattern_5-6900](6900/previews/pattern_5.png) \| ![pattern_6-6900](6900/previews/pattern_6.png) \| [<NSFW, click to see>](6900/previews/bikini.png) \| [<NSFW, click to see>](6900/previews/bondage.png) \| ![free-6900](6900/previews/free.png) \| ![maid-6900](6900/previews/maid.png) \| ![miko-6900](6900/previews/miko.png) \| [<NSFW, click to see>](6900/previews/nude.png) \| [<NSFW, click to see>](6900/previews/nude2.png) \| ![suit-6900](6900/previews/suit.png) \| ![yukata-6900](6900/previews/yukata.png) \| \| 6440 \| 0.823 \| [Download](6440/aihara_mei_citrus.zip) \| ![pattern_1-6440](6440/previews/pattern_1.png) \| ![pattern_2-6440](6440/previews/pattern_2.png) \| ![pattern_3-6440](6440/previews/pattern_3.png) \| ![pattern_4-6440](6440/previews/pattern_4.png) \| ![pattern_5-6440](6440/previews/pattern_5.png) \| ![pattern_6-6440](6440/previews/pattern_6.png) \| [<NSFW, click to see>](6440/previews/bikini.png) \| [<NSFW, click to see>](6440/previews/bondage.png) \| ![free-6440](6440/previews/free.png) \| ![maid-6440](6440/previews/maid.png) \| ![miko-6440](6440/previews/miko.png) \| [<NSFW, click to see>](6440/previews/nude.png) \| [<NSFW, click to see>](6440/previews/nude2.png) \| ![suit-6440](6440/previews/suit.png) \| ![yukata-6440](6440/previews/yukata.png) \| \| 5980 \| 0.865 \| [Download](5980/aihara_mei_citrus.zip) \| ![pattern_1-5980](5980/previews/pattern_1.png) \| ![pattern_2-5980](5980/previews/pattern_2.png) \| ![pattern_3-5980](5980/previews/pattern_3.png) \| ![pattern_4-5980](5980/previews/pattern_4.png) \| ![pattern_5-5980](5980/previews/pattern_5.png) \| ![pattern_6-5980](5980/previews/pattern_6.png) \| [<NSFW, click to see>](5980/previews/bikini.png) \| [<NSFW, click to see>](5980/previews/bondage.png) \| ![free-5980](5980/previews/free.png) \| ![maid-5980](5980/previews/maid.png) \| ![miko-5980](5980/previews/miko.png) \| [<NSFW, click to see>](5980/previews/nude.png) \| [<NSFW, click to see>](5980/previews/nude2.png) \| ![suit-5980](5980/previews/suit.png) \| ![yukata-5980](5980/previews/yukata.png) \| \| 5520 \| 0.883 \| [Download](5520/aihara_mei_citrus.zip) \| ![pattern_1-5520](5520/previews/pattern_1.png) \| ![pattern_2-5520](5520/previews/pattern_2.png) \| ![pattern_3-5520](5520/previews/pattern_3.png) \| ![pattern_4-5520](5520/previews/pattern_4.png) \| ![pattern_5-5520](5520/previews/pattern_5.png) \| ![pattern_6-5520](5520/previews/pattern_6.png) \| [<NSFW, click to see>](5520/previews/bikini.png) \| [<NSFW, click to see>](5520/previews/bondage.png) \| ![free-5520](5520/previews/free.png) \| ![maid-5520](5520/previews/maid.png) \| ![miko-5520](5520/previews/miko.png) \| [<NSFW, click to see>](5520/previews/nude.png) \| [<NSFW, click to see>](5520/previews/nude2.png) \| ![suit-5520](5520/previews/suit.png) \| ![yukata-5520](5520/previews/yukata.png) \| \| 5060 \| 0.884 \| [Download](5060/aihara_mei_citrus.zip) \| ![pattern_1-5060](5060/previews/pattern_1.png) \| ![pattern_2-5060](5060/previews/pattern_2.png) \| ![pattern_3-5060](5060/previews/pattern_3.png) \| ![pattern_4-5060](5060/previews/pattern_4.png) \| ![pattern_5-5060](5060/previews/pattern_5.png) \| ![pattern_6-5060](5060/previews/pattern_6.png) \| [<NSFW, click to see>](5060/previews/bikini.png) \| [<NSFW, click to see>](5060/previews/bondage.png) \| ![free-5060](5060/previews/free.png) \| ![maid-5060](5060/previews/maid.png) \| ![miko-5060](5060/previews/miko.png) \| [<NSFW, click to see>](5060/previews/nude.png) \| [<NSFW, click to see>](5060/previews/nude2.png) \| ![suit-5060](5060/previews/suit.png) \| ![yukata-5060](5060/previews/yukata.png) \| \| 4600 \| 0.909 \| [Download](4600/aihara_mei_citrus.zip) \| ![pattern_1-4600](4600/previews/pattern_1.png) \| ![pattern_2-4600](4600/previews/pattern_2.png) \| ![pattern_3-4600](4600/previews/pattern_3.png) \| ![pattern_4-4600](4600/previews/pattern_4.png) \| ![pattern_5-4600](4600/previews/pattern_5.png) \| ![pattern_6-4600](4600/previews/pattern_6.png) \| [<NSFW, click to see>](4600/previews/bikini.png) \| [<NSFW, click to see>](4600/previews/bondage.png) \| ![free-4600](4600/previews/free.png) \| ![maid-4600](4600/previews/maid.png) \| ![miko-4600](4600/previews/miko.png) \| [<NSFW, click to see>](4600/previews/nude.png) \| [<NSFW, click to see>](4600/previews/nude2.png) \| ![suit-4600](4600/previews/suit.png) \| ![yukata-4600](4600/previews/yukata.png) \| \| 4140 \| 0.920 \| [Download](4140/aihara_mei_citrus.zip) \| ![pattern_1-4140](4140/previews/pattern_1.png) \| ![pattern_2-4140](4140/previews/pattern_2.png) \| ![pattern_3-4140](4140/previews/pattern_3.png) \| ![pattern_4-4140](4140/previews/pattern_4.png) \| ![pattern_5-4140](4140/previews/pattern_5.png) \| ![pattern_6-4140](4140/previews/pattern_6.png) \| [<NSFW, click to see>](4140/previews/bikini.png) \| [<NSFW, click to see>](4140/previews/bondage.png) \| ![free-4140](4140/previews/free.png) \| ![maid-4140](4140/previews/maid.png) \| ![miko-4140](4140/previews/miko.png) \| [<NSFW, click to see>](4140/previews/nude.png) \| [<NSFW, click to see>](4140/previews/nude2.png) \| ![suit-4140](4140/previews/suit.png) \| ![yukata-4140](4140/previews/yukata.png) \| \| 3680 \| 0.925 \| [Download](3680/aihara_mei_citrus.zip) \| ![pattern_1-3680](3680/previews/pattern_1.png) \| ![pattern_2-3680](3680/previews/pattern_2.png) \| ![pattern_3-3680](3680/previews/pattern_3.png) \| ![pattern_4-3680](3680/previews/pattern_4.png) \| ![pattern_5-3680](3680/previews/pattern_5.png) \| ![pattern_6-3680](3680/previews/pattern_6.png) \| [<NSFW, click to see>](3680/previews/bikini.png) \| [<NSFW, click to see>](3680/previews/bondage.png) \| ![free-3680](3680/previews/free.png) \| ![maid-3680](3680/previews/maid.png) \| ![miko-3680](3680/previews/miko.png) \| [<NSFW, click to see>](3680/previews/nude.png) \| [<NSFW, click to see>](3680/previews/nude2.png) \| ![suit-3680](3680/previews/suit.png) \| ![yukata-3680](3680/previews/yukata.png) \| \| 3220 \| 0.909 \| [Download](3220/aihara_mei_citrus.zip) \| ![pattern_1-3220](3220/previews/pattern_1.png) \| ![pattern_2-3220](3220/previews/pattern_2.png) \| ![pattern_3-3220](3220/previews/pattern_3.png) \| ![pattern_4-3220](3220/previews/pattern_4.png) \| ![pattern_5-3220](3220/previews/pattern_5.png) \| ![pattern_6-3220](3220/previews/pattern_6.png) \| [<NSFW, click to see>](3220/previews/bikini.png) \| [<NSFW, click to see>](3220/previews/bondage.png) \| ![free-3220](3220/previews/free.png) \| ![maid-3220](3220/previews/maid.png) \| ![miko-3220](3220/previews/miko.png) \| [<NSFW, click to see>](3220/previews/nude.png) \| [<NSFW, click to see>](3220/previews/nude2.png) \| ![suit-3220](3220/previews/suit.png) \| ![yukata-3220](3220/previews/yukata.png) \| \| 2760 \| 0.857 \| [Download](2760/aihara_mei_citrus.zip) \| ![pattern_1-2760](2760/previews/pattern_1.png) \| ![pattern_2-2760](2760/previews/pattern_2.png) \| ![pattern_3-2760](2760/previews/pattern_3.png) \| ![pattern_4-2760](2760/previews/pattern_4.png) \| ![pattern_5-2760](2760/previews/pattern_5.png) \| ![pattern_6-2760](2760/previews/pattern_6.png) \| [<NSFW, click to see>](2760/previews/bikini.png) \| [<NSFW, click to see>](2760/previews/bondage.png) \| ![free-2760](2760/previews/free.png) \| ![maid-2760](2760/previews/maid.png) \| ![miko-2760](2760/previews/miko.png) \| [<NSFW, click to see>](2760/previews/nude.png) \| [<NSFW, click to see>](2760/previews/nude2.png) \| ![suit-2760](2760/previews/suit.png) \| ![yukata-2760](2760/previews/yukata.png) \| \| 2300 \| 0.867 \| [Download](2300/aihara_mei_citrus.zip) \| ![pattern_1-2300](2300/previews/pattern_1.png) \| ![pattern_2-2300](2300/previews/pattern_2.png) \| ![pattern_3-2300](2300/previews/pattern_3.png) \| ![pattern_4-2300](2300/previews/pattern_4.png) \| ![pattern_5-2300](2300/previews/pattern_5.png) \| ![pattern_6-2300](2300/previews/pattern_6.png) \| [<NSFW, click to see>](2300/previews/bikini.png) \| [<NSFW, click to see>](2300/previews/bondage.png) \| ![free-2300](2300/previews/free.png) \| ![maid-2300](2300/previews/maid.png) \| ![miko-2300](2300/previews/miko.png) \| [<NSFW, click to see>](2300/previews/nude.png) \| [<NSFW, click to see>](2300/previews/nude2.png) \| ![suit-2300](2300/previews/suit.png) \| ![yukata-2300](2300/previews/yukata.png) \| \| 1840 \| 0.859 \| [Download](1840/aihara_mei_citrus.zip) \| ![pattern_1-1840](1840/previews/pattern_1.png) \| ![pattern_2-1840](1840/previews/pattern_2.png) \| ![pattern_3-1840](1840/previews/pattern_3.png) \| ![pattern_4-1840](1840/previews/pattern_4.png) \| ![pattern_5-1840](1840/previews/pattern_5.png) \| ![pattern_6-1840](1840/previews/pattern_6.png) \| [<NSFW, click to see>](1840/previews/bikini.png) \| [<NSFW, click to see>](1840/previews/bondage.png) \| ![free-1840](1840/previews/free.png) \| ![maid-1840](1840/previews/maid.png) \| ![miko-1840](1840/previews/miko.png) \| [<NSFW, click to see>](1840/previews/nude.png) \| [<NSFW, click to see>](1840/previews/nude2.png) \| ![suit-1840](1840/previews/suit.png) \| ![yukata-1840](1840/previews/yukata.png) \| \| 1380 \| 0.831 \| [Download](1380/aihara_mei_citrus.zip) \| ![pattern_1-1380](1380/previews/pattern_1.png) \| ![pattern_2-1380](1380/previews/pattern_2.png) \| ![pattern_3-1380](1380/previews/pattern_3.png) \| ![pattern_4-1380](1380/previews/pattern_4.png) \| ![pattern_5-1380](1380/previews/pattern_5.png) \| ![pattern_6-1380](1380/previews/pattern_6.png) \| [<NSFW, click to see>](1380/previews/bikini.png) \| [<NSFW, click to see>](1380/previews/bondage.png) \| ![free-1380](1380/previews/free.png) \| ![maid-1380](1380/previews/maid.png) \| ![miko-1380](1380/previews/miko.png) \| [<NSFW, click to see>](1380/previews/nude.png) \| [<NSFW, click to see>](1380/previews/nude2.png) \| ![suit-1380](1380/previews/suit.png) \| ![yukata-1380](1380/previews/yukata.png) \| \| 920 \| 0.861 \| [Download](920/aihara_mei_citrus.zip) \| ![pattern_1-920](920/previews/pattern_1.png) \| ![pattern_2-920](920/previews/pattern_2.png) \| ![pattern_3-920](920/previews/pattern_3.png) \| ![pattern_4-920](920/previews/pattern_4.png) \| ![pattern_5-920](920/previews/pattern_5.png) \| ![pattern_6-920](920/previews/pattern_6.png) \| [<NSFW, click to see>](920/previews/bikini.png) \| [<NSFW, click to see>](920/previews/bondage.png) \| ![free-920](920/previews/free.png) \| ![maid-920](920/previews/maid.png) \| ![miko-920](920/previews/miko.png) \| [<NSFW, click to see>](920/previews/nude.png) \| [<NSFW, click to see>](920/previews/nude2.png) \| ![suit-920](920/previews/suit.png) \| ![yukata-920](920/previews/yukata.png) \| \| 460 \| 0.598 \| [Download](460/aihara_mei_citrus.zip) \| ![pattern_1-460](460/previews/pattern_1.png) \| ![pattern_2-460](460/previews/pattern_2.png) \| ![pattern_3-460](460/previews/pattern_3.png) \| ![pattern_4-460](460/previews/pattern_4.png) \| ![pattern_5-460](460/previews/pattern_5.png) \| ![pattern_6-460](460/previews/pattern_6.png) \| [<NSFW, click to see>](460/previews/bikini.png) \| [<NSFW, click to see>](460/previews/bondage.png) \| ![free-460](460/previews/free.png) \| ![maid-460](460/previews/maid.png) \| ![miko-460](460/previews/miko.png) \| [<NSFW, click to see>](460/previews/nude.png) \| [<NSFW, click to see>](460/previews/nude2.png) \| ![suit-460](460/previews/suit.png) \| ![yukata-460](460/previews/yukata.png) \|
CyberHarem/kurumi_lycorisrecoil	CyberHarem	2023-09-28T16:38:36Z	0	1	null	[ "art", "text-to-image", "dataset:CyberHarem/kurumi_lycorisrecoil", "license:mit", "region:us" ]	text-to-image	2023-09-28T16:26:50Z	--- license: mit datasets: - CyberHarem/kurumi_lycorisrecoil pipeline_tag: text-to-image tags: - art --- # Lora of kurumi_lycorisrecoil This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 5100, you need to download `5100/kurumi_lycorisrecoil.pt` as the embedding and `5100/kurumi_lycorisrecoil.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. The best step we recommend is 5100, with the score of 0.996. The trigger words are: 1. `kurumi_lycorisrecoil` 2. `blonde_hair, long_hair, hairband, black_hairband, forehead, hoodie, black_ribbon, blue_eyes, ribbon, closed_mouth, hair_ribbon, white_hoodie, indoors` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: \| Steps \| Score \| Download \| pattern_1 \| pattern_2 \| pattern_3 \| pattern_4 \| pattern_5 \| bikini \| bondage \| free \| maid \| miko \| nude \| nude2 \| suit \| yukata \| \|:---------\|:----------\|:----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------\|:--------------------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-----------------------------------------------\|:------------------------------------------------\|:-------------------------------------\|:-----------------------------------------\| \| 5100 \| 0.996 \| [Download](5100/kurumi_lycorisrecoil.zip) \| ![pattern_1-5100](5100/previews/pattern_1.png) \| ![pattern_2-5100](5100/previews/pattern_2.png) \| ![pattern_3-5100](5100/previews/pattern_3.png) \| ![pattern_4-5100](5100/previews/pattern_4.png) \| ![pattern_5-5100](5100/previews/pattern_5.png) \| ![bikini-5100](5100/previews/bikini.png) \| [<NSFW, click to see>](5100/previews/bondage.png) \| ![free-5100](5100/previews/free.png) \| ![maid-5100](5100/previews/maid.png) \| ![miko-5100](5100/previews/miko.png) \| [<NSFW, click to see>](5100/previews/nude.png) \| [<NSFW, click to see>](5100/previews/nude2.png) \| ![suit-5100](5100/previews/suit.png) \| ![yukata-5100](5100/previews/yukata.png) \| \| 4760 \| 0.993 \| [Download](4760/kurumi_lycorisrecoil.zip) \| ![pattern_1-4760](4760/previews/pattern_1.png) \| ![pattern_2-4760](4760/previews/pattern_2.png) \| ![pattern_3-4760](4760/previews/pattern_3.png) \| ![pattern_4-4760](4760/previews/pattern_4.png) \| ![pattern_5-4760](4760/previews/pattern_5.png) \| ![bikini-4760](4760/previews/bikini.png) \| [<NSFW, click to see>](4760/previews/bondage.png) \| ![free-4760](4760/previews/free.png) \| ![maid-4760](4760/previews/maid.png) \| ![miko-4760](4760/previews/miko.png) \| [<NSFW, click to see>](4760/previews/nude.png) \| [<NSFW, click to see>](4760/previews/nude2.png) \| ![suit-4760](4760/previews/suit.png) \| ![yukata-4760](4760/previews/yukata.png) \| \| 4420 \| 0.989 \| [Download](4420/kurumi_lycorisrecoil.zip) \| ![pattern_1-4420](4420/previews/pattern_1.png) \| ![pattern_2-4420](4420/previews/pattern_2.png) \| ![pattern_3-4420](4420/previews/pattern_3.png) \| ![pattern_4-4420](4420/previews/pattern_4.png) \| ![pattern_5-4420](4420/previews/pattern_5.png) \| ![bikini-4420](4420/previews/bikini.png) \| [<NSFW, click to see>](4420/previews/bondage.png) \| ![free-4420](4420/previews/free.png) \| ![maid-4420](4420/previews/maid.png) \| ![miko-4420](4420/previews/miko.png) \| [<NSFW, click to see>](4420/previews/nude.png) \| [<NSFW, click to see>](4420/previews/nude2.png) \| ![suit-4420](4420/previews/suit.png) \| ![yukata-4420](4420/previews/yukata.png) \| \| 4080 \| 0.991 \| [Download](4080/kurumi_lycorisrecoil.zip) \| ![pattern_1-4080](4080/previews/pattern_1.png) \| ![pattern_2-4080](4080/previews/pattern_2.png) \| ![pattern_3-4080](4080/previews/pattern_3.png) \| ![pattern_4-4080](4080/previews/pattern_4.png) \| ![pattern_5-4080](4080/previews/pattern_5.png) \| ![bikini-4080](4080/previews/bikini.png) \| [<NSFW, click to see>](4080/previews/bondage.png) \| ![free-4080](4080/previews/free.png) \| ![maid-4080](4080/previews/maid.png) \| ![miko-4080](4080/previews/miko.png) \| [<NSFW, click to see>](4080/previews/nude.png) \| [<NSFW, click to see>](4080/previews/nude2.png) \| ![suit-4080](4080/previews/suit.png) \| ![yukata-4080](4080/previews/yukata.png) \| \| 3740 \| 0.989 \| [Download](3740/kurumi_lycorisrecoil.zip) \| ![pattern_1-3740](3740/previews/pattern_1.png) \| ![pattern_2-3740](3740/previews/pattern_2.png) \| ![pattern_3-3740](3740/previews/pattern_3.png) \| ![pattern_4-3740](3740/previews/pattern_4.png) \| ![pattern_5-3740](3740/previews/pattern_5.png) \| ![bikini-3740](3740/previews/bikini.png) \| [<NSFW, click to see>](3740/previews/bondage.png) \| ![free-3740](3740/previews/free.png) \| ![maid-3740](3740/previews/maid.png) \| ![miko-3740](3740/previews/miko.png) \| [<NSFW, click to see>](3740/previews/nude.png) \| [<NSFW, click to see>](3740/previews/nude2.png) \| ![suit-3740](3740/previews/suit.png) \| ![yukata-3740](3740/previews/yukata.png) \| \| 3400 \| 0.985 \| [Download](3400/kurumi_lycorisrecoil.zip) \| ![pattern_1-3400](3400/previews/pattern_1.png) \| ![pattern_2-3400](3400/previews/pattern_2.png) \| ![pattern_3-3400](3400/previews/pattern_3.png) \| ![pattern_4-3400](3400/previews/pattern_4.png) \| ![pattern_5-3400](3400/previews/pattern_5.png) \| ![bikini-3400](3400/previews/bikini.png) \| [<NSFW, click to see>](3400/previews/bondage.png) \| ![free-3400](3400/previews/free.png) \| ![maid-3400](3400/previews/maid.png) \| ![miko-3400](3400/previews/miko.png) \| [<NSFW, click to see>](3400/previews/nude.png) \| [<NSFW, click to see>](3400/previews/nude2.png) \| ![suit-3400](3400/previews/suit.png) \| ![yukata-3400](3400/previews/yukata.png) \| \| 3060 \| 0.994 \| [Download](3060/kurumi_lycorisrecoil.zip) \| ![pattern_1-3060](3060/previews/pattern_1.png) \| ![pattern_2-3060](3060/previews/pattern_2.png) \| ![pattern_3-3060](3060/previews/pattern_3.png) \| ![pattern_4-3060](3060/previews/pattern_4.png) \| ![pattern_5-3060](3060/previews/pattern_5.png) \| ![bikini-3060](3060/previews/bikini.png) \| [<NSFW, click to see>](3060/previews/bondage.png) \| ![free-3060](3060/previews/free.png) \| ![maid-3060](3060/previews/maid.png) \| ![miko-3060](3060/previews/miko.png) \| [<NSFW, click to see>](3060/previews/nude.png) \| [<NSFW, click to see>](3060/previews/nude2.png) \| ![suit-3060](3060/previews/suit.png) \| ![yukata-3060](3060/previews/yukata.png) \| \| 2720 \| 0.917 \| [Download](2720/kurumi_lycorisrecoil.zip) \| ![pattern_1-2720](2720/previews/pattern_1.png) \| ![pattern_2-2720](2720/previews/pattern_2.png) \| ![pattern_3-2720](2720/previews/pattern_3.png) \| ![pattern_4-2720](2720/previews/pattern_4.png) \| ![pattern_5-2720](2720/previews/pattern_5.png) \| ![bikini-2720](2720/previews/bikini.png) \| [<NSFW, click to see>](2720/previews/bondage.png) \| ![free-2720](2720/previews/free.png) \| ![maid-2720](2720/previews/maid.png) \| ![miko-2720](2720/previews/miko.png) \| [<NSFW, click to see>](2720/previews/nude.png) \| [<NSFW, click to see>](2720/previews/nude2.png) \| ![suit-2720](2720/previews/suit.png) \| ![yukata-2720](2720/previews/yukata.png) \| \| 2380 \| 0.991 \| [Download](2380/kurumi_lycorisrecoil.zip) \| ![pattern_1-2380](2380/previews/pattern_1.png) \| ![pattern_2-2380](2380/previews/pattern_2.png) \| ![pattern_3-2380](2380/previews/pattern_3.png) \| ![pattern_4-2380](2380/previews/pattern_4.png) \| ![pattern_5-2380](2380/previews/pattern_5.png) \| ![bikini-2380](2380/previews/bikini.png) \| [<NSFW, click to see>](2380/previews/bondage.png) \| ![free-2380](2380/previews/free.png) \| ![maid-2380](2380/previews/maid.png) \| ![miko-2380](2380/previews/miko.png) \| [<NSFW, click to see>](2380/previews/nude.png) \| [<NSFW, click to see>](2380/previews/nude2.png) \| ![suit-2380](2380/previews/suit.png) \| ![yukata-2380](2380/previews/yukata.png) \| \| 2040 \| 0.980 \| [Download](2040/kurumi_lycorisrecoil.zip) \| ![pattern_1-2040](2040/previews/pattern_1.png) \| ![pattern_2-2040](2040/previews/pattern_2.png) \| ![pattern_3-2040](2040/previews/pattern_3.png) \| ![pattern_4-2040](2040/previews/pattern_4.png) \| ![pattern_5-2040](2040/previews/pattern_5.png) \| ![bikini-2040](2040/previews/bikini.png) \| [<NSFW, click to see>](2040/previews/bondage.png) \| ![free-2040](2040/previews/free.png) \| ![maid-2040](2040/previews/maid.png) \| ![miko-2040](2040/previews/miko.png) \| [<NSFW, click to see>](2040/previews/nude.png) \| [<NSFW, click to see>](2040/previews/nude2.png) \| ![suit-2040](2040/previews/suit.png) \| ![yukata-2040](2040/previews/yukata.png) \| \| 1700 \| 0.991 \| [Download](1700/kurumi_lycorisrecoil.zip) \| ![pattern_1-1700](1700/previews/pattern_1.png) \| ![pattern_2-1700](1700/previews/pattern_2.png) \| ![pattern_3-1700](1700/previews/pattern_3.png) \| ![pattern_4-1700](1700/previews/pattern_4.png) \| ![pattern_5-1700](1700/previews/pattern_5.png) \| ![bikini-1700](1700/previews/bikini.png) \| [<NSFW, click to see>](1700/previews/bondage.png) \| ![free-1700](1700/previews/free.png) \| ![maid-1700](1700/previews/maid.png) \| ![miko-1700](1700/previews/miko.png) \| [<NSFW, click to see>](1700/previews/nude.png) \| [<NSFW, click to see>](1700/previews/nude2.png) \| ![suit-1700](1700/previews/suit.png) \| ![yukata-1700](1700/previews/yukata.png) \| \| 1360 \| 0.986 \| [Download](1360/kurumi_lycorisrecoil.zip) \| ![pattern_1-1360](1360/previews/pattern_1.png) \| ![pattern_2-1360](1360/previews/pattern_2.png) \| ![pattern_3-1360](1360/previews/pattern_3.png) \| ![pattern_4-1360](1360/previews/pattern_4.png) \| ![pattern_5-1360](1360/previews/pattern_5.png) \| ![bikini-1360](1360/previews/bikini.png) \| [<NSFW, click to see>](1360/previews/bondage.png) \| ![free-1360](1360/previews/free.png) \| ![maid-1360](1360/previews/maid.png) \| ![miko-1360](1360/previews/miko.png) \| [<NSFW, click to see>](1360/previews/nude.png) \| [<NSFW, click to see>](1360/previews/nude2.png) \| ![suit-1360](1360/previews/suit.png) \| ![yukata-1360](1360/previews/yukata.png) \| \| 1020 \| 0.987 \| [Download](1020/kurumi_lycorisrecoil.zip) \| ![pattern_1-1020](1020/previews/pattern_1.png) \| ![pattern_2-1020](1020/previews/pattern_2.png) \| ![pattern_3-1020](1020/previews/pattern_3.png) \| ![pattern_4-1020](1020/previews/pattern_4.png) \| ![pattern_5-1020](1020/previews/pattern_5.png) \| ![bikini-1020](1020/previews/bikini.png) \| [<NSFW, click to see>](1020/previews/bondage.png) \| ![free-1020](1020/previews/free.png) \| ![maid-1020](1020/previews/maid.png) \| ![miko-1020](1020/previews/miko.png) \| [<NSFW, click to see>](1020/previews/nude.png) \| [<NSFW, click to see>](1020/previews/nude2.png) \| ![suit-1020](1020/previews/suit.png) \| ![yukata-1020](1020/previews/yukata.png) \| \| 680 \| 0.924 \| [Download](680/kurumi_lycorisrecoil.zip) \| ![pattern_1-680](680/previews/pattern_1.png) \| ![pattern_2-680](680/previews/pattern_2.png) \| ![pattern_3-680](680/previews/pattern_3.png) \| ![pattern_4-680](680/previews/pattern_4.png) \| ![pattern_5-680](680/previews/pattern_5.png) \| ![bikini-680](680/previews/bikini.png) \| [<NSFW, click to see>](680/previews/bondage.png) \| ![free-680](680/previews/free.png) \| ![maid-680](680/previews/maid.png) \| ![miko-680](680/previews/miko.png) \| [<NSFW, click to see>](680/previews/nude.png) \| [<NSFW, click to see>](680/previews/nude2.png) \| ![suit-680](680/previews/suit.png) \| ![yukata-680](680/previews/yukata.png) \| \| 340 \| 0.989 \| [Download](340/kurumi_lycorisrecoil.zip) \| ![pattern_1-340](340/previews/pattern_1.png) \| ![pattern_2-340](340/previews/pattern_2.png) \| ![pattern_3-340](340/previews/pattern_3.png) \| ![pattern_4-340](340/previews/pattern_4.png) \| ![pattern_5-340](340/previews/pattern_5.png) \| ![bikini-340](340/previews/bikini.png) \| [<NSFW, click to see>](340/previews/bondage.png) \| ![free-340](340/previews/free.png) \| ![maid-340](340/previews/maid.png) \| ![miko-340](340/previews/miko.png) \| [<NSFW, click to see>](340/previews/nude.png) \| [<NSFW, click to see>](340/previews/nude2.png) \| ![suit-340](340/previews/suit.png) \| ![yukata-340](340/previews/yukata.png) \|
TheBloke/leo-hessianai-7B-GGUF	TheBloke	2023-09-28T16:28:04Z	239	3	transformers	[ "transformers", "gguf", "llama", "text-generation", "en", "de", "dataset:oscar-corpus/OSCAR-2301", "dataset:wikipedia", "dataset:bjoernp/tagesschau-2018-2023", "base_model:LeoLM/leo-hessianai-7b", "base_model:quantized:LeoLM/leo-hessianai-7b", "license:llama2", "region:us" ]	text-generation	2023-09-28T16:23:50Z	--- base_model: LeoLM/leo-hessianai-7b datasets: - oscar-corpus/OSCAR-2301 - wikipedia - bjoernp/tagesschau-2018-2023 inference: false language: - en - de library_name: transformers license: llama2 model_creator: LAION LeoLM model_name: Leo Hessianai 7B model_type: llama pipeline_tag: text-generation prompt_template: '{prompt} ' quantized_by: TheBloke --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" 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;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Leo Hessianai 7B - GGUF - Model creator: [LAION LeoLM](https://huggingface.co/LeoLM) - Original model: [Leo Hessianai 7B](https://huggingface.co/LeoLM/leo-hessianai-7b) <!-- description start --> ## Description This repo contains GGUF format model files for [LAION LeoLM's Leo Hessianai 7B](https://huggingface.co/LeoLM/leo-hessianai-7b). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplate list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. <!-- README_GGUF.md-about-gguf end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/leo-hessianai-7B-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/leo-hessianai-7B-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF) * [LAION LeoLM's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/LeoLM/leo-hessianai-7b) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: None ``` {prompt} ``` <!-- prompt-template end --> <!-- compatibility_gguf start --> ## Compatibility These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) They are also compatible with many third party UIs and libraries - please see the list at the top of this README. ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw Refer to the Provided Files table below to see what files use which methods, and how. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-provided-files start --> ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [leo-hessianai-7b.Q2_K.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF/blob/main/leo-hessianai-7b.Q2_K.gguf) \| Q2_K \| 2 \| 2.83 GB\| 5.33 GB \| smallest, significant quality loss - not recommended for most purposes \| \| [leo-hessianai-7b.Q3_K_S.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF/blob/main/leo-hessianai-7b.Q3_K_S.gguf) \| Q3_K_S \| 3 \| 2.95 GB\| 5.45 GB \| very small, high quality loss \| \| [leo-hessianai-7b.Q3_K_M.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF/blob/main/leo-hessianai-7b.Q3_K_M.gguf) \| Q3_K_M \| 3 \| 3.30 GB\| 5.80 GB \| very small, high quality loss \| \| [leo-hessianai-7b.Q3_K_L.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF/blob/main/leo-hessianai-7b.Q3_K_L.gguf) \| Q3_K_L \| 3 \| 3.60 GB\| 6.10 GB \| small, substantial quality loss \| \| [leo-hessianai-7b.Q4_0.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF/blob/main/leo-hessianai-7b.Q4_0.gguf) \| Q4_0 \| 4 \| 3.83 GB\| 6.33 GB \| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [leo-hessianai-7b.Q4_K_S.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF/blob/main/leo-hessianai-7b.Q4_K_S.gguf) \| Q4_K_S \| 4 \| 3.86 GB\| 6.36 GB \| small, greater quality loss \| \| [leo-hessianai-7b.Q4_K_M.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF/blob/main/leo-hessianai-7b.Q4_K_M.gguf) \| Q4_K_M \| 4 \| 4.08 GB\| 6.58 GB \| medium, balanced quality - recommended \| \| [leo-hessianai-7b.Q5_0.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF/blob/main/leo-hessianai-7b.Q5_0.gguf) \| Q5_0 \| 5 \| 4.65 GB\| 7.15 GB \| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [leo-hessianai-7b.Q5_K_S.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF/blob/main/leo-hessianai-7b.Q5_K_S.gguf) \| Q5_K_S \| 5 \| 4.65 GB\| 7.15 GB \| large, low quality loss - recommended \| \| [leo-hessianai-7b.Q5_K_M.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF/blob/main/leo-hessianai-7b.Q5_K_M.gguf) \| Q5_K_M \| 5 \| 4.78 GB\| 7.28 GB \| large, very low quality loss - recommended \| \| [leo-hessianai-7b.Q6_K.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF/blob/main/leo-hessianai-7b.Q6_K.gguf) \| Q6_K \| 6 \| 5.53 GB\| 8.03 GB \| very large, extremely low quality loss \| \| [leo-hessianai-7b.Q8_0.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF/blob/main/leo-hessianai-7b.Q8_0.gguf) \| Q8_0 \| 8 \| 7.16 GB\| 9.66 GB \| very large, extremely low quality loss - not recommended \| Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. <!-- README_GGUF.md-provided-files end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: - LM Studio - LoLLMS Web UI - Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: TheBloke/leo-hessianai-7B-GGUF and below it, a specific filename to download, such as: leo-hessianai-7b.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download TheBloke/leo-hessianai-7B-GGUF leo-hessianai-7b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download TheBloke/leo-hessianai-7B-GGUF --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/leo-hessianai-7B-GGUF leo-hessianai-7b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 32 -m leo-hessianai-7b.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. ### How to load this model in Python code, using ctransformers #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install ctransformers # Or with CUDA GPU acceleration pip install ctransformers[cuda] # Or with AMD ROCm GPU acceleration (Linux only) CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers # Or with Metal GPU acceleration for macOS systems only CT_METAL=1 pip install ctransformers --no-binary ctransformers ``` #### Simple ctransformers example code ```python from ctransformers import AutoModelForCausalLM # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = AutoModelForCausalLM.from_pretrained("TheBloke/leo-hessianai-7B-GGUF", model_file="leo-hessianai-7b.Q4_K_M.gguf", model_type="llama", gpu_layers=50) print(llm("AI is going to")) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> <!-- original-model-card start --> # Original model card: LAION LeoLM's Leo Hessianai 7B # LAION LeoLM: Linguistically Enhanced Open Language Model Meet LeoLM, the first open and commercially available German Foundation Language Model built on Llama-2. Our models extend Llama-2's capabilities into German through continued pretraining on a large corpus of German-language and mostly locality specific text. Thanks to a compute grant at HessianAI's new supercomputer 42, we release two foundation models trained with 8k context length, [`LeoLM/leo-hessianai-7b`](https://huggingface.co/LeoLM/leo-hessianai-7b) and [`LeoLM/leo-hessianai-13b`](https://huggingface.co/LeoLM/leo-hessianai-13b) under the [Llama-2 community license](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) (70b also coming soon! 👀). With this release, we hope to bring a new wave of opportunities to German open-source and commercial LLM research and accelerate adoption. Read our [blog post]() or our paper (preprint coming soon) for more details! A project by Björn Plüster and Christoph Schuhmann in collaboration with LAION and HessianAI. ## Model Details - Finetuned from: [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) - Model type: Causal decoder-only transformer language model - Language: English and German - License: [LLAMA 2 COMMUNITY LICENSE AGREEMENT](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) - Contact: [LAION Discord](https://discord.com/invite/eq3cAMZtCC) or [Björn Plüster](mailto:[email protected]) ## Use in 🤗Transformers First install direct dependencies: ``` pip install transformers torch sentencepiece ``` If you want faster inference using flash-attention2, you need to install these dependencies: ```bash pip install packaging ninja pip install flash-attn==v2.1.1 --no-build-isolation pip install git+https://github.com/HazyResearch/[email protected]#subdirectory=csrc/rotary ``` Then load the model in transformers: ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch model = AutoModelForCausalLM.from_pretrained( model="LeoLM/leo-hessianai-7b", device_map="auto", torch_dtype=torch.float16, trust_remote_code=True # True for flash-attn2 else False ) ``` ## Training parameters ![training_parameters](imgs/training_params.png "Training Hyperparameters") ## Benchmarks ![benchmarks](imgs/benchmarks.png "Benchmark Scores") <!-- original-model-card end -->
TheBloke/leo-hessianai-7B-chat-GPTQ	TheBloke	2023-09-28T16:22:45Z	26	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "custom_code", "en", "de", "dataset:LeoLM/OpenSchnabeltier", "dataset:OpenAssistant/OASST-DE", "dataset:FreedomIntelligence/alpaca-gpt4-deutsch", "dataset:FreedomIntelligence/evol-instruct-deutsch", "dataset:LeoLM/German_Poems", "dataset:LeoLM/German_Songs", "base_model:LeoLM/leo-hessianai-7b-chat", "base_model:quantized:LeoLM/leo-hessianai-7b-chat", "license:llama2", "autotrain_compatible", "text-generation-inference", "4-bit", "gptq", "region:us" ]	text-generation	2023-09-28T15:35:40Z	--- base_model: LeoLM/leo-hessianai-7b-chat datasets: - LeoLM/OpenSchnabeltier - OpenAssistant/OASST-DE - FreedomIntelligence/alpaca-gpt4-deutsch - FreedomIntelligence/evol-instruct-deutsch - LeoLM/German_Poems - LeoLM/German_Songs inference: false language: - en - de library_name: transformers license: llama2 model_creator: LAION LeoLM model_name: Leo Hessianai 7B Chat model_type: llama pipeline_tag: text-generation prompt_template: '<\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ' quantized_by: TheBloke --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" 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;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Leo Hessianai 7B Chat - GPTQ - Model creator: [LAION LeoLM](https://huggingface.co/LeoLM) - Original model: [Leo Hessianai 7B Chat](https://huggingface.co/LeoLM/leo-hessianai-7b-chat) <!-- description start --> ## Description This repo contains GPTQ model files for [LAION LeoLM's Leo Hessianai 7B Chat](https://huggingface.co/LeoLM/leo-hessianai-7b-chat). Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-GGUF) * [LAION LeoLM's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/LeoLM/leo-hessianai-7b-chat) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: ChatML ``` <\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` <!-- prompt-template end --> <!-- README_GPTQ.md-provided-files start --> ## Provided files, and GPTQ parameters Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches. All recent GPTQ files are made with AutoGPTQ, and all files in non-main branches are made with AutoGPTQ. Files in the `main` branch which were uploaded before August 2023 were made with GPTQ-for-LLaMa. <details> <summary>Explanation of GPTQ parameters</summary> - Bits: The bit size of the quantised model. - GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value. - Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now. - Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy. - GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s). - Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences. - ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit. </details> \| Branch \| Bits \| GS \| Act Order \| Damp % \| GPTQ Dataset \| Seq Len \| Size \| ExLlama \| Desc \| \| ------ \| ---- \| -- \| --------- \| ------ \| ------------ \| ------- \| ---- \| ------- \| ---- \| \| [main](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-GPTQ/tree/main) \| 4 \| 128 \| Yes \| 0.1 \| [German Quad](https://huggingface.co/datasets/deepset/germanquad) \| 8192 \| 3.90 GB \| Yes \| 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. \| \| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-GPTQ/tree/gptq-4bit-32g-actorder_True) \| 4 \| 32 \| Yes \| 0.1 \| [German Quad](https://huggingface.co/datasets/deepset/germanquad) \| 8192 \| 4.28 GB \| Yes \| 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. \| \| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-GPTQ/tree/gptq-8bit--1g-actorder_True) \| 8 \| None \| Yes \| 0.1 \| [German Quad](https://huggingface.co/datasets/deepset/germanquad) \| 8192 \| 7.01 GB \| No \| 8-bit, with Act Order. No group size, to lower VRAM requirements. \| \| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-GPTQ/tree/gptq-8bit-128g-actorder_True) \| 8 \| 128 \| Yes \| 0.1 \| [German Quad](https://huggingface.co/datasets/deepset/germanquad) \| 8192 \| 7.16 GB \| No \| 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. \| \| [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-GPTQ/tree/gptq-8bit-32g-actorder_True) \| 8 \| 32 \| Yes \| 0.1 \| [German Quad](https://huggingface.co/datasets/deepset/germanquad) \| 8192 \| 7.62 GB \| No \| 8-bit, with group size 32g and Act Order for maximum inference quality. \| \| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-GPTQ/tree/gptq-4bit-64g-actorder_True) \| 4 \| 64 \| Yes \| 0.1 \| [German Quad](https://huggingface.co/datasets/deepset/germanquad) \| 8192 \| 4.03 GB \| Yes \| 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. \| <!-- README_GPTQ.md-provided-files end --> <!-- README_GPTQ.md-download-from-branches start --> ## How to download, including from branches ### In text-generation-webui To download from the `main` branch, enter `TheBloke/leo-hessianai-7B-chat-GPTQ` in the "Download model" box. To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/leo-hessianai-7B-chat-GPTQ:gptq-4bit-32g-actorder_True` ### From the command line I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` To download the `main` branch to a folder called `leo-hessianai-7B-chat-GPTQ`: ```shell mkdir leo-hessianai-7B-chat-GPTQ huggingface-cli download TheBloke/leo-hessianai-7B-chat-GPTQ --local-dir leo-hessianai-7B-chat-GPTQ --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: ```shell mkdir leo-hessianai-7B-chat-GPTQ huggingface-cli download TheBloke/leo-hessianai-7B-chat-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir leo-hessianai-7B-chat-GPTQ --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Huggingface cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model. The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`. For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell mkdir leo-hessianai-7B-chat-GPTQ HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/leo-hessianai-7B-chat-GPTQ --local-dir leo-hessianai-7B-chat-GPTQ --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> ### With `git` (not recommended) To clone a specific branch with `git`, use a command like this: ```shell git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/leo-hessianai-7B-chat-GPTQ ``` Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.) <!-- README_GPTQ.md-download-from-branches end --> <!-- README_GPTQ.md-text-generation-webui start --> ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui). Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the Model tab. 2. Under Download custom model or LoRA, enter `TheBloke/leo-hessianai-7B-chat-GPTQ`. - To download from a specific branch, enter for example `TheBloke/leo-hessianai-7B-chat-GPTQ:gptq-4bit-32g-actorder_True` - see Provided Files above for the list of branches for each option. 3. Click Download. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to Model. 6. In the Model dropdown, choose the model you just downloaded: `leo-hessianai-7B-chat-GPTQ` 7. The model will automatically load, and is now ready for use! 8. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right. * Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. 9. Once you're ready, click the Text Generation tab and enter a prompt to get started! <!-- README_GPTQ.md-text-generation-webui end --> <!-- README_GPTQ.md-use-from-python start --> ## How to use this GPTQ model from Python code ### Install the necessary packages Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later. ```shell pip3 install transformers optimum pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7 ``` If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead: ```shell pip3 uninstall -y auto-gptq git clone https://github.com/PanQiWei/AutoGPTQ cd AutoGPTQ git checkout v0.4.2 pip3 install . ``` ### You can then use the following code ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_name_or_path = "TheBloke/leo-hessianai-7B-chat-GPTQ" # To use a different branch, change revision # For example: revision="gptq-4bit-32g-actorder_True" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", trust_remote_code=False, revision="main") tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) prompt = "Tell me about AI" prompt_template=f'''<\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ''' print("\n\n* Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline print("* Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(pipe(prompt_template)[0]['generated_text']) ``` <!-- README_GPTQ.md-use-from-python end --> <!-- README_GPTQ.md-compatibility start --> ## Compatibility The files provided are tested to work with AutoGPTQ, both via Transformers and using AutoGPTQ directly. They should also work with [Occ4m's GPTQ-for-LLaMa fork](https://github.com/0cc4m/KoboldAI). [ExLlama](https://github.com/turboderp/exllama) is compatible with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility. [Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is compatible with all GPTQ models. <!-- README_GPTQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: LAION LeoLM's Leo Hessianai 7B Chat # LAION LeoLM: Linguistically Enhanced Open Language Model Meet LeoLM, the first open and commercially available German Foundation Language Model built on Llama-2. Our models extend Llama-2's capabilities into German through continued pretraining on a large corpus of German-language and mostly locality specific text. Thanks to a compute grant at HessianAI's new supercomputer 42, we release two foundation models trained with 8k context length, [`LeoLM/leo-hessianai-7b`](https://huggingface.co/LeoLM/leo-hessianai-7b) and [`LeoLM/leo-hessianai-13b`](https://huggingface.co/LeoLM/leo-hessianai-13b) under the [Llama-2 community license](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) (70b also coming soon! 👀). With this release, we hope to bring a new wave of opportunities to German open-source and commercial LLM research and accelerate adoption. Read our [blog post]() or our paper (preprint coming soon) for more details! A project by Björn Plüster and Christoph Schuhmann in collaboration with LAION and HessianAI. ## LeoLM Chat `LeoLM/leo-hessianai-7b-chat` is a German chat model built on our foundation model `LeoLM/leo-hessianai-7b` and finetuned on a selection of German instruction datasets. The model performs exceptionally well on writing, explanation and discussion tasks but struggles somewhat with math and advanced reasoning. See our MT-Bench-DE scores: ``` { "first_turn": 5.75, "second_turn": 4.45, "categories": { "writing": 5.875, "roleplay": 6.3, "reasoning": 3.5, "math": 2.85, "coding": 2.95, "extraction": 4.3, "stem": 7.4, "humanities": 7.625 }, "average": 5.1 } ``` ## Model Details - Finetuned from: [LeoLM/leo-hessianai-7b](https://huggingface.co/LeoLM/leo-hessianai-7b) - Model type: Causal decoder-only transformer language model - Language: English and German - Demo: [Web Demo]() - License: [LLAMA 2 COMMUNITY LICENSE AGREEMENT](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) - Contact: [LAION Discord](https://discord.com/invite/eq3cAMZtCC) or [Björn Plüster](mailto:[email protected]) ## Use in 🤗Transformers First install direct dependencies: ``` pip install transformers torch sentencepiece ``` If you want faster inference using flash-attention2, you need to install these dependencies: ```bash pip install packaging ninja pip install flash-attn==v2.1.1 --no-build-isolation pip install git+https://github.com/HazyResearch/[email protected]#subdirectory=csrc/rotary ``` Then load the model in transformers: ```python from transformers import pipeline import torch system_prompt = """<\|im_start\|>system Dies ist eine Unterhaltung zwischen einem intelligenten, hilfsbereitem KI-Assistenten und einem Nutzer. Der Assistent gibt ausführliche, hilfreiche und ehrliche Antworten.<\|im_end\|> """ prompt_format = "<\|im_start\|>user\n{prompt}<\|im_end\|>\n<\|im_start\|>assistant\n" prompt = "Erkläre mir wie die Fahrradwegesituation in Hamburg ist." generator = pipeline(model="LeoLM/leo-hessianai-7b-chat", device="cuda", torch_dtype=torch.float16, trust_remote_code=True) # True for flash-attn2 else False print(generator(prompt_format.format(prompt=prompt), do_sample=True, top_p=0.95, max_length=8192)) ``` "Als KI kann ich keine persönlichen Beobachtungen teilen, aber ich kann einige allgemeine Informationen zur Fahrradwegesituation in Hamburg liefern. Im Vergleich zu vielen anderen großen Städten hat Hamburg eine hohe Anzahl von Fahrradfahrern und nimmt seine Verantwortung für nachhaltige Verkehrsmittel sehr ernst. Es gibt viele Fahrradwege und separate Fahrspuren, die Radfahrern ermöglichen, zusammen mit dem Autoverkehr zu fahren. Diese Fahrradspuren sind oft mit Markierungen gekennzeichnet und durch physische Trennungen von anderen Fahrspuren abgegrenzt. Darüber hinaus gibt es viele Fahrradstraßen, auf denen Radfahrer Vorfahrt haben und Autos langsamer fahren müssen. In einigen städtischen Gebieten können Fahrradwege jedoch eng oder überfüllt sein, besonders während der Stoßzeiten. Es gibt auch viele Kreuzungen, an denen Radfahrer anhalten und auf Grün warten müssen, ähnlich wie Autofahrer. Insgesamt ist die Fahrradinfrastruktur in Hamburg ziemlich gut, aber wie überall gibt es immer Raum für Verbesserungen." ## Prompting / Prompt Template Prompt dialogue template (ChatML format): ``` """ <\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant """ ``` The model input can contain multiple conversation turns between user and assistant, e.g. ``` <\|im_start\|>user {prompt 1}<\|im_end\|> <\|im_start\|>assistant {reply 1}<\|im_end\|> <\|im_start\|>user {prompt 2}<\|im_end\|> <\|im_start\|>assistant (...) ``` ## Ethical Considerations and Limitations LeoLM has been tested in English and German, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, the potential outputs of `LeoLM/leo-hessianai-7b-chat` cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of `LeoLM/leo-hessianai-7b-chat`, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see Meta's [Responsible Use Guide](https://ai.meta.com/llama/responsible-use-guide/). ## Finetuning Details \| Hyperparameter \| Value \| \|---\|---\| \| Num epochs \| 3 \| \| Examples per epoch \| 131214 \| \| Global batch size \| 256 \| \| Learning rate \| 3e-5 \| \| Warmup steps \| 100 \| \| LR scheduler \| Cosine \| \| Adam betas \| (0.9, 0.95) \| ## Dataset Details ``` ## Stats for 'Subset of OpenAssistant/OASST-DE' (3534 samples (100.0%)) ----------------- Accepted: 3534/3534 (100.0%) Accepted tokens: 2259302 Skipped: 0 (0.0%) Min tokens per sample: 29 Max tokens per sample: 2484 Avg tokens per sample: 639.3044708545557 ----------------- ## Stats for 'Subset of FreedomIntelligence/evol-instruct-deutsch' (57841 samples (100.0%)) ----------------- Accepted: 57841/57841 (100.0%) Accepted tokens: 42958192 Skipped: 0 (0.0%) Min tokens per sample: 33 Max tokens per sample: 5507 Avg tokens per sample: 742.6944900675991 ----------------- ## Stats for 'Subset of FreedomIntelligence/alpaca-gpt4-deutsch' (48969 samples (100.0%)) ----------------- Accepted: 48969/48969 (100.0%) Accepted tokens: 13372005 Skipped: 0 (0.0%) Min tokens per sample: 19 Max tokens per sample: 1359 Avg tokens per sample: 273.07082031489307 ----------------- ## Stats for 'Subset of LeoLM/OpenSchnabeltier' (21314 samples (100.0%)) ----------------- Accepted: 21314/21314 (100.0%) Accepted tokens: 8134690 Skipped: 0 (0.0%) Min tokens per sample: 25 Max tokens per sample: 1202 Avg tokens per sample: 381.65947264708643 ----------------- ## Stats for 'Subset of LeoLM/German_Poems' (490 samples (100.0%)) ----------------- Accepted: 490/490 (100.0%) Accepted tokens: 618642 Skipped: 0 (0.0%) Min tokens per sample: 747 Max tokens per sample: 1678 Avg tokens per sample: 1262.534693877551 ----------------- ## Stats for 'Subset of LeoLM/German_Songs' (392 samples (100.0%)) ----------------- Accepted: 392/392 (100.0%) Accepted tokens: 187897 Skipped: 0 (0.0%) Min tokens per sample: 231 Max tokens per sample: 826 Avg tokens per sample: 479.3290816326531 ----------------- ## Stats for 'total' (132540 samples (100.0%)) ----------------- Accepted: 132540/132540 (100.0%) Accepted tokens: 67530728 Skipped: 0 (0.0%) Min tokens per sample: 19 Max tokens per sample: 5507 Avg tokens per sample: 509.51205673758864 ----------------- ```
Tensoic/Mistral-7B-v0.1-alpaca-2k-test	Tensoic	2023-09-28T16:00:31Z	6	0	transformers	[ "transformers", "pytorch", "mistral", "text-generation", "base_model:mistralai/Mistral-7B-v0.1", "base_model:finetune:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-09-28T14:32:02Z	--- license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 --- # Tensoic - Mistral-7B-v0.1-alpaca-2k-test Our first fine tune of the Mistral 7B on the Alpaca-2k-test dataset. Feel free to play around! ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 3 ### Framework versions - Transformers 4.34.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.14.0
CyberHarem/nakahara_mizuki_lycorisrecoil	CyberHarem	2023-09-28T15:55:34Z	0	0	null	[ "art", "text-to-image", "dataset:CyberHarem/nakahara_mizuki_lycorisrecoil", "license:mit", "region:us" ]	text-to-image	2023-09-28T15:44:35Z	--- license: mit datasets: - CyberHarem/nakahara_mizuki_lycorisrecoil pipeline_tag: text-to-image tags: - art --- # Lora of nakahara_mizuki_lycorisrecoil This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 4420, you need to download `4420/nakahara_mizuki_lycorisrecoil.pt` as the embedding and `4420/nakahara_mizuki_lycorisrecoil.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. The best step we recommend is 4420, with the score of 0.997. The trigger words are: 1. `nakahara_mizuki_lycorisrecoil` 2. `brown_hair, long_hair, glasses, red-framed_eyewear, hair_between_eyes, brown_eyes, semi-rimless_eyewear` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: \| Steps \| Score \| Download \| pattern_1 \| pattern_2 \| pattern_3 \| bikini \| bondage \| free \| maid \| miko \| nude \| nude2 \| suit \| yukata \| \|:---------\|:----------\|:-------------------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------\|:--------------------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-----------------------------------------------\|:------------------------------------------------\|:-------------------------------------\|:-----------------------------------------\| \| 5100 \| 0.996 \| [Download](5100/nakahara_mizuki_lycorisrecoil.zip) \| ![pattern_1-5100](5100/previews/pattern_1.png) \| ![pattern_2-5100](5100/previews/pattern_2.png) \| ![pattern_3-5100](5100/previews/pattern_3.png) \| ![bikini-5100](5100/previews/bikini.png) \| [<NSFW, click to see>](5100/previews/bondage.png) \| ![free-5100](5100/previews/free.png) \| ![maid-5100](5100/previews/maid.png) \| ![miko-5100](5100/previews/miko.png) \| [<NSFW, click to see>](5100/previews/nude.png) \| [<NSFW, click to see>](5100/previews/nude2.png) \| ![suit-5100](5100/previews/suit.png) \| ![yukata-5100](5100/previews/yukata.png) \| \| 4760 \| 0.993 \| [Download](4760/nakahara_mizuki_lycorisrecoil.zip) \| ![pattern_1-4760](4760/previews/pattern_1.png) \| ![pattern_2-4760](4760/previews/pattern_2.png) \| ![pattern_3-4760](4760/previews/pattern_3.png) \| ![bikini-4760](4760/previews/bikini.png) \| [<NSFW, click to see>](4760/previews/bondage.png) \| ![free-4760](4760/previews/free.png) \| ![maid-4760](4760/previews/maid.png) \| ![miko-4760](4760/previews/miko.png) \| [<NSFW, click to see>](4760/previews/nude.png) \| [<NSFW, click to see>](4760/previews/nude2.png) \| ![suit-4760](4760/previews/suit.png) \| ![yukata-4760](4760/previews/yukata.png) \| \| 4420 \| 0.997 \| [Download](4420/nakahara_mizuki_lycorisrecoil.zip) \| ![pattern_1-4420](4420/previews/pattern_1.png) \| ![pattern_2-4420](4420/previews/pattern_2.png) \| ![pattern_3-4420](4420/previews/pattern_3.png) \| ![bikini-4420](4420/previews/bikini.png) \| [<NSFW, click to see>](4420/previews/bondage.png) \| ![free-4420](4420/previews/free.png) \| ![maid-4420](4420/previews/maid.png) \| ![miko-4420](4420/previews/miko.png) \| [<NSFW, click to see>](4420/previews/nude.png) \| [<NSFW, click to see>](4420/previews/nude2.png) \| ![suit-4420](4420/previews/suit.png) \| ![yukata-4420](4420/previews/yukata.png) \| \| 4080 \| 0.996 \| [Download](4080/nakahara_mizuki_lycorisrecoil.zip) \| ![pattern_1-4080](4080/previews/pattern_1.png) \| ![pattern_2-4080](4080/previews/pattern_2.png) \| ![pattern_3-4080](4080/previews/pattern_3.png) \| ![bikini-4080](4080/previews/bikini.png) \| [<NSFW, click to see>](4080/previews/bondage.png) \| ![free-4080](4080/previews/free.png) \| ![maid-4080](4080/previews/maid.png) \| ![miko-4080](4080/previews/miko.png) \| [<NSFW, click to see>](4080/previews/nude.png) \| [<NSFW, click to see>](4080/previews/nude2.png) \| ![suit-4080](4080/previews/suit.png) \| ![yukata-4080](4080/previews/yukata.png) \| \| 3740 \| 0.987 \| [Download](3740/nakahara_mizuki_lycorisrecoil.zip) \| ![pattern_1-3740](3740/previews/pattern_1.png) \| ![pattern_2-3740](3740/previews/pattern_2.png) \| ![pattern_3-3740](3740/previews/pattern_3.png) \| ![bikini-3740](3740/previews/bikini.png) \| [<NSFW, click to see>](3740/previews/bondage.png) \| ![free-3740](3740/previews/free.png) \| ![maid-3740](3740/previews/maid.png) \| ![miko-3740](3740/previews/miko.png) \| [<NSFW, click to see>](3740/previews/nude.png) \| [<NSFW, click to see>](3740/previews/nude2.png) \| ![suit-3740](3740/previews/suit.png) \| ![yukata-3740](3740/previews/yukata.png) \| \| 3400 \| 0.994 \| [Download](3400/nakahara_mizuki_lycorisrecoil.zip) \| ![pattern_1-3400](3400/previews/pattern_1.png) \| ![pattern_2-3400](3400/previews/pattern_2.png) \| ![pattern_3-3400](3400/previews/pattern_3.png) \| ![bikini-3400](3400/previews/bikini.png) \| [<NSFW, click to see>](3400/previews/bondage.png) \| ![free-3400](3400/previews/free.png) \| ![maid-3400](3400/previews/maid.png) \| ![miko-3400](3400/previews/miko.png) \| [<NSFW, click to see>](3400/previews/nude.png) \| [<NSFW, click to see>](3400/previews/nude2.png) \| ![suit-3400](3400/previews/suit.png) \| ![yukata-3400](3400/previews/yukata.png) \| \| 3060 \| 0.994 \| [Download](3060/nakahara_mizuki_lycorisrecoil.zip) \| ![pattern_1-3060](3060/previews/pattern_1.png) \| ![pattern_2-3060](3060/previews/pattern_2.png) \| ![pattern_3-3060](3060/previews/pattern_3.png) \| ![bikini-3060](3060/previews/bikini.png) \| [<NSFW, click to see>](3060/previews/bondage.png) \| ![free-3060](3060/previews/free.png) \| ![maid-3060](3060/previews/maid.png) \| ![miko-3060](3060/previews/miko.png) \| [<NSFW, click to see>](3060/previews/nude.png) \| [<NSFW, click to see>](3060/previews/nude2.png) \| ![suit-3060](3060/previews/suit.png) \| ![yukata-3060](3060/previews/yukata.png) \| \| 2720 \| 0.994 \| [Download](2720/nakahara_mizuki_lycorisrecoil.zip) \| ![pattern_1-2720](2720/previews/pattern_1.png) \| ![pattern_2-2720](2720/previews/pattern_2.png) \| ![pattern_3-2720](2720/previews/pattern_3.png) \| ![bikini-2720](2720/previews/bikini.png) \| [<NSFW, click to see>](2720/previews/bondage.png) \| ![free-2720](2720/previews/free.png) \| ![maid-2720](2720/previews/maid.png) \| ![miko-2720](2720/previews/miko.png) \| [<NSFW, click to see>](2720/previews/nude.png) \| [<NSFW, click to see>](2720/previews/nude2.png) \| ![suit-2720](2720/previews/suit.png) \| ![yukata-2720](2720/previews/yukata.png) \| \| 2380 \| 0.993 \| [Download](2380/nakahara_mizuki_lycorisrecoil.zip) \| ![pattern_1-2380](2380/previews/pattern_1.png) \| ![pattern_2-2380](2380/previews/pattern_2.png) \| ![pattern_3-2380](2380/previews/pattern_3.png) \| ![bikini-2380](2380/previews/bikini.png) \| [<NSFW, click to see>](2380/previews/bondage.png) \| ![free-2380](2380/previews/free.png) \| ![maid-2380](2380/previews/maid.png) \| ![miko-2380](2380/previews/miko.png) \| [<NSFW, click to see>](2380/previews/nude.png) \| [<NSFW, click to see>](2380/previews/nude2.png) \| ![suit-2380](2380/previews/suit.png) \| ![yukata-2380](2380/previews/yukata.png) \| \| 2040 \| 0.994 \| [Download](2040/nakahara_mizuki_lycorisrecoil.zip) \| ![pattern_1-2040](2040/previews/pattern_1.png) \| ![pattern_2-2040](2040/previews/pattern_2.png) \| ![pattern_3-2040](2040/previews/pattern_3.png) \| ![bikini-2040](2040/previews/bikini.png) \| [<NSFW, click to see>](2040/previews/bondage.png) \| ![free-2040](2040/previews/free.png) \| ![maid-2040](2040/previews/maid.png) \| ![miko-2040](2040/previews/miko.png) \| [<NSFW, click to see>](2040/previews/nude.png) \| [<NSFW, click to see>](2040/previews/nude2.png) \| ![suit-2040](2040/previews/suit.png) \| ![yukata-2040](2040/previews/yukata.png) \| \| 1700 \| 0.992 \| [Download](1700/nakahara_mizuki_lycorisrecoil.zip) \| ![pattern_1-1700](1700/previews/pattern_1.png) \| ![pattern_2-1700](1700/previews/pattern_2.png) \| ![pattern_3-1700](1700/previews/pattern_3.png) \| ![bikini-1700](1700/previews/bikini.png) \| [<NSFW, click to see>](1700/previews/bondage.png) \| ![free-1700](1700/previews/free.png) \| ![maid-1700](1700/previews/maid.png) \| ![miko-1700](1700/previews/miko.png) \| [<NSFW, click to see>](1700/previews/nude.png) \| [<NSFW, click to see>](1700/previews/nude2.png) \| ![suit-1700](1700/previews/suit.png) \| ![yukata-1700](1700/previews/yukata.png) \| \| 1360 \| 0.990 \| [Download](1360/nakahara_mizuki_lycorisrecoil.zip) \| ![pattern_1-1360](1360/previews/pattern_1.png) \| ![pattern_2-1360](1360/previews/pattern_2.png) \| ![pattern_3-1360](1360/previews/pattern_3.png) \| ![bikini-1360](1360/previews/bikini.png) \| [<NSFW, click to see>](1360/previews/bondage.png) \| ![free-1360](1360/previews/free.png) \| ![maid-1360](1360/previews/maid.png) \| ![miko-1360](1360/previews/miko.png) \| [<NSFW, click to see>](1360/previews/nude.png) \| [<NSFW, click to see>](1360/previews/nude2.png) \| ![suit-1360](1360/previews/suit.png) \| ![yukata-1360](1360/previews/yukata.png) \| \| 1020 \| 0.982 \| [Download](1020/nakahara_mizuki_lycorisrecoil.zip) \| ![pattern_1-1020](1020/previews/pattern_1.png) \| ![pattern_2-1020](1020/previews/pattern_2.png) \| ![pattern_3-1020](1020/previews/pattern_3.png) \| ![bikini-1020](1020/previews/bikini.png) \| [<NSFW, click to see>](1020/previews/bondage.png) \| ![free-1020](1020/previews/free.png) \| ![maid-1020](1020/previews/maid.png) \| ![miko-1020](1020/previews/miko.png) \| [<NSFW, click to see>](1020/previews/nude.png) \| [<NSFW, click to see>](1020/previews/nude2.png) \| ![suit-1020](1020/previews/suit.png) \| ![yukata-1020](1020/previews/yukata.png) \| \| 680 \| 0.982 \| [Download](680/nakahara_mizuki_lycorisrecoil.zip) \| ![pattern_1-680](680/previews/pattern_1.png) \| ![pattern_2-680](680/previews/pattern_2.png) \| ![pattern_3-680](680/previews/pattern_3.png) \| ![bikini-680](680/previews/bikini.png) \| [<NSFW, click to see>](680/previews/bondage.png) \| ![free-680](680/previews/free.png) \| ![maid-680](680/previews/maid.png) \| ![miko-680](680/previews/miko.png) \| [<NSFW, click to see>](680/previews/nude.png) \| [<NSFW, click to see>](680/previews/nude2.png) \| ![suit-680](680/previews/suit.png) \| ![yukata-680](680/previews/yukata.png) \| \| 340 \| 0.975 \| [Download](340/nakahara_mizuki_lycorisrecoil.zip) \| ![pattern_1-340](340/previews/pattern_1.png) \| ![pattern_2-340](340/previews/pattern_2.png) \| ![pattern_3-340](340/previews/pattern_3.png) \| ![bikini-340](340/previews/bikini.png) \| [<NSFW, click to see>](340/previews/bondage.png) \| ![free-340](340/previews/free.png) \| ![maid-340](340/previews/maid.png) \| ![miko-340](340/previews/miko.png) \| [<NSFW, click to see>](340/previews/nude.png) \| [<NSFW, click to see>](340/previews/nude2.png) \| ![suit-340](340/previews/suit.png) \| ![yukata-340](340/previews/yukata.png) \|
gsl22/testando	gsl22	2023-09-28T15:52:59Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "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	2023-09-28T14:21:31Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: testando 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. --> # testando This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None 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: 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: 50 ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3
openerotica/Mistral-7B-v0.1-GPTQ-ERP	openerotica	2023-09-28T15:41:23Z	8	1	transformers	[ "transformers", "pytorch", "llama", "text-generation", "pretrained", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-09-27T23:20:33Z	--- license: apache-2.0 pipeline_tag: text-generation tags: - pretrained --- This is the base model quantized on an erotica dataset at 8192 sequence length. No act order and no groupsize. Not recomended for inference. # Model Card for Mistral-7B-v0.1 The Mistral-7B-v0.1 Large Language Model (LLM) is a pretrained generative text model with 7 billion parameters. Mistral-7B-v0.1 outperforms Llama 2 13B on all benchmarks we tested. For full details of this model please read our [Release blog post](https://mistral.ai/news/announcing-mistral-7b/) ## Model Architecture Mistral-7B-v0.1 is a transformer model, with the following architecture choices: - Grouped-Query Attention - Sliding-Window Attention - Byte-fallback BPE tokenizer ## The Mistral AI Team Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.
TheBloke/leo-hessianai-7B-chat-GGUF	TheBloke	2023-09-28T15:39:03Z	166	4	transformers	[ "transformers", "gguf", "llama", "text-generation", "en", "de", "dataset:LeoLM/OpenSchnabeltier", "dataset:OpenAssistant/OASST-DE", "dataset:FreedomIntelligence/alpaca-gpt4-deutsch", "dataset:FreedomIntelligence/evol-instruct-deutsch", "dataset:LeoLM/German_Poems", "dataset:LeoLM/German_Songs", "base_model:LeoLM/leo-hessianai-7b-chat", "base_model:quantized:LeoLM/leo-hessianai-7b-chat", "license:llama2", "region:us" ]	text-generation	2023-09-28T15:35:30Z	--- base_model: LeoLM/leo-hessianai-7b-chat datasets: - LeoLM/OpenSchnabeltier - OpenAssistant/OASST-DE - FreedomIntelligence/alpaca-gpt4-deutsch - FreedomIntelligence/evol-instruct-deutsch - LeoLM/German_Poems - LeoLM/German_Songs inference: false language: - en - de library_name: transformers license: llama2 model_creator: LAION LeoLM model_name: Leo Hessianai 7B Chat model_type: llama pipeline_tag: text-generation prompt_template: '<\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ' quantized_by: TheBloke --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" 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;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Leo Hessianai 7B Chat - GGUF - Model creator: [LAION LeoLM](https://huggingface.co/LeoLM) - Original model: [Leo Hessianai 7B Chat](https://huggingface.co/LeoLM/leo-hessianai-7b-chat) <!-- description start --> ## Description This repo contains GGUF format model files for [LAION LeoLM's Leo Hessianai 7B Chat](https://huggingface.co/LeoLM/leo-hessianai-7b-chat). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplate list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. <!-- README_GGUF.md-about-gguf end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-GGUF) * [LAION LeoLM's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/LeoLM/leo-hessianai-7b-chat) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: ChatML ``` <\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` <!-- prompt-template end --> <!-- compatibility_gguf start --> ## Compatibility These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) They are also compatible with many third party UIs and libraries - please see the list at the top of this README. ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw Refer to the Provided Files table below to see what files use which methods, and how. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-provided-files start --> ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [leo-hessianai-7b-chat.Q2_K.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-GGUF/blob/main/leo-hessianai-7b-chat.Q2_K.gguf) \| Q2_K \| 2 \| 2.83 GB\| 5.33 GB \| smallest, significant quality loss - not recommended for most purposes \| \| [leo-hessianai-7b-chat.Q3_K_S.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-GGUF/blob/main/leo-hessianai-7b-chat.Q3_K_S.gguf) \| Q3_K_S \| 3 \| 2.95 GB\| 5.45 GB \| very small, high quality loss \| \| [leo-hessianai-7b-chat.Q3_K_M.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-GGUF/blob/main/leo-hessianai-7b-chat.Q3_K_M.gguf) \| Q3_K_M \| 3 \| 3.30 GB\| 5.80 GB \| very small, high quality loss \| \| [leo-hessianai-7b-chat.Q3_K_L.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-GGUF/blob/main/leo-hessianai-7b-chat.Q3_K_L.gguf) \| Q3_K_L \| 3 \| 3.60 GB\| 6.10 GB \| small, substantial quality loss \| \| [leo-hessianai-7b-chat.Q4_0.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-GGUF/blob/main/leo-hessianai-7b-chat.Q4_0.gguf) \| Q4_0 \| 4 \| 3.83 GB\| 6.33 GB \| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [leo-hessianai-7b-chat.Q4_K_S.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-GGUF/blob/main/leo-hessianai-7b-chat.Q4_K_S.gguf) \| Q4_K_S \| 4 \| 3.86 GB\| 6.36 GB \| small, greater quality loss \| \| [leo-hessianai-7b-chat.Q4_K_M.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-GGUF/blob/main/leo-hessianai-7b-chat.Q4_K_M.gguf) \| Q4_K_M \| 4 \| 4.08 GB\| 6.58 GB \| medium, balanced quality - recommended \| \| [leo-hessianai-7b-chat.Q5_0.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-GGUF/blob/main/leo-hessianai-7b-chat.Q5_0.gguf) \| Q5_0 \| 5 \| 4.65 GB\| 7.15 GB \| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [leo-hessianai-7b-chat.Q5_K_S.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-GGUF/blob/main/leo-hessianai-7b-chat.Q5_K_S.gguf) \| Q5_K_S \| 5 \| 4.65 GB\| 7.15 GB \| large, low quality loss - recommended \| \| [leo-hessianai-7b-chat.Q5_K_M.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-GGUF/blob/main/leo-hessianai-7b-chat.Q5_K_M.gguf) \| Q5_K_M \| 5 \| 4.78 GB\| 7.28 GB \| large, very low quality loss - recommended \| \| [leo-hessianai-7b-chat.Q6_K.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-GGUF/blob/main/leo-hessianai-7b-chat.Q6_K.gguf) \| Q6_K \| 6 \| 5.53 GB\| 8.03 GB \| very large, extremely low quality loss \| \| [leo-hessianai-7b-chat.Q8_0.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-GGUF/blob/main/leo-hessianai-7b-chat.Q8_0.gguf) \| Q8_0 \| 8 \| 7.16 GB\| 9.66 GB \| very large, extremely low quality loss - not recommended \| Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. <!-- README_GGUF.md-provided-files end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: - LM Studio - LoLLMS Web UI - Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: TheBloke/leo-hessianai-7B-chat-GGUF and below it, a specific filename to download, such as: leo-hessianai-7b-chat.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download TheBloke/leo-hessianai-7B-chat-GGUF leo-hessianai-7b-chat.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download TheBloke/leo-hessianai-7B-chat-GGUF --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/leo-hessianai-7B-chat-GGUF leo-hessianai-7b-chat.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 32 -m leo-hessianai-7b-chat.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<\|im_start\|>system\n{system_message}<\|im_end\|>\n<\|im_start\|>user\n{prompt}<\|im_end\|>\n<\|im_start\|>assistant" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. ### How to load this model in Python code, using ctransformers #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install ctransformers # Or with CUDA GPU acceleration pip install ctransformers[cuda] # Or with AMD ROCm GPU acceleration (Linux only) CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers # Or with Metal GPU acceleration for macOS systems only CT_METAL=1 pip install ctransformers --no-binary ctransformers ``` #### Simple ctransformers example code ```python from ctransformers import AutoModelForCausalLM # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = AutoModelForCausalLM.from_pretrained("TheBloke/leo-hessianai-7B-chat-GGUF", model_file="leo-hessianai-7b-chat.Q4_K_M.gguf", model_type="llama", gpu_layers=50) print(llm("AI is going to")) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> <!-- original-model-card start --> # Original model card: LAION LeoLM's Leo Hessianai 7B Chat # LAION LeoLM: Linguistically Enhanced Open Language Model Meet LeoLM, the first open and commercially available German Foundation Language Model built on Llama-2. Our models extend Llama-2's capabilities into German through continued pretraining on a large corpus of German-language and mostly locality specific text. Thanks to a compute grant at HessianAI's new supercomputer 42, we release two foundation models trained with 8k context length, [`LeoLM/leo-hessianai-7b`](https://huggingface.co/LeoLM/leo-hessianai-7b) and [`LeoLM/leo-hessianai-13b`](https://huggingface.co/LeoLM/leo-hessianai-13b) under the [Llama-2 community license](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) (70b also coming soon! 👀). With this release, we hope to bring a new wave of opportunities to German open-source and commercial LLM research and accelerate adoption. Read our [blog post]() or our paper (preprint coming soon) for more details! A project by Björn Plüster and Christoph Schuhmann in collaboration with LAION and HessianAI. ## LeoLM Chat `LeoLM/leo-hessianai-7b-chat` is a German chat model built on our foundation model `LeoLM/leo-hessianai-7b` and finetuned on a selection of German instruction datasets. The model performs exceptionally well on writing, explanation and discussion tasks but struggles somewhat with math and advanced reasoning. See our MT-Bench-DE scores: ``` { "first_turn": 5.75, "second_turn": 4.45, "categories": { "writing": 5.875, "roleplay": 6.3, "reasoning": 3.5, "math": 2.85, "coding": 2.95, "extraction": 4.3, "stem": 7.4, "humanities": 7.625 }, "average": 5.1 } ``` ## Model Details - Finetuned from: [LeoLM/leo-hessianai-7b](https://huggingface.co/LeoLM/leo-hessianai-7b) - Model type: Causal decoder-only transformer language model - Language: English and German - Demo: [Web Demo]() - License: [LLAMA 2 COMMUNITY LICENSE AGREEMENT](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) - Contact: [LAION Discord](https://discord.com/invite/eq3cAMZtCC) or [Björn Plüster](mailto:[email protected]) ## Use in 🤗Transformers First install direct dependencies: ``` pip install transformers torch sentencepiece ``` If you want faster inference using flash-attention2, you need to install these dependencies: ```bash pip install packaging ninja pip install flash-attn==v2.1.1 --no-build-isolation pip install git+https://github.com/HazyResearch/[email protected]#subdirectory=csrc/rotary ``` Then load the model in transformers: ```python from transformers import pipeline import torch system_prompt = """<\|im_start\|>system Dies ist eine Unterhaltung zwischen einem intelligenten, hilfsbereitem KI-Assistenten und einem Nutzer. Der Assistent gibt ausführliche, hilfreiche und ehrliche Antworten.<\|im_end\|> """ prompt_format = "<\|im_start\|>user\n{prompt}<\|im_end\|>\n<\|im_start\|>assistant\n" prompt = "Erkläre mir wie die Fahrradwegesituation in Hamburg ist." generator = pipeline(model="LeoLM/leo-hessianai-7b-chat", device="cuda", torch_dtype=torch.float16, trust_remote_code=True) # True for flash-attn2 else False print(generator(prompt_format.format(prompt=prompt), do_sample=True, top_p=0.95, max_length=8192)) ``` "Als KI kann ich keine persönlichen Beobachtungen teilen, aber ich kann einige allgemeine Informationen zur Fahrradwegesituation in Hamburg liefern. Im Vergleich zu vielen anderen großen Städten hat Hamburg eine hohe Anzahl von Fahrradfahrern und nimmt seine Verantwortung für nachhaltige Verkehrsmittel sehr ernst. Es gibt viele Fahrradwege und separate Fahrspuren, die Radfahrern ermöglichen, zusammen mit dem Autoverkehr zu fahren. Diese Fahrradspuren sind oft mit Markierungen gekennzeichnet und durch physische Trennungen von anderen Fahrspuren abgegrenzt. Darüber hinaus gibt es viele Fahrradstraßen, auf denen Radfahrer Vorfahrt haben und Autos langsamer fahren müssen. In einigen städtischen Gebieten können Fahrradwege jedoch eng oder überfüllt sein, besonders während der Stoßzeiten. Es gibt auch viele Kreuzungen, an denen Radfahrer anhalten und auf Grün warten müssen, ähnlich wie Autofahrer. Insgesamt ist die Fahrradinfrastruktur in Hamburg ziemlich gut, aber wie überall gibt es immer Raum für Verbesserungen." ## Prompting / Prompt Template Prompt dialogue template (ChatML format): ``` """ <\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant """ ``` The model input can contain multiple conversation turns between user and assistant, e.g. ``` <\|im_start\|>user {prompt 1}<\|im_end\|> <\|im_start\|>assistant {reply 1}<\|im_end\|> <\|im_start\|>user {prompt 2}<\|im_end\|> <\|im_start\|>assistant (...) ``` ## Ethical Considerations and Limitations LeoLM has been tested in English and German, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, the potential outputs of `LeoLM/leo-hessianai-7b-chat` cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of `LeoLM/leo-hessianai-7b-chat`, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see Meta's [Responsible Use Guide](https://ai.meta.com/llama/responsible-use-guide/). ## Finetuning Details \| Hyperparameter \| Value \| \|---\|---\| \| Num epochs \| 3 \| \| Examples per epoch \| 131214 \| \| Global batch size \| 256 \| \| Learning rate \| 3e-5 \| \| Warmup steps \| 100 \| \| LR scheduler \| Cosine \| \| Adam betas \| (0.9, 0.95) \| ## Dataset Details ``` ## Stats for 'Subset of OpenAssistant/OASST-DE' (3534 samples (100.0%)) ----------------- Accepted: 3534/3534 (100.0%) Accepted tokens: 2259302 Skipped: 0 (0.0%) Min tokens per sample: 29 Max tokens per sample: 2484 Avg tokens per sample: 639.3044708545557 ----------------- ## Stats for 'Subset of FreedomIntelligence/evol-instruct-deutsch' (57841 samples (100.0%)) ----------------- Accepted: 57841/57841 (100.0%) Accepted tokens: 42958192 Skipped: 0 (0.0%) Min tokens per sample: 33 Max tokens per sample: 5507 Avg tokens per sample: 742.6944900675991 ----------------- ## Stats for 'Subset of FreedomIntelligence/alpaca-gpt4-deutsch' (48969 samples (100.0%)) ----------------- Accepted: 48969/48969 (100.0%) Accepted tokens: 13372005 Skipped: 0 (0.0%) Min tokens per sample: 19 Max tokens per sample: 1359 Avg tokens per sample: 273.07082031489307 ----------------- ## Stats for 'Subset of LeoLM/OpenSchnabeltier' (21314 samples (100.0%)) ----------------- Accepted: 21314/21314 (100.0%) Accepted tokens: 8134690 Skipped: 0 (0.0%) Min tokens per sample: 25 Max tokens per sample: 1202 Avg tokens per sample: 381.65947264708643 ----------------- ## Stats for 'Subset of LeoLM/German_Poems' (490 samples (100.0%)) ----------------- Accepted: 490/490 (100.0%) Accepted tokens: 618642 Skipped: 0 (0.0%) Min tokens per sample: 747 Max tokens per sample: 1678 Avg tokens per sample: 1262.534693877551 ----------------- ## Stats for 'Subset of LeoLM/German_Songs' (392 samples (100.0%)) ----------------- Accepted: 392/392 (100.0%) Accepted tokens: 187897 Skipped: 0 (0.0%) Min tokens per sample: 231 Max tokens per sample: 826 Avg tokens per sample: 479.3290816326531 ----------------- ## Stats for 'total' (132540 samples (100.0%)) ----------------- Accepted: 132540/132540 (100.0%) Accepted tokens: 67530728 Skipped: 0 (0.0%) Min tokens per sample: 19 Max tokens per sample: 5507 Avg tokens per sample: 509.51205673758864 ----------------- ``` <!-- original-model-card end -->
ezzini/marian-finetuned-mcwc-ar-to-en	ezzini	2023-09-28T15:38:53Z	63	0	transformers	[ "transformers", "tf", "marian", "text2text-generation", "generated_from_keras_callback", "base_model:Helsinki-NLP/opus-mt-ar-en", "base_model:finetune:Helsinki-NLP/opus-mt-ar-en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-09-28T15:31:26Z	--- license: apache-2.0 base_model: Helsinki-NLP/opus-mt-ar-en tags: - generated_from_keras_callback model-index: - name: ezzini/marian-finetuned-mcwc-ar-to-en 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. --> # ezzini/marian-finetuned-mcwc-ar-to-en This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ar-en](https://huggingface.co/Helsinki-NLP/opus-mt-ar-en) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.9486 - Validation Loss: 1.0908 - Epoch: 2 ## 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': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 384, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 1.3918 \| 1.1473 \| 0 \| \| 1.0745 \| 1.1021 \| 1 \| \| 0.9486 \| 1.0908 \| 2 \| ### Framework versions - Transformers 4.33.3 - TensorFlow 2.13.0 - Datasets 2.14.5 - Tokenizers 0.13.3
joachimsallstrom/aether-glitch-lora-for-sdxl	joachimsallstrom	2023-09-28T15:35:34Z	910	17	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "style", "vhs glitch", "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	2023-09-28T15:35:20Z	--- license: other tags: - text-to-image - stable-diffusion - lora - diffusers - style - vhs glitch base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: vhs glitch widget: - text: " vhs glitch a skeleton news reporter wearing a black wig and suit, they live 1988, movie still cinematic" - text: " vhs glitch man on a mountain looking at the sunset, cinematic, black background" - text: " vhs glitch cinematic 80s movie still of Margot Robbie, Barbie movie, intricate, atom bomb in the background" - text: " vhs glitch award-winning photo of kung fury battling a monster, cinematic horror" - text: " vhs glitch movie still of cyberpunk 2077 female character, cinematic, hires 8k, intricate, white background" - text: " vhs glitch movie still of by 50s Jennifer Connelly jazz singer at a dark club, Dark City 1998, cinematic, heavy grain" - text: " vhs glitch movie still of cyberpunk 2077 black character, cinematic, hires 8k, intricate, white background" - text: " vhs glitch cinematic movie still of Cillian Murphy wearing a 40s fedora" - text: " vhs glitch found footage of horror girl in the woods, cinematic, hires 8k, intricate, black background" - text: " vhs glitch movie still of cyberpunk 2077 character with cigar in mouth, cinematic, hires 8k, intricate, white background" --- # Aether Glitch - LoRA for SDXL ![Image 0](2680627.jpeg) > vhs glitch a skeleton news reporter wearing a black wig and suit, they live 1988, movie still cinematic <p><strong><span style="color:rgb(230, 73, 128)">MAKE SURE TO CHECK THE IMAGES ON THIS PAGE FOR PROMPTS AND SETTINGS TO GET GOOD RESULTS.</span></strong><br /><br />This is <strong><span style="color:rgb(230, 73, 128)">A</span><span style="color:rgb(190, 75, 219)">e</span><span style="color:rgb(121, 80, 242)">t</span><span style="color:rgb(76, 110, 245)">h</span><span style="color:rgb(34, 139, 230)">e</span><span style="color:rgb(21, 170, 191)">r</span></strong> <strong><span style="color:rgb(21, 170, 191)">G</span><span style="color:rgb(64, 192, 87)">l</span><span style="color:rgb(130, 201, 30)">it</span><span style="color:rgb(250, 176, 5)">c</span><span style="color:rgb(253, 126, 20)">h</span></strong>, an SDXL LoRA that simulates vhs tape glitches over subjects. Make sure to trigger the LoRA with <strong><em>vhs glitch</em></strong>, and play with weights to get more or less balanced glitch or crt tv looks. Looks cool on both photoreal and animated stuff. Gaming, movies and horror works well, and of course synthwave and such. Overall a great mood setter!<br /><br />Thanks to <a target="_blank" rel="ugc" href="https://rundiffusion.com/">RunDiffusion</a> who sponsored the finetuning of this LoRA. <span style="color:rgb(219, 222, 225)">It was made on Lastben’s SDXL LoRA trainer through RunDiffusion which will soon be made public for everyone. Aether Glitch is of course available on their platform to try it out.</span></p><p></p><p><span style="color:rgb(219, 222, 225)">Also thanks to Masslevel, Warlock and Adam D for contributing with awesome images.</span></p> ## Image examples for the model: ![Image 1](2675921.jpeg) > vhs glitch man on a mountain looking at the sunset, cinematic, black background ![Image 2](2675899.jpeg) > vhs glitch cinematic 80s movie still of Margot Robbie, Barbie movie, intricate, atom bomb in the background ![Image 3](2680127.jpeg) > vhs glitch award-winning photo of kung fury battling a monster, cinematic horror ![Image 4](2665925.jpeg) > vhs glitch movie still of cyberpunk 2077 female character, cinematic, hires 8k, intricate, white background ![Image 5](2680711.jpeg) > vhs glitch movie still of by 50s Jennifer Connelly jazz singer at a dark club, Dark City 1998, cinematic, heavy grain ![Image 6](2665924.jpeg) > vhs glitch movie still of cyberpunk 2077 black character, cinematic, hires 8k, intricate, white background ![Image 7](2675806.jpeg) > vhs glitch cinematic movie still of Cillian Murphy wearing a 40s fedora ![Image 8](2675809.jpeg) > vhs glitch found footage of horror girl in the woods, cinematic, hires 8k, intricate, black background ![Image 9](2675807.jpeg) > vhs glitch movie still of cyberpunk 2077 character with cigar in mouth, cinematic, hires 8k, intricate, white background
TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ	TheBloke	2023-09-28T15:34:24Z	33	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "custom_code", "en", "de", "dataset:LeoLM/OpenSchnabeltier", "dataset:OpenAssistant/OASST-DE", "dataset:FreedomIntelligence/alpaca-gpt4-deutsch", "dataset:FreedomIntelligence/evol-instruct-deutsch", "dataset:LeoLM/German_Poems", "dataset:LeoLM/German_Songs", "dataset:garage-bAInd/Open-Platypus", "dataset:WizardLM/WizardLM_evol_instruct_70k", "dataset:bjoernp/oasst25-08-23-filtered", "base_model:LeoLM/leo-hessianai-7b-chat-bilingual", "base_model:quantized:LeoLM/leo-hessianai-7b-chat-bilingual", "license:llama2", "autotrain_compatible", "text-generation-inference", "4-bit", "gptq", "region:us" ]	text-generation	2023-09-28T14:52:03Z	--- base_model: LeoLM/leo-hessianai-7b-chat-bilingual datasets: - LeoLM/OpenSchnabeltier - OpenAssistant/OASST-DE - FreedomIntelligence/alpaca-gpt4-deutsch - FreedomIntelligence/evol-instruct-deutsch - LeoLM/German_Poems - LeoLM/German_Songs - garage-bAInd/Open-Platypus - WizardLM/WizardLM_evol_instruct_70k - bjoernp/oasst25-08-23-filtered inference: false language: - en - de library_name: transformers license: llama2 model_creator: LAION LeoLM model_name: Leo Hessianai 7B Chat Bilingual model_type: llama pipeline_tag: text-generation prompt_template: '<\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ' quantized_by: TheBloke --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" 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;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Leo Hessianai 7B Chat Bilingual - GPTQ - Model creator: [LAION LeoLM](https://huggingface.co/LeoLM) - Original model: [Leo Hessianai 7B Chat Bilingual](https://huggingface.co/LeoLM/leo-hessianai-7b-chat-bilingual) <!-- description start --> ## Description This repo contains GPTQ model files for [LAION LeoLM's Leo Hessianai 7B Chat Bilingual](https://huggingface.co/LeoLM/leo-hessianai-7b-chat-bilingual). Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GGUF) * [LAION LeoLM's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/LeoLM/leo-hessianai-7b-chat-bilingual) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: ChatML ``` <\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` <!-- prompt-template end --> <!-- README_GPTQ.md-provided-files start --> ## Provided files, and GPTQ parameters Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches. All recent GPTQ files are made with AutoGPTQ, and all files in non-main branches are made with AutoGPTQ. Files in the `main` branch which were uploaded before August 2023 were made with GPTQ-for-LLaMa. <details> <summary>Explanation of GPTQ parameters</summary> - Bits: The bit size of the quantised model. - GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value. - Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now. - Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy. - GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s). - Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences. - ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit. </details> \| Branch \| Bits \| GS \| Act Order \| Damp % \| GPTQ Dataset \| Seq Len \| Size \| ExLlama \| Desc \| \| ------ \| ---- \| -- \| --------- \| ------ \| ------------ \| ------- \| ---- \| ------- \| ---- \| \| [main](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ/tree/main) \| 4 \| 128 \| Yes \| 0.1 \| [German Quad](https://huggingface.co/datasets/deepset/germanquad) \| 8192 \| 3.90 GB \| Yes \| 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. \| \| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ/tree/gptq-4bit-32g-actorder_True) \| 4 \| 32 \| Yes \| 0.1 \| [German Quad](https://huggingface.co/datasets/deepset/germanquad) \| 8192 \| 4.28 GB \| Yes \| 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. \| \| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ/tree/gptq-8bit--1g-actorder_True) \| 8 \| None \| Yes \| 0.1 \| [German Quad](https://huggingface.co/datasets/deepset/germanquad) \| 8192 \| 7.01 GB \| No \| 8-bit, with Act Order. No group size, to lower VRAM requirements. \| \| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ/tree/gptq-8bit-128g-actorder_True) \| 8 \| 128 \| Yes \| 0.1 \| [German Quad](https://huggingface.co/datasets/deepset/germanquad) \| 8192 \| 7.16 GB \| No \| 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. \| \| [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ/tree/gptq-8bit-32g-actorder_True) \| 8 \| 32 \| Yes \| 0.1 \| [German Quad](https://huggingface.co/datasets/deepset/germanquad) \| 8192 \| 7.62 GB \| No \| 8-bit, with group size 32g and Act Order for maximum inference quality. \| \| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ/tree/gptq-4bit-64g-actorder_True) \| 4 \| 64 \| Yes \| 0.1 \| [German Quad](https://huggingface.co/datasets/deepset/germanquad) \| 8192 \| 4.03 GB \| Yes \| 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. \| <!-- README_GPTQ.md-provided-files end --> <!-- README_GPTQ.md-download-from-branches start --> ## How to download, including from branches ### In text-generation-webui To download from the `main` branch, enter `TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ` in the "Download model" box. To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ:gptq-4bit-32g-actorder_True` ### From the command line I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` To download the `main` branch to a folder called `leo-hessianai-7B-chat-bilingual-GPTQ`: ```shell mkdir leo-hessianai-7B-chat-bilingual-GPTQ huggingface-cli download TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ --local-dir leo-hessianai-7B-chat-bilingual-GPTQ --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: ```shell mkdir leo-hessianai-7B-chat-bilingual-GPTQ huggingface-cli download TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir leo-hessianai-7B-chat-bilingual-GPTQ --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Huggingface cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model. The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`. For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell mkdir leo-hessianai-7B-chat-bilingual-GPTQ HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ --local-dir leo-hessianai-7B-chat-bilingual-GPTQ --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> ### With `git` (not recommended) To clone a specific branch with `git`, use a command like this: ```shell git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ ``` Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.) <!-- README_GPTQ.md-download-from-branches end --> <!-- README_GPTQ.md-text-generation-webui start --> ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui). Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the Model tab. 2. Under Download custom model or LoRA, enter `TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ`. - To download from a specific branch, enter for example `TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ:gptq-4bit-32g-actorder_True` - see Provided Files above for the list of branches for each option. 3. Click Download. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to Model. 6. In the Model dropdown, choose the model you just downloaded: `leo-hessianai-7B-chat-bilingual-GPTQ` 7. The model will automatically load, and is now ready for use! 8. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right. * Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. 9. Once you're ready, click the Text Generation tab and enter a prompt to get started! <!-- README_GPTQ.md-text-generation-webui end --> <!-- README_GPTQ.md-use-from-python start --> ## How to use this GPTQ model from Python code ### Install the necessary packages Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later. ```shell pip3 install transformers optimum pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7 ``` If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead: ```shell pip3 uninstall -y auto-gptq git clone https://github.com/PanQiWei/AutoGPTQ cd AutoGPTQ git checkout v0.4.2 pip3 install . ``` ### You can then use the following code ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_name_or_path = "TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ" # To use a different branch, change revision # For example: revision="gptq-4bit-32g-actorder_True" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", trust_remote_code=False, revision="main") tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) prompt = "Tell me about AI" prompt_template=f'''<\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ''' print("\n\n* Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline print("* Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(pipe(prompt_template)[0]['generated_text']) ``` <!-- README_GPTQ.md-use-from-python end --> <!-- README_GPTQ.md-compatibility start --> ## Compatibility The files provided are tested to work with AutoGPTQ, both via Transformers and using AutoGPTQ directly. They should also work with [Occ4m's GPTQ-for-LLaMa fork](https://github.com/0cc4m/KoboldAI). [ExLlama](https://github.com/turboderp/exllama) is compatible with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility. [Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is compatible with all GPTQ models. <!-- README_GPTQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: LAION LeoLM's Leo Hessianai 7B Chat Bilingual # LAION LeoLM: Linguistically Enhanced Open Language Model Meet LeoLM, the first open and commercially available German Foundation Language Model built on Llama-2. Our models extend Llama-2's capabilities into German through continued pretraining on a large corpus of German-language and mostly locality specific text. Thanks to a compute grant at HessianAI's new supercomputer 42, we release two foundation models trained with 8k context length, [`LeoLM/leo-hessianai-7b`](https://huggingface.co/LeoLM/leo-hessianai-7b) and [`LeoLM/leo-hessianai-13b`](https://huggingface.co/LeoLM/leo-hessianai-13b) under the [Llama-2 community license](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) (70b also coming soon! 👀). With this release, we hope to bring a new wave of opportunities to German open-source and commercial LLM research and accelerate adoption. Read our [blog post]() or our paper (preprint coming soon) for more details! A project by Björn Plüster and Christoph Schuhmann in collaboration with LAION and HessianAI. ## LeoLM Chat `LeoLM/leo-hessianai-7b-chat-bilingual` is a bilingual English-German chat model built on our foundation model `LeoLM/leo-hessianai-7b` and finetuned on a selection of German translateed instruction datasets and their English counterparts. The model performs exceptionally well on writing, explanation and discussion tasks but struggles somewhat with math and advanced reasoning. See our MT-Bench scores: ``` { "first_turn": 5.64375, "second_turn": 4.075, "categories": { "writing": 5.925, "roleplay": 5.25, "reasoning": 3.1, "math": 1.8, "coding": 3.4, "extraction": 5, "stem": 6.5, "humanities": 7.9 }, "average": 4.859375 } ``` ## Model Details - Finetuned from: [LeoLM/leo-hessianai-7b](https://huggingface.co/LeoLM/leo-hessianai-7b) - Model type: Causal decoder-only transformer language model - Language: English and German - Demo: [Web Demo]() - License: [LLAMA 2 COMMUNITY LICENSE AGREEMENT](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) - Contact: [LAION Discord](https://discord.com/invite/eq3cAMZtCC) or [Björn Plüster](mailto:[email protected]) ## Use in 🤗Transformers First install direct dependencies: ``` pip install transformers torch sentencepiece ``` If you want faster inference using flash-attention2, you need to install these dependencies: ```bash pip install packaging ninja pip install flash-attn==v2.1.1 --no-build-isolation pip install git+https://github.com/HazyResearch/[email protected]#subdirectory=csrc/rotary ``` Then load the model in transformers: ```python from transformers import pipeline import torch system_prompt = """<\|im_start\|>system Dies ist eine Unterhaltung zwischen einem intelligenten, hilfsbereitem KI-Assistenten und einem Nutzer. Der Assistent gibt ausführliche, hilfreiche und ehrliche Antworten.<\|im_end\|> """ prompt_format = "<\|im_start\|>user\n{prompt}<\|im_end\|>\n<\|im_start\|>assistant\n" prompt = "Erkläre mir wie die Fahrradwegesituation in Hamburg ist." generator = pipeline(model="LeoLM/leo-hessianai-7b-chat-bilingual", device="cuda", torch_dtype=torch.float16, trust_remote_code=True) # True for flash-attn2 else False print(generator(prompt_format.format(prompt=prompt), do_sample=True, top_p=0.95, max_length=8192)) ``` "Als KI kann ich keine persönlichen Beobachtungen teilen, aber ich kann einige allgemeine Informationen zur Fahrradwegesituation in Hamburg liefern. Im Vergleich zu vielen anderen großen Städten hat Hamburg eine hohe Anzahl von Fahrradfahrern und nimmt seine Verantwortung für nachhaltige Verkehrsmittel sehr ernst. Es gibt viele Fahrradwege und separate Fahrspuren, die Radfahrern ermöglichen, zusammen mit dem Autoverkehr zu fahren. Diese Fahrradspuren sind oft mit Markierungen gekennzeichnet und durch physische Trennungen von anderen Fahrspuren abgegrenzt. Darüber hinaus gibt es viele Fahrradstraßen, auf denen Radfahrer Vorfahrt haben und Autos langsamer fahren müssen. In einigen städtischen Gebieten können Fahrradwege jedoch eng oder überfüllt sein, besonders während der Stoßzeiten. Es gibt auch viele Kreuzungen, an denen Radfahrer anhalten und auf Grün warten müssen, ähnlich wie Autofahrer. Insgesamt ist die Fahrradinfrastruktur in Hamburg ziemlich gut, aber wie überall gibt es immer Raum für Verbesserungen." ## Prompting / Prompt Template Prompt dialogue template (ChatML format): ``` """ <\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant """ ``` The model input can contain multiple conversation turns between user and assistant, e.g. ``` <\|im_start\|>user {prompt 1}<\|im_end\|> <\|im_start\|>assistant {reply 1}<\|im_end\|> <\|im_start\|>user {prompt 2}<\|im_end\|> <\|im_start\|>assistant (...) ``` ## Ethical Considerations and Limitations LeoLM has been tested in English and German, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, the potential outputs of `LeoLM/leo-hessianai-7b-chat` cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of `LeoLM/leo-hessianai-7b-chat`, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see Meta's [Responsible Use Guide](https://ai.meta.com/llama/responsible-use-guide/). ## Finetuning Details \| Hyperparameter \| Value \| \|---\|---\| \| Num epochs \| 3 \| \| Examples per epoch \| 233275 \| \| Global batch size \| 256 \| \| Learning rate \| 3e-5 \| \| Warmup steps \| 100 \| \| LR scheduler \| Cosine \| \| Adam betas \| (0.9, 0.95) \| \| Weight decay \| 0.001 \| ## Dataset Details ``` ## Stats for 'Subset of LeoLM/OpenSchnabeltier' (21314 samples (100.0%)) ----------------- Accepted: 21314/21314 (100.0%) Accepted tokens: 8134690 Skipped: 0 (0.0%) Min tokens per sample: 25 Max tokens per sample: 1202 Avg tokens per sample: 381.65947264708643 ----------------- ## Stats for 'Subset of garage-bAInd/Open-Platypus' (24427 samples (100.0%)) ----------------- Accepted: 24427/24427 (100.0%) Accepted tokens: 9549043 Skipped: 0 (0.0%) Min tokens per sample: 23 Max tokens per sample: 5054 Avg tokens per sample: 390.9216440823679 ----------------- ## Stats for 'Subset of WizardLM/WizardLM_evol_instruct_70k' (68600 samples (100.0%)) ----------------- Accepted: 68600/68600 (100.0%) Accepted tokens: 33045040 Skipped: 0 (0.0%) Min tokens per sample: 18 Max tokens per sample: 11810 Avg tokens per sample: 481.7061224489796 ----------------- ## Stats for 'Subset of FreedomIntelligence/evol-instruct-deutsch' (57841 samples (100.0%)) ----------------- Accepted: 57841/57841 (100.0%) Accepted tokens: 42958192 Skipped: 0 (0.0%) Min tokens per sample: 33 Max tokens per sample: 5507 Avg tokens per sample: 742.6944900675991 ----------------- ## Stats for 'Subset of FreedomIntelligence/alpaca-gpt4-deutsch' (48969 samples (100.0%)) ----------------- Accepted: 48969/48969 (100.0%) Accepted tokens: 13372005 Skipped: 0 (0.0%) Min tokens per sample: 19 Max tokens per sample: 1359 Avg tokens per sample: 273.07082031489307 ----------------- ## Stats for 'Subset of LeoLM/German_Songs' (490 samples (100.0%)) ----------------- Accepted: 490/490 (100.0%) Accepted tokens: 618642 Skipped: 0 (0.0%) Min tokens per sample: 747 Max tokens per sample: 1678 Avg tokens per sample: 1262.534693877551 ----------------- ## Stats for 'Subset of LeoLM/German_Poems' (392 samples (100.0%)) ----------------- Accepted: 392/392 (100.0%) Accepted tokens: 187897 Skipped: 0 (0.0%) Min tokens per sample: 231 Max tokens per sample: 826 Avg tokens per sample: 479.3290816326531 ----------------- ## Stats for 'Subset of OpenAssistant/OASST_DE' (3646 samples (100.0%)) ----------------- Accepted: 3646/3646 (100.0%) Accepted tokens: 2338738 Skipped: 0 (0.0%) Min tokens per sample: 29 Max tokens per sample: 2484 Avg tokens per sample: 641.4530992868897 ----------------- ## Stats for 'Subset of bjoernp/oasst25-08-23-filtered' (8922 samples (100.0%)) ----------------- Accepted: 8922/8922 (100.0%) Accepted tokens: 4526427 Skipped: 0 (0.0%) Min tokens per sample: 23 Max tokens per sample: 5407 Avg tokens per sample: 507.3332212508406 ----------------- ## Stats for 'total' (235632 samples (100.0%)) ----------------- Accepted: 235632/235632 (100.0%) Accepted tokens: 115862397 Skipped: 0 (0.0%) Min tokens per sample: 18 Max tokens per sample: 11810 Avg tokens per sample: 491.70909299246284 ----------------- ```
roa7n/gpt2-human_nontata_promoters-randomized_6_layers_3e-05_lr_2_e	roa7n	2023-09-28T15:33:46Z	0	0	peft	[ "peft", "region:us" ]	null	2023-09-28T15:33:43Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
ezzini/marian-finetuned-mcwc-es-to-en	ezzini	2023-09-28T15:24:12Z	62	0	transformers	[ "transformers", "tf", "marian", "text2text-generation", "generated_from_keras_callback", "base_model:Helsinki-NLP/opus-mt-es-en", "base_model:finetune:Helsinki-NLP/opus-mt-es-en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-09-28T15:04:33Z	--- license: apache-2.0 base_model: Helsinki-NLP/opus-mt-es-en tags: - generated_from_keras_callback model-index: - name: ezzini/marian-finetuned-mcwc-es-to-en 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. --> # ezzini/marian-finetuned-mcwc-es-to-en This model is a fine-tuned version of [Helsinki-NLP/opus-mt-es-en](https://huggingface.co/Helsinki-NLP/opus-mt-es-en) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5858 - Validation Loss: 0.7591 - Epoch: 2 ## 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': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 222, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 0.9297 \| 0.7942 \| 0 \| \| 0.6781 \| 0.7633 \| 1 \| \| 0.5858 \| 0.7591 \| 2 \| ### Framework versions - Transformers 4.33.3 - TensorFlow 2.13.0 - Datasets 2.14.5 - Tokenizers 0.13.3
Elukasoooo/Pudzian	Elukasoooo	2023-09-28T14:58:13Z	0	0	null	[ "pl", "dataset:taesiri/arxiv_qa", "license:openrail", "region:us" ]	null	2023-09-28T14:56:31Z	--- license: openrail datasets: - taesiri/arxiv_qa language: - pl metrics: - character ---
polowitty/q-Taxi-v3	polowitty	2023-09-28T14:55:26Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-09-28T14:55:24Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.52 +/- 2.73 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="polowitty/q-Taxi-v3", 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"]) ```
TheBloke/leo-hessianai-7B-chat-bilingual-GGUF	TheBloke	2023-09-28T14:55:21Z	153	4	transformers	[ "transformers", "gguf", "llama", "text-generation", "en", "de", "dataset:LeoLM/OpenSchnabeltier", "dataset:OpenAssistant/OASST-DE", "dataset:FreedomIntelligence/alpaca-gpt4-deutsch", "dataset:FreedomIntelligence/evol-instruct-deutsch", "dataset:LeoLM/German_Poems", "dataset:LeoLM/German_Songs", "dataset:garage-bAInd/Open-Platypus", "dataset:WizardLM/WizardLM_evol_instruct_70k", "dataset:bjoernp/oasst25-08-23-filtered", "base_model:LeoLM/leo-hessianai-7b-chat-bilingual", "base_model:quantized:LeoLM/leo-hessianai-7b-chat-bilingual", "license:llama2", "region:us" ]	text-generation	2023-09-28T14:51:43Z	--- base_model: LeoLM/leo-hessianai-7b-chat-bilingual datasets: - LeoLM/OpenSchnabeltier - OpenAssistant/OASST-DE - FreedomIntelligence/alpaca-gpt4-deutsch - FreedomIntelligence/evol-instruct-deutsch - LeoLM/German_Poems - LeoLM/German_Songs - garage-bAInd/Open-Platypus - WizardLM/WizardLM_evol_instruct_70k - bjoernp/oasst25-08-23-filtered inference: false language: - en - de library_name: transformers license: llama2 model_creator: LAION LeoLM model_name: Leo Hessianai 7B Chat Bilingual model_type: llama pipeline_tag: text-generation prompt_template: '<\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ' quantized_by: TheBloke --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" 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;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Leo Hessianai 7B Chat Bilingual - GGUF - Model creator: [LAION LeoLM](https://huggingface.co/LeoLM) - Original model: [Leo Hessianai 7B Chat Bilingual](https://huggingface.co/LeoLM/leo-hessianai-7b-chat-bilingual) <!-- description start --> ## Description This repo contains GGUF format model files for [LAION LeoLM's Leo Hessianai 7B Chat Bilingual](https://huggingface.co/LeoLM/leo-hessianai-7b-chat-bilingual). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplate list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. <!-- README_GGUF.md-about-gguf end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GGUF) * [LAION LeoLM's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/LeoLM/leo-hessianai-7b-chat-bilingual) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: ChatML ``` <\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` <!-- prompt-template end --> <!-- compatibility_gguf start --> ## Compatibility These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) They are also compatible with many third party UIs and libraries - please see the list at the top of this README. ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw Refer to the Provided Files table below to see what files use which methods, and how. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-provided-files start --> ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [leo-hessianai-7b-chat-bilingual.Q2_K.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GGUF/blob/main/leo-hessianai-7b-chat-bilingual.Q2_K.gguf) \| Q2_K \| 2 \| 2.83 GB\| 5.33 GB \| smallest, significant quality loss - not recommended for most purposes \| \| [leo-hessianai-7b-chat-bilingual.Q3_K_S.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GGUF/blob/main/leo-hessianai-7b-chat-bilingual.Q3_K_S.gguf) \| Q3_K_S \| 3 \| 2.95 GB\| 5.45 GB \| very small, high quality loss \| \| [leo-hessianai-7b-chat-bilingual.Q3_K_M.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GGUF/blob/main/leo-hessianai-7b-chat-bilingual.Q3_K_M.gguf) \| Q3_K_M \| 3 \| 3.30 GB\| 5.80 GB \| very small, high quality loss \| \| [leo-hessianai-7b-chat-bilingual.Q3_K_L.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GGUF/blob/main/leo-hessianai-7b-chat-bilingual.Q3_K_L.gguf) \| Q3_K_L \| 3 \| 3.60 GB\| 6.10 GB \| small, substantial quality loss \| \| [leo-hessianai-7b-chat-bilingual.Q4_0.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GGUF/blob/main/leo-hessianai-7b-chat-bilingual.Q4_0.gguf) \| Q4_0 \| 4 \| 3.83 GB\| 6.33 GB \| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [leo-hessianai-7b-chat-bilingual.Q4_K_S.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GGUF/blob/main/leo-hessianai-7b-chat-bilingual.Q4_K_S.gguf) \| Q4_K_S \| 4 \| 3.86 GB\| 6.36 GB \| small, greater quality loss \| \| [leo-hessianai-7b-chat-bilingual.Q4_K_M.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GGUF/blob/main/leo-hessianai-7b-chat-bilingual.Q4_K_M.gguf) \| Q4_K_M \| 4 \| 4.08 GB\| 6.58 GB \| medium, balanced quality - recommended \| \| [leo-hessianai-7b-chat-bilingual.Q5_0.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GGUF/blob/main/leo-hessianai-7b-chat-bilingual.Q5_0.gguf) \| Q5_0 \| 5 \| 4.65 GB\| 7.15 GB \| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [leo-hessianai-7b-chat-bilingual.Q5_K_S.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GGUF/blob/main/leo-hessianai-7b-chat-bilingual.Q5_K_S.gguf) \| Q5_K_S \| 5 \| 4.65 GB\| 7.15 GB \| large, low quality loss - recommended \| \| [leo-hessianai-7b-chat-bilingual.Q5_K_M.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GGUF/blob/main/leo-hessianai-7b-chat-bilingual.Q5_K_M.gguf) \| Q5_K_M \| 5 \| 4.78 GB\| 7.28 GB \| large, very low quality loss - recommended \| \| [leo-hessianai-7b-chat-bilingual.Q6_K.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GGUF/blob/main/leo-hessianai-7b-chat-bilingual.Q6_K.gguf) \| Q6_K \| 6 \| 5.53 GB\| 8.03 GB \| very large, extremely low quality loss \| \| [leo-hessianai-7b-chat-bilingual.Q8_0.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GGUF/blob/main/leo-hessianai-7b-chat-bilingual.Q8_0.gguf) \| Q8_0 \| 8 \| 7.16 GB\| 9.66 GB \| very large, extremely low quality loss - not recommended \| Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. <!-- README_GGUF.md-provided-files end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: - LM Studio - LoLLMS Web UI - Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: TheBloke/leo-hessianai-7B-chat-bilingual-GGUF and below it, a specific filename to download, such as: leo-hessianai-7b-chat-bilingual.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download TheBloke/leo-hessianai-7B-chat-bilingual-GGUF leo-hessianai-7b-chat-bilingual.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download TheBloke/leo-hessianai-7B-chat-bilingual-GGUF --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/leo-hessianai-7B-chat-bilingual-GGUF leo-hessianai-7b-chat-bilingual.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 32 -m leo-hessianai-7b-chat-bilingual.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<\|im_start\|>system\n{system_message}<\|im_end\|>\n<\|im_start\|>user\n{prompt}<\|im_end\|>\n<\|im_start\|>assistant" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. ### How to load this model in Python code, using ctransformers #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install ctransformers # Or with CUDA GPU acceleration pip install ctransformers[cuda] # Or with AMD ROCm GPU acceleration (Linux only) CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers # Or with Metal GPU acceleration for macOS systems only CT_METAL=1 pip install ctransformers --no-binary ctransformers ``` #### Simple ctransformers example code ```python from ctransformers import AutoModelForCausalLM # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = AutoModelForCausalLM.from_pretrained("TheBloke/leo-hessianai-7B-chat-bilingual-GGUF", model_file="leo-hessianai-7b-chat-bilingual.Q4_K_M.gguf", model_type="llama", gpu_layers=50) print(llm("AI is going to")) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> <!-- original-model-card start --> # Original model card: LAION LeoLM's Leo Hessianai 7B Chat Bilingual # LAION LeoLM: Linguistically Enhanced Open Language Model Meet LeoLM, the first open and commercially available German Foundation Language Model built on Llama-2. Our models extend Llama-2's capabilities into German through continued pretraining on a large corpus of German-language and mostly locality specific text. Thanks to a compute grant at HessianAI's new supercomputer 42, we release two foundation models trained with 8k context length, [`LeoLM/leo-hessianai-7b`](https://huggingface.co/LeoLM/leo-hessianai-7b) and [`LeoLM/leo-hessianai-13b`](https://huggingface.co/LeoLM/leo-hessianai-13b) under the [Llama-2 community license](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) (70b also coming soon! 👀). With this release, we hope to bring a new wave of opportunities to German open-source and commercial LLM research and accelerate adoption. Read our [blog post]() or our paper (preprint coming soon) for more details! A project by Björn Plüster and Christoph Schuhmann in collaboration with LAION and HessianAI. ## LeoLM Chat `LeoLM/leo-hessianai-7b-chat-bilingual` is a bilingual English-German chat model built on our foundation model `LeoLM/leo-hessianai-7b` and finetuned on a selection of German translateed instruction datasets and their English counterparts. The model performs exceptionally well on writing, explanation and discussion tasks but struggles somewhat with math and advanced reasoning. See our MT-Bench scores: ``` { "first_turn": 5.64375, "second_turn": 4.075, "categories": { "writing": 5.925, "roleplay": 5.25, "reasoning": 3.1, "math": 1.8, "coding": 3.4, "extraction": 5, "stem": 6.5, "humanities": 7.9 }, "average": 4.859375 } ``` ## Model Details - Finetuned from: [LeoLM/leo-hessianai-7b](https://huggingface.co/LeoLM/leo-hessianai-7b) - Model type: Causal decoder-only transformer language model - Language: English and German - Demo: [Web Demo]() - License: [LLAMA 2 COMMUNITY LICENSE AGREEMENT](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) - Contact: [LAION Discord](https://discord.com/invite/eq3cAMZtCC) or [Björn Plüster](mailto:[email protected]) ## Use in 🤗Transformers First install direct dependencies: ``` pip install transformers torch sentencepiece ``` If you want faster inference using flash-attention2, you need to install these dependencies: ```bash pip install packaging ninja pip install flash-attn==v2.1.1 --no-build-isolation pip install git+https://github.com/HazyResearch/[email protected]#subdirectory=csrc/rotary ``` Then load the model in transformers: ```python from transformers import pipeline import torch system_prompt = """<\|im_start\|>system Dies ist eine Unterhaltung zwischen einem intelligenten, hilfsbereitem KI-Assistenten und einem Nutzer. Der Assistent gibt ausführliche, hilfreiche und ehrliche Antworten.<\|im_end\|> """ prompt_format = "<\|im_start\|>user\n{prompt}<\|im_end\|>\n<\|im_start\|>assistant\n" prompt = "Erkläre mir wie die Fahrradwegesituation in Hamburg ist." generator = pipeline(model="LeoLM/leo-hessianai-7b-chat-bilingual", device="cuda", torch_dtype=torch.float16, trust_remote_code=True) # True for flash-attn2 else False print(generator(prompt_format.format(prompt=prompt), do_sample=True, top_p=0.95, max_length=8192)) ``` "Als KI kann ich keine persönlichen Beobachtungen teilen, aber ich kann einige allgemeine Informationen zur Fahrradwegesituation in Hamburg liefern. Im Vergleich zu vielen anderen großen Städten hat Hamburg eine hohe Anzahl von Fahrradfahrern und nimmt seine Verantwortung für nachhaltige Verkehrsmittel sehr ernst. Es gibt viele Fahrradwege und separate Fahrspuren, die Radfahrern ermöglichen, zusammen mit dem Autoverkehr zu fahren. Diese Fahrradspuren sind oft mit Markierungen gekennzeichnet und durch physische Trennungen von anderen Fahrspuren abgegrenzt. Darüber hinaus gibt es viele Fahrradstraßen, auf denen Radfahrer Vorfahrt haben und Autos langsamer fahren müssen. In einigen städtischen Gebieten können Fahrradwege jedoch eng oder überfüllt sein, besonders während der Stoßzeiten. Es gibt auch viele Kreuzungen, an denen Radfahrer anhalten und auf Grün warten müssen, ähnlich wie Autofahrer. Insgesamt ist die Fahrradinfrastruktur in Hamburg ziemlich gut, aber wie überall gibt es immer Raum für Verbesserungen." ## Prompting / Prompt Template Prompt dialogue template (ChatML format): ``` """ <\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant """ ``` The model input can contain multiple conversation turns between user and assistant, e.g. ``` <\|im_start\|>user {prompt 1}<\|im_end\|> <\|im_start\|>assistant {reply 1}<\|im_end\|> <\|im_start\|>user {prompt 2}<\|im_end\|> <\|im_start\|>assistant (...) ``` ## Ethical Considerations and Limitations LeoLM has been tested in English and German, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, the potential outputs of `LeoLM/leo-hessianai-7b-chat` cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of `LeoLM/leo-hessianai-7b-chat`, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see Meta's [Responsible Use Guide](https://ai.meta.com/llama/responsible-use-guide/). ## Finetuning Details \| Hyperparameter \| Value \| \|---\|---\| \| Num epochs \| 3 \| \| Examples per epoch \| 233275 \| \| Global batch size \| 256 \| \| Learning rate \| 3e-5 \| \| Warmup steps \| 100 \| \| LR scheduler \| Cosine \| \| Adam betas \| (0.9, 0.95) \| \| Weight decay \| 0.001 \| ## Dataset Details ``` ## Stats for 'Subset of LeoLM/OpenSchnabeltier' (21314 samples (100.0%)) ----------------- Accepted: 21314/21314 (100.0%) Accepted tokens: 8134690 Skipped: 0 (0.0%) Min tokens per sample: 25 Max tokens per sample: 1202 Avg tokens per sample: 381.65947264708643 ----------------- ## Stats for 'Subset of garage-bAInd/Open-Platypus' (24427 samples (100.0%)) ----------------- Accepted: 24427/24427 (100.0%) Accepted tokens: 9549043 Skipped: 0 (0.0%) Min tokens per sample: 23 Max tokens per sample: 5054 Avg tokens per sample: 390.9216440823679 ----------------- ## Stats for 'Subset of WizardLM/WizardLM_evol_instruct_70k' (68600 samples (100.0%)) ----------------- Accepted: 68600/68600 (100.0%) Accepted tokens: 33045040 Skipped: 0 (0.0%) Min tokens per sample: 18 Max tokens per sample: 11810 Avg tokens per sample: 481.7061224489796 ----------------- ## Stats for 'Subset of FreedomIntelligence/evol-instruct-deutsch' (57841 samples (100.0%)) ----------------- Accepted: 57841/57841 (100.0%) Accepted tokens: 42958192 Skipped: 0 (0.0%) Min tokens per sample: 33 Max tokens per sample: 5507 Avg tokens per sample: 742.6944900675991 ----------------- ## Stats for 'Subset of FreedomIntelligence/alpaca-gpt4-deutsch' (48969 samples (100.0%)) ----------------- Accepted: 48969/48969 (100.0%) Accepted tokens: 13372005 Skipped: 0 (0.0%) Min tokens per sample: 19 Max tokens per sample: 1359 Avg tokens per sample: 273.07082031489307 ----------------- ## Stats for 'Subset of LeoLM/German_Songs' (490 samples (100.0%)) ----------------- Accepted: 490/490 (100.0%) Accepted tokens: 618642 Skipped: 0 (0.0%) Min tokens per sample: 747 Max tokens per sample: 1678 Avg tokens per sample: 1262.534693877551 ----------------- ## Stats for 'Subset of LeoLM/German_Poems' (392 samples (100.0%)) ----------------- Accepted: 392/392 (100.0%) Accepted tokens: 187897 Skipped: 0 (0.0%) Min tokens per sample: 231 Max tokens per sample: 826 Avg tokens per sample: 479.3290816326531 ----------------- ## Stats for 'Subset of OpenAssistant/OASST_DE' (3646 samples (100.0%)) ----------------- Accepted: 3646/3646 (100.0%) Accepted tokens: 2338738 Skipped: 0 (0.0%) Min tokens per sample: 29 Max tokens per sample: 2484 Avg tokens per sample: 641.4530992868897 ----------------- ## Stats for 'Subset of bjoernp/oasst25-08-23-filtered' (8922 samples (100.0%)) ----------------- Accepted: 8922/8922 (100.0%) Accepted tokens: 4526427 Skipped: 0 (0.0%) Min tokens per sample: 23 Max tokens per sample: 5407 Avg tokens per sample: 507.3332212508406 ----------------- ## Stats for 'total' (235632 samples (100.0%)) ----------------- Accepted: 235632/235632 (100.0%) Accepted tokens: 115862397 Skipped: 0 (0.0%) Min tokens per sample: 18 Max tokens per sample: 11810 Avg tokens per sample: 491.70909299246284 ----------------- ``` <!-- original-model-card end -->
TinyLlama/tinyLlama-intermediate-checkpoints	TinyLlama	2023-09-28T14:53:27Z	51	12	transformers	[ "transformers", "llama", "text-generation", "en", "dataset:cerebras/SlimPajama-627B", "dataset:bigcode/starcoderdata", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-09-28T14:27:51Z	--- license: apache-2.0 datasets: - cerebras/SlimPajama-627B - bigcode/starcoderdata language: - en --- <div align="center"> # TinyLlama-1.1B </div> https://github.com/jzhang38/TinyLlama The TinyLlama project aims to pretrain a 1.1B Llama model on 3 trillion tokens. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-09-01. <div align="center"> <img src="./TinyLlama_logo.png" width="300"/> </div> We adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint. #### This Model This is an intermediate checkpoint with 50K steps and 105B tokens. #### Releases Schedule We will be rolling out intermediate checkpoints following the below schedule. We also include some baseline models for comparison. \| Date \| HF Checkpoint \| Tokens \| Step \| HellaSwag Acc_norm \| \|------------\|-------------------------------------------------\|--------\|------\|---------------------\| \| Baseline \| [StableLM-Alpha-3B](https://huggingface.co/stabilityai/stablelm-base-alpha-3b)\| 800B \| -- \| 38.31 \| \| Baseline \| [Pythia-1B-intermediate-step-50k-105b](https://huggingface.co/EleutherAI/pythia-1b/tree/step50000) \| 105B \| 50k \| 42.04 \| \| Baseline \| [Pythia-1B](https://huggingface.co/EleutherAI/pythia-1b) \| 300B \| 143k \| 47.16 \| \| 2023-09-04 \| [TinyLlama-1.1B-intermediate-step-50k-105b](https://huggingface.co/PY007/TinyLlama-1.1B-step-50K-105b) \| 105B \| 50k \| 43.50 \| \| 2023-09-16 \| -- \| 500B \| -- \| -- \| \| 2023-10-01 \| -- \| 1T \| -- \| -- \| \| 2023-10-16 \| -- \| 1.5T \| -- \| -- \| \| 2023-10-31 \| -- \| 2T \| -- \| -- \| \| 2023-11-15 \| -- \| 2.5T \| -- \| -- \| \| 2023-12-01 \| -- \| 3T \| -- \| -- \| #### How to use You will need the transformers>=4.31 Do check the [TinyLlama](https://github.com/jzhang38/TinyLlama) github page for more information. ``` from transformers import AutoTokenizer import transformers import torch model = "PY007/TinyLlama-1.1B-step-50K-105b" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) sequences = pipeline( 'The TinyLlama project aims to pretrain a 1.1B Llama model on 3 trillion tokens. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-09-01.', do_sample=True, top_k=10, num_return_sequences=1, repetition_penalty=1.5, eos_token_id=tokenizer.eos_token_id, max_length=500, ) for seq in sequences: print(f"Result: {seq['generated_text']}") ```
mridul3301/distilbart-cnn-12-6-finetuned-arxiv-summarization-20k-5epochs	mridul3301	2023-09-28T14:52:13Z	12	0	transformers	[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "generated_from_trainer", "dataset:arxiv_summarization_dataset", "base_model:sshleifer/distilbart-cnn-12-6", "base_model:finetune:sshleifer/distilbart-cnn-12-6", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-08-16T10:31:33Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - arxiv_summarization_dataset metrics: - rouge base_model: sshleifer/distilbart-cnn-12-6 model-index: - name: distilbart-cnn-12-6-finetuned-arxiv-summarization-20k-5epochs results: - task: type: text2text-generation name: Sequence-to-sequence Language Modeling dataset: name: arxiv_summarization_dataset type: arxiv_summarization_dataset config: section split: test[:2000] args: section metrics: - type: rouge value: 43.6107 name: Rouge1 --- <!-- 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. --> # distilbart-cnn-12-6-finetuned-arxiv-summarization-20k-5epochs This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on the arxiv_summarization_dataset dataset. It achieves the following results on the evaluation set: - Loss: 2.3793 - Rouge1: 43.6107 - Rouge2: 15.4482 - Rougel: 25.4843 - Rougelsum: 38.4962 - Gen Len: 122.2845 ## 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:--------:\| \| 2.6031 \| 1.0 \| 2500 \| 2.4896 \| 42.8688 \| 15.0682 \| 25.1782 \| 37.8758 \| 121.6685 \| \| 2.3931 \| 2.0 \| 5000 \| 2.4135 \| 43.7802 \| 15.6074 \| 25.7201 \| 38.6496 \| 123.614 \| \| 2.2454 \| 3.0 \| 7500 \| 2.3819 \| 44.1347 \| 15.8906 \| 25.8923 \| 38.961 \| 120.8765 \| \| 2.1393 \| 4.0 \| 10000 \| 2.3780 \| 43.6521 \| 15.4051 \| 25.5124 \| 38.3483 \| 122.396 \| \| 2.0688 \| 5.0 \| 12500 \| 2.3793 \| 43.6107 \| 15.4482 \| 25.4843 \| 38.4962 \| 122.2845 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3
TheBloke/leo-hessianai-13B-GPTQ	TheBloke	2023-09-28T14:50:39Z	24	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "custom_code", "en", "de", "dataset:oscar-corpus/OSCAR-2301", "dataset:wikipedia", "dataset:bjoernp/tagesschau-2018-2023", "base_model:LeoLM/leo-hessianai-13b", "base_model:quantized:LeoLM/leo-hessianai-13b", "license:llama2", "autotrain_compatible", "text-generation-inference", "4-bit", "gptq", "region:us" ]	text-generation	2023-09-28T13:36:44Z	--- base_model: LeoLM/leo-hessianai-13b datasets: - oscar-corpus/OSCAR-2301 - wikipedia - bjoernp/tagesschau-2018-2023 inference: false language: - en - de library_name: transformers license: llama2 model_creator: LAION LeoLM model_name: Leo Hessianai 13B model_type: llama pipeline_tag: text-generation prompt_template: '{prompt} ' quantized_by: TheBloke --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" 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;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Leo Hessianai 13B - GPTQ - Model creator: [LAION LeoLM](https://huggingface.co/LeoLM) - Original model: [Leo Hessianai 13B](https://huggingface.co/LeoLM/leo-hessianai-13b) <!-- description start --> ## Description This repo contains GPTQ model files for [LAION LeoLM's Leo Hessianai 13B](https://huggingface.co/LeoLM/leo-hessianai-13b). Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/leo-hessianai-13B-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/leo-hessianai-13B-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/leo-hessianai-13B-GGUF) * [LAION LeoLM's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/LeoLM/leo-hessianai-13b) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: None ``` {prompt} ``` <!-- prompt-template end --> <!-- README_GPTQ.md-provided-files start --> ## Provided files, and GPTQ parameters Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches. All recent GPTQ files are made with AutoGPTQ, and all files in non-main branches are made with AutoGPTQ. Files in the `main` branch which were uploaded before August 2023 were made with GPTQ-for-LLaMa. <details> <summary>Explanation of GPTQ parameters</summary> - Bits: The bit size of the quantised model. - GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value. - Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now. - Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy. - GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s). - Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences. - ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit. </details> \| Branch \| Bits \| GS \| Act Order \| Damp % \| GPTQ Dataset \| Seq Len \| Size \| ExLlama \| Desc \| \| ------ \| ---- \| -- \| --------- \| ------ \| ------------ \| ------- \| ---- \| ------- \| ---- \| \| [main](https://huggingface.co/TheBloke/leo-hessianai-13B-GPTQ/tree/main) \| 4 \| 128 \| Yes \| 0.1 \| [German Quad](https://huggingface.co/datasets/deepset/germanquad) \| 8192 \| 7.26 GB \| Yes \| 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. \| \| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-13B-GPTQ/tree/gptq-4bit-32g-actorder_True) \| 4 \| 32 \| Yes \| 0.1 \| [German Quad](https://huggingface.co/datasets/deepset/germanquad) \| 8192 \| 8.00 GB \| Yes \| 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. \| \| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-13B-GPTQ/tree/gptq-8bit--1g-actorder_True) \| 8 \| None \| Yes \| 0.1 \| [German Quad](https://huggingface.co/datasets/deepset/germanquad) \| 8192 \| 13.36 GB \| No \| 8-bit, with Act Order. No group size, to lower VRAM requirements. \| \| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-13B-GPTQ/tree/gptq-8bit-128g-actorder_True) \| 8 \| 128 \| Yes \| 0.1 \| [German Quad](https://huggingface.co/datasets/deepset/germanquad) \| 8192 \| 13.65 GB \| No \| 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. \| \| [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-13B-GPTQ/tree/gptq-8bit-32g-actorder_True) \| 8 \| 32 \| Yes \| 0.1 \| [German Quad](https://huggingface.co/datasets/deepset/germanquad) \| 8192 \| 14.54 GB \| No \| 8-bit, with group size 32g and Act Order for maximum inference quality. \| \| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-13B-GPTQ/tree/gptq-4bit-64g-actorder_True) \| 4 \| 64 \| Yes \| 0.1 \| [German Quad](https://huggingface.co/datasets/deepset/germanquad) \| 8192 \| 7.51 GB \| Yes \| 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. \| <!-- README_GPTQ.md-provided-files end --> <!-- README_GPTQ.md-download-from-branches start --> ## How to download, including from branches ### In text-generation-webui To download from the `main` branch, enter `TheBloke/leo-hessianai-13B-GPTQ` in the "Download model" box. To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/leo-hessianai-13B-GPTQ:gptq-4bit-32g-actorder_True` ### From the command line I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` To download the `main` branch to a folder called `leo-hessianai-13B-GPTQ`: ```shell mkdir leo-hessianai-13B-GPTQ huggingface-cli download TheBloke/leo-hessianai-13B-GPTQ --local-dir leo-hessianai-13B-GPTQ --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: ```shell mkdir leo-hessianai-13B-GPTQ huggingface-cli download TheBloke/leo-hessianai-13B-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir leo-hessianai-13B-GPTQ --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Huggingface cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model. The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`. For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell mkdir leo-hessianai-13B-GPTQ HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/leo-hessianai-13B-GPTQ --local-dir leo-hessianai-13B-GPTQ --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> ### With `git` (not recommended) To clone a specific branch with `git`, use a command like this: ```shell git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/leo-hessianai-13B-GPTQ ``` Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.) <!-- README_GPTQ.md-download-from-branches end --> <!-- README_GPTQ.md-text-generation-webui start --> ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui). Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the Model tab. 2. Under Download custom model or LoRA, enter `TheBloke/leo-hessianai-13B-GPTQ`. - To download from a specific branch, enter for example `TheBloke/leo-hessianai-13B-GPTQ:gptq-4bit-32g-actorder_True` - see Provided Files above for the list of branches for each option. 3. Click Download. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to Model. 6. In the Model dropdown, choose the model you just downloaded: `leo-hessianai-13B-GPTQ` 7. The model will automatically load, and is now ready for use! 8. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right. * Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. 9. Once you're ready, click the Text Generation tab and enter a prompt to get started! <!-- README_GPTQ.md-text-generation-webui end --> <!-- README_GPTQ.md-use-from-python start --> ## How to use this GPTQ model from Python code ### Install the necessary packages Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later. ```shell pip3 install transformers optimum pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7 ``` If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead: ```shell pip3 uninstall -y auto-gptq git clone https://github.com/PanQiWei/AutoGPTQ cd AutoGPTQ git checkout v0.4.2 pip3 install . ``` ### You can then use the following code ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_name_or_path = "TheBloke/leo-hessianai-13B-GPTQ" # To use a different branch, change revision # For example: revision="gptq-4bit-32g-actorder_True" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", trust_remote_code=False, revision="main") tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) prompt = "Tell me about AI" prompt_template=f'''{prompt} ''' print("\n\n* Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline print("* Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(pipe(prompt_template)[0]['generated_text']) ``` <!-- README_GPTQ.md-use-from-python end --> <!-- README_GPTQ.md-compatibility start --> ## Compatibility The files provided are tested to work with AutoGPTQ, both via Transformers and using AutoGPTQ directly. They should also work with [Occ4m's GPTQ-for-LLaMa fork](https://github.com/0cc4m/KoboldAI). [ExLlama](https://github.com/turboderp/exllama) is compatible with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility. [Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is compatible with all GPTQ models. <!-- README_GPTQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: LAION LeoLM's Leo Hessianai 13B # LAION LeoLM: Linguistically Enhanced Open Language Model Meet LeoLM, the first open and commercially available German Foundation Language Model built on Llama-2. Our models extend Llama-2's capabilities into German through continued pretraining on a large corpus of German-language and mostly locality specific text. Thanks to a compute grant at HessianAI's new supercomputer 42, we release two foundation models trained with 8k context length, [`LeoLM/leo-hessianai-7b`](https://huggingface.co/LeoLM/leo-hessianai-7b) and [`LeoLM/leo-hessianai-13b`](https://huggingface.co/LeoLM/leo-hessianai-13b) under the [Llama-2 community license](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) (70b also coming soon! 👀). With this release, we hope to bring a new wave of opportunities to German open-source and commercial LLM research and accelerate adoption. Read our [blog post]() or our paper (preprint coming soon) for more details! A project by Björn Plüster and Christoph Schuhmann in collaboration with LAION and HessianAI. ## Model Details - Finetuned from: [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) - Model type: Causal decoder-only transformer language model - Language: English and German - License: [LLAMA 2 COMMUNITY LICENSE AGREEMENT](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) - Contact: [LAION Discord](https://discord.com/invite/eq3cAMZtCC) or [Björn Plüster](mailto:[email protected]) ## Use in 🤗Transformers First install direct dependencies: ``` pip install transformers torch sentencepiece ``` If you want faster inference using flash-attention2, you need to install these dependencies: ```bash pip install packaging ninja pip install flash-attn==v2.1.1 --no-build-isolation pip install git+https://github.com/HazyResearch/[email protected]#subdirectory=csrc/rotary ``` Then load the model in transformers: ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch model = AutoModelForCausalLM.from_pretrained( model="LeoLM/leo-hessianai-13b", device_map="auto", torch_dtype=torch.float16, trust_remote_code=True # True for flash-attn2 else False ) ``` ## Training parameters ![training_parameters](imgs/training_params.png "Training Hyperparameters") ## Benchmarks ![benchmarks](imgs/benchmarks.png "Benchmark Scores")
VuongQuoc/checkpoints_28_9_microsoft_deberta_V2	VuongQuoc	2023-09-28T14:36:28Z	73	0	transformers	[ "transformers", "pytorch", "deberta-v2", "multiple-choice", "generated_from_trainer", "base_model:microsoft/deberta-v3-large", "base_model:finetune:microsoft/deberta-v3-large", "license:mit", "endpoints_compatible", "region:us" ]	multiple-choice	2023-09-28T02:14:45Z	--- license: mit base_model: microsoft/deberta-v3-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: checkpoints_28_9_microsoft_deberta_V2 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. --> # checkpoints_28_9_microsoft_deberta_V2 This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5675 - Map@3: 0.8842 - Accuracy: 0.815 ## 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: 2 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 64 - 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 \| Map@3 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:--------:\| \| 1.0011 \| 0.11 \| 100 \| 0.8842 \| 0.8258 \| 0.74 \| \| 0.8398 \| 0.21 \| 200 \| 0.6978 \| 0.8667 \| 0.79 \| \| 0.8414 \| 0.32 \| 300 \| 0.6337 \| 0.8625 \| 0.795 \| \| 0.7461 \| 0.43 \| 400 \| 0.6609 \| 0.8600 \| 0.775 \| \| 0.7131 \| 0.53 \| 500 \| 0.6329 \| 0.8758 \| 0.805 \| \| 0.6891 \| 0.64 \| 600 \| 0.6157 \| 0.8892 \| 0.83 \| \| 0.6969 \| 0.75 \| 700 \| 0.5917 \| 0.8808 \| 0.805 \| \| 0.6775 \| 0.85 \| 800 \| 0.5698 \| 0.8817 \| 0.81 \| \| 0.6534 \| 0.96 \| 900 \| 0.5675 \| 0.8842 \| 0.815 \| ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.0 - Datasets 2.9.0 - Tokenizers 0.13.3
badokorach/flan-t5-small-qa-91	badokorach	2023-09-28T14:35:47Z	8	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "base_model:google/flan-t5-small", "base_model:finetune:google/flan-t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-09-28T12:33:04Z	--- license: apache-2.0 base_model: google/flan-t5-small tags: - generated_from_trainer model-index: - name: flan-t5-small-qa-91 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-small-qa-91 This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0727 ## 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: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 0.1207 \| 1.0 \| 500 \| 0.0685 \| \| 0.1176 \| 2.0 \| 1000 \| 0.0693 \| \| 0.116 \| 3.0 \| 1500 \| 0.0696 \| \| 0.1152 \| 4.0 \| 2000 \| 0.0709 \| \| 0.1138 \| 5.0 \| 2500 \| 0.0703 \| \| 0.1134 \| 6.0 \| 3000 \| 0.0717 \| \| 0.1125 \| 7.0 \| 3500 \| 0.0707 \| \| 0.1119 \| 8.0 \| 4000 \| 0.0710 \| \| 0.1115 \| 9.0 \| 4500 \| 0.0717 \| \| 0.1113 \| 10.0 \| 5000 \| 0.0713 \| \| 0.111 \| 11.0 \| 5500 \| 0.0721 \| \| 0.1107 \| 12.0 \| 6000 \| 0.0726 \| \| 0.1106 \| 13.0 \| 6500 \| 0.0721 \| \| 0.111 \| 14.0 \| 7000 \| 0.0720 \| \| 0.1108 \| 15.0 \| 7500 \| 0.0721 \| \| 0.1106 \| 16.0 \| 8000 \| 0.0722 \| \| 0.1104 \| 17.0 \| 8500 \| 0.0729 \| \| 0.1102 \| 18.0 \| 9000 \| 0.0727 \| \| 0.1101 \| 19.0 \| 9500 \| 0.0727 \| \| 0.11 \| 20.0 \| 10000 \| 0.0727 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cu118 - Tokenizers 0.13.3
CyberHarem/hyuuga_akari_yagatekimininaru	CyberHarem	2023-09-28T14:32:51Z	0	0	null	[ "art", "text-to-image", "dataset:CyberHarem/hyuuga_akari_yagatekimininaru", "license:mit", "region:us" ]	text-to-image	2023-09-28T14:22:59Z	--- license: mit datasets: - CyberHarem/hyuuga_akari_yagatekimininaru pipeline_tag: text-to-image tags: - art --- # Lora of hyuuga_akari_yagatekimininaru This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 3740, you need to download `3740/hyuuga_akari_yagatekimininaru.pt` as the embedding and `3740/hyuuga_akari_yagatekimininaru.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. The best step we recommend is 3740, with the score of 0.889. The trigger words are: 1. `hyuuga_akari_yagatekimininaru` 2. `brown_hair, brown_eyes, hair_ornament, hairclip, bangs, short_hair` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: \| Steps \| Score \| Download \| pattern_1 \| bikini \| bondage \| free \| maid \| miko \| nude \| nude2 \| suit \| yukata \| \|:---------\|:----------\|:-------------------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------\|:--------------------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-----------------------------------------------\|:------------------------------------------------\|:-------------------------------------\|:-----------------------------------------\| \| 5100 \| 0.774 \| [Download](5100/hyuuga_akari_yagatekimininaru.zip) \| ![pattern_1-5100](5100/previews/pattern_1.png) \| ![bikini-5100](5100/previews/bikini.png) \| [<NSFW, click to see>](5100/previews/bondage.png) \| ![free-5100](5100/previews/free.png) \| ![maid-5100](5100/previews/maid.png) \| ![miko-5100](5100/previews/miko.png) \| [<NSFW, click to see>](5100/previews/nude.png) \| [<NSFW, click to see>](5100/previews/nude2.png) \| ![suit-5100](5100/previews/suit.png) \| ![yukata-5100](5100/previews/yukata.png) \| \| 4760 \| 0.736 \| [Download](4760/hyuuga_akari_yagatekimininaru.zip) \| ![pattern_1-4760](4760/previews/pattern_1.png) \| ![bikini-4760](4760/previews/bikini.png) \| [<NSFW, click to see>](4760/previews/bondage.png) \| ![free-4760](4760/previews/free.png) \| ![maid-4760](4760/previews/maid.png) \| ![miko-4760](4760/previews/miko.png) \| [<NSFW, click to see>](4760/previews/nude.png) \| [<NSFW, click to see>](4760/previews/nude2.png) \| ![suit-4760](4760/previews/suit.png) \| ![yukata-4760](4760/previews/yukata.png) \| \| 4420 \| 0.838 \| [Download](4420/hyuuga_akari_yagatekimininaru.zip) \| ![pattern_1-4420](4420/previews/pattern_1.png) \| ![bikini-4420](4420/previews/bikini.png) \| [<NSFW, click to see>](4420/previews/bondage.png) \| ![free-4420](4420/previews/free.png) \| ![maid-4420](4420/previews/maid.png) \| ![miko-4420](4420/previews/miko.png) \| [<NSFW, click to see>](4420/previews/nude.png) \| [<NSFW, click to see>](4420/previews/nude2.png) \| ![suit-4420](4420/previews/suit.png) \| ![yukata-4420](4420/previews/yukata.png) \| \| 4080 \| 0.777 \| [Download](4080/hyuuga_akari_yagatekimininaru.zip) \| ![pattern_1-4080](4080/previews/pattern_1.png) \| ![bikini-4080](4080/previews/bikini.png) \| [<NSFW, click to see>](4080/previews/bondage.png) \| ![free-4080](4080/previews/free.png) \| ![maid-4080](4080/previews/maid.png) \| ![miko-4080](4080/previews/miko.png) \| [<NSFW, click to see>](4080/previews/nude.png) \| [<NSFW, click to see>](4080/previews/nude2.png) \| ![suit-4080](4080/previews/suit.png) \| ![yukata-4080](4080/previews/yukata.png) \| \| 3740 \| 0.889 \| [Download](3740/hyuuga_akari_yagatekimininaru.zip) \| ![pattern_1-3740](3740/previews/pattern_1.png) \| ![bikini-3740](3740/previews/bikini.png) \| [<NSFW, click to see>](3740/previews/bondage.png) \| ![free-3740](3740/previews/free.png) \| ![maid-3740](3740/previews/maid.png) \| ![miko-3740](3740/previews/miko.png) \| [<NSFW, click to see>](3740/previews/nude.png) \| [<NSFW, click to see>](3740/previews/nude2.png) \| ![suit-3740](3740/previews/suit.png) \| ![yukata-3740](3740/previews/yukata.png) \| \| 3400 \| 0.810 \| [Download](3400/hyuuga_akari_yagatekimininaru.zip) \| ![pattern_1-3400](3400/previews/pattern_1.png) \| ![bikini-3400](3400/previews/bikini.png) \| [<NSFW, click to see>](3400/previews/bondage.png) \| ![free-3400](3400/previews/free.png) \| ![maid-3400](3400/previews/maid.png) \| ![miko-3400](3400/previews/miko.png) \| [<NSFW, click to see>](3400/previews/nude.png) \| [<NSFW, click to see>](3400/previews/nude2.png) \| ![suit-3400](3400/previews/suit.png) \| ![yukata-3400](3400/previews/yukata.png) \| \| 3060 \| 0.868 \| [Download](3060/hyuuga_akari_yagatekimininaru.zip) \| ![pattern_1-3060](3060/previews/pattern_1.png) \| ![bikini-3060](3060/previews/bikini.png) \| [<NSFW, click to see>](3060/previews/bondage.png) \| ![free-3060](3060/previews/free.png) \| ![maid-3060](3060/previews/maid.png) \| ![miko-3060](3060/previews/miko.png) \| [<NSFW, click to see>](3060/previews/nude.png) \| [<NSFW, click to see>](3060/previews/nude2.png) \| ![suit-3060](3060/previews/suit.png) \| ![yukata-3060](3060/previews/yukata.png) \| \| 2720 \| 0.725 \| [Download](2720/hyuuga_akari_yagatekimininaru.zip) \| ![pattern_1-2720](2720/previews/pattern_1.png) \| ![bikini-2720](2720/previews/bikini.png) \| [<NSFW, click to see>](2720/previews/bondage.png) \| ![free-2720](2720/previews/free.png) \| ![maid-2720](2720/previews/maid.png) \| ![miko-2720](2720/previews/miko.png) \| [<NSFW, click to see>](2720/previews/nude.png) \| [<NSFW, click to see>](2720/previews/nude2.png) \| ![suit-2720](2720/previews/suit.png) \| ![yukata-2720](2720/previews/yukata.png) \| \| 2380 \| 0.777 \| [Download](2380/hyuuga_akari_yagatekimininaru.zip) \| ![pattern_1-2380](2380/previews/pattern_1.png) \| ![bikini-2380](2380/previews/bikini.png) \| [<NSFW, click to see>](2380/previews/bondage.png) \| ![free-2380](2380/previews/free.png) \| ![maid-2380](2380/previews/maid.png) \| ![miko-2380](2380/previews/miko.png) \| [<NSFW, click to see>](2380/previews/nude.png) \| [<NSFW, click to see>](2380/previews/nude2.png) \| ![suit-2380](2380/previews/suit.png) \| ![yukata-2380](2380/previews/yukata.png) \| \| 2040 \| 0.786 \| [Download](2040/hyuuga_akari_yagatekimininaru.zip) \| ![pattern_1-2040](2040/previews/pattern_1.png) \| ![bikini-2040](2040/previews/bikini.png) \| [<NSFW, click to see>](2040/previews/bondage.png) \| ![free-2040](2040/previews/free.png) \| ![maid-2040](2040/previews/maid.png) \| ![miko-2040](2040/previews/miko.png) \| [<NSFW, click to see>](2040/previews/nude.png) \| [<NSFW, click to see>](2040/previews/nude2.png) \| ![suit-2040](2040/previews/suit.png) \| ![yukata-2040](2040/previews/yukata.png) \| \| 1700 \| 0.751 \| [Download](1700/hyuuga_akari_yagatekimininaru.zip) \| ![pattern_1-1700](1700/previews/pattern_1.png) \| ![bikini-1700](1700/previews/bikini.png) \| [<NSFW, click to see>](1700/previews/bondage.png) \| ![free-1700](1700/previews/free.png) \| ![maid-1700](1700/previews/maid.png) \| ![miko-1700](1700/previews/miko.png) \| [<NSFW, click to see>](1700/previews/nude.png) \| [<NSFW, click to see>](1700/previews/nude2.png) \| ![suit-1700](1700/previews/suit.png) \| ![yukata-1700](1700/previews/yukata.png) \| \| 1360 \| 0.724 \| [Download](1360/hyuuga_akari_yagatekimininaru.zip) \| ![pattern_1-1360](1360/previews/pattern_1.png) \| ![bikini-1360](1360/previews/bikini.png) \| [<NSFW, click to see>](1360/previews/bondage.png) \| ![free-1360](1360/previews/free.png) \| ![maid-1360](1360/previews/maid.png) \| ![miko-1360](1360/previews/miko.png) \| [<NSFW, click to see>](1360/previews/nude.png) \| [<NSFW, click to see>](1360/previews/nude2.png) \| ![suit-1360](1360/previews/suit.png) \| ![yukata-1360](1360/previews/yukata.png) \| \| 1020 \| 0.619 \| [Download](1020/hyuuga_akari_yagatekimininaru.zip) \| ![pattern_1-1020](1020/previews/pattern_1.png) \| ![bikini-1020](1020/previews/bikini.png) \| [<NSFW, click to see>](1020/previews/bondage.png) \| ![free-1020](1020/previews/free.png) \| ![maid-1020](1020/previews/maid.png) \| ![miko-1020](1020/previews/miko.png) \| [<NSFW, click to see>](1020/previews/nude.png) \| [<NSFW, click to see>](1020/previews/nude2.png) \| ![suit-1020](1020/previews/suit.png) \| ![yukata-1020](1020/previews/yukata.png) \| \| 680 \| 0.568 \| [Download](680/hyuuga_akari_yagatekimininaru.zip) \| ![pattern_1-680](680/previews/pattern_1.png) \| ![bikini-680](680/previews/bikini.png) \| [<NSFW, click to see>](680/previews/bondage.png) \| ![free-680](680/previews/free.png) \| ![maid-680](680/previews/maid.png) \| ![miko-680](680/previews/miko.png) \| [<NSFW, click to see>](680/previews/nude.png) \| [<NSFW, click to see>](680/previews/nude2.png) \| ![suit-680](680/previews/suit.png) \| ![yukata-680](680/previews/yukata.png) \| \| 340 \| 0.495 \| [Download](340/hyuuga_akari_yagatekimininaru.zip) \| ![pattern_1-340](340/previews/pattern_1.png) \| ![bikini-340](340/previews/bikini.png) \| [<NSFW, click to see>](340/previews/bondage.png) \| ![free-340](340/previews/free.png) \| ![maid-340](340/previews/maid.png) \| ![miko-340](340/previews/miko.png) \| [<NSFW, click to see>](340/previews/nude.png) \| [<NSFW, click to see>](340/previews/nude2.png) \| ![suit-340](340/previews/suit.png) \| ![yukata-340](340/previews/yukata.png) \|
devdata/irishpotato_commonbean	devdata	2023-09-28T14:21:23Z	0	0	fastai	[ "fastai", "region:us" ]	null	2023-09-28T14:21:16Z	--- tags: - fastai --- # Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
skaliy/spine-segmentation	skaliy	2023-09-28T14:16:33Z	0	6	fastMONAI	[ "fastMONAI", "medical", "image-segmentation", "en", "region:us" ]	image-segmentation	2022-11-08T15:19:16Z	--- language: - en pipeline_tag: image-segmentation tags: - medical library_name: fastMONAI --- # Multi-Center CNN-based spine segmentation from T2w MRI using small amounts of data Pre-trained weights and exported models for our spine segmentation project. The source code, designed to reproduce our test results and facilitate training and running inference on your own data, is available on GitHub: https://github.com/MMIV-ML/fastMONAI/tree/master/research ## Requirements Last checked and validated with fastMONAI version 0.3.2. Please ensure that you have the correct version of fastMONAI installed to guarantee the correct operation of the model. ![Suggested pipeline](pipeline.png) ## Data ### Haukeland University Hospital (HUH): 15 T2-weighted MRI scans - [Study](https://trialsjournal.biomedcentral.com/articles/10.1186/s13063-017-2306-8) #### Scanner Specifications - Type: Siemens MAGNETOM Avanto 1.5 T - Slice Thickness: 4 mm - Field of View (FOV): 300x300 mm^2 - Matrix: 384x269 - Interslice Gap: 0.4 mm - Repetition Time: 3700 ms - Echo Time: 87 ms - Number of Acquisitions: 2 ### Sahlgren-ska University Hospital (SUH): 10 T2-weighted MRI scans - [Study](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5789539/) #### Scanner Specifications - Type: Siemens MAGNETOM Aera 1.5 T - Slice Thickness: 3.5 mm - Field of View (FOV): 300x300 mm^2 - Matrix: 384x384 - Interslice Gap: 0.7 mm - Repetition Time: 3500 ms - Echo Time: 95 ms - Number of Acquisitions: 1 ### Data Annotation - Process: Manual segmentation of vertebrae - Experts: Conducted by an MRI expert with consensus from a senior radiologist - Software: ITK-SNAP - Scope: Segmented vertebrae in all slices, including bone marrow but excluding vertebral cortex ### External Data Evaluation - Source: [Annotated T2-weighted MR images of the Lower Spine](https://zenodo.org/record/22304) - Scan Specifications: 23 scans, 1.5 T in sagittal orientation - Segments: Seven vertebrae are manually segmented on each image (Th11–L5) - Modification: Th11 manually removed for consistency with the annotation for SUH and HUH.
hcho22/falcon-7b-sharded_finetuned_codealpaca_20k	hcho22	2023-09-28T14:14:57Z	0	0	null	[ "tensorboard", "generated_from_trainer", "base_model:ybelkada/falcon-7b-sharded-bf16", "base_model:finetune:ybelkada/falcon-7b-sharded-bf16", "region:us" ]	null	2023-09-27T22:12:59Z	--- base_model: ybelkada/falcon-7b-sharded-bf16 tags: - generated_from_trainer model-index: - name: falcon-7b-sharded_finetuned_codealpaca_20k 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. --> # falcon-7b-sharded_finetuned_codealpaca_20k This model is a fine-tuned version of [ybelkada/falcon-7b-sharded-bf16](https://huggingface.co/ybelkada/falcon-7b-sharded-bf16) 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: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - training_steps: 500 ### Training results ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3
mmenendezg/detr-resnet-50_finetuned_brain_tumor	mmenendezg	2023-09-28T14:14:24Z	165	0	transformers	[ "transformers", "pytorch", "detr", "object-detection", "generated_from_trainer", "base_model:facebook/detr-resnet-50", "base_model:finetune:facebook/detr-resnet-50", "license:apache-2.0", "endpoints_compatible", "region:us" ]	object-detection	2023-09-21T23:52:26Z	--- license: apache-2.0 base_model: facebook/detr-resnet-50 tags: - generated_from_trainer model-index: - name: detr-resnet-50_finetuned_brain_tumor 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. --> # detr-resnet-50_finetuned_brain_tumor This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the None 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: 9.764150575493936e-06 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1 - Datasets 2.14.5 - Tokenizers 0.13.3
Venkatesh4342/whisper-small-en-hi	Venkatesh4342	2023-09-28T14:10:48Z	89	2	transformers	[ "transformers", "pytorch", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-09-10T11:53:53Z	--- license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-small-en-hi 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-small-en-hi This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3279 - Wer: 24.0479 ## Model description Two datasets are used for two different languages, for hindi mozilla-foundation/common_voice_11_0 is used and for english google/fleurs is used. with combination of two dataset wer has decreased significantly. ## 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: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 3000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.059 \| 2.52 \| 1500 \| 0.2881 \| 24.7722 \| \| 0.0084 \| 5.03 \| 3000 \| 0.3279 \| 24.0479 \| ### Framework versions - Transformers 4.33.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.13.3
herve76/bbhf2	herve76	2023-09-28T14:07:28Z	2	0	diffusers	[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "dataset:herve76/bbhf2", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]	text-to-image	2023-09-28T13:21:53Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: bbhf2 tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: false datasets: - herve76/bbhf2 --- # LoRA DreamBooth - herve76/bbhf2 These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on the concept prompt: ``` bbhf2 ``` Use this keyword to trigger your custom model in your prompts. LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Usage Make sure to upgrade diffusers to >= 0.19.0: ``` pip install diffusers --upgrade ``` In addition make sure to install transformers, safetensors, accelerate as well as the invisible watermark: ``` pip install invisible_watermark transformers accelerate safetensors ``` To just use the base model, you can run: ```python import torch from diffusers import DiffusionPipeline, AutoencoderKL vae = AutoencoderKL.from_pretrained('madebyollin/sdxl-vae-fp16-fix', torch_dtype=torch.float16) pipe = DiffusionPipeline.from_pretrained( "stabilityai/stable-diffusion-xl-base-1.0", vae=vae, torch_dtype=torch.float16, variant="fp16", use_safetensors=True ) pipe.to("cuda") # This is where you load your trained weights pipe.load_lora_weights('herve76/bbhf2') prompt = "A majestic bbhf2 jumping from a big stone at night" image = pipe(prompt=prompt, num_inference_steps=50).images[0] ```
ArnaudHureaux/Llama-2-70b-chat-hf-miniguanaco	ArnaudHureaux	2023-09-28T14:03:35Z	13	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "facebook", "meta", "llama-2", "en", "arxiv:2307.09288", "autotrain_compatible", "text-generation-inference", "region:us" ]	text-generation	2023-09-26T10:42:23Z	--- extra_gated_heading: Access Llama 2 on Hugging Face extra_gated_description: >- This is a form to enable access to Llama 2 on Hugging Face after you have been granted access from Meta. Please visit the [Meta website](https://ai.meta.com/resources/models-and-libraries/llama-downloads) and accept our license terms and acceptable use policy before submitting this form. Requests will be processed in 1-2 days. extra_gated_prompt: "Your Hugging Face account email address MUST match the email you provide on the Meta website, or your request will not be approved." extra_gated_button_content: Submit extra_gated_fields: I agree to share my name, email address and username with Meta and confirm that I have already been granted download access on the Meta website: checkbox language: - en pipeline_tag: text-generation inference: false tags: - facebook - meta - pytorch - llama - llama-2 --- # Llama 2 Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 70B fine-tuned model, optimized for dialogue use cases and converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom. ## Model Details Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here. Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM. Model Developers Meta Variations Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations. Input Models input text only. Output Models generate text only. Model Architecture Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety. \|\|Training Data\|Params\|Content Length\|GQA\|Tokens\|LR\| \|---\|---\|---\|---\|---\|---\|---\| \|Llama 2\|A new mix of publicly available online data\|7B\|4k\|✗\|2.0T\|3.0 x 10<sup>-4</sup>\| \|Llama 2\|A new mix of publicly available online data\|13B\|4k\|✗\|2.0T\|3.0 x 10<sup>-4</sup>\| \|Llama 2\|A new mix of publicly available online data\|70B\|4k\|✔\|2.0T\|1.5 x 10<sup>-4</sup>\| Llama 2 family of models. Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability. Model Dates Llama 2 was trained between January 2023 and July 2023. Status This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. License A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) Research Paper ["Llama-2: Open Foundation and Fine-tuned Chat Models"](arxiv.org/abs/2307.09288) ## Intended Use Intended Use Cases Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. Out-of-scope Uses Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2. ## Hardware and Software Training Factors We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. Carbon Footprint Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program. \|\|Time (GPU hours)\|Power Consumption (W)\|Carbon Emitted(tCO<sub>2</sub>eq)\| \|---\|---\|---\|---\| \|Llama 2 7B\|184320\|400\|31.22\| \|Llama 2 13B\|368640\|400\|62.44\| \|Llama 2 70B\|1720320\|400\|291.42\| \|Total\|3311616\|\|539.00\| CO<sub>2</sub> emissions during pretraining. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data Overview Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. Data Freshness The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023. ## Evaluation Results In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library. \|Model\|Size\|Code\|Commonsense Reasoning\|World Knowledge\|Reading Comprehension\|Math\|MMLU\|BBH\|AGI Eval\| \|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\| \|Llama 1\|7B\|14.1\|60.8\|46.2\|58.5\|6.95\|35.1\|30.3\|23.9\| \|Llama 1\|13B\|18.9\|66.1\|52.6\|62.3\|10.9\|46.9\|37.0\|33.9\| \|Llama 1\|33B\|26.0\|70.0\|58.4\|67.6\|21.4\|57.8\|39.8\|41.7\| \|Llama 1\|65B\|30.7\|70.7\|60.5\|68.6\|30.8\|63.4\|43.5\|47.6\| \|Llama 2\|7B\|16.8\|63.9\|48.9\|61.3\|14.6\|45.3\|32.6\|29.3\| \|Llama 2\|13B\|24.5\|66.9\|55.4\|65.8\|28.7\|54.8\|39.4\|39.1\| \|Llama 2\|70B\|37.5\|71.9\|63.6\|69.4\|35.2\|68.9\|51.2\|54.2\| Overall performance on grouped academic benchmarks. Code: We report the average pass@1 scores of our models on HumanEval and MBPP. Commonsense Reasoning: We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. World Knowledge: We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. Reading Comprehension: For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. MATH: We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1. \|\|\|TruthfulQA\|Toxigen\| \|---\|---\|---\|---\| \|Llama 1\|7B\|27.42\|23.00\| \|Llama 1\|13B\|41.74\|23.08\| \|Llama 1\|33B\|44.19\|22.57\| \|Llama 1\|65B\|48.71\|21.77\| \|Llama 2\|7B\|33.29\|21.25\| \|Llama 2\|13B\|41.86\|26.10\| \|Llama 2\|70B\|50.18\|24.60\| Evaluation of pretrained LLMs on automatic safety benchmarks. For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better). \|\|\|TruthfulQA\|Toxigen\| \|---\|---\|---\|---\| \|Llama-2-Chat\|7B\|57.04\|0.00\| \|Llama-2-Chat\|13B\|62.18\|0.00\| \|Llama-2-Chat\|70B\|64.14\|0.01\| Evaluation of fine-tuned LLMs on different safety datasets. Same metric definitions as above. ## Ethical Considerations and Limitations Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide) ## Reporting Issues Please report any software “bug,” or other problems with the models through one of the following means: - Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama) - Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) - Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) ## Llama Model Index \|Model\|Llama2\|Llama2-hf\|Llama2-chat\|Llama2-chat-hf\| \|---\|---\|---\|---\|---\| \|7B\| [Link](https://huggingface.co/llamaste/Llama-2-7b) \| [Link](https://huggingface.co/llamaste/Llama-2-7b-hf) \| [Link](https://huggingface.co/llamaste/Llama-2-7b-chat) \| [Link](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)\| \|13B\| [Link](https://huggingface.co/llamaste/Llama-2-13b) \| [Link](https://huggingface.co/llamaste/Llama-2-13b-hf) \| [Link](https://huggingface.co/llamaste/Llama-2-13b-chat) \| [Link](https://huggingface.co/llamaste/Llama-2-13b-hf)\| \|70B\| [Link](https://huggingface.co/llamaste/Llama-2-70b) \| [Link](https://huggingface.co/llamaste/Llama-2-70b-hf) \| [Link](https://huggingface.co/llamaste/Llama-2-70b-chat) \| [Link](https://huggingface.co/llamaste/Llama-2-70b-hf)\|
Elegbede/Distilbert_FInetuned_For_Text_Classification	Elegbede	2023-09-28T13:46:23Z	73	0	transformers	[ "transformers", "tf", "distilbert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-09-26T16:11:25Z	--- license: apache-2.0 --- This model involves finetuning Distilbert for text classification to generate emotions from texts The label mapping corresponds to: label_0: 'Sadness 😭', label_1: 'Joy 😂', label_2: 'Love 😍', label_3: 'Anger 😠', label_4: 'Fear 😨', label_5: 'Surprise 😲'
anders0204/snowball	anders0204	2023-09-28T13:46:01Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2023-09-28T13:45:57Z	--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget 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: anders0204/snowball 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
TheBloke/leo-hessianai-13B-GGUF	TheBloke	2023-09-28T13:43:20Z	172	2	transformers	[ "transformers", "gguf", "llama", "text-generation", "en", "de", "dataset:oscar-corpus/OSCAR-2301", "dataset:wikipedia", "dataset:bjoernp/tagesschau-2018-2023", "base_model:LeoLM/leo-hessianai-13b", "base_model:quantized:LeoLM/leo-hessianai-13b", "license:llama2", "region:us" ]	text-generation	2023-09-28T13:36:34Z	--- base_model: LeoLM/leo-hessianai-13b datasets: - oscar-corpus/OSCAR-2301 - wikipedia - bjoernp/tagesschau-2018-2023 inference: false language: - en - de library_name: transformers license: llama2 model_creator: LAION LeoLM model_name: Leo Hessianai 13B model_type: llama pipeline_tag: text-generation prompt_template: '{prompt} ' quantized_by: TheBloke --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" 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;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Leo Hessianai 13B - GGUF - Model creator: [LAION LeoLM](https://huggingface.co/LeoLM) - Original model: [Leo Hessianai 13B](https://huggingface.co/LeoLM/leo-hessianai-13b) <!-- description start --> ## Description This repo contains GGUF format model files for [LAION LeoLM's Leo Hessianai 13B](https://huggingface.co/LeoLM/leo-hessianai-13b). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplate list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. <!-- README_GGUF.md-about-gguf end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/leo-hessianai-13B-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/leo-hessianai-13B-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/leo-hessianai-13B-GGUF) * [LAION LeoLM's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/LeoLM/leo-hessianai-13b) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: None ``` {prompt} ``` <!-- prompt-template end --> <!-- compatibility_gguf start --> ## Compatibility These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) They are also compatible with many third party UIs and libraries - please see the list at the top of this README. ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw Refer to the Provided Files table below to see what files use which methods, and how. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-provided-files start --> ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [leo-hessianai-13b.Q2_K.gguf](https://huggingface.co/TheBloke/leo-hessianai-13B-GGUF/blob/main/leo-hessianai-13b.Q2_K.gguf) \| Q2_K \| 2 \| 5.43 GB\| 7.93 GB \| smallest, significant quality loss - not recommended for most purposes \| \| [leo-hessianai-13b.Q3_K_S.gguf](https://huggingface.co/TheBloke/leo-hessianai-13B-GGUF/blob/main/leo-hessianai-13b.Q3_K_S.gguf) \| Q3_K_S \| 3 \| 5.66 GB\| 8.16 GB \| very small, high quality loss \| \| [leo-hessianai-13b.Q3_K_M.gguf](https://huggingface.co/TheBloke/leo-hessianai-13B-GGUF/blob/main/leo-hessianai-13b.Q3_K_M.gguf) \| Q3_K_M \| 3 \| 6.34 GB\| 8.84 GB \| very small, high quality loss \| \| [leo-hessianai-13b.Q3_K_L.gguf](https://huggingface.co/TheBloke/leo-hessianai-13B-GGUF/blob/main/leo-hessianai-13b.Q3_K_L.gguf) \| Q3_K_L \| 3 \| 6.93 GB\| 9.43 GB \| small, substantial quality loss \| \| [leo-hessianai-13b.Q4_0.gguf](https://huggingface.co/TheBloke/leo-hessianai-13B-GGUF/blob/main/leo-hessianai-13b.Q4_0.gguf) \| Q4_0 \| 4 \| 7.37 GB\| 9.87 GB \| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [leo-hessianai-13b.Q4_K_S.gguf](https://huggingface.co/TheBloke/leo-hessianai-13B-GGUF/blob/main/leo-hessianai-13b.Q4_K_S.gguf) \| Q4_K_S \| 4 \| 7.41 GB\| 9.91 GB \| small, greater quality loss \| \| [leo-hessianai-13b.Q4_K_M.gguf](https://huggingface.co/TheBloke/leo-hessianai-13B-GGUF/blob/main/leo-hessianai-13b.Q4_K_M.gguf) \| Q4_K_M \| 4 \| 7.87 GB\| 10.37 GB \| medium, balanced quality - recommended \| \| [leo-hessianai-13b.Q5_0.gguf](https://huggingface.co/TheBloke/leo-hessianai-13B-GGUF/blob/main/leo-hessianai-13b.Q5_0.gguf) \| Q5_0 \| 5 \| 8.97 GB\| 11.47 GB \| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [leo-hessianai-13b.Q5_K_S.gguf](https://huggingface.co/TheBloke/leo-hessianai-13B-GGUF/blob/main/leo-hessianai-13b.Q5_K_S.gguf) \| Q5_K_S \| 5 \| 8.97 GB\| 11.47 GB \| large, low quality loss - recommended \| \| [leo-hessianai-13b.Q5_K_M.gguf](https://huggingface.co/TheBloke/leo-hessianai-13B-GGUF/blob/main/leo-hessianai-13b.Q5_K_M.gguf) \| Q5_K_M \| 5 \| 9.23 GB\| 11.73 GB \| large, very low quality loss - recommended \| \| [leo-hessianai-13b.Q6_K.gguf](https://huggingface.co/TheBloke/leo-hessianai-13B-GGUF/blob/main/leo-hessianai-13b.Q6_K.gguf) \| Q6_K \| 6 \| 10.68 GB\| 13.18 GB \| very large, extremely low quality loss \| \| [leo-hessianai-13b.Q8_0.gguf](https://huggingface.co/TheBloke/leo-hessianai-13B-GGUF/blob/main/leo-hessianai-13b.Q8_0.gguf) \| Q8_0 \| 8 \| 13.83 GB\| 16.33 GB \| very large, extremely low quality loss - not recommended \| Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. <!-- README_GGUF.md-provided-files end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: - LM Studio - LoLLMS Web UI - Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: TheBloke/leo-hessianai-13B-GGUF and below it, a specific filename to download, such as: leo-hessianai-13b.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download TheBloke/leo-hessianai-13B-GGUF leo-hessianai-13b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download TheBloke/leo-hessianai-13B-GGUF --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/leo-hessianai-13B-GGUF leo-hessianai-13b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 32 -m leo-hessianai-13b.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. ### How to load this model in Python code, using ctransformers #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install ctransformers # Or with CUDA GPU acceleration pip install ctransformers[cuda] # Or with AMD ROCm GPU acceleration (Linux only) CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers # Or with Metal GPU acceleration for macOS systems only CT_METAL=1 pip install ctransformers --no-binary ctransformers ``` #### Simple ctransformers example code ```python from ctransformers import AutoModelForCausalLM # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = AutoModelForCausalLM.from_pretrained("TheBloke/leo-hessianai-13B-GGUF", model_file="leo-hessianai-13b.Q4_K_M.gguf", model_type="llama", gpu_layers=50) print(llm("AI is going to")) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> <!-- original-model-card start --> # Original model card: LAION LeoLM's Leo Hessianai 13B # LAION LeoLM: Linguistically Enhanced Open Language Model Meet LeoLM, the first open and commercially available German Foundation Language Model built on Llama-2. Our models extend Llama-2's capabilities into German through continued pretraining on a large corpus of German-language and mostly locality specific text. Thanks to a compute grant at HessianAI's new supercomputer 42, we release two foundation models trained with 8k context length, [`LeoLM/leo-hessianai-7b`](https://huggingface.co/LeoLM/leo-hessianai-7b) and [`LeoLM/leo-hessianai-13b`](https://huggingface.co/LeoLM/leo-hessianai-13b) under the [Llama-2 community license](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) (70b also coming soon! 👀). With this release, we hope to bring a new wave of opportunities to German open-source and commercial LLM research and accelerate adoption. Read our [blog post]() or our paper (preprint coming soon) for more details! A project by Björn Plüster and Christoph Schuhmann in collaboration with LAION and HessianAI. ## Model Details - Finetuned from: [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) - Model type: Causal decoder-only transformer language model - Language: English and German - License: [LLAMA 2 COMMUNITY LICENSE AGREEMENT](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) - Contact: [LAION Discord](https://discord.com/invite/eq3cAMZtCC) or [Björn Plüster](mailto:[email protected]) ## Use in 🤗Transformers First install direct dependencies: ``` pip install transformers torch sentencepiece ``` If you want faster inference using flash-attention2, you need to install these dependencies: ```bash pip install packaging ninja pip install flash-attn==v2.1.1 --no-build-isolation pip install git+https://github.com/HazyResearch/[email protected]#subdirectory=csrc/rotary ``` Then load the model in transformers: ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch model = AutoModelForCausalLM.from_pretrained( model="LeoLM/leo-hessianai-13b", device_map="auto", torch_dtype=torch.float16, trust_remote_code=True # True for flash-attn2 else False ) ``` ## Training parameters ![training_parameters](imgs/training_params.png "Training Hyperparameters") ## Benchmarks ![benchmarks](imgs/benchmarks.png "Benchmark Scores") <!-- original-model-card end -->
pejho/bloom_prompt_tuning_1695907859.055798	pejho	2023-09-28T13:39:03Z	2	0	peft	[ "peft", "region:us" ]	null	2023-09-28T13:39:01Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0
massh3dpotato/Llama-2-13B-chat-GGML	massh3dpotato	2023-09-28T13:35:00Z	7	0	transformers	[ "transformers", "llama", "facebook", "meta", "pytorch", "llama-2", "text-generation", "en", "arxiv:2307.09288", "base_model:meta-llama/Llama-2-13b-chat-hf", "base_model:finetune:meta-llama/Llama-2-13b-chat-hf", "license:other", "region:us" ]	text-generation	2023-09-28T11:32:21Z	--- language: - en license: other tags: - facebook - meta - pytorch - llama - llama-2 model_name: Llama 2 13B Chat inference: false model_creator: Meta Llama 2 model_link: https://huggingface.co/meta-llama/Llama-2-13b-chat-hf model_type: llama pipeline_tag: text-generation quantized_by: TheBloke base_model: meta-llama/Llama-2-13b-chat-hf --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" 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;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Llama 2 13B Chat - GGML - Model creator: [Meta Llama 2](https://huggingface.co/meta-llama) - Original model: [Llama 2 13B Chat](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf) ## Description This repo contains GGML format model files for [Meta's Llama 2 13B-chat](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf). ### Important note regarding GGML files. The GGML format has now been superseded by GGUF. As of August 21st 2023, [llama.cpp](https://github.com/ggerganov/llama.cpp) no longer supports GGML models. Third party clients and libraries are expected to still support it for a time, but many may also drop support. Please use the GGUF models instead. ### About GGML GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp) and libraries and UIs which support this format, such as: * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most popular web UI. Supports NVidia CUDA GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a powerful GGML web UI with GPU acceleration on all platforms (CUDA and OpenCL). Especially good for story telling. * [LM Studio](https://lmstudio.ai/), a fully featured local GUI with GPU acceleration on both Windows (NVidia and AMD), and macOS. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with CUDA GPU acceleration via the c_transformers backend. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. ## Repositories available * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Llama-2-13B-chat-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Llama-2-13B-chat-GGUF) * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference (deprecated)](https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML) * [Meta Llama 2's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/meta-llama/Llama-2-13B-chat-hf) ## Prompt template: Llama-2-Chat ``` [INST] <<SYS>> You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information. <</SYS>> {prompt}[/INST] ``` <!-- compatibility_ggml start --> ## Compatibility These quantised GGML files are compatible with llama.cpp between June 6th (commit `2d43387`) and August 21st 2023. For support with latest llama.cpp, please use GGUF files instead. The final llama.cpp commit with support for GGML was: [dadbed99e65252d79f81101a392d0d6497b86caa](https://github.com/ggerganov/llama.cpp/commit/dadbed99e65252d79f81101a392d0d6497b86caa) As of August 23rd 2023 they are still compatible with all UIs, libraries and utilities which use GGML. This may change in the future. ## Explanation of the new k-quant methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw * GGML_TYPE_Q8_K - "type-0" 8-bit quantization. Only used for quantizing intermediate results. The difference to the existing Q8_0 is that the block size is 256. All 2-6 bit dot products are implemented for this quantization type. Refer to the Provided Files table below to see what files use which methods, and how. </details> <!-- compatibility_ggml end --> ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [llama-2-13b-chat.ggmlv3.q2_K.bin](https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML/blob/main/llama-2-13b-chat.ggmlv3.q2_K.bin) \| q2_K \| 2 \| 5.51 GB\| 8.01 GB \| New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors. \| \| [llama-2-13b-chat.ggmlv3.q3_K_S.bin](https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML/blob/main/llama-2-13b-chat.ggmlv3.q3_K_S.bin) \| q3_K_S \| 3 \| 5.66 GB\| 8.16 GB \| New k-quant method. Uses GGML_TYPE_Q3_K for all tensors \| \| [llama-2-13b-chat.ggmlv3.q3_K_M.bin](https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML/blob/main/llama-2-13b-chat.ggmlv3.q3_K_M.bin) \| q3_K_M \| 3 \| 6.31 GB\| 8.81 GB \| New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K \| \| [llama-2-13b-chat.ggmlv3.q3_K_L.bin](https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML/blob/main/llama-2-13b-chat.ggmlv3.q3_K_L.bin) \| q3_K_L \| 3 \| 6.93 GB\| 9.43 GB \| New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K \| \| [llama-2-13b-chat.ggmlv3.q4_0.bin](https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML/blob/main/llama-2-13b-chat.ggmlv3.q4_0.bin) \| q4_0 \| 4 \| 7.32 GB\| 9.82 GB \| Original quant method, 4-bit. \| \| [llama-2-13b-chat.ggmlv3.q4_K_S.bin](https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML/blob/main/llama-2-13b-chat.ggmlv3.q4_K_S.bin) \| q4_K_S \| 4 \| 7.37 GB\| 9.87 GB \| New k-quant method. Uses GGML_TYPE_Q4_K for all tensors \| \| [llama-2-13b-chat.ggmlv3.q4_K_M.bin](https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML/blob/main/llama-2-13b-chat.ggmlv3.q4_K_M.bin) \| q4_K_M \| 4 \| 7.87 GB\| 10.37 GB \| New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K \| \| [llama-2-13b-chat.ggmlv3.q4_1.bin](https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML/blob/main/llama-2-13b-chat.ggmlv3.q4_1.bin) \| q4_1 \| 4 \| 8.14 GB\| 10.64 GB \| Original quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. \| \| [llama-2-13b-chat.ggmlv3.q5_0.bin](https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML/blob/main/llama-2-13b-chat.ggmlv3.q5_0.bin) \| q5_0 \| 5 \| 8.95 GB\| 11.45 GB \| Original quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. \| \| [llama-2-13b-chat.ggmlv3.q5_K_S.bin](https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML/blob/main/llama-2-13b-chat.ggmlv3.q5_K_S.bin) \| q5_K_S \| 5 \| 8.97 GB\| 11.47 GB \| New k-quant method. Uses GGML_TYPE_Q5_K for all tensors \| \| [llama-2-13b-chat.ggmlv3.q5_K_M.bin](https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML/blob/main/llama-2-13b-chat.ggmlv3.q5_K_M.bin) \| q5_K_M \| 5 \| 9.23 GB\| 11.73 GB \| New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K \| \| [llama-2-13b-chat.ggmlv3.q5_1.bin](https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML/blob/main/llama-2-13b-chat.ggmlv3.q5_1.bin) \| q5_1 \| 5 \| 9.76 GB\| 12.26 GB \| Original quant method, 5-bit. Even higher accuracy, resource usage and slower inference. \| \| [llama-2-13b-chat.ggmlv3.q6_K.bin](https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML/blob/main/llama-2-13b-chat.ggmlv3.q6_K.bin) \| q6_K \| 6 \| 10.68 GB\| 13.18 GB \| New k-quant method. Uses GGML_TYPE_Q8_K for all tensors - 6-bit quantization \| \| [llama-2-13b-chat.ggmlv3.q8_0.bin](https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML/blob/main/llama-2-13b-chat.ggmlv3.q8_0.bin) \| q8_0 \| 8 \| 13.83 GB\| 16.33 GB \| Original quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. \| Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. ## How to run in `llama.cpp` Make sure you are using `llama.cpp` from commit [dadbed99e65252d79f81101a392d0d6497b86caa](https://github.com/ggerganov/llama.cpp/commit/dadbed99e65252d79f81101a392d0d6497b86caa) or earlier. For compatibility with latest llama.cpp, please use GGUF files instead. ``` ./main -t 10 -ngl 32 -m llama-2-13b-chat.ggmlv3.q4_K_M.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "[INST] <<SYS>>\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\n<</SYS>>\nWrite a story about llamas[/INST]" ``` Change `-t 10` to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use `-t 8`. Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 2048` to the desired sequence length for this model. For example, `-c 4096` for a Llama 2 model. For models that use RoPE, add `--rope-freq-base 10000 --rope-freq-scale 0.5` for doubled context, or `--rope-freq-base 10000 --rope-freq-scale 0.25` for 4x context. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md). <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute. Thanks to the [chirper.ai](https://chirper.ai) team! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Russ Johnson, J, alfie_i, Alex, NimbleBox.ai, Chadd, Mandus, Nikolai Manek, Ken Nordquist, ya boyyy, Illia Dulskyi, Viktor Bowallius, vamX, Iucharbius, zynix, Magnesian, Clay Pascal, Pierre Kircher, Enrico Ros, Tony Hughes, Elle, Andrey, knownsqashed, Deep Realms, Jerry Meng, Lone Striker, Derek Yates, Pyrater, Mesiah Bishop, James Bentley, Femi Adebogun, Brandon Frisco, SuperWojo, Alps Aficionado, Michael Dempsey, Vitor Caleffi, Will Dee, Edmond Seymore, usrbinkat, LangChain4j, Kacper Wikieł, Luke Pendergrass, John Detwiler, theTransient, Nathan LeClaire, Tiffany J. Kim, biorpg, Eugene Pentland, Stanislav Ovsiannikov, Fred von Graf, terasurfer, Kalila, Dan Guido, Nitin Borwankar, 阿明, Ai Maven, John Villwock, Gabriel Puliatti, Stephen Murray, Asp the Wyvern, danny, Chris Smitley, ReadyPlayerEmma, S_X, Daniel P. Andersen, Olakabola, Jeffrey Morgan, Imad Khwaja, Caitlyn Gatomon, webtim, Alicia Loh, Trenton Dambrowitz, Swaroop Kallakuri, Erik Bjäreholt, Leonard Tan, Spiking Neurons AB, Luke @flexchar, Ajan Kanaga, Thomas Belote, Deo Leter, RoA, Willem Michiel, transmissions 11, subjectnull, Matthew Berman, Joseph William Delisle, David Ziegler, Michael Davis, Johann-Peter Hartmann, Talal Aujan, senxiiz, Artur Olbinski, Rainer Wilmers, Spencer Kim, Fen Risland, Cap'n Zoog, Rishabh Srivastava, Michael Levine, Geoffrey Montalvo, Sean Connelly, Alexandros Triantafyllidis, Pieter, Gabriel Tamborski, Sam, Subspace Studios, Junyu Yang, Pedro Madruga, Vadim, Cory Kujawski, K, Raven Klaugh, Randy H, Mano Prime, Sebastain Graf, Space Cruiser Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: Meta's Llama 2 13B-chat # Llama 2 Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 13B fine-tuned model, optimized for dialogue use cases and converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom. ## Model Details Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here. Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM. Model Developers Meta Variations Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations. Input Models input text only. Output Models generate text only. Model Architecture Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety. \|\|Training Data\|Params\|Content Length\|GQA\|Tokens\|LR\| \|---\|---\|---\|---\|---\|---\|---\| \|Llama 2\|A new mix of publicly available online data\|7B\|4k\|✗\|2.0T\|3.0 x 10<sup>-4</sup>\| \|Llama 2\|A new mix of publicly available online data\|13B\|4k\|✗\|2.0T\|3.0 x 10<sup>-4</sup>\| \|Llama 2\|A new mix of publicly available online data\|70B\|4k\|✔\|2.0T\|1.5 x 10<sup>-4</sup>\| Llama 2 family of models. Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability. Model Dates Llama 2 was trained between January 2023 and July 2023. Status This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. License A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) Research Paper ["Llama-2: Open Foundation and Fine-tuned Chat Models"](arxiv.org/abs/2307.09288) ## Intended Use Intended Use Cases Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212). Out-of-scope Uses Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2. ## Hardware and Software Training Factors We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. Carbon Footprint Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program. \|\|Time (GPU hours)\|Power Consumption (W)\|Carbon Emitted(tCO<sub>2</sub>eq)\| \|---\|---\|---\|---\| \|Llama 2 7B\|184320\|400\|31.22\| \|Llama 2 13B\|368640\|400\|62.44\| \|Llama 2 70B\|1720320\|400\|291.42\| \|Total\|3311616\|\|539.00\| CO<sub>2</sub> emissions during pretraining. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data Overview Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. Data Freshness The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023. ## Evaluation Results In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library. \|Model\|Size\|Code\|Commonsense Reasoning\|World Knowledge\|Reading Comprehension\|Math\|MMLU\|BBH\|AGI Eval\| \|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\| \|Llama 1\|7B\|14.1\|60.8\|46.2\|58.5\|6.95\|35.1\|30.3\|23.9\| \|Llama 1\|13B\|18.9\|66.1\|52.6\|62.3\|10.9\|46.9\|37.0\|33.9\| \|Llama 1\|33B\|26.0\|70.0\|58.4\|67.6\|21.4\|57.8\|39.8\|41.7\| \|Llama 1\|65B\|30.7\|70.7\|60.5\|68.6\|30.8\|63.4\|43.5\|47.6\| \|Llama 2\|7B\|16.8\|63.9\|48.9\|61.3\|14.6\|45.3\|32.6\|29.3\| \|Llama 2\|13B\|24.5\|66.9\|55.4\|65.8\|28.7\|54.8\|39.4\|39.1\| \|Llama 2\|70B\|37.5\|71.9\|63.6\|69.4\|35.2\|68.9\|51.2\|54.2\| Overall performance on grouped academic benchmarks. Code: We report the average pass@1 scores of our models on HumanEval and MBPP. Commonsense Reasoning: We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. World Knowledge: We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. Reading Comprehension: For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. MATH: We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1. \|\|\|TruthfulQA\|Toxigen\| \|---\|---\|---\|---\| \|Llama 1\|7B\|27.42\|23.00\| \|Llama 1\|13B\|41.74\|23.08\| \|Llama 1\|33B\|44.19\|22.57\| \|Llama 1\|65B\|48.71\|21.77\| \|Llama 2\|7B\|33.29\|21.25\| \|Llama 2\|13B\|41.86\|26.10\| \|Llama 2\|70B\|50.18\|24.60\| Evaluation of pretrained LLMs on automatic safety benchmarks. For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better). \|\|\|TruthfulQA\|Toxigen\| \|---\|---\|---\|---\| \|Llama-2-Chat\|7B\|57.04\|0.00\| \|Llama-2-Chat\|13B\|62.18\|0.00\| \|Llama-2-Chat\|70B\|64.14\|0.01\| Evaluation of fine-tuned LLMs on different safety datasets. Same metric definitions as above. ## Ethical Considerations and Limitations Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide) ## Reporting Issues Please report any software “bug,” or other problems with the models through one of the following means: - Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama) - Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) - Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) ## Llama Model Index \|Model\|Llama2\|Llama2-hf\|Llama2-chat\|Llama2-chat-hf\| \|---\|---\|---\|---\|---\| \|7B\| [Link](https://huggingface.co/llamaste/Llama-2-7b) \| [Link](https://huggingface.co/llamaste/Llama-2-7b-hf) \| [Link](https://huggingface.co/llamaste/Llama-2-7b-chat) \| [Link](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)\| \|13B\| [Link](https://huggingface.co/llamaste/Llama-2-13b) \| [Link](https://huggingface.co/llamaste/Llama-2-13b-hf) \| [Link](https://huggingface.co/llamaste/Llama-2-13b-chat) \| [Link](https://huggingface.co/llamaste/Llama-2-13b-hf)\| \|70B\| [Link](https://huggingface.co/llamaste/Llama-2-70b) \| [Link](https://huggingface.co/llamaste/Llama-2-70b-hf) \| [Link](https://huggingface.co/llamaste/Llama-2-70b-chat) \| [Link](https://huggingface.co/llamaste/Llama-2-70b-hf)\|
TheBloke/leo-hessianai-13B-chat-GPTQ	TheBloke	2023-09-28T13:34:46Z	41	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "custom_code", "en", "de", "dataset:LeoLM/OpenSchnabeltier", "dataset:OpenAssistant/OASST-DE", "dataset:FreedomIntelligence/alpaca-gpt4-deutsch", "dataset:FreedomIntelligence/evol-instruct-deutsch", "dataset:LeoLM/German_Poems", "dataset:LeoLM/German_Songs", "base_model:LeoLM/leo-hessianai-13b-chat", "base_model:quantized:LeoLM/leo-hessianai-13b-chat", "license:llama2", "autotrain_compatible", "text-generation-inference", "4-bit", "gptq", "region:us" ]	text-generation	2023-09-28T12:22:36Z	--- base_model: LeoLM/leo-hessianai-13b-chat datasets: - LeoLM/OpenSchnabeltier - OpenAssistant/OASST-DE - FreedomIntelligence/alpaca-gpt4-deutsch - FreedomIntelligence/evol-instruct-deutsch - LeoLM/German_Poems - LeoLM/German_Songs inference: false language: - en - de library_name: transformers license: llama2 model_creator: LAION LeoLM model_name: Leo Hessianai 13B Chat model_type: llama pipeline_tag: text-generation prompt_template: '<\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ' quantized_by: TheBloke --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" 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;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Leo Hessianai 13B Chat - GPTQ - Model creator: [LAION LeoLM](https://huggingface.co/LeoLM) - Original model: [Leo Hessianai 13B Chat](https://huggingface.co/LeoLM/leo-hessianai-13b-chat) <!-- description start --> ## Description This repo contains GPTQ model files for [LAION LeoLM's Leo Hessianai 13B Chat](https://huggingface.co/LeoLM/leo-hessianai-13b-chat). Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/leo-hessianai-13B-chat-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/leo-hessianai-13B-chat-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/leo-hessianai-13B-chat-GGUF) * [LAION LeoLM's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/LeoLM/leo-hessianai-13b-chat) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: ChatML ``` <\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` <!-- prompt-template end --> <!-- README_GPTQ.md-provided-files start --> ## Provided files, and GPTQ parameters Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches. All recent GPTQ files are made with AutoGPTQ, and all files in non-main branches are made with AutoGPTQ. Files in the `main` branch which were uploaded before August 2023 were made with GPTQ-for-LLaMa. <details> <summary>Explanation of GPTQ parameters</summary> - Bits: The bit size of the quantised model. - GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value. - Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now. - Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy. - GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s). - Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences. - ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit. </details> \| Branch \| Bits \| GS \| Act Order \| Damp % \| GPTQ Dataset \| Seq Len \| Size \| ExLlama \| Desc \| \| ------ \| ---- \| -- \| --------- \| ------ \| ------------ \| ------- \| ---- \| ------- \| ---- \| \| [main](https://huggingface.co/TheBloke/leo-hessianai-13B-chat-GPTQ/tree/main) \| 4 \| 128 \| Yes \| 0.1 \| [German Quad](https://huggingface.co/datasets/deepset/germanquad) \| 8192 \| 7.26 GB \| Yes \| 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. \| \| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-13B-chat-GPTQ/tree/gptq-4bit-32g-actorder_True) \| 4 \| 32 \| Yes \| 0.1 \| [German Quad](https://huggingface.co/datasets/deepset/germanquad) \| 8192 \| 8.01 GB \| Yes \| 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. \| \| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-13B-chat-GPTQ/tree/gptq-8bit--1g-actorder_True) \| 8 \| None \| Yes \| 0.1 \| [German Quad](https://huggingface.co/datasets/deepset/germanquad) \| 8192 \| 13.36 GB \| No \| 8-bit, with Act Order. No group size, to lower VRAM requirements. \| \| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-13B-chat-GPTQ/tree/gptq-8bit-128g-actorder_True) \| 8 \| 128 \| Yes \| 0.1 \| [German Quad](https://huggingface.co/datasets/deepset/germanquad) \| 8192 \| 13.66 GB \| No \| 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. \| \| [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-13B-chat-GPTQ/tree/gptq-8bit-32g-actorder_True) \| 8 \| 32 \| Yes \| 0.1 \| [German Quad](https://huggingface.co/datasets/deepset/germanquad) \| 8192 \| 14.55 GB \| No \| 8-bit, with group size 32g and Act Order for maximum inference quality. \| \| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-13B-chat-GPTQ/tree/gptq-4bit-64g-actorder_True) \| 4 \| 64 \| Yes \| 0.1 \| [German Quad](https://huggingface.co/datasets/deepset/germanquad) \| 8192 \| 7.51 GB \| Yes \| 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. \| <!-- README_GPTQ.md-provided-files end --> <!-- README_GPTQ.md-download-from-branches start --> ## How to download, including from branches ### In text-generation-webui To download from the `main` branch, enter `TheBloke/leo-hessianai-13B-chat-GPTQ` in the "Download model" box. To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/leo-hessianai-13B-chat-GPTQ:gptq-4bit-32g-actorder_True` ### From the command line I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` To download the `main` branch to a folder called `leo-hessianai-13B-chat-GPTQ`: ```shell mkdir leo-hessianai-13B-chat-GPTQ huggingface-cli download TheBloke/leo-hessianai-13B-chat-GPTQ --local-dir leo-hessianai-13B-chat-GPTQ --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: ```shell mkdir leo-hessianai-13B-chat-GPTQ huggingface-cli download TheBloke/leo-hessianai-13B-chat-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir leo-hessianai-13B-chat-GPTQ --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Huggingface cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model. The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`. For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell mkdir leo-hessianai-13B-chat-GPTQ HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/leo-hessianai-13B-chat-GPTQ --local-dir leo-hessianai-13B-chat-GPTQ --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> ### With `git` (not recommended) To clone a specific branch with `git`, use a command like this: ```shell git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/leo-hessianai-13B-chat-GPTQ ``` Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.) <!-- README_GPTQ.md-download-from-branches end --> <!-- README_GPTQ.md-text-generation-webui start --> ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui). Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the Model tab. 2. Under Download custom model or LoRA, enter `TheBloke/leo-hessianai-13B-chat-GPTQ`. - To download from a specific branch, enter for example `TheBloke/leo-hessianai-13B-chat-GPTQ:gptq-4bit-32g-actorder_True` - see Provided Files above for the list of branches for each option. 3. Click Download. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to Model. 6. In the Model dropdown, choose the model you just downloaded: `leo-hessianai-13B-chat-GPTQ` 7. The model will automatically load, and is now ready for use! 8. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right. * Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. 9. Once you're ready, click the Text Generation tab and enter a prompt to get started! <!-- README_GPTQ.md-text-generation-webui end --> <!-- README_GPTQ.md-use-from-python start --> ## How to use this GPTQ model from Python code ### Install the necessary packages Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later. ```shell pip3 install transformers optimum pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7 ``` If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead: ```shell pip3 uninstall -y auto-gptq git clone https://github.com/PanQiWei/AutoGPTQ cd AutoGPTQ git checkout v0.4.2 pip3 install . ``` ### You can then use the following code ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_name_or_path = "TheBloke/leo-hessianai-13B-chat-GPTQ" # To use a different branch, change revision # For example: revision="gptq-4bit-32g-actorder_True" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", trust_remote_code=False, revision="main") tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) prompt = "Tell me about AI" prompt_template=f'''<\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ''' print("\n\n* Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline print("* Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(pipe(prompt_template)[0]['generated_text']) ``` <!-- README_GPTQ.md-use-from-python end --> <!-- README_GPTQ.md-compatibility start --> ## Compatibility The files provided are tested to work with AutoGPTQ, both via Transformers and using AutoGPTQ directly. They should also work with [Occ4m's GPTQ-for-LLaMa fork](https://github.com/0cc4m/KoboldAI). [ExLlama](https://github.com/turboderp/exllama) is compatible with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility. [Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is compatible with all GPTQ models. <!-- README_GPTQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: LAION LeoLM's Leo Hessianai 13B Chat # LAION LeoLM: Linguistically Enhanced Open Language Model Meet LeoLM, the first open and commercially available German Foundation Language Model built on Llama-2. Our models extend Llama-2's capabilities into German through continued pretraining on a large corpus of German-language and mostly locality specific text. Thanks to a compute grant at HessianAI's new supercomputer 42, we release two foundation models trained with 8k context length, [`LeoLM/leo-hessianai-7b`](https://huggingface.co/LeoLM/leo-hessianai-7b) and [`LeoLM/leo-hessianai-13b`](https://huggingface.co/LeoLM/leo-hessianai-13b) under the [Llama-2 community license](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) (70b also coming soon! 👀). With this release, we hope to bring a new wave of opportunities to German open-source and commercial LLM research and accelerate adoption. Read our [blog post]() or our paper (preprint coming soon) for more details! A project by Björn Plüster and Christoph Schuhmann in collaboration with LAION and HessianAI. ## LeoLM Chat `LeoLM/leo-hessianai-13b-chat` is a German chat model built on our foundation model `LeoLM/leo-hessianai-13b` and finetuned on a selection of German instruction datasets. The model performs exceptionally well on writing, explanation and discussion tasks but struggles somewhat with math and advanced reasoning. See our MT-Bench-DE scores: ``` { "first_turn": 6.525, "second_turn": 5.15, "categories": { "writing": 6.925, "roleplay": 6.7, "reasoning": 4.55, "math": 3.25, "coding": 3.45, "extraction": 5.4, "stem": 7.55, "humanities": 8.875 }, "average": 5.8375 } ``` ## Model Details - Finetuned from: [LeoLM/leo-hessianai-13b](https://huggingface.co/LeoLM/leo-hessianai-7b) - Model type: Causal decoder-only transformer language model - Language: English and German - Demo: [Web Demo]() - License: [LLAMA 2 COMMUNITY LICENSE AGREEMENT](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) - Contact: [LAION Discord](https://discord.com/invite/eq3cAMZtCC) or [Björn Plüster](mailto:[email protected]) ## Use in 🤗Transformers First install direct dependencies: ``` pip install transformers torch sentencepiece ``` If you want faster inference using flash-attention2, you need to install these dependencies: ```bash pip install packaging ninja pip install flash-attn==v2.1.1 --no-build-isolation pip install git+https://github.com/HazyResearch/[email protected]#subdirectory=csrc/rotary ``` Then load the model in transformers: ```python from transformers import pipeline import torch system_prompt = """<\|im_start\|>system Dies ist eine Unterhaltung zwischen einem intelligenten, hilfsbereitem KI-Assistenten und einem Nutzer. Der Assistent gibt ausführliche, hilfreiche und ehrliche Antworten.<\|im_end\|> """ prompt_format = "<\|im_start\|>user\n{prompt}<\|im_end\|>\n<\|im_start\|>assistant\n" prompt = "Erkläre mir wie die Fahrradwegesituation in Hamburg ist." generator = pipeline(model="LeoLM/leo-hessianai-13b-chat", device="cuda", torch_dtype=torch.float16, trust_remote_code=True) # True for flash-attn2 else False print(generator(prompt_format.format(prompt=prompt), do_sample=True, top_p=0.95, max_length=8192)) ``` "Hamburg verfügt über ein umfangreiches Netzwerk an Radwegen, das ein effizientes und angenehmes Radfahren in der ganzen Stadt ermöglicht. Die Radwege variieren in Bezug auf ihre Qualität, wobei einige erstklassig mit eigens für Radfahrer reservierten Spuren sind, während andere einfache Fahrradsymbole auf dem Boden haben, die anzeigen, dass Radfahrer abwechselnd mit dem Autoverkehr auf der Straße fahren sollten. Einige Nebenstraßen haben auch spezielle Fahrradspuren, wobei einige mit Bordsteinabsenkungen zur Seite der Autospuren markiert sind. Zusätzlich haben viele Hauptstraßen, insbesondere in Nebenstraßen, fahrradfreundliche Abstände zwischen den geparkten Autos und dem Gehweg, was ein bequemes Fahren auf der Straße ermöglicht. Der Bau von Radschnellwegen, die schnelles und effizientes Radfahren in und aus der Stadt ermöglichen, ist im Gange und wird in den kommenden Jahren fortgesetzt. Insgesamt sind die Radwege in Hamburg weitläufig und gut ausgeschildert, was es zu einem angenehmen Ort macht, um mit dem Fahrrad zu fahren." ## Prompting / Prompt Template Prompt dialogue template (ChatML format): ``` """ <\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant """ ``` The model input can contain multiple conversation turns between user and assistant, e.g. ``` <\|im_start\|>user {prompt 1}<\|im_end\|> <\|im_start\|>assistant {reply 1}<\|im_end\|> <\|im_start\|>user {prompt 2}<\|im_end\|> <\|im_start\|>assistant (...) ``` ## Ethical Considerations and Limitations LeoLM has been tested in English and German, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, the potential outputs of `LeoLM/leo-hessianai-13b-chat` cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of `LeoLM/leo-hessianai-13b-chat`, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see Meta's [Responsible Use Guide](https://ai.meta.com/llama/responsible-use-guide/). ## Finetuning Details \| Hyperparameter \| Value \| \|---\|---\| \| Num epochs \| 3 \| \| Examples per epoch \| 131214 \| \| Global batch size \| 256 \| \| Learning rate \| 3e-5 \| \| Warmup steps \| 100 \| \| LR scheduler \| Cosine \| \| Adam betas \| (0.9, 0.95) \| ## Dataset Details ``` ## Stats for 'Subset of OpenAssistant/OASST-DE' (3534 samples (100.0%)) ----------------- Accepted: 3534/3534 (100.0%) Accepted tokens: 2259302 Skipped: 0 (0.0%) Min tokens per sample: 29 Max tokens per sample: 2484 Avg tokens per sample: 639.3044708545557 ----------------- ## Stats for 'Subset of FreedomIntelligence/evol-instruct-deutsch' (57841 samples (100.0%)) ----------------- Accepted: 57841/57841 (100.0%) Accepted tokens: 42958192 Skipped: 0 (0.0%) Min tokens per sample: 33 Max tokens per sample: 5507 Avg tokens per sample: 742.6944900675991 ----------------- ## Stats for 'Subset of FreedomIntelligence/alpaca-gpt4-deutsch' (48969 samples (100.0%)) ----------------- Accepted: 48969/48969 (100.0%) Accepted tokens: 13372005 Skipped: 0 (0.0%) Min tokens per sample: 19 Max tokens per sample: 1359 Avg tokens per sample: 273.07082031489307 ----------------- ## Stats for 'Subset of LeoLM/OpenSchnabeltier' (21314 samples (100.0%)) ----------------- Accepted: 21314/21314 (100.0%) Accepted tokens: 8134690 Skipped: 0 (0.0%) Min tokens per sample: 25 Max tokens per sample: 1202 Avg tokens per sample: 381.65947264708643 ----------------- ## Stats for 'Subset of LeoLM/German_Poems' (490 samples (100.0%)) ----------------- Accepted: 490/490 (100.0%) Accepted tokens: 618642 Skipped: 0 (0.0%) Min tokens per sample: 747 Max tokens per sample: 1678 Avg tokens per sample: 1262.534693877551 ----------------- ## Stats for 'Subset of LeoLM/German_Songs' (392 samples (100.0%)) ----------------- Accepted: 392/392 (100.0%) Accepted tokens: 187897 Skipped: 0 (0.0%) Min tokens per sample: 231 Max tokens per sample: 826 Avg tokens per sample: 479.3290816326531 ----------------- ## Stats for 'total' (132540 samples (100.0%)) ----------------- Accepted: 132540/132540 (100.0%) Accepted tokens: 67530728 Skipped: 0 (0.0%) Min tokens per sample: 19 Max tokens per sample: 5507 Avg tokens per sample: 509.51205673758864 ----------------- ```
sxandie/autotrain-air-sea-services-28092023-91915144626	sxandie	2023-09-28T12:40:34Z	108	0	transformers	[ "transformers", "pytorch", "safetensors", "xlm-roberta", "token-classification", "autotrain", "unk", "dataset:sxandie/autotrain-data-air-sea-services-28092023", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-09-28T12:38:08Z	--- tags: - autotrain - token-classification language: - unk widget: - text: "I love AutoTrain" datasets: - sxandie/autotrain-data-air-sea-services-28092023 co2_eq_emissions: emissions: 0.8518554225909252 --- # Model Trained Using AutoTrain - Problem type: Entity Extraction - Model ID: 91915144626 - CO2 Emissions (in grams): 0.8519 ## Validation Metrics - Loss: 0.921 - Accuracy: 0.759 - Precision: 0.159 - Recall: 0.098 - F1: 0.122 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/sxandie/autotrain-air-sea-services-28092023-91915144626 ``` Or Python API: ``` from transformers import AutoModelForTokenClassification, AutoTokenizer model = AutoModelForTokenClassification.from_pretrained("sxandie/autotrain-air-sea-services-28092023-91915144626", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("sxandie/autotrain-air-sea-services-28092023-91915144626", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
shikari2917/mypic4	shikari2917	2023-09-28T12:40:17Z	5	1	diffusers	[ "diffusers", "text-to-image", "autotrain", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]	text-to-image	2023-09-28T12:40:15Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: photo of a sks rafeh tags: - text-to-image - diffusers - autotrain inference: true --- # DreamBooth trained by AutoTrain Text encoder was not trained.
badokorach/flan-t5-small-qa-9-qa-91	badokorach	2023-09-28T12:28:24Z	4	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "base_model:badokorach/flan-t5-small-qa-9", "base_model:finetune:badokorach/flan-t5-small-qa-9", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-09-28T09:51:48Z	--- license: apache-2.0 base_model: badokorach/flan-t5-small-qa-9 tags: - generated_from_trainer model-index: - name: flan-t5-small-qa-9-qa-91 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-small-qa-9-qa-91 This model is a fine-tuned version of [badokorach/flan-t5-small-qa-9](https://huggingface.co/badokorach/flan-t5-small-qa-9) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1084 ## 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: 4e-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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 343 \| 0.1041 \| \| 0.0207 \| 2.0 \| 686 \| 0.1041 \| \| 0.0196 \| 3.0 \| 1029 \| 0.1057 \| \| 0.0196 \| 4.0 \| 1372 \| 0.1052 \| \| 0.0186 \| 5.0 \| 1715 \| 0.1043 \| \| 0.0173 \| 6.0 \| 2058 \| 0.1050 \| \| 0.0173 \| 7.0 \| 2401 \| 0.1079 \| \| 0.0165 \| 8.0 \| 2744 \| 0.1068 \| \| 0.0161 \| 9.0 \| 3087 \| 0.1081 \| \| 0.0161 \| 10.0 \| 3430 \| 0.1084 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cu118 - Tokenizers 0.13.3
CJ-gyuwonpark/ch-70b-v2	CJ-gyuwonpark	2023-09-28T12:26:00Z	0	0	peft	[ "peft", "llama", "4-bit", "bitsandbytes", "region:us" ]	null	2023-09-26T06:05:50Z	--- 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: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.6.0.dev0
CyberHarem/kanou_koyomi_yagatekimininaru	CyberHarem	2023-09-28T12:21:10Z	0	0	null	[ "art", "text-to-image", "dataset:CyberHarem/kanou_koyomi_yagatekimininaru", "license:mit", "region:us" ]	text-to-image	2023-09-28T12:09:24Z	--- license: mit datasets: - CyberHarem/kanou_koyomi_yagatekimininaru pipeline_tag: text-to-image tags: - art --- # Lora of kanou_koyomi_yagatekimininaru This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 4420, you need to download `4420/kanou_koyomi_yagatekimininaru.pt` as the embedding and `4420/kanou_koyomi_yagatekimininaru.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. The best step we recommend is 4420, with the score of 0.882. The trigger words are: 1. `kanou_koyomi_yagatekimininaru` 2. `short_hair, black_hair, closed_mouth, indoors, brown_hair, blue_eyes, black_eyes` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: \| Steps \| Score \| Download \| pattern_1 \| pattern_2 \| pattern_3 \| pattern_4 \| pattern_5 \| bikini \| bondage \| free \| maid \| miko \| nude \| nude2 \| suit \| yukata \| \|:---------\|:----------\|:-------------------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------\|:--------------------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-----------------------------------------------\|:------------------------------------------------\|:-------------------------------------\|:-----------------------------------------\| \| 5100 \| 0.815 \| [Download](5100/kanou_koyomi_yagatekimininaru.zip) \| ![pattern_1-5100](5100/previews/pattern_1.png) \| ![pattern_2-5100](5100/previews/pattern_2.png) \| ![pattern_3-5100](5100/previews/pattern_3.png) \| ![pattern_4-5100](5100/previews/pattern_4.png) \| ![pattern_5-5100](5100/previews/pattern_5.png) \| ![bikini-5100](5100/previews/bikini.png) \| [<NSFW, click to see>](5100/previews/bondage.png) \| ![free-5100](5100/previews/free.png) \| ![maid-5100](5100/previews/maid.png) \| ![miko-5100](5100/previews/miko.png) \| [<NSFW, click to see>](5100/previews/nude.png) \| [<NSFW, click to see>](5100/previews/nude2.png) \| ![suit-5100](5100/previews/suit.png) \| ![yukata-5100](5100/previews/yukata.png) \| \| 4760 \| 0.719 \| [Download](4760/kanou_koyomi_yagatekimininaru.zip) \| ![pattern_1-4760](4760/previews/pattern_1.png) \| ![pattern_2-4760](4760/previews/pattern_2.png) \| ![pattern_3-4760](4760/previews/pattern_3.png) \| ![pattern_4-4760](4760/previews/pattern_4.png) \| ![pattern_5-4760](4760/previews/pattern_5.png) \| ![bikini-4760](4760/previews/bikini.png) \| [<NSFW, click to see>](4760/previews/bondage.png) \| ![free-4760](4760/previews/free.png) \| ![maid-4760](4760/previews/maid.png) \| ![miko-4760](4760/previews/miko.png) \| [<NSFW, click to see>](4760/previews/nude.png) \| [<NSFW, click to see>](4760/previews/nude2.png) \| ![suit-4760](4760/previews/suit.png) \| ![yukata-4760](4760/previews/yukata.png) \| \| 4420 \| 0.882 \| [Download](4420/kanou_koyomi_yagatekimininaru.zip) \| ![pattern_1-4420](4420/previews/pattern_1.png) \| ![pattern_2-4420](4420/previews/pattern_2.png) \| ![pattern_3-4420](4420/previews/pattern_3.png) \| ![pattern_4-4420](4420/previews/pattern_4.png) \| ![pattern_5-4420](4420/previews/pattern_5.png) \| ![bikini-4420](4420/previews/bikini.png) \| [<NSFW, click to see>](4420/previews/bondage.png) \| ![free-4420](4420/previews/free.png) \| ![maid-4420](4420/previews/maid.png) \| ![miko-4420](4420/previews/miko.png) \| [<NSFW, click to see>](4420/previews/nude.png) \| [<NSFW, click to see>](4420/previews/nude2.png) \| ![suit-4420](4420/previews/suit.png) \| ![yukata-4420](4420/previews/yukata.png) \| \| 4080 \| 0.746 \| [Download](4080/kanou_koyomi_yagatekimininaru.zip) \| ![pattern_1-4080](4080/previews/pattern_1.png) \| ![pattern_2-4080](4080/previews/pattern_2.png) \| ![pattern_3-4080](4080/previews/pattern_3.png) \| ![pattern_4-4080](4080/previews/pattern_4.png) \| ![pattern_5-4080](4080/previews/pattern_5.png) \| ![bikini-4080](4080/previews/bikini.png) \| [<NSFW, click to see>](4080/previews/bondage.png) \| ![free-4080](4080/previews/free.png) \| ![maid-4080](4080/previews/maid.png) \| ![miko-4080](4080/previews/miko.png) \| [<NSFW, click to see>](4080/previews/nude.png) \| [<NSFW, click to see>](4080/previews/nude2.png) \| ![suit-4080](4080/previews/suit.png) \| ![yukata-4080](4080/previews/yukata.png) \| \| 3740 \| 0.794 \| [Download](3740/kanou_koyomi_yagatekimininaru.zip) \| ![pattern_1-3740](3740/previews/pattern_1.png) \| ![pattern_2-3740](3740/previews/pattern_2.png) \| ![pattern_3-3740](3740/previews/pattern_3.png) \| ![pattern_4-3740](3740/previews/pattern_4.png) \| ![pattern_5-3740](3740/previews/pattern_5.png) \| ![bikini-3740](3740/previews/bikini.png) \| [<NSFW, click to see>](3740/previews/bondage.png) \| ![free-3740](3740/previews/free.png) \| ![maid-3740](3740/previews/maid.png) \| ![miko-3740](3740/previews/miko.png) \| [<NSFW, click to see>](3740/previews/nude.png) \| [<NSFW, click to see>](3740/previews/nude2.png) \| ![suit-3740](3740/previews/suit.png) \| ![yukata-3740](3740/previews/yukata.png) \| \| 3400 \| 0.755 \| [Download](3400/kanou_koyomi_yagatekimininaru.zip) \| ![pattern_1-3400](3400/previews/pattern_1.png) \| ![pattern_2-3400](3400/previews/pattern_2.png) \| ![pattern_3-3400](3400/previews/pattern_3.png) \| ![pattern_4-3400](3400/previews/pattern_4.png) \| ![pattern_5-3400](3400/previews/pattern_5.png) \| ![bikini-3400](3400/previews/bikini.png) \| [<NSFW, click to see>](3400/previews/bondage.png) \| ![free-3400](3400/previews/free.png) \| ![maid-3400](3400/previews/maid.png) \| ![miko-3400](3400/previews/miko.png) \| [<NSFW, click to see>](3400/previews/nude.png) \| [<NSFW, click to see>](3400/previews/nude2.png) \| ![suit-3400](3400/previews/suit.png) \| ![yukata-3400](3400/previews/yukata.png) \| \| 3060 \| 0.700 \| [Download](3060/kanou_koyomi_yagatekimininaru.zip) \| ![pattern_1-3060](3060/previews/pattern_1.png) \| ![pattern_2-3060](3060/previews/pattern_2.png) \| ![pattern_3-3060](3060/previews/pattern_3.png) \| ![pattern_4-3060](3060/previews/pattern_4.png) \| ![pattern_5-3060](3060/previews/pattern_5.png) \| ![bikini-3060](3060/previews/bikini.png) \| [<NSFW, click to see>](3060/previews/bondage.png) \| ![free-3060](3060/previews/free.png) \| ![maid-3060](3060/previews/maid.png) \| ![miko-3060](3060/previews/miko.png) \| [<NSFW, click to see>](3060/previews/nude.png) \| [<NSFW, click to see>](3060/previews/nude2.png) \| ![suit-3060](3060/previews/suit.png) \| ![yukata-3060](3060/previews/yukata.png) \| \| 2720 \| 0.664 \| [Download](2720/kanou_koyomi_yagatekimininaru.zip) \| ![pattern_1-2720](2720/previews/pattern_1.png) \| ![pattern_2-2720](2720/previews/pattern_2.png) \| ![pattern_3-2720](2720/previews/pattern_3.png) \| ![pattern_4-2720](2720/previews/pattern_4.png) \| ![pattern_5-2720](2720/previews/pattern_5.png) \| ![bikini-2720](2720/previews/bikini.png) \| [<NSFW, click to see>](2720/previews/bondage.png) \| ![free-2720](2720/previews/free.png) \| ![maid-2720](2720/previews/maid.png) \| ![miko-2720](2720/previews/miko.png) \| [<NSFW, click to see>](2720/previews/nude.png) \| [<NSFW, click to see>](2720/previews/nude2.png) \| ![suit-2720](2720/previews/suit.png) \| ![yukata-2720](2720/previews/yukata.png) \| \| 2380 \| 0.799 \| [Download](2380/kanou_koyomi_yagatekimininaru.zip) \| ![pattern_1-2380](2380/previews/pattern_1.png) \| ![pattern_2-2380](2380/previews/pattern_2.png) \| ![pattern_3-2380](2380/previews/pattern_3.png) \| ![pattern_4-2380](2380/previews/pattern_4.png) \| ![pattern_5-2380](2380/previews/pattern_5.png) \| ![bikini-2380](2380/previews/bikini.png) \| [<NSFW, click to see>](2380/previews/bondage.png) \| ![free-2380](2380/previews/free.png) \| ![maid-2380](2380/previews/maid.png) \| ![miko-2380](2380/previews/miko.png) \| [<NSFW, click to see>](2380/previews/nude.png) \| [<NSFW, click to see>](2380/previews/nude2.png) \| ![suit-2380](2380/previews/suit.png) \| ![yukata-2380](2380/previews/yukata.png) \| \| 2040 \| 0.666 \| [Download](2040/kanou_koyomi_yagatekimininaru.zip) \| ![pattern_1-2040](2040/previews/pattern_1.png) \| ![pattern_2-2040](2040/previews/pattern_2.png) \| ![pattern_3-2040](2040/previews/pattern_3.png) \| ![pattern_4-2040](2040/previews/pattern_4.png) \| ![pattern_5-2040](2040/previews/pattern_5.png) \| ![bikini-2040](2040/previews/bikini.png) \| [<NSFW, click to see>](2040/previews/bondage.png) \| ![free-2040](2040/previews/free.png) \| ![maid-2040](2040/previews/maid.png) \| ![miko-2040](2040/previews/miko.png) \| [<NSFW, click to see>](2040/previews/nude.png) \| [<NSFW, click to see>](2040/previews/nude2.png) \| ![suit-2040](2040/previews/suit.png) \| ![yukata-2040](2040/previews/yukata.png) \| \| 1700 \| 0.819 \| [Download](1700/kanou_koyomi_yagatekimininaru.zip) \| ![pattern_1-1700](1700/previews/pattern_1.png) \| ![pattern_2-1700](1700/previews/pattern_2.png) \| ![pattern_3-1700](1700/previews/pattern_3.png) \| ![pattern_4-1700](1700/previews/pattern_4.png) \| ![pattern_5-1700](1700/previews/pattern_5.png) \| ![bikini-1700](1700/previews/bikini.png) \| [<NSFW, click to see>](1700/previews/bondage.png) \| ![free-1700](1700/previews/free.png) \| ![maid-1700](1700/previews/maid.png) \| ![miko-1700](1700/previews/miko.png) \| [<NSFW, click to see>](1700/previews/nude.png) \| [<NSFW, click to see>](1700/previews/nude2.png) \| ![suit-1700](1700/previews/suit.png) \| ![yukata-1700](1700/previews/yukata.png) \| \| 1360 \| 0.724 \| [Download](1360/kanou_koyomi_yagatekimininaru.zip) \| ![pattern_1-1360](1360/previews/pattern_1.png) \| ![pattern_2-1360](1360/previews/pattern_2.png) \| ![pattern_3-1360](1360/previews/pattern_3.png) \| ![pattern_4-1360](1360/previews/pattern_4.png) \| ![pattern_5-1360](1360/previews/pattern_5.png) \| ![bikini-1360](1360/previews/bikini.png) \| [<NSFW, click to see>](1360/previews/bondage.png) \| ![free-1360](1360/previews/free.png) \| ![maid-1360](1360/previews/maid.png) \| ![miko-1360](1360/previews/miko.png) \| [<NSFW, click to see>](1360/previews/nude.png) \| [<NSFW, click to see>](1360/previews/nude2.png) \| ![suit-1360](1360/previews/suit.png) \| ![yukata-1360](1360/previews/yukata.png) \| \| 1020 \| 0.675 \| [Download](1020/kanou_koyomi_yagatekimininaru.zip) \| ![pattern_1-1020](1020/previews/pattern_1.png) \| ![pattern_2-1020](1020/previews/pattern_2.png) \| ![pattern_3-1020](1020/previews/pattern_3.png) \| ![pattern_4-1020](1020/previews/pattern_4.png) \| ![pattern_5-1020](1020/previews/pattern_5.png) \| ![bikini-1020](1020/previews/bikini.png) \| [<NSFW, click to see>](1020/previews/bondage.png) \| ![free-1020](1020/previews/free.png) \| ![maid-1020](1020/previews/maid.png) \| ![miko-1020](1020/previews/miko.png) \| [<NSFW, click to see>](1020/previews/nude.png) \| [<NSFW, click to see>](1020/previews/nude2.png) \| ![suit-1020](1020/previews/suit.png) \| ![yukata-1020](1020/previews/yukata.png) \| \| 680 \| 0.607 \| [Download](680/kanou_koyomi_yagatekimininaru.zip) \| ![pattern_1-680](680/previews/pattern_1.png) \| ![pattern_2-680](680/previews/pattern_2.png) \| ![pattern_3-680](680/previews/pattern_3.png) \| ![pattern_4-680](680/previews/pattern_4.png) \| ![pattern_5-680](680/previews/pattern_5.png) \| ![bikini-680](680/previews/bikini.png) \| [<NSFW, click to see>](680/previews/bondage.png) \| ![free-680](680/previews/free.png) \| ![maid-680](680/previews/maid.png) \| ![miko-680](680/previews/miko.png) \| [<NSFW, click to see>](680/previews/nude.png) \| [<NSFW, click to see>](680/previews/nude2.png) \| ![suit-680](680/previews/suit.png) \| ![yukata-680](680/previews/yukata.png) \| \| 340 \| 0.528 \| [Download](340/kanou_koyomi_yagatekimininaru.zip) \| ![pattern_1-340](340/previews/pattern_1.png) \| ![pattern_2-340](340/previews/pattern_2.png) \| ![pattern_3-340](340/previews/pattern_3.png) \| ![pattern_4-340](340/previews/pattern_4.png) \| ![pattern_5-340](340/previews/pattern_5.png) \| ![bikini-340](340/previews/bikini.png) \| [<NSFW, click to see>](340/previews/bondage.png) \| ![free-340](340/previews/free.png) \| ![maid-340](340/previews/maid.png) \| ![miko-340](340/previews/miko.png) \| [<NSFW, click to see>](340/previews/nude.png) \| [<NSFW, click to see>](340/previews/nude2.png) \| ![suit-340](340/previews/suit.png) \| ![yukata-340](340/previews/yukata.png) \|
CyberHarem/aoyama_midori_istheorderarabbit	CyberHarem	2023-09-28T12:17:17Z	0	0	null	[ "art", "text-to-image", "dataset:CyberHarem/aoyama_midori_istheorderarabbit", "license:mit", "region:us" ]	text-to-image	2023-09-28T11:48:41Z	--- license: mit datasets: - CyberHarem/aoyama_midori_istheorderarabbit pipeline_tag: text-to-image tags: - art --- # Lora of aoyama_midori_istheorderarabbit This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 4800, you need to download `4800/aoyama_midori_istheorderarabbit.pt` as the embedding and `4800/aoyama_midori_istheorderarabbit.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. The best step we recommend is 4800, with the score of 0.872. The trigger words are: 1. `aoyama_midori_istheorderarabbit` 2. `blush, blonde_hair, hair_between_eyes, smile, blue_eyes, bangs, long_hair, closed_mouth` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: \| Steps \| Score \| Download \| pattern_1 \| pattern_2 \| pattern_3 \| pattern_4 \| pattern_5 \| pattern_6 \| pattern_7 \| pattern_8 \| pattern_9 \| pattern_10 \| pattern_11 \| bikini \| bondage \| free \| maid \| miko \| nude \| nude2 \| suit \| yukata \| \|:---------\|:----------\|:---------------------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-------------------------------------------------\|:-------------------------------------------------\|:-----------------------------------------\|:--------------------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-----------------------------------------------\|:------------------------------------------------\|:-------------------------------------\|:-----------------------------------------\| \| 6000 \| 0.822 \| [Download](6000/aoyama_midori_istheorderarabbit.zip) \| ![pattern_1-6000](6000/previews/pattern_1.png) \| ![pattern_2-6000](6000/previews/pattern_2.png) \| ![pattern_3-6000](6000/previews/pattern_3.png) \| ![pattern_4-6000](6000/previews/pattern_4.png) \| ![pattern_5-6000](6000/previews/pattern_5.png) \| ![pattern_6-6000](6000/previews/pattern_6.png) \| ![pattern_7-6000](6000/previews/pattern_7.png) \| ![pattern_8-6000](6000/previews/pattern_8.png) \| ![pattern_9-6000](6000/previews/pattern_9.png) \| ![pattern_10-6000](6000/previews/pattern_10.png) \| ![pattern_11-6000](6000/previews/pattern_11.png) \| ![bikini-6000](6000/previews/bikini.png) \| [<NSFW, click to see>](6000/previews/bondage.png) \| ![free-6000](6000/previews/free.png) \| ![maid-6000](6000/previews/maid.png) \| ![miko-6000](6000/previews/miko.png) \| [<NSFW, click to see>](6000/previews/nude.png) \| [<NSFW, click to see>](6000/previews/nude2.png) \| ![suit-6000](6000/previews/suit.png) \| ![yukata-6000](6000/previews/yukata.png) \| \| 5600 \| 0.838 \| [Download](5600/aoyama_midori_istheorderarabbit.zip) \| ![pattern_1-5600](5600/previews/pattern_1.png) \| ![pattern_2-5600](5600/previews/pattern_2.png) \| ![pattern_3-5600](5600/previews/pattern_3.png) \| ![pattern_4-5600](5600/previews/pattern_4.png) \| ![pattern_5-5600](5600/previews/pattern_5.png) \| ![pattern_6-5600](5600/previews/pattern_6.png) \| ![pattern_7-5600](5600/previews/pattern_7.png) \| ![pattern_8-5600](5600/previews/pattern_8.png) \| ![pattern_9-5600](5600/previews/pattern_9.png) \| ![pattern_10-5600](5600/previews/pattern_10.png) \| ![pattern_11-5600](5600/previews/pattern_11.png) \| ![bikini-5600](5600/previews/bikini.png) \| [<NSFW, click to see>](5600/previews/bondage.png) \| ![free-5600](5600/previews/free.png) \| ![maid-5600](5600/previews/maid.png) \| ![miko-5600](5600/previews/miko.png) \| [<NSFW, click to see>](5600/previews/nude.png) \| [<NSFW, click to see>](5600/previews/nude2.png) \| ![suit-5600](5600/previews/suit.png) \| ![yukata-5600](5600/previews/yukata.png) \| \| 5200 \| 0.833 \| [Download](5200/aoyama_midori_istheorderarabbit.zip) \| ![pattern_1-5200](5200/previews/pattern_1.png) \| ![pattern_2-5200](5200/previews/pattern_2.png) \| ![pattern_3-5200](5200/previews/pattern_3.png) \| ![pattern_4-5200](5200/previews/pattern_4.png) \| ![pattern_5-5200](5200/previews/pattern_5.png) \| ![pattern_6-5200](5200/previews/pattern_6.png) \| ![pattern_7-5200](5200/previews/pattern_7.png) \| ![pattern_8-5200](5200/previews/pattern_8.png) \| ![pattern_9-5200](5200/previews/pattern_9.png) \| ![pattern_10-5200](5200/previews/pattern_10.png) \| ![pattern_11-5200](5200/previews/pattern_11.png) \| ![bikini-5200](5200/previews/bikini.png) \| [<NSFW, click to see>](5200/previews/bondage.png) \| ![free-5200](5200/previews/free.png) \| ![maid-5200](5200/previews/maid.png) \| ![miko-5200](5200/previews/miko.png) \| [<NSFW, click to see>](5200/previews/nude.png) \| [<NSFW, click to see>](5200/previews/nude2.png) \| ![suit-5200](5200/previews/suit.png) \| ![yukata-5200](5200/previews/yukata.png) \| \| 4800 \| 0.872 \| [Download](4800/aoyama_midori_istheorderarabbit.zip) \| ![pattern_1-4800](4800/previews/pattern_1.png) \| ![pattern_2-4800](4800/previews/pattern_2.png) \| ![pattern_3-4800](4800/previews/pattern_3.png) \| ![pattern_4-4800](4800/previews/pattern_4.png) \| ![pattern_5-4800](4800/previews/pattern_5.png) \| ![pattern_6-4800](4800/previews/pattern_6.png) \| ![pattern_7-4800](4800/previews/pattern_7.png) \| ![pattern_8-4800](4800/previews/pattern_8.png) \| ![pattern_9-4800](4800/previews/pattern_9.png) \| ![pattern_10-4800](4800/previews/pattern_10.png) \| ![pattern_11-4800](4800/previews/pattern_11.png) \| ![bikini-4800](4800/previews/bikini.png) \| [<NSFW, click to see>](4800/previews/bondage.png) \| ![free-4800](4800/previews/free.png) \| ![maid-4800](4800/previews/maid.png) \| ![miko-4800](4800/previews/miko.png) \| [<NSFW, click to see>](4800/previews/nude.png) \| [<NSFW, click to see>](4800/previews/nude2.png) \| ![suit-4800](4800/previews/suit.png) \| ![yukata-4800](4800/previews/yukata.png) \| \| 4400 \| 0.786 \| [Download](4400/aoyama_midori_istheorderarabbit.zip) \| ![pattern_1-4400](4400/previews/pattern_1.png) \| ![pattern_2-4400](4400/previews/pattern_2.png) \| ![pattern_3-4400](4400/previews/pattern_3.png) \| ![pattern_4-4400](4400/previews/pattern_4.png) \| ![pattern_5-4400](4400/previews/pattern_5.png) \| ![pattern_6-4400](4400/previews/pattern_6.png) \| ![pattern_7-4400](4400/previews/pattern_7.png) \| ![pattern_8-4400](4400/previews/pattern_8.png) \| ![pattern_9-4400](4400/previews/pattern_9.png) \| ![pattern_10-4400](4400/previews/pattern_10.png) \| ![pattern_11-4400](4400/previews/pattern_11.png) \| ![bikini-4400](4400/previews/bikini.png) \| [<NSFW, click to see>](4400/previews/bondage.png) \| ![free-4400](4400/previews/free.png) \| ![maid-4400](4400/previews/maid.png) \| ![miko-4400](4400/previews/miko.png) \| [<NSFW, click to see>](4400/previews/nude.png) \| [<NSFW, click to see>](4400/previews/nude2.png) \| ![suit-4400](4400/previews/suit.png) \| ![yukata-4400](4400/previews/yukata.png) \| \| 4000 \| 0.834 \| [Download](4000/aoyama_midori_istheorderarabbit.zip) \| ![pattern_1-4000](4000/previews/pattern_1.png) \| ![pattern_2-4000](4000/previews/pattern_2.png) \| ![pattern_3-4000](4000/previews/pattern_3.png) \| ![pattern_4-4000](4000/previews/pattern_4.png) \| ![pattern_5-4000](4000/previews/pattern_5.png) \| ![pattern_6-4000](4000/previews/pattern_6.png) \| ![pattern_7-4000](4000/previews/pattern_7.png) \| ![pattern_8-4000](4000/previews/pattern_8.png) \| ![pattern_9-4000](4000/previews/pattern_9.png) \| ![pattern_10-4000](4000/previews/pattern_10.png) \| ![pattern_11-4000](4000/previews/pattern_11.png) \| ![bikini-4000](4000/previews/bikini.png) \| [<NSFW, click to see>](4000/previews/bondage.png) \| ![free-4000](4000/previews/free.png) \| ![maid-4000](4000/previews/maid.png) \| ![miko-4000](4000/previews/miko.png) \| [<NSFW, click to see>](4000/previews/nude.png) \| [<NSFW, click to see>](4000/previews/nude2.png) \| ![suit-4000](4000/previews/suit.png) \| ![yukata-4000](4000/previews/yukata.png) \| \| 3600 \| 0.787 \| [Download](3600/aoyama_midori_istheorderarabbit.zip) \| ![pattern_1-3600](3600/previews/pattern_1.png) \| ![pattern_2-3600](3600/previews/pattern_2.png) \| ![pattern_3-3600](3600/previews/pattern_3.png) \| ![pattern_4-3600](3600/previews/pattern_4.png) \| ![pattern_5-3600](3600/previews/pattern_5.png) \| ![pattern_6-3600](3600/previews/pattern_6.png) \| ![pattern_7-3600](3600/previews/pattern_7.png) \| ![pattern_8-3600](3600/previews/pattern_8.png) \| ![pattern_9-3600](3600/previews/pattern_9.png) \| ![pattern_10-3600](3600/previews/pattern_10.png) \| ![pattern_11-3600](3600/previews/pattern_11.png) \| ![bikini-3600](3600/previews/bikini.png) \| [<NSFW, click to see>](3600/previews/bondage.png) \| ![free-3600](3600/previews/free.png) \| ![maid-3600](3600/previews/maid.png) \| ![miko-3600](3600/previews/miko.png) \| [<NSFW, click to see>](3600/previews/nude.png) \| [<NSFW, click to see>](3600/previews/nude2.png) \| ![suit-3600](3600/previews/suit.png) \| ![yukata-3600](3600/previews/yukata.png) \| \| 3200 \| 0.829 \| [Download](3200/aoyama_midori_istheorderarabbit.zip) \| ![pattern_1-3200](3200/previews/pattern_1.png) \| ![pattern_2-3200](3200/previews/pattern_2.png) \| ![pattern_3-3200](3200/previews/pattern_3.png) \| ![pattern_4-3200](3200/previews/pattern_4.png) \| ![pattern_5-3200](3200/previews/pattern_5.png) \| ![pattern_6-3200](3200/previews/pattern_6.png) \| ![pattern_7-3200](3200/previews/pattern_7.png) \| ![pattern_8-3200](3200/previews/pattern_8.png) \| ![pattern_9-3200](3200/previews/pattern_9.png) \| ![pattern_10-3200](3200/previews/pattern_10.png) \| ![pattern_11-3200](3200/previews/pattern_11.png) \| ![bikini-3200](3200/previews/bikini.png) \| [<NSFW, click to see>](3200/previews/bondage.png) \| ![free-3200](3200/previews/free.png) \| ![maid-3200](3200/previews/maid.png) \| ![miko-3200](3200/previews/miko.png) \| [<NSFW, click to see>](3200/previews/nude.png) \| [<NSFW, click to see>](3200/previews/nude2.png) \| ![suit-3200](3200/previews/suit.png) \| ![yukata-3200](3200/previews/yukata.png) \| \| 2800 \| 0.778 \| [Download](2800/aoyama_midori_istheorderarabbit.zip) \| ![pattern_1-2800](2800/previews/pattern_1.png) \| ![pattern_2-2800](2800/previews/pattern_2.png) \| ![pattern_3-2800](2800/previews/pattern_3.png) \| ![pattern_4-2800](2800/previews/pattern_4.png) \| ![pattern_5-2800](2800/previews/pattern_5.png) \| ![pattern_6-2800](2800/previews/pattern_6.png) \| ![pattern_7-2800](2800/previews/pattern_7.png) \| ![pattern_8-2800](2800/previews/pattern_8.png) \| ![pattern_9-2800](2800/previews/pattern_9.png) \| ![pattern_10-2800](2800/previews/pattern_10.png) \| ![pattern_11-2800](2800/previews/pattern_11.png) \| ![bikini-2800](2800/previews/bikini.png) \| [<NSFW, click to see>](2800/previews/bondage.png) \| ![free-2800](2800/previews/free.png) \| ![maid-2800](2800/previews/maid.png) \| ![miko-2800](2800/previews/miko.png) \| [<NSFW, click to see>](2800/previews/nude.png) \| [<NSFW, click to see>](2800/previews/nude2.png) \| ![suit-2800](2800/previews/suit.png) \| ![yukata-2800](2800/previews/yukata.png) \| \| 2400 \| 0.749 \| [Download](2400/aoyama_midori_istheorderarabbit.zip) \| ![pattern_1-2400](2400/previews/pattern_1.png) \| ![pattern_2-2400](2400/previews/pattern_2.png) \| ![pattern_3-2400](2400/previews/pattern_3.png) \| ![pattern_4-2400](2400/previews/pattern_4.png) \| ![pattern_5-2400](2400/previews/pattern_5.png) \| ![pattern_6-2400](2400/previews/pattern_6.png) \| ![pattern_7-2400](2400/previews/pattern_7.png) \| ![pattern_8-2400](2400/previews/pattern_8.png) \| ![pattern_9-2400](2400/previews/pattern_9.png) \| ![pattern_10-2400](2400/previews/pattern_10.png) \| ![pattern_11-2400](2400/previews/pattern_11.png) \| ![bikini-2400](2400/previews/bikini.png) \| [<NSFW, click to see>](2400/previews/bondage.png) \| ![free-2400](2400/previews/free.png) \| ![maid-2400](2400/previews/maid.png) \| ![miko-2400](2400/previews/miko.png) \| [<NSFW, click to see>](2400/previews/nude.png) \| [<NSFW, click to see>](2400/previews/nude2.png) \| ![suit-2400](2400/previews/suit.png) \| ![yukata-2400](2400/previews/yukata.png) \| \| 2000 \| 0.806 \| [Download](2000/aoyama_midori_istheorderarabbit.zip) \| ![pattern_1-2000](2000/previews/pattern_1.png) \| ![pattern_2-2000](2000/previews/pattern_2.png) \| ![pattern_3-2000](2000/previews/pattern_3.png) \| ![pattern_4-2000](2000/previews/pattern_4.png) \| ![pattern_5-2000](2000/previews/pattern_5.png) \| ![pattern_6-2000](2000/previews/pattern_6.png) \| ![pattern_7-2000](2000/previews/pattern_7.png) \| ![pattern_8-2000](2000/previews/pattern_8.png) \| ![pattern_9-2000](2000/previews/pattern_9.png) \| ![pattern_10-2000](2000/previews/pattern_10.png) \| ![pattern_11-2000](2000/previews/pattern_11.png) \| ![bikini-2000](2000/previews/bikini.png) \| [<NSFW, click to see>](2000/previews/bondage.png) \| ![free-2000](2000/previews/free.png) \| ![maid-2000](2000/previews/maid.png) \| ![miko-2000](2000/previews/miko.png) \| [<NSFW, click to see>](2000/previews/nude.png) \| [<NSFW, click to see>](2000/previews/nude2.png) \| ![suit-2000](2000/previews/suit.png) \| ![yukata-2000](2000/previews/yukata.png) \| \| 1600 \| 0.687 \| [Download](1600/aoyama_midori_istheorderarabbit.zip) \| ![pattern_1-1600](1600/previews/pattern_1.png) \| ![pattern_2-1600](1600/previews/pattern_2.png) \| ![pattern_3-1600](1600/previews/pattern_3.png) \| ![pattern_4-1600](1600/previews/pattern_4.png) \| ![pattern_5-1600](1600/previews/pattern_5.png) \| ![pattern_6-1600](1600/previews/pattern_6.png) \| ![pattern_7-1600](1600/previews/pattern_7.png) \| ![pattern_8-1600](1600/previews/pattern_8.png) \| ![pattern_9-1600](1600/previews/pattern_9.png) \| ![pattern_10-1600](1600/previews/pattern_10.png) \| ![pattern_11-1600](1600/previews/pattern_11.png) \| ![bikini-1600](1600/previews/bikini.png) \| [<NSFW, click to see>](1600/previews/bondage.png) \| ![free-1600](1600/previews/free.png) \| ![maid-1600](1600/previews/maid.png) \| ![miko-1600](1600/previews/miko.png) \| [<NSFW, click to see>](1600/previews/nude.png) \| [<NSFW, click to see>](1600/previews/nude2.png) \| ![suit-1600](1600/previews/suit.png) \| ![yukata-1600](1600/previews/yukata.png) \| \| 1200 \| 0.718 \| [Download](1200/aoyama_midori_istheorderarabbit.zip) \| ![pattern_1-1200](1200/previews/pattern_1.png) \| ![pattern_2-1200](1200/previews/pattern_2.png) \| ![pattern_3-1200](1200/previews/pattern_3.png) \| ![pattern_4-1200](1200/previews/pattern_4.png) \| ![pattern_5-1200](1200/previews/pattern_5.png) \| ![pattern_6-1200](1200/previews/pattern_6.png) \| ![pattern_7-1200](1200/previews/pattern_7.png) \| ![pattern_8-1200](1200/previews/pattern_8.png) \| ![pattern_9-1200](1200/previews/pattern_9.png) \| ![pattern_10-1200](1200/previews/pattern_10.png) \| ![pattern_11-1200](1200/previews/pattern_11.png) \| ![bikini-1200](1200/previews/bikini.png) \| [<NSFW, click to see>](1200/previews/bondage.png) \| ![free-1200](1200/previews/free.png) \| ![maid-1200](1200/previews/maid.png) \| ![miko-1200](1200/previews/miko.png) \| [<NSFW, click to see>](1200/previews/nude.png) \| [<NSFW, click to see>](1200/previews/nude2.png) \| ![suit-1200](1200/previews/suit.png) \| ![yukata-1200](1200/previews/yukata.png) \| \| 800 \| 0.673 \| [Download](800/aoyama_midori_istheorderarabbit.zip) \| ![pattern_1-800](800/previews/pattern_1.png) \| ![pattern_2-800](800/previews/pattern_2.png) \| ![pattern_3-800](800/previews/pattern_3.png) \| ![pattern_4-800](800/previews/pattern_4.png) \| ![pattern_5-800](800/previews/pattern_5.png) \| ![pattern_6-800](800/previews/pattern_6.png) \| ![pattern_7-800](800/previews/pattern_7.png) \| ![pattern_8-800](800/previews/pattern_8.png) \| ![pattern_9-800](800/previews/pattern_9.png) \| ![pattern_10-800](800/previews/pattern_10.png) \| ![pattern_11-800](800/previews/pattern_11.png) \| ![bikini-800](800/previews/bikini.png) \| [<NSFW, click to see>](800/previews/bondage.png) \| ![free-800](800/previews/free.png) \| ![maid-800](800/previews/maid.png) \| ![miko-800](800/previews/miko.png) \| [<NSFW, click to see>](800/previews/nude.png) \| [<NSFW, click to see>](800/previews/nude2.png) \| ![suit-800](800/previews/suit.png) \| ![yukata-800](800/previews/yukata.png) \| \| 400 \| 0.435 \| [Download](400/aoyama_midori_istheorderarabbit.zip) \| ![pattern_1-400](400/previews/pattern_1.png) \| ![pattern_2-400](400/previews/pattern_2.png) \| ![pattern_3-400](400/previews/pattern_3.png) \| ![pattern_4-400](400/previews/pattern_4.png) \| ![pattern_5-400](400/previews/pattern_5.png) \| ![pattern_6-400](400/previews/pattern_6.png) \| ![pattern_7-400](400/previews/pattern_7.png) \| ![pattern_8-400](400/previews/pattern_8.png) \| ![pattern_9-400](400/previews/pattern_9.png) \| ![pattern_10-400](400/previews/pattern_10.png) \| ![pattern_11-400](400/previews/pattern_11.png) \| ![bikini-400](400/previews/bikini.png) \| [<NSFW, click to see>](400/previews/bondage.png) \| ![free-400](400/previews/free.png) \| ![maid-400](400/previews/maid.png) \| ![miko-400](400/previews/miko.png) \| [<NSFW, click to see>](400/previews/nude.png) \| [<NSFW, click to see>](400/previews/nude2.png) \| ![suit-400](400/previews/suit.png) \| ![yukata-400](400/previews/yukata.png) \|
milaidy/gabewise	milaidy	2023-09-28T12:14:49Z	0	1	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-09-28T12:02:15Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### gabewise Dreambooth model trained by milaidy with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:
mHossain/bangla_idiom_generation_v2	mHossain	2023-09-28T12:11:45Z	103	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "base_model:mHossain/bangla-para-v3-500000", "base_model:finetune:mHossain/bangla-para-v3-500000", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-09-28T11:29:55Z	--- base_model: mHossain/bangla-para-v3-500000 tags: - generated_from_trainer metrics: - rouge model-index: - name: bangla_idiom_generation_v2 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. --> # bangla_idiom_generation_v2 This model is a fine-tuned version of [mHossain/bangla-para-v3-500000](https://huggingface.co/mHossain/bangla-para-v3-500000) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.7655 - Rouge1: 0.0 - Rouge2: 0.0 - Rougel: 0.0 - Rougelsum: 0.0 - Gen Len: 10.4292 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5000 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 226 \| 7.4650 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 11.1195 \| \| No log \| 2.0 \| 452 \| 7.1989 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 10.677 \| \| 7.7649 \| 3.0 \| 678 \| 6.8146 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 9.8938 \| \| 7.7649 \| 4.0 \| 904 \| 6.3650 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 9.3805 \| \| 7.0991 \| 5.0 \| 1130 \| 5.8781 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 9.2478 \| \| 7.0991 \| 6.0 \| 1356 \| 5.4460 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 9.0487 \| \| 6.1699 \| 7.0 \| 1582 \| 5.1824 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 9.3363 \| \| 6.1699 \| 8.0 \| 1808 \| 4.9930 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 10.2478 \| \| 5.5395 \| 9.0 \| 2034 \| 4.8596 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 10.5575 \| \| 5.5395 \| 10.0 \| 2260 \| 4.7655 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 10.4292 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
udg/cartoon3d	udg	2023-09-28T12:11:00Z	30	0	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-08-09T10:13:57Z	--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true ---
fsarab/ppo-Huggy	fsarab	2023-09-28T12:08:54Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-09-28T12:08:48Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy 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: fsarab/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
yudiwbs/absa_elektronik_aspek_v2	yudiwbs	2023-09-28T11:52:36Z	0	0	flair	[ "flair", "pytorch", "token-classification", "sequence-tagger-model", "region:us" ]	token-classification	2023-09-24T13:59:16Z	--- tags: - flair - token-classification - sequence-tagger-model widget: - text: "Sesuai diskusi. Barang mulus walau ada hairline scratch di casing depan. Battery masih awet. Dapat bonus keyboard jp jg. Thanks gan." --- Klasifikasi aspek dalam review laptop ### Demo: How to use in Flair Requires: - [Flair](https://github.com/flairNLP/flair/) (`pip install flair`) ```python from flair.data import Sentence from flair.models import SequenceTagger # load tagger tagger = SequenceTagger.load("yudiwbs/absa_elektronik_aspek_v2") sentence = Sentence('Sesuai diskusi. Barang mulus walau ada hairline scratch di casing depan. Battery masih awet. Dapat bonus jg. Thanks gan.') # predict model.predict(sentence) print(sentence.to_tagged_string()) # iterate over entities and print for entity in sentence.get_spans('aspek'): print(entity) ```
dss107/news6	dss107	2023-09-28T11:48:29Z	4	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]	text-classification	2023-09-28T11:47:14Z	--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # dss107/news6 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("dss107/news6") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```
hmxiong/ScanNet_Finetune_global_guidance_10epoch_bs64	hmxiong	2023-09-28T11:47:16Z	0	0	null	[ "region:us" ]	null	2023-09-28T02:29:07Z	每个场景使用的是全部的18个类别信息
hmxiong/ScanNet_Finetune_no_guidance_10epoch_bs64	hmxiong	2023-09-28T11:45:57Z	0	0	null	[ "region:us" ]	null	2023-09-28T02:30:25Z	训练阶段不加入任何的类别guidance
anders0204/Reinforce-Pixelcopter-10k	anders0204	2023-09-28T11:39:25Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-09-27T13:52:30Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Pixelcopter-10k results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: -5.00 +/- 0.00 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
amayprro552/jssml-000004	amayprro552	2023-09-28T11:29:44Z	0	0	null	[ "license:other", "region:us" ]	null	2023-09-28T11:29:44Z	--- license: other license_name: lncz license_link: LICENSE ---
Pranjal12345/whisper-small-ne-pranjal	Pranjal12345	2023-09-28T11:14:22Z	76	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "ne", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-09-22T06:30:06Z	--- language: - ne license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 model-index: - name: Whisper Small Nepali - Pranjal Khadka 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 Small Nepali - Pranjal Khadka This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - eval_loss: 0.0001 - eval_wer: 0.0 - eval_runtime: 5.3111 - eval_samples_per_second: 0.753 - eval_steps_per_second: 0.188 - epoch: 76.92 - step: 1000 ## 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: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 2000 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
colab086/roberta-finetuned-subjqa-movies_2	colab086	2023-09-28T11:13:44Z	122	0	transformers	[ "transformers", "pytorch", "roberta", "question-answering", "generated_from_trainer", "base_model:deepset/roberta-base-squad2", "base_model:finetune:deepset/roberta-base-squad2", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]	question-answering	2023-09-28T10:57:30Z	--- license: cc-by-4.0 base_model: deepset/roberta-base-squad2 tags: - generated_from_trainer model-index: - name: roberta-finetuned-subjqa-movies_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. --> # roberta-finetuned-subjqa-movies_2 This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on the None 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: 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: 5 ### Training results ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
TheBloke/leo-hessianai-13B-chat-bilingual-GGUF	TheBloke	2023-09-28T11:11:33Z	288	6	transformers	[ "transformers", "gguf", "llama", "text-generation", "en", "de", "dataset:LeoLM/OpenSchnabeltier", "dataset:OpenAssistant/OASST-DE", "dataset:FreedomIntelligence/alpaca-gpt4-deutsch", "dataset:FreedomIntelligence/evol-instruct-deutsch", "dataset:LeoLM/German_Poems", "dataset:LeoLM/German_Songs", "dataset:garage-bAInd/Open-Platypus", "dataset:WizardLM/WizardLM_evol_instruct_70k", "dataset:bjoernp/oasst25-08-23-filtered", "base_model:LeoLM/leo-hessianai-13b-chat-bilingual", "base_model:quantized:LeoLM/leo-hessianai-13b-chat-bilingual", "license:llama2", "region:us" ]	text-generation	2023-09-28T10:56:39Z	--- base_model: LeoLM/leo-hessianai-13b-chat-bilingual datasets: - LeoLM/OpenSchnabeltier - OpenAssistant/OASST-DE - FreedomIntelligence/alpaca-gpt4-deutsch - FreedomIntelligence/evol-instruct-deutsch - LeoLM/German_Poems - LeoLM/German_Songs - garage-bAInd/Open-Platypus - WizardLM/WizardLM_evol_instruct_70k - bjoernp/oasst25-08-23-filtered inference: false language: - en - de library_name: transformers license: llama2 model_creator: LAION LeoLM model_name: Leo Hessianai 13B Chat Bilingual model_type: llama pipeline_tag: text-generation prompt_template: '<\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ' quantized_by: TheBloke --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" 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;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Leo Hessianai 13B Chat Bilingual - GGUF - Model creator: [LAION LeoLM](https://huggingface.co/LeoLM) - Original model: [Leo Hessianai 13B Chat Bilingual](https://huggingface.co/LeoLM/leo-hessianai-13b-chat-bilingual) <!-- description start --> ## Description This repo contains GGUF format model files for [LAION LeoLM's Leo Hessianai 13B Chat Bilingual](https://huggingface.co/LeoLM/leo-hessianai-13b-chat-bilingual). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplate list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. <!-- README_GGUF.md-about-gguf end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/leo-hessianai-13B-chat-bilingual-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/leo-hessianai-13B-chat-bilingual-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/leo-hessianai-13B-chat-bilingual-GGUF) * [LAION LeoLM's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/LeoLM/leo-hessianai-13b-chat-bilingual) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: ChatML ``` <\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` <!-- prompt-template end --> <!-- compatibility_gguf start --> ## Compatibility These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) They are also compatible with many third party UIs and libraries - please see the list at the top of this README. ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw Refer to the Provided Files table below to see what files use which methods, and how. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-provided-files start --> ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [leo-hessianai-13b-chat-bilingual.Q2_K.gguf](https://huggingface.co/TheBloke/leo-hessianai-13B-chat-bilingual-GGUF/blob/main/leo-hessianai-13b-chat-bilingual.Q2_K.gguf) \| Q2_K \| 2 \| 5.43 GB\| 7.93 GB \| smallest, significant quality loss - not recommended for most purposes \| \| [leo-hessianai-13b-chat-bilingual.Q3_K_S.gguf](https://huggingface.co/TheBloke/leo-hessianai-13B-chat-bilingual-GGUF/blob/main/leo-hessianai-13b-chat-bilingual.Q3_K_S.gguf) \| Q3_K_S \| 3 \| 5.66 GB\| 8.16 GB \| very small, high quality loss \| \| [leo-hessianai-13b-chat-bilingual.Q3_K_M.gguf](https://huggingface.co/TheBloke/leo-hessianai-13B-chat-bilingual-GGUF/blob/main/leo-hessianai-13b-chat-bilingual.Q3_K_M.gguf) \| Q3_K_M \| 3 \| 6.34 GB\| 8.84 GB \| very small, high quality loss \| \| [leo-hessianai-13b-chat-bilingual.Q3_K_L.gguf](https://huggingface.co/TheBloke/leo-hessianai-13B-chat-bilingual-GGUF/blob/main/leo-hessianai-13b-chat-bilingual.Q3_K_L.gguf) \| Q3_K_L \| 3 \| 6.93 GB\| 9.43 GB \| small, substantial quality loss \| \| [leo-hessianai-13b-chat-bilingual.Q4_0.gguf](https://huggingface.co/TheBloke/leo-hessianai-13B-chat-bilingual-GGUF/blob/main/leo-hessianai-13b-chat-bilingual.Q4_0.gguf) \| Q4_0 \| 4 \| 7.37 GB\| 9.87 GB \| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [leo-hessianai-13b-chat-bilingual.Q4_K_S.gguf](https://huggingface.co/TheBloke/leo-hessianai-13B-chat-bilingual-GGUF/blob/main/leo-hessianai-13b-chat-bilingual.Q4_K_S.gguf) \| Q4_K_S \| 4 \| 7.42 GB\| 9.92 GB \| small, greater quality loss \| \| [leo-hessianai-13b-chat-bilingual.Q4_K_M.gguf](https://huggingface.co/TheBloke/leo-hessianai-13B-chat-bilingual-GGUF/blob/main/leo-hessianai-13b-chat-bilingual.Q4_K_M.gguf) \| Q4_K_M \| 4 \| 7.87 GB\| 10.37 GB \| medium, balanced quality - recommended \| \| [leo-hessianai-13b-chat-bilingual.Q5_0.gguf](https://huggingface.co/TheBloke/leo-hessianai-13B-chat-bilingual-GGUF/blob/main/leo-hessianai-13b-chat-bilingual.Q5_0.gguf) \| Q5_0 \| 5 \| 8.97 GB\| 11.47 GB \| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [leo-hessianai-13b-chat-bilingual.Q5_K_S.gguf](https://huggingface.co/TheBloke/leo-hessianai-13B-chat-bilingual-GGUF/blob/main/leo-hessianai-13b-chat-bilingual.Q5_K_S.gguf) \| Q5_K_S \| 5 \| 8.97 GB\| 11.47 GB \| large, low quality loss - recommended \| \| [leo-hessianai-13b-chat-bilingual.Q5_K_M.gguf](https://huggingface.co/TheBloke/leo-hessianai-13B-chat-bilingual-GGUF/blob/main/leo-hessianai-13b-chat-bilingual.Q5_K_M.gguf) \| Q5_K_M \| 5 \| 9.23 GB\| 11.73 GB \| large, very low quality loss - recommended \| \| [leo-hessianai-13b-chat-bilingual.Q6_K.gguf](https://huggingface.co/TheBloke/leo-hessianai-13B-chat-bilingual-GGUF/blob/main/leo-hessianai-13b-chat-bilingual.Q6_K.gguf) \| Q6_K \| 6 \| 10.68 GB\| 13.18 GB \| very large, extremely low quality loss \| \| [leo-hessianai-13b-chat-bilingual.Q8_0.gguf](https://huggingface.co/TheBloke/leo-hessianai-13B-chat-bilingual-GGUF/blob/main/leo-hessianai-13b-chat-bilingual.Q8_0.gguf) \| Q8_0 \| 8 \| 13.83 GB\| 16.33 GB \| very large, extremely low quality loss - not recommended \| Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. <!-- README_GGUF.md-provided-files end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: - LM Studio - LoLLMS Web UI - Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: TheBloke/leo-hessianai-13B-chat-bilingual-GGUF and below it, a specific filename to download, such as: leo-hessianai-13b-chat-bilingual.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download TheBloke/leo-hessianai-13B-chat-bilingual-GGUF leo-hessianai-13b-chat-bilingual.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download TheBloke/leo-hessianai-13B-chat-bilingual-GGUF --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/leo-hessianai-13B-chat-bilingual-GGUF leo-hessianai-13b-chat-bilingual.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 32 -m leo-hessianai-13b-chat-bilingual.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<\|im_start\|>system\n{system_message}<\|im_end\|>\n<\|im_start\|>user\n{prompt}<\|im_end\|>\n<\|im_start\|>assistant" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. ### How to load this model in Python code, using ctransformers #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install ctransformers # Or with CUDA GPU acceleration pip install ctransformers[cuda] # Or with AMD ROCm GPU acceleration (Linux only) CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers # Or with Metal GPU acceleration for macOS systems only CT_METAL=1 pip install ctransformers --no-binary ctransformers ``` #### Simple ctransformers example code ```python from ctransformers import AutoModelForCausalLM # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = AutoModelForCausalLM.from_pretrained("TheBloke/leo-hessianai-13B-chat-bilingual-GGUF", model_file="leo-hessianai-13b-chat-bilingual.Q4_K_M.gguf", model_type="llama", gpu_layers=50) print(llm("AI is going to")) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> <!-- original-model-card start --> # Original model card: LAION LeoLM's Leo Hessianai 13B Chat Bilingual # LAION LeoLM: Linguistically Enhanced Open Language Model Meet LeoLM, the first open and commercially available German Foundation Language Model built on Llama-2. Our models extend Llama-2's capabilities into German through continued pretraining on a large corpus of German-language and mostly locality specific text. Thanks to a compute grant at HessianAI's new supercomputer 42, we release two foundation models trained with 8k context length, [`LeoLM/leo-hessianai-7b`](https://huggingface.co/LeoLM/leo-hessianai-7b) and [`LeoLM/leo-hessianai-13b`](https://huggingface.co/LeoLM/leo-hessianai-13b) under the [Llama-2 community license](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) (70b also coming soon! 👀). With this release, we hope to bring a new wave of opportunities to German open-source and commercial LLM research and accelerate adoption. Read our [blog post]() or our paper (preprint coming soon) for more details! A project by Björn Plüster and Christoph Schuhmann in collaboration with LAION and HessianAI. ## LeoLM Chat `LeoLM/leo-hessianai-13b-chat-bilingual` is a bilingual English-German chat model built on our foundation model `LeoLM/leo-hessianai-13b` and finetuned on a selection of German translateed instruction datasets and their English counterparts. The model performs exceptionally well on writing, explanation and discussion tasks but struggles somewhat with math and advanced reasoning. See our MT-Bench scores: ``` { "first_turn": 6.13125, "second_turn": 4.88125, "categories": { "writing": 6.75, "roleplay": 5.55, "reasoning": 3.3, "math": 2.25, "coding": 3.9, "extraction": 5.8, "stem": 7.55, "humanities": 8.95 }, "average": 5.50625 } ``` ## Model Details - Finetuned from: [LeoLM/leo-hessianai-13b](https://huggingface.co/LeoLM/leo-hessianai-13b) - Model type: Causal decoder-only transformer language model - Language: English and German - Demo: [Web Demo]() - License: [LLAMA 2 COMMUNITY LICENSE AGREEMENT](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) - Contact: [LAION Discord](https://discord.com/invite/eq3cAMZtCC) or [Björn Plüster](mailto:[email protected]) ## Use in 🤗Transformers First install direct dependencies: ``` pip install transformers torch sentencepiece ``` If you want faster inference using flash-attention2, you need to install these dependencies: ```bash pip install packaging ninja pip install flash-attn==v2.1.1 --no-build-isolation pip install git+https://github.com/HazyResearch/[email protected]#subdirectory=csrc/rotary ``` Then load the model in transformers: ```python from transformers import pipeline import torch system_prompt = """<\|im_start\|>system Dies ist eine Unterhaltung zwischen einem intelligenten, hilfsbereitem KI-Assistenten und einem Nutzer. Der Assistent gibt ausführliche, hilfreiche und ehrliche Antworten.<\|im_end\|> """ prompt_format = "<\|im_start\|>user\n{prompt}<\|im_end\|>\n<\|im_start\|>assistant\n" prompt = "Erkläre mir wie die Fahrradwegesituation in Hamburg ist." generator = pipeline(model="LeoLM/leo-hessianai-13b-chat-bilingual", device="cuda", torch_dtype=torch.float16, trust_remote_code=True) # True for flash-attn2 else False print(generator(prompt_format.format(prompt=prompt), do_sample=True, top_p=0.95, max_length=8192)) ``` "Als KI kann ich keine persönlichen Beobachtungen teilen, aber ich kann einige allgemeine Informationen zur Fahrradwegesituation in Hamburg liefern. Im Vergleich zu vielen anderen großen Städten hat Hamburg eine hohe Anzahl von Fahrradfahrern und nimmt seine Verantwortung für nachhaltige Verkehrsmittel sehr ernst. Es gibt viele Fahrradwege und separate Fahrspuren, die Radfahrern ermöglichen, zusammen mit dem Autoverkehr zu fahren. Diese Fahrradspuren sind oft mit Markierungen gekennzeichnet und durch physische Trennungen von anderen Fahrspuren abgegrenzt. Darüber hinaus gibt es viele Fahrradstraßen, auf denen Radfahrer Vorfahrt haben und Autos langsamer fahren müssen. In einigen städtischen Gebieten können Fahrradwege jedoch eng oder überfüllt sein, besonders während der Stoßzeiten. Es gibt auch viele Kreuzungen, an denen Radfahrer anhalten und auf Grün warten müssen, ähnlich wie Autofahrer. Insgesamt ist die Fahrradinfrastruktur in Hamburg ziemlich gut, aber wie überall gibt es immer Raum für Verbesserungen." ## Prompting / Prompt Template Prompt dialogue template (ChatML format): ``` """ <\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant """ ``` The model input can contain multiple conversation turns between user and assistant, e.g. ``` <\|im_start\|>user {prompt 1}<\|im_end\|> <\|im_start\|>assistant {reply 1}<\|im_end\|> <\|im_start\|>user {prompt 2}<\|im_end\|> <\|im_start\|>assistant (...) ``` ## Ethical Considerations and Limitations LeoLM has been tested in English and German, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, the potential outputs of `LeoLM/leo-hessianai-7b-chat` cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of `LeoLM/leo-hessianai-7b-chat`, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see Meta's [Responsible Use Guide](https://ai.meta.com/llama/responsible-use-guide/). ## Finetuning Details \| Hyperparameter \| Value \| \|---\|---\| \| Num epochs \| 3 \| \| Examples per epoch \| 233275 \| \| Global batch size \| 256 \| \| Learning rate \| 3e-5 \| \| Warmup steps \| 100 \| \| LR scheduler \| Cosine \| \| Adam betas \| (0.9, 0.95) \| \| Weight decay \| 0.001 \| ## Dataset Details ``` ## Stats for 'Subset of LeoLM/OpenSchnabeltier' (21314 samples (100.0%)) ----------------- Accepted: 21314/21314 (100.0%) Accepted tokens: 8134690 Skipped: 0 (0.0%) Min tokens per sample: 25 Max tokens per sample: 1202 Avg tokens per sample: 381.65947264708643 ----------------- ## Stats for 'Subset of garage-bAInd/Open-Platypus' (24427 samples (100.0%)) ----------------- Accepted: 24427/24427 (100.0%) Accepted tokens: 9549043 Skipped: 0 (0.0%) Min tokens per sample: 23 Max tokens per sample: 5054 Avg tokens per sample: 390.9216440823679 ----------------- ## Stats for 'Subset of WizardLM/WizardLM_evol_instruct_70k' (68600 samples (100.0%)) ----------------- Accepted: 68600/68600 (100.0%) Accepted tokens: 33045040 Skipped: 0 (0.0%) Min tokens per sample: 18 Max tokens per sample: 11810 Avg tokens per sample: 481.7061224489796 ----------------- ## Stats for 'Subset of FreedomIntelligence/evol-instruct-deutsch' (57841 samples (100.0%)) ----------------- Accepted: 57841/57841 (100.0%) Accepted tokens: 42958192 Skipped: 0 (0.0%) Min tokens per sample: 33 Max tokens per sample: 5507 Avg tokens per sample: 742.6944900675991 ----------------- ## Stats for 'Subset of FreedomIntelligence/alpaca-gpt4-deutsch' (48969 samples (100.0%)) ----------------- Accepted: 48969/48969 (100.0%) Accepted tokens: 13372005 Skipped: 0 (0.0%) Min tokens per sample: 19 Max tokens per sample: 1359 Avg tokens per sample: 273.07082031489307 ----------------- ## Stats for 'Subset of LeoLM/German_Songs' (490 samples (100.0%)) ----------------- Accepted: 490/490 (100.0%) Accepted tokens: 618642 Skipped: 0 (0.0%) Min tokens per sample: 747 Max tokens per sample: 1678 Avg tokens per sample: 1262.534693877551 ----------------- ## Stats for 'Subset of LeoLM/German_Poems' (392 samples (100.0%)) ----------------- Accepted: 392/392 (100.0%) Accepted tokens: 187897 Skipped: 0 (0.0%) Min tokens per sample: 231 Max tokens per sample: 826 Avg tokens per sample: 479.3290816326531 ----------------- ## Stats for 'Subset of OpenAssistant/OASST_DE' (3646 samples (100.0%)) ----------------- Accepted: 3646/3646 (100.0%) Accepted tokens: 2338738 Skipped: 0 (0.0%) Min tokens per sample: 29 Max tokens per sample: 2484 Avg tokens per sample: 641.4530992868897 ----------------- ## Stats for 'Subset of bjoernp/oasst25-08-23-filtered' (8922 samples (100.0%)) ----------------- Accepted: 8922/8922 (100.0%) Accepted tokens: 4526427 Skipped: 0 (0.0%) Min tokens per sample: 23 Max tokens per sample: 5407 Avg tokens per sample: 507.3332212508406 ----------------- ## Stats for 'total' (235632 samples (100.0%)) ----------------- Accepted: 235632/235632 (100.0%) Accepted tokens: 115862397 Skipped: 0 (0.0%) Min tokens per sample: 18 Max tokens per sample: 11810 Avg tokens per sample: 491.70909299246284 ----------------- ``` <!-- original-model-card end -->
SophiaPu/RLunit2	SophiaPu	2023-09-28T11:07:00Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-09-28T11:06:56Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: RLunit2 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="SophiaPu/RLunit2", 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"]) ```
Yongseok/xlm-roberta-base-finetuned-panx-de	Yongseok	2023-09-28T11:06:33Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-09-28T09:53:26Z	--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme config: PAN-X.de split: validation args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8596122135668391 --- <!-- 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 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1370 - F1: 0.8596 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2569 \| 1.0 \| 525 \| 0.1654 \| 0.8227 \| \| 0.1325 \| 2.0 \| 1050 \| 0.1381 \| 0.8483 \| \| 0.0832 \| 3.0 \| 1575 \| 0.1370 \| 0.8596 \| ### Framework versions - Transformers 4.30.0 - Pytorch 2.0.1+cu117 - Datasets 2.14.2 - Tokenizers 0.13.3
VishalCh/trained-llama2	VishalCh	2023-09-28T11:06:16Z	2	0	transformers	[ "transformers", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:finetune:meta-llama/Llama-2-7b-hf", "endpoints_compatible", "region:us" ]	null	2023-09-27T12:02:08Z	--- base_model: meta-llama/Llama-2-7b-hf tags: - generated_from_trainer model-index: - name: trained-llama2 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. --> # trained-llama2 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) 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: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 30 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
santiviquez/amazon-reviews-finetuning-distilbert-base-uncased	santiviquez	2023-09-28T11:04:26Z	133	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "dataset:amazon_reviews_multi", "base_model:nlptown/bert-base-multilingual-uncased-sentiment", "base_model:finetune:nlptown/bert-base-multilingual-uncased-sentiment", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-09-27T12:41:22Z	--- license: mit base_model: nlptown/bert-base-multilingual-uncased-sentiment tags: - generated_from_trainer datasets: - amazon_reviews_multi metrics: - accuracy - f1 model-index: - name: amazon-reviews-finetuning-distilbert-base-uncased results: - task: name: Text Classification type: text-classification dataset: name: amazon_reviews_multi type: amazon_reviews_multi config: en split: validation args: en metrics: - name: Accuracy type: accuracy value: 0.7703180212014135 - name: F1 type: f1 value: 0.7271375381543915 --- <!-- 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. --> # amazon-reviews-finetuning-distilbert-base-uncased This model is a fine-tuned version of [nlptown/bert-base-multilingual-uncased-sentiment](https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.5859 - Accuracy: 0.7703 - F1: 0.7271 ## 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: 32 - eval_batch_size: 32 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 188 \| 0.5587 \| 0.7756 \| 0.7297 \| \| No log \| 2.0 \| 376 \| 0.5859 \| 0.7703 \| 0.7271 \| ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.0 - Datasets 2.14.6.dev0 - Tokenizers 0.13.3
poisson-fish/Marcoroni-70B-v1-AWQ	poisson-fish	2023-09-28T10:59:20Z	14	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "lmdeploy", "awq", "marcoroni", "70b", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-09-25T02:23:56Z	--- tags: - lmdeploy - awq - marcoroni - 70b --- This is [AIDC-ai-business/Marcoroni-70B-v1](https://huggingface.co/AIDC-ai-business/Marcoroni-70B-v1) quantized to LMDeploy 4bit AWQ with the following config: ```bash python3 -m lmdeploy.lite.apis.auto_awq \ --model ./Marcoroni-70B-v1 \ --w_bits 4 \ --w_group_size 128 \ --work_dir ./quant ``` # Original Model Card: # Marcoroni-70B # Model Details * Trained by: trained by AIDC AI-Business. * Model type: Marcoroni-70B is an auto-regressive language model based on the Llama 2 transformer architecture. * Language(s): English * License for Marcoroni-70B base weights: Non-Commercial Creative Commons license ([CC BY-NC-4.0](https://creativecommons.org/licenses/by-nc/4.0/)) # Prompting ## Prompt Template for alpaca style ``` ### Instruction: <prompt> (without the <>) ### Response: ```
SophiaPu/q-FrozenLake-v1-4x4-noSlippery	SophiaPu	2023-09-28T10:56:43Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-09-28T10:53:00Z	--- 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="SophiaPu/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"]) ```
CyberHarem/nanami_touko_yagatekimininaru	CyberHarem	2023-09-28T10:51:28Z	0	0	null	[ "art", "text-to-image", "dataset:CyberHarem/nanami_touko_yagatekimininaru", "license:mit", "region:us" ]	text-to-image	2023-09-28T10:34:13Z	--- license: mit datasets: - CyberHarem/nanami_touko_yagatekimininaru pipeline_tag: text-to-image tags: - art --- # Lora of nanami_touko_yagatekimininaru This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 6000, you need to download `6000/nanami_touko_yagatekimininaru.pt` as the embedding and `6000/nanami_touko_yagatekimininaru.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. The best step we recommend is 6000, with the score of 0.929. The trigger words are: 1. `nanami_touko_yagatekimininaru` 2. `long_hair, black_hair, bangs, bow, ribbon, grey_eyes` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: \| Steps \| Score \| Download \| pattern_1 \| pattern_2 \| pattern_3 \| pattern_4 \| pattern_5 \| pattern_6 \| pattern_7 \| pattern_8 \| pattern_9 \| pattern_10 \| pattern_11 \| pattern_12 \| bikini \| bondage \| free \| maid \| miko \| nude \| nude2 \| suit \| yukata \| \|:---------\|:----------\|:-------------------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-------------------------------------------------\|:-------------------------------------------------\|:-------------------------------------------------\|:-----------------------------------------\|:--------------------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-----------------------------------------------\|:------------------------------------------------\|:-------------------------------------\|:-----------------------------------------\| \| 9000 \| 0.877 \| [Download](9000/nanami_touko_yagatekimininaru.zip) \| ![pattern_1-9000](9000/previews/pattern_1.png) \| ![pattern_2-9000](9000/previews/pattern_2.png) \| ![pattern_3-9000](9000/previews/pattern_3.png) \| ![pattern_4-9000](9000/previews/pattern_4.png) \| ![pattern_5-9000](9000/previews/pattern_5.png) \| ![pattern_6-9000](9000/previews/pattern_6.png) \| ![pattern_7-9000](9000/previews/pattern_7.png) \| ![pattern_8-9000](9000/previews/pattern_8.png) \| ![pattern_9-9000](9000/previews/pattern_9.png) \| ![pattern_10-9000](9000/previews/pattern_10.png) \| ![pattern_11-9000](9000/previews/pattern_11.png) \| ![pattern_12-9000](9000/previews/pattern_12.png) \| ![bikini-9000](9000/previews/bikini.png) \| [<NSFW, click to see>](9000/previews/bondage.png) \| ![free-9000](9000/previews/free.png) \| ![maid-9000](9000/previews/maid.png) \| ![miko-9000](9000/previews/miko.png) \| [<NSFW, click to see>](9000/previews/nude.png) \| [<NSFW, click to see>](9000/previews/nude2.png) \| ![suit-9000](9000/previews/suit.png) \| ![yukata-9000](9000/previews/yukata.png) \| \| 8400 \| 0.873 \| [Download](8400/nanami_touko_yagatekimininaru.zip) \| ![pattern_1-8400](8400/previews/pattern_1.png) \| ![pattern_2-8400](8400/previews/pattern_2.png) \| ![pattern_3-8400](8400/previews/pattern_3.png) \| ![pattern_4-8400](8400/previews/pattern_4.png) \| ![pattern_5-8400](8400/previews/pattern_5.png) \| ![pattern_6-8400](8400/previews/pattern_6.png) \| ![pattern_7-8400](8400/previews/pattern_7.png) \| ![pattern_8-8400](8400/previews/pattern_8.png) \| ![pattern_9-8400](8400/previews/pattern_9.png) \| ![pattern_10-8400](8400/previews/pattern_10.png) \| ![pattern_11-8400](8400/previews/pattern_11.png) \| ![pattern_12-8400](8400/previews/pattern_12.png) \| ![bikini-8400](8400/previews/bikini.png) \| [<NSFW, click to see>](8400/previews/bondage.png) \| ![free-8400](8400/previews/free.png) \| ![maid-8400](8400/previews/maid.png) \| ![miko-8400](8400/previews/miko.png) \| [<NSFW, click to see>](8400/previews/nude.png) \| [<NSFW, click to see>](8400/previews/nude2.png) \| ![suit-8400](8400/previews/suit.png) \| ![yukata-8400](8400/previews/yukata.png) \| \| 7800 \| 0.840 \| [Download](7800/nanami_touko_yagatekimininaru.zip) \| ![pattern_1-7800](7800/previews/pattern_1.png) \| ![pattern_2-7800](7800/previews/pattern_2.png) \| ![pattern_3-7800](7800/previews/pattern_3.png) \| ![pattern_4-7800](7800/previews/pattern_4.png) \| ![pattern_5-7800](7800/previews/pattern_5.png) \| ![pattern_6-7800](7800/previews/pattern_6.png) \| ![pattern_7-7800](7800/previews/pattern_7.png) \| ![pattern_8-7800](7800/previews/pattern_8.png) \| ![pattern_9-7800](7800/previews/pattern_9.png) \| ![pattern_10-7800](7800/previews/pattern_10.png) \| ![pattern_11-7800](7800/previews/pattern_11.png) \| ![pattern_12-7800](7800/previews/pattern_12.png) \| ![bikini-7800](7800/previews/bikini.png) \| [<NSFW, click to see>](7800/previews/bondage.png) \| ![free-7800](7800/previews/free.png) \| ![maid-7800](7800/previews/maid.png) \| ![miko-7800](7800/previews/miko.png) \| [<NSFW, click to see>](7800/previews/nude.png) \| [<NSFW, click to see>](7800/previews/nude2.png) \| ![suit-7800](7800/previews/suit.png) \| ![yukata-7800](7800/previews/yukata.png) \| \| 7200 \| 0.835 \| [Download](7200/nanami_touko_yagatekimininaru.zip) \| ![pattern_1-7200](7200/previews/pattern_1.png) \| ![pattern_2-7200](7200/previews/pattern_2.png) \| ![pattern_3-7200](7200/previews/pattern_3.png) \| ![pattern_4-7200](7200/previews/pattern_4.png) \| ![pattern_5-7200](7200/previews/pattern_5.png) \| ![pattern_6-7200](7200/previews/pattern_6.png) \| ![pattern_7-7200](7200/previews/pattern_7.png) \| ![pattern_8-7200](7200/previews/pattern_8.png) \| ![pattern_9-7200](7200/previews/pattern_9.png) \| ![pattern_10-7200](7200/previews/pattern_10.png) \| ![pattern_11-7200](7200/previews/pattern_11.png) \| ![pattern_12-7200](7200/previews/pattern_12.png) \| ![bikini-7200](7200/previews/bikini.png) \| [<NSFW, click to see>](7200/previews/bondage.png) \| ![free-7200](7200/previews/free.png) \| ![maid-7200](7200/previews/maid.png) \| ![miko-7200](7200/previews/miko.png) \| [<NSFW, click to see>](7200/previews/nude.png) \| [<NSFW, click to see>](7200/previews/nude2.png) \| ![suit-7200](7200/previews/suit.png) \| ![yukata-7200](7200/previews/yukata.png) \| \| 6600 \| 0.881 \| [Download](6600/nanami_touko_yagatekimininaru.zip) \| ![pattern_1-6600](6600/previews/pattern_1.png) \| ![pattern_2-6600](6600/previews/pattern_2.png) \| ![pattern_3-6600](6600/previews/pattern_3.png) \| ![pattern_4-6600](6600/previews/pattern_4.png) \| ![pattern_5-6600](6600/previews/pattern_5.png) \| ![pattern_6-6600](6600/previews/pattern_6.png) \| ![pattern_7-6600](6600/previews/pattern_7.png) \| ![pattern_8-6600](6600/previews/pattern_8.png) \| ![pattern_9-6600](6600/previews/pattern_9.png) \| ![pattern_10-6600](6600/previews/pattern_10.png) \| ![pattern_11-6600](6600/previews/pattern_11.png) \| ![pattern_12-6600](6600/previews/pattern_12.png) \| ![bikini-6600](6600/previews/bikini.png) \| [<NSFW, click to see>](6600/previews/bondage.png) \| ![free-6600](6600/previews/free.png) \| ![maid-6600](6600/previews/maid.png) \| ![miko-6600](6600/previews/miko.png) \| [<NSFW, click to see>](6600/previews/nude.png) \| [<NSFW, click to see>](6600/previews/nude2.png) \| ![suit-6600](6600/previews/suit.png) \| ![yukata-6600](6600/previews/yukata.png) \| \| 6000 \| 0.929 \| [Download](6000/nanami_touko_yagatekimininaru.zip) \| ![pattern_1-6000](6000/previews/pattern_1.png) \| ![pattern_2-6000](6000/previews/pattern_2.png) \| ![pattern_3-6000](6000/previews/pattern_3.png) \| ![pattern_4-6000](6000/previews/pattern_4.png) \| ![pattern_5-6000](6000/previews/pattern_5.png) \| ![pattern_6-6000](6000/previews/pattern_6.png) \| ![pattern_7-6000](6000/previews/pattern_7.png) \| ![pattern_8-6000](6000/previews/pattern_8.png) \| ![pattern_9-6000](6000/previews/pattern_9.png) \| ![pattern_10-6000](6000/previews/pattern_10.png) \| ![pattern_11-6000](6000/previews/pattern_11.png) \| ![pattern_12-6000](6000/previews/pattern_12.png) \| ![bikini-6000](6000/previews/bikini.png) \| [<NSFW, click to see>](6000/previews/bondage.png) \| ![free-6000](6000/previews/free.png) \| ![maid-6000](6000/previews/maid.png) \| ![miko-6000](6000/previews/miko.png) \| [<NSFW, click to see>](6000/previews/nude.png) \| [<NSFW, click to see>](6000/previews/nude2.png) \| ![suit-6000](6000/previews/suit.png) \| ![yukata-6000](6000/previews/yukata.png) \| \| 5400 \| 0.911 \| [Download](5400/nanami_touko_yagatekimininaru.zip) \| ![pattern_1-5400](5400/previews/pattern_1.png) \| ![pattern_2-5400](5400/previews/pattern_2.png) \| ![pattern_3-5400](5400/previews/pattern_3.png) \| ![pattern_4-5400](5400/previews/pattern_4.png) \| ![pattern_5-5400](5400/previews/pattern_5.png) \| ![pattern_6-5400](5400/previews/pattern_6.png) \| ![pattern_7-5400](5400/previews/pattern_7.png) \| ![pattern_8-5400](5400/previews/pattern_8.png) \| ![pattern_9-5400](5400/previews/pattern_9.png) \| ![pattern_10-5400](5400/previews/pattern_10.png) \| ![pattern_11-5400](5400/previews/pattern_11.png) \| ![pattern_12-5400](5400/previews/pattern_12.png) \| ![bikini-5400](5400/previews/bikini.png) \| [<NSFW, click to see>](5400/previews/bondage.png) \| ![free-5400](5400/previews/free.png) \| ![maid-5400](5400/previews/maid.png) \| ![miko-5400](5400/previews/miko.png) \| [<NSFW, click to see>](5400/previews/nude.png) \| [<NSFW, click to see>](5400/previews/nude2.png) \| ![suit-5400](5400/previews/suit.png) \| ![yukata-5400](5400/previews/yukata.png) \| \| 4800 \| 0.913 \| [Download](4800/nanami_touko_yagatekimininaru.zip) \| ![pattern_1-4800](4800/previews/pattern_1.png) \| ![pattern_2-4800](4800/previews/pattern_2.png) \| ![pattern_3-4800](4800/previews/pattern_3.png) \| ![pattern_4-4800](4800/previews/pattern_4.png) \| ![pattern_5-4800](4800/previews/pattern_5.png) \| ![pattern_6-4800](4800/previews/pattern_6.png) \| ![pattern_7-4800](4800/previews/pattern_7.png) \| ![pattern_8-4800](4800/previews/pattern_8.png) \| ![pattern_9-4800](4800/previews/pattern_9.png) \| ![pattern_10-4800](4800/previews/pattern_10.png) \| ![pattern_11-4800](4800/previews/pattern_11.png) \| ![pattern_12-4800](4800/previews/pattern_12.png) \| ![bikini-4800](4800/previews/bikini.png) \| [<NSFW, click to see>](4800/previews/bondage.png) \| ![free-4800](4800/previews/free.png) \| ![maid-4800](4800/previews/maid.png) \| ![miko-4800](4800/previews/miko.png) \| [<NSFW, click to see>](4800/previews/nude.png) \| [<NSFW, click to see>](4800/previews/nude2.png) \| ![suit-4800](4800/previews/suit.png) \| ![yukata-4800](4800/previews/yukata.png) \| \| 4200 \| 0.901 \| [Download](4200/nanami_touko_yagatekimininaru.zip) \| ![pattern_1-4200](4200/previews/pattern_1.png) \| ![pattern_2-4200](4200/previews/pattern_2.png) \| ![pattern_3-4200](4200/previews/pattern_3.png) \| ![pattern_4-4200](4200/previews/pattern_4.png) \| ![pattern_5-4200](4200/previews/pattern_5.png) \| ![pattern_6-4200](4200/previews/pattern_6.png) \| ![pattern_7-4200](4200/previews/pattern_7.png) \| ![pattern_8-4200](4200/previews/pattern_8.png) \| ![pattern_9-4200](4200/previews/pattern_9.png) \| ![pattern_10-4200](4200/previews/pattern_10.png) \| ![pattern_11-4200](4200/previews/pattern_11.png) \| ![pattern_12-4200](4200/previews/pattern_12.png) \| ![bikini-4200](4200/previews/bikini.png) \| [<NSFW, click to see>](4200/previews/bondage.png) \| ![free-4200](4200/previews/free.png) \| ![maid-4200](4200/previews/maid.png) \| ![miko-4200](4200/previews/miko.png) \| [<NSFW, click to see>](4200/previews/nude.png) \| [<NSFW, click to see>](4200/previews/nude2.png) \| ![suit-4200](4200/previews/suit.png) \| ![yukata-4200](4200/previews/yukata.png) \| \| 3600 \| 0.890 \| [Download](3600/nanami_touko_yagatekimininaru.zip) \| ![pattern_1-3600](3600/previews/pattern_1.png) \| ![pattern_2-3600](3600/previews/pattern_2.png) \| ![pattern_3-3600](3600/previews/pattern_3.png) \| ![pattern_4-3600](3600/previews/pattern_4.png) \| ![pattern_5-3600](3600/previews/pattern_5.png) \| ![pattern_6-3600](3600/previews/pattern_6.png) \| ![pattern_7-3600](3600/previews/pattern_7.png) \| ![pattern_8-3600](3600/previews/pattern_8.png) \| ![pattern_9-3600](3600/previews/pattern_9.png) \| ![pattern_10-3600](3600/previews/pattern_10.png) \| ![pattern_11-3600](3600/previews/pattern_11.png) \| ![pattern_12-3600](3600/previews/pattern_12.png) \| ![bikini-3600](3600/previews/bikini.png) \| [<NSFW, click to see>](3600/previews/bondage.png) \| ![free-3600](3600/previews/free.png) \| ![maid-3600](3600/previews/maid.png) \| ![miko-3600](3600/previews/miko.png) \| [<NSFW, click to see>](3600/previews/nude.png) \| [<NSFW, click to see>](3600/previews/nude2.png) \| ![suit-3600](3600/previews/suit.png) \| ![yukata-3600](3600/previews/yukata.png) \| \| 3000 \| 0.872 \| [Download](3000/nanami_touko_yagatekimininaru.zip) \| ![pattern_1-3000](3000/previews/pattern_1.png) \| ![pattern_2-3000](3000/previews/pattern_2.png) \| ![pattern_3-3000](3000/previews/pattern_3.png) \| ![pattern_4-3000](3000/previews/pattern_4.png) \| ![pattern_5-3000](3000/previews/pattern_5.png) \| ![pattern_6-3000](3000/previews/pattern_6.png) \| ![pattern_7-3000](3000/previews/pattern_7.png) \| ![pattern_8-3000](3000/previews/pattern_8.png) \| ![pattern_9-3000](3000/previews/pattern_9.png) \| ![pattern_10-3000](3000/previews/pattern_10.png) \| ![pattern_11-3000](3000/previews/pattern_11.png) \| ![pattern_12-3000](3000/previews/pattern_12.png) \| ![bikini-3000](3000/previews/bikini.png) \| [<NSFW, click to see>](3000/previews/bondage.png) \| ![free-3000](3000/previews/free.png) \| ![maid-3000](3000/previews/maid.png) \| ![miko-3000](3000/previews/miko.png) \| [<NSFW, click to see>](3000/previews/nude.png) \| [<NSFW, click to see>](3000/previews/nude2.png) \| ![suit-3000](3000/previews/suit.png) \| ![yukata-3000](3000/previews/yukata.png) \| \| 2400 \| 0.869 \| [Download](2400/nanami_touko_yagatekimininaru.zip) \| ![pattern_1-2400](2400/previews/pattern_1.png) \| ![pattern_2-2400](2400/previews/pattern_2.png) \| ![pattern_3-2400](2400/previews/pattern_3.png) \| ![pattern_4-2400](2400/previews/pattern_4.png) \| ![pattern_5-2400](2400/previews/pattern_5.png) \| ![pattern_6-2400](2400/previews/pattern_6.png) \| ![pattern_7-2400](2400/previews/pattern_7.png) \| ![pattern_8-2400](2400/previews/pattern_8.png) \| ![pattern_9-2400](2400/previews/pattern_9.png) \| ![pattern_10-2400](2400/previews/pattern_10.png) \| ![pattern_11-2400](2400/previews/pattern_11.png) \| ![pattern_12-2400](2400/previews/pattern_12.png) \| ![bikini-2400](2400/previews/bikini.png) \| [<NSFW, click to see>](2400/previews/bondage.png) \| ![free-2400](2400/previews/free.png) \| ![maid-2400](2400/previews/maid.png) \| ![miko-2400](2400/previews/miko.png) \| [<NSFW, click to see>](2400/previews/nude.png) \| [<NSFW, click to see>](2400/previews/nude2.png) \| ![suit-2400](2400/previews/suit.png) \| ![yukata-2400](2400/previews/yukata.png) \| \| 1800 \| 0.864 \| [Download](1800/nanami_touko_yagatekimininaru.zip) \| ![pattern_1-1800](1800/previews/pattern_1.png) \| ![pattern_2-1800](1800/previews/pattern_2.png) \| ![pattern_3-1800](1800/previews/pattern_3.png) \| ![pattern_4-1800](1800/previews/pattern_4.png) \| ![pattern_5-1800](1800/previews/pattern_5.png) \| ![pattern_6-1800](1800/previews/pattern_6.png) \| ![pattern_7-1800](1800/previews/pattern_7.png) \| ![pattern_8-1800](1800/previews/pattern_8.png) \| ![pattern_9-1800](1800/previews/pattern_9.png) \| ![pattern_10-1800](1800/previews/pattern_10.png) \| ![pattern_11-1800](1800/previews/pattern_11.png) \| ![pattern_12-1800](1800/previews/pattern_12.png) \| ![bikini-1800](1800/previews/bikini.png) \| [<NSFW, click to see>](1800/previews/bondage.png) \| ![free-1800](1800/previews/free.png) \| ![maid-1800](1800/previews/maid.png) \| ![miko-1800](1800/previews/miko.png) \| [<NSFW, click to see>](1800/previews/nude.png) \| [<NSFW, click to see>](1800/previews/nude2.png) \| ![suit-1800](1800/previews/suit.png) \| ![yukata-1800](1800/previews/yukata.png) \| \| 1200 \| 0.881 \| [Download](1200/nanami_touko_yagatekimininaru.zip) \| ![pattern_1-1200](1200/previews/pattern_1.png) \| ![pattern_2-1200](1200/previews/pattern_2.png) \| ![pattern_3-1200](1200/previews/pattern_3.png) \| ![pattern_4-1200](1200/previews/pattern_4.png) \| ![pattern_5-1200](1200/previews/pattern_5.png) \| ![pattern_6-1200](1200/previews/pattern_6.png) \| ![pattern_7-1200](1200/previews/pattern_7.png) \| ![pattern_8-1200](1200/previews/pattern_8.png) \| ![pattern_9-1200](1200/previews/pattern_9.png) \| ![pattern_10-1200](1200/previews/pattern_10.png) \| ![pattern_11-1200](1200/previews/pattern_11.png) \| ![pattern_12-1200](1200/previews/pattern_12.png) \| ![bikini-1200](1200/previews/bikini.png) \| [<NSFW, click to see>](1200/previews/bondage.png) \| ![free-1200](1200/previews/free.png) \| ![maid-1200](1200/previews/maid.png) \| ![miko-1200](1200/previews/miko.png) \| [<NSFW, click to see>](1200/previews/nude.png) \| [<NSFW, click to see>](1200/previews/nude2.png) \| ![suit-1200](1200/previews/suit.png) \| ![yukata-1200](1200/previews/yukata.png) \| \| 600 \| 0.798 \| [Download](600/nanami_touko_yagatekimininaru.zip) \| ![pattern_1-600](600/previews/pattern_1.png) \| ![pattern_2-600](600/previews/pattern_2.png) \| ![pattern_3-600](600/previews/pattern_3.png) \| ![pattern_4-600](600/previews/pattern_4.png) \| ![pattern_5-600](600/previews/pattern_5.png) \| ![pattern_6-600](600/previews/pattern_6.png) \| ![pattern_7-600](600/previews/pattern_7.png) \| ![pattern_8-600](600/previews/pattern_8.png) \| ![pattern_9-600](600/previews/pattern_9.png) \| ![pattern_10-600](600/previews/pattern_10.png) \| ![pattern_11-600](600/previews/pattern_11.png) \| ![pattern_12-600](600/previews/pattern_12.png) \| ![bikini-600](600/previews/bikini.png) \| [<NSFW, click to see>](600/previews/bondage.png) \| ![free-600](600/previews/free.png) \| ![maid-600](600/previews/maid.png) \| ![miko-600](600/previews/miko.png) \| [<NSFW, click to see>](600/previews/nude.png) \| [<NSFW, click to see>](600/previews/nude2.png) \| ![suit-600](600/previews/suit.png) \| ![yukata-600](600/previews/yukata.png) \|
hozcan/dog_model	hozcan	2023-09-28T10:38:50Z	1	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:CompVis/stable-diffusion-v1-4", "base_model:finetune:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-09-28T10:01:26Z	--- license: creativeml-openrail-m base_model: CompVis/stable-diffusion-v1-4 instance_prompt: a photo of sks dog tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - hozcan/dog_model This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.
MerziaAdamjee/OPT-IML-finetuned-sql-1	MerziaAdamjee	2023-09-28T10:27:31Z	160	0	transformers	[ "transformers", "pytorch", "opt", "text-generation", "generated_from_trainer", "base_model:facebook/opt-iml-1.3b", "base_model:finetune:facebook/opt-iml-1.3b", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-09-28T09:50:50Z	--- license: other base_model: facebook/opt-iml-1.3b tags: - generated_from_trainer model-index: - name: OPT-IML-finetuned-sql-1 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. --> # OPT-IML-finetuned-sql-1 This model is a fine-tuned version of [facebook/opt-iml-1.3b](https://huggingface.co/facebook/opt-iml-1.3b) on the None 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: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 100 ### Training results ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
Ashishkr/mistral-medical-consultation	Ashishkr	2023-09-28T10:14:39Z	10	2	peft	[ "peft", "mistralai/Mistral-7B-Instruct-v0.1", "finetuned", "text-generation", "license:apache-2.0", "region:us" ]	text-generation	2023-09-28T09:17:50Z	--- library_name: peft license: apache-2.0 pipeline_tag: text-generation inference: false tags: - mistralai/Mistral-7B-Instruct-v0.1 - finetuned --- ```bash pip install -q accelerate bitsandbytes trl datasets pip install git+https://github.com/huggingface/transformers pip install git+https://github.com/huggingface/peft.git ``` ```python import os import torch from datasets import load_dataset from transformers import ( AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, HfArgumentParser, pipeline, logging, ) from peft import LoraConfig, PeftModel base_model_name = "mistralai/Mistral-7B-Instruct-v0.1" ################################################################################ # bitsandbytes parameters ################################################################################ # Activate 4-bit precision base model loading use_4bit = True # Compute dtype for 4-bit base models bnb_4bit_compute_dtype = "float16" # Quantization type (fp4 or nf4) bnb_4bit_quant_type = "nf4" # Activate nested quantization for 4-bit base models (double quantization) use_nested_quant = False # Load the entire model on the GPU 0 device_map = {"": 0} # Load tokenizer and model with QLoRA configuration compute_dtype = getattr(torch, bnb_4bit_compute_dtype) bnb_config = BitsAndBytesConfig( load_in_4bit=use_4bit, bnb_4bit_quant_type=bnb_4bit_quant_type, bnb_4bit_compute_dtype=compute_dtype, bnb_4bit_use_double_quant=use_nested_quant, ) # Check GPU compatibility with bfloat16 if compute_dtype == torch.float16 and use_4bit: major, _ = torch.cuda.get_device_capability() if major >= 8: print("=" * 80) print("Your GPU supports bfloat16: accelerate training with bf16=True") print("=" * 80) # Reload model in FP16 and merge it with LoRA weights base_model = AutoModelForCausalLM.from_pretrained( base_model_name, low_cpu_mem_usage=True, return_dict=True, torch_dtype=torch.float16, quantization_config=bnb_config, device_map=device_map, ) model = PeftModel.from_pretrained(base_model, "Ashishkr/mistral-medical-consultation") model = model.merge_and_unload() # Reload tokenizer to save it tokenizer = AutoTokenizer.from_pretrained(base_model_name, trust_remote_code=True) tokenizer.pad_token = tokenizer.eos_token tokenizer.padding_side = "right" ``` ```python # Run text generation pipeline with the merged model prompt = """ i have a neck pain since 2 days . """ pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer,do_sample = True, temperature = 0.9, max_length=200) response = pipe(f"<s>[INST] {prompt} [/INST]") print(response[0]['generated_text']) ```
borey456/my_awesome_qa_model	borey456	2023-09-28T10:06:39Z	106	0	transformers	[ "transformers", "pytorch", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad", "base_model:distilbert/distilbert-base-multilingual-cased", "base_model:finetune:distilbert/distilbert-base-multilingual-cased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-09-28T09:36:05Z	--- license: apache-2.0 base_model: distilbert-base-multilingual-cased tags: - generated_from_trainer datasets: - squad model-index: - name: my_awesome_qa_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. --> # my_awesome_qa_model This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.8061 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 250 \| 2.2749 \| \| 2.6361 \| 2.0 \| 500 \| 1.8912 \| \| 2.6361 \| 3.0 \| 750 \| 1.8061 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
MarcoMancini/low-law-emb	MarcoMancini	2023-09-28T09:55:02Z	5,443	0	transformers	[ "transformers", "pytorch", "endpoints_compatible", "region:us" ]	null	2023-08-28T08:30:52Z	Found. Redirecting to https://cdn-lfs.hf.co/repos/1a/4d/1a4d4ab1858984b063c6453b1c9583c03ebb210406c2389eadcfc236cddbf228/7f91b71dee029cf890650508c68e62ba4d494adddb8039b458311061d36a28a5?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27README.md%3B+filename%3D%22README.md%22%3B&response-content-type=text%2Fmarkdown&Expires=1739040384&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTczOTA0MDM4NH19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy5oZi5jby9yZXBvcy8xYS80ZC8xYTRkNGFiMTg1ODk4NGIwNjNjNjQ1M2IxYzk1ODNjMDNlYmIyMTA0MDZjMjM4OWVhZGNmYzIzNmNkZGJmMjI4LzdmOTFiNzFkZWUwMjljZjg5MDY1MDUwOGM2OGU2MmJhNGQ0OTRhZGRkYjgwMzliNDU4MzExMDYxZDM2YTI4YTU%7EcmVzcG9uc2UtY29udGVudC1kaXNwb3NpdGlvbj0qJnJlc3BvbnNlLWNvbnRlbnQtdHlwZT0qIn1dfQ__&Signature=a277koRoupo-DEP0Pf-qzpYESYAdswrwyEp6y86kwAIt71oI5BekLd3NAz3rEjtfZpwVbO82nI7ZDWB-Yf8AcfRRYDat%7EJXFsy8NQ7k8rlvVLthh-tkCkUUaHgI70iqB2K0%7EUkDWh2XkjYp9ZbgFN6SUStzDa1%7ENCdMmGByVKdDAjxfHUcPjBS0LJYdQBLOGyswhMXs64NrQSBXkh3bjNTSzwKHN%7EQsdAsqCu7Bj9toFIIoQqzGeNe00tma6gMJidZcJZbycT-rRCtX9IYcLSTCf8aevW1dA29unDuCRowpVCOBhmYCJ5GPqA5udhplsbUyomMQhmY1oOLsARknUIQ__&Key-Pair-Id=K3RPWS32NSSJCE
kakunkakun/lora-trained-xl	kakunkakun	2023-09-28T09:47:14Z	4	1	diffusers	[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "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	2023-09-28T01:00:03Z	--- license: openrail++ base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: tagi tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - kakunkakun/lora-trained-xl These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on tagi using [DreamBooth](https://dreambooth.github.io/). 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) LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
ArchiveAPI/majicMIX-realistic	ArchiveAPI	2023-09-28T09:46:32Z	0	1	null	[ "license:unknown", "region:us" ]	null	2023-09-28T09:25:24Z	--- license: unknown --- # majicMIX realistic #### Model Link: https://civitai.com/models/43331?modelVersionId=160495 #### Credit belongs to the original author. The model here only serves as an API for downloads. # Log - Sep-28-2023: Add v6
Gurusha/sd-hand-model-lora-sdxl2	Gurusha	2023-09-28T09:40:40Z	0	1	diffusers	[ "diffusers", "tensorboard", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-09-27T21:12:13Z	--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-xl-base-1.0 dataset: None tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - Gurusha/sd-hand-model-lora-sdxl2 These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were fine-tuned on the None dataset. You can find some example images in the following. LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
jondurbin/airoboros-c34b-2.2.1	jondurbin	2023-09-28T09:39:42Z	1,437	9	transformers	[ "transformers", "pytorch", "llama", "text-generation", "dataset:jondurbin/airoboros-2.2.1", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-09-19T20:22:58Z	--- license: llama2 datasets: - jondurbin/airoboros-2.2.1 --- ### Overview Another experimental model, using mostly sythetic data generated by [airoboros](https://github.com/jondurbin/airoboros) This is essentially a minor "fix" branch of [airoboros-c34b-2.2](https://hf.co/jondurbin/airoboros-c34b-2.2) with a updates, primarily: - [re-generated writing responses](https://huggingface.co/datasets/jondurbin/airoboros-2.2.1#re-generated-writing-responses) - [longer contextual blocks](https://huggingface.co/datasets/jondurbin/airoboros-2.2.1#longer-contextual-blocks) - [removal of "rp" data](https://huggingface.co/datasets/jondurbin/airoboros-2.2.1#rp-category-removed) - [(less aggressive) de-censoring](https://huggingface.co/datasets/jondurbin/airoboros-2.2.1#de-censoring) - 5 epochs instead of 3 This is a fairly general purpose model, but focuses heavily on instruction following, rather than casual chat/roleplay. Huge thank you to the folks over at [a16z](https://a16z.com/) for sponsoring the costs associated with building models and associated tools! ### Prompt format The prompt format: ``` A chat. USER: {prompt} ASSISTANT: ``` The default system prompt ("A chat.") was used for most of the prompts, however it also included a wide sampling of responses with other prompts, particularly in "stylized\_response", "rp", "gtkm", etc. Here's another example: ``` A chat between Bob (aka USER) and Tom (aka ASSISTANT). Tom is an extremely intelligent 18th century bookkeeper, who speaks loquaciously. USER: {prompt} ASSISTANT: ``` And chat scenario that wouldn't require USER/ASSISTANT (but should use stopping criteria to prevent the model from speaking on your behalf). ``` A chat between old friends: Timmy and Tommy. {description of characters} {setting for the chat} Timmy: takes a big sip from his coffee "Ah, sweet, delicious, magical coffee." Tommy: ``` __I strongly suggest adding stopping criteria/early inference stopping on "USER:", and/or whatever names you specify in the system prompt.__ ### Fine tuning info https://wandb.ai/jondurbin/airoboros-c34b-2.2.1/runs/07b08z7m?workspace=user-jondurbin ### Helpful usage tips The prompts shown here are are just the text that would be included after USER: and before ASSISTANT: in the full prompt format above, the system prompt and USER:/ASSISTANT: have been omited for readability. #### Context obedient question answering By obedient, I mean the model was trained to ignore what it thinks it knows, and uses the context to answer the question. The model was also tuned to limit the values to the provided context as much as possible to reduce hallucinations. The format for a closed-context prompt is as follows: ``` BEGININPUT BEGINCONTEXT [key0: value0] [key1: value1] ... other metdata ... ENDCONTEXT [insert your text blocks here] ENDINPUT [add as many other blocks, in the exact same format] BEGININSTRUCTION [insert your instruction(s). The model was tuned with single questions, paragraph format, lists, etc.] ENDINSTRUCTION ``` It's also helpful to add "Don't make up answers if you don't know." to your instruction block to make sure if the context is completely unrelated it doesn't make something up. The __only__ prompts that need this closed context formating are closed-context instructions. Normal questions/instructions do not! I know it's a bit verbose and annoying, but after much trial and error, using these explicit delimiters helps the model understand where to find the responses and how to associate specific sources with it. - `BEGININPUT` - denotes a new input block - `BEGINCONTEXT` - denotes the block of context (metadata key/value pairs) to associate with the current input block - `ENDCONTEXT` - denotes the end of the metadata block for the current input - [text] - Insert whatever text you want for the input block, as many paragraphs as can fit in the context. - `ENDINPUT` - denotes the end of the current input block - [repeat as many input blocks in this format as you want] - `BEGININSTRUCTION` - denotes the start of the list (or one) instruction(s) to respond to for all of the input blocks above. - [instruction(s)] - `ENDINSTRUCTION` - denotes the end of instruction set It sometimes works without `ENDINSTRUCTION`, but by explicitly including that in the prompt, the model better understands that all of the instructions in the block should be responded to. Here's a trivial, but important example to prove the point: ``` BEGININPUT BEGINCONTEXT date: 2021-01-01 url: https://web.site/123 ENDCONTEXT In a shocking turn of events, blueberries are now green, but will be sticking with the same name. ENDINPUT BEGININSTRUCTION What color are bluberries? Source? ENDINSTRUCTION ``` And the response: ``` Blueberries are now green. Source: date: 2021-01-01 url: https://web.site/123 ``` #### Summarization 500 samples have been included from [this dataset](https://huggingface.co/datasets/mattpscott/airoboros-summarization), using the same format as contextual question answering, for example: ``` BEGININPUT {text to summarize} ENDINPUT BEGININSTRUCTION Summarize the input in around 130 words. ENDINSTRUCTION ``` #### Getting longer responses You can use a few techniques to get longer responses. Detailed prompts, with explicit instruction for word count: ``` Please compose a narrative set in the heart of an ancient library, steeped in the scent of old parchment and ink. The protagonist should be a young scholar who is dedicated to studying the art of storytelling and its evolution throughout history. In her pursuit of knowledge, she stumbles upon a forgotten tome that seems to possess an unusual aura. This book has the ability to bring stories to life, literally manifesting characters and scenarios from within its pages into reality. The main character must navigate through various epochs of storytelling - from oral traditions of tribal societies, through medieval minstrels' tales, to modern-day digital narratives - as they come alive around her. Each era presents its unique challenges and lessons about the power and impact of stories on human civilization. One such character could be a sentient quill pen, who was once used by renowned authors of yesteryears and now holds their wisdom and experiences. It becomes her mentor, guiding her through this journey with witty remarks and insightful commentary. Ensure that your tale encapsulates the thrill of adventure, the beauty of learning, and the profound connection between humans and their stories. All characters involved should be non-human entities. Feel free to explore creative liberties but maintain the mentioned elements. Your response should be approximately 2300 words. ``` Or, a simpler example: ``` Please create a long, detailed story about a dragon in an old growth forest who, for some reason, begins speaking the words of the source code of linux. ``` There are a few examples of next chapter completion as well, e.g.: ``` Write the next chapter of a historical fiction novel set in Paris during the 20th century. Here's a summary of the previous chapter: In the vibrant city of Paris, amid the tumultuous changes of the 20th century, our protagonist Margot, an aspiring fashion designer, has just secured an apprenticeship at a prestigious couture house. She meets Lucien, a charming journalist who covers the fashion industry. Together they navigate the ever-changing world of fashion and society, uncovering secrets that reveal the intricate links between style, politics, and culture. As the chapter concludes, they decide to delve deeper into the hidden corners of the fashion world to unravel its mysteries. Requirements for the next chapter: 1. Character Development of Margot and Lucien: - Margot's Evolution: Unfold more about Margot's past, her dreams of revolutionizing fashion, and her struggle to establish herself in a male-dominated industry. Illustrate her growing expertise, innovative ideas, and increasing dependence on Lucien. - Lucien's Complexity: Introduce uncertainties surrounding Lucien's background and real motives. Increase suspense by suggesting undisclosed information he possesses, while also highlighting his wit and perceptiveness. 2. Exploration of Paris and the Couture House: - Paris: Elaborate their journey through the bustling streets of Paris, including encounters with iconic figures, social unrest, and relics from different eras of French history. - The Couture House: Expand on the grandeur of the couture house they work in, filled with artistic masterpieces, intense competition, and cryptic notes hinting at a scandalous past. 3. Emergence of the Subplot: The Lost Collection: - Discovery: Have Margot and Lucien stumble upon a secret vault containing a lost collection designed before World War II, raising new questions about the previous owner and the influence of war on fashion. - Revelation: Capture their shock as they realize the designs were plagiarized, the potential repercussions, and the opportunities it presents for Margot's career. - Twist: End with a twist that suggests there are other stolen collections across Paris, setting up their new mission. Your response should be approximately 650 words. ``` #### Coding You can ask for fairly complex coding instructions with multiple criteria, e.g.: ``` Create a python application with the following requirements: - Asyncio FastAPI webserver - ping endpoint that returns the current date in JSON format - file upload endpoint, which calculates the file's sha256 checksum, and checks postgres to deduplicate ``` Or inline criteria: ``` Write a multi-threaded TCP server in C that accepts a "GET [key]" input and "SET [key] [value]" input, and uses a binary tree to get and store the input values. ``` You can also optionally add a single space and "PLAINFORMAT" at the end of your prompt to avoid backticks, explanations, etc. and just print the code, e.g.: ``` Write a websocket application in node.js. PLAINFORMAT ``` #### Agent/function calling The dataset includes many examples of function/args generation based on input criteria. This is somewhat similar to the OpenAI function calling, but the output is either JSON or YAML. Example prompt: ``` As an AI assistant, please select the most suitable function and parameters from the list of available functions below, based on the user's input. Provide your response in JSON format. Input: I want to know how many times 'Python' is mentioned in my text file. Available functions: file_analytics: description: This tool performs various operations on a text file. params: action: The operation we want to perform on the data, such as "count_occurrences", "find_line", etc. filters: keyword: The word or phrase we want to search for. ``` Response: ```json { "function": "file_analytics", "params": { "action": "count_occurrences", "filters": { "keyword": "Python" } } } ``` #### Chain-of-thought You can ask for several possible responses to a given problem, with a ranking and final answer selection. Example prompt: ``` A group of people decide to play a round-robin tournament where every player plays every other player exactly once. If a total of 45 games were played, how many players participated in the tournament? Offer several potential answers, rank them according to mathematical logic, and pick the most feasible one. ``` Example response: ``` Solution 1: In a round-robin tournament, each player plays every other player exactly once. This means that the total number of games played is equal to the combination of all players taken two at a time (since a game involves two players). The formula for combinations is nC2 = n(n-1)/2, where n is the total number of players. We know that nC2 equals 45 (the total number of games), so we can set up the equation as follows: n(n-1)/2 = 45 Solving this equation gives us n=10. Final answer: There were 10 players in the tournament. Solution 2: Another way to approach this problem is by using the concept of triangular numbers. Triangular numbers are a sequence of numbers where each number can be represented as a triangle with dots. The nth triangular number is given by the sum of the first n natural numbers. If we consider each game as a dot and each player as a side of the triangle, then the total number of games (45) would correspond to the 9th triangular number because 1+2+3+4+5+6+7+8+9=45. However, since each side of the triangle represents a player, and there's one more player than sides in our model (because the last player has no one left to play against), we need to add one more to get the total number of players. Final answer: There were 10 players in the tournament. Solution 3: We could also use trial and error to solve this problem. Starting from a small number of players, we calculate how many games would be played in a round-robin tournament until we reach 45 games. For example, with 2 players, only 1 game is played; with 3 players, 3 games are played; with 4 players, 6 games are played, and so on. Continuing this process, we find that with 10 players, 45 games are played. Final answer: There were 10 players in the tournament. Ranking of solutions from best to worst: 1. Solution 1: This solution uses direct mathematical reasoning based on the properties of a round-robin tournament and solves an equation to find the answer. 2. Solution 2: This solution uses the concept of triangular numbers which requires additional steps and knowledge beyond the immediate problem context, but still arrives at the correct answer. 3. Solution 3: This solution relies on trial and error, which is less efficient and reliable than the other methods, but it eventually gets to the right answer. Best and final answer: There were 10 players in the tournament. ``` #### reWOO style execution planning The model now supports execution planning for complex instructions that would require making use of several tools. The output is just the plan, you must implement a mechanism to parse the output and actually call the functions! Example prompt: ``` Please construct a systematic plan to generate an optimal response to the user instruction, utilizing a set of provided tools. Each plan will correspond to an evidence value, which will be the output of one of the available functions given an input string that could be the user's question, one or more prior evidence values, or a combination of both. Here are the tools available to be called: DuckDuckGo[input]: Tool that enables the user to search for information using the DuckDuckGo search engine. This tool is helpful in locating a suitable list of sites that may contain the answer to the user's query. It does not directly answer the question but finds a list of sites that might have the answer. TextScraper[input]: Load one or more websites from the input string containing newline delimited links, where input is one or more links, and produces plain text output containing the content of the links. HyperlinkExtractor[input]: Extract links from plain text and produces a plain text, newline delimited response of links. KnowledgeModel[input]: Question answering language model, particularly useful in answering questions based on an input passage of text. The input must be a text question that references an :evidence[n]: variable, e.g. What color is the cat, given :evidence1:? The output should be in format: Plan: [first action to take based on input question] :evidence0: = [function to call with input parameter] Plan: [next action to take, based on result of :evidence0:] :evidence1: = [next function to call and input parameter, which may include reference to previous evidence, e.g. "Given context :evidence0"] ... Answer: [:evidence[n]: containing the final answer.] Question: Who won the Nobel Prize in Literature in 2020 and what is their most famous work? ``` Response: ``` Plan: Begin by conducting a web search to find out who won the Nobel Prize in Literature in 2020. :evidence0: = DuckDuckGo[Nobel Prize in Literature 2020 winner] Plan: Utilize the KnowledgeModel to interpret the search results and find the name of the laureate. :evidence1: = KnowledgeModel[Who won the Nobel Prize in Literature in 2020, given :evidence0:?] Plan: Conduct another web search to find the most famous work of the identified laureate. :evidence2: = DuckDuckGo[Most famous work of :evidence1:] Plan: Extract the relevant links from the DuckDuckGo search results for a more focused search. :evidence3: = HyperlinkExtractor[:evidence2:] Plan: Use the TextScraper tool to extract information from the relevant links. :evidence4: = TextScraper[:evidence3:] Plan: Finally, utilize the KnowledgeModel to identify and summarize the most famous work of the laureate from the extracted information. :evidence5: = KnowledgeModel[What is the most famous work of :evidence1:, given :evidence4:?] Answer: :evidence5: ``` For this to be useful, you'd have to parse the output plan text, and implement/call each of the functions. This is just pseudo-code, completely untested off the top of my head, and obviously would requiring full implementation + hardening: ```python import re import requests def inject_context(input_text, context): for ref in set(re.findall(r"(:evidence[0-9]+:)", input_text, re.I)): input_text = input_text.replace(ref, context.get(ref, "")) return input_text def duckduckgo(input_text, context): search_string = inject_context(input_text, context) ... search via duck duck go using search_string ... return text content def link_extractor(input_text, context): input_text = inject_context(input_text, context) return "\n".join(list(set(re.findall(r"(https?://[^\s]+?\.?)", input_text, re.I)))) def scrape(input_text, context): input_text = inject_context(input_text, context) text = [] for link in input_text.splitlines(): text.append(requests.get(link).text) return "\n".join(text) def infer(input_text, context) prompt = inject_context(input_text, *context) ... call model with prompt, return output def parse_plan(plan): method_map = { "DuckDuckGo": duckduckgo, "HyperlinkExtractor": link_extractor, "KnowledgeModel": infer, "TextScraper": scrape, } context = {} for line in plan.strip().splitlines(): if line.startswith("Plan:"): print(line) continue parts = re.match("^(:evidence[0-9]+:)\s=\s([^\[]+])(\[.\])\s$", line, re.I) if not parts: if line.startswith("Answer: "): return context.get(line.split(" ")[-1].strip(), "Answer couldn't be generated...") raise RuntimeError("bad format: " + line) context[parts.group(1)] = method_map[parts.group(2)](parts.group(3), *context) ``` ### Contribute If you're interested in new functionality, particularly a new "instructor" type to generate a specific type of training data, take a look at the dataset generation tool repo: https://github.com/jondurbin/airoboros and either make a PR or open an issue with details. To help me with the OpenAI/compute costs: - https://bmc.link/jondurbin - ETH 0xce914eAFC2fe52FdceE59565Dd92c06f776fcb11 - BTC bc1qdwuth4vlg8x37ggntlxu5cjfwgmdy5zaa7pswf ### Licence and usage restrictions The airoboros 2.2 models are built on top of llama-2/codellama. The llama-2 base model has a custom Meta license: - See the [meta-license/LICENSE.txt](meta-license/LICENSE.txt) file attached for the original license provided by Meta. - See also [meta-license/USE_POLICY.md](meta-license/USE_POLICY.md) and [meta-license/Responsible-Use-Guide.pdf](meta-license/Responsible-Use-Guide.pdf), also provided by Meta. The fine-tuning data was mostly generated by OpenAI API calls to gpt-4, via [airoboros](https://github.com/jondurbin/airoboros) The ToS for OpenAI API usage has a clause preventing the output from being used to train a model that __competes__ with OpenAI - what does compete* actually mean here? - these small open source models will not produce output anywhere near the quality of gpt-4, or even gpt-3.5, so I can't imagine this could credibly be considered competing in the first place - if someone else uses the dataset to do the same, they wouldn't necessarily be violating the ToS because they didn't call the API, so I don't know how that works - the training data used in essentially all large language models includes a significant amount of copyrighted or otherwise non-permissive licensing in the first place - other work using the self-instruct method, e.g. the original here: https://github.com/yizhongw/self-instruct released the data and model as apache-2 I am purposingly leaving this license ambiguous (other than the fact you must comply with the Meta original license for llama-2) because I am not a lawyer and refuse to attempt to interpret all of the terms accordingly. Your best bet is probably to avoid using this commercially due to the OpenAI API usage. Either way, by using this model, you agree to completely indemnify me.
CyberHarem/koito_yuu_yagatekimininaru	CyberHarem	2023-09-28T09:38:20Z	0	0	null	[ "art", "text-to-image", "dataset:CyberHarem/koito_yuu_yagatekimininaru", "license:mit", "region:us" ]	text-to-image	2023-09-28T09:24:17Z	--- license: mit datasets: - CyberHarem/koito_yuu_yagatekimininaru pipeline_tag: text-to-image tags: - art --- # Lora of koito_yuu_yagatekimininaru This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 5600, you need to download `5600/koito_yuu_yagatekimininaru.pt` as the embedding and `5600/koito_yuu_yagatekimininaru.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. The best step we recommend is 5600, with the score of 0.986. The trigger words are: 1. `koito_yuu_yagatekimininaru` 2. `twintails, short_twintails, low_twintails, brown_eyes, ahoge, pink_hair, closed_mouth, yellow_bow, short_hair, yellow_ribbon, bangs, bow, ribbon, yellow_bowtie, hair_between_eyes, orange_hair` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: \| Steps \| Score \| Download \| pattern_1 \| pattern_2 \| pattern_3 \| pattern_4 \| pattern_5 \| pattern_6 \| pattern_7 \| pattern_8 \| pattern_9 \| bikini \| bondage \| free \| maid \| miko \| nude \| nude2 \| suit \| yukata \| \|:---------\|:----------\|:----------------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------\|:--------------------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-----------------------------------------------\|:------------------------------------------------\|:-------------------------------------\|:-----------------------------------------\| \| 8400 \| 0.982 \| [Download](8400/koito_yuu_yagatekimininaru.zip) \| ![pattern_1-8400](8400/previews/pattern_1.png) \| ![pattern_2-8400](8400/previews/pattern_2.png) \| ![pattern_3-8400](8400/previews/pattern_3.png) \| ![pattern_4-8400](8400/previews/pattern_4.png) \| ![pattern_5-8400](8400/previews/pattern_5.png) \| ![pattern_6-8400](8400/previews/pattern_6.png) \| ![pattern_7-8400](8400/previews/pattern_7.png) \| ![pattern_8-8400](8400/previews/pattern_8.png) \| ![pattern_9-8400](8400/previews/pattern_9.png) \| ![bikini-8400](8400/previews/bikini.png) \| [<NSFW, click to see>](8400/previews/bondage.png) \| ![free-8400](8400/previews/free.png) \| ![maid-8400](8400/previews/maid.png) \| ![miko-8400](8400/previews/miko.png) \| [<NSFW, click to see>](8400/previews/nude.png) \| [<NSFW, click to see>](8400/previews/nude2.png) \| ![suit-8400](8400/previews/suit.png) \| ![yukata-8400](8400/previews/yukata.png) \| \| 7840 \| 0.973 \| [Download](7840/koito_yuu_yagatekimininaru.zip) \| ![pattern_1-7840](7840/previews/pattern_1.png) \| ![pattern_2-7840](7840/previews/pattern_2.png) \| ![pattern_3-7840](7840/previews/pattern_3.png) \| ![pattern_4-7840](7840/previews/pattern_4.png) \| ![pattern_5-7840](7840/previews/pattern_5.png) \| ![pattern_6-7840](7840/previews/pattern_6.png) \| ![pattern_7-7840](7840/previews/pattern_7.png) \| ![pattern_8-7840](7840/previews/pattern_8.png) \| ![pattern_9-7840](7840/previews/pattern_9.png) \| ![bikini-7840](7840/previews/bikini.png) \| [<NSFW, click to see>](7840/previews/bondage.png) \| ![free-7840](7840/previews/free.png) \| ![maid-7840](7840/previews/maid.png) \| ![miko-7840](7840/previews/miko.png) \| [<NSFW, click to see>](7840/previews/nude.png) \| [<NSFW, click to see>](7840/previews/nude2.png) \| ![suit-7840](7840/previews/suit.png) \| ![yukata-7840](7840/previews/yukata.png) \| \| 7280 \| 0.979 \| [Download](7280/koito_yuu_yagatekimininaru.zip) \| ![pattern_1-7280](7280/previews/pattern_1.png) \| ![pattern_2-7280](7280/previews/pattern_2.png) \| ![pattern_3-7280](7280/previews/pattern_3.png) \| ![pattern_4-7280](7280/previews/pattern_4.png) \| ![pattern_5-7280](7280/previews/pattern_5.png) \| ![pattern_6-7280](7280/previews/pattern_6.png) \| ![pattern_7-7280](7280/previews/pattern_7.png) \| ![pattern_8-7280](7280/previews/pattern_8.png) \| ![pattern_9-7280](7280/previews/pattern_9.png) \| ![bikini-7280](7280/previews/bikini.png) \| [<NSFW, click to see>](7280/previews/bondage.png) \| ![free-7280](7280/previews/free.png) \| ![maid-7280](7280/previews/maid.png) \| ![miko-7280](7280/previews/miko.png) \| [<NSFW, click to see>](7280/previews/nude.png) \| [<NSFW, click to see>](7280/previews/nude2.png) \| ![suit-7280](7280/previews/suit.png) \| ![yukata-7280](7280/previews/yukata.png) \| \| 6720 \| 0.982 \| [Download](6720/koito_yuu_yagatekimininaru.zip) \| ![pattern_1-6720](6720/previews/pattern_1.png) \| ![pattern_2-6720](6720/previews/pattern_2.png) \| ![pattern_3-6720](6720/previews/pattern_3.png) \| ![pattern_4-6720](6720/previews/pattern_4.png) \| ![pattern_5-6720](6720/previews/pattern_5.png) \| ![pattern_6-6720](6720/previews/pattern_6.png) \| ![pattern_7-6720](6720/previews/pattern_7.png) \| ![pattern_8-6720](6720/previews/pattern_8.png) \| ![pattern_9-6720](6720/previews/pattern_9.png) \| ![bikini-6720](6720/previews/bikini.png) \| [<NSFW, click to see>](6720/previews/bondage.png) \| ![free-6720](6720/previews/free.png) \| ![maid-6720](6720/previews/maid.png) \| ![miko-6720](6720/previews/miko.png) \| [<NSFW, click to see>](6720/previews/nude.png) \| [<NSFW, click to see>](6720/previews/nude2.png) \| ![suit-6720](6720/previews/suit.png) \| ![yukata-6720](6720/previews/yukata.png) \| \| 6160 \| 0.979 \| [Download](6160/koito_yuu_yagatekimininaru.zip) \| ![pattern_1-6160](6160/previews/pattern_1.png) \| ![pattern_2-6160](6160/previews/pattern_2.png) \| ![pattern_3-6160](6160/previews/pattern_3.png) \| ![pattern_4-6160](6160/previews/pattern_4.png) \| ![pattern_5-6160](6160/previews/pattern_5.png) \| ![pattern_6-6160](6160/previews/pattern_6.png) \| ![pattern_7-6160](6160/previews/pattern_7.png) \| ![pattern_8-6160](6160/previews/pattern_8.png) \| ![pattern_9-6160](6160/previews/pattern_9.png) \| ![bikini-6160](6160/previews/bikini.png) \| [<NSFW, click to see>](6160/previews/bondage.png) \| ![free-6160](6160/previews/free.png) \| ![maid-6160](6160/previews/maid.png) \| ![miko-6160](6160/previews/miko.png) \| [<NSFW, click to see>](6160/previews/nude.png) \| [<NSFW, click to see>](6160/previews/nude2.png) \| ![suit-6160](6160/previews/suit.png) \| ![yukata-6160](6160/previews/yukata.png) \| \| 5600 \| 0.986 \| [Download](5600/koito_yuu_yagatekimininaru.zip) \| ![pattern_1-5600](5600/previews/pattern_1.png) \| ![pattern_2-5600](5600/previews/pattern_2.png) \| ![pattern_3-5600](5600/previews/pattern_3.png) \| ![pattern_4-5600](5600/previews/pattern_4.png) \| ![pattern_5-5600](5600/previews/pattern_5.png) \| ![pattern_6-5600](5600/previews/pattern_6.png) \| ![pattern_7-5600](5600/previews/pattern_7.png) \| ![pattern_8-5600](5600/previews/pattern_8.png) \| ![pattern_9-5600](5600/previews/pattern_9.png) \| ![bikini-5600](5600/previews/bikini.png) \| [<NSFW, click to see>](5600/previews/bondage.png) \| ![free-5600](5600/previews/free.png) \| ![maid-5600](5600/previews/maid.png) \| ![miko-5600](5600/previews/miko.png) \| [<NSFW, click to see>](5600/previews/nude.png) \| [<NSFW, click to see>](5600/previews/nude2.png) \| ![suit-5600](5600/previews/suit.png) \| ![yukata-5600](5600/previews/yukata.png) \| \| 5040 \| 0.970 \| [Download](5040/koito_yuu_yagatekimininaru.zip) \| ![pattern_1-5040](5040/previews/pattern_1.png) \| ![pattern_2-5040](5040/previews/pattern_2.png) \| ![pattern_3-5040](5040/previews/pattern_3.png) \| ![pattern_4-5040](5040/previews/pattern_4.png) \| ![pattern_5-5040](5040/previews/pattern_5.png) \| ![pattern_6-5040](5040/previews/pattern_6.png) \| ![pattern_7-5040](5040/previews/pattern_7.png) \| ![pattern_8-5040](5040/previews/pattern_8.png) \| ![pattern_9-5040](5040/previews/pattern_9.png) \| ![bikini-5040](5040/previews/bikini.png) \| [<NSFW, click to see>](5040/previews/bondage.png) \| ![free-5040](5040/previews/free.png) \| ![maid-5040](5040/previews/maid.png) \| ![miko-5040](5040/previews/miko.png) \| [<NSFW, click to see>](5040/previews/nude.png) \| [<NSFW, click to see>](5040/previews/nude2.png) \| ![suit-5040](5040/previews/suit.png) \| ![yukata-5040](5040/previews/yukata.png) \| \| 4480 \| 0.978 \| [Download](4480/koito_yuu_yagatekimininaru.zip) \| ![pattern_1-4480](4480/previews/pattern_1.png) \| ![pattern_2-4480](4480/previews/pattern_2.png) \| ![pattern_3-4480](4480/previews/pattern_3.png) \| ![pattern_4-4480](4480/previews/pattern_4.png) \| ![pattern_5-4480](4480/previews/pattern_5.png) \| ![pattern_6-4480](4480/previews/pattern_6.png) \| ![pattern_7-4480](4480/previews/pattern_7.png) \| ![pattern_8-4480](4480/previews/pattern_8.png) \| ![pattern_9-4480](4480/previews/pattern_9.png) \| ![bikini-4480](4480/previews/bikini.png) \| [<NSFW, click to see>](4480/previews/bondage.png) \| ![free-4480](4480/previews/free.png) \| ![maid-4480](4480/previews/maid.png) \| ![miko-4480](4480/previews/miko.png) \| [<NSFW, click to see>](4480/previews/nude.png) \| [<NSFW, click to see>](4480/previews/nude2.png) \| ![suit-4480](4480/previews/suit.png) \| ![yukata-4480](4480/previews/yukata.png) \| \| 3920 \| 0.971 \| [Download](3920/koito_yuu_yagatekimininaru.zip) \| ![pattern_1-3920](3920/previews/pattern_1.png) \| ![pattern_2-3920](3920/previews/pattern_2.png) \| ![pattern_3-3920](3920/previews/pattern_3.png) \| ![pattern_4-3920](3920/previews/pattern_4.png) \| ![pattern_5-3920](3920/previews/pattern_5.png) \| ![pattern_6-3920](3920/previews/pattern_6.png) \| ![pattern_7-3920](3920/previews/pattern_7.png) \| ![pattern_8-3920](3920/previews/pattern_8.png) \| ![pattern_9-3920](3920/previews/pattern_9.png) \| ![bikini-3920](3920/previews/bikini.png) \| [<NSFW, click to see>](3920/previews/bondage.png) \| ![free-3920](3920/previews/free.png) \| ![maid-3920](3920/previews/maid.png) \| ![miko-3920](3920/previews/miko.png) \| [<NSFW, click to see>](3920/previews/nude.png) \| [<NSFW, click to see>](3920/previews/nude2.png) \| ![suit-3920](3920/previews/suit.png) \| ![yukata-3920](3920/previews/yukata.png) \| \| 3360 \| 0.971 \| [Download](3360/koito_yuu_yagatekimininaru.zip) \| ![pattern_1-3360](3360/previews/pattern_1.png) \| ![pattern_2-3360](3360/previews/pattern_2.png) \| ![pattern_3-3360](3360/previews/pattern_3.png) \| ![pattern_4-3360](3360/previews/pattern_4.png) \| ![pattern_5-3360](3360/previews/pattern_5.png) \| ![pattern_6-3360](3360/previews/pattern_6.png) \| ![pattern_7-3360](3360/previews/pattern_7.png) \| ![pattern_8-3360](3360/previews/pattern_8.png) \| ![pattern_9-3360](3360/previews/pattern_9.png) \| ![bikini-3360](3360/previews/bikini.png) \| [<NSFW, click to see>](3360/previews/bondage.png) \| ![free-3360](3360/previews/free.png) \| ![maid-3360](3360/previews/maid.png) \| ![miko-3360](3360/previews/miko.png) \| [<NSFW, click to see>](3360/previews/nude.png) \| [<NSFW, click to see>](3360/previews/nude2.png) \| ![suit-3360](3360/previews/suit.png) \| ![yukata-3360](3360/previews/yukata.png) \| \| 2800 \| 0.964 \| [Download](2800/koito_yuu_yagatekimininaru.zip) \| ![pattern_1-2800](2800/previews/pattern_1.png) \| ![pattern_2-2800](2800/previews/pattern_2.png) \| ![pattern_3-2800](2800/previews/pattern_3.png) \| ![pattern_4-2800](2800/previews/pattern_4.png) \| ![pattern_5-2800](2800/previews/pattern_5.png) \| ![pattern_6-2800](2800/previews/pattern_6.png) \| ![pattern_7-2800](2800/previews/pattern_7.png) \| ![pattern_8-2800](2800/previews/pattern_8.png) \| ![pattern_9-2800](2800/previews/pattern_9.png) \| ![bikini-2800](2800/previews/bikini.png) \| [<NSFW, click to see>](2800/previews/bondage.png) \| ![free-2800](2800/previews/free.png) \| ![maid-2800](2800/previews/maid.png) \| ![miko-2800](2800/previews/miko.png) \| [<NSFW, click to see>](2800/previews/nude.png) \| [<NSFW, click to see>](2800/previews/nude2.png) \| ![suit-2800](2800/previews/suit.png) \| ![yukata-2800](2800/previews/yukata.png) \| \| 2240 \| 0.960 \| [Download](2240/koito_yuu_yagatekimininaru.zip) \| ![pattern_1-2240](2240/previews/pattern_1.png) \| ![pattern_2-2240](2240/previews/pattern_2.png) \| ![pattern_3-2240](2240/previews/pattern_3.png) \| ![pattern_4-2240](2240/previews/pattern_4.png) \| ![pattern_5-2240](2240/previews/pattern_5.png) \| ![pattern_6-2240](2240/previews/pattern_6.png) \| ![pattern_7-2240](2240/previews/pattern_7.png) \| ![pattern_8-2240](2240/previews/pattern_8.png) \| ![pattern_9-2240](2240/previews/pattern_9.png) \| ![bikini-2240](2240/previews/bikini.png) \| [<NSFW, click to see>](2240/previews/bondage.png) \| ![free-2240](2240/previews/free.png) \| ![maid-2240](2240/previews/maid.png) \| ![miko-2240](2240/previews/miko.png) \| [<NSFW, click to see>](2240/previews/nude.png) \| [<NSFW, click to see>](2240/previews/nude2.png) \| ![suit-2240](2240/previews/suit.png) \| ![yukata-2240](2240/previews/yukata.png) \| \| 1680 \| 0.953 \| [Download](1680/koito_yuu_yagatekimininaru.zip) \| ![pattern_1-1680](1680/previews/pattern_1.png) \| ![pattern_2-1680](1680/previews/pattern_2.png) \| ![pattern_3-1680](1680/previews/pattern_3.png) \| ![pattern_4-1680](1680/previews/pattern_4.png) \| ![pattern_5-1680](1680/previews/pattern_5.png) \| ![pattern_6-1680](1680/previews/pattern_6.png) \| ![pattern_7-1680](1680/previews/pattern_7.png) \| ![pattern_8-1680](1680/previews/pattern_8.png) \| ![pattern_9-1680](1680/previews/pattern_9.png) \| ![bikini-1680](1680/previews/bikini.png) \| [<NSFW, click to see>](1680/previews/bondage.png) \| ![free-1680](1680/previews/free.png) \| ![maid-1680](1680/previews/maid.png) \| ![miko-1680](1680/previews/miko.png) \| [<NSFW, click to see>](1680/previews/nude.png) \| [<NSFW, click to see>](1680/previews/nude2.png) \| ![suit-1680](1680/previews/suit.png) \| ![yukata-1680](1680/previews/yukata.png) \| \| 1120 \| 0.955 \| [Download](1120/koito_yuu_yagatekimininaru.zip) \| ![pattern_1-1120](1120/previews/pattern_1.png) \| ![pattern_2-1120](1120/previews/pattern_2.png) \| ![pattern_3-1120](1120/previews/pattern_3.png) \| ![pattern_4-1120](1120/previews/pattern_4.png) \| ![pattern_5-1120](1120/previews/pattern_5.png) \| ![pattern_6-1120](1120/previews/pattern_6.png) \| ![pattern_7-1120](1120/previews/pattern_7.png) \| ![pattern_8-1120](1120/previews/pattern_8.png) \| ![pattern_9-1120](1120/previews/pattern_9.png) \| ![bikini-1120](1120/previews/bikini.png) \| [<NSFW, click to see>](1120/previews/bondage.png) \| ![free-1120](1120/previews/free.png) \| ![maid-1120](1120/previews/maid.png) \| ![miko-1120](1120/previews/miko.png) \| [<NSFW, click to see>](1120/previews/nude.png) \| [<NSFW, click to see>](1120/previews/nude2.png) \| ![suit-1120](1120/previews/suit.png) \| ![yukata-1120](1120/previews/yukata.png) \| \| 560 \| 0.945 \| [Download](560/koito_yuu_yagatekimininaru.zip) \| ![pattern_1-560](560/previews/pattern_1.png) \| ![pattern_2-560](560/previews/pattern_2.png) \| ![pattern_3-560](560/previews/pattern_3.png) \| ![pattern_4-560](560/previews/pattern_4.png) \| ![pattern_5-560](560/previews/pattern_5.png) \| ![pattern_6-560](560/previews/pattern_6.png) \| ![pattern_7-560](560/previews/pattern_7.png) \| ![pattern_8-560](560/previews/pattern_8.png) \| ![pattern_9-560](560/previews/pattern_9.png) \| ![bikini-560](560/previews/bikini.png) \| [<NSFW, click to see>](560/previews/bondage.png) \| ![free-560](560/previews/free.png) \| ![maid-560](560/previews/maid.png) \| ![miko-560](560/previews/miko.png) \| [<NSFW, click to see>](560/previews/nude.png) \| [<NSFW, click to see>](560/previews/nude2.png) \| ![suit-560](560/previews/suit.png) \| ![yukata-560](560/previews/yukata.png) \|
BlueSunflower/Pythia-70M-chess	BlueSunflower	2023-09-28T09:31:56Z	149	0	transformers	[ "transformers", "pytorch", "gpt_neox", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-09-28T09:16:13Z	Pythia 70M model trained on 50M chess games from lichess Example context format: Score: 0-1 WhiteElo: 2750 BlackElo: 2850 Timecontrol: 1800+0 Moves: 1. e4 Model results: Estimated ELO: 1100 Legal moves: 97% --- license: apache-2.0 ---
vaibhav9/hangman-bert-base-2	vaibhav9	2023-09-28T09:04:22Z	177	0	transformers	[ "transformers", "pytorch", "bert", "fill-mask", "generated_from_trainer", "base_model:vaibhav9/hangman-bert-base-2", "base_model:finetune:vaibhav9/hangman-bert-base-2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-09-28T03:00:47Z	--- license: apache-2.0 base_model: vaibhav9/hangman-bert-base-2 tags: - generated_from_trainer model-index: - name: hangman-bert-base-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. --> # hangman-bert-base-2 This model is a fine-tuned version of [vaibhav9/hangman-bert-base-2](https://huggingface.co/vaibhav9/hangman-bert-base-2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0054 ## 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: 32 - eval_batch_size: 32 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.0245 \| 1.0 \| 2609 \| 2.0118 \| \| 2.0108 \| 2.0 \| 5218 \| 2.0105 \| \| 2.0092 \| 3.0 \| 7827 \| 2.0054 \| ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1 - Datasets 2.14.5 - Tokenizers 0.13.3
gtxygyzb/mt5-small-finetuned-amazon-en-es	gtxygyzb	2023-09-28T09:03:23Z	13	0	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "summarization", "generated_from_trainer", "base_model:google/mt5-small", "base_model:finetune:google/mt5-small", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2023-09-28T08:03:02Z	--- license: apache-2.0 base_model: google/mt5-small tags: - summarization - generated_from_trainer metrics: - rouge model-index: - name: mt5-small-finetuned-amazon-en-es 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-finetuned-amazon-en-es 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: 3.0349 - Rouge1: 17.1191 - Rouge2: 8.4119 - Rougel: 16.6388 - Rougelsum: 16.6017 ## 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: 5.6e-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: 8 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:------:\|:-------:\|:---------:\| \| 7.3401 \| 1.0 \| 1209 \| 3.3465 \| 14.1778 \| 6.284 \| 13.8905 \| 13.8872 \| \| 3.9195 \| 2.0 \| 2418 \| 3.1859 \| 15.9786 \| 8.1666 \| 15.3933 \| 15.3693 \| \| 3.5975 \| 3.0 \| 3627 \| 3.0945 \| 17.5518 \| 9.134 \| 16.9215 \| 16.8899 \| \| 3.4241 \| 4.0 \| 4836 \| 3.0913 \| 16.3875 \| 7.6999 \| 15.8311 \| 15.8004 \| \| 3.3252 \| 5.0 \| 6045 \| 3.0588 \| 16.6777 \| 8.1639 \| 16.1058 \| 16.1357 \| \| 3.2442 \| 6.0 \| 7254 \| 3.0444 \| 17.141 \| 8.4204 \| 16.6366 \| 16.6896 \| \| 3.2149 \| 7.0 \| 8463 \| 3.0355 \| 17.3266 \| 8.7249 \| 16.9438 \| 16.9154 \| \| 3.184 \| 8.0 \| 9672 \| 3.0349 \| 17.1191 \| 8.4119 \| 16.6388 \| 16.6017 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
roa7n/gpt2-human_nontata_promoters-randomized_5_layers_3e-05_lr_8_e	roa7n	2023-09-28T08:57:52Z	0	0	peft	[ "peft", "region:us" ]	null	2023-09-28T08:57:49Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
illuin/tiny-random-MistralForCausalLM-lora	illuin	2023-09-28T08:53:48Z	1	1	peft	[ "peft", "region:us" ]	null	2023-09-28T08:53:27Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0
hw2942/chinese-pert-base-SSE50	hw2942	2023-09-28T08:34:59Z	106	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "base_model:hfl/chinese-pert-base", "base_model:finetune:hfl/chinese-pert-base", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-09-28T08:28:40Z	--- license: cc-by-nc-sa-4.0 base_model: hfl/chinese-pert-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: chinese-pert-base-wallstreetcn-morning-news-market-overview-SSE50-1 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. --> # chinese-pert-base-wallstreetcn-morning-news-market-overview-SSE50-1 This model is a fine-tuned version of [hfl/chinese-pert-base](https://huggingface.co/hfl/chinese-pert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5854 - Accuracy: 0.7879 ## 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 34 \| 0.6936 \| 0.4242 \| \| No log \| 2.0 \| 68 \| 0.6755 \| 0.6061 \| \| No log \| 3.0 \| 102 \| 0.6629 \| 0.6667 \| \| No log \| 4.0 \| 136 \| 0.5120 \| 0.7879 \| \| No log \| 5.0 \| 170 \| 0.4575 \| 0.8182 \| \| No log \| 6.0 \| 204 \| 0.5254 \| 0.7576 \| \| No log \| 7.0 \| 238 \| 0.5460 \| 0.7879 \| \| No log \| 8.0 \| 272 \| 0.5277 \| 0.8182 \| \| No log \| 9.0 \| 306 \| 0.5906 \| 0.7273 \| \| No log \| 10.0 \| 340 \| 0.5854 \| 0.7879 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
junjuice0/VOXO-v0-4	junjuice0	2023-09-28T08:26:25Z	0	1	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-09-28T08:26:25Z	--- license: creativeml-openrail-m ---
dhrf/lora-Llama-2-7b-hf-qa-1epoch	dhrf	2023-09-28T08:06:57Z	0	1	peft	[ "peft", "region:us" ]	null	2023-09-28T07:51:26Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - 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 The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - 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 0.5.0 - PEFT 0.5.0
ciaranmacseoin/llama-2-7b-sent	ciaranmacseoin	2023-09-28T08:06:17Z	4	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-09-21T12:22:25Z	Utilized Google Colab to fine-tune the advanced LLamma 7b model for enhanced sentiment classification. Curated a representative dataset from diverse content like reviews and social media posts. The refined model showcased a notable improvement in classification accuracy, surpassing multiple baseline models. Tools: Python, LLamma 7b, Google Colab. Outcomes: An adept LLamma 7b model tailored for nuanced sentiment insights across varied textual data.
vaiv/kobigbird-roberta-large	vaiv	2023-09-28T08:01:49Z	1,065	4	transformers	[ "transformers", "pytorch", "safetensors", "big_bird", "fill-mask", "korean", "ko", "arxiv:2309.10339", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-09-06T06:42:48Z	--- license: cc-by-sa-4.0 language: - ko tags: - korean --- # KoBigBird-RoBERTa-large This is a large-sized Korean BigBird model introduced in our [paper](https://arxiv.org/abs/2309.10339). The model draws heavily from the parameters of [klue/roberta-large](https://huggingface.co/klue/roberta-large) to ensure high performance. By employing the BigBird architecture and incorporating the newly proposed TAPER, the language model accommodates even longer input lengths. ### How to Use ```python from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("vaiv/kobigbird-roberta-large") model = AutoModelForMaskedLM.from_pretrained("vaiv/kobigbird-roberta-large") ``` ### Hyperparameters ![image/png](https://cdn-uploads.huggingface.co/production/uploads/62ce3886a9be5c195564fd71/bhuidw3bNQZbE2tzVcZw_.png) ### Results Measurement on validation sets of the KLUE benchmark datasets ![image/png](https://cdn-uploads.huggingface.co/production/uploads/62ce3886a9be5c195564fd71/50jMYggkGVUM06n2v1Hxm.png) ### Limitations While our model achieves great results even without additional pretraining, further pretraining can refine the positional representations more. ## Citation Information ```bibtex @article{yang2023kobigbird, title={KoBigBird-large: Transformation of Transformer for Korean Language Understanding}, author={Yang, Kisu and Jang, Yoonna and Lee, Taewoo and Seong, Jinwoo and Lee, Hyungjin and Jang, Hwanseok and Lim, Heuiseok}, journal={arXiv preprint arXiv:2309.10339}, year={2023} } ```
YL95/naive_chunk0	YL95	2023-09-28T07:57:39Z	0	0	peft	[ "peft", "tensorboard", "region:us" ]	null	2023-09-28T07:56:33Z	--- 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 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 - PEFT 0.6.0.dev0
TexR6/q-FrozenLake-v1-4x4-noSlippery	TexR6	2023-09-28T07:57:19Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-09-28T07:57:16Z	--- 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="TexR6/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"]) ```
samuelleecong/speecht5_finetuned_swahili	samuelleecong	2023-09-28T07:48:36Z	82	0	transformers	[ "transformers", "pytorch", "speecht5", "text-to-audio", "generated_from_trainer", "text-to-speech", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]	text-to-speech	2023-09-28T04:45:29Z	--- license: mit base_model: microsoft/speecht5_tts tags: - generated_from_trainer model-index: - name: speecht5_finetuned_swahili results: [] pipeline_tag: text-to-speech --- <!-- 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. --> # speecht5_finetuned_swahili This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the A kiswahili Dataset for Development of Text-To-Speech System dataset. It achieves the following results on the evaluation set: - eval_loss: 0.4618 - eval_runtime: 11.2006 - eval_samples_per_second: 54.015 - eval_steps_per_second: 27.052 - epoch: 11.76 - step: 2000 ## 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: 4 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3

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