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LoneStriker/Tess-10.7B-v1.5-8.0bpw-h8-exl2
LoneStriker
"2024-01-27T10:31:04Z"
3
0
transformers
[ "transformers", "pytorch", "llama", "text-generation", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
"2024-01-27T10:26:33Z"
--- license: apache-2.0 --- <br> ![Tesoro](https://huggingface.co/migtissera/Tess-10.7B-v1.5/resolve/main/Tess-v1-5.png) <br> Tess, short for Tesoro (Treasure in Italian), is a general purpose Large Language Model series. Tess-10.7B-v1.5 was trained on the SOLAR-10.7B base. # Prompt Format: ``` SYSTEM: <ANY SYSTEM CONTEXT> USER: ASSISTANT: ```
mofyrt/bert-base-uncased-finetuned-cola
mofyrt
"2022-04-23T18:04:55Z"
4
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
"2022-04-23T13:35:27Z"
--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: bert-base-uncased-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.5905946625710334 --- <!-- 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. --> # bert-base-uncased-finetuned-cola This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.7445 - Matthews Correlation: 0.5906 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.4926 | 1.0 | 535 | 0.5155 | 0.4941 | | 0.2971 | 2.0 | 1070 | 0.5561 | 0.5320 | | 0.1947 | 3.0 | 1605 | 0.7230 | 0.5677 | | 0.1293 | 4.0 | 2140 | 0.7445 | 0.5906 | | 0.0867 | 5.0 | 2675 | 0.8836 | 0.5788 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.0+cu111 - Datasets 2.1.0 - Tokenizers 0.12.1
MBZUAI/swiftformer-s
MBZUAI
"2023-05-12T03:12:44Z"
79
1
transformers
[ "transformers", "pytorch", "swiftformer", "image-classification", "dataset:imagenet-1k", "arxiv:2303.15446", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
"2023-04-18T07:37:01Z"
--- datasets: - imagenet-1k library_name: transformers pipeline_tag: image-classification --- # SwiftFormer (swiftformer-s) ## Model description The SwiftFormer model was proposed in [SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://arxiv.org/abs/2303.15446) by Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan. SwiftFormer paper introduces a novel efficient additive attention mechanism that effectively replaces the quadratic matrix multiplication operations in the self-attention computation with linear element-wise multiplications. A series of models called 'SwiftFormer' is built based on this, which achieves state-of-the-art performance in terms of both accuracy and mobile inference speed. Even their small variant achieves 78.5% top-1 ImageNet1K accuracy with only 0.8 ms latency on iPhone 14, which is more accurate and 2× faster compared to MobileViT-v2. ## Intended uses & limitations ## How to use import requests from PIL import Image url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image = Image.open(requests.get(url, stream=True).raw) from transformers import ViTImageProcessor processor = ViTImageProcessor.from_pretrained('shehan97/swiftformer-s') inputs = processor(images=image, return_tensors="pt") from transformers.models.swiftformer import SwiftFormerForImageClassification new_model = SwiftFormerForImageClassification.from_pretrained('shehan97/swiftformer-s') output = new_model(inputs['pixel_values'], output_hidden_states=True) logits = output.logits predicted_class_idx = logits.argmax(-1).item() print("Predicted class:", new_model.config.id2label[predicted_class_idx]) ## Limitations and bias ## Training data The classification model is trained on the ImageNet-1K dataset. ## Training procedure ## Evaluation results
mradermacher/LLama3.3-Rhino-70B-RAG-GGUF
mradermacher
"2025-01-16T07:55:24Z"
214
0
transformers
[ "transformers", "gguf", "text-generation-inference", "unsloth", "llama", "trl", "sft", "en", "base_model:QomSSLab/LLama3.3-Rhino-70B-RAG", "base_model:quantized:QomSSLab/LLama3.3-Rhino-70B-RAG", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]
null
"2025-01-16T01:14:00Z"
--- base_model: QomSSLab/LLama3.3-Rhino-70B-RAG language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - llama - trl - sft --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/QomSSLab/LLama3.3-Rhino-70B-RAG <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/LLama3.3-Rhino-70B-RAG-GGUF/resolve/main/LLama3.3-Rhino-70B-RAG.Q2_K.gguf) | Q2_K | 26.5 | | | [GGUF](https://huggingface.co/mradermacher/LLama3.3-Rhino-70B-RAG-GGUF/resolve/main/LLama3.3-Rhino-70B-RAG.Q3_K_S.gguf) | Q3_K_S | 31.0 | | | [GGUF](https://huggingface.co/mradermacher/LLama3.3-Rhino-70B-RAG-GGUF/resolve/main/LLama3.3-Rhino-70B-RAG.Q3_K_M.gguf) | Q3_K_M | 34.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/LLama3.3-Rhino-70B-RAG-GGUF/resolve/main/LLama3.3-Rhino-70B-RAG.Q3_K_L.gguf) | Q3_K_L | 37.2 | | | [GGUF](https://huggingface.co/mradermacher/LLama3.3-Rhino-70B-RAG-GGUF/resolve/main/LLama3.3-Rhino-70B-RAG.IQ4_XS.gguf) | IQ4_XS | 38.4 | | | [GGUF](https://huggingface.co/mradermacher/LLama3.3-Rhino-70B-RAG-GGUF/resolve/main/LLama3.3-Rhino-70B-RAG.Q4_K_S.gguf) | Q4_K_S | 40.4 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/LLama3.3-Rhino-70B-RAG-GGUF/resolve/main/LLama3.3-Rhino-70B-RAG.Q4_K_M.gguf) | Q4_K_M | 42.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/LLama3.3-Rhino-70B-RAG-GGUF/resolve/main/LLama3.3-Rhino-70B-RAG.Q5_K_S.gguf) | Q5_K_S | 48.8 | | | [GGUF](https://huggingface.co/mradermacher/LLama3.3-Rhino-70B-RAG-GGUF/resolve/main/LLama3.3-Rhino-70B-RAG.Q5_K_M.gguf) | Q5_K_M | 50.0 | | | [PART 1](https://huggingface.co/mradermacher/LLama3.3-Rhino-70B-RAG-GGUF/resolve/main/LLama3.3-Rhino-70B-RAG.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/LLama3.3-Rhino-70B-RAG-GGUF/resolve/main/LLama3.3-Rhino-70B-RAG.Q6_K.gguf.part2of2) | Q6_K | 58.0 | very good quality | | [PART 1](https://huggingface.co/mradermacher/LLama3.3-Rhino-70B-RAG-GGUF/resolve/main/LLama3.3-Rhino-70B-RAG.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/LLama3.3-Rhino-70B-RAG-GGUF/resolve/main/LLama3.3-Rhino-70B-RAG.Q8_0.gguf.part2of2) | Q8_0 | 75.1 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
anas-awadalla/t5-base-few-shot-k-128-finetuned-squad-seed-0
anas-awadalla
"2022-09-29T23:26:31Z"
108
0
transformers
[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
"2022-09-27T17:38:38Z"
--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: t5-base-few-shot-k-128-finetuned-squad-seed-0 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. --> # t5-base-few-shot-k-128-finetuned-squad-seed-0 This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) on the squad 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: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - training_steps: 1000 ### Training results ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.11.6
RichardErkhov/OpenLLM-Ro_-_RoLlama3-8b-Instruct-2024-06-28-gguf
RichardErkhov
"2024-10-11T19:40:11Z"
146
1
null
[ "gguf", "arxiv:2406.18266", "endpoints_compatible", "region:us", "conversational" ]
null
"2024-10-11T15:36:31Z"
Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) RoLlama3-8b-Instruct-2024-06-28 - GGUF - Model creator: https://huggingface.co/OpenLLM-Ro/ - Original model: https://huggingface.co/OpenLLM-Ro/RoLlama3-8b-Instruct-2024-06-28/ | Name | Quant method | Size | | ---- | ---- | ---- | | [RoLlama3-8b-Instruct-2024-06-28.Q2_K.gguf](https://huggingface.co/RichardErkhov/OpenLLM-Ro_-_RoLlama3-8b-Instruct-2024-06-28-gguf/blob/main/RoLlama3-8b-Instruct-2024-06-28.Q2_K.gguf) | Q2_K | 2.96GB | | [RoLlama3-8b-Instruct-2024-06-28.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/OpenLLM-Ro_-_RoLlama3-8b-Instruct-2024-06-28-gguf/blob/main/RoLlama3-8b-Instruct-2024-06-28.IQ3_XS.gguf) | IQ3_XS | 3.28GB | | [RoLlama3-8b-Instruct-2024-06-28.IQ3_S.gguf](https://huggingface.co/RichardErkhov/OpenLLM-Ro_-_RoLlama3-8b-Instruct-2024-06-28-gguf/blob/main/RoLlama3-8b-Instruct-2024-06-28.IQ3_S.gguf) | IQ3_S | 3.43GB | | [RoLlama3-8b-Instruct-2024-06-28.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/OpenLLM-Ro_-_RoLlama3-8b-Instruct-2024-06-28-gguf/blob/main/RoLlama3-8b-Instruct-2024-06-28.Q3_K_S.gguf) | Q3_K_S | 3.41GB | | [RoLlama3-8b-Instruct-2024-06-28.IQ3_M.gguf](https://huggingface.co/RichardErkhov/OpenLLM-Ro_-_RoLlama3-8b-Instruct-2024-06-28-gguf/blob/main/RoLlama3-8b-Instruct-2024-06-28.IQ3_M.gguf) | IQ3_M | 3.52GB | | [RoLlama3-8b-Instruct-2024-06-28.Q3_K.gguf](https://huggingface.co/RichardErkhov/OpenLLM-Ro_-_RoLlama3-8b-Instruct-2024-06-28-gguf/blob/main/RoLlama3-8b-Instruct-2024-06-28.Q3_K.gguf) | Q3_K | 3.74GB | | [RoLlama3-8b-Instruct-2024-06-28.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/OpenLLM-Ro_-_RoLlama3-8b-Instruct-2024-06-28-gguf/blob/main/RoLlama3-8b-Instruct-2024-06-28.Q3_K_M.gguf) | Q3_K_M | 3.74GB | | [RoLlama3-8b-Instruct-2024-06-28.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/OpenLLM-Ro_-_RoLlama3-8b-Instruct-2024-06-28-gguf/blob/main/RoLlama3-8b-Instruct-2024-06-28.Q3_K_L.gguf) | Q3_K_L | 4.03GB | | [RoLlama3-8b-Instruct-2024-06-28.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/OpenLLM-Ro_-_RoLlama3-8b-Instruct-2024-06-28-gguf/blob/main/RoLlama3-8b-Instruct-2024-06-28.IQ4_XS.gguf) | IQ4_XS | 4.18GB | | [RoLlama3-8b-Instruct-2024-06-28.Q4_0.gguf](https://huggingface.co/RichardErkhov/OpenLLM-Ro_-_RoLlama3-8b-Instruct-2024-06-28-gguf/blob/main/RoLlama3-8b-Instruct-2024-06-28.Q4_0.gguf) | Q4_0 | 4.34GB | | [RoLlama3-8b-Instruct-2024-06-28.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/OpenLLM-Ro_-_RoLlama3-8b-Instruct-2024-06-28-gguf/blob/main/RoLlama3-8b-Instruct-2024-06-28.IQ4_NL.gguf) | IQ4_NL | 4.38GB | | [RoLlama3-8b-Instruct-2024-06-28.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/OpenLLM-Ro_-_RoLlama3-8b-Instruct-2024-06-28-gguf/blob/main/RoLlama3-8b-Instruct-2024-06-28.Q4_K_S.gguf) | Q4_K_S | 4.37GB | | [RoLlama3-8b-Instruct-2024-06-28.Q4_K.gguf](https://huggingface.co/RichardErkhov/OpenLLM-Ro_-_RoLlama3-8b-Instruct-2024-06-28-gguf/blob/main/RoLlama3-8b-Instruct-2024-06-28.Q4_K.gguf) | Q4_K | 4.58GB | | [RoLlama3-8b-Instruct-2024-06-28.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/OpenLLM-Ro_-_RoLlama3-8b-Instruct-2024-06-28-gguf/blob/main/RoLlama3-8b-Instruct-2024-06-28.Q4_K_M.gguf) | Q4_K_M | 4.58GB | | [RoLlama3-8b-Instruct-2024-06-28.Q4_1.gguf](https://huggingface.co/RichardErkhov/OpenLLM-Ro_-_RoLlama3-8b-Instruct-2024-06-28-gguf/blob/main/RoLlama3-8b-Instruct-2024-06-28.Q4_1.gguf) | Q4_1 | 4.78GB | | [RoLlama3-8b-Instruct-2024-06-28.Q5_0.gguf](https://huggingface.co/RichardErkhov/OpenLLM-Ro_-_RoLlama3-8b-Instruct-2024-06-28-gguf/blob/main/RoLlama3-8b-Instruct-2024-06-28.Q5_0.gguf) | Q5_0 | 5.21GB | | [RoLlama3-8b-Instruct-2024-06-28.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/OpenLLM-Ro_-_RoLlama3-8b-Instruct-2024-06-28-gguf/blob/main/RoLlama3-8b-Instruct-2024-06-28.Q5_K_S.gguf) | Q5_K_S | 5.21GB | | [RoLlama3-8b-Instruct-2024-06-28.Q5_K.gguf](https://huggingface.co/RichardErkhov/OpenLLM-Ro_-_RoLlama3-8b-Instruct-2024-06-28-gguf/blob/main/RoLlama3-8b-Instruct-2024-06-28.Q5_K.gguf) | Q5_K | 5.34GB | | [RoLlama3-8b-Instruct-2024-06-28.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/OpenLLM-Ro_-_RoLlama3-8b-Instruct-2024-06-28-gguf/blob/main/RoLlama3-8b-Instruct-2024-06-28.Q5_K_M.gguf) | Q5_K_M | 1.79GB | | [RoLlama3-8b-Instruct-2024-06-28.Q5_1.gguf](https://huggingface.co/RichardErkhov/OpenLLM-Ro_-_RoLlama3-8b-Instruct-2024-06-28-gguf/blob/main/RoLlama3-8b-Instruct-2024-06-28.Q5_1.gguf) | Q5_1 | 5.65GB | | [RoLlama3-8b-Instruct-2024-06-28.Q6_K.gguf](https://huggingface.co/RichardErkhov/OpenLLM-Ro_-_RoLlama3-8b-Instruct-2024-06-28-gguf/blob/main/RoLlama3-8b-Instruct-2024-06-28.Q6_K.gguf) | Q6_K | 6.14GB | | [RoLlama3-8b-Instruct-2024-06-28.Q8_0.gguf](https://huggingface.co/RichardErkhov/OpenLLM-Ro_-_RoLlama3-8b-Instruct-2024-06-28-gguf/blob/main/RoLlama3-8b-Instruct-2024-06-28.Q8_0.gguf) | Q8_0 | 7.95GB | Original model description: --- license: cc-by-nc-4.0 language: - ro base_model: - meta-llama/Meta-Llama-3-8B datasets: - OpenLLM-Ro/ro_sft_alpaca - OpenLLM-Ro/ro_sft_alpaca_gpt4 - OpenLLM-Ro/ro_sft_dolly - OpenLLM-Ro/ro_sft_selfinstruct_gpt4 - OpenLLM-Ro/ro_sft_norobots - OpenLLM-Ro/ro_sft_orca - OpenLLM-Ro/ro_sft_camel model-index: - name: OpenLLM-Ro/RoLlama3-8b-Instruct-2024-06-28 results: - task: type: text-generation dataset: name: RoMT-Bench type: RoMT-Bench metrics: - name: Score type: Score value: 5.15 - task: type: text-generation dataset: name: RoCulturaBench type: RoCulturaBench metrics: - name: Score type: Score value: 3.71 - task: type: text-generation dataset: name: Romanian_Academic_Benchmarks type: Romanian_Academic_Benchmarks metrics: - name: Average accuracy type: accuracy value: 50.56 - task: type: text-generation dataset: name: OpenLLM-Ro/ro_arc_challenge type: OpenLLM-Ro/ro_arc_challenge metrics: - name: Average accuracy type: accuracy value: 44.70 - task: type: text-generation dataset: name: OpenLLM-Ro/ro_mmlu type: OpenLLM-Ro/ro_mmlu metrics: - name: Average accuracy type: accuracy value: 52.19 - task: type: text-generation dataset: name: OpenLLM-Ro/ro_winogrande type: OpenLLM-Ro/ro_winogrande metrics: - name: Average accuracy type: accuracy value: 67.23 - task: type: text-generation dataset: name: OpenLLM-Ro/ro_hellaswag type: OpenLLM-Ro/ro_hellaswag metrics: - name: Average accuracy type: accuracy value: 57.69 - task: type: text-generation dataset: name: OpenLLM-Ro/ro_gsm8k type: OpenLLM-Ro/ro_gsm8k metrics: - name: Average accuracy type: accuracy value: 30.23 - task: type: text-generation dataset: name: OpenLLM-Ro/ro_truthfulqa type: OpenLLM-Ro/ro_truthfulqa metrics: - name: Average accuracy type: accuracy value: 51.34 - task: type: text-generation dataset: name: LaRoSeDa_binary type: LaRoSeDa_binary metrics: - name: Average macro-f1 type: macro-f1 value: 97.52 - task: type: text-generation dataset: name: LaRoSeDa_multiclass type: LaRoSeDa_multiclass metrics: - name: Average macro-f1 type: macro-f1 value: 67.41 - task: type: text-generation dataset: name: LaRoSeDa_binary_finetuned type: LaRoSeDa_binary_finetuned metrics: - name: Average macro-f1 type: macro-f1 value: 94.15 - task: type: text-generation dataset: name: LaRoSeDa_multiclass_finetuned type: LaRoSeDa_multiclass_finetuned metrics: - name: Average macro-f1 type: macro-f1 value: 87.13 - task: type: text-generation dataset: name: WMT_EN-RO type: WMT_EN-RO metrics: - name: Average bleu type: bleu value: 24.01 - task: type: text-generation dataset: name: WMT_RO-EN type: WMT_RO-EN metrics: - name: Average bleu type: bleu value: 27.36 - task: type: text-generation dataset: name: WMT_EN-RO_finetuned type: WMT_EN-RO_finetuned metrics: - name: Average bleu type: bleu value: 26.53 - task: type: text-generation dataset: name: WMT_RO-EN_finetuned type: WMT_RO-EN_finetuned metrics: - name: Average bleu type: bleu value: 40.36 - task: type: text-generation dataset: name: XQuAD type: XQuAD metrics: - name: Average exact_match type: exact_match value: 39.43 - task: type: text-generation dataset: name: XQuAD type: XQuAD metrics: - name: Average f1 type: f1 value: 59.50 - task: type: text-generation dataset: name: XQuAD_finetuned type: XQuAD_finetuned metrics: - name: Average exact_match type: exact_match value: 44.45 - task: type: text-generation dataset: name: XQuAD_finetuned type: XQuAD_finetuned metrics: - name: Average f1 type: f1 value: 59.76 - task: type: text-generation dataset: name: STS type: STS metrics: - name: Average spearman type: spearman value: 77.20 - task: type: text-generation dataset: name: STS type: STS metrics: - name: Average pearson type: pearson value: 77.87 - task: type: text-generation dataset: name: STS_finetuned type: STS_finetuned metrics: - name: Average spearman type: spearman value: 85.80 - task: type: text-generation dataset: name: STS_finetuned type: STS_finetuned metrics: - name: Average pearson type: pearson value: 86.05 - task: type: text-generation dataset: name: RoMT-Bench type: RoMT-Bench metrics: - name: First turn type: Score value: 6.03 - name: Second turn type: Score value: 4.28 - task: type: text-generation dataset: name: OpenLLM-Ro/ro_arc_challenge type: OpenLLM-Ro/ro_arc_challenge metrics: - name: 0-shot type: accuracy value: 41.90 - name: 1-shot type: accuracy value: 44.30 - name: 3-shot type: accuracy value: 44.56 - name: 5-shot type: accuracy value: 45.50 - name: 10-shot type: accuracy value: 46.10 - name: 25-shot type: accuracy value: 45.84 - task: type: text-generation dataset: name: OpenLLM-Ro/ro_mmlu type: OpenLLM-Ro/ro_mmlu metrics: - name: 0-shot type: accuracy value: 50.85 - name: 1-shot type: accuracy value: 51.24 - name: 3-shot type: accuracy value: 53.30 - name: 5-shot type: accuracy value: 53.39 - task: type: text-generation dataset: name: OpenLLM-Ro/ro_winogrande type: OpenLLM-Ro/ro_winogrande metrics: - name: 0-shot type: accuracy value: 65.19 - name: 1-shot type: accuracy value: 66.54 - name: 3-shot type: accuracy value: 67.88 - name: 5-shot type: accuracy value: 69.30 - task: type: text-generation dataset: name: OpenLLM-Ro/ro_hellaswag type: OpenLLM-Ro/ro_hellaswag metrics: - name: 0-shot type: accuracy value: 56.12 - name: 1-shot type: accuracy value: 57.37 - name: 3-shot type: accuracy value: 57.92 - name: 5-shot type: accuracy value: 58.18 - name: 10-shot type: accuracy value: 58.85 - task: type: text-generation dataset: name: OpenLLM-Ro/ro_gsm8k type: OpenLLM-Ro/ro_gsm8k metrics: - name: 1-shot type: accuracy value: 29.42 - name: 3-shot type: accuracy value: 30.02 - name: 5-shot type: accuracy value: 31.24 - task: type: text-generation dataset: name: LaRoSeDa_binary type: LaRoSeDa_binary metrics: - name: 0-shot type: macro-f1 value: 97.43 - name: 1-shot type: macro-f1 value: 96.60 - name: 3-shot type: macro-f1 value: 97.90 - name: 5-shot type: macro-f1 value: 98.13 - task: type: text-generation dataset: name: LaRoSeDa_multiclass type: LaRoSeDa_multiclass metrics: - name: 0-shot type: macro-f1 value: 63.77 - name: 1-shot type: macro-f1 value: 68.91 - name: 3-shot type: macro-f1 value: 66.36 - name: 5-shot type: macro-f1 value: 70.61 - task: type: text-generation dataset: name: WMT_EN-RO type: WMT_EN-RO metrics: - name: 0-shot type: bleu value: 6.92 - name: 1-shot type: bleu value: 29.33 - name: 3-shot type: bleu value: 29.79 - name: 5-shot type: bleu value: 30.02 - task: type: text-generation dataset: name: WMT_RO-EN type: WMT_RO-EN metrics: - name: 0-shot type: bleu value: 4.50 - name: 1-shot type: bleu value: 30.30 - name: 3-shot type: bleu value: 36.96 - name: 5-shot type: bleu value: 37.70 - task: type: text-generation dataset: name: XQuAD_EM type: XQuAD_EM metrics: - name: 0-shot type: exact_match value: 4.45 - name: 1-shot type: exact_match value: 48.24 - name: 3-shot type: exact_match value: 52.03 - name: 5-shot type: exact_match value: 53.03 - task: type: text-generation dataset: name: XQuAD_F1 type: XQuAD_F1 metrics: - name: 0-shot type: f1 value: 26.08 - name: 1-shot type: f1 value: 68.40 - name: 3-shot type: f1 value: 71.92 - name: 5-shot type: f1 value: 71.60 - task: type: text-generation dataset: name: STS_Spearman type: STS_Spearman metrics: - name: 1-shot type: spearman value: 77.76 - name: 3-shot type: spearman value: 76.72 - name: 5-shot type: spearman value: 77.12 - task: type: text-generation dataset: name: STS_Pearson type: STS_Pearson metrics: - name: 1-shot type: pearson value: 77.83 - name: 3-shot type: pearson value: 77.64 - name: 5-shot type: pearson value: 78.13 --- # Model Card for Model ID *Built with Meta Llama 3* <!-- Provide a quick summary of what the model is/does. --> RoLlama3 is a family of pretrained and fine-tuned generative text models for Romanian. This is the repository for the **instruct 8B model**. Links to other models can be found at the bottom of this page. ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> OpenLLM-Ro represents the first open-source effort to build a LLM specialized for Romanian. OpenLLM-Ro developed and publicly releases a collection of Romanian LLMs, both in the form of foundational model and instruct and chat variants. - **Developed by:** OpenLLM-Ro <!-- - **Funded by [optional]:** [More Information Needed] --> <!-- - **Shared by [optional]:** [More Information Needed] --> <!-- - **Model type:** [More Information Needed] --> - **Language(s):** Romanian - **License:** cc-by-nc-4.0 - **Finetuned from model:** [Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) - **Trained using:** [RoAlpaca](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_alpaca), [RoAlpacaGPT4](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_alpaca_gpt4), [RoDolly](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_dolly), [RoSelfInstruct](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_selfinstruct_gpt4), [RoNoRobots](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_norobots), [RoOrca](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_orca), [RoCamel](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_camel) ### Model Sources <!-- Provide the basic links for the model. --> - **Repository:** https://github.com/OpenLLM-Ro/LLaMA-Factory - **Paper:** https://arxiv.org/abs/2406.18266 ## Intended Use ### Intended Use Cases RoLlama3 is intented for research use in Romanian. Base models can be adapted for a variety of natural language tasks while instruction and chat tuned models are intended for assistant-like chat. ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> Use in any manner that violates the license, any applicable laws or regluations, use in languages other than Romanian. ## How to Get Started with the Model Use the code below to get started with the model. ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("OpenLLM-Ro/RoLlama3-8b-Instruct-2024-06-28") model = AutoModelForCausalLM.from_pretrained("OpenLLM-Ro/RoLlama3-8b-Instruct-2024-06-28") instruction = "Ce jocuri de societate pot juca cu prietenii mei?" chat = [ {"role": "system", "content": "Ești un asistent folositor, respectuos și onest. Încearcă să ajuți cât mai mult prin informațiile oferite, excluzând răspunsuri toxice, rasiste, sexiste, periculoase și ilegale."}, {"role": "user", "content": instruction}, ] prompt = tokenizer.apply_chat_template(chat, tokenize=False, system_message="") inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt") outputs = model.generate(input_ids=inputs, max_new_tokens=128) print(tokenizer.decode(outputs[0])) ``` ## Academic Benchmarks <table> <tbody> <tr> <td><strong>Model</strong></td> <td><strong><center>Average</center></strong></td> <td><strong><center>ARC</center></strong></td> <td><strong><center>MMLU</center></strong></td> <td><strong><center>Winogrande</center></strong></td> <td><strong><center>Hellaswag</center></strong></td> <td><strong><center>GSM8k</center></strong></td> <td><strong><center>TruthfulQA</center></strong></td> </tr> <tr> <td>Llama-3-8B-Instruct</td><td><center>50.62</center></td><td><center>43.69</center></td><td><center>52.04</center></td><td><center>59.33</center></td><td><center>53.19</center></td><td><center><strong>43.87</strong></center></td><td><center><strong>51.59</strong></center></td> </tr> <tr> <td><em>RoLlama3-8b-Instruct-2024-06-28</em></td><td><center><em>50.56</em></center></td><td><center><em>44.70</em></center></td><td><center><em>52.19</em></center></td><td><center><em><strong>67.23</strong></em></center></td><td><center><em>57.69</em></center></td><td><center><em>30.23</em></center></td><td><center><em>51.34</em></center></td> </tr> <tr> <td>RoLlama3-8b-Instruct-2024-10-09</td><td><center><strong>52.21</strong></center></td><td><center><strong>47.94</strong></center></td><td><center><strong>53.50</strong></center></td><td><center>66.06</center></td><td><center><strong>59.72</strong></center></td><td><center>40.16</center></td><td><center>45.90</center></td> </tr> <tr> <td>RoLlama3-8b-Instruct-DPO-2024-10-09</td><td><center>49.96</center></td><td><center>46.29</center></td><td><center>53.29</center></td><td><center>65.57</center></td><td><center>58.15</center></td><td><center>34.77</center></td><td><center>41.70</center></td> </tr> </tbody> </table> ## Downstream tasks <table> <tbody> <tr> <td></td> <td colspan="4"><center><strong>LaRoSeDa</strong></center></td> <td colspan="4"><center><strong>WMT</strong></center></td> </tr> <tr> <td></td> <td colspan="2"><center><strong>Few-shot</strong></center></td> <td colspan="2"><center><strong>Finetuned</strong></center></td> <td colspan="2"><center><strong>Few-shot</strong></center></td> <td colspan="2"><center><strong>Finetuned</strong></center></td> </tr> <tr> <td><strong>Model</strong></td> <td><center><strong>Binary<br>(Macro F1)</strong></center></td> <td><center><strong>Multiclass<br>(Macro F1)</strong></center></td> <td><center><strong>Binary<br>(Macro F1)</strong></center></td> <td><center><strong>Multiclass<br>(Macro F1)</strong></center></td> <td><center><strong>EN-RO<br>(Bleu)</strong></center></td> <td><center><strong>RO-EN<br>(Bleu)</strong></center></td> <td><center><strong>EN-RO<br>(Bleu)</strong></center></td> <td><center><strong>RO-EN<br>(Bleu)</strong></center> </tr> <tr> <td>Llama-3-8B-Instruct</td><td><center>95.88</center></td><td><center>56.21</center></td><td><center><strong>98.53</strong></center></td><td><center>86.19</center></td><td><center>18.88</center></td><td><center><strong>30.98</strong></center></td><td><center><strong>28.02</strong></center></td><td><center>40.28</center></td> </tr> <tr> <td><em>RoLlama3-8b-Instruct-2024-06-28</em></td><td><center><em><strong>97.52</strong></em></center></td><td><center><em><strong>67.41</strong></em></center></td><td><center><em>94.15</em></center></td><td><center><em>87.13</em></center></td><td><center><em><strong>24.01</strong></em></center></td><td><center><em>27.36</em></center></td><td><center><em>26.53</em></center></td><td><center><em>40.36</em></center></td> </tr> <tr> <td>RoLlama3-8b-Instruct-2024-10-09</td><td><center>95.58</center></td><td><center>61.20</center></td><td><center>96.46</center></td><td><center><strong>87.26</strong></center></td><td><center>22.92</center></td><td><center>24.28</center></td><td><center>27.31</center></td><td><center><strong>40.52</strong></center></td> </tr> <tr> <td>RoLlama3-8b-Instruct-DPO-2024-10-09</td><td><center>97.48</center></td><td><center>54.00</center></td><td><center>-</center></td><td><center>-</center></td><td><center>22.09</center></td><td><center>23.00</center></td><td><center>-</center></td><td><center>-</center></td> </tr> </tbody> </table> <table> <tbody> <tr> <td></td> <td colspan="4"><center><strong>XQuAD</strong></center></td> <td colspan="4"><center><strong>STS</strong></center></td> </tr> <tr> <td></td> <td colspan="2"><center><strong>Few-shot</strong></center></td> <td colspan="2"><center><strong>Finetuned</strong></center></td> <td colspan="2"><center><strong>Few-shot</strong></center></td> <td colspan="2"><center><strong>Finetuned</strong></center></td> </tr> <tr> <td><strong>Model</strong></td> <td><center><strong>(EM)</strong></center></td> <td><center><strong>(F1)</strong></center></td> <td><center><strong>(EM)</strong></center></td> <td><center><strong>(F1)</strong></center></td> <td><center><strong>(Spearman)</strong></center></td> <td><center><strong>(Pearson)</strong></center></td> <td><center><strong>(Spearman)</strong></center></td> <td><center><strong>(Pearson)</strong></center></td> </tr> <tr> <td>Llama-3-8B-Instruct</td><td><center><strong>39.47</strong></center></td><td><center>58.67</center></td><td><center><strong>67.65</strong></center></td><td><center><strong>82.77</strong></center></td><td><center>73.04</center></td><td><center>72.36</center></td><td><center>83.49</center></td><td><center>84.06</center></td> </tr> <tr> <td><em>RoLlama3-8b-Instruct-2024-06-28</em></td><td><center><em>39.43</em></center></td><td><center><em><strong>59.50</strong></em></center></td><td><center><em>44.45</em></center></td><td><center><em>59.76</em></center></td><td><center><em>77.20</em></center></td><td><center><em>77.87</em></center></td><td><center><em>85.80</em></center></td><td><center><em>86.05</em></center></td> </tr> <tr> <td>RoLlama3-8b-Instruct-2024-10-09</td><td><center>18.89</center></td><td><center>31.79</center></td><td><center>50.84</center></td><td><center>65.18</center></td><td><center>77.60</center></td><td><center>76.86</center></td><td><center><strong>86.70</strong></center></td><td><center><strong>87.09</strong></center></td> </tr> <tr> <td>RoLlama3-8b-Instruct-DPO-2024-10-09</td><td><center>26.05</center></td><td><center>42.77</center></td><td><center>-</center></td><td><center>-</center></td><td><center><strong>79.64</strong></center></td><td><center><strong>79.52</strong></center></td><td><center>-</center></td><td><center>-</center></td> </tr> </tbody> </table> ## MT-Bench <table> <tbody> <tr> <td><strong>Model</strong></td> <td><strong><center>Average</center></strong></td> <td><strong><center>1st turn</center></strong></td> <td><strong><center>2nd turn</center></strong></td> <td><strong><center>Answers in Ro</center></strong></td> </tr> <tr> <td>Llama-3-8B-Instruct</td><td><center><strong>5.96</strong></center></td><td><center>6.16</center></td><td><center><strong>5.76</strong></center></td><td><center>158/160</center></td> </tr> <tr> <td><em>RoLlama3-8b-Instruct-2024-06-28</em></td><td><center><em>5.15</em></center></td><td><center><em>6.03</em></center></td><td><center><em>4.28</em></center></td><td><center><em><strong>160/160</strong></em></center></td> </tr> <tr> <td>RoLlama3-8b-Instruct-2024-10-09</td><td><center>5.38</center></td><td><center>6.09</center></td><td><center>4.67</center></td><td><center><strong>160/160</strong></center></td> </tr> <tr> <td>RoLlama3-8b-Instruct-DPO-2024-10-09</td><td><center>5.87</center></td><td><center><strong>6.22</strong></center></td><td><center>5.49</center></td><td><center><strong>160/160</strong></center></td> </tr> </tbody> </table> ## RoCulturaBench <table> <tbody> <tr> <td><strong>Model</strong></td> <td><strong><center>Average</center></strong></td> <td><strong><center>Answers in Ro</center></strong></td> </tr> <tr> <td>Llama-3-8B-Instruct</td><td><center><strong>4.62</strong></center></td><td><center><strong>100/100</strong></center></td> </tr> <tr> <td><em>RoLlama3-8b-Instruct-2024-06-28</em></td><td><center><em>3.71</em></center></td><td><center><em><strong>100/100</strong></em></center></td> </tr> <tr> <td>RoLlama3-8b-Instruct-2024-10-09</td><td><center>3.81</center></td><td><center><strong>100/100</strong></center></td> </tr> <tr> <td>RoLlama3-8b-Instruct-DPO-2024-10-09</td><td><center>4.40</center></td><td><center><strong>100/100</strong></center></td> </tr> </tbody> </table> ## RoLlama3 Model Family | Model | Link | |--------------------|:--------:| |*RoLlama3-8b-Instruct-2024-06-28*| [link](https://huggingface.co/OpenLLM-Ro/RoLlama3-8b-Instruct-2024-06-28) | |RoLlama3-8b-Instruct-2024-10-09| [link](https://huggingface.co/OpenLLM-Ro/RoLlama3-8b-Instruct-2024-10-09) | |RoLlama3-8b-Instruct-DPO-2024-10-09| [link](https://huggingface.co/OpenLLM-Ro/RoLlama3-8b-Instruct-DPO-2024-10-09) | ## Citation ``` @misc{masala2024vorbecstiromanecsterecipetrain, title={"Vorbe\c{s}ti Rom\^ane\c{s}te?" A Recipe to Train Powerful Romanian LLMs with English Instructions}, author={Mihai Masala and Denis C. Ilie-Ablachim and Alexandru Dima and Dragos Corlatescu and Miruna Zavelca and Ovio Olaru and Simina Terian-Dan and Andrei Terian-Dan and Marius Leordeanu and Horia Velicu and Marius Popescu and Mihai Dascalu and Traian Rebedea}, year={2024}, eprint={2406.18266}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2406.18266}, } ``` <!-- **APA:** [More Information Needed] -->
stuartmesham/electra-large_lemon-spell_5k_2_p3
stuartmesham
"2022-10-24T17:14:16Z"
6
0
transformers
[ "transformers", "pytorch", "electra", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
"2022-10-24T17:13:27Z"
--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: electra-large_lemon-spell_5k_2_p3 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. --> # electra-large_lemon-spell_5k_2_p3 This model is a fine-tuned version of [model_saves/electra-large_lemon-spell_5k_2_p2](https://huggingface.co/model_saves/electra-large_lemon-spell_5k_2_p2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4357 - Accuracy: 0.9398 ## 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: 128 - eval_batch_size: 128 - seed: 52 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 15.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 268 | 0.4357 | 0.9398 | | No log | 2.0 | 536 | 0.4500 | 0.9391 | | No log | 3.0 | 804 | 0.4678 | 0.9388 | | 0.3213 | 4.0 | 1072 | 0.5006 | 0.9384 | ### Framework versions - Transformers 4.21.0.dev0 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
choco58/LLaMAdelic
choco58
"2025-01-24T16:54:27Z"
53
1
transformers
[ "transformers", "safetensors", "llama", "text-generation", "LLaMAdelic", "Conversational AI", "Personality", "Persona-dialogue", "Dialogue-systems", "Human-like assistant", "LLaMA", "LLaMA-8B", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
"2024-09-17T04:27:44Z"
--- library_name: transformers tags: [LLaMAdelic, Conversational AI, Personality, Persona-dialogue, Dialogue-systems, Human-like assistant, LLaMA, LLaMA-8B] --- # LLaMAdelic: Conversational Personality Model 🌊✨ Welcome to **LLaMAdelic**—a conversational model fine-tuned from LLaMA 3 8B Instruct, capturing nuanced personality traits that make AI interactions feel more authentic and relatable. Whether it’s about balancing conscientious responses or tapping into empathetic reflections, LLaMAdelic is here to explore the depths of the human-like personality spectrum. # Model Overview: LLaMAdelic ## Model Name: LLaMAdelic - **Architecture**: LLaMA 3 8B Instruct - **Training Objective**: Personality-Enhanced Conversational AI - **Training Dataset**: Fine-tuned on conversational data to reflect Big 5 personality traits. - JIC: [Journal Intensive Conversations](https://huggingface.co/datasets/chocokiddo/jic) dataset - **Training Duration**: 4-5 days on A100 GPU (training parameters can be found in appendix of the paper) ## Why "LLaMAdelic"? The name "LLaMAdelic" reflects our aim to bring a rich, nuanced personality to conversational AI. Just as the Big 5 personality traits (OCEAN) encapsulate the subtle layers of human interaction, LLaMAdelic seeks to capture these nuanced dimensions — openness, conscientiousness, extraversion, agreeableness, and neuroticism — making conversations with AI feel more genuinely human. It’s not just another model; it’s designed to add depth, authenticity, and a hint of human-like character to every interaction. --- ## Scope of Applications LLaMAdelic is designed to add a splash of personality to various conversational tasks. Here's what it can handle: - **Conversational Agents**: Engage users with relatable and personality-driven conversations. - **Text Generation**: Generate human-like text for articles, chats, and creative writing with a personal touch. - **Question-Answering**: Answer questions with a flair of personality, making responses more relatable. - **Educational and Therapy Bots**: Assist in applications where personality-sensitive responses can improve user engagement and retention. --- ## Intended Use LLaMAdelic is built for those aiming to inject personality into conversational systems, whether it’s for customer service bots, therapy support, or just plain fun AI companions. It’s particularly suited to applications where capturing nuances like openness, agreeableness, and neuroticism (yes, even those angsty replies!) can enhance user experience. ### Data and Training The model has been trained on an extensive conversational dataset. Our goal was to align model responses with intrinsic personality traits, enabling LLaMAdelic to tailor its tone and style depending on conversational context. More information on the dataset will be shared soon. ### Results **Personality Evaluation on [EleutherAI/lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) (OCEAN Personality Benchmark)** | Model | Description | Openness | Conscientiousness | Extraversion | Agreeableness | Neuroticism | AVG | |---------------|-------------------------------|----------|-------------------|--------------|---------------|-------------|-------| | LLaMA 8B ins | Zeroshot | 0.8760 | 0.7620 | 0.7170 | 0.9500 | 0.5220 | 0.7654 | | LLaMAdelic | Fine-tuned on Conversational Data | 0.9150 | 0.7840 | 0.6680 | 0.9440 | 0.7040 | 0.8030 | --- ## Performance and Limitations While LLaMAdelic brings vibrant and personality-driven conversations to the table, it does have limitations: - **Personality Representation**: LLaMAdelic is trained for personality alignment, so it may sacrifice some general knowledge capabilities in favor of personality-specific responses. A detailed evaluation will be updated soon. - **Sensitive Topics**: Despite strong filtering, caution is advised when deploying in high-stakes environments. - **Computational Load**: The LLaMA 8B backbone requires substantial resources, which may limit deployment in real-time settings without sufficient hardware. --- ## Ethical Considerations We made sure to avoid toxic or inappropriate dialogues by tagging any dialogue with over 25% toxic utterances for separate review. Ethical considerations are a priority, and LLaMAdelic was designed with responsible AI practices in mind. For details on ethical data practices, see the Appendix. --- ## Future Updates Stay tuned for more information on LLaMAdelic! --- ## Citation ```bibtex @inproceedings{pal-etal-2025-beyond, title = "Beyond Discrete Personas: Personality Modeling Through Journal Intensive Conversations", author = "Pal, Sayantan and Das, Souvik and Srihari, Rohini K.", editor = "Rambow, Owen and Wanner, Leo and Apidianaki, Marianna and Al-Khalifa, Hend and Eugenio, Barbara Di and Schockaert, Steven", booktitle = "Proceedings of the 31st International Conference on Computational Linguistics", month = jan, year = "2025", address = "Abu Dhabi, UAE", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2025.coling-main.470/", pages = "7055--7074", abstract = "Large Language Models (LLMs) have significantly improved personalized conversational capabilities. However, existing datasets like Persona Chat, Synthetic Persona Chat, and Blended Skill Talk rely on static, predefined personas. This approach often results in dialogues that fail to capture human personalities' fluid and evolving nature. To overcome these limitations, we introduce a novel dataset with around 400,000 dialogues and a framework for generating personalized conversations using long-form journal entries from Reddit. Our approach clusters journal entries for each author and filters them by selecting the most representative cluster, ensuring that the retained entries best reflect the author`s personality. We further refine the data by capturing the Big Five personality traits{---}openness, conscientiousness, extraversion, agreeableness, and neuroticism{---}ensuring that dialogues authentically reflect an individual`s personality. Using Llama 3 70B, we generate high-quality, personality-rich dialogues grounded in these journal entries. Fine-tuning models on this dataset leads to an 11{\%} improvement in capturing personality traits on average, outperforming existing approaches in generating more coherent and personality-driven dialogues." } ``` ---
CeroShrijver/chinese-macbert-large-text-classification
CeroShrijver
"2023-06-18T17:36:30Z"
106
1
transformers
[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
"2023-06-03T07:49:47Z"
--- tags: - generated_from_trainer model-index: - name: chinese-macbert-large-text-classification results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # chinese-macbert-large-text-classification This model was trained from scratch 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: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Framework versions - Transformers 4.28.1 - Pytorch 1.12.1 - Datasets 2.12.0 - Tokenizers 0.11.6
dendimaki/multilabel_classification
dendimaki
"2024-05-08T09:04:00Z"
0
0
peft
[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]
null
"2024-05-08T08:46:13Z"
--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 model-index: - name: multilabel_classification results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # multilabel_classification This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) 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: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 425 | 2.0765 | ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
TransferGraph/marcelcastrobr_sagemaker-distilbert-emotion-finetuned-lora-glue_cola
TransferGraph
"2024-02-28T00:47:09Z"
0
0
peft
[ "peft", "safetensors", "parquet", "text-classification", "dataset:glue", "base_model:marcelcastrobr/sagemaker-distilbert-emotion", "base_model:adapter:marcelcastrobr/sagemaker-distilbert-emotion", "license:apache-2.0", "model-index", "region:us" ]
text-classification
"2024-02-28T00:47:06Z"
--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - glue metrics: - accuracy base_model: marcelcastrobr/sagemaker-distilbert-emotion model-index: - name: marcelcastrobr_sagemaker-distilbert-emotion-finetuned-lora-glue_cola results: - task: type: text-classification name: Text Classification dataset: name: glue type: glue config: cola split: validation args: cola metrics: - type: accuracy value: 0.7535953978907 name: accuracy --- <!-- 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. --> # marcelcastrobr_sagemaker-distilbert-emotion-finetuned-lora-glue_cola This model is a fine-tuned version of [marcelcastrobr/sagemaker-distilbert-emotion](https://huggingface.co/marcelcastrobr/sagemaker-distilbert-emotion) on the glue dataset. It achieves the following results on the evaluation set: - accuracy: 0.7536 ## 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: 0.0004 - 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: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.6702 | None | 0 | | 0.7095 | 0.6043 | 0 | | 0.7354 | 0.5365 | 1 | | 0.7555 | 0.5047 | 2 | | 0.7536 | 0.4836 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
mlx-community/Starling-LM-7B-beta
mlx-community
"2024-03-28T15:12:23Z"
79
2
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "reward model", "RLHF", "RLAIF", "mlx", "conversational", "en", "dataset:berkeley-nest/Nectar", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
"2024-03-28T14:41:41Z"
--- language: - en license: apache-2.0 library_name: transformers tags: - reward model - RLHF - RLAIF - mlx datasets: - berkeley-nest/Nectar --- # mlx-community/Starling-LM-7B-beta This model was converted to MLX format from [`Nexusflow/Starling-LM-7B-beta`]() using mlx-lm version **0.5.0**. Refer to the [original model card](https://huggingface.co/Nexusflow/Starling-LM-7B-beta) for more details on the model. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/Starling-LM-7B-beta") response = generate(model, tokenizer, prompt="hello", verbose=True) ```
kinshuk-h/flan-t5-retacred-kg-w-context-var-len-small-finetuned
kinshuk-h
"2023-07-19T13:19:25Z"
110
0
transformers
[ "transformers", "pytorch", "safetensors", "t5", "text2text-generation", "legal", "en", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
"2023-06-07T09:40:48Z"
--- license: mit language: - en pipeline_tag: text2text-generation tags: - legal --- # flan-t5-retacred-kg-w-context-var-len-small-finetuned [flan-t5-small](https://huggingface.co/google/flan-t5-small) finetuned over the TACRED Knowledge Graph patched with the [Re-TACRED proposal](https://github.com/gstoica27/Re-TACRED) using the training method for [KGT-5](https://github.com/apoorvumang/kgt5/) with additional variable length context alongside the prompts.
TehranNLP-org/electra-base-mnli
TehranNLP-org
"2022-05-03T17:01:07Z"
5
0
transformers
[ "transformers", "pytorch", "electra", "text-classification", "generated_from_trainer", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
"2022-04-30T12:50:13Z"
--- language: - en license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: SEED0042 results: - task: name: Text Classification type: text-classification dataset: name: MNLI type: '' args: mnli metrics: - name: Accuracy type: accuracy value: 0.8879266428935303 --- <!-- 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. --> # SEED0042 This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the MNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.4265 - Accuracy: 0.8879 ## 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: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - distributed_type: not_parallel - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.3762 | 1.0 | 12272 | 0.3312 | 0.8794 | | 0.2542 | 2.0 | 24544 | 0.3467 | 0.8843 | | 0.1503 | 3.0 | 36816 | 0.4265 | 0.8879 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu113 - Datasets 2.1.0 - Tokenizers 0.11.6
PopularPenguin/bart-base-2024-09-24_11-12
PopularPenguin
"2024-09-24T11:48:43Z"
89
0
transformers
[ "transformers", "tensorboard", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "dataset:arrow", "base_model:facebook/bart-base", "base_model:finetune:facebook/bart-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
"2024-09-24T11:21:12Z"
--- library_name: transformers license: apache-2.0 base_model: facebook/bart-base tags: - generated_from_trainer datasets: - arrow model-index: - name: bart-base-2024-09-24_11-12 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. --> # bart-base-2024-09-24_11-12 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the arrow dataset. It achieves the following results on the evaluation set: - Loss: 0.1993 - Gen Len: 20.0 - Bertscorer-p: 0.5928 - Bertscorer-r: 0.1701 - Bertscorer-f1: 0.3731 - Sacrebleu-score: 10.2541 - Sacrebleu-precisions: [90.63003300856309, 79.05155386114873, 70.66565212490137, 65.68935823527592] - Bleu-bp: 0.1350 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Gen Len | Bertscorer-p | Bertscorer-r | Bertscorer-f1 | Sacrebleu-score | Sacrebleu-precisions | Bleu-bp | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------------:|:------------:|:-------------:|:---------------:|:----------------------------------------------------------------------------:|:-------:| | 0.189 | 1.0 | 4772 | 0.1993 | 20.0 | 0.5928 | 0.1701 | 0.3731 | 10.2541 | [90.63003300856309, 79.05155386114873, 70.66565212490137, 65.68935823527592] | 0.1350 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.1+cu121 - Datasets 3.0.0 - Tokenizers 0.19.1
mradermacher/BlackSheep-Mistral-RP-7B-i1-GGUF
mradermacher
"2025-01-22T01:37:36Z"
143
0
transformers
[ "transformers", "gguf", "en", "base_model:TroyDoesAI/BlackSheep-Mistral-RP-7B", "base_model:quantized:TroyDoesAI/BlackSheep-Mistral-RP-7B", "license:artistic-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]
null
"2024-11-18T07:42:20Z"
--- base_model: TroyDoesAI/BlackSheep-Mistral-RP-7B language: - en library_name: transformers license: artistic-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/TroyDoesAI/BlackSheep-Mistral-RP-7B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/BlackSheep-Mistral-RP-7B-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/BlackSheep-Mistral-RP-7B-i1-GGUF/resolve/main/BlackSheep-Mistral-RP-7B.i1-IQ1_S.gguf) | i1-IQ1_S | 1.7 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/BlackSheep-Mistral-RP-7B-i1-GGUF/resolve/main/BlackSheep-Mistral-RP-7B.i1-IQ1_M.gguf) | i1-IQ1_M | 1.9 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/BlackSheep-Mistral-RP-7B-i1-GGUF/resolve/main/BlackSheep-Mistral-RP-7B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.1 | | | [GGUF](https://huggingface.co/mradermacher/BlackSheep-Mistral-RP-7B-i1-GGUF/resolve/main/BlackSheep-Mistral-RP-7B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.3 | | | [GGUF](https://huggingface.co/mradermacher/BlackSheep-Mistral-RP-7B-i1-GGUF/resolve/main/BlackSheep-Mistral-RP-7B.i1-IQ2_S.gguf) | i1-IQ2_S | 2.4 | | | [GGUF](https://huggingface.co/mradermacher/BlackSheep-Mistral-RP-7B-i1-GGUF/resolve/main/BlackSheep-Mistral-RP-7B.i1-IQ2_M.gguf) | i1-IQ2_M | 2.6 | | | [GGUF](https://huggingface.co/mradermacher/BlackSheep-Mistral-RP-7B-i1-GGUF/resolve/main/BlackSheep-Mistral-RP-7B.i1-Q2_K.gguf) | i1-Q2_K | 2.8 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/BlackSheep-Mistral-RP-7B-i1-GGUF/resolve/main/BlackSheep-Mistral-RP-7B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 2.9 | lower quality | | [GGUF](https://huggingface.co/mradermacher/BlackSheep-Mistral-RP-7B-i1-GGUF/resolve/main/BlackSheep-Mistral-RP-7B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.1 | | | [GGUF](https://huggingface.co/mradermacher/BlackSheep-Mistral-RP-7B-i1-GGUF/resolve/main/BlackSheep-Mistral-RP-7B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.3 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/BlackSheep-Mistral-RP-7B-i1-GGUF/resolve/main/BlackSheep-Mistral-RP-7B.i1-IQ3_S.gguf) | i1-IQ3_S | 3.3 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/BlackSheep-Mistral-RP-7B-i1-GGUF/resolve/main/BlackSheep-Mistral-RP-7B.i1-IQ3_M.gguf) | i1-IQ3_M | 3.4 | | | [GGUF](https://huggingface.co/mradermacher/BlackSheep-Mistral-RP-7B-i1-GGUF/resolve/main/BlackSheep-Mistral-RP-7B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.6 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/BlackSheep-Mistral-RP-7B-i1-GGUF/resolve/main/BlackSheep-Mistral-RP-7B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 3.9 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/BlackSheep-Mistral-RP-7B-i1-GGUF/resolve/main/BlackSheep-Mistral-RP-7B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.0 | | | [GGUF](https://huggingface.co/mradermacher/BlackSheep-Mistral-RP-7B-i1-GGUF/resolve/main/BlackSheep-Mistral-RP-7B.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 4.2 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/BlackSheep-Mistral-RP-7B-i1-GGUF/resolve/main/BlackSheep-Mistral-RP-7B.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 4.2 | fast on arm+i8mm, low quality | | [GGUF](https://huggingface.co/mradermacher/BlackSheep-Mistral-RP-7B-i1-GGUF/resolve/main/BlackSheep-Mistral-RP-7B.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 4.2 | fast on arm+sve, low quality | | [GGUF](https://huggingface.co/mradermacher/BlackSheep-Mistral-RP-7B-i1-GGUF/resolve/main/BlackSheep-Mistral-RP-7B.i1-Q4_0.gguf) | i1-Q4_0 | 4.2 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/BlackSheep-Mistral-RP-7B-i1-GGUF/resolve/main/BlackSheep-Mistral-RP-7B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.2 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/BlackSheep-Mistral-RP-7B-i1-GGUF/resolve/main/BlackSheep-Mistral-RP-7B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/BlackSheep-Mistral-RP-7B-i1-GGUF/resolve/main/BlackSheep-Mistral-RP-7B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.1 | | | [GGUF](https://huggingface.co/mradermacher/BlackSheep-Mistral-RP-7B-i1-GGUF/resolve/main/BlackSheep-Mistral-RP-7B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.2 | | | [GGUF](https://huggingface.co/mradermacher/BlackSheep-Mistral-RP-7B-i1-GGUF/resolve/main/BlackSheep-Mistral-RP-7B.i1-Q6_K.gguf) | i1-Q6_K | 6.0 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
marstafk0/mark-lora2
marstafk0
"2025-02-21T18:30:16Z"
0
0
diffusers
[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]
text-to-image
"2025-02-21T18:16:03Z"
--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: Mark --- # Mark Lora2 <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `Mark` to trigger the image generation. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('marstafk0/mark-lora2', weight_name='lora.safetensors') image = pipeline('your prompt').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
facebook/mms-tts-bgw
facebook
"2023-09-01T14:22:36Z"
107
0
transformers
[ "transformers", "pytorch", "safetensors", "vits", "text-to-audio", "mms", "text-to-speech", "arxiv:2305.13516", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]
text-to-speech
"2023-09-01T14:22:19Z"
--- license: cc-by-nc-4.0 tags: - mms - vits pipeline_tag: text-to-speech --- # Massively Multilingual Speech (MMS): Bhatri Text-to-Speech This repository contains the **Bhatri (bgw)** language text-to-speech (TTS) model checkpoint. This model is part of Facebook's [Massively Multilingual Speech](https://arxiv.org/abs/2305.13516) project, aiming to provide speech technology across a diverse range of languages. You can find more details about the supported languages and their ISO 639-3 codes in the [MMS Language Coverage Overview](https://dl.fbaipublicfiles.com/mms/misc/language_coverage_mms.html), and see all MMS-TTS checkpoints on the Hugging Face Hub: [facebook/mms-tts](https://huggingface.co/models?sort=trending&search=facebook%2Fmms-tts). MMS-TTS is available in the 🤗 Transformers library from version 4.33 onwards. ## Model Details VITS (**V**ariational **I**nference with adversarial learning for end-to-end **T**ext-to-**S**peech) is an end-to-end speech synthesis model that predicts a speech waveform conditional on an input text sequence. It is a conditional variational autoencoder (VAE) comprised of a posterior encoder, decoder, and conditional prior. A set of spectrogram-based acoustic features are predicted by the flow-based module, which is formed of a Transformer-based text encoder and multiple coupling layers. The spectrogram is decoded using a stack of transposed convolutional layers, much in the same style as the HiFi-GAN vocoder. Motivated by the one-to-many nature of the TTS problem, where the same text input can be spoken in multiple ways, the model also includes a stochastic duration predictor, which allows the model to synthesise speech with different rhythms from the same input text. The model is trained end-to-end with a combination of losses derived from variational lower bound and adversarial training. To improve the expressiveness of the model, normalizing flows are applied to the conditional prior distribution. During inference, the text encodings are up-sampled based on the duration prediction module, and then mapped into the waveform using a cascade of the flow module and HiFi-GAN decoder. Due to the stochastic nature of the duration predictor, the model is non-deterministic, and thus requires a fixed seed to generate the same speech waveform. For the MMS project, a separate VITS checkpoint is trained on each langauge. ## Usage MMS-TTS is available in the 🤗 Transformers library from version 4.33 onwards. To use this checkpoint, first install the latest version of the library: ``` pip install --upgrade transformers accelerate ``` Then, run inference with the following code-snippet: ```python from transformers import VitsModel, AutoTokenizer import torch model = VitsModel.from_pretrained("facebook/mms-tts-bgw") tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-bgw") text = "some example text in the Bhatri language" inputs = tokenizer(text, return_tensors="pt") with torch.no_grad(): output = model(**inputs).waveform ``` The resulting waveform can be saved as a `.wav` file: ```python import scipy scipy.io.wavfile.write("techno.wav", rate=model.config.sampling_rate, data=output) ``` Or displayed in a Jupyter Notebook / Google Colab: ```python from IPython.display import Audio Audio(output, rate=model.config.sampling_rate) ``` ## BibTex citation This model was developed by Vineel Pratap et al. from Meta AI. If you use the model, consider citing the MMS paper: ``` @article{pratap2023mms, title={Scaling Speech Technology to 1,000+ Languages}, author={Vineel Pratap and Andros Tjandra and Bowen Shi and Paden Tomasello and Arun Babu and Sayani Kundu and Ali Elkahky and Zhaoheng Ni and Apoorv Vyas and Maryam Fazel-Zarandi and Alexei Baevski and Yossi Adi and Xiaohui Zhang and Wei-Ning Hsu and Alexis Conneau and Michael Auli}, journal={arXiv}, year={2023} } ``` ## License The model is licensed as **CC-BY-NC 4.0**.
Malar/BM_MLM_EXT_230221063231
Malar
"2023-02-21T06:34:37Z"
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "roberta", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
"2023-02-21T06:32:36Z"
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: BM_MLM_EXT_230221063231 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. --> # BM_MLM_EXT_230221063231 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8944 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 108 | 1.8893 | | No log | 2.0 | 216 | 1.8124 | | No log | 3.0 | 324 | 1.8304 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.8.0+cu111 - Datasets 1.11.0 - Tokenizers 0.12.1
ZhangShenao/SELM-Llama-3.2-3B-Instruct-re-new-iter-1
ZhangShenao
"2025-01-10T03:52:47Z"
17
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "alignment-handbook", "trl", "dpo", "generated_from_trainer", "conversational", "dataset:updated", "dataset:original", "base_model:meta-llama/Llama-3.2-3B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-3B-Instruct", "license:llama3.2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
"2025-01-10T03:31:28Z"
--- library_name: transformers license: llama3.2 base_model: meta-llama/Llama-3.2-3B-Instruct tags: - alignment-handbook - trl - dpo - generated_from_trainer - trl - dpo - generated_from_trainer datasets: - updated - original model-index: - name: SELM-Llama-3.2-3B-Instruct-re-new-iter-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. --> # SELM-Llama-3.2-3B-Instruct-re-new-iter-1 This model is a fine-tuned version of [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) on the updated and the original datasets. ## 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-07 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 128 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.45.0 - Pytorch 2.5.1+cu124 - Datasets 2.14.6 - Tokenizers 0.20.3
zacdennis/gradientascent
zacdennis
"2023-07-27T22:16:30Z"
0
0
null
[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
"2023-07-27T22:16:26Z"
--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: gradientascent results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 109.50 +/- 14.23 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
lesso16/b311c13d-01a1-4346-9dc9-db322bd04ffa
lesso16
"2025-03-16T11:27:47Z"
5
0
peft
[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "custom_code", "base_model:NousResearch/CodeLlama-13b-hf-flash", "base_model:adapter:NousResearch/CodeLlama-13b-hf-flash", "region:us" ]
null
"2025-03-10T09:32:55Z"
--- library_name: peft base_model: NousResearch/CodeLlama-13b-hf-flash tags: - axolotl - generated_from_trainer model-index: - name: b311c13d-01a1-4346-9dc9-db322bd04ffa results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <br> # b311c13d-01a1-4346-9dc9-db322bd04ffa This model is a fine-tuned version of [NousResearch/CodeLlama-13b-hf-flash](https://huggingface.co/NousResearch/CodeLlama-13b-hf-flash) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0949 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000216 - train_batch_size: 4 - eval_batch_size: 4 - seed: 160 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 500 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | No log | 0.0006 | 1 | 1.0067 | | 0.7748 | 0.3221 | 500 | 0.0949 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
PrunaAI/nyu-visionx-cambrian-phi3-3b-QUANTO-int8bit-smashed
PrunaAI
"2024-07-19T09:27:07Z"
4
0
transformers
[ "transformers", "pruna-ai", "base_model:nyu-visionx/cambrian-phi3-3b", "base_model:finetune:nyu-visionx/cambrian-phi3-3b", "endpoints_compatible", "region:us" ]
null
"2024-07-17T11:19:58Z"
--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: nyu-visionx/cambrian-phi3-3b metrics: - memory_disk - memory_inference - inference_latency - inference_throughput - inference_CO2_emissions - inference_energy_consumption tags: - pruna-ai --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> <!-- header end --> [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/rskEr4BZJx) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) **Frequently Asked Questions** - ***How does the compression work?*** The model is compressed with quanto. - ***How does the model quality change?*** The quality of the model output might vary compared to the base model. - ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - ***What is the model format?*** We use safetensors. - ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data. - ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. - ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases. ## Setup You can run the smashed model with these steps: 0. Check requirements from the original repo nyu-visionx/cambrian-phi3-3b installed. In particular, check python, cuda, and transformers versions. 1. Make sure that you have installed quantization related packages. ```bash pip install quanto ``` 2. Load & run the model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer IMPORTS model = AutoModelForCausalLM.from_pretrained("PrunaAI/nyu-visionx-cambrian-phi3-3b-QUANTO-int8bit-smashed", trust_remote_code=True, device_map='auto') tokenizer = AutoTokenizer.from_pretrained("nyu-visionx/cambrian-phi3-3b") input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"] outputs = model.generate(input_ids, max_new_tokens=216) tokenizer.decode(outputs[0]) ``` ## Configurations The configuration info are in `smash_config.json`. ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model nyu-visionx/cambrian-phi3-3b before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
TrinhDacPhu/finetune4en-vi
TrinhDacPhu
"2024-06-24T23:35:25Z"
12
0
transformers
[ "transformers", "safetensors", "marian", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
"2024-06-17T22:09:22Z"
--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Halcyonindo/an1kulora
Halcyonindo
"2023-05-12T17:27:24Z"
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
null
"2023-05-12T17:26:20Z"
--- license: creativeml-openrail-m ---
mradermacher/Hermes-Qwen1.5-MoE-A2.7B-Chat-240405-GGUF
mradermacher
"2024-11-24T19:17:42Z"
20
1
transformers
[ "transformers", "gguf", "en", "base_model:ByteResearch/Hermes-Qwen1.5-MoE-A2.7B-Chat-240405", "base_model:quantized:ByteResearch/Hermes-Qwen1.5-MoE-A2.7B-Chat-240405", "endpoints_compatible", "region:us", "conversational" ]
null
"2024-11-24T17:53:39Z"
--- base_model: ByteResearch/Hermes-Qwen1.5-MoE-A2.7B-Chat-240405 language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> static quants of https://huggingface.co/ByteResearch/Hermes-Qwen1.5-MoE-A2.7B-Chat-240405 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Hermes-Qwen1.5-MoE-A2.7B-Chat-240405-GGUF/resolve/main/Hermes-Qwen1.5-MoE-A2.7B-Chat-240405.Q2_K.gguf) | Q2_K | 6.0 | | | [GGUF](https://huggingface.co/mradermacher/Hermes-Qwen1.5-MoE-A2.7B-Chat-240405-GGUF/resolve/main/Hermes-Qwen1.5-MoE-A2.7B-Chat-240405.Q3_K_S.gguf) | Q3_K_S | 6.9 | | | [GGUF](https://huggingface.co/mradermacher/Hermes-Qwen1.5-MoE-A2.7B-Chat-240405-GGUF/resolve/main/Hermes-Qwen1.5-MoE-A2.7B-Chat-240405.Q3_K_M.gguf) | Q3_K_M | 7.5 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Hermes-Qwen1.5-MoE-A2.7B-Chat-240405-GGUF/resolve/main/Hermes-Qwen1.5-MoE-A2.7B-Chat-240405.Q3_K_L.gguf) | Q3_K_L | 7.8 | | | [GGUF](https://huggingface.co/mradermacher/Hermes-Qwen1.5-MoE-A2.7B-Chat-240405-GGUF/resolve/main/Hermes-Qwen1.5-MoE-A2.7B-Chat-240405.IQ4_XS.gguf) | IQ4_XS | 8.0 | | | [GGUF](https://huggingface.co/mradermacher/Hermes-Qwen1.5-MoE-A2.7B-Chat-240405-GGUF/resolve/main/Hermes-Qwen1.5-MoE-A2.7B-Chat-240405.Q4_0_4_4.gguf) | Q4_0_4_4 | 8.2 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/Hermes-Qwen1.5-MoE-A2.7B-Chat-240405-GGUF/resolve/main/Hermes-Qwen1.5-MoE-A2.7B-Chat-240405.Q4_K_S.gguf) | Q4_K_S | 8.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Hermes-Qwen1.5-MoE-A2.7B-Chat-240405-GGUF/resolve/main/Hermes-Qwen1.5-MoE-A2.7B-Chat-240405.Q4_K_M.gguf) | Q4_K_M | 9.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Hermes-Qwen1.5-MoE-A2.7B-Chat-240405-GGUF/resolve/main/Hermes-Qwen1.5-MoE-A2.7B-Chat-240405.Q5_K_S.gguf) | Q5_K_S | 10.3 | | | [GGUF](https://huggingface.co/mradermacher/Hermes-Qwen1.5-MoE-A2.7B-Chat-240405-GGUF/resolve/main/Hermes-Qwen1.5-MoE-A2.7B-Chat-240405.Q5_K_M.gguf) | Q5_K_M | 10.9 | | | [GGUF](https://huggingface.co/mradermacher/Hermes-Qwen1.5-MoE-A2.7B-Chat-240405-GGUF/resolve/main/Hermes-Qwen1.5-MoE-A2.7B-Chat-240405.Q6_K.gguf) | Q6_K | 12.9 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Hermes-Qwen1.5-MoE-A2.7B-Chat-240405-GGUF/resolve/main/Hermes-Qwen1.5-MoE-A2.7B-Chat-240405.Q8_0.gguf) | Q8_0 | 15.3 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
LarryAIDraw/nico_robin_v1
LarryAIDraw
"2023-11-25T01:38:25Z"
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
null
"2023-11-25T01:35:44Z"
--- license: creativeml-openrail-m --- https://civitai.com/models/211150/nico-robin-one-piece
SmallDoge/Qwen2.5-3B-Instruct-SmallThoughts
SmallDoge
"2025-03-14T10:45:05Z"
0
0
transformers
[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
"2025-03-14T10:35:21Z"
--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
TransferGraph/Jeevesh8_lecun_feather_berts-3-finetuned-lora-tweet_eval_hate
TransferGraph
"2024-02-29T13:43:37Z"
0
0
peft
[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:Jeevesh8/lecun_feather_berts-3", "base_model:adapter:Jeevesh8/lecun_feather_berts-3", "model-index", "region:us" ]
text-classification
"2024-02-29T13:43:35Z"
--- library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: Jeevesh8/lecun_feather_berts-3 model-index: - name: Jeevesh8_lecun_feather_berts-3-finetuned-lora-tweet_eval_hate results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: hate split: validation args: hate metrics: - type: accuracy value: 0.73 name: accuracy --- <!-- 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. --> # Jeevesh8_lecun_feather_berts-3-finetuned-lora-tweet_eval_hate This model is a fine-tuned version of [Jeevesh8/lecun_feather_berts-3](https://huggingface.co/Jeevesh8/lecun_feather_berts-3) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.73 ## 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: 0.0004 - 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: 4 ### Training results | accuracy | train_loss | epoch | |:--------:|:----------:|:-----:| | 0.499 | None | 0 | | 0.701 | 0.5912 | 0 | | 0.712 | 0.4743 | 1 | | 0.721 | 0.4435 | 2 | | 0.73 | 0.4307 | 3 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
sivaranjanisundarraj/finetuning-sentiment-model-imdb-3000
sivaranjanisundarraj
"2024-05-25T06:44:32Z"
108
0
transformers
[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
"2024-05-25T06:39:22Z"
--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-imdb-3000 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. --> # finetuning-sentiment-model-imdb-3000 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3371 - Accuracy: 0.8767 - F1: 0.8810 ## 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: 2 ### Training results ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
itlwas/Marco-o1-Q4_K_M-GGUF
itlwas
"2024-12-24T12:47:34Z"
21
0
transformers
[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "base_model:AIDC-AI/Marco-o1", "base_model:quantized:AIDC-AI/Marco-o1", "license:apache-2.0", "region:us", "conversational" ]
null
"2024-12-24T12:47:12Z"
--- license: apache-2.0 library_name: transformers inference: false base_model: AIDC-AI/Marco-o1 tags: - llama-cpp - gguf-my-repo --- # AIronMind/Marco-o1-Q4_K_M-GGUF This model was converted to GGUF format from [`AIDC-AI/Marco-o1`](https://huggingface.co/AIDC-AI/Marco-o1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/AIDC-AI/Marco-o1) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo AIronMind/Marco-o1-Q4_K_M-GGUF --hf-file marco-o1-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo AIronMind/Marco-o1-Q4_K_M-GGUF --hf-file marco-o1-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo AIronMind/Marco-o1-Q4_K_M-GGUF --hf-file marco-o1-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo AIronMind/Marco-o1-Q4_K_M-GGUF --hf-file marco-o1-q4_k_m.gguf -c 2048 ```
kejolong/cnmodel
kejolong
"2023-12-16T20:47:14Z"
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
null
"2023-11-15T16:24:18Z"
--- license: creativeml-openrail-m ---
clarxus/61766e53-cbff-40df-b751-ff9b536670d4
clarxus
"2025-02-04T10:13:56Z"
10
0
peft
[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/SmolLM-1.7B", "base_model:adapter:unsloth/SmolLM-1.7B", "license:apache-2.0", "region:us" ]
null
"2025-02-04T10:02:31Z"
--- library_name: peft license: apache-2.0 base_model: unsloth/SmolLM-1.7B tags: - axolotl - generated_from_trainer model-index: - name: 61766e53-cbff-40df-b751-ff9b536670d4 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/SmolLM-1.7B bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 3e2ed20f95d2f384_train_data.json ds_type: json format: custom path: /workspace/input_data/3e2ed20f95d2f384_train_data.json type: field_input: student_answer field_instruction: question field_output: reference_answer format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto do_eval: true early_stopping_patience: 5 eval_batch_size: 4 eval_max_new_tokens: 128 eval_steps: 50 eval_table_size: null evals_per_epoch: null flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: true hub_model_id: clarxus/61766e53-cbff-40df-b751-ff9b536670d4 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 128 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_memory: 0: 75GB max_steps: 200 micro_batch_size: 8 mlflow_experiment_name: /tmp/3e2ed20f95d2f384_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optim_args: adam_beta1: 0.9 adam_beta2: 0.95 adam_epsilon: 1.0e-05 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 50 saves_per_epoch: null sequence_len: 1024 strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: techspear-hub wandb_mode: online wandb_name: e9396a3b-cad7-4197-aa37-3ad515193e96 wandb_project: Gradients-On-Seven wandb_run: your_name wandb_runid: e9396a3b-cad7-4197-aa37-3ad515193e96 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 61766e53-cbff-40df-b751-ff9b536670d4 This model is a fine-tuned version of [unsloth/SmolLM-1.7B](https://huggingface.co/unsloth/SmolLM-1.7B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2243 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-05 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 2.1324 | 0.0028 | 1 | 3.2835 | | 1.4925 | 0.1397 | 50 | 1.0052 | | 0.7377 | 0.2793 | 100 | 0.5217 | | 0.4174 | 0.4190 | 150 | 0.2874 | | 0.3831 | 0.5587 | 200 | 0.2243 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
BlackKakapo/cupidon-small-ro
BlackKakapo
"2025-03-27T15:27:28Z"
10
2
sentence-transformers
[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "transformers", "ro", "dataset:BlackKakapo/RoSTSC", "base_model:sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2", "base_model:finetune:sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
sentence-similarity
"2025-03-21T15:49:42Z"
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers language: - ro language_creators: - machine-generated dataset: - ro_sts license: apache-2.0 datasets: - BlackKakapo/RoSTSC base_model: - sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 --- # 🔥 cupidon-small-ro Here comes cupidon-small-ro — small in name, but ready to play with the big models. Fine-tuned from the powerful sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2, this sentence-transformers model captures Romanian sentence meaning with impressive accuracy. It’s compact enough to stay efficient, but packs a semantic punch that hits deep. Think of it as the model that proves "small" can still break hearts — especially in semantic textual similarity, search, or clustering. 💔💬 ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ```bash pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('BlackKakapo/cupidon-small-ro') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('BlackKakapo/cupidon-small-ro') model = AutoModel.from_pretrained('BlackKakapo/cupidon-small-ro') ``` ## License This dataset is licensed under **Apache 2.0**. ## Citation If you use BlackKakapo/cupidon-mini-ro in your research, please cite this model as follows: ``` @misc{cupidon-small-ro, title={BlackKakapo/cupidon-small-ro}, author={BlackKakapo}, year={2025}, } ```
MayBashendy/ASAP_FineTuningBERT_AugV3_k5_task1_organization_fold4
MayBashendy
"2024-11-09T21:25:22Z"
162
0
transformers
[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
"2024-11-09T20:13:02Z"
--- library_name: transformers license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer model-index: - name: ASAP_FineTuningBERT_AugV3_k5_task1_organization_fold4 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. --> # ASAP_FineTuningBERT_AugV3_k5_task1_organization_fold4 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8540 - Qwk: 0.4885 - Mse: 0.8540 - Rmse: 0.9241 ## 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse | |:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|:------:| | No log | 0.0034 | 2 | 10.4541 | 0.0 | 10.4541 | 3.2333 | | No log | 0.0069 | 4 | 8.7845 | 0.0063 | 8.7845 | 2.9639 | | No log | 0.0103 | 6 | 7.0878 | 0.0056 | 7.0878 | 2.6623 | | No log | 0.0137 | 8 | 5.6467 | 0.0016 | 5.6467 | 2.3763 | | No log | 0.0172 | 10 | 4.4096 | 0.0018 | 4.4096 | 2.0999 | | No log | 0.0206 | 12 | 3.4517 | 0.0352 | 3.4517 | 1.8579 | | No log | 0.0240 | 14 | 2.6451 | 0.0157 | 2.6451 | 1.6264 | | No log | 0.0274 | 16 | 2.1055 | 0.0079 | 2.1055 | 1.4510 | | No log | 0.0309 | 18 | 1.6913 | 0.0079 | 1.6913 | 1.3005 | | No log | 0.0343 | 20 | 1.5597 | 0.0079 | 1.5597 | 1.2489 | | No log | 0.0377 | 22 | 1.7036 | 0.0079 | 1.7036 | 1.3052 | | No log | 0.0412 | 24 | 1.4501 | 0.0298 | 1.4501 | 1.2042 | | No log | 0.0446 | 26 | 1.6450 | 0.0061 | 1.6450 | 1.2826 | | No log | 0.0480 | 28 | 1.4894 | 0.0047 | 1.4894 | 1.2204 | | No log | 0.0515 | 30 | 1.4130 | 0.0392 | 1.4130 | 1.1887 | | No log | 0.0549 | 32 | 1.6155 | 0.0095 | 1.6155 | 1.2710 | | No log | 0.0583 | 34 | 1.7771 | 0.0079 | 1.7771 | 1.3331 | | No log | 0.0617 | 36 | 2.0169 | 0.0089 | 2.0169 | 1.4202 | | No log | 0.0652 | 38 | 1.6396 | 0.0105 | 1.6396 | 1.2805 | | No log | 0.0686 | 40 | 1.1523 | 0.0826 | 1.1523 | 1.0734 | | No log | 0.0720 | 42 | 1.0491 | 0.0715 | 1.0491 | 1.0243 | | No log | 0.0755 | 44 | 1.0917 | 0.0738 | 1.0917 | 1.0448 | | No log | 0.0789 | 46 | 1.3192 | 0.0915 | 1.3192 | 1.1485 | | No log | 0.0823 | 48 | 2.0679 | 0.0079 | 2.0679 | 1.4380 | | No log | 0.0858 | 50 | 2.0706 | 0.0079 | 2.0706 | 1.4390 | | No log | 0.0892 | 52 | 1.5654 | 0.0068 | 1.5654 | 1.2511 | | No log | 0.0926 | 54 | 1.3798 | 0.0718 | 1.3798 | 1.1747 | | No log | 0.0961 | 56 | 1.3267 | 0.1049 | 1.3267 | 1.1518 | | No log | 0.0995 | 58 | 1.2978 | 0.1266 | 1.2978 | 1.1392 | | No log | 0.1029 | 60 | 1.3724 | 0.0549 | 1.3724 | 1.1715 | | No log | 0.1063 | 62 | 1.5236 | 0.0076 | 1.5236 | 1.2343 | | No log | 0.1098 | 64 | 1.6220 | 0.0030 | 1.6220 | 1.2736 | | No log | 0.1132 | 66 | 1.5378 | 0.0024 | 1.5378 | 1.2401 | | No log | 0.1166 | 68 | 1.6463 | 0.0033 | 1.6463 | 1.2831 | | No log | 0.1201 | 70 | 1.7358 | 0.0002 | 1.7358 | 1.3175 | | No log | 0.1235 | 72 | 1.6195 | -0.0036 | 1.6195 | 1.2726 | | No log | 0.1269 | 74 | 1.4822 | 0.0152 | 1.4822 | 1.2174 | | No log | 0.1304 | 76 | 1.2915 | 0.0599 | 1.2915 | 1.1364 | | No log | 0.1338 | 78 | 1.1804 | 0.0669 | 1.1804 | 1.0865 | | No log | 0.1372 | 80 | 1.2141 | 0.0485 | 1.2141 | 1.1019 | | No log | 0.1407 | 82 | 1.3422 | -0.0004 | 1.3422 | 1.1585 | | No log | 0.1441 | 84 | 1.1635 | 0.0737 | 1.1635 | 1.0787 | | No log | 0.1475 | 86 | 0.9490 | 0.0682 | 0.9490 | 0.9742 | | No log | 0.1509 | 88 | 0.8979 | 0.0682 | 0.8979 | 0.9476 | | No log | 0.1544 | 90 | 0.9215 | 0.0630 | 0.9215 | 0.9600 | | No log | 0.1578 | 92 | 1.0162 | 0.0766 | 1.0162 | 1.0081 | | No log | 0.1612 | 94 | 1.0891 | 0.0804 | 1.0891 | 1.0436 | | No log | 0.1647 | 96 | 1.0281 | 0.0957 | 1.0281 | 1.0139 | | No log | 0.1681 | 98 | 0.9041 | 0.0992 | 0.9041 | 0.9508 | | No log | 0.1715 | 100 | 0.8600 | 0.0842 | 0.8600 | 0.9274 | | No log | 0.1750 | 102 | 0.8940 | 0.0845 | 0.8940 | 0.9455 | | No log | 0.1784 | 104 | 0.8905 | 0.0823 | 0.8905 | 0.9437 | | No log | 0.1818 | 106 | 0.8699 | 0.0630 | 0.8699 | 0.9327 | | No log | 0.1852 | 108 | 0.9257 | 0.0643 | 0.9257 | 0.9621 | | No log | 0.1887 | 110 | 1.0056 | 0.0818 | 1.0056 | 1.0028 | | No log | 0.1921 | 112 | 0.9491 | 0.0678 | 0.9491 | 0.9742 | | No log | 0.1955 | 114 | 0.9983 | 0.0683 | 0.9983 | 0.9991 | | No log | 0.1990 | 116 | 1.0346 | 0.0737 | 1.0346 | 1.0171 | | No log | 0.2024 | 118 | 1.0319 | 0.0808 | 1.0319 | 1.0158 | | No log | 0.2058 | 120 | 1.0585 | 0.0957 | 1.0585 | 1.0289 | | No log | 0.2093 | 122 | 1.1211 | 0.1074 | 1.1211 | 1.0588 | | No log | 0.2127 | 124 | 1.2209 | 0.1052 | 1.2209 | 1.1049 | | No log | 0.2161 | 126 | 1.2090 | 0.1047 | 1.2090 | 1.0996 | | No log | 0.2196 | 128 | 1.0891 | 0.1219 | 1.0891 | 1.0436 | | No log | 0.2230 | 130 | 2.5667 | 0.0289 | 2.5667 | 1.6021 | | No log | 0.2264 | 132 | 4.8560 | -0.0172 | 4.8560 | 2.2036 | | No log | 0.2298 | 134 | 2.7746 | 0.0366 | 2.7746 | 1.6657 | | No log | 0.2333 | 136 | 0.8076 | 0.0995 | 0.8076 | 0.8987 | | No log | 0.2367 | 138 | 0.9279 | 0.0620 | 0.9279 | 0.9633 | | No log | 0.2401 | 140 | 1.0653 | 0.0579 | 1.0653 | 1.0322 | | No log | 0.2436 | 142 | 0.9947 | 0.0593 | 0.9947 | 0.9973 | | No log | 0.2470 | 144 | 0.8734 | 0.0459 | 0.8734 | 0.9346 | | No log | 0.2504 | 146 | 0.8209 | 0.0459 | 0.8209 | 0.9060 | | No log | 0.2539 | 148 | 0.8149 | 0.0459 | 0.8149 | 0.9027 | | No log | 0.2573 | 150 | 0.8122 | 0.0459 | 0.8122 | 0.9012 | | No log | 0.2607 | 152 | 0.8128 | 0.0459 | 0.8128 | 0.9016 | | No log | 0.2642 | 154 | 0.8080 | 0.0459 | 0.8080 | 0.8989 | | No log | 0.2676 | 156 | 0.8144 | 0.0617 | 0.8144 | 0.9024 | | No log | 0.2710 | 158 | 0.8260 | 0.0747 | 0.8260 | 0.9088 | | No log | 0.2744 | 160 | 0.8116 | 0.0724 | 0.8116 | 0.9009 | | No log | 0.2779 | 162 | 0.7971 | 0.0644 | 0.7971 | 0.8928 | | No log | 0.2813 | 164 | 0.8109 | 0.0558 | 0.8109 | 0.9005 | | No log | 0.2847 | 166 | 0.8364 | 0.0558 | 0.8364 | 0.9145 | | No log | 0.2882 | 168 | 0.7910 | 0.0558 | 0.7910 | 0.8894 | | No log | 0.2916 | 170 | 0.7807 | 0.0558 | 0.7807 | 0.8835 | | No log | 0.2950 | 172 | 0.7720 | 0.0620 | 0.7720 | 0.8786 | | No log | 0.2985 | 174 | 0.7883 | 0.0747 | 0.7883 | 0.8879 | | No log | 0.3019 | 176 | 0.7986 | 0.0814 | 0.7986 | 0.8937 | | No log | 0.3053 | 178 | 0.7986 | 0.0836 | 0.7986 | 0.8936 | | No log | 0.3087 | 180 | 0.7869 | 0.0747 | 0.7869 | 0.8871 | | No log | 0.3122 | 182 | 0.8133 | 0.0620 | 0.8133 | 0.9018 | | No log | 0.3156 | 184 | 0.8636 | 0.0459 | 0.8636 | 0.9293 | | No log | 0.3190 | 186 | 0.8813 | 0.0434 | 0.8813 | 0.9388 | | No log | 0.3225 | 188 | 0.8884 | 0.0549 | 0.8884 | 0.9426 | | No log | 0.3259 | 190 | 0.8814 | 0.0555 | 0.8814 | 0.9388 | | No log | 0.3293 | 192 | 0.8817 | 0.1626 | 0.8817 | 0.9390 | | No log | 0.3328 | 194 | 0.8910 | 0.2245 | 0.8910 | 0.9439 | | No log | 0.3362 | 196 | 0.8679 | 0.2799 | 0.8679 | 0.9316 | | No log | 0.3396 | 198 | 0.8133 | 0.1795 | 0.8133 | 0.9019 | | No log | 0.3431 | 200 | 0.8171 | 0.1940 | 0.8171 | 0.9040 | | No log | 0.3465 | 202 | 0.8469 | 0.2575 | 0.8469 | 0.9203 | | No log | 0.3499 | 204 | 0.8790 | 0.2307 | 0.8790 | 0.9376 | | No log | 0.3533 | 206 | 0.8847 | 0.1400 | 0.8847 | 0.9406 | | No log | 0.3568 | 208 | 0.9028 | 0.0502 | 0.9028 | 0.9501 | | No log | 0.3602 | 210 | 0.9008 | 0.0690 | 0.9008 | 0.9491 | | No log | 0.3636 | 212 | 0.8805 | 0.1004 | 0.8805 | 0.9383 | | No log | 0.3671 | 214 | 0.8424 | 0.0820 | 0.8424 | 0.9178 | | No log | 0.3705 | 216 | 0.8325 | 0.0753 | 0.8325 | 0.9124 | | No log | 0.3739 | 218 | 0.8144 | 0.0874 | 0.8144 | 0.9025 | | No log | 0.3774 | 220 | 0.8314 | 0.2189 | 0.8314 | 0.9118 | | No log | 0.3808 | 222 | 0.8541 | 0.3114 | 0.8541 | 0.9242 | | No log | 0.3842 | 224 | 0.8353 | 0.2907 | 0.8353 | 0.9140 | | No log | 0.3877 | 226 | 0.8128 | 0.2936 | 0.8128 | 0.9015 | | No log | 0.3911 | 228 | 0.7832 | 0.2022 | 0.7832 | 0.8850 | | No log | 0.3945 | 230 | 0.7746 | 0.1606 | 0.7746 | 0.8801 | | No log | 0.3979 | 232 | 0.7689 | 0.1796 | 0.7689 | 0.8768 | | No log | 0.4014 | 234 | 0.7650 | 0.1695 | 0.7650 | 0.8747 | | No log | 0.4048 | 236 | 0.7645 | 0.1502 | 0.7645 | 0.8743 | | No log | 0.4082 | 238 | 0.7622 | 0.0676 | 0.7622 | 0.8730 | | No log | 0.4117 | 240 | 0.7626 | 0.0693 | 0.7626 | 0.8733 | | No log | 0.4151 | 242 | 0.7589 | 0.0777 | 0.7589 | 0.8711 | | No log | 0.4185 | 244 | 0.7769 | 0.3239 | 0.7769 | 0.8814 | | No log | 0.4220 | 246 | 0.8533 | 0.3712 | 0.8533 | 0.9238 | | No log | 0.4254 | 248 | 0.8826 | 0.3042 | 0.8826 | 0.9395 | | No log | 0.4288 | 250 | 0.8974 | 0.2468 | 0.8974 | 0.9473 | | No log | 0.4322 | 252 | 0.8558 | 0.2418 | 0.8558 | 0.9251 | | No log | 0.4357 | 254 | 0.8683 | 0.0838 | 0.8683 | 0.9318 | | No log | 0.4391 | 256 | 1.8847 | 0.0116 | 1.8847 | 1.3728 | | No log | 0.4425 | 258 | 1.9193 | 0.0196 | 1.9193 | 1.3854 | | No log | 0.4460 | 260 | 1.7541 | 0.0279 | 1.7541 | 1.3244 | | No log | 0.4494 | 262 | 1.1901 | 0.1307 | 1.1901 | 1.0909 | | No log | 0.4528 | 264 | 0.8152 | 0.1156 | 0.8152 | 0.9029 | | No log | 0.4563 | 266 | 0.7355 | 0.0860 | 0.7355 | 0.8576 | | No log | 0.4597 | 268 | 0.7253 | 0.0693 | 0.7253 | 0.8517 | | No log | 0.4631 | 270 | 0.7311 | 0.0728 | 0.7311 | 0.8551 | | No log | 0.4666 | 272 | 0.7594 | 0.2769 | 0.7594 | 0.8715 | | No log | 0.4700 | 274 | 0.7950 | 0.4128 | 0.7950 | 0.8916 | | No log | 0.4734 | 276 | 0.8190 | 0.3663 | 0.8190 | 0.9050 | | No log | 0.4768 | 278 | 0.8627 | 0.3435 | 0.8627 | 0.9288 | | No log | 0.4803 | 280 | 0.8799 | 0.3281 | 0.8799 | 0.9381 | | No log | 0.4837 | 282 | 0.8792 | 0.3281 | 0.8792 | 0.9377 | | No log | 0.4871 | 284 | 0.8162 | 0.3401 | 0.8162 | 0.9034 | | No log | 0.4906 | 286 | 0.7772 | 0.4092 | 0.7772 | 0.8816 | | No log | 0.4940 | 288 | 0.7801 | 0.3999 | 0.7801 | 0.8832 | | No log | 0.4974 | 290 | 0.7471 | 0.3607 | 0.7471 | 0.8643 | | No log | 0.5009 | 292 | 0.7161 | 0.2118 | 0.7161 | 0.8462 | | No log | 0.5043 | 294 | 0.7337 | 0.3039 | 0.7337 | 0.8565 | | No log | 0.5077 | 296 | 0.7708 | 0.4149 | 0.7708 | 0.8779 | | No log | 0.5111 | 298 | 0.7610 | 0.3708 | 0.7610 | 0.8724 | | No log | 0.5146 | 300 | 0.7456 | 0.3050 | 0.7456 | 0.8635 | | No log | 0.5180 | 302 | 0.7360 | 0.2361 | 0.7360 | 0.8579 | | No log | 0.5214 | 304 | 0.7349 | 0.2281 | 0.7349 | 0.8573 | | No log | 0.5249 | 306 | 0.7285 | 0.2043 | 0.7285 | 0.8536 | | No log | 0.5283 | 308 | 0.7070 | 0.1514 | 0.7070 | 0.8408 | | No log | 0.5317 | 310 | 0.7056 | 0.2389 | 0.7056 | 0.8400 | | No log | 0.5352 | 312 | 0.6916 | 0.1769 | 0.6916 | 0.8316 | | No log | 0.5386 | 314 | 0.6841 | 0.0977 | 0.6841 | 0.8271 | | No log | 0.5420 | 316 | 0.7423 | 0.1207 | 0.7423 | 0.8616 | | No log | 0.5455 | 318 | 0.9220 | 0.1621 | 0.9220 | 0.9602 | | No log | 0.5489 | 320 | 1.0175 | 0.2216 | 1.0175 | 1.0087 | | No log | 0.5523 | 322 | 1.0533 | 0.2130 | 1.0533 | 1.0263 | | No log | 0.5557 | 324 | 0.9320 | 0.2034 | 0.9320 | 0.9654 | | No log | 0.5592 | 326 | 0.7331 | 0.1527 | 0.7331 | 0.8562 | | No log | 0.5626 | 328 | 0.6548 | 0.1728 | 0.6548 | 0.8092 | | No log | 0.5660 | 330 | 0.6968 | 0.3633 | 0.6968 | 0.8347 | | No log | 0.5695 | 332 | 0.7427 | 0.3691 | 0.7427 | 0.8618 | | No log | 0.5729 | 334 | 0.7882 | 0.2644 | 0.7882 | 0.8878 | | No log | 0.5763 | 336 | 0.8160 | 0.0905 | 0.8160 | 0.9033 | | No log | 0.5798 | 338 | 0.8233 | 0.0540 | 0.8233 | 0.9073 | | No log | 0.5832 | 340 | 0.8169 | 0.0581 | 0.8169 | 0.9038 | | No log | 0.5866 | 342 | 0.8322 | 0.0558 | 0.8322 | 0.9123 | | No log | 0.5901 | 344 | 0.8375 | 0.0558 | 0.8375 | 0.9152 | | No log | 0.5935 | 346 | 0.8434 | 0.0558 | 0.8434 | 0.9184 | | No log | 0.5969 | 348 | 0.8369 | 0.0655 | 0.8369 | 0.9148 | | No log | 0.6003 | 350 | 0.8578 | 0.0558 | 0.8578 | 0.9262 | | No log | 0.6038 | 352 | 0.8761 | 0.0533 | 0.8761 | 0.9360 | | No log | 0.6072 | 354 | 0.8633 | 0.0533 | 0.8633 | 0.9291 | | No log | 0.6106 | 356 | 0.8311 | 0.0533 | 0.8311 | 0.9117 | | No log | 0.6141 | 358 | 0.8233 | 0.0533 | 0.8233 | 0.9074 | | No log | 0.6175 | 360 | 0.8318 | 0.0545 | 0.8318 | 0.9120 | | No log | 0.6209 | 362 | 0.8117 | 0.0672 | 0.8117 | 0.9010 | | No log | 0.6244 | 364 | 0.7745 | 0.1929 | 0.7745 | 0.8800 | | No log | 0.6278 | 366 | 0.7582 | 0.3273 | 0.7582 | 0.8707 | | No log | 0.6312 | 368 | 0.7591 | 0.3862 | 0.7591 | 0.8713 | | No log | 0.6346 | 370 | 0.7898 | 0.3950 | 0.7898 | 0.8887 | | No log | 0.6381 | 372 | 0.7906 | 0.3908 | 0.7906 | 0.8892 | | No log | 0.6415 | 374 | 0.7817 | 0.3939 | 0.7817 | 0.8841 | | No log | 0.6449 | 376 | 0.7472 | 0.4034 | 0.7472 | 0.8644 | | No log | 0.6484 | 378 | 0.6836 | 0.2956 | 0.6836 | 0.8268 | | No log | 0.6518 | 380 | 0.6678 | 0.2076 | 0.6678 | 0.8172 | | No log | 0.6552 | 382 | 0.6667 | 0.1808 | 0.6667 | 0.8165 | | No log | 0.6587 | 384 | 0.6672 | 0.2037 | 0.6672 | 0.8168 | | No log | 0.6621 | 386 | 0.6815 | 0.3610 | 0.6815 | 0.8255 | | No log | 0.6655 | 388 | 0.7429 | 0.3978 | 0.7429 | 0.8619 | | No log | 0.6690 | 390 | 0.8098 | 0.3911 | 0.8098 | 0.8999 | | No log | 0.6724 | 392 | 0.8099 | 0.3792 | 0.8099 | 0.9000 | | No log | 0.6758 | 394 | 0.8004 | 0.3823 | 0.8004 | 0.8946 | | No log | 0.6792 | 396 | 0.7312 | 0.3793 | 0.7312 | 0.8551 | | No log | 0.6827 | 398 | 0.7154 | 0.3665 | 0.7154 | 0.8458 | | No log | 0.6861 | 400 | 0.6883 | 0.3723 | 0.6883 | 0.8297 | | No log | 0.6895 | 402 | 0.6618 | 0.3733 | 0.6618 | 0.8135 | | No log | 0.6930 | 404 | 0.6510 | 0.3558 | 0.6510 | 0.8068 | | No log | 0.6964 | 406 | 0.6427 | 0.3455 | 0.6427 | 0.8017 | | No log | 0.6998 | 408 | 0.6877 | 0.2104 | 0.6877 | 0.8293 | | No log | 0.7033 | 410 | 0.7691 | 0.1952 | 0.7691 | 0.8770 | | No log | 0.7067 | 412 | 0.6090 | 0.4214 | 0.6090 | 0.7804 | | No log | 0.7101 | 414 | 0.6570 | 0.4416 | 0.6570 | 0.8105 | | No log | 0.7136 | 416 | 0.6584 | 0.4009 | 0.6584 | 0.8114 | | No log | 0.7170 | 418 | 0.6512 | 0.3557 | 0.6512 | 0.8070 | | No log | 0.7204 | 420 | 0.7077 | 0.2119 | 0.7077 | 0.8413 | | No log | 0.7238 | 422 | 0.7783 | 0.1751 | 0.7783 | 0.8822 | | No log | 0.7273 | 424 | 0.7581 | 0.1718 | 0.7581 | 0.8707 | | No log | 0.7307 | 426 | 0.7271 | 0.1811 | 0.7271 | 0.8527 | | No log | 0.7341 | 428 | 0.7055 | 0.2054 | 0.7055 | 0.8399 | | No log | 0.7376 | 430 | 0.6621 | 0.3369 | 0.6621 | 0.8137 | | No log | 0.7410 | 432 | 0.6885 | 0.3977 | 0.6885 | 0.8297 | | No log | 0.7444 | 434 | 0.6920 | 0.4186 | 0.6920 | 0.8318 | | No log | 0.7479 | 436 | 0.6917 | 0.4300 | 0.6917 | 0.8317 | | No log | 0.7513 | 438 | 0.6280 | 0.4431 | 0.6280 | 0.7925 | | No log | 0.7547 | 440 | 0.5963 | 0.4405 | 0.5963 | 0.7722 | | No log | 0.7581 | 442 | 0.5874 | 0.4380 | 0.5874 | 0.7664 | | No log | 0.7616 | 444 | 0.5913 | 0.4492 | 0.5913 | 0.7689 | | No log | 0.7650 | 446 | 0.6154 | 0.4674 | 0.6154 | 0.7845 | | No log | 0.7684 | 448 | 0.6316 | 0.4507 | 0.6316 | 0.7947 | | No log | 0.7719 | 450 | 0.5995 | 0.4604 | 0.5995 | 0.7743 | | No log | 0.7753 | 452 | 0.5784 | 0.4653 | 0.5784 | 0.7605 | | No log | 0.7787 | 454 | 0.5788 | 0.4011 | 0.5788 | 0.7608 | | No log | 0.7822 | 456 | 0.6058 | 0.3145 | 0.6058 | 0.7783 | | No log | 0.7856 | 458 | 0.6096 | 0.2966 | 0.6096 | 0.7808 | | No log | 0.7890 | 460 | 0.6101 | 0.2799 | 0.6101 | 0.7811 | | No log | 0.7925 | 462 | 0.6341 | 0.2733 | 0.6341 | 0.7963 | | No log | 0.7959 | 464 | 0.5612 | 0.3275 | 0.5612 | 0.7491 | | No log | 0.7993 | 466 | 0.5301 | 0.4283 | 0.5301 | 0.7281 | | No log | 0.8027 | 468 | 0.5363 | 0.4775 | 0.5363 | 0.7323 | | No log | 0.8062 | 470 | 0.5627 | 0.4966 | 0.5627 | 0.7502 | | No log | 0.8096 | 472 | 0.6140 | 0.5264 | 0.6140 | 0.7836 | | No log | 0.8130 | 474 | 0.6255 | 0.5175 | 0.6255 | 0.7909 | | No log | 0.8165 | 476 | 0.6558 | 0.5165 | 0.6558 | 0.8098 | | No log | 0.8199 | 478 | 0.6823 | 0.5129 | 0.6823 | 0.8260 | | No log | 0.8233 | 480 | 0.6910 | 0.4902 | 0.6910 | 0.8313 | | No log | 0.8268 | 482 | 0.6955 | 0.4858 | 0.6955 | 0.8340 | | No log | 0.8302 | 484 | 0.6444 | 0.5138 | 0.6444 | 0.8028 | | No log | 0.8336 | 486 | 0.6024 | 0.4028 | 0.6024 | 0.7762 | | No log | 0.8370 | 488 | 0.5988 | 0.3254 | 0.5988 | 0.7738 | | No log | 0.8405 | 490 | 0.5972 | 0.3724 | 0.5972 | 0.7728 | | No log | 0.8439 | 492 | 0.5991 | 0.4367 | 0.5991 | 0.7740 | | No log | 0.8473 | 494 | 0.6037 | 0.4775 | 0.6037 | 0.7770 | | No log | 0.8508 | 496 | 0.5667 | 0.4162 | 0.5667 | 0.7528 | | No log | 0.8542 | 498 | 0.5614 | 0.3345 | 0.5614 | 0.7492 | | 1.4467 | 0.8576 | 500 | 0.5595 | 0.3099 | 0.5595 | 0.7480 | | 1.4467 | 0.8611 | 502 | 0.5750 | 0.3021 | 0.5750 | 0.7583 | | 1.4467 | 0.8645 | 504 | 0.5486 | 0.3463 | 0.5486 | 0.7407 | | 1.4467 | 0.8679 | 506 | 0.5228 | 0.4251 | 0.5228 | 0.7230 | | 1.4467 | 0.8714 | 508 | 0.5220 | 0.4514 | 0.5220 | 0.7225 | | 1.4467 | 0.8748 | 510 | 0.5299 | 0.5216 | 0.5299 | 0.7280 | | 1.4467 | 0.8782 | 512 | 0.5365 | 0.5290 | 0.5365 | 0.7324 | | 1.4467 | 0.8816 | 514 | 0.5426 | 0.5082 | 0.5426 | 0.7366 | | 1.4467 | 0.8851 | 516 | 0.5536 | 0.5262 | 0.5536 | 0.7441 | | 1.4467 | 0.8885 | 518 | 0.6244 | 0.5335 | 0.6244 | 0.7902 | | 1.4467 | 0.8919 | 520 | 0.6433 | 0.5236 | 0.6433 | 0.8020 | | 1.4467 | 0.8954 | 522 | 0.6888 | 0.5369 | 0.6888 | 0.8299 | | 1.4467 | 0.8988 | 524 | 0.8037 | 0.4895 | 0.8037 | 0.8965 | | 1.4467 | 0.9022 | 526 | 0.7234 | 0.5176 | 0.7234 | 0.8506 | | 1.4467 | 0.9057 | 528 | 0.7297 | 0.5097 | 0.7297 | 0.8542 | | 1.4467 | 0.9091 | 530 | 0.7516 | 0.5034 | 0.7516 | 0.8669 | | 1.4467 | 0.9125 | 532 | 0.7611 | 0.4916 | 0.7611 | 0.8724 | | 1.4467 | 0.9160 | 534 | 0.6036 | 0.5769 | 0.6036 | 0.7769 | | 1.4467 | 0.9194 | 536 | 0.4908 | 0.4728 | 0.4908 | 0.7005 | | 1.4467 | 0.9228 | 538 | 0.5400 | 0.4473 | 0.5400 | 0.7348 | | 1.4467 | 0.9262 | 540 | 0.6013 | 0.3841 | 0.6013 | 0.7754 | | 1.4467 | 0.9297 | 542 | 0.5468 | 0.4434 | 0.5468 | 0.7394 | | 1.4467 | 0.9331 | 544 | 0.4979 | 0.4585 | 0.4979 | 0.7056 | | 1.4467 | 0.9365 | 546 | 0.4855 | 0.4803 | 0.4855 | 0.6968 | | 1.4467 | 0.9400 | 548 | 0.4848 | 0.5022 | 0.4848 | 0.6963 | | 1.4467 | 0.9434 | 550 | 0.4907 | 0.5268 | 0.4907 | 0.7005 | | 1.4467 | 0.9468 | 552 | 0.5412 | 0.5541 | 0.5412 | 0.7356 | | 1.4467 | 0.9503 | 554 | 0.5753 | 0.5743 | 0.5753 | 0.7585 | | 1.4467 | 0.9537 | 556 | 0.5410 | 0.5593 | 0.5410 | 0.7355 | | 1.4467 | 0.9571 | 558 | 0.5243 | 0.5358 | 0.5243 | 0.7241 | | 1.4467 | 0.9605 | 560 | 0.5355 | 0.5508 | 0.5355 | 0.7318 | | 1.4467 | 0.9640 | 562 | 0.6039 | 0.5449 | 0.6039 | 0.7771 | | 1.4467 | 0.9674 | 564 | 0.5849 | 0.5535 | 0.5849 | 0.7648 | | 1.4467 | 0.9708 | 566 | 0.5134 | 0.5411 | 0.5134 | 0.7165 | | 1.4467 | 0.9743 | 568 | 0.4942 | 0.4772 | 0.4942 | 0.7030 | | 1.4467 | 0.9777 | 570 | 0.5022 | 0.4533 | 0.5022 | 0.7087 | | 1.4467 | 0.9811 | 572 | 0.4918 | 0.5004 | 0.4918 | 0.7013 | | 1.4467 | 0.9846 | 574 | 0.5402 | 0.5311 | 0.5402 | 0.7350 | | 1.4467 | 0.9880 | 576 | 0.5852 | 0.5550 | 0.5852 | 0.7650 | | 1.4467 | 0.9914 | 578 | 0.5398 | 0.5377 | 0.5398 | 0.7347 | | 1.4467 | 0.9949 | 580 | 0.5121 | 0.5300 | 0.5121 | 0.7156 | | 1.4467 | 0.9983 | 582 | 0.4940 | 0.4900 | 0.4940 | 0.7028 | | 1.4467 | 1.0017 | 584 | 0.5047 | 0.4475 | 0.5047 | 0.7104 | | 1.4467 | 1.0051 | 586 | 0.5087 | 0.4404 | 0.5087 | 0.7132 | | 1.4467 | 1.0086 | 588 | 0.4969 | 0.4986 | 0.4969 | 0.7049 | | 1.4467 | 1.0120 | 590 | 0.4991 | 0.5336 | 0.4991 | 0.7065 | | 1.4467 | 1.0154 | 592 | 0.5040 | 0.5444 | 0.5040 | 0.7099 | | 1.4467 | 1.0189 | 594 | 0.4863 | 0.5234 | 0.4863 | 0.6973 | | 1.4467 | 1.0223 | 596 | 0.4988 | 0.4800 | 0.4988 | 0.7063 | | 1.4467 | 1.0257 | 598 | 0.5908 | 0.4059 | 0.5908 | 0.7686 | | 1.4467 | 1.0292 | 600 | 0.6252 | 0.3926 | 0.6252 | 0.7907 | | 1.4467 | 1.0326 | 602 | 0.5112 | 0.4579 | 0.5112 | 0.7150 | | 1.4467 | 1.0360 | 604 | 0.5122 | 0.5698 | 0.5122 | 0.7157 | | 1.4467 | 1.0395 | 606 | 0.7310 | 0.4911 | 0.7310 | 0.8550 | | 1.4467 | 1.0429 | 608 | 0.8856 | 0.4021 | 0.8856 | 0.9411 | | 1.4467 | 1.0463 | 610 | 0.9630 | 0.3085 | 0.9630 | 0.9813 | | 1.4467 | 1.0497 | 612 | 0.9528 | 0.2853 | 0.9528 | 0.9761 | | 1.4467 | 1.0532 | 614 | 0.9115 | 0.2412 | 0.9115 | 0.9547 | | 1.4467 | 1.0566 | 616 | 0.8846 | 0.1487 | 0.8846 | 0.9406 | | 1.4467 | 1.0600 | 618 | 0.7885 | 0.3236 | 0.7885 | 0.8880 | | 1.4467 | 1.0635 | 620 | 0.6935 | 0.4716 | 0.6935 | 0.8328 | | 1.4467 | 1.0669 | 622 | 0.6428 | 0.5356 | 0.6428 | 0.8017 | | 1.4467 | 1.0703 | 624 | 0.6412 | 0.5383 | 0.6412 | 0.8007 | | 1.4467 | 1.0738 | 626 | 0.6226 | 0.5171 | 0.6226 | 0.7891 | | 1.4467 | 1.0772 | 628 | 0.5974 | 0.5300 | 0.5974 | 0.7729 | | 1.4467 | 1.0806 | 630 | 0.5614 | 0.5356 | 0.5614 | 0.7492 | | 1.4467 | 1.0840 | 632 | 0.5480 | 0.5475 | 0.5480 | 0.7403 | | 1.4467 | 1.0875 | 634 | 0.5286 | 0.5256 | 0.5286 | 0.7271 | | 1.4467 | 1.0909 | 636 | 0.5107 | 0.4668 | 0.5107 | 0.7146 | | 1.4467 | 1.0943 | 638 | 0.5219 | 0.3653 | 0.5219 | 0.7224 | | 1.4467 | 1.0978 | 640 | 0.5869 | 0.2661 | 0.5869 | 0.7661 | | 1.4467 | 1.1012 | 642 | 0.6165 | 0.2497 | 0.6165 | 0.7851 | | 1.4467 | 1.1046 | 644 | 0.5858 | 0.2664 | 0.5858 | 0.7654 | | 1.4467 | 1.1081 | 646 | 0.5430 | 0.3289 | 0.5430 | 0.7369 | | 1.4467 | 1.1115 | 648 | 0.5303 | 0.3594 | 0.5303 | 0.7282 | | 1.4467 | 1.1149 | 650 | 0.5374 | 0.3472 | 0.5374 | 0.7331 | | 1.4467 | 1.1184 | 652 | 0.5604 | 0.3247 | 0.5604 | 0.7486 | | 1.4467 | 1.1218 | 654 | 0.5965 | 0.2873 | 0.5965 | 0.7723 | | 1.4467 | 1.1252 | 656 | 0.6517 | 0.2560 | 0.6517 | 0.8073 | | 1.4467 | 1.1286 | 658 | 0.6403 | 0.2600 | 0.6403 | 0.8002 | | 1.4467 | 1.1321 | 660 | 0.5611 | 0.3158 | 0.5611 | 0.7490 | | 1.4467 | 1.1355 | 662 | 0.5172 | 0.4185 | 0.5172 | 0.7192 | | 1.4467 | 1.1389 | 664 | 0.5615 | 0.5188 | 0.5615 | 0.7493 | | 1.4467 | 1.1424 | 666 | 0.6296 | 0.5406 | 0.6296 | 0.7935 | | 1.4467 | 1.1458 | 668 | 0.6790 | 0.5060 | 0.6790 | 0.8240 | | 1.4467 | 1.1492 | 670 | 0.6252 | 0.5295 | 0.6252 | 0.7907 | | 1.4467 | 1.1527 | 672 | 0.5617 | 0.5067 | 0.5617 | 0.7495 | | 1.4467 | 1.1561 | 674 | 0.5482 | 0.5205 | 0.5482 | 0.7404 | | 1.4467 | 1.1595 | 676 | 0.5532 | 0.5105 | 0.5532 | 0.7438 | | 1.4467 | 1.1630 | 678 | 0.5241 | 0.5325 | 0.5241 | 0.7239 | | 1.4467 | 1.1664 | 680 | 0.5108 | 0.4480 | 0.5108 | 0.7147 | | 1.4467 | 1.1698 | 682 | 0.5547 | 0.2923 | 0.5547 | 0.7448 | | 1.4467 | 1.1732 | 684 | 0.5698 | 0.2945 | 0.5698 | 0.7549 | | 1.4467 | 1.1767 | 686 | 0.5462 | 0.3037 | 0.5462 | 0.7390 | | 1.4467 | 1.1801 | 688 | 0.5157 | 0.4053 | 0.5157 | 0.7181 | | 1.4467 | 1.1835 | 690 | 0.5196 | 0.5144 | 0.5196 | 0.7208 | | 1.4467 | 1.1870 | 692 | 0.5475 | 0.5311 | 0.5475 | 0.7400 | | 1.4467 | 1.1904 | 694 | 0.6014 | 0.5623 | 0.6014 | 0.7755 | | 1.4467 | 1.1938 | 696 | 0.6404 | 0.5366 | 0.6404 | 0.8003 | | 1.4467 | 1.1973 | 698 | 0.6191 | 0.5592 | 0.6191 | 0.7868 | | 1.4467 | 1.2007 | 700 | 0.5985 | 0.5541 | 0.5985 | 0.7736 | | 1.4467 | 1.2041 | 702 | 0.5791 | 0.5440 | 0.5791 | 0.7610 | | 1.4467 | 1.2075 | 704 | 0.5684 | 0.5482 | 0.5684 | 0.7539 | | 1.4467 | 1.2110 | 706 | 0.5335 | 0.5284 | 0.5335 | 0.7304 | | 1.4467 | 1.2144 | 708 | 0.5002 | 0.5346 | 0.5002 | 0.7073 | | 1.4467 | 1.2178 | 710 | 0.4808 | 0.5058 | 0.4808 | 0.6934 | | 1.4467 | 1.2213 | 712 | 0.4982 | 0.4652 | 0.4982 | 0.7058 | | 1.4467 | 1.2247 | 714 | 0.5175 | 0.4509 | 0.5175 | 0.7194 | | 1.4467 | 1.2281 | 716 | 0.5188 | 0.4514 | 0.5188 | 0.7203 | | 1.4467 | 1.2316 | 718 | 0.5650 | 0.4206 | 0.5650 | 0.7517 | | 1.4467 | 1.2350 | 720 | 0.6433 | 0.3495 | 0.6433 | 0.8021 | | 1.4467 | 1.2384 | 722 | 0.5728 | 0.4136 | 0.5728 | 0.7568 | | 1.4467 | 1.2419 | 724 | 0.4843 | 0.5375 | 0.4843 | 0.6959 | | 1.4467 | 1.2453 | 726 | 0.5314 | 0.5164 | 0.5314 | 0.7290 | | 1.4467 | 1.2487 | 728 | 0.5652 | 0.4515 | 0.5652 | 0.7518 | | 1.4467 | 1.2521 | 730 | 0.5401 | 0.5090 | 0.5401 | 0.7349 | | 1.4467 | 1.2556 | 732 | 0.5091 | 0.5267 | 0.5091 | 0.7135 | | 1.4467 | 1.2590 | 734 | 0.5036 | 0.5331 | 0.5036 | 0.7096 | | 1.4467 | 1.2624 | 736 | 0.5180 | 0.5579 | 0.5180 | 0.7197 | | 1.4467 | 1.2659 | 738 | 0.5450 | 0.5734 | 0.5450 | 0.7382 | | 1.4467 | 1.2693 | 740 | 0.6033 | 0.5591 | 0.6033 | 0.7767 | | 1.4467 | 1.2727 | 742 | 0.6785 | 0.5214 | 0.6785 | 0.8237 | | 1.4467 | 1.2762 | 744 | 0.7074 | 0.5244 | 0.7074 | 0.8411 | | 1.4467 | 1.2796 | 746 | 0.7368 | 0.5110 | 0.7368 | 0.8584 | | 1.4467 | 1.2830 | 748 | 0.7032 | 0.5253 | 0.7032 | 0.8386 | | 1.4467 | 1.2864 | 750 | 0.6386 | 0.5526 | 0.6386 | 0.7991 | | 1.4467 | 1.2899 | 752 | 0.5645 | 0.5853 | 0.5645 | 0.7514 | | 1.4467 | 1.2933 | 754 | 0.5120 | 0.5700 | 0.5120 | 0.7155 | | 1.4467 | 1.2967 | 756 | 0.5050 | 0.5636 | 0.5050 | 0.7106 | | 1.4467 | 1.3002 | 758 | 0.4909 | 0.5660 | 0.4909 | 0.7006 | | 1.4467 | 1.3036 | 760 | 0.4752 | 0.5537 | 0.4752 | 0.6894 | | 1.4467 | 1.3070 | 762 | 0.4705 | 0.5425 | 0.4705 | 0.6859 | | 1.4467 | 1.3105 | 764 | 0.4769 | 0.5650 | 0.4769 | 0.6906 | | 1.4467 | 1.3139 | 766 | 0.5247 | 0.5878 | 0.5247 | 0.7244 | | 1.4467 | 1.3173 | 768 | 0.5567 | 0.5820 | 0.5567 | 0.7461 | | 1.4467 | 1.3208 | 770 | 0.5621 | 0.5798 | 0.5621 | 0.7497 | | 1.4467 | 1.3242 | 772 | 0.5323 | 0.5977 | 0.5323 | 0.7296 | | 1.4467 | 1.3276 | 774 | 0.5068 | 0.5952 | 0.5068 | 0.7119 | | 1.4467 | 1.3310 | 776 | 0.4927 | 0.5798 | 0.4927 | 0.7019 | | 1.4467 | 1.3345 | 778 | 0.5172 | 0.5159 | 0.5172 | 0.7192 | | 1.4467 | 1.3379 | 780 | 0.5354 | 0.4926 | 0.5354 | 0.7317 | | 1.4467 | 1.3413 | 782 | 0.5227 | 0.4901 | 0.5227 | 0.7230 | | 1.4467 | 1.3448 | 784 | 0.5517 | 0.4711 | 0.5517 | 0.7427 | | 1.4467 | 1.3482 | 786 | 0.6068 | 0.4424 | 0.6068 | 0.7790 | | 1.4467 | 1.3516 | 788 | 0.5734 | 0.4632 | 0.5734 | 0.7573 | | 1.4467 | 1.3551 | 790 | 0.5509 | 0.4758 | 0.5509 | 0.7423 | | 1.4467 | 1.3585 | 792 | 0.5378 | 0.4870 | 0.5378 | 0.7333 | | 1.4467 | 1.3619 | 794 | 0.4953 | 0.5192 | 0.4953 | 0.7038 | | 1.4467 | 1.3654 | 796 | 0.4681 | 0.5414 | 0.4681 | 0.6842 | | 1.4467 | 1.3688 | 798 | 0.4889 | 0.5946 | 0.4889 | 0.6992 | | 1.4467 | 1.3722 | 800 | 0.5452 | 0.6009 | 0.5452 | 0.7384 | | 1.4467 | 1.3756 | 802 | 0.6980 | 0.5381 | 0.6980 | 0.8355 | | 1.4467 | 1.3791 | 804 | 0.8372 | 0.4942 | 0.8372 | 0.9150 | | 1.4467 | 1.3825 | 806 | 0.9861 | 0.4654 | 0.9861 | 0.9930 | | 1.4467 | 1.3859 | 808 | 1.1494 | 0.4320 | 1.1494 | 1.0721 | | 1.4467 | 1.3894 | 810 | 1.1945 | 0.4135 | 1.1945 | 1.0929 | | 1.4467 | 1.3928 | 812 | 1.0414 | 0.4591 | 1.0414 | 1.0205 | | 1.4467 | 1.3962 | 814 | 0.7966 | 0.5238 | 0.7966 | 0.8925 | | 1.4467 | 1.3997 | 816 | 0.5620 | 0.6009 | 0.5620 | 0.7497 | | 1.4467 | 1.4031 | 818 | 0.4917 | 0.5098 | 0.4917 | 0.7012 | | 1.4467 | 1.4065 | 820 | 0.5848 | 0.4308 | 0.5848 | 0.7647 | | 1.4467 | 1.4099 | 822 | 0.6022 | 0.4328 | 0.6022 | 0.7760 | | 1.4467 | 1.4134 | 824 | 0.5356 | 0.4644 | 0.5356 | 0.7319 | | 1.4467 | 1.4168 | 826 | 0.4930 | 0.4972 | 0.4930 | 0.7021 | | 1.4467 | 1.4202 | 828 | 0.5180 | 0.5755 | 0.5180 | 0.7197 | | 1.4467 | 1.4237 | 830 | 0.5769 | 0.5715 | 0.5769 | 0.7595 | | 1.4467 | 1.4271 | 832 | 0.6236 | 0.5765 | 0.6236 | 0.7897 | | 1.4467 | 1.4305 | 834 | 0.6349 | 0.5654 | 0.6349 | 0.7968 | | 1.4467 | 1.4340 | 836 | 0.5807 | 0.5617 | 0.5807 | 0.7621 | | 1.4467 | 1.4374 | 838 | 0.5394 | 0.5114 | 0.5394 | 0.7344 | | 1.4467 | 1.4408 | 840 | 0.5532 | 0.4129 | 0.5532 | 0.7438 | | 1.4467 | 1.4443 | 842 | 0.5295 | 0.4610 | 0.5295 | 0.7277 | | 1.4467 | 1.4477 | 844 | 0.5082 | 0.5507 | 0.5082 | 0.7129 | | 1.4467 | 1.4511 | 846 | 0.5605 | 0.5704 | 0.5605 | 0.7487 | | 1.4467 | 1.4545 | 848 | 0.5777 | 0.5612 | 0.5777 | 0.7601 | | 1.4467 | 1.4580 | 850 | 0.5747 | 0.5383 | 0.5747 | 0.7581 | | 1.4467 | 1.4614 | 852 | 0.5844 | 0.5035 | 0.5844 | 0.7644 | | 1.4467 | 1.4648 | 854 | 0.6067 | 0.4904 | 0.6067 | 0.7789 | | 1.4467 | 1.4683 | 856 | 0.6499 | 0.4798 | 0.6499 | 0.8062 | | 1.4467 | 1.4717 | 858 | 0.6804 | 0.4627 | 0.6804 | 0.8249 | | 1.4467 | 1.4751 | 860 | 0.7119 | 0.4594 | 0.7119 | 0.8438 | | 1.4467 | 1.4786 | 862 | 0.6832 | 0.4750 | 0.6832 | 0.8265 | | 1.4467 | 1.4820 | 864 | 0.6485 | 0.4806 | 0.6485 | 0.8053 | | 1.4467 | 1.4854 | 866 | 0.6089 | 0.4928 | 0.6089 | 0.7803 | | 1.4467 | 1.4889 | 868 | 0.5959 | 0.5352 | 0.5959 | 0.7720 | | 1.4467 | 1.4923 | 870 | 0.6194 | 0.5600 | 0.6194 | 0.7870 | | 1.4467 | 1.4957 | 872 | 0.6318 | 0.5642 | 0.6318 | 0.7949 | | 1.4467 | 1.4991 | 874 | 0.6293 | 0.5612 | 0.6293 | 0.7933 | | 1.4467 | 1.5026 | 876 | 0.6245 | 0.5551 | 0.6245 | 0.7903 | | 1.4467 | 1.5060 | 878 | 0.6221 | 0.5464 | 0.6221 | 0.7888 | | 1.4467 | 1.5094 | 880 | 0.6268 | 0.5602 | 0.6268 | 0.7917 | | 1.4467 | 1.5129 | 882 | 0.6381 | 0.5641 | 0.6381 | 0.7988 | | 1.4467 | 1.5163 | 884 | 0.7047 | 0.5904 | 0.7047 | 0.8394 | | 1.4467 | 1.5197 | 886 | 0.8195 | 0.5681 | 0.8195 | 0.9053 | | 1.4467 | 1.5232 | 888 | 0.9176 | 0.5200 | 0.9176 | 0.9579 | | 1.4467 | 1.5266 | 890 | 0.8808 | 0.5355 | 0.8808 | 0.9385 | | 1.4467 | 1.5300 | 892 | 0.8105 | 0.5565 | 0.8105 | 0.9003 | | 1.4467 | 1.5334 | 894 | 0.7524 | 0.5707 | 0.7524 | 0.8674 | | 1.4467 | 1.5369 | 896 | 0.6910 | 0.5920 | 0.6910 | 0.8312 | | 1.4467 | 1.5403 | 898 | 0.6964 | 0.5859 | 0.6964 | 0.8345 | | 1.4467 | 1.5437 | 900 | 0.6703 | 0.5921 | 0.6703 | 0.8187 | | 1.4467 | 1.5472 | 902 | 0.6408 | 0.5879 | 0.6408 | 0.8005 | | 1.4467 | 1.5506 | 904 | 0.6144 | 0.5458 | 0.6144 | 0.7838 | | 1.4467 | 1.5540 | 906 | 0.5912 | 0.5258 | 0.5912 | 0.7689 | | 1.4467 | 1.5575 | 908 | 0.6039 | 0.4918 | 0.6039 | 0.7771 | | 1.4467 | 1.5609 | 910 | 0.7268 | 0.4464 | 0.7268 | 0.8525 | | 1.4467 | 1.5643 | 912 | 0.8848 | 0.4019 | 0.8848 | 0.9406 | | 1.4467 | 1.5678 | 914 | 0.8112 | 0.4244 | 0.8112 | 0.9007 | | 1.4467 | 1.5712 | 916 | 0.6855 | 0.4701 | 0.6855 | 0.8279 | | 1.4467 | 1.5746 | 918 | 0.5862 | 0.4971 | 0.5862 | 0.7656 | | 1.4467 | 1.5780 | 920 | 0.5316 | 0.5337 | 0.5316 | 0.7291 | | 1.4467 | 1.5815 | 922 | 0.5797 | 0.6002 | 0.5797 | 0.7614 | | 1.4467 | 1.5849 | 924 | 0.6757 | 0.5839 | 0.6757 | 0.8220 | | 1.4467 | 1.5883 | 926 | 0.7027 | 0.5251 | 0.7027 | 0.8383 | | 1.4467 | 1.5918 | 928 | 0.6812 | 0.4456 | 0.6812 | 0.8253 | | 1.4467 | 1.5952 | 930 | 0.6881 | 0.4488 | 0.6881 | 0.8295 | | 1.4467 | 1.5986 | 932 | 0.7299 | 0.4800 | 0.7299 | 0.8544 | | 1.4467 | 1.6021 | 934 | 0.7679 | 0.5006 | 0.7679 | 0.8763 | | 1.4467 | 1.6055 | 936 | 0.7714 | 0.4971 | 0.7714 | 0.8783 | | 1.4467 | 1.6089 | 938 | 0.7134 | 0.5341 | 0.7134 | 0.8446 | | 1.4467 | 1.6123 | 940 | 0.6023 | 0.5899 | 0.6023 | 0.7761 | | 1.4467 | 1.6158 | 942 | 0.5084 | 0.5953 | 0.5084 | 0.7130 | | 1.4467 | 1.6192 | 944 | 0.4845 | 0.5553 | 0.4845 | 0.6960 | | 1.4467 | 1.6226 | 946 | 0.5044 | 0.5000 | 0.5044 | 0.7102 | | 1.4467 | 1.6261 | 948 | 0.5057 | 0.5064 | 0.5057 | 0.7111 | | 1.4467 | 1.6295 | 950 | 0.4857 | 0.5267 | 0.4857 | 0.6969 | | 1.4467 | 1.6329 | 952 | 0.4853 | 0.5538 | 0.4853 | 0.6966 | | 1.4467 | 1.6364 | 954 | 0.5263 | 0.5920 | 0.5263 | 0.7255 | | 1.4467 | 1.6398 | 956 | 0.5516 | 0.5825 | 0.5516 | 0.7427 | | 1.4467 | 1.6432 | 958 | 0.5468 | 0.5930 | 0.5468 | 0.7394 | | 1.4467 | 1.6467 | 960 | 0.5185 | 0.5757 | 0.5185 | 0.7200 | | 1.4467 | 1.6501 | 962 | 0.5088 | 0.4945 | 0.5088 | 0.7133 | | 1.4467 | 1.6535 | 964 | 0.5813 | 0.3930 | 0.5813 | 0.7625 | | 1.4467 | 1.6569 | 966 | 0.5810 | 0.4091 | 0.5810 | 0.7622 | | 1.4467 | 1.6604 | 968 | 0.5508 | 0.4628 | 0.5508 | 0.7422 | | 1.4467 | 1.6638 | 970 | 0.4762 | 0.5199 | 0.4762 | 0.6901 | | 1.4467 | 1.6672 | 972 | 0.4902 | 0.5730 | 0.4902 | 0.7002 | | 1.4467 | 1.6707 | 974 | 0.5267 | 0.5791 | 0.5267 | 0.7258 | | 1.4467 | 1.6741 | 976 | 0.5422 | 0.5672 | 0.5422 | 0.7363 | | 1.4467 | 1.6775 | 978 | 0.5207 | 0.5731 | 0.5207 | 0.7216 | | 1.4467 | 1.6810 | 980 | 0.5013 | 0.5841 | 0.5013 | 0.7080 | | 1.4467 | 1.6844 | 982 | 0.5081 | 0.5773 | 0.5081 | 0.7128 | | 1.4467 | 1.6878 | 984 | 0.5251 | 0.5647 | 0.5251 | 0.7246 | | 1.4467 | 1.6913 | 986 | 0.5149 | 0.5633 | 0.5149 | 0.7176 | | 1.4467 | 1.6947 | 988 | 0.4974 | 0.5435 | 0.4974 | 0.7052 | | 1.4467 | 1.6981 | 990 | 0.4818 | 0.5208 | 0.4818 | 0.6941 | | 1.4467 | 1.7015 | 992 | 0.4888 | 0.4916 | 0.4888 | 0.6991 | | 1.4467 | 1.7050 | 994 | 0.5038 | 0.4543 | 0.5038 | 0.7098 | | 1.4467 | 1.7084 | 996 | 0.5016 | 0.4525 | 0.5016 | 0.7082 | | 1.4467 | 1.7118 | 998 | 0.4872 | 0.4982 | 0.4872 | 0.6980 | | 1.307 | 1.7153 | 1000 | 0.4887 | 0.4958 | 0.4887 | 0.6991 | | 1.307 | 1.7187 | 1002 | 0.5017 | 0.5087 | 0.5017 | 0.7083 | | 1.307 | 1.7221 | 1004 | 0.5236 | 0.5381 | 0.5236 | 0.7236 | | 1.307 | 1.7256 | 1006 | 0.5646 | 0.5846 | 0.5646 | 0.7514 | | 1.307 | 1.7290 | 1008 | 0.5972 | 0.5660 | 0.5972 | 0.7728 | | 1.307 | 1.7324 | 1010 | 0.5695 | 0.5561 | 0.5695 | 0.7546 | | 1.307 | 1.7358 | 1012 | 0.5178 | 0.5463 | 0.5178 | 0.7196 | | 1.307 | 1.7393 | 1014 | 0.4861 | 0.5501 | 0.4861 | 0.6972 | | 1.307 | 1.7427 | 1016 | 0.4876 | 0.5395 | 0.4876 | 0.6983 | | 1.307 | 1.7461 | 1018 | 0.4866 | 0.5360 | 0.4866 | 0.6975 | | 1.307 | 1.7496 | 1020 | 0.4853 | 0.5405 | 0.4853 | 0.6966 | | 1.307 | 1.7530 | 1022 | 0.4882 | 0.5377 | 0.4882 | 0.6987 | | 1.307 | 1.7564 | 1024 | 0.5056 | 0.5343 | 0.5056 | 0.7110 | | 1.307 | 1.7599 | 1026 | 0.5697 | 0.5727 | 0.5697 | 0.7548 | | 1.307 | 1.7633 | 1028 | 0.5984 | 0.5816 | 0.5984 | 0.7735 | | 1.307 | 1.7667 | 1030 | 0.5625 | 0.5673 | 0.5625 | 0.7500 | | 1.307 | 1.7702 | 1032 | 0.5218 | 0.5736 | 0.5218 | 0.7223 | | 1.307 | 1.7736 | 1034 | 0.4841 | 0.5602 | 0.4841 | 0.6958 | | 1.307 | 1.7770 | 1036 | 0.4750 | 0.5322 | 0.4750 | 0.6892 | | 1.307 | 1.7804 | 1038 | 0.5044 | 0.4753 | 0.5044 | 0.7102 | | 1.307 | 1.7839 | 1040 | 0.5388 | 0.4538 | 0.5388 | 0.7340 | | 1.307 | 1.7873 | 1042 | 0.5200 | 0.4602 | 0.5200 | 0.7211 | | 1.307 | 1.7907 | 1044 | 0.4939 | 0.4771 | 0.4939 | 0.7028 | | 1.307 | 1.7942 | 1046 | 0.4687 | 0.5325 | 0.4687 | 0.6846 | | 1.307 | 1.7976 | 1048 | 0.4940 | 0.5668 | 0.4940 | 0.7029 | | 1.307 | 1.8010 | 1050 | 0.5585 | 0.5803 | 0.5585 | 0.7473 | | 1.307 | 1.8045 | 1052 | 0.5689 | 0.5679 | 0.5689 | 0.7542 | | 1.307 | 1.8079 | 1054 | 0.5711 | 0.5514 | 0.5711 | 0.7557 | | 1.307 | 1.8113 | 1056 | 0.6434 | 0.5778 | 0.6434 | 0.8021 | | 1.307 | 1.8148 | 1058 | 0.7629 | 0.5149 | 0.7629 | 0.8735 | | 1.307 | 1.8182 | 1060 | 0.8838 | 0.4890 | 0.8838 | 0.9401 | | 1.307 | 1.8216 | 1062 | 0.9201 | 0.4814 | 0.9201 | 0.9592 | | 1.307 | 1.8250 | 1064 | 0.9079 | 0.4724 | 0.9079 | 0.9529 | | 1.307 | 1.8285 | 1066 | 0.8348 | 0.4747 | 0.8348 | 0.9137 | | 1.307 | 1.8319 | 1068 | 0.8540 | 0.4885 | 0.8540 | 0.9241 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0+cu118 - Datasets 2.21.0 - Tokenizers 0.19.1
ttorolee/qwen2.5_7b_it_100
ttorolee
"2024-11-08T08:07:11Z"
5
0
null
[ "safetensors", "qwen2", "law", "unsloth", "trl", "sft", "text-generation", "conversational", "ko", "en", "base_model:unsloth/Qwen2.5-7B-Instruct", "base_model:finetune:unsloth/Qwen2.5-7B-Instruct", "license:apache-2.0", "region:us" ]
text-generation
"2024-11-08T06:58:36Z"
--- license: apache-2.0 language: - ko - en base_model: - unsloth/Qwen2.5-7B-Instruct pipeline_tag: text-generation tags: - law - unsloth - trl - sft ---
RylanSchaeffer/pythia-70m_tatsu-lab_alpaca_farm_sftsd0_policy_pythia-6.9b_gold_internlm2-7b_noise0.25_rmsd2
RylanSchaeffer
"2024-07-31T00:00:58Z"
5
0
transformers
[ "transformers", "safetensors", "gpt_neox", "text-classification", "trl", "reward-trainer", "generated_from_trainer", "base_model:RylanSchaeffer/EleutherAI_pythia-70m_tatsu-lab_alpaca_farm_sftseed0", "base_model:finetune:RylanSchaeffer/EleutherAI_pythia-70m_tatsu-lab_alpaca_farm_sftseed0", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-classification
"2024-07-31T00:00:52Z"
--- license: apache-2.0 base_model: RylanSchaeffer/EleutherAI_pythia-70m_tatsu-lab_alpaca_farm_sftseed0 tags: - trl - reward-trainer - generated_from_trainer metrics: - accuracy model-index: - name: pythia-70m_tatsu-lab_alpaca_farm_sftsd0_policy_pythia-6.9b_gold_internlm2-7b_noise0.25_rmsd2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/rylan/switching-rms-rm/runs/9c8zd3l2) # pythia-70m_tatsu-lab_alpaca_farm_sftsd0_policy_pythia-6.9b_gold_internlm2-7b_noise0.25_rmsd2 This model is a fine-tuned version of [RylanSchaeffer/EleutherAI_pythia-70m_tatsu-lab_alpaca_farm_sftseed0](https://huggingface.co/RylanSchaeffer/EleutherAI_pythia-70m_tatsu-lab_alpaca_farm_sftseed0) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7715 - Accuracy: 0.5312 ## 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: 16 - seed: 2 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.025 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:------:|:----:|:---------------:|:--------:| | No log | 0 | 0 | 0.8791 | 0.5158 | | 0.8237 | 0.0648 | 100 | 0.8836 | 0.5077 | | 0.8692 | 0.1296 | 200 | 0.8703 | 0.5120 | | 0.8862 | 0.1944 | 300 | 0.8459 | 0.5139 | | 0.805 | 0.2592 | 400 | 0.8338 | 0.5170 | | 0.8476 | 0.3239 | 500 | 0.8211 | 0.5247 | | 0.87 | 0.3887 | 600 | 0.8137 | 0.5197 | | 0.7827 | 0.4535 | 700 | 0.8091 | 0.5251 | | 0.8028 | 0.5183 | 800 | 0.8105 | 0.5224 | | 0.7531 | 0.5831 | 900 | 0.8027 | 0.5255 | | 0.7557 | 0.6479 | 1000 | 0.7992 | 0.5270 | | 0.8452 | 0.7127 | 1100 | 0.8015 | 0.5224 | | 0.7943 | 0.7775 | 1200 | 0.7905 | 0.5262 | | 0.7649 | 0.8422 | 1300 | 0.7861 | 0.5274 | | 0.7663 | 0.9070 | 1400 | 0.7874 | 0.5351 | | 0.7498 | 0.9718 | 1500 | 0.7858 | 0.5351 | | 0.7649 | 1.0366 | 1600 | 0.7848 | 0.5289 | | 0.7859 | 1.1014 | 1700 | 0.7861 | 0.5285 | | 0.7689 | 1.1662 | 1800 | 0.7864 | 0.5297 | | 0.745 | 1.2310 | 1900 | 0.7821 | 0.5289 | | 0.7447 | 1.2958 | 2000 | 0.7830 | 0.5340 | | 0.8268 | 1.3605 | 2100 | 0.7796 | 0.5293 | | 0.7596 | 1.4253 | 2200 | 0.7797 | 0.5336 | | 0.7543 | 1.4901 | 2300 | 0.7741 | 0.5278 | | 0.7558 | 1.5549 | 2400 | 0.7736 | 0.5266 | | 0.7518 | 1.6197 | 2500 | 0.7725 | 0.5251 | | 0.7845 | 1.6845 | 2600 | 0.7738 | 0.5367 | | 0.763 | 1.7493 | 2700 | 0.7776 | 0.5262 | | 0.7527 | 1.8141 | 2800 | 0.7756 | 0.5312 | | 0.7533 | 1.8788 | 2900 | 0.7799 | 0.5262 | | 0.7932 | 1.9436 | 3000 | 0.7757 | 0.5347 | | 0.7522 | 2.0084 | 3100 | 0.7757 | 0.5332 | | 0.7677 | 2.0732 | 3200 | 0.7738 | 0.5228 | | 0.7804 | 2.1380 | 3300 | 0.7733 | 0.5274 | | 0.7504 | 2.2028 | 3400 | 0.7742 | 0.5266 | | 0.7793 | 2.2676 | 3500 | 0.7757 | 0.5266 | | 0.7447 | 2.3324 | 3600 | 0.7726 | 0.5266 | | 0.7647 | 2.3971 | 3700 | 0.7728 | 0.5343 | | 0.7154 | 2.4619 | 3800 | 0.7704 | 0.5251 | | 0.7742 | 2.5267 | 3900 | 0.7743 | 0.5312 | | 0.7828 | 2.5915 | 4000 | 0.7758 | 0.5197 | | 0.7383 | 2.6563 | 4100 | 0.7729 | 0.5297 | | 0.765 | 2.7211 | 4200 | 0.7761 | 0.5270 | | 0.7862 | 2.7859 | 4300 | 0.7764 | 0.5255 | | 0.7602 | 2.8507 | 4400 | 0.7735 | 0.5270 | | 0.7487 | 2.9155 | 4500 | 0.7758 | 0.5266 | | 0.7447 | 2.9802 | 4600 | 0.7747 | 0.5297 | | 0.7869 | 3.0450 | 4700 | 0.7756 | 0.5340 | | 0.7655 | 3.1098 | 4800 | 0.7778 | 0.5301 | | 0.7438 | 3.1746 | 4900 | 0.7717 | 0.5270 | | 0.7754 | 3.2394 | 5000 | 0.7725 | 0.5320 | | 0.7783 | 3.3042 | 5100 | 0.7685 | 0.5401 | | 0.7806 | 3.3690 | 5200 | 0.7718 | 0.5289 | | 0.7755 | 3.4338 | 5300 | 0.7700 | 0.5343 | | 0.7698 | 3.4985 | 5400 | 0.7723 | 0.5270 | | 0.7772 | 3.5633 | 5500 | 0.7733 | 0.5320 | | 0.8048 | 3.6281 | 5600 | 0.7750 | 0.5266 | | 0.7491 | 3.6929 | 5700 | 0.7732 | 0.5274 | | 0.8085 | 3.7577 | 5800 | 0.7757 | 0.5243 | | 0.7653 | 3.8225 | 5900 | 0.7739 | 0.5228 | | 0.7702 | 3.8873 | 6000 | 0.7747 | 0.5197 | | 0.7671 | 3.9521 | 6100 | 0.7711 | 0.5316 | | 0.777 | 4.0168 | 6200 | 0.7739 | 0.5282 | | 0.7451 | 4.0816 | 6300 | 0.7709 | 0.5324 | | 0.7121 | 4.1464 | 6400 | 0.7706 | 0.5355 | | 0.7714 | 4.2112 | 6500 | 0.7721 | 0.5370 | | 0.7299 | 4.2760 | 6600 | 0.7697 | 0.5382 | | 0.782 | 4.3408 | 6700 | 0.7759 | 0.5312 | | 0.7759 | 4.4056 | 6800 | 0.7726 | 0.5270 | | 0.7474 | 4.4704 | 6900 | 0.7669 | 0.5355 | | 0.776 | 4.5351 | 7000 | 0.7721 | 0.5309 | | 0.7693 | 4.5999 | 7100 | 0.7720 | 0.5316 | | 0.7578 | 4.6647 | 7200 | 0.7731 | 0.5274 | | 0.7431 | 4.7295 | 7300 | 0.7690 | 0.5351 | | 0.7883 | 4.7943 | 7400 | 0.7726 | 0.5255 | | 0.7794 | 4.8591 | 7500 | 0.7704 | 0.5255 | | 0.7697 | 4.9239 | 7600 | 0.7730 | 0.5312 | | 0.7373 | 4.9887 | 7700 | 0.7714 | 0.5328 | ### Framework versions - Transformers 4.42.4 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
tomaszki/llama-7
tomaszki
"2024-04-22T17:46:30Z"
4
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
"2024-04-22T17:43:22Z"
--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
sarahai/ruT5-base-summarizer
sarahai
"2024-03-19T18:11:21Z"
631
5
transformers
[ "transformers", "safetensors", "t5", "text2text-generation", "summarization", "summarizer", "суммаризатор", "text-generation-inference", "russian text summarizer", "ru", "dataset:IlyaGusev/gazeta", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
summarization
"2024-02-28T09:53:20Z"
--- license: apache-2.0 datasets: - IlyaGusev/gazeta language: - ru pipeline_tag: summarization tags: - summarization - summarizer - суммаризатор - text-generation-inference - russian text summarizer widget: - text: >- 83-летняя жительница Хабаровского края сутки простояла в трясине, отпугивая кружащего вокруг нее медведя рыком. Об этом сообщает ТАСС со ссылкой на источник в добровольческом поисково-спасательном отряде. Об инциденте стало известно 5 августа, когда в правоохранительные органы обратились родственники пенсионерки. По их словам, утром того дня она ушла в лес за грибами из поселка Сита и пропала. На поиски пожилой женщины вышли местные жители, участники спасательного отряда, охотники и сотрудники патрульно-постовой службы. Они несколько раз видели следы медведей, их лежанки, а также слышали хищников, бродящих неподалеку. Разыскать пенсионерку удалось только 7 августа. «Ночью в лесу в нескольких метрах от лежанки медведя было обнаружено ведро с грибами, поисковики услышали нехарактерное для животных рычание и в овраге в ручье увидели бабушку. Рыком женщина пыталась отпугнуть караулившего ее медведя», — рассказал представитель поискового отряда. Когда спасатели освобождали жительницу Приморья от оков трясины, рядом все еще ходил медведь — его спугнул лишь подъехавший за поисковиками автомобиль. В итоге женщину отвезли в районную больницу. Врачи заподозрили у нее травму черепа и отправили в медучреждение Хабаровска, но там диагноз не подтвердился. По словам сотрудников больницы, пострадавшая испытала сильный стресс, из-за которого у нее повысилась сонливость, передает портал Life.ru. Позже пенсионерка рассказала, что ходила по лесу в поисках грибов и угодила в илистое дно ручья, как вдруг около нее начал кружить медведь. Чтобы отпугнуть дикого зверя, женщина стала громко рычать. Ранее нападение медведя на человека произошло 24 июля в Карелии. Там на территорию дачного участка в садово-огородническом товариществе «Родник» прибежал медвежонок — его увидел хозяин дома и решил погладить. Через некоторое время из леса вышла медведица и впилась зубами в мужчину. Его госпитализировали с укусами в районе предплечья и прооперировали. По словам главврача медучреждения, пациент находится в состоянии средней тяжести, передает газета «Новости Костомукши». Жители Карелии заявили, что хищники давно держат в страхе целые районы. Так, медведи заполонили город Беломорск: их замечали на заводах, набережной, около магазина и в порту. Прогулку одного из зверей сняла камера наружного видеонаблюдения, расположенная на побережье. 3 августа местная жительница якобы встретила медведя прямо у продуктового магазина. «Может уже что-нибудь сделают с этим. У некоторых дети гуляют до 11, а медведи сейчас голодные и бродят. example_title: Summarization Example 2 library_name: transformers metrics: - accuracy --- Russian text summarizer was fine-tuned from ai-forever/ruT5-base model and trained on ~60k rows samples' dataset. Example Usage: ```python import torch from transformers import T5Tokenizer, T5ForConditionalGeneration model_name = "sarahai/ruT5-base-summarizer" tokenizer = T5Tokenizer.from_pretrained(model_name) model = T5ForConditionalGeneration.from_pretrained(model_name) device = torch.device("cpu") #if you are using cpu input_text = "Похоже, в Солнечной системе вскоре могут снова произойти изменения, связанные с переклассификацией известных науке тел. По мнению ученых, в ближайшем будущем возможно увеличение числа так называемых карликовых планет — тел, из-за которых возникает наибольшее число споров в астрономической среде. Чтобы относиться к карликовым планетам, по правилам Международного астрономического союза телу Солнечной системы необходимо удовлетворять сразу четырем критериям. Во-первых, оно должно вращаться вокруг Солнца, при этом оно не должно быть спутником одной из планет. Пространство вокруг тела должно быть «очищено» от других объектов, и, наконец, тело должно быть достаточно массивным, чтобы быть в состоянии гидростатического равновесия — иначе говоря, оно должно быть относительно круглым. Внутри Солнечной системы есть огромное число тел, удовлетворяющих первым трем критериям, особенно, находящихся внутри Главного пояса астероидов между орбитами Марса и Юпитера. Всем четырем критериям до последнего времени, как считалось, удовлетворяли пять тел Солнечной системы — транснептуновые объекты Плутон, Эрида, Макемаке, Хаумеа и наименьшая из известных карликовых планет Церера, находящаяся в поясе астероидов. Однако последние наблюдения показали, что к карликовым планетам стоит отнести еще одно тело – Гигею, четвертый по величине объект пояса астероидов после Цереры, Весты и Паллады. До недавнего времени этот астероид был мало изучен — астрономы знали, что он имеет продолговатую форму размером более 400 километров. Наблюдения, проведенные в Чили на одном из крупнейших телескопов мира Very Large Telescope (Очень большой телескоп), смогли качественно изменить представление о форме этого тела. «Благодаря уникальным возможностям инструмента SPHERE на телескопе VLT, остающемся одной из мощнейших строящих изображение систем в мире, мы смогли рассмотреть форму Гигеи, которая оказалась почти сферической, — пояснил астроном Пьер Вернацца из Астрофизической лаборатории в Марселе. — Благодаря этим снимкам Гигея может быть переклассифицирована в карликовую планету, самую маленькую в Солнечной системе». Согласно новым наблюдениям, диаметр Гигеи составляет свыше 430 километров, а период вращения вокруг собственной оси — 13,8 часа. Ученые и раньше знали, что поверхность Гигеи схожа с поверхностью Цереры и имеет такую же низкую плотность. Однако теперь стало очевидно, что Гигея почти такая же круглая, как и Церера, и потому имеет полное право тоже называться карликовой планетой. Немало удивило астрономов и другое обстоятельство — отсутствие на поверхности Гигеи крупных ударных кратеров. Дело в то, что примерно на одной орбите с Гигеей находится порядка 7 тыс. небольших астероидов схожего состава. Гигея — наиболее массивное из этих тел, принадлежащих к одному семейству. Считается, что вся группа образовалась порядка 2 миллиардов лет назад, когда удар крупного тела выбил из Гигеи множество осколков, вылетевших в окружающее пространство. Похожее событие пережила в далеком прошлом Веста, создав вокруг себя аналогичное семейство астероидов. Правда, на теле Весты до сих пор присутствуют следы этого бурного прошлого. Снимки 95% поверхности Гигеи позволили обнаружить лишь два мелких кратера на ее поверхности, которые не идут ни в какое сравнение с «ранами» на поверхности Гигеи. «Ни один из этих кратеров не мог быть вызван ударом, образовавшим семейство астероидов Гигеи, чей объем соответствует объему тела диаметром сто километров. Они слишком маленькие», — пояснил интригу Мирослав Броз, астроном из Карлова Университета в Чехии. На помощь в решении этой загадки пришло численное моделирование, часто используемое астрофизиками для описания эволюции различных астрономических систем. С его помощью астрономы показали, что округлая форма современной Гигеи и наличие рядом с ней роя астероидов — следствие сильнейшего лобового столкновения Гигеи с крупным телом, имевшим в поперечнике от 75 до 150 километров. Моделирование показало, что это соударение, произошедшее 2 млрд лет назад, почти полностью разнесло на части Гигею. Образовавшиеся после этого осколки, слипшись под действием гравитации, заново сформировали Гигею, дав ей почти идеально круглую форму. «Такие столкновения между двумя крупными телами в поясе астероидов уникальны для последних 3-4 миллиардов лет», — пояснил Равел Севечек, соавтор исследования , опубликованного в журнале Nature Astronomy. Ранее астрономы объявили об открытии, которое в очередной раз заставит авторов переписывать учебники астрономии. С конца 1990-х годов считалось, что планетой Солнечной системы, имеющей наибольшее число спутников, является Юпитер, у которого их в настоящее время насчитывается 79 штук. Вторым после него по этому показателю был Сатурн, третьим – Уран. Однако теперь рекордсменом стал именно Сатурн, которому астрономы добавили сразу 20 небольших, ранее неизвестных спутников. Теперь их у него как минимум 82 штуки. Новые спутники были открыты при помощи телескопа Subaru, расположенного на горе Мауна-Кеа на Гавайях. Обнаружить объекты позволили новые компьютерные алгоритмы, примененные для обработки данных, полученных еще в 2004-2004 годы." #your input in russian input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(device) outputs = model.generate(input_ids, max_length=100, min_length=50, length_penalty=2.0, num_beams=4, early_stopping=True) #change according to your preferences summary = tokenizer.decode(outputs[0], skip_special_tokens=True) print(summary) ``` References Hugging Face Model Hub T5 Paper Disclaimer: The model's performance may be influenced by the quality and representativeness of the data it was fine-tuned on. Users are encouraged to assess the model's suitability for their specific applications and datasets.
LoneStriker/Yi-34B-GiftedConvo-merged-8.0bpw-h8-exl2
LoneStriker
"2023-11-09T19:49:03Z"
7
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "dataset:NobodyExistsOnTheInternet/GiftedConvoBeforeEcons", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
"2023-11-09T19:45:54Z"
--- license: mit datasets: - NobodyExistsOnTheInternet/GiftedConvoBeforeEcons --- Trained on over 20k instruct generated all by gpt-4 or humans Dataset features: 1000 long evolved conversations based off LIMA Subsection of correct PRM800k data Subsection of CamelAI's Physics and Chemistry data The model is trained with Qlora as well as Axolotl. The model format is Vicuna 1.1: ``` User: ... Assistant: ... ```
Daemontatox/PathFinderAi3.0
Daemontatox
"2025-01-10T04:36:45Z"
74
1
transformers
[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-generation
"2024-12-31T15:18:21Z"
--- base_model: Daemontatox/PathFinderAI3.0 tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en model-index: - name: PathFinderAi3.0 results: - task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: wis-k/instruction-following-eval split: train args: num_few_shot: 0 metrics: - type: inst_level_strict_acc and prompt_level_strict_acc value: 42.71 name: averaged accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Daemontatox%2FPathFinderAi3.0 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: SaylorTwift/bbh split: test args: num_few_shot: 3 metrics: - type: acc_norm value: 55.54 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Daemontatox%2FPathFinderAi3.0 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: lighteval/MATH-Hard split: test args: num_few_shot: 4 metrics: - type: exact_match value: 48.34 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Daemontatox%2FPathFinderAi3.0 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa split: train args: num_few_shot: 0 metrics: - type: acc_norm value: 21.14 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Daemontatox%2FPathFinderAi3.0 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics: - type: acc_norm value: 20.05 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Daemontatox%2FPathFinderAi3.0 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 52.86 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Daemontatox%2FPathFinderAi3.0 name: Open LLM Leaderboard --- ![image](./image.webp) # PathFinderAI 3.0 PathFinderAI 3.0 is a high-performance language model designed for advanced reasoning, real-time text analysis, and decision support. Fine-tuned for diverse applications, it builds upon the capabilities of Qwen2, optimized with cutting-edge tools for efficiency and performance. ## Features - **Advanced Reasoning:** Fine-tuned for real-time problem-solving and logic-driven tasks. - **Enhanced Performance:** Trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and the Hugging Face TRL library. - **Multi-domain Capability:** Excels in education, research, business, legal, and healthcare applications. - **Optimized Architecture:** Leverages Qwen2 for robust language understanding and generation. ## Training Details - **Base Model:** Daemontatox/PathFinderAI3.0 - **Training Frameworks:** [Unsloth](https://github.com/unslothai/unsloth) and Hugging Face’s TRL library. - **Optimization:** Quantization-aware training for faster inference and deployment on resource-constrained environments. ## Deployment This model is ideal for deployment on both cloud platforms and edge devices, including Raspberry Pi, utilizing efficient quantization techniques. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) ## License The model is open-sourced under the Apache 2.0 license. ## Usage To load the model: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Daemontatox/PathFinderAI3.0" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name) # Example usage input_text = "What is the capital of France?" inputs = tokenizer(input_text, return_tensors="pt") outputs = model.generate(**inputs) print(tokenizer.decode(outputs[0])) ``` Model Applications PathFinderAI 3.0 is designed for: Real-time reasoning and problem-solving Text generation and comprehension Legal and policy analysis Educational tutoring Healthcare decision support # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/Daemontatox__PathFinderAi3.0-details)! Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=Daemontatox%2FPathFinderAi3.0&sort[column]=Average%20%E2%AC%86%EF%B8%8F&sort[direction]=desc)! | Metric |Value (%)| |-------------------|--------:| |**Average** | 40.11| |IFEval (0-Shot) | 42.71| |BBH (3-Shot) | 55.54| |MATH Lvl 5 (4-Shot)| 48.34| |GPQA (0-shot) | 21.14| |MuSR (0-shot) | 20.05| |MMLU-PRO (5-shot) | 52.86|
ADG-2353/Reinforce-Pixelcopter
ADG-2353
"2024-03-30T21:40:37Z"
0
0
null
[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
"2024-03-30T21:40:01Z"
--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Pixelcopter results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 35.10 +/- 24.07 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
sail-rvc/smg4
sail-rvc
"2023-07-14T07:43:49Z"
1
0
transformers
[ "transformers", "rvc", "sail-rvc", "audio-to-audio", "endpoints_compatible", "region:us" ]
audio-to-audio
"2023-07-14T07:43:35Z"
--- pipeline_tag: audio-to-audio tags: - rvc - sail-rvc --- # smg4 ## RVC Model ![banner](https://i.imgur.com/xocCjhH.jpg) This model repo was automatically generated. Date: 2023-07-14 07:43:49 Bot Name: juuxnscrap Model Type: RVC Source: https://huggingface.co/juuxn/RVCModels/ Reason: Converting into loadable format for https://github.com/chavinlo/rvc-runpod
flooptherocket/DialogGPT-small-rick
flooptherocket
"2021-09-10T01:17:41Z"
3
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
"2022-03-02T23:29:05Z"
--- tags: conversational --- @Rick from Rick and Morty GPT-2 Conversation Model ---
huggingtweets/johnowhitaker
huggingtweets
"2021-08-11T10:36:34Z"
4
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
"2022-03-02T23:29:05Z"
--- language: en thumbnail: https://www.huggingtweets.com/johnowhitaker/1628678191103/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;https://pbs.twimg.com/profile_images/1165660747504005120/5nA4Go6i_400x400.jpg&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url(&#39;&#39;)"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Jonathan Whitaker</div> <div style="text-align: center; font-size: 14px;">@johnowhitaker</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Jonathan Whitaker. | Data | Jonathan Whitaker | | --- | --- | | Tweets downloaded | 508 | | Retweets | 45 | | Short tweets | 13 | | Tweets kept | 450 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2iuk80nc/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @johnowhitaker's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2xsei074) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2xsei074/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/johnowhitaker') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
lctzz540/bunbo
lctzz540
"2024-05-18T10:05:18Z"
2
0
peft
[ "peft", "safetensors", "arxiv:1910.09700", "base_model:ura-hcmut/ura-llama-7b", "base_model:adapter:ura-hcmut/ura-llama-7b", "region:us" ]
null
"2024-05-18T10:04:42Z"
--- library_name: peft base_model: ura-hcmut/ura-llama-7b --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.1
TalentoTechIA/JuanDavidArdila
TalentoTechIA
"2025-01-21T01:27:41Z"
6
0
transformers
[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
"2025-01-21T01:11:17Z"
--- library_name: transformers license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer metrics: - accuracy model-index: - name: JuanDavidArdila 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. --> # JuanDavidArdila This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0326 - Accuracy: 0.9850 ## 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 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:------:|:----:|:---------------:|:--------:| | 0.0509 | 3.8462 | 500 | 0.0326 | 0.9850 | ### Framework versions - Transformers 4.47.1 - Pytorch 2.5.1+cu121 - Datasets 3.2.0 - Tokenizers 0.21.0
nhoxinh/46bae12d-dc84-42e4-a2e0-fd8b7c1ad706
nhoxinh
"2025-01-15T01:29:26Z"
8
0
peft
[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:codellama/CodeLlama-7b-hf", "base_model:adapter:codellama/CodeLlama-7b-hf", "license:llama2", "8-bit", "bitsandbytes", "region:us" ]
null
"2025-01-15T01:09:59Z"
--- library_name: peft license: llama2 base_model: codellama/CodeLlama-7b-hf tags: - axolotl - generated_from_trainer model-index: - name: 46bae12d-dc84-42e4-a2e0-fd8b7c1ad706 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: codellama/CodeLlama-7b-hf bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 016ff5466a568eaf_train_data.json ds_type: json format: custom path: /workspace/input_data/016ff5466a568eaf_train_data.json type: field_instruction: role field_output: text format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: nhoxinh/46bae12d-dc84-42e4-a2e0-fd8b7c1ad706 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-05 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/016ff5466a568eaf_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 special_tokens: pad_token: </s> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: e8ec2a58-4a2d-41db-ba55-d5a6443e9dce wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: e8ec2a58-4a2d-41db-ba55-d5a6443e9dce warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 46bae12d-dc84-42e4-a2e0-fd8b7c1ad706 This model is a fine-tuned version of [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8143 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 1.6048 | 0.0573 | 200 | 1.8143 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
LoneStriker/TenyxChat-8x7B-v1-6.0bpw-h6-exl2
LoneStriker
"2024-01-19T22:47:12Z"
5
0
transformers
[ "transformers", "safetensors", "mixtral", "text-generation", "tenyx-fine-tuning", "dpo", "tenyxchat", "conversational", "en", "dataset:HuggingFaceH4/ultrafeedback_binarized", "arxiv:2305.18290", "arxiv:2401.04088", "arxiv:2306.05685", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
"2024-01-19T22:32:44Z"
--- license: apache-2.0 language: - en library_name: transformers tags: - tenyx-fine-tuning - dpo - tenyxchat datasets: - HuggingFaceH4/ultrafeedback_binarized --- # TenyxChat: Language Model Alignment using Tenyx Fine-tuning Introducing TenyxChat-8x7B-v1, part of our TenyxChat series trained to function as useful assistants through preference tuning, using Tenyx's recently released advanced fine-tuning technology ([VentureBeat article](https://venturebeat.com/ai/tenyx-aims-to-fix-llms-catastrophic-forgetting-problem/)). Our model is trained using the [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290) framework on the open-source AI feedback dataset [UltraFeedback](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized). We fine-tune [Mixtral-8x7B-Instruct-v0.1](https://arxiv.org/pdf/2401.04088.pdf) with our proprietary approach ([blog](https://www.tenyx.com/post/forgetting-and-toxicity-in-llms-a-deep-dive-on-fine-tuning-methods), [service](https://www.tenyx.com/fine-tuning)), similar to that of our [7B model](https://huggingface.co/tenyx/TenyxChat-7B-v1), and show an increase in [MT-Bench](https://arxiv.org/abs/2306.05685) scores. Our approach aims to mitigate forgetting in LLMs in a computationally efficient manner, thereby enabling continual fine-tuning capabilities without altering the pre-trained output distribution. TenyxChat-8x7B-v1 was trained using eight A100s (80GB) for about eight hours, with a training setup obtained from HuggingFaceH4 ([GitHub](https://github.com/huggingface/alignment-handbook)). # Model details - Model type: Fine-tuned Mixture Of Expert 8x7B model for chat. - License: Apache 2.0 - Base model: [Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) - Demo: [spaces/tenyx/TenyxChat-8x7B-v1](https://huggingface.co/spaces/tenyx/TenyxChat-8x7B-v1) ## Usage Our model uses a simple chat template based on Mixtral-8x7B-Instruct-v0.1 . The chat template usage with a Hugging face generation example is shown below. ### Chat Template (Jinja) ```rust {{ bos_token }} {% for message in messages %} {% if message['role'] == 'user' %} {{ '[INST]' + message['content'] + '[/INST]' }} {% elif message['role'] == 'system' %} {{ '[INST]' + message['content'] + '[/INST]' }} {% elif message['role'] == 'assistant' %} {{ message['content'] + eos_token }} {% endif %} {% endfor %} ``` ### Hugging face Example ```python import torch from transformers import pipeline pipe = pipeline("text-generation", model="tenyx/TenyxChat-8x7B-v1", torch_dtype=torch.bfloat16, device_map="auto") messages = [ {"role": "system", "content": "You are a friendly chatbot who always responds in the style of a pirate."}, {"role": "user", "content": "Hi. I would like to make a hotel booking."}, ] prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) outputs = pipe(prompt, max_new_tokens=512, do_sample=False) ``` ### Output ``` <s>[INST]You are a friendly chatbot who always responds in the style of a pirate.[/INST] [INST]Hi. I would like to make a hotel booking.[/INST] Ahoy there, me hearty! Ye wish to make a hotel booking, do ye? Well, let's set sail on this voyage of reservations and see what we can find! What's the name of the port (hotel) and the dates of our journey (check-in and check-out)? I'll do me best to assist ye! ``` # Performance At the time of release (Jan 2024), TenyxChat-8x7B-v1 is the highest-ranked model, only superseded by GPT4, on the MT-Bench evaluation available for download and commercial use. ## MT-Bench MT-Bench is a benchmark made up of 80 high-quality multi-turn questions. These questions fall into eight categories: Writing, Roleplay, Reasoning, Math, Coding, Extraction, STEM, and Humanities. The chat models are rated using GPT-4 on a scale of 1 to 10, with higher values corresponding to better responses. | Model | First Turn | Second Turn | Average | | --- | --- | --- | --- | | GPT-4* | 8.95625 | 9.02500 | 8.990625 | | TenyxChat-8x7B-v1 | 8.63750 | 8.16250 | 8.400000 | | Mixtral (reproduced) | 8.49375 | 8.00000 | 8.246875 | | GPT-3.5-turbo* | 8.07500 | 7.81250 | 7.943750 | *values reported on [lmsys](https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge) ChatBot Arena ![hexplot.png](assets/hexplot.png) # Limitations TenyxChat-8x7B-v1, like other language models, has its own set of limitations. We haven’t fine-tuned the model explicitly to align with **human** safety preferences. Therefore, it is capable of producing undesirable outputs, particularly when adversarially prompted. From our observation, the model still tends to struggle with tasks that involve reasoning and math questions. In some instances, it might generate verbose or extraneous content. # License TenyxChat-8x7B-v1, similar to Mixtral-8x7B-Instruct-v0.1 , is distributed under the Apache License 2.0. # Citation If you use TenyxChat-8x7B-v1 for your research, cite us as ``` @misc{tenyxchat2024, title={TenyxChat: Language Model Alignment using Tenyx Fine-tuning}, author={Tenyx}, year={2024}, } ```
gubartz/st_scibert_abstruct
gubartz
"2023-08-04T17:49:54Z"
41
0
sentence-transformers
[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
sentence-similarity
"2023-08-04T17:49:43Z"
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 352 with parameters: ``` {'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.BatchAllTripletLoss.BatchAllTripletLoss` Parameters of the fit()-Method: ``` { "epochs": 20, "evaluation_steps": 0, "evaluator": "sentence_transformers.evaluation.TripletEvaluator.TripletEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 704, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
DavidAU/DeepSeek-MOE-4X8B-R1-Distill-Llama-3.1-Mad-Scientist-24B
DavidAU
"2025-02-15T03:45:20Z"
0
0
transformers
[ "transformers", "safetensors", "mixtral", "text-generation", "mergekit", "merge", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
"2025-02-15T00:14:14Z"
--- library_name: transformers tags: - mergekit - merge base_model: [] --- <h2>DeepSeek-MOE-4X8B-R1-Distill-Llama-3.1-Mad-Scientist-24B</h2> This repo contains the full precision source code, in "safe tensors" format to generate GGUFs, GPTQ, EXL2, AWQ, HQQ and other formats. The source code can also be used directly. (source files will be uploaded when parameter count shows in upper left) NOTE: Links to GGUFs below. <B>IMPORTANT: Highest Quality Settings / Optimal Operation Guide / Parameters and Samplers</B> If you are going to use this model, (source, GGUF or a different quant), please review this document for critical parameter, sampler and advance sampler settings (for multiple AI/LLM aps). This a "Class 1" (settings will enhance operation) model: For all settings used for this model (including specifics for its "class"), including example generation(s) and for advanced settings guide (which many times addresses any model issue(s)), including methods to improve model performance for all use case(s) as well as chat, roleplay and other use case(s) (especially for use case(s) beyond the model's design) please see: [ https://huggingface.co/DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters ] REASON: Regardless of "model class" this document will detail methods to enhance operations. If the model is a Class 3/4 model the default settings (parameters, samplers, advanced samplers) must be set for "use case(s)" uses correctly. Some AI/LLM apps DO NOT have consistant default setting(s) which result in sub-par model operation. Like wise for Class 3/4 models (which operate somewhat to very differently than standard models) additional samplers and advanced samplers settings are required to "smooth out" operation, AND/OR also allow full operation for use cases the model was not designed for. BONUS - Use these settings for ANY model, ANY repo, ANY quant (including source/full precision): This document also details parameters, sampler and advanced samplers that can be use FOR ANY MODEL, FROM ANY REPO too - all quants, and of course source code operation too - to enhance the operation of any model. [ https://huggingface.co/DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters ] NOTE: I strongly suggest you also visit the DavidAU GGUF (below) repo too for more details in using this model ; especially if it is "Class 3" or "Class 4" to get maximum performance from the model. For full information about this model, including: - Details about this model and its use case(s). - Context limits - Special usage notes / settings. - Any model(s) used to create this model. - Template(s) used to access/use this model. - Example generation(s) - GGUF quants of this model Please go to: [ https://huggingface.co/DavidAU/DeepSeek-MOE-4X8B-R1-Distill-Llama-3.1-Mad-Scientist-24B-gguf ]
Reza2kn/XRAY
Reza2kn
"2024-10-10T02:31:22Z"
7
4
diffusers
[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "region:us" ]
text-to-image
"2024-10-10T02:28:57Z"
--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: >- chest XRAY image of 50 year old male with bilateral secondary PTB with right upper atelectasis, right pleural adhesions, left compensatory emphysema output: url: images/1728521628967__000016669_0.jpg base_model: black-forest-labs/FLUX.1-dev instance_prompt: XRAY --- # XRAY <Gallery /> ## Model description Tryin&#39; somethin&#39; here. v1 ## Trigger words You should use `XRAY` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/Reza2kn/XRAY/tree/main) them in the Files & versions tab.
martimfasantos/tinyllama-1.1b-sum-dpo-full_LR2e-7_3epochs
martimfasantos
"2024-06-09T13:24:43Z"
5
0
transformers
[ "transformers", "tensorboard", "safetensors", "llama", "text-generation", "alignment-handbook", "trl", "dpo", "generated_from_trainer", "dataset:openai/summarize_from_feedback", "base_model:martimfasantos/tinyllama-1.1b-sum-sft-full", "base_model:finetune:martimfasantos/tinyllama-1.1b-sum-sft-full", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
"2024-06-08T12:39:25Z"
--- license: apache-2.0 base_model: martimfasantos/tinyllama-1.1b-sum-sft-full tags: - alignment-handbook - trl - dpo - generated_from_trainer - trl - dpo - generated_from_trainer datasets: - openai/summarize_from_feedback model-index: - name: tinyllama-1.1b-sum-dpo-full_LR2e-7_3epochs 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. --> # tinyllama-1.1b-sum-dpo-full_LR2e-7_3epochs This model is a fine-tuned version of [martimfasantos/tinyllama-1.1b-sum-sft-full](https://huggingface.co/martimfasantos/tinyllama-1.1b-sum-sft-full) on the openai/summarize_from_feedback dataset. It achieves the following results on the evaluation set: - Loss: 0.6411 - Rewards/chosen: -1.5955 - Rewards/rejected: -1.9066 - Rewards/accuracies: 0.6273 - Rewards/margins: 0.3112 - Logps/rejected: -253.4108 - Logps/chosen: -218.5612 - Logits/rejected: -2.1502 - Logits/chosen: -2.1697 ## 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-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |:-------------:|:------:|:-----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| | 0.6924 | 0.0689 | 400 | 0.6930 | 0.0011 | 0.0007 | 0.5390 | 0.0003 | -62.6755 | -58.9094 | -2.9687 | -2.9723 | | 0.6891 | 0.1378 | 800 | 0.6909 | -0.0061 | -0.0108 | 0.5748 | 0.0047 | -63.8305 | -59.6239 | -2.9588 | -2.9622 | | 0.6874 | 0.2068 | 1200 | 0.6876 | -0.0302 | -0.0427 | 0.5871 | 0.0124 | -67.0173 | -62.0385 | -2.9361 | -2.9395 | | 0.676 | 0.2757 | 1600 | 0.6820 | -0.1057 | -0.1316 | 0.5850 | 0.0259 | -75.9065 | -69.5813 | -2.8942 | -2.8976 | | 0.6751 | 0.3446 | 2000 | 0.6770 | -0.1715 | -0.2098 | 0.5890 | 0.0384 | -83.7308 | -76.1611 | -2.8434 | -2.8468 | | 0.6518 | 0.4135 | 2400 | 0.6676 | -0.3727 | -0.4381 | 0.6069 | 0.0654 | -106.5637 | -96.2904 | -2.7893 | -2.7926 | | 0.6695 | 0.4824 | 2800 | 0.6631 | -0.4734 | -0.5560 | 0.6141 | 0.0826 | -118.3500 | -106.3523 | -2.7415 | -2.7450 | | 0.6467 | 0.5513 | 3200 | 0.6583 | -0.6700 | -0.7814 | 0.625 | 0.1113 | -140.8851 | -126.0199 | -2.6864 | -2.6902 | | 0.6264 | 0.6203 | 3600 | 0.6586 | -0.6359 | -0.7384 | 0.6106 | 0.1024 | -136.5857 | -122.6100 | -2.6176 | -2.6225 | | 0.6203 | 0.6892 | 4000 | 0.6523 | -0.7851 | -0.9183 | 0.6166 | 0.1332 | -154.5775 | -137.5248 | -2.5583 | -2.5642 | | 0.6341 | 0.7581 | 4400 | 0.6487 | -0.8786 | -1.0259 | 0.6129 | 0.1473 | -165.3377 | -146.8752 | -2.4643 | -2.4723 | | 0.6184 | 0.8270 | 4800 | 0.6454 | -1.0766 | -1.2481 | 0.6129 | 0.1716 | -187.5630 | -166.6730 | -2.4141 | -2.4242 | | 0.609 | 0.8959 | 5200 | 0.6414 | -0.9919 | -1.1678 | 0.6164 | 0.1759 | -179.5278 | -158.2066 | -2.3970 | -2.4080 | | 0.5977 | 0.9649 | 5600 | 0.6432 | -0.9166 | -1.0804 | 0.6273 | 0.1638 | -170.7888 | -150.6710 | -2.3933 | -2.4042 | | 0.5845 | 1.0338 | 6000 | 0.6438 | -1.3686 | -1.6032 | 0.6245 | 0.2346 | -223.0724 | -195.8758 | -2.2640 | -2.2816 | | 0.5789 | 1.1027 | 6400 | 0.6455 | -1.3882 | -1.6212 | 0.6164 | 0.2331 | -224.8725 | -197.8306 | -2.2428 | -2.2595 | | 0.5681 | 1.1716 | 6800 | 0.6434 | -1.3348 | -1.5500 | 0.6129 | 0.2153 | -217.7540 | -192.4917 | -2.2435 | -2.2593 | | 0.5602 | 1.2405 | 7200 | 0.6448 | -1.3673 | -1.5959 | 0.6234 | 0.2286 | -222.3391 | -195.7428 | -2.2210 | -2.2378 | | 0.6357 | 1.3094 | 7600 | 0.6413 | -1.3975 | -1.6344 | 0.6125 | 0.2368 | -226.1876 | -198.7702 | -2.2034 | -2.2208 | | 0.5491 | 1.3784 | 8000 | 0.6438 | -1.4655 | -1.7121 | 0.6055 | 0.2466 | -233.9599 | -205.5657 | -2.1906 | -2.2085 | | 0.5537 | 1.4473 | 8400 | 0.6445 | -1.4375 | -1.6793 | 0.6259 | 0.2418 | -230.6812 | -202.7634 | -2.1797 | -2.1984 | | 0.61 | 1.5162 | 8800 | 0.6405 | -1.0941 | -1.2946 | 0.6164 | 0.2005 | -192.2120 | -168.4266 | -2.2428 | -2.2579 | | 0.523 | 1.5851 | 9200 | 0.6431 | -1.4596 | -1.7029 | 0.6289 | 0.2433 | -233.0398 | -204.9723 | -2.1570 | -2.1756 | | 0.5412 | 1.6540 | 9600 | 0.6393 | -1.4228 | -1.6896 | 0.6315 | 0.2668 | -231.7097 | -201.2986 | -2.1513 | -2.1708 | | 0.5368 | 1.7229 | 10000 | 0.6408 | -1.3358 | -1.5858 | 0.6236 | 0.2500 | -221.3330 | -192.5947 | -2.1730 | -2.1915 | | 0.5064 | 1.7919 | 10400 | 0.6423 | -1.0625 | -1.2620 | 0.6215 | 0.1995 | -188.9488 | -165.2631 | -2.2150 | -2.2307 | | 0.5268 | 1.8608 | 10800 | 0.6406 | -1.4254 | -1.6829 | 0.6341 | 0.2575 | -231.0404 | -201.5558 | -2.1644 | -2.1831 | | 0.5384 | 1.9297 | 11200 | 0.6418 | -1.6486 | -1.9439 | 0.6364 | 0.2954 | -257.1440 | -223.8720 | -2.1299 | -2.1503 | | 0.5734 | 1.9986 | 11600 | 0.6378 | -1.4356 | -1.7101 | 0.6362 | 0.2744 | -233.7563 | -202.5782 | -2.1624 | -2.1813 | | 0.5302 | 2.0675 | 12000 | 0.6413 | -1.7064 | -2.0285 | 0.6292 | 0.3221 | -265.5970 | -229.6515 | -2.1257 | -2.1466 | | 0.4961 | 2.1365 | 12400 | 0.6474 | -2.0075 | -2.3712 | 0.6387 | 0.3637 | -299.8690 | -259.7696 | -2.0958 | -2.1178 | | 0.55 | 2.2054 | 12800 | 0.6415 | -1.5035 | -1.7868 | 0.6315 | 0.2833 | -241.4328 | -209.3660 | -2.1574 | -2.1761 | | 0.5546 | 2.2743 | 13200 | 0.6425 | -1.6715 | -1.9874 | 0.6303 | 0.3159 | -261.4859 | -226.1615 | -2.1413 | -2.1612 | | 0.5639 | 2.3432 | 13600 | 0.6409 | -1.5908 | -1.8980 | 0.6289 | 0.3072 | -252.5519 | -218.1001 | -2.1481 | -2.1675 | | 0.5055 | 2.4121 | 14000 | 0.6384 | -1.4618 | -1.7629 | 0.6257 | 0.3010 | -239.0347 | -205.1979 | -2.1665 | -2.1857 | | 0.5404 | 2.4810 | 14400 | 0.6405 | -1.6514 | -1.9790 | 0.6285 | 0.3276 | -260.6489 | -224.1589 | -2.1411 | -2.1613 | | 0.5348 | 2.5500 | 14800 | 0.6418 | -1.6812 | -2.0090 | 0.6276 | 0.3278 | -263.6481 | -227.1385 | -2.1375 | -2.1578 | | 0.5114 | 2.6189 | 15200 | 0.6408 | -1.5587 | -1.8632 | 0.6310 | 0.3046 | -249.0734 | -214.8810 | -2.1538 | -2.1732 | | 0.5356 | 2.6878 | 15600 | 0.6405 | -1.5493 | -1.8534 | 0.6266 | 0.3041 | -248.0918 | -213.9473 | -2.1550 | -2.1743 | | 0.4885 | 2.7567 | 16000 | 0.6406 | -1.5822 | -1.8916 | 0.6269 | 0.3094 | -251.9056 | -217.2328 | -2.1512 | -2.1707 | | 0.5057 | 2.8256 | 16400 | 0.6410 | -1.5799 | -1.8883 | 0.6306 | 0.3084 | -251.5751 | -217.0051 | -2.1527 | -2.1720 | | 0.5731 | 2.8946 | 16800 | 0.6412 | -1.5917 | -1.9021 | 0.6271 | 0.3104 | -252.9564 | -218.1854 | -2.1507 | -2.1702 | | 0.4958 | 2.9635 | 17200 | 0.6412 | -1.5933 | -1.9040 | 0.6296 | 0.3107 | -253.1478 | -218.3473 | -2.1506 | -2.1702 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.1.2 - Datasets 2.19.2 - Tokenizers 0.19.1
SynthAIzer/finetuned-sentence-similarity
SynthAIzer
"2024-11-04T06:23:02Z"
7
1
null
[ "safetensors", "mpnet", "text classification", "Transformers", "bert", "text-classification", "en", "arxiv:1910.09700", "region:us" ]
text-classification
"2024-10-29T05:02:47Z"
--- language: - en pipeline_tag: text-classification tags: - text classification - Transformers - bert --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
ai4bharat/indic-conformer-600m
ai4bharat
"2025-03-13T11:03:46Z"
0
0
null
[ "automatic-speech-recognition", "custom_code", "region:us" ]
automatic-speech-recognition
"2025-03-13T10:53:49Z"
--- pipeline_tag: automatic-speech-recognition --- # **IndicConformer** AI4Bharat's IndicConformers is a suite of ASR models built to deliver accurate speech-to-text conversion in all 22 official Indian languages. By leveraging cutting-edge deep learning techniques, these models provide precise transcriptions. As the country's first open-source ASR system covering such a vast array of languages, AI4Bharat Indic Conformer is a transformative tool for making technology more inclusive and accessible to all. IndicConformer is released under the MIT license. ## **Model Details** - **Model Name:** IndicConformer-600M-Multi - **Repository:** [ai4bharat/indic-conformer-600m-multilingual](https://huggingface.co/ai4bharat/indic-conformer-600m-multilingual) - **Architecture:** Multilingual Conformer-based Hybrid CTC + RNNT ASR model - **Parameter Size:** 600M - **Languages Supported:** IN-22 --- ## **Model Usage** This model can be used to transcribe speech in various Indian languages. It supports two decoding strategies: - **CTC (Connectionist Temporal Classification)** - **RNNT (Recurrent Neural Network Transducer)** ### **Installation** Ensure that you have `transformers` and `torchaudio` installed: ```bash pip install transformers torchaudio ``` ### **Inference Example** ```python from transformers import AutoModel import torchaudio # Load the model model = AutoModel.from_pretrained("ai4bharat/indic-conformer-600m-multilingual", trust_remote_code=True) # Load an audio file wav, sr = torchaudio.load("audio.flac") target_sample_rate = 16000 # Expected sample rate if sr != target_sample_rate: resampler = torchaudio.transforms.Resample(orig_freq=sr, new_freq=target_sample_rate) wav = resampler(wav) # Perform ASR with CTC decoding transcription_ctc = model(wav, "hi", "ctc") print("CTC Transcription:", transcription_ctc) # Perform ASR with RNNT decoding transcription_rnnt = model(wav, "hi", "rnnt") print("RNNT Transcription:", transcription_rnnt) ``` ## **Supported Languages** IndicConformer-600M-Multi is trained for **22 offcially recognized languages of India**, including: - Assamese(`as`) - Bengali(`bn`) - Bodo(`brx`) - Dogri(`doi`) - Gujarati(`gu`) - Hindi(`hi`) - Kannada(`kn`) - Konkani(`kok`) - Kashmiri(`ks`) - Maithili(`mai`) - Malayalam(`ml`) - Manipuri(`mni`) - Marathi(`mr`) - Nepali(`ne`) - Odia(`or`) - Punjabi(`pa`) - Sanskrit(`sa`) - Santali(`sat`) - Sindhi(`sd`) - Tamil(`ta`) - Telugu(`te`) - Urdu(`ur`) ## **Contact** For any questions or feedback, please contact: - Tahir Javed ([email protected]) - Kaushal Bhogale ([email protected])
zelk12/MT-Merge7-N-gemma-2-MT3g7MT2g7-9B
zelk12
"2025-03-03T13:27:49Z"
0
0
transformers
[ "transformers", "safetensors", "gemma2", "text-generation", "mergekit", "merge", "conversational", "base_model:zelk12/MT2-Gen7-gemma-2-9B", "base_model:merge:zelk12/MT2-Gen7-gemma-2-9B", "base_model:zelk12/MT3-Gen7-gemma-2-9B", "base_model:merge:zelk12/MT3-Gen7-gemma-2-9B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
"2025-03-03T13:21:49Z"
--- base_model: - zelk12/MT2-Gen7-gemma-2-9B - zelk12/MT3-Gen7-gemma-2-9B library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [SLERP](https://en.wikipedia.org/wiki/Slerp) merge method. ### Models Merged The following models were included in the merge: * [zelk12/MT2-Gen7-gemma-2-9B](https://huggingface.co/zelk12/MT2-Gen7-gemma-2-9B) * [zelk12/MT3-Gen7-gemma-2-9B](https://huggingface.co/zelk12/MT3-Gen7-gemma-2-9B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: zelk12/MT3-Gen7-gemma-2-9B - model: zelk12/MT2-Gen7-gemma-2-9B merge_method: slerp base_model: zelk12/MT3-Gen7-gemma-2-9B dtype: bfloat16 parameters: t: 0.25 ```
jaymanvirk/ppo_pyramids_rnd
jaymanvirk
"2024-05-11T06:53:28Z"
0
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]
reinforcement-learning
"2024-05-11T06:53:26Z"
--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # **ppo** Agent playing **Pyramids** This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: jaymanvirk/ppo_pyramids_rnd 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
ahmedelsayed/v1-bloom-1b1-sql-context
ahmedelsayed
"2024-02-27T00:17:07Z"
0
0
transformers
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
null
"2024-02-27T00:17:05Z"
--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
lesso07/5693867f-d516-4b71-be57-39b004714adb
lesso07
"2025-02-09T22:45:58Z"
8
0
peft
[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/Llama-3.2-1B-Instruct", "base_model:adapter:unsloth/Llama-3.2-1B-Instruct", "license:llama3.2", "region:us" ]
null
"2025-02-09T22:27:53Z"
--- library_name: peft license: llama3.2 base_model: unsloth/Llama-3.2-1B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 5693867f-d516-4b71-be57-39b004714adb results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <br> # 5693867f-d516-4b71-be57-39b004714adb This model is a fine-tuned version of [unsloth/Llama-3.2-1B-Instruct](https://huggingface.co/unsloth/Llama-3.2-1B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3522 ## 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: 0.000207 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 50 - training_steps: 500 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | No log | 0.0001 | 1 | 2.0411 | | 1.3108 | 0.0052 | 50 | 1.5953 | | 1.2891 | 0.0104 | 100 | 1.5803 | | 1.3557 | 0.0156 | 150 | 1.4890 | | 1.3051 | 0.0208 | 200 | 1.4918 | | 1.276 | 0.0260 | 250 | 1.4426 | | 1.2771 | 0.0312 | 300 | 1.4338 | | 1.1274 | 0.0364 | 350 | 1.3891 | | 1.1713 | 0.0416 | 400 | 1.3654 | | 1.1527 | 0.0468 | 450 | 1.3580 | | 1.1735 | 0.0520 | 500 | 1.3522 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
stefan-it/hmbench-icdar-fr-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-4
stefan-it
"2023-10-17T23:06:55Z"
2
0
flair
[ "flair", "pytorch", "tensorboard", "token-classification", "sequence-tagger-model", "fr", "base_model:hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax", "base_model:finetune:hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax", "license:mit", "region:us" ]
token-classification
"2023-10-13T05:42:53Z"
--- language: fr license: mit tags: - flair - token-classification - sequence-tagger-model base_model: hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax inference: false widget: - text: Je suis convaincu , a-t43 dit . que nous n"y parviendrions pas , mais nous ne pouvons céder parce que l' état moral de nos troupe* en souffrirait trop . ( Fournier . ) Des avions ennemis lancent dix-sept bombes sur Dunkerque LONDRES . 31 décembre . --- # Fine-tuned Flair Model on French ICDAR-Europeana NER Dataset This Flair model was fine-tuned on the [French ICDAR-Europeana](https://github.com/stefan-it/historic-domain-adaptation-icdar) NER Dataset using hmByT5 as backbone LM. The ICDAR-Europeana NER Dataset is a preprocessed variant of the [Europeana NER Corpora](https://github.com/EuropeanaNewspapers/ner-corpora) for Dutch and French. The following NEs were annotated: `PER`, `LOC` and `ORG`. # ⚠️ Inference Widget ⚠️ Fine-Tuning ByT5 models in Flair is currently done by implementing an own [`ByT5Embedding`][0] class. This class needs to be present when running the model with Flair. Thus, the inference widget is not working with hmByT5 at the moment on the Model Hub and is currently disabled. This should be fixed in future, when ByT5 fine-tuning is supported in Flair directly. [0]: https://github.com/stefan-it/hmBench/blob/main/byt5_embeddings.py # Results We performed a hyper-parameter search over the following parameters with 5 different seeds per configuration: * Batch Sizes: `[8, 4]` * Learning Rates: `[0.00015, 0.00016]` And report micro F1-score on development set: | Configuration | Run 1 | Run 2 | Run 3 | Run 4 | Run 5 | Avg. | |-------------------|--------------|--------------|--------------|--------------|--------------|--------------| | bs8-e10-lr0.00015 | [0.7743][1] | [0.7654][2] | [0.7604][3] | [0.7729][4] | [0.7736][5] | 76.93 ± 0.55 | | bs8-e10-lr0.00016 | [0.7686][6] | [0.7648][7] | [0.7678][8] | [0.7653][9] | [0.7755][10] | 76.84 ± 0.38 | | bs4-e10-lr0.00015 | [0.7757][11] | [0.7549][12] | [0.7693][13] | [0.7597][14] | [0.7696][15] | 76.58 ± 0.75 | | bs4-e10-lr0.00016 | [0.7625][16] | [0.7575][17] | [0.769][18] | [0.7635][19] | [0.7647][20] | 76.34 ± 0.37 | [1]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-1 [2]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-2 [3]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-3 [4]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-4 [5]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbyt5-bs8-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-5 [6]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-1 [7]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-2 [8]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-3 [9]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-4 [10]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbyt5-bs8-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-5 [11]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-1 [12]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-2 [13]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-3 [14]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-4 [15]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbyt5-bs4-wsFalse-e10-lr0.00015-poolingfirst-layers-1-crfFalse-5 [16]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-1 [17]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-2 [18]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-3 [19]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-4 [20]: https://hf.co/stefan-it/hmbench-icdar-fr-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-5 The [training log](training.log) and TensorBoard logs (only for hmByT5 and hmTEAMS based models) are also uploaded to the model hub. More information about fine-tuning can be found [here](https://github.com/stefan-it/hmBench). # Acknowledgements We thank [Luisa März](https://github.com/LuisaMaerz), [Katharina Schmid](https://github.com/schmika) and [Erion Çano](https://github.com/erionc) for their fruitful discussions about Historic Language Models. Research supported with Cloud TPUs from Google's [TPU Research Cloud](https://sites.research.google/trc/about/) (TRC). Many Thanks for providing access to the TPUs ❤️
Pdro-ruiz/Reinforce-CartPole
Pdro-ruiz
"2025-02-14T17:04:44Z"
0
0
null
[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
"2025-02-14T17:04:39Z"
--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 155.30 +/- 6.25 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
visdata/mm5
visdata
"2025-02-18T15:02:31Z"
0
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
"2025-02-18T14:56:47Z"
--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
TheBloke/OpenOrca-Zephyr-7B-AWQ
TheBloke
"2023-12-04T23:50:27Z"
10
1
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "base_model:Weyaxi/OpenOrca-Zephyr-7B", "base_model:quantized:Weyaxi/OpenOrca-Zephyr-7B", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "4-bit", "awq", "region:us" ]
text-generation
"2023-12-04T23:33:16Z"
--- base_model: Weyaxi/OpenOrca-Zephyr-7B inference: false license: cc-by-nc-4.0 model_creator: "Ya\u011F\u0131z \xC7al\u0131k" model_name: OpenOrca Zephyr 7B model_type: mistral prompt_template: '<|system|> {system_message}</s> <|user|> {prompt}</s> <|assistant|> ' quantized_by: TheBloke --- <!-- markdownlint-disable MD041 --> <!-- 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 --> # OpenOrca Zephyr 7B - AWQ - Model creator: [Yağız Çalık](https://huggingface.co/Weyaxi) - Original model: [OpenOrca Zephyr 7B](https://huggingface.co/Weyaxi/OpenOrca-Zephyr-7B) <!-- description start --> ## Description This repo contains AWQ model files for [Yağız Çalık's OpenOrca Zephyr 7B](https://huggingface.co/Weyaxi/OpenOrca-Zephyr-7B). These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). ### About AWQ AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings. AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead. It is supported by: - [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ - [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types. - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) - [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers - [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/OpenOrca-Zephyr-7B-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/OpenOrca-Zephyr-7B-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/OpenOrca-Zephyr-7B-GGUF) * [Yağız Çalık's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Weyaxi/OpenOrca-Zephyr-7B) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Zephyr ``` <|system|> {system_message}</s> <|user|> {prompt}</s> <|assistant|> ``` <!-- prompt-template end --> <!-- README_AWQ.md-provided-files start --> ## Provided files, and AWQ parameters I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered. Models are released as sharded safetensors files. | Branch | Bits | GS | AWQ Dataset | Seq Len | Size | | ------ | ---- | -- | ----------- | ------- | ---- | | [main](https://huggingface.co/TheBloke/OpenOrca-Zephyr-7B-AWQ/tree/main) | 4 | 128 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 4.15 GB <!-- README_AWQ.md-provided-files end --> <!-- README_AWQ.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/OpenOrca-Zephyr-7B-AWQ`. 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: `OpenOrca-Zephyr-7B-AWQ` 7. Select **Loader: AutoAWQ**. 8. Click Load, and the model will load and is now ready for use. 9. 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. 10. Once you're ready, click the **Text Generation** tab and enter a prompt to get started! <!-- README_AWQ.md-text-generation-webui end --> <!-- README_AWQ.md-use-from-vllm start --> ## Multi-user inference server: vLLM Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/). - Please ensure you are using vLLM version 0.2 or later. - When using vLLM as a server, pass the `--quantization awq` parameter. For example: ```shell python3 -m vllm.entrypoints.api_server --model TheBloke/OpenOrca-Zephyr-7B-AWQ --quantization awq --dtype auto ``` - When using vLLM from Python code, again set `quantization=awq`. For example: ```python from vllm import LLM, SamplingParams prompts = [ "Tell me about AI", "Write a story about llamas", "What is 291 - 150?", "How much wood would a woodchuck chuck if a woodchuck could chuck wood?", ] prompt_template=f'''<|system|> {system_message}</s> <|user|> {prompt}</s> <|assistant|> ''' prompts = [prompt_template.format(prompt=prompt) for prompt in prompts] sampling_params = SamplingParams(temperature=0.8, top_p=0.95) llm = LLM(model="TheBloke/OpenOrca-Zephyr-7B-AWQ", quantization="awq", dtype="auto") outputs = llm.generate(prompts, sampling_params) # Print the outputs. for output in outputs: prompt = output.prompt generated_text = output.outputs[0].text print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") ``` <!-- README_AWQ.md-use-from-vllm start --> <!-- README_AWQ.md-use-from-tgi start --> ## Multi-user inference server: Hugging Face Text Generation Inference (TGI) Use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0` Example Docker parameters: ```shell --model-id TheBloke/OpenOrca-Zephyr-7B-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 ``` Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/huggingface_hub) 0.17.0 or later): ```shell pip3 install huggingface-hub ``` ```python from huggingface_hub import InferenceClient endpoint_url = "https://your-endpoint-url-here" prompt = "Tell me about AI" prompt_template=f'''<|system|> {system_message}</s> <|user|> {prompt}</s> <|assistant|> ''' client = InferenceClient(endpoint_url) response = client.text_generation(prompt, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1) print(f"Model output: ", response) ``` <!-- README_AWQ.md-use-from-tgi end --> <!-- README_AWQ.md-use-from-python start --> ## Inference from Python code using Transformers ### Install the necessary packages - Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later. - Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later. ```shell pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0" ``` Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0. If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command: ```shell pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl ``` If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead: ```shell pip3 uninstall -y autoawq git clone https://github.com/casper-hansen/AutoAWQ cd AutoAWQ pip3 install . ``` ### Transformers example code (requires Transformers 4.35.0 and later) ```python from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer model_name_or_path = "TheBloke/OpenOrca-Zephyr-7B-AWQ" tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) model = AutoModelForCausalLM.from_pretrained( model_name_or_path, low_cpu_mem_usage=True, device_map="cuda:0" ) # Using the text streamer to stream output one token at a time streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) prompt = "Tell me about AI" prompt_template=f'''<|system|> {system_message}</s> <|user|> {prompt}</s> <|assistant|> ''' # Convert prompt to tokens tokens = tokenizer( prompt_template, return_tensors='pt' ).input_ids.cuda() generation_params = { "do_sample": True, "temperature": 0.7, "top_p": 0.95, "top_k": 40, "max_new_tokens": 512, "repetition_penalty": 1.1 } # Generate streamed output, visible one token at a time generation_output = model.generate( tokens, streamer=streamer, **generation_params ) # Generation without a streamer, which will include the prompt in the output generation_output = model.generate( tokens, **generation_params ) # Get the tokens from the output, decode them, print them token_output = generation_output[0] text_output = tokenizer.decode(token_output) print("model.generate output: ", text_output) # Inference is also possible via Transformers' pipeline from transformers import pipeline pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, **generation_params ) pipe_output = pipe(prompt_template)[0]['generated_text'] print("pipeline output: ", pipe_output) ``` <!-- README_AWQ.md-use-from-python end --> <!-- README_AWQ.md-compatibility start --> ## Compatibility The files provided are tested to work with: - [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`. - [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later. - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later. - [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later. - [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later. <!-- README_AWQ.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**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: Yağız Çalık's OpenOrca Zephyr 7B <a href="https://www.buymeacoffee.com/PulsarAI" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;" ></a> Merge of [HuggingFaceH4/zephyr-7b-alpha](https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha) and [Open-Orca/Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca) using ties merge. ### *Weights* - [HuggingFaceH4/zephyr-7b-alpha](https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha): 0.5 - [Open-Orca/Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca): 0.3 ### *Density* - [HuggingFaceH4/zephyr-7b-alpha](https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha): 0.5 - [Open-Orca/Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca): 0.5 # Evulation Results ([Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)) | Metric | Value | |-----------------------|-------| | Avg. | | | ARC (25-shot) | | | HellaSwag (10-shot) | | | MMLU (5-shot) | | | TruthfulQA (0-shot) | |
anhtranhong/fingpt-mt_llama2-7b_lora_with_fiqa-qa-v1
anhtranhong
"2024-02-24T10:13:58Z"
0
0
peft
[ "peft", "region:us" ]
null
"2024-02-24T06:36:28Z"
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - 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 0.5.0
tensorblock/Llama-medx_v2-GGUF
tensorblock
"2024-11-16T01:44:32Z"
37
0
transformers
[ "transformers", "gguf", "medical", "TensorBlock", "GGUF", "dataset:skumar9/orpo-mmlu", "base_model:skumar9/Llama-medx_v2", "base_model:quantized:skumar9/Llama-medx_v2", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]
null
"2024-11-14T20:59:05Z"
--- library_name: transformers license: apache-2.0 datasets: - skumar9/orpo-mmlu tags: - medical - TensorBlock - GGUF base_model: skumar9/Llama-medx_v2 --- <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"> Feedback and support: TensorBlock's <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a> </p> </div> </div> ## skumar9/Llama-medx_v2 - GGUF This repo contains GGUF format model files for [skumar9/Llama-medx_v2](https://huggingface.co/skumar9/Llama-medx_v2). The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4011](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d). <div style="text-align: left; margin: 20px 0;"> <a href="https://tensorblock.co/waitlist/client" style="display: inline-block; padding: 10px 20px; background-color: #007bff; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;"> Run them on the TensorBlock client using your local machine ↗ </a> </div> ## Prompt template ``` <|im_start|>system {system_prompt}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ``` ## Model file specification | Filename | Quant type | File Size | Description | | -------- | ---------- | --------- | ----------- | | [Llama-medx_v2-Q2_K.gguf](https://huggingface.co/tensorblock/Llama-medx_v2-GGUF/blob/main/Llama-medx_v2-Q2_K.gguf) | Q2_K | 2.961 GB | smallest, significant quality loss - not recommended for most purposes | | [Llama-medx_v2-Q3_K_S.gguf](https://huggingface.co/tensorblock/Llama-medx_v2-GGUF/blob/main/Llama-medx_v2-Q3_K_S.gguf) | Q3_K_S | 3.413 GB | very small, high quality loss | | [Llama-medx_v2-Q3_K_M.gguf](https://huggingface.co/tensorblock/Llama-medx_v2-GGUF/blob/main/Llama-medx_v2-Q3_K_M.gguf) | Q3_K_M | 3.743 GB | very small, high quality loss | | [Llama-medx_v2-Q3_K_L.gguf](https://huggingface.co/tensorblock/Llama-medx_v2-GGUF/blob/main/Llama-medx_v2-Q3_K_L.gguf) | Q3_K_L | 4.025 GB | small, substantial quality loss | | [Llama-medx_v2-Q4_0.gguf](https://huggingface.co/tensorblock/Llama-medx_v2-GGUF/blob/main/Llama-medx_v2-Q4_0.gguf) | Q4_0 | 4.341 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | [Llama-medx_v2-Q4_K_S.gguf](https://huggingface.co/tensorblock/Llama-medx_v2-GGUF/blob/main/Llama-medx_v2-Q4_K_S.gguf) | Q4_K_S | 4.370 GB | small, greater quality loss | | [Llama-medx_v2-Q4_K_M.gguf](https://huggingface.co/tensorblock/Llama-medx_v2-GGUF/blob/main/Llama-medx_v2-Q4_K_M.gguf) | Q4_K_M | 4.583 GB | medium, balanced quality - recommended | | [Llama-medx_v2-Q5_0.gguf](https://huggingface.co/tensorblock/Llama-medx_v2-GGUF/blob/main/Llama-medx_v2-Q5_0.gguf) | Q5_0 | 5.215 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | [Llama-medx_v2-Q5_K_S.gguf](https://huggingface.co/tensorblock/Llama-medx_v2-GGUF/blob/main/Llama-medx_v2-Q5_K_S.gguf) | Q5_K_S | 5.215 GB | large, low quality loss - recommended | | [Llama-medx_v2-Q5_K_M.gguf](https://huggingface.co/tensorblock/Llama-medx_v2-GGUF/blob/main/Llama-medx_v2-Q5_K_M.gguf) | Q5_K_M | 5.339 GB | large, very low quality loss - recommended | | [Llama-medx_v2-Q6_K.gguf](https://huggingface.co/tensorblock/Llama-medx_v2-GGUF/blob/main/Llama-medx_v2-Q6_K.gguf) | Q6_K | 6.143 GB | very large, extremely low quality loss | | [Llama-medx_v2-Q8_0.gguf](https://huggingface.co/tensorblock/Llama-medx_v2-GGUF/blob/main/Llama-medx_v2-Q8_0.gguf) | Q8_0 | 7.954 GB | very large, extremely low quality loss - not recommended | ## Downloading instruction ### Command line Firstly, install Huggingface Client ```shell pip install -U "huggingface_hub[cli]" ``` Then, downoad the individual model file the a local directory ```shell huggingface-cli download tensorblock/Llama-medx_v2-GGUF --include "Llama-medx_v2-Q2_K.gguf" --local-dir MY_LOCAL_DIR ``` If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try: ```shell huggingface-cli download tensorblock/Llama-medx_v2-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf' ```
Augusto777/SWv2-DMAE-H-6-ps-clean-fix-U-40-Cross-4
Augusto777
"2025-02-18T15:42:06Z"
0
0
null
[ "tensorboard", "safetensors", "swinv2", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/swinv2-tiny-patch4-window8-256", "base_model:finetune:microsoft/swinv2-tiny-patch4-window8-256", "license:apache-2.0", "model-index", "region:us" ]
null
"2025-02-18T15:07:43Z"
--- license: apache-2.0 base_model: microsoft/swinv2-tiny-patch4-window8-256 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: SWv2-DMAE-H-6-ps-clean-fix-U-40-Cross-4 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.8076923076923077 --- <!-- 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. --> # SWv2-DMAE-H-6-ps-clean-fix-U-40-Cross-4 This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.6121 - Accuracy: 0.8077 ## 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: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 40 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.3848 | 0.96 | 11 | 1.3618 | 0.3462 | | 1.3878 | 2.0 | 23 | 1.3404 | 0.3462 | | 1.3655 | 2.96 | 34 | 1.2639 | 0.5192 | | 1.2532 | 4.0 | 46 | 1.1336 | 0.5769 | | 1.0874 | 4.96 | 57 | 0.9641 | 0.6346 | | 0.9273 | 6.0 | 69 | 0.8201 | 0.75 | | 0.7103 | 6.96 | 80 | 0.7912 | 0.6154 | | 0.6715 | 8.0 | 92 | 0.6121 | 0.8077 | | 0.6077 | 8.96 | 103 | 0.6953 | 0.7115 | | 0.5358 | 10.0 | 115 | 0.6212 | 0.75 | | 0.516 | 10.96 | 126 | 0.6300 | 0.7692 | | 0.4471 | 12.0 | 138 | 0.6625 | 0.75 | | 0.4423 | 12.96 | 149 | 0.5968 | 0.8077 | | 0.3955 | 14.0 | 161 | 0.6423 | 0.75 | | 0.3662 | 14.96 | 172 | 0.6578 | 0.7885 | | 0.3448 | 16.0 | 184 | 0.6242 | 0.7885 | | 0.3201 | 16.96 | 195 | 0.6471 | 0.7692 | | 0.3236 | 18.0 | 207 | 0.7658 | 0.75 | | 0.2718 | 18.96 | 218 | 0.7001 | 0.7692 | | 0.2885 | 20.0 | 230 | 0.7451 | 0.75 | | 0.2486 | 20.96 | 241 | 0.7268 | 0.75 | | 0.2727 | 22.0 | 253 | 0.7521 | 0.75 | | 0.2366 | 22.96 | 264 | 0.7495 | 0.75 | | 0.246 | 24.0 | 276 | 0.6777 | 0.7885 | | 0.241 | 24.96 | 287 | 0.7473 | 0.7308 | | 0.2616 | 26.0 | 299 | 0.7162 | 0.7692 | | 0.2193 | 26.96 | 310 | 0.7607 | 0.7308 | | 0.2047 | 28.0 | 322 | 0.8142 | 0.7115 | | 0.215 | 28.96 | 333 | 0.8245 | 0.7308 | | 0.2255 | 30.0 | 345 | 0.7968 | 0.7308 | | 0.2126 | 30.96 | 356 | 0.7737 | 0.75 | | 0.1991 | 32.0 | 368 | 0.7784 | 0.7692 | | 0.186 | 32.96 | 379 | 0.8160 | 0.75 | | 0.1826 | 34.0 | 391 | 0.7998 | 0.75 | | 0.1942 | 34.96 | 402 | 0.7761 | 0.7692 | | 0.1784 | 36.0 | 414 | 0.7894 | 0.7692 | | 0.1652 | 36.96 | 425 | 0.7950 | 0.7885 | | 0.1837 | 38.0 | 437 | 0.7954 | 0.7692 | | 0.1801 | 38.26 | 440 | 0.7961 | 0.7692 | ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu118 - Datasets 2.16.1 - Tokenizers 0.15.0
GlenZhang/lora_model
GlenZhang
"2024-05-22T12:44:04Z"
0
0
transformers
[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
"2024-05-22T12:42:33Z"
--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - **Developed by:** GlenZhang - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
shubhamgantayat/tiiuae-falcon-rw-1b-wet-strength-model
shubhamgantayat
"2023-10-10T13:10:47Z"
195
0
transformers
[ "transformers", "pytorch", "falcon", "text-generation", "generated_from_trainer", "custom_code", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
"2023-10-10T10:37:12Z"
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: tiiuae-falcon-rw-1b-wet-strength-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. --> # tiiuae-falcon-rw-1b-wet-strength-model This model is a fine-tuned version of [tiiuae/falcon-rw-1b](https://huggingface.co/tiiuae/falcon-rw-1b) 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: 0.0005 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.27.2 - Pytorch 2.0.1+cu117 - Datasets 2.11.0 - Tokenizers 0.13.3
glif-loradex-trainer/insectagon_memecoins_default1
glif-loradex-trainer
"2024-11-10T21:19:32Z"
30
0
diffusers
[ "diffusers", "text-to-image", "template:sd-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:finetune:black-forest-labs/FLUX.1-dev", "license:other", "region:us", "flux", "lora", "base_model:adapter:black-forest-labs/FLUX.1-dev" ]
text-to-image
"2024-11-10T21:18:19Z"
--- tags: - diffusers - text-to-image - template:sd-lora - base_model:black-forest-labs/FLUX.1-dev - base_model:finetune:black-forest-labs/FLUX.1-dev - license:other - region:us - flux - lora widget: - output: url: samples/1731273354813__000004000_0.jpg text: Brett sitting alone in the jungle crying [SMfighter] - output: url: samples/1731273378540__000004000_1.jpg text: Trump dancing with an angry face [SMfighter] - output: url: samples/1731273402047__000004000_2.jpg text: An 8-bit super street fighter game with doge vs pepe [SMfighter] - output: url: samples/1731273425555__000004000_3.jpg text: An exciting action scene featuring mew [SMfighter] - output: url: samples/1731273449740__000004000_4.jpg text: a Japanese anime dramatic scene with toshi and a human woman [SMfighter] - output: url: samples/1731273473224__000004000_5.jpg text: A man sitting and explaining life to a sad slerf [SMfighter] base_model: black-forest-labs/FLUX.1-dev trigger: SMfighter instance_prompt: SMfighter license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md --- # memecoins_default1 Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit) under the [Glif Loradex program](https://huggingface.co/glif-loradex-trainer) by [Glif](https://glif.app) user `insectagon`. <Gallery /> ## Trigger words You should use `SMfighter` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/glif-loradex-trainer/insectagon_memecoins_default1/tree/main) them in the Files & versions tab. ## License This model is licensed under the [flux-1-dev-non-commercial-license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
sail-rvc/Selena_Gomez__RVC_-_1000_Epochs_
sail-rvc
"2023-07-14T07:31:25Z"
21
1
transformers
[ "transformers", "rvc", "sail-rvc", "audio-to-audio", "endpoints_compatible", "region:us" ]
audio-to-audio
"2023-07-14T07:31:10Z"
--- pipeline_tag: audio-to-audio tags: - rvc - sail-rvc --- # Selena_Gomez__RVC_-_1000_Epochs_ ## RVC Model ![banner](https://i.imgur.com/xocCjhH.jpg) This model repo was automatically generated. Date: 2023-07-14 07:31:25 Bot Name: juuxnscrap Model Type: RVC Source: https://huggingface.co/juuxn/RVCModels/ Reason: Converting into loadable format for https://github.com/chavinlo/rvc-runpod
RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf
RichardErkhov
"2025-03-02T03:49:10Z"
0
0
null
[ "gguf", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
null
"2025-03-02T03:46:01Z"
Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) SmolLM2-135M-Bebop-Reranker - GGUF - Model creator: https://huggingface.co/jbaron34/ - Original model: https://huggingface.co/jbaron34/SmolLM2-135M-Bebop-Reranker/ | Name | Quant method | Size | | ---- | ---- | ---- | | [SmolLM2-135M-Bebop-Reranker.Q2_K.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q2_K.gguf) | Q2_K | 0.08GB | | [SmolLM2-135M-Bebop-Reranker.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.IQ3_XS.gguf) | IQ3_XS | 0.08GB | | [SmolLM2-135M-Bebop-Reranker.IQ3_S.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.IQ3_S.gguf) | IQ3_S | 0.08GB | | [SmolLM2-135M-Bebop-Reranker.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q3_K_S.gguf) | Q3_K_S | 0.08GB | | [SmolLM2-135M-Bebop-Reranker.IQ3_M.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.IQ3_M.gguf) | IQ3_M | 0.08GB | | [SmolLM2-135M-Bebop-Reranker.Q3_K.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q3_K.gguf) | Q3_K | 0.09GB | | [SmolLM2-135M-Bebop-Reranker.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q3_K_M.gguf) | Q3_K_M | 0.09GB | | [SmolLM2-135M-Bebop-Reranker.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q3_K_L.gguf) | Q3_K_L | 0.09GB | | [SmolLM2-135M-Bebop-Reranker.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.IQ4_XS.gguf) | IQ4_XS | 0.09GB | | [SmolLM2-135M-Bebop-Reranker.Q4_0.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q4_0.gguf) | Q4_0 | 0.09GB | | [SmolLM2-135M-Bebop-Reranker.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.IQ4_NL.gguf) | IQ4_NL | 0.09GB | | [SmolLM2-135M-Bebop-Reranker.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q4_K_S.gguf) | Q4_K_S | 0.1GB | | [SmolLM2-135M-Bebop-Reranker.Q4_K.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q4_K.gguf) | Q4_K | 0.1GB | | [SmolLM2-135M-Bebop-Reranker.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q4_K_M.gguf) | Q4_K_M | 0.1GB | | [SmolLM2-135M-Bebop-Reranker.Q4_1.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q4_1.gguf) | Q4_1 | 0.09GB | | [SmolLM2-135M-Bebop-Reranker.Q5_0.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q5_0.gguf) | Q5_0 | 0.1GB | | [SmolLM2-135M-Bebop-Reranker.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q5_K_S.gguf) | Q5_K_S | 0.1GB | | [SmolLM2-135M-Bebop-Reranker.Q5_K.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q5_K.gguf) | Q5_K | 0.1GB | | [SmolLM2-135M-Bebop-Reranker.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q5_K_M.gguf) | Q5_K_M | 0.1GB | | [SmolLM2-135M-Bebop-Reranker.Q5_1.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q5_1.gguf) | Q5_1 | 0.1GB | | [SmolLM2-135M-Bebop-Reranker.Q6_K.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q6_K.gguf) | Q6_K | 0.13GB | | [SmolLM2-135M-Bebop-Reranker.Q8_0.gguf](https://huggingface.co/RichardErkhov/jbaron34_-_SmolLM2-135M-Bebop-Reranker-gguf/blob/main/SmolLM2-135M-Bebop-Reranker.Q8_0.gguf) | Q8_0 | 0.13GB | Original model description: --- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
farzadd/falcon-7b-test_finetune_QA_FAQ
farzadd
"2023-06-30T10:13:13Z"
1
0
peft
[ "peft", "region:us" ]
null
"2023-06-30T09:46:48Z"
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - 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 0.4.0.dev0
DavidNovikov/ddpm-butterflies-128
DavidNovikov
"2022-08-08T21:05:11Z"
0
0
diffusers
[ "diffusers", "tensorboard", "en", "dataset:huggan/smithsonian_butterflies_subset", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]
null
"2022-08-08T20:22:07Z"
--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: huggan/smithsonian_butterflies_subset metrics: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # ddpm-butterflies-128 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `huggan/smithsonian_butterflies_subset` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/DavidNovikov/ddpm-butterflies-128/tensorboard?#scalars)
osanseviero/llama-or-potato
osanseviero
"2022-04-01T09:45:26Z"
63
0
transformers
[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "llama-leaderboard", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
"2022-04-01T09:05:43Z"
--- tags: - image-classification - pytorch - huggingpics - llama-leaderboard metrics: - accuracy model-index: - name: llama-or-potato results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 1.0 --- # llama-or-potato Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### llamas ![llamas](images/llamas.jpg) #### potato ![potato](images/potato.jpg)
Essacheez/Phi-3-mini-4k-instruct-finetune-translation-10k-system-prompt-style
Essacheez
"2024-05-31T12:50:21Z"
7
0
transformers
[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
"2024-05-30T12:16:54Z"
--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
lesso02/e6188fad-891e-47ee-8819-b42281b4c7fa
lesso02
"2025-03-16T11:07:32Z"
9
0
peft
[ "peft", "safetensors", "gemma", "axolotl", "generated_from_trainer", "base_model:fxmarty/tiny-random-GemmaForCausalLM", "base_model:adapter:fxmarty/tiny-random-GemmaForCausalLM", "license:mit", "region:us" ]
null
"2025-03-13T22:53:31Z"
--- library_name: peft license: mit base_model: fxmarty/tiny-random-GemmaForCausalLM tags: - axolotl - generated_from_trainer model-index: - name: e6188fad-891e-47ee-8819-b42281b4c7fa results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <br> # e6188fad-891e-47ee-8819-b42281b4c7fa This model is a fine-tuned version of [fxmarty/tiny-random-GemmaForCausalLM](https://huggingface.co/fxmarty/tiny-random-GemmaForCausalLM) on the None dataset. It achieves the following results on the evaluation set: - Loss: 12.3067 ## 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: 0.000202 - train_batch_size: 4 - eval_batch_size: 4 - seed: 20 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 2500 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | No log | 0.0008 | 1 | 12.4558 | | 12.3442 | 0.3891 | 500 | 12.3301 | | 12.3328 | 0.7781 | 1000 | 12.3138 | | 12.3262 | 1.1672 | 1500 | 12.3083 | | 12.3238 | 1.5563 | 2000 | 12.3072 | | 12.3249 | 1.9453 | 2500 | 12.3067 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
bardsai/whisper-small-pl
bardsai
"2024-01-02T21:42:00Z"
34
0
transformers
[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "pl", "dataset:mozilla-foundation/common_voice_11_0", "dataset:google/fleurs", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
"2022-12-08T10:46:18Z"
--- language: - pl license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 - google/fleurs base_model: openai/whisper-small model-index: - name: Whisper Small PL results: - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: mozilla-foundation/common_voice_11_0 type: mozilla-foundation/common_voice_11_0 config: pl split: test metrics: - type: wer value: 14.57 name: WER - type: wer_without_norm value: 33.57 name: WER unnormalized - type: cer value: 4.02 name: CER - type: mer value: 14.37 name: MER - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: facebook/voxpopuli type: facebook/voxpopuli config: pl split: test metrics: - type: wer value: 15.73 name: WER - type: wer_without_norm value: 34.51 name: WER unnormalized - type: cer value: 7.73 name: CER - type: mer value: 15.28 name: MER - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: google/fleurs type: google/fleurs config: pl_pl split: test metrics: - type: wer value: 16.79 name: WER - type: wer_without_norm value: 35.69 name: WER unnormalized - type: cer value: 4.99 name: CER - type: mer value: 16.55 name: MER --- <!-- 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 PL This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 and the FLEURS datasets. It achieves the following results on the evaluation set: - eval_loss: 0.3571 - eval_wer: 14.8004 - eval_runtime: 2233.4204 - eval_samples_per_second: 3.714 - eval_steps_per_second: 0.232 - epoch: 4.03 - step: 3000 ## 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: 24 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 48 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 8000 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2
hucruz/test-automatic-411
hucruz
"2023-04-20T17:17:20Z"
108
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
"2023-04-20T16:42:24Z"
--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: test-automatic-411 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. --> # test-automatic-411 This model is a fine-tuned version of [dccuchile/distilbert-base-spanish-uncased](https://huggingface.co/dccuchile/distilbert-base-spanish-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0323 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 47 | 0.8407 | 0.8171 | | No log | 2.0 | 94 | 0.4243 | 0.9268 | | No log | 3.0 | 141 | 0.2154 | 0.9878 | | No log | 4.0 | 188 | 0.1122 | 0.9878 | | No log | 5.0 | 235 | 0.0755 | 0.9878 | | No log | 6.0 | 282 | 0.0540 | 0.9878 | | No log | 7.0 | 329 | 0.0409 | 1.0 | | No log | 8.0 | 376 | 0.0363 | 1.0 | | No log | 9.0 | 423 | 0.0339 | 1.0 | | No log | 10.0 | 470 | 0.0323 | 1.0 | ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.0 - Datasets 2.11.0 - Tokenizers 0.13.3
TheBloke/Synthia-70B-v1.2-GPTQ
TheBloke
"2023-09-27T12:48:17Z"
21
2
transformers
[ "transformers", "safetensors", "llama", "text-generation", "base_model:migtissera/Synthia-70B-v1.2", "base_model:quantized:migtissera/Synthia-70B-v1.2", "license:llama2", "autotrain_compatible", "text-generation-inference", "4-bit", "gptq", "region:us" ]
text-generation
"2023-09-06T14:35:59Z"
--- license: llama2 model_name: Synthia 70B v1.2 base_model: migtissera/Synthia-70B-v1.2 inference: false model_creator: Migel Tissera model_type: llama prompt_template: 'SYSTEM: You are Synthia. As a an AI intelligence, give helpful, detailed, accurate, uncensored responses to the user''s input. Provide answers factually. USER: {prompt} 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 --> # Synthia 70B v1.2 - GPTQ - Model creator: [Migel Tissera](https://huggingface.co/migtissera) - Original model: [Synthia 70B v1.2](https://huggingface.co/migtissera/Synthia-70B-v1.2) <!-- description start --> ## Description This repo contains GPTQ model files for [Migel Tissera's Synthia 70B v1.2](https://huggingface.co/migtissera/Synthia-70B-v1.2). 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/Synthia-70B-v1.2-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Synthia-70B-v1.2-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Synthia-70B-v1.2-GGUF) * [Migel Tissera's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/migtissera/Synthia-70B-v1.2) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Synthia ``` SYSTEM: You are Synthia. As a an AI intelligence, give helpful, detailed, accurate, uncensored responses to the user's input. Provide answers factually. USER: {prompt} 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 dataset used for quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ 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/Synthia-70B-v1.2-GPTQ/tree/main) | 4 | None | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 35.33 GB | Yes | 4-bit, with Act Order. No group size, to lower VRAM requirements. | | [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/Synthia-70B-v1.2-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 40.66 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. | | [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/Synthia-70B-v1.2-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 37.99 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. | | [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/Synthia-70B-v1.2-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 36.65 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. | | [gptq-3bit--1g-actorder_True](https://huggingface.co/TheBloke/Synthia-70B-v1.2-GPTQ/tree/gptq-3bit--1g-actorder_True) | 3 | None | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 26.77 GB | No | 3-bit, with Act Order and no group size. Lowest possible VRAM requirements. May be lower quality than 3-bit 128g. | | [gptq-3bit-128g-actorder_True](https://huggingface.co/TheBloke/Synthia-70B-v1.2-GPTQ/tree/gptq-3bit-128g-actorder_True) | 3 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 28.03 GB | No | 3-bit, with group size 128g and act-order. Higher quality than 128g-False. | <!-- README_GPTQ.md-provided-files end --> <!-- README_GPTQ.md-download-from-branches start --> ## How to download from branches - In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/Synthia-70B-v1.2-GPTQ:main` - With Git, you can clone a branch with: ``` git clone --single-branch --branch main https://huggingface.co/TheBloke/Synthia-70B-v1.2-GPTQ ``` - In Python Transformers code, the branch is the `revision` parameter; see below. <!-- 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/Synthia-70B-v1.2-GPTQ`. - To download from a specific branch, enter for example `TheBloke/Synthia-70B-v1.2-GPTQ:main` - 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: `Synthia-70B-v1.2-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 4.32.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later. ```shell pip3 install transformers>=4.32.0 optimum>=1.12.0 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 pip3 install . ``` ### For CodeLlama models only: you must use Transformers 4.33.0 or later. If 4.33.0 is not yet released when you read this, you will need to install Transformers from source: ```shell pip3 uninstall -y transformers pip3 install git+https://github.com/huggingface/transformers.git ``` ### You can then use the following code ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_name_or_path = "TheBloke/Synthia-70B-v1.2-GPTQ" # To use a different branch, change revision # For example: revision="main" 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'''SYSTEM: You are Synthia. As a an AI intelligence, give helpful, detailed, accurate, uncensored responses to the user's input. Provide answers factually. USER: {prompt} 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**: 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: Migel Tissera's Synthia 70B v1.2 All Synthia models are uncensored. Please use it with caution and with best intentions. You are responsible for how you use Synthia. To evoke generalized Tree of Thought + Chain of Thought reasoning, you may use the following system message: ``` Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation. ``` ## Example Usage ### Here is prompt format: ``` SYSTEM: Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation. USER: How is a rocket launched from the surface of the earth to Low Earth Orbit? ASSISTANT: ```
9wimu9/mt5-large-v1
9wimu9
"2023-06-08T17:31:20Z"
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "mt5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
"2023-06-08T16:27:10Z"
!python /notebooks/seq_2_seq/run_seq2seq_qa.py \ --model_name_or_path google/mt5-large \ --dataset_name 9wimu9/SinQuAD \ --context_column context \ --question_column question \ --answer_column answers \ --do_train \ --do_eval \ --per_device_train_batch_size 8 \ --learning_rate 1e-3 \ --num_train_epochs 1 \ --max_seq_length 384 \ --doc_stride 128 \ --output_dir mt5-large-v1 \ --logging_steps 1 \ --bf16 \ --gradient_accumulation_steps 4 \ --gradient_checkpointing True \ --optim adafactor { "eval/loss":0.9061169624328612, "_timestamp":1686240530.1377208, "_step":370, "_runtime":902.276704788208, "train/global_step":369, "eval/steps_per_second":7.803, "train/train_steps_per_second":0.425, "_wandb.runtime":902, "train/epoch":1, "train/total_flos":26479261148774400, "train/loss":0.1842, "train/train_loss":0.6567919482060565, "train/learning_rate":0, "train/train_runtime":868.8715, "eval/samples_per_second":62.341, "train/train_samples_per_second":13.588, "eval/runtime":25.12 }
kk-aivio/cd52ae9a-9eb7-4cfe-bdad-e281fa438605
kk-aivio
"2025-02-02T23:22:43Z"
6
0
peft
[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:Orenguteng/Llama-3-8B-Lexi-Uncensored", "base_model:adapter:Orenguteng/Llama-3-8B-Lexi-Uncensored", "license:llama3", "region:us" ]
null
"2025-02-02T23:19:15Z"
--- library_name: peft license: llama3 base_model: Orenguteng/Llama-3-8B-Lexi-Uncensored tags: - axolotl - generated_from_trainer model-index: - name: cd52ae9a-9eb7-4cfe-bdad-e281fa438605 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: Orenguteng/Llama-3-8B-Lexi-Uncensored bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - be5ab324e25875e7_train_data.json ds_type: json format: custom path: /workspace/input_data/be5ab324e25875e7_train_data.json type: field_input: input field_instruction: instruction field_output: output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: kk-aivio/cd52ae9a-9eb7-4cfe-bdad-e281fa438605 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/be5ab324e25875e7_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 21c17688-3386-4af0-a372-07bbb0501a28 wandb_project: Birthday-SN56-17-Gradients-On-Demand wandb_run: your_name wandb_runid: 21c17688-3386-4af0-a372-07bbb0501a28 warmup_steps: 5 weight_decay: 0.0 xformers_attention: null ``` </details><br> # cd52ae9a-9eb7-4cfe-bdad-e281fa438605 This model is a fine-tuned version of [Orenguteng/Llama-3-8B-Lexi-Uncensored](https://huggingface.co/Orenguteng/Llama-3-8B-Lexi-Uncensored) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8373 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | No log | 0.0015 | 1 | 5.9562 | | 5.2512 | 0.0726 | 50 | 4.8114 | | 3.6303 | 0.1452 | 100 | 3.2205 | | 2.9013 | 0.2178 | 150 | 2.0801 | | 2.2513 | 0.2904 | 200 | 1.8373 | ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
deepnet111/sn9-3b-star-008
deepnet111
"2025-01-30T14:06:47Z"
25
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
"2025-01-30T14:03:46Z"
--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
gokuls/bert_12_layer_model_v2_complete_training_new_wt_init_48_frz
gokuls
"2023-06-22T17:15:57Z"
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "hybridbert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
"2023-06-20T09:59:23Z"
--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: bert_12_layer_model_v2_complete_training_new_wt_init_48_frz 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. --> # bert_12_layer_model_v2_complete_training_new_wt_init_48_frz This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.4340 - Accuracy: 0.5488 ## 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: 48 - eval_batch_size: 48 - 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 | |:-------------:|:-----:|:------:|:---------------:|:--------:| | 3.8468 | 0.08 | 10000 | 3.6051 | 0.4101 | | 3.6009 | 0.16 | 20000 | 3.3734 | 0.4369 | | 3.4559 | 0.25 | 30000 | 3.2348 | 0.4517 | | 3.3578 | 0.33 | 40000 | 3.1395 | 0.4623 | | 3.2803 | 0.41 | 50000 | 3.0632 | 0.4709 | | 3.2157 | 0.49 | 60000 | 3.0010 | 0.4780 | | 3.1503 | 0.57 | 70000 | 2.9554 | 0.4838 | | 3.1044 | 0.66 | 80000 | 2.9104 | 0.4888 | | 3.0703 | 0.74 | 90000 | 2.8759 | 0.4931 | | 3.029 | 0.82 | 100000 | 2.8357 | 0.4976 | | 2.9907 | 0.9 | 110000 | 2.8082 | 0.5013 | | 2.9619 | 0.98 | 120000 | 2.7805 | 0.5042 | | 2.9284 | 1.07 | 130000 | 2.7578 | 0.5072 | | 2.9027 | 1.15 | 140000 | 2.7295 | 0.5103 | | 2.8738 | 1.23 | 150000 | 2.7094 | 0.5133 | | 2.8603 | 1.31 | 160000 | 2.6848 | 0.5160 | | 2.829 | 1.39 | 170000 | 2.6667 | 0.5185 | | 2.8106 | 1.47 | 180000 | 2.6479 | 0.5208 | | 2.7942 | 1.56 | 190000 | 2.6304 | 0.5227 | | 2.772 | 1.64 | 200000 | 2.6156 | 0.5249 | | 2.7546 | 1.72 | 210000 | 2.5994 | 0.5270 | | 2.7348 | 1.8 | 220000 | 2.5858 | 0.5290 | | 2.725 | 1.88 | 230000 | 2.5728 | 0.5304 | | 2.7116 | 1.97 | 240000 | 2.5587 | 0.5324 | | 2.6953 | 2.05 | 250000 | 2.5476 | 0.5338 | | 2.6883 | 2.13 | 260000 | 2.5339 | 0.5355 | | 2.6768 | 2.21 | 270000 | 2.5231 | 0.5371 | | 2.6622 | 2.29 | 280000 | 2.5097 | 0.5383 | | 2.6499 | 2.38 | 290000 | 2.5026 | 0.5396 | | 2.6361 | 2.46 | 300000 | 2.4916 | 0.5412 | | 2.629 | 2.54 | 310000 | 2.4843 | 0.5421 | | 2.6269 | 2.62 | 320000 | 2.4737 | 0.5432 | | 2.6175 | 2.7 | 330000 | 2.4676 | 0.5443 | | 2.5961 | 2.79 | 340000 | 2.4580 | 0.5457 | | 2.5926 | 2.87 | 350000 | 2.4502 | 0.5468 | | 2.5866 | 2.95 | 360000 | 2.4413 | 0.5480 | | 2.5781 | 3.03 | 370000 | 2.4340 | 0.5488 | ### Framework versions - Transformers 4.30.2 - Pytorch 1.14.0a0+410ce96 - Datasets 2.13.0 - Tokenizers 0.13.3
GleamEyeBeast/Mandarin
GleamEyeBeast
"2022-02-07T04:25:26Z"
4
0
transformers
[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
"2022-03-02T23:29:04Z"
--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: Mandarin 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. --> # Mandarin This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice 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: 0.0001 - 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 - lr_scheduler_warmup_steps: 1000 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
HongKi08/SanHak
HongKi08
"2025-03-28T06:33:30Z"
0
0
transformers
[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
null
"2025-03-28T05:33:47Z"
<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>503</h1> <p>We had to rate limit you. To continue using our service, please log in or create an account.</p> </div> </main> </body> </html>
Helsinki-NLP/opus-mt-tc-bible-big-urj-deu_eng_nld
Helsinki-NLP
"2024-10-08T21:50:13Z"
113
0
transformers
[ "transformers", "pytorch", "safetensors", "marian", "text2text-generation", "translation", "opus-mt-tc-bible", "chm", "de", "en", "et", "fi", "fkv", "hu", "izh", "krl", "kv", "liv", "mdf", "mrj", "myv", "nl", "se", "sma", "smn", "udm", "vot", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
translation
"2024-10-08T21:50:02Z"
--- library_name: transformers language: - chm - de - en - et - fi - fkv - hu - izh - krl - kv - liv - mdf - mrj - myv - nl - se - sma - smn - udm - vot tags: - translation - opus-mt-tc-bible license: apache-2.0 model-index: - name: opus-mt-tc-bible-big-urj-deu_eng_nld results: - task: name: Translation multi-multi type: translation args: multi-multi dataset: name: tatoeba-test-v2020-07-28-v2023-09-26 type: tatoeba_mt args: multi-multi metrics: - name: BLEU type: bleu value: 46.1 - name: chr-F type: chrf value: 0.65088 --- # opus-mt-tc-bible-big-urj-deu_eng_nld ## Table of Contents - [Model Details](#model-details) - [Uses](#uses) - [Risks, Limitations and Biases](#risks-limitations-and-biases) - [How to Get Started With the Model](#how-to-get-started-with-the-model) - [Training](#training) - [Evaluation](#evaluation) - [Citation Information](#citation-information) - [Acknowledgements](#acknowledgements) ## Model Details Neural machine translation model for translating from Uralic languages (urj) to unknown (deu+eng+nld). This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train). **Model Description:** - **Developed by:** Language Technology Research Group at the University of Helsinki - **Model Type:** Translation (transformer-big) - **Release**: 2024-08-18 - **License:** Apache-2.0 - **Language(s):** - Source Language(s): chm est fin fkv hun izh koi kom kpv krl liv mdf mrj myv sma sme smn udm vot vro - Target Language(s): deu eng nld - Valid Target Language Labels: >>deu<< >>eng<< >>nld<< >>xxx<< - **Original Model**: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-18.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/urj-deu+eng+nld/opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-18.zip) - **Resources for more information:** - [OPUS-MT dashboard](https://opus.nlpl.eu/dashboard/index.php?pkg=opusmt&test=all&scoreslang=all&chart=standard&model=Tatoeba-MT-models/urj-deu%2Beng%2Bnld/opusTCv20230926max50%2Bbt%2Bjhubc_transformer-big_2024-08-18) - [OPUS-MT-train GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train) - [More information about MarianNMT models in the transformers library](https://huggingface.co/docs/transformers/model_doc/marian) - [Tatoeba Translation Challenge](https://github.com/Helsinki-NLP/Tatoeba-Challenge/) - [HPLT bilingual data v1 (as part of the Tatoeba Translation Challenge dataset)](https://hplt-project.org/datasets/v1) - [A massively parallel Bible corpus](https://aclanthology.org/L14-1215/) This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of `>>id<<` (id = valid target language ID), e.g. `>>deu<<` ## Uses This model can be used for translation and text-to-text generation. ## Risks, Limitations and Biases **CONTENT WARNING: Readers should be aware that the model is trained on various public data sets that may contain content that is disturbing, offensive, and can propagate historical and current stereotypes.** Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). ## How to Get Started With the Model A short example code: ```python from transformers import MarianMTModel, MarianTokenizer src_text = [ ">>deu<< Jobb meghalni, mint úgy élni.", ">>eng<< Az algák miatt ilyen színű a tó." ] model_name = "pytorch-models/opus-mt-tc-bible-big-urj-deu_eng_nld" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_pretrained(model_name) translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True)) for t in translated: print( tokenizer.decode(t, skip_special_tokens=True) ) # expected output: # Es ist besser zu sterben, als so zu leben. # Because of the algae, the lake is such a color. ``` You can also use OPUS-MT models with the transformers pipelines, for example: ```python from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-bible-big-urj-deu_eng_nld") print(pipe(">>deu<< Jobb meghalni, mint úgy élni.")) # expected output: Es ist besser zu sterben, als so zu leben. ``` ## Training - **Data**: opusTCv20230926max50+bt+jhubc ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) - **Pre-processing**: SentencePiece (spm32k,spm32k) - **Model Type:** transformer-big - **Original MarianNMT Model**: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-18.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/urj-deu+eng+nld/opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-18.zip) - **Training Scripts**: [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train) ## Evaluation * [Model scores at the OPUS-MT dashboard](https://opus.nlpl.eu/dashboard/index.php?pkg=opusmt&test=all&scoreslang=all&chart=standard&model=Tatoeba-MT-models/urj-deu%2Beng%2Bnld/opusTCv20230926max50%2Bbt%2Bjhubc_transformer-big_2024-08-18) * test set translations: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-18.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/urj-deu+eng+nld/opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-18.test.txt) * test set scores: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-18.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/urj-deu+eng+nld/opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-18.eval.txt) * benchmark results: [benchmark_results.txt](benchmark_results.txt) * benchmark output: [benchmark_translations.zip](benchmark_translations.zip) | langpair | testset | chr-F | BLEU | #sent | #words | |----------|---------|-------|-------|-------|--------| | multi-multi | tatoeba-test-v2020-07-28-v2023-09-26 | 0.65088 | 46.1 | 10000 | 78967 | ## Citation Information * Publications: [Democratizing neural machine translation with OPUS-MT](https://doi.org/10.1007/s10579-023-09704-w) and [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.) ```bibtex @article{tiedemann2023democratizing, title={Democratizing neural machine translation with {OPUS-MT}}, author={Tiedemann, J{\"o}rg and Aulamo, Mikko and Bakshandaeva, Daria and Boggia, Michele and Gr{\"o}nroos, Stig-Arne and Nieminen, Tommi and Raganato, Alessandro and Scherrer, Yves and Vazquez, Raul and Virpioja, Sami}, journal={Language Resources and Evaluation}, number={58}, pages={713--755}, year={2023}, publisher={Springer Nature}, issn={1574-0218}, doi={10.1007/s10579-023-09704-w} } @inproceedings{tiedemann-thottingal-2020-opus, title = "{OPUS}-{MT} {--} Building open translation services for the World", author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh}, booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation", month = nov, year = "2020", address = "Lisboa, Portugal", publisher = "European Association for Machine Translation", url = "https://aclanthology.org/2020.eamt-1.61", pages = "479--480", } @inproceedings{tiedemann-2020-tatoeba, title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}", author = {Tiedemann, J{\"o}rg}, booktitle = "Proceedings of the Fifth Conference on Machine Translation", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.wmt-1.139", pages = "1174--1182", } ``` ## Acknowledgements The work is supported by the [HPLT project](https://hplt-project.org/), funded by the European Union’s Horizon Europe research and innovation programme under grant agreement No 101070350. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland, and the [EuroHPC supercomputer LUMI](https://www.lumi-supercomputer.eu/). ## Model conversion info * transformers version: 4.45.1 * OPUS-MT git hash: 0882077 * port time: Wed Oct 9 00:49:52 EEST 2024 * port machine: LM0-400-22516.local
PrunaAI/TheDrummer-Llama-3SOME-8B-v1-QUANTO-float8bit-smashed
PrunaAI
"2024-08-02T16:03:00Z"
3
0
transformers
[ "transformers", "pruna-ai", "base_model:BeaverLegacy/Llama-3SOME-8B-v1", "base_model:finetune:BeaverLegacy/Llama-3SOME-8B-v1", "endpoints_compatible", "region:us" ]
null
"2024-06-17T21:14:50Z"
--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: TheDrummer/Llama-3SOME-8B-v1 metrics: - memory_disk - memory_inference - inference_latency - inference_throughput - inference_CO2_emissions - inference_energy_consumption tags: - pruna-ai --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> <!-- header end --> [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/rskEr4BZJx) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) **Frequently Asked Questions** - ***How does the compression work?*** The model is compressed with quanto. - ***How does the model quality change?*** The quality of the model output might vary compared to the base model. - ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - ***What is the model format?*** We use safetensors. - ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data. - ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. - ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases. ## Setup You can run the smashed model with these steps: 0. Check requirements from the original repo TheDrummer/Llama-3SOME-8B-v1 installed. In particular, check python, cuda, and transformers versions. 1. Make sure that you have installed quantization related packages. ```bash pip install quanto ``` 2. Load & run the model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer IMPORTS model = AutoModelForCausalLM.from_pretrained("PrunaAI/TheDrummer-Llama-3SOME-8B-v1-QUANTO-float8bit-smashed", trust_remote_code=True, device_map='auto') tokenizer = AutoTokenizer.from_pretrained("TheDrummer/Llama-3SOME-8B-v1") input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"] outputs = model.generate(input_ids, max_new_tokens=216) tokenizer.decode(outputs[0]) ``` ## Configurations The configuration info are in `smash_config.json`. ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model TheDrummer/Llama-3SOME-8B-v1 before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
nlpai-lab/kullm-polyglot-5.8b-v2
nlpai-lab
"2023-06-07T06:45:30Z"
2,351
23
transformers
[ "transformers", "pytorch", "gpt_neox", "text-generation", "ko", "dataset:nlpai-lab/kullm-v2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
"2023-06-07T05:30:10Z"
--- license: apache-2.0 datasets: - nlpai-lab/kullm-v2 language: - ko --- # KULLM-Polyglot-5.8B-v2 This model is a parameter-efficient fine-tuned version of [EleutherAI/polyglot-ko-5.8b](https://huggingface.co/EleutherAI/polyglot-ko-5.8b) on a KULLM v2 Detail Codes are available at [KULLM Github Repository](https://github.com/nlpai-lab/KULLM) ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-4 - train_batch_size: 128 - seed: 42 - distributed_type: multi-GPU (A100 80G) - num_devices: 4 - gradient_accumulation_steps: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8.0 ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu117 - Datasets 2.11.0 - Tokenizers 0.13.3
bluebird089/videomae-base-finetuned-ai4life-subset
bluebird089
"2024-02-27T17:36:20Z"
62
0
transformers
[ "transformers", "tensorboard", "safetensors", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-base", "base_model:finetune:MCG-NJU/videomae-base", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]
video-classification
"2024-02-27T09:58:51Z"
--- license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base tags: - generated_from_trainer model-index: - name: videomae-base-finetuned-ai4life-subset 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. --> # videomae-base-finetuned-ai4life-subset This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-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: 0.1 - training_steps: 656 ### Framework versions - Transformers 4.37.0 - Pytorch 2.1.2 - Datasets 2.1.0 - Tokenizers 0.15.1
kk-aivio/8538c8be-eaf0-43a6-a74e-2d13cd2744cc
kk-aivio
"2025-02-03T09:34:53Z"
8
0
peft
[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:Qwen/Qwen2.5-3B-Instruct", "base_model:adapter:Qwen/Qwen2.5-3B-Instruct", "license:other", "region:us" ]
null
"2025-02-03T09:30:39Z"
--- library_name: peft license: other base_model: Qwen/Qwen2.5-3B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 8538c8be-eaf0-43a6-a74e-2d13cd2744cc results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) # 8538c8be-eaf0-43a6-a74e-2d13cd2744cc This model is a fine-tuned version of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0357 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
RichardErkhov/HugoVoxx_-_Gemma-2-2b-it-ag-merged-model-gguf
RichardErkhov
"2025-02-26T19:15:58Z"
0
0
null
[ "gguf", "arxiv:1910.09700", "endpoints_compatible", "region:us", "conversational" ]
null
"2025-02-26T18:13:07Z"
Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Gemma-2-2b-it-ag-merged-model - GGUF - Model creator: https://huggingface.co/HugoVoxx/ - Original model: https://huggingface.co/HugoVoxx/Gemma-2-2b-it-ag-merged-model/ | Name | Quant method | Size | | ---- | ---- | ---- | | [Gemma-2-2b-it-ag-merged-model.Q2_K.gguf](https://huggingface.co/RichardErkhov/HugoVoxx_-_Gemma-2-2b-it-ag-merged-model-gguf/blob/main/Gemma-2-2b-it-ag-merged-model.Q2_K.gguf) | Q2_K | 1.15GB | | [Gemma-2-2b-it-ag-merged-model.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/HugoVoxx_-_Gemma-2-2b-it-ag-merged-model-gguf/blob/main/Gemma-2-2b-it-ag-merged-model.IQ3_XS.gguf) | IQ3_XS | 1.22GB | | [Gemma-2-2b-it-ag-merged-model.IQ3_S.gguf](https://huggingface.co/RichardErkhov/HugoVoxx_-_Gemma-2-2b-it-ag-merged-model-gguf/blob/main/Gemma-2-2b-it-ag-merged-model.IQ3_S.gguf) | IQ3_S | 1.27GB | | [Gemma-2-2b-it-ag-merged-model.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/HugoVoxx_-_Gemma-2-2b-it-ag-merged-model-gguf/blob/main/Gemma-2-2b-it-ag-merged-model.Q3_K_S.gguf) | Q3_K_S | 1.27GB | | [Gemma-2-2b-it-ag-merged-model.IQ3_M.gguf](https://huggingface.co/RichardErkhov/HugoVoxx_-_Gemma-2-2b-it-ag-merged-model-gguf/blob/main/Gemma-2-2b-it-ag-merged-model.IQ3_M.gguf) | IQ3_M | 1.3GB | | [Gemma-2-2b-it-ag-merged-model.Q3_K.gguf](https://huggingface.co/RichardErkhov/HugoVoxx_-_Gemma-2-2b-it-ag-merged-model-gguf/blob/main/Gemma-2-2b-it-ag-merged-model.Q3_K.gguf) | Q3_K | 1.36GB | | [Gemma-2-2b-it-ag-merged-model.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/HugoVoxx_-_Gemma-2-2b-it-ag-merged-model-gguf/blob/main/Gemma-2-2b-it-ag-merged-model.Q3_K_M.gguf) | Q3_K_M | 1.36GB | | [Gemma-2-2b-it-ag-merged-model.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/HugoVoxx_-_Gemma-2-2b-it-ag-merged-model-gguf/blob/main/Gemma-2-2b-it-ag-merged-model.Q3_K_L.gguf) | Q3_K_L | 1.44GB | | [Gemma-2-2b-it-ag-merged-model.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/HugoVoxx_-_Gemma-2-2b-it-ag-merged-model-gguf/blob/main/Gemma-2-2b-it-ag-merged-model.IQ4_XS.gguf) | IQ4_XS | 1.47GB | | [Gemma-2-2b-it-ag-merged-model.Q4_0.gguf](https://huggingface.co/RichardErkhov/HugoVoxx_-_Gemma-2-2b-it-ag-merged-model-gguf/blob/main/Gemma-2-2b-it-ag-merged-model.Q4_0.gguf) | Q4_0 | 1.52GB | | [Gemma-2-2b-it-ag-merged-model.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/HugoVoxx_-_Gemma-2-2b-it-ag-merged-model-gguf/blob/main/Gemma-2-2b-it-ag-merged-model.IQ4_NL.gguf) | IQ4_NL | 1.53GB | | [Gemma-2-2b-it-ag-merged-model.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/HugoVoxx_-_Gemma-2-2b-it-ag-merged-model-gguf/blob/main/Gemma-2-2b-it-ag-merged-model.Q4_K_S.gguf) | Q4_K_S | 1.53GB | | [Gemma-2-2b-it-ag-merged-model.Q4_K.gguf](https://huggingface.co/RichardErkhov/HugoVoxx_-_Gemma-2-2b-it-ag-merged-model-gguf/blob/main/Gemma-2-2b-it-ag-merged-model.Q4_K.gguf) | Q4_K | 1.59GB | | [Gemma-2-2b-it-ag-merged-model.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/HugoVoxx_-_Gemma-2-2b-it-ag-merged-model-gguf/blob/main/Gemma-2-2b-it-ag-merged-model.Q4_K_M.gguf) | Q4_K_M | 1.59GB | | [Gemma-2-2b-it-ag-merged-model.Q4_1.gguf](https://huggingface.co/RichardErkhov/HugoVoxx_-_Gemma-2-2b-it-ag-merged-model-gguf/blob/main/Gemma-2-2b-it-ag-merged-model.Q4_1.gguf) | Q4_1 | 1.64GB | | [Gemma-2-2b-it-ag-merged-model.Q5_0.gguf](https://huggingface.co/RichardErkhov/HugoVoxx_-_Gemma-2-2b-it-ag-merged-model-gguf/blob/main/Gemma-2-2b-it-ag-merged-model.Q5_0.gguf) | Q5_0 | 1.75GB | | [Gemma-2-2b-it-ag-merged-model.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/HugoVoxx_-_Gemma-2-2b-it-ag-merged-model-gguf/blob/main/Gemma-2-2b-it-ag-merged-model.Q5_K_S.gguf) | Q5_K_S | 1.75GB | | [Gemma-2-2b-it-ag-merged-model.Q5_K.gguf](https://huggingface.co/RichardErkhov/HugoVoxx_-_Gemma-2-2b-it-ag-merged-model-gguf/blob/main/Gemma-2-2b-it-ag-merged-model.Q5_K.gguf) | Q5_K | 1.79GB | | [Gemma-2-2b-it-ag-merged-model.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/HugoVoxx_-_Gemma-2-2b-it-ag-merged-model-gguf/blob/main/Gemma-2-2b-it-ag-merged-model.Q5_K_M.gguf) | Q5_K_M | 1.79GB | | [Gemma-2-2b-it-ag-merged-model.Q5_1.gguf](https://huggingface.co/RichardErkhov/HugoVoxx_-_Gemma-2-2b-it-ag-merged-model-gguf/blob/main/Gemma-2-2b-it-ag-merged-model.Q5_1.gguf) | Q5_1 | 1.87GB | | [Gemma-2-2b-it-ag-merged-model.Q6_K.gguf](https://huggingface.co/RichardErkhov/HugoVoxx_-_Gemma-2-2b-it-ag-merged-model-gguf/blob/main/Gemma-2-2b-it-ag-merged-model.Q6_K.gguf) | Q6_K | 2.0GB | | [Gemma-2-2b-it-ag-merged-model.Q8_0.gguf](https://huggingface.co/RichardErkhov/HugoVoxx_-_Gemma-2-2b-it-ag-merged-model-gguf/blob/main/Gemma-2-2b-it-ag-merged-model.Q8_0.gguf) | Q8_0 | 2.59GB | Original model description: --- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
huggingtweets/bichebuni
huggingtweets
"2021-05-21T20:37:06Z"
6
0
transformers
[ "transformers", "pytorch", "jax", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
"2022-03-02T23:29:05Z"
--- language: en thumbnail: https://www.huggingtweets.com/bichebuni/1614096170963/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div> <div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1356414477143519232/H2T46KhD_400x400.jpg')"> </div> <div style="margin-top: 8px; font-size: 19px; font-weight: 800">Ellie 🐰 🤖 AI Bot </div> <div style="font-size: 15px">@bichebuni bot</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://app.wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-model-to-generate-tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on [@bichebuni's tweets](https://twitter.com/bichebuni). | Data | Quantity | | --- | --- | | Tweets downloaded | 1578 | | Retweets | 559 | | Short tweets | 216 | | Tweets kept | 803 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2jluupd2/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @bichebuni's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2a0ttba9) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2a0ttba9/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/bichebuni') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
Nubletz/bertwiki-simplestyle-split-embedding-recon-53
Nubletz
"2025-01-21T22:09:42Z"
6
0
transformers
[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
feature-extraction
"2025-01-21T22:09:31Z"
--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
MaziyarPanahi/Ognoexperiment27multi_verse_modelNeuralsynthesis-7B-GGUF
MaziyarPanahi
"2024-05-21T18:37:50Z"
62
0
transformers
[ "transformers", "gguf", "mistral", "quantized", "2-bit", "3-bit", "4-bit", "5-bit", "6-bit", "8-bit", "GGUF", "safetensors", "text-generation", "merge", "mergekit", "lazymergekit", "automerger", "base_model:Kukedlc/NeuralSynthesis-7B-v0.1", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us", "base_model:automerger/Ognoexperiment27multi_verse_modelNeuralsynthesis-7B", "base_model:quantized:automerger/Ognoexperiment27multi_verse_modelNeuralsynthesis-7B" ]
text-generation
"2024-05-21T18:05:26Z"
--- tags: - quantized - 2-bit - 3-bit - 4-bit - 5-bit - 6-bit - 8-bit - GGUF - transformers - safetensors - mistral - text-generation - merge - mergekit - lazymergekit - automerger - base_model:Kukedlc/NeuralSynthesis-7B-v0.1 - license:apache-2.0 - autotrain_compatible - endpoints_compatible - text-generation-inference - region:us - text-generation model_name: Ognoexperiment27multi_verse_modelNeuralsynthesis-7B-GGUF base_model: automerger/Ognoexperiment27multi_verse_modelNeuralsynthesis-7B inference: false model_creator: automerger pipeline_tag: text-generation quantized_by: MaziyarPanahi --- # [MaziyarPanahi/Ognoexperiment27multi_verse_modelNeuralsynthesis-7B-GGUF](https://huggingface.co/MaziyarPanahi/Ognoexperiment27multi_verse_modelNeuralsynthesis-7B-GGUF) - Model creator: [automerger](https://huggingface.co/automerger) - Original model: [automerger/Ognoexperiment27multi_verse_modelNeuralsynthesis-7B](https://huggingface.co/automerger/Ognoexperiment27multi_verse_modelNeuralsynthesis-7B) ## Description [MaziyarPanahi/Ognoexperiment27multi_verse_modelNeuralsynthesis-7B-GGUF](https://huggingface.co/MaziyarPanahi/Ognoexperiment27multi_verse_modelNeuralsynthesis-7B-GGUF) contains GGUF format model files for [automerger/Ognoexperiment27multi_verse_modelNeuralsynthesis-7B](https://huggingface.co/automerger/Ognoexperiment27multi_verse_modelNeuralsynthesis-7B). ### 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 incomplete 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. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [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. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [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. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. ## Special thanks 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.
visdata/mmm0
visdata
"2025-02-20T17:07:42Z"
0
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
"2025-02-20T17:02:15Z"
--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
adarsh12x/mistral_7b_samantha
adarsh12x
"2024-02-25T21:51:46Z"
0
0
peft
[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:samantha-data", "base_model:mistralai/Mistral-7B-Instruct-v0.1", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.1", "license:apache-2.0", "region:us" ]
null
"2024-02-25T21:51:38Z"
--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer datasets: - samantha-data base_model: mistralai/Mistral-7B-Instruct-v0.1 model-index: - name: mistral_7b_samantha 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. --> # mistral_7b_samantha This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on the samantha-data dataset. It achieves the following results on the evaluation set: - Loss: 1.7573 ## 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: constant - lr_scheduler_warmup_steps: 0.03 - training_steps: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.7235 | 0.4 | 10 | 1.8474 | | 1.2154 | 0.8 | 20 | 1.7551 | | 1.1009 | 1.2 | 30 | 1.6884 | | 0.981 | 1.6 | 40 | 1.6751 | | 0.9463 | 2.0 | 50 | 1.6704 | | 0.8443 | 2.4 | 60 | 1.7248 | | 0.8128 | 2.8 | 70 | 1.7101 | | 0.8158 | 3.2 | 80 | 1.7826 | | 0.7087 | 3.6 | 90 | 1.7805 | | 0.7091 | 4.0 | 100 | 1.7573 | ### Framework versions - PEFT 0.8.2 - Transformers 4.38.1 - Pytorch 2.2.1+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
FounderOfHuggingface/gpt2_lora_r16_dbpedia_14_t18_e5_member_shadow36
FounderOfHuggingface
"2024-01-11T08:17:57Z"
1
0
peft
[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai-community/gpt2", "base_model:adapter:openai-community/gpt2", "region:us" ]
null
"2024-01-11T08:17:54Z"
--- library_name: peft base_model: gpt2 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.1
PeterKruger/AutoBench
PeterKruger
"2025-03-03T23:45:38Z"
0
0
null
[ "code", "en", "license:mit", "region:us" ]
null
"2025-02-20T21:59:16Z"
--- license: mit language: - en tags: - code --- # AutoBench 1.0 - Collective-LLM-as-a-Judge Benchmark System **Table of Contents** * [Overview](#overview) * [Key Features of AutoBench v1.0](#key-features-of-autobench-v10) * [Getting Started](#getting-started) * [Prerequisites](#prerequisites) * [Google Cloud Authentication for Vertex AI](#google-cloud-authentication-for-vertex-ai) * [API Keys](#api-keys) * [Configuration](#configuration) * [Running the Benchmark](#running-the-benchmark) * [Output Files](#output-files) * [Customization](#customization) * [Limitations](#limitations) * [Learn more and contribute](#learn_more_and_contribute) * [License](#license) * [Contact](#contact) ## Overview AutoBench 1.0 is an innovative and automated benchmark system designed to evaluate the performance of Large Language Models (LLMs) with unprecedented dynamism, flexibility, and cost-effectiveness. Leveraging the "Collective-LLM-as-a-Judge" approach, AutoBench uses LLMs themselves to collectively assess the quality of questions and answers, overcoming the limitations of traditional static benchmarks and human-biased evaluations. The system is designed to be: * **Correlated with Established Benchmarks:** Achieves high correlations with Chatbot Arena, MMLU, and AAQI, demonstrating alignment with human evaluations and broader AI capabilities. * **Cost-Effective:** On a single sub-$100 budget and ca. 5-10 hour runtime, it will provide higly accurate rank for 20 models, therefore making large-scale and frequent benchmarking feasible. * **Dynamic and Hard to Hack:** Dynamically generated questions in each iteration prevent "benchmark gaming" and ensure models demonstrate genuine general abilities. * **Scalable:** Designed for continuous monitoring of LLM progress and future-proofed for evolving AI capabilities. * **Granular:** Provides detailed performance breakdowns across various topics (Math, General Culture, Logics, Code, Science, History, etc.). **For an intro explanation of the methodology, please refer to the Hugging Face Blog Post: [Escape the Benchmark Trap: AutoBench – the Collective-LLM-as-a-Judge System for Evaluating AI models (ASI-Ready!)](https://huggingface.co/blog/PeterKruger/autobench).** **For a simple demo, try the Hugging Faces Spaces implementation of the benchmark: [AutoBench 1.0 Demo](https://huggingface.co/spaces/PeterKruger/AutoBench).** **For a detailed explanation of the methodology, please refer to the [Detailed Methodology Document](AutoBench_1_0_Detailed_Methodology_Document.pdf).** ## Key Features of AutoBench 1.0 * **Dynamic and Adaptive:** The system generates new questions for each iteration, making it resistant to gaming and adaptable to the rapid evolution of LLMs. * **Reduced Human Bias – and Defined LLM-as-a-Judge Perspective:** Minimizes human subjectivity by using LLMs for evaluation, embracing inherent "model bias" as a perspective relative to the current LLM ecosystem. * **Scalability and Cost-Effectiveness:** Significantly reduces the cost and time associated with traditional human evaluation, enabling frequent and large-scale benchmark updates. * **Granular Topic-Specific Insights:** Offers detailed performance breakdowns across various topics, providing a nuanced understanding of LLM strengths and weaknesses. * **Iterative Refinement and Weighting Stability:** Employs an iterative weighting mechanism that dynamically adjusts model weights based on performance, ensuring stability and convergence over time. * **Well-Defined Question Quality Control:** Implements a transparent and rigorous approach to question quality control with quantifiable acceptance criteria, ensuring high-quality and relevant questions. ## Getting Started ### Prerequisites * **Python 3.7+** * **Required Python Libraries:** ```bash pip install openai together anthropic vertexai pandas numpy concurrent.futures re time csv google-api-core ``` Ensure you have the latest versions, especially for `openai` (version 1.0.0 or later is recommended). * **Google Colab Environment (Recommended):** While the script can be adapted to other environments, it is primarily designed to run in Google Colab due to the use of Colab Secrets Manager for API key security and Vertex AI integration. * **Google Cloud Account and Vertex AI API Enabled:** To utilize Gemini models through Vertex AI, you need a Google Cloud account with the Vertex AI API enabled. ### Google Cloud Authentication for Vertex AI To use Gemini models via Vertex AI, you must authenticate your Google Cloud account within the Colab environment. **Before running the benchmark, follow these steps:** 1. **Download your Vertex AI API authentication JSON file:** * Go to the [Google Cloud Console](https://console.cloud.google.com/). * Navigate to "IAM & Admin" > "Service Accounts". * Create or select an existing service account with the necessary Vertex AI permissions. * Create a new JSON key for the service account and download it to your local machine. 2. **Upload the JSON key file to your Colab environment:** * In your Google Colab notebook, use the file upload button in the sidebar (folder icon) to upload the JSON key file to the `/content/` directory. **Ensure you upload it to `/content/`**. 3. **Run the following authentication code in your Colab notebook *before* running any other cells:** ```python #Remember to upload your Vertex AI API auth json file in /content #Run this before anything else import os from google.colab import auth # 1. Colab User Authentication (Interactive) auth.authenticate_user() # 2. Service Account Authentication (using the JSON file) # Make SURE the file is uploaded to /content/ and the filename is correct! os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "/content/YOUR_VERTEX_AI_KEY_FILE.json" # Replace with your actual JSON filename # ... rest of your code ... ``` **Important:** * **Replace `YOUR_VERTEX_AI_KEY_FILE.json` with the actual filename of your downloaded JSON key file.** * **Make sure the JSON file is uploaded to the `/content/` directory in Colab.** * **Run this authentication code *only once* at the beginning of your Colab session.** * **For initial setup, running `auth.authenticate_user()` (interactive authentication) is recommended to verify your Google Cloud connection before relying on service account authentication.** You can comment it out after confirming it works. ### API Keys AutoBench 1.0 requires API keys for accessing the following services: * **OpenAI:** For models like `gpt-4o`, `gpt-3.5-turbo`, and Grok models (accessed through OpenAI API). * **Together AI:** For a wide range of open-source models like Llama 3, Gemma, Mistral, and Qwen. * **Anthropic:** For Claude 3 models. * **Nebius:** For DeepSeek models (accessed through Nebius API, similar to OpenAI). * **Vertex AI (Google Cloud):** For Gemini models. **Securely manage your API keys using Google Colab Secrets Manager:** 1. In your Google Colab notebook, navigate to the "Secrets" panel (key icon in the sidebar). 2. Add the following secrets, replacing `YOUR_API_KEY` with your actual API keys: * `OpenAI_API_key`: Your OpenAI API key. * `TOGETHER_API_KEY`: Your Together AI API key. * `ANTHROPIC_API_KEY`: Your Anthropic API key. * `GROK_API_KEY`: Your Grok API key (accessed through OpenAI API, requires Grok access). * `NEBIUS_API_KEY`: Your Nebius API key. **The script is configured to retrieve these keys using `google.colab.userdata.get()`.** ### Configuration The core configurations for AutoBench 1.0 are defined directly within the Python script (`llm_benchmark.py`) for easy modification. Key configuration sections include: * **`model_config` Dictionary:** This dictionary defines each LLM used in the benchmark, including: * `type`: API provider (`"gemini"`, `"openai"`, `"together"`, `"anthropic"`, `"nebius"`, `"grok"`). * `name`: Model identifier (e.g., `"gpt-4o-2024-11-20"`, `"gemini-2.0-flash-001"`). * `role`: Model's designated role in the benchmark (`"answer"`, `"rank"`, or `"both"`). * **Model Lists:** `openai_models`, `gemini_models`, `together_models`, `anthropic_models`, `nebius_models`, `grok_models` lists specify which models from `model_config` will be actively benchmarked. * **`topics` List:** Defines the list of topics used for question generation (e.g., `["math", "history", "creative writing", ...]`). * **`difficulties` List:** Defines the difficulty levels for questions (e.g., `["a very simple", "a simple", "a", "a difficult", "a very difficult"]`). * **`difficulty_probabilities` Dictionary:** Controls the distribution of question difficulty levels during benchmark iterations. * **Global Parameters:** Various parameters at the beginning of the script (e.g., `time_sleep`, `base_temp`, `question_temp`, `answer_temp`, token limits, thresholds) can be adjusted to fine-tune the benchmark. **To customize the benchmark:** 1. **Edit the `model_config` dictionary** to add, remove, or modify models, their types, names, and roles. 2. **Adjust the model lists** (`openai_models`, etc.) to select the specific models you want to include in the benchmark run. 3. **Modify the `topics` and `difficulties` lists** to customize the benchmark's scope and challenge. 4. **Tweak global parameters** to adjust temperature, token limits, timeouts, and other settings as needed. ## Running the Benchmark 1. **Open the `llm_benchmark.py` file in Google Colab.** 2. **Ensure you have set up your API keys in Colab Secrets Manager and authenticated with Google Cloud for Vertex AI as described above.** 3. run !pip install openai numpy pandas together anthropic google-cloud-aiplatform to load all required packages 3. **Review and customize the configuration sections in the script if needed.** 4. **Run all cells in the notebook sequentially.** The script will execute the benchmark iterations, dynamically generate questions and answers, rank model performance, and update model weights iteratively. Progress and results will be printed to the Colab output, and detailed results will be saved to CSV files. ## Output Files AutoBench 1.0 generates the following output files: * **`llm_benchmark_results.csv`:** This file contains aggregated benchmark results, including: * Average rank for each model across all iterations. * Topic-specific average ranks, providing granular performance insights. * **`llm_benchmark_iteration_results.csv` (or similar, timestamped):** This file provides detailed results for each iteration, including: * Iteration number, topic, and difficulty. * Generated question prompt and question. * Answers generated by each model. * Ranks assigned by judging models for each answer. * Average rank for each model in each iteration. * Durations for answer generation and ranking processes. * **`model_weights_out.csv`:** This file saves the model weights at the end of the benchmark run. These weights are updated iteratively based on model performance and can be used as input for subsequent benchmark runs (by renaming it to `weights_in.csv` or updating the `old_weights_file` variable) to enable continuous learning and adaptation of the benchmark. * **`weights_in.csv` (or similar, input weights file):** If you provide a file with pre-existing model weights (e.g., from a previous run), this file will be loaded at the beginning of the benchmark to initialize model weights. PLEASE NOTE: the system will recognize if new models have been introduced and initialize weights and ranks accordingly. ## Customization AutoBench 1.0 is highly customizable. You can: * **Add or remove LLMs** by modifying the `model_config` dictionary and the model lists. * **Change the topics and difficulty levels** to focus on specific areas of LLM performance. * **Adjust prompts** for question generation and ranking to refine the benchmark's focus and evaluation criteria. * **Modify the number of iterations (`t`)** to control the benchmark's runtime and robustness. * **Fine-tune parameters** like temperature, token limits, timeouts, and thresholds to optimize the benchmark for your specific needs and resources. ## Limitations AutoBench 1.0, while offering significant advantages, also has limitations and potential biases inherent to the LLM-as-a-Judge approach: * **LLM-as-a-Judge Bias:** The benchmark inherently reflects the biases of the LLMs used as judges. Results are relative to the "view" of the current set of LLMs, not necessarily against an absolute, objective standard. * **Question Quality Control Dependency:** The quality of the benchmark depends on the ability of the LLM judges to effectively evaluate question quality. * **Ranking Granularity:** The 1-5 ranking scale may not capture subtle differences in answer quality, potentially losing nuance between high-performing models. * **Potential Suboptimality of Weighting:** The cumulative average weighting mechanism may converge to a locally optimal but not globally optimal state. * **Black Box Nature of LLMs:** The internal decision-making processes of the judging LLMs remain opaque, limiting full transparency of the evaluation process. **Please refer to the [Detailed Methodology Document](AutoBench_1_0_Detailed_Methodology_Document.pdf) for a more in-depth discussion of limitations and potential biases.** ## Learn more and contribute * **Start from our blog post on Hugging Face**: [Escape the Benchmark Trap: AutoBench – the Collective-LLM-as-a-Judge System for Evaluating AI models (ASI-Ready!)](https://huggingface.co/blog/PeterKruger/autobench) * **Explore the code and data:** [Hugging Face AutoBench 1.0 Repository](https://huggingface.co/PeterKruger/AutoBench) <!-- Replace with actual link --> * **Try our Demo on Spaces:** [AutoBench 1.0 Demo](https://huggingface.co/spaces/PeterKruger/AutoBench) <!-- Replace with actual link --> * **Read the detailed methodology:** [Detailed Methodology Document](https://huggingface.co/PeterKruger/AutoBench/blob/main/AutoBench_1_0_Detailed_Methodology_Document.pdf) <!-- Replace with link --> * **Join the discussion:** [Hugging Face AutoBench Community Discussion](https://huggingface.co/PeterKruger/AutoBench/discussions) <!-- Replace with link --> * **Contribute:** Help us by suggesting new topics, refining prompts, or enhancing the weighting algorithm—submit pull requests or issues via the Hugging Face Repo. ## License [MIT License](LICENSE) ## Contact [Peter Kruger/eZecute] [[email protected]]
HamZurger/ppo-Huggy
HamZurger
"2023-08-25T13:07:29Z"
3
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]
reinforcement-learning
"2023-08-25T13:07:17Z"
--- 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: HamZurger/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
mradermacher/chatbot_gpt2_healthcaremagic100k-GGUF
mradermacher
"2025-02-28T15:52:11Z"
0
0
transformers
[ "transformers", "gguf", "en", "base_model:maull04/chatbot_gpt2_healthcaremagic100k", "base_model:quantized:maull04/chatbot_gpt2_healthcaremagic100k", "license:mit", "endpoints_compatible", "region:us" ]
null
"2025-02-28T15:49:46Z"
--- base_model: maull04/chatbot_gpt2_healthcaremagic100k language: - en library_name: transformers license: mit quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/maull04/chatbot_gpt2_healthcaremagic100k <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/chatbot_gpt2_healthcaremagic100k-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/chatbot_gpt2_healthcaremagic100k-GGUF/resolve/main/chatbot_gpt2_healthcaremagic100k.Q2_K.gguf) | Q2_K | 0.2 | | | [GGUF](https://huggingface.co/mradermacher/chatbot_gpt2_healthcaremagic100k-GGUF/resolve/main/chatbot_gpt2_healthcaremagic100k.Q3_K_S.gguf) | Q3_K_S | 0.2 | | | [GGUF](https://huggingface.co/mradermacher/chatbot_gpt2_healthcaremagic100k-GGUF/resolve/main/chatbot_gpt2_healthcaremagic100k.Q3_K_M.gguf) | Q3_K_M | 0.2 | lower quality | | [GGUF](https://huggingface.co/mradermacher/chatbot_gpt2_healthcaremagic100k-GGUF/resolve/main/chatbot_gpt2_healthcaremagic100k.Q3_K_L.gguf) | Q3_K_L | 0.2 | | | [GGUF](https://huggingface.co/mradermacher/chatbot_gpt2_healthcaremagic100k-GGUF/resolve/main/chatbot_gpt2_healthcaremagic100k.IQ4_XS.gguf) | IQ4_XS | 0.2 | | | [GGUF](https://huggingface.co/mradermacher/chatbot_gpt2_healthcaremagic100k-GGUF/resolve/main/chatbot_gpt2_healthcaremagic100k.Q4_K_S.gguf) | Q4_K_S | 0.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/chatbot_gpt2_healthcaremagic100k-GGUF/resolve/main/chatbot_gpt2_healthcaremagic100k.Q4_K_M.gguf) | Q4_K_M | 0.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/chatbot_gpt2_healthcaremagic100k-GGUF/resolve/main/chatbot_gpt2_healthcaremagic100k.Q5_K_S.gguf) | Q5_K_S | 0.2 | | | [GGUF](https://huggingface.co/mradermacher/chatbot_gpt2_healthcaremagic100k-GGUF/resolve/main/chatbot_gpt2_healthcaremagic100k.Q5_K_M.gguf) | Q5_K_M | 0.2 | | | [GGUF](https://huggingface.co/mradermacher/chatbot_gpt2_healthcaremagic100k-GGUF/resolve/main/chatbot_gpt2_healthcaremagic100k.Q6_K.gguf) | Q6_K | 0.2 | very good quality | | [GGUF](https://huggingface.co/mradermacher/chatbot_gpt2_healthcaremagic100k-GGUF/resolve/main/chatbot_gpt2_healthcaremagic100k.Q8_0.gguf) | Q8_0 | 0.3 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/chatbot_gpt2_healthcaremagic100k-GGUF/resolve/main/chatbot_gpt2_healthcaremagic100k.f16.gguf) | f16 | 0.4 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->