ftopal/huggingface-models-raw · Datasets at Hugging Face

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null	null		{}	AImonkeys/mistralXdocker	null	[ "region:us" ]	null	2024-04-30T05:47:21+00:00
null	null	# kat33/Mixtral-8x7B-Instruct-v0.1-Q6_K-GGUF This model was converted to GGUF format from [`mistralai/Mixtral-8x7B-Instruct-v0.1`](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) 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/mistralai/Mixtral-8x7B-Instruct-v0.1) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo kat33/Mixtral-8x7B-Instruct-v0.1-Q6_K-GGUF --model mixtral-8x7b-instruct-v0.1.Q6_K.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo kat33/Mixtral-8x7B-Instruct-v0.1-Q6_K-GGUF --model mixtral-8x7b-instruct-v0.1.Q6_K.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. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m mixtral-8x7b-instruct-v0.1.Q6_K.gguf -n 128 ```	{"language": ["fr", "it", "de", "es", "en"], "license": "apache-2.0", "tags": ["llama-cpp", "gguf-my-repo"], "inference": {"parameters": {"temperature": 0.5}}, "widget": [{"messages": [{"role": "user", "content": "What is your favorite condiment?"}]}]}	kat33/Mixtral-8x7B-Instruct-v0.1-Q6_K-GGUF	null	[ "gguf", "llama-cpp", "gguf-my-repo", "fr", "it", "de", "es", "en", "license:apache-2.0", "region:us" ]	null	2024-04-30T05:47:29+00:00
null	peft	<!-- 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. --> # GUE_prom_prom_core_notata-seqsight_32768_512_43M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_notata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_notata) dataset. It achieves the following results on the evaluation set: - Loss: 0.3840 - F1 Score: 0.8338 - Accuracy: 0.8338 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.5472 \| 0.6 \| 200 \| 0.4181 \| 0.8117 \| 0.8119 \| \| 0.4381 \| 1.2 \| 400 \| 0.4003 \| 0.8190 \| 0.8191 \| \| 0.4205 \| 1.81 \| 600 \| 0.3911 \| 0.8243 \| 0.8244 \| \| 0.4179 \| 2.41 \| 800 \| 0.3876 \| 0.8264 \| 0.8266 \| \| 0.4072 \| 3.01 \| 1000 \| 0.3833 \| 0.8287 \| 0.8289 \| \| 0.4051 \| 3.61 \| 1200 \| 0.3853 \| 0.8272 \| 0.8276 \| \| 0.4021 \| 4.22 \| 1400 \| 0.3797 \| 0.8318 \| 0.8319 \| \| 0.4066 \| 4.82 \| 1600 \| 0.3777 \| 0.8310 \| 0.8312 \| \| 0.3943 \| 5.42 \| 1800 \| 0.3787 \| 0.8297 \| 0.8297 \| \| 0.3998 \| 6.02 \| 2000 \| 0.3801 \| 0.8315 \| 0.8319 \| \| 0.3971 \| 6.63 \| 2200 \| 0.3780 \| 0.8335 \| 0.8336 \| \| 0.392 \| 7.23 \| 2400 \| 0.3841 \| 0.8294 \| 0.8300 \| \| 0.3939 \| 7.83 \| 2600 \| 0.3736 \| 0.8331 \| 0.8332 \| \| 0.3904 \| 8.43 \| 2800 \| 0.3861 \| 0.8293 \| 0.8300 \| \| 0.3951 \| 9.04 \| 3000 \| 0.3779 \| 0.8299 \| 0.8302 \| \| 0.387 \| 9.64 \| 3200 \| 0.3752 \| 0.8328 \| 0.8329 \| \| 0.3886 \| 10.24 \| 3400 \| 0.3737 \| 0.8326 \| 0.8327 \| \| 0.3848 \| 10.84 \| 3600 \| 0.3716 \| 0.8332 \| 0.8332 \| \| 0.3857 \| 11.45 \| 3800 \| 0.3736 \| 0.8307 \| 0.8308 \| \| 0.3849 \| 12.05 \| 4000 \| 0.3704 \| 0.8332 \| 0.8332 \| \| 0.3814 \| 12.65 \| 4200 \| 0.3767 \| 0.8328 \| 0.8331 \| \| 0.3859 \| 13.25 \| 4400 \| 0.3726 \| 0.8339 \| 0.8340 \| \| 0.3851 \| 13.86 \| 4600 \| 0.3712 \| 0.8315 \| 0.8315 \| \| 0.383 \| 14.46 \| 4800 \| 0.3728 \| 0.8327 \| 0.8329 \| \| 0.3822 \| 15.06 \| 5000 \| 0.3713 \| 0.8318 \| 0.8319 \| \| 0.3802 \| 15.66 \| 5200 \| 0.3708 \| 0.8330 \| 0.8331 \| \| 0.3821 \| 16.27 \| 5400 \| 0.3712 \| 0.8321 \| 0.8321 \| \| 0.3788 \| 16.87 \| 5600 \| 0.3812 \| 0.8313 \| 0.8319 \| \| 0.375 \| 17.47 \| 5800 \| 0.3789 \| 0.8334 \| 0.8338 \| \| 0.385 \| 18.07 \| 6000 \| 0.3745 \| 0.8341 \| 0.8346 \| \| 0.3775 \| 18.67 \| 6200 \| 0.3698 \| 0.8334 \| 0.8336 \| \| 0.379 \| 19.28 \| 6400 \| 0.3706 \| 0.8330 \| 0.8331 \| \| 0.3764 \| 19.88 \| 6600 \| 0.3706 \| 0.8324 \| 0.8327 \| \| 0.3714 \| 20.48 \| 6800 \| 0.3743 \| 0.8340 \| 0.8344 \| \| 0.3842 \| 21.08 \| 7000 \| 0.3683 \| 0.8345 \| 0.8347 \| \| 0.3801 \| 21.69 \| 7200 \| 0.3683 \| 0.8347 \| 0.8347 \| \| 0.3727 \| 22.29 \| 7400 \| 0.3686 \| 0.8348 \| 0.8349 \| \| 0.3725 \| 22.89 \| 7600 \| 0.3691 \| 0.8333 \| 0.8334 \| \| 0.3754 \| 23.49 \| 7800 \| 0.3689 \| 0.8342 \| 0.8344 \| \| 0.3772 \| 24.1 \| 8000 \| 0.3725 \| 0.8335 \| 0.8338 \| \| 0.3773 \| 24.7 \| 8200 \| 0.3736 \| 0.8335 \| 0.8340 \| \| 0.371 \| 25.3 \| 8400 \| 0.3721 \| 0.8337 \| 0.8340 \| \| 0.379 \| 25.9 \| 8600 \| 0.3688 \| 0.8335 \| 0.8336 \| \| 0.3786 \| 26.51 \| 8800 \| 0.3682 \| 0.8347 \| 0.8347 \| \| 0.3773 \| 27.11 \| 9000 \| 0.3680 \| 0.8329 \| 0.8331 \| \| 0.3799 \| 27.71 \| 9200 \| 0.3692 \| 0.8329 \| 0.8331 \| \| 0.3689 \| 28.31 \| 9400 \| 0.3715 \| 0.8326 \| 0.8329 \| \| 0.3744 \| 28.92 \| 9600 \| 0.3692 \| 0.8334 \| 0.8336 \| \| 0.3783 \| 29.52 \| 9800 \| 0.3690 \| 0.8334 \| 0.8336 \| \| 0.3679 \| 30.12 \| 10000 \| 0.3695 \| 0.8334 \| 0.8336 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_prom_prom_core_notata-seqsight_32768_512_43M-L1_f", "results": []}]}	mahdibaghbanzadeh/GUE_prom_prom_core_notata-seqsight_32768_512_43M-L1_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]	null	2024-04-30T05:48:08+00:00
text-generation	transformers	# Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```	{"license": "other", "library_name": "transformers", "tags": ["autotrain", "text-generation-inference", "text-generation", "peft"], "widget": [{"messages": [{"role": "user", "content": "What is your favorite condiment?"}]}]}	nanxiz/autotrain-h731u-jdfg6	null	[ "transformers", "tensorboard", "safetensors", "autotrain", "text-generation-inference", "text-generation", "peft", "conversational", "license:other", "endpoints_compatible", "region:us" ]	null	2024-04-30T05:48:39+00:00
null	null	<!-- 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. --> # O0430HMA14 This model is a fine-tuned version of [allenai/OLMo-1B](https://huggingface.co/allenai/OLMo-1B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0186 ## 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 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 100 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.558 \| 0.09 \| 10 \| 0.2938 \| \| 0.1782 \| 0.18 \| 20 \| 0.1518 \| \| 0.1488 \| 0.27 \| 30 \| 0.1634 \| \| 0.1562 \| 0.36 \| 40 \| 0.1549 \| \| 0.1523 \| 0.45 \| 50 \| 0.1528 \| \| 0.1532 \| 0.54 \| 60 \| 0.1495 \| \| 0.1487 \| 0.63 \| 70 \| 0.1476 \| \| 0.1493 \| 0.73 \| 80 \| 0.1547 \| \| 0.148 \| 0.82 \| 90 \| 0.1499 \| \| 0.1487 \| 0.91 \| 100 \| 0.1516 \| \| 0.1516 \| 1.0 \| 110 \| 0.1509 \| \| 0.1464 \| 1.09 \| 120 \| 0.1491 \| \| 0.2792 \| 1.18 \| 130 \| 2.5830 \| \| 1.2568 \| 1.27 \| 140 \| 0.1547 \| \| 0.1824 \| 1.36 \| 150 \| 0.1368 \| \| 0.341 \| 1.45 \| 160 \| 0.3759 \| \| 0.1732 \| 1.54 \| 170 \| 0.0789 \| \| 0.444 \| 1.63 \| 180 \| 0.0761 \| \| 0.0692 \| 1.72 \| 190 \| 0.0591 \| \| 0.0553 \| 1.81 \| 200 \| 0.0601 \| \| 0.0576 \| 1.9 \| 210 \| 0.0560 \| \| 0.0578 \| 1.99 \| 220 \| 0.0525 \| \| 0.0498 \| 2.08 \| 230 \| 0.0459 \| \| 0.0412 \| 2.18 \| 240 \| 0.0334 \| \| 0.0359 \| 2.27 \| 250 \| 0.0302 \| \| 0.0315 \| 2.36 \| 260 \| 0.0261 \| \| 0.0254 \| 2.45 \| 270 \| 0.0243 \| \| 0.0179 \| 2.54 \| 280 \| 0.0219 \| \| 0.0251 \| 2.63 \| 290 \| 0.0211 \| \| 0.0226 \| 2.72 \| 300 \| 0.0195 \| \| 0.0216 \| 2.81 \| 310 \| 0.0197 \| \| 0.0231 \| 2.9 \| 320 \| 0.0186 \| \| 0.0224 \| 2.99 \| 330 \| 0.0186 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "allenai/OLMo-1B", "model-index": [{"name": "O0430HMA14", "results": []}]}	Litzy619/O0430HMA14	null	[ "safetensors", "generated_from_trainer", "base_model:allenai/OLMo-1B", "license:apache-2.0", "region:us" ]	null	2024-04-30T05:49:17+00:00
null	null		{}	oguzkurt/layoutparser-onnx	null	[ "onnx", "region:us" ]	null	2024-04-30T05:49:28+00:00
null	null	<!-- 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. --> # O0430HMA15 This model is a fine-tuned version of [allenai/OLMo-1B](https://huggingface.co/allenai/OLMo-1B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0266 ## 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 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 100 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.5644 \| 0.09 \| 10 \| 0.2800 \| \| 0.178 \| 0.18 \| 20 \| 0.1523 \| \| 0.1487 \| 0.27 \| 30 \| 0.1618 \| \| 0.1564 \| 0.36 \| 40 \| 0.1585 \| \| 0.1535 \| 0.45 \| 50 \| 0.1523 \| \| 0.1531 \| 0.54 \| 60 \| 0.1488 \| \| 0.1503 \| 0.63 \| 70 \| 0.1486 \| \| 0.1497 \| 0.73 \| 80 \| 0.1558 \| \| 0.147 \| 0.82 \| 90 \| 0.1492 \| \| 0.1496 \| 0.91 \| 100 \| 0.1499 \| \| 0.1507 \| 1.0 \| 110 \| 0.1486 \| \| 0.1469 \| 1.09 \| 120 \| 0.1510 \| \| 0.1478 \| 1.18 \| 130 \| 0.1494 \| \| 0.1483 \| 1.27 \| 140 \| 0.1481 \| \| 0.1499 \| 1.36 \| 150 \| 0.1506 \| \| 0.146 \| 1.45 \| 160 \| 0.1442 \| \| 0.3204 \| 1.54 \| 170 \| 2.2831 \| \| 0.367 \| 1.63 \| 180 \| 0.2210 \| \| 0.0994 \| 1.72 \| 190 \| 0.0781 \| \| 0.0734 \| 1.81 \| 200 \| 0.0705 \| \| 0.0635 \| 1.9 \| 210 \| 0.0575 \| \| 0.0585 \| 1.99 \| 220 \| 0.0566 \| \| 0.0659 \| 2.08 \| 230 \| 0.0568 \| \| 0.0521 \| 2.18 \| 240 \| 0.0482 \| \| 0.0439 \| 2.27 \| 250 \| 0.0367 \| \| 0.0508 \| 2.36 \| 260 \| 0.0361 \| \| 0.037 \| 2.45 \| 270 \| 0.0350 \| \| 0.0269 \| 2.54 \| 280 \| 0.0289 \| \| 0.0326 \| 2.63 \| 290 \| 0.0277 \| \| 0.0316 \| 2.72 \| 300 \| 0.0298 \| \| 0.0286 \| 2.81 \| 310 \| 0.0278 \| \| 0.028 \| 2.9 \| 320 \| 0.0270 \| \| 0.0307 \| 2.99 \| 330 \| 0.0266 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "allenai/OLMo-1B", "model-index": [{"name": "O0430HMA15", "results": []}]}	Litzy619/O0430HMA15	null	[ "safetensors", "generated_from_trainer", "base_model:allenai/OLMo-1B", "license:apache-2.0", "region:us" ]	null	2024-04-30T05:50:59+00:00
text-to-image	diffusers	# 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 🧨 diffusers 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]	{"library_name": "diffusers"}	Niggendar/mugenmalumixSDXL_v30	null	[ "diffusers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	null	2024-04-30T05:51:08+00:00
null	null	# NikolayKozloff/tweety-tatar-base-7b-2024-v1-Q8_0-GGUF This model was converted to GGUF format from [`Tweeties/tweety-tatar-base-7b-2024-v1`](https://huggingface.co/Tweeties/tweety-tatar-base-7b-2024-v1) 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/Tweeties/tweety-tatar-base-7b-2024-v1) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo NikolayKozloff/tweety-tatar-base-7b-2024-v1-Q8_0-GGUF --model tweety-tatar-base-7b-2024-v1.Q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo NikolayKozloff/tweety-tatar-base-7b-2024-v1-Q8_0-GGUF --model tweety-tatar-base-7b-2024-v1.Q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m tweety-tatar-base-7b-2024-v1.Q8_0.gguf -n 128 ```	{"language": ["tt"], "license": "apache-2.0", "tags": ["tweety", "llama-cpp", "gguf-my-repo"], "datasets": ["oscar-corpus/OSCAR-2301"], "base_model": "mistralai/Mistral-7B-Instruct-v0.2"}	NikolayKozloff/tweety-tatar-base-7b-2024-v1-GGUF	null	[ "gguf", "tweety", "llama-cpp", "gguf-my-repo", "tt", "dataset:oscar-corpus/OSCAR-2301", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "region:us" ]	null	2024-04-30T05:51:28+00:00
null	null		{"license": "afl-3.0"}	agknoows/OppapaJesus	null	[ "license:afl-3.0", "region:us" ]	null	2024-04-30T05:53:45+00:00
text2text-generation	transformers		{}	shenkha/DGSlow_Bartbase_BST	null	[ "transformers", "pytorch", "bart", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-30T05:54:55+00:00
text-generation	transformers		{}	arctic126/hospital_mm4-3b	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-30T05:55:25+00:00
null	peft	<!-- 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. --> # GUE_prom_prom_core_notata-seqsight_32768_512_43M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_notata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_notata) dataset. It achieves the following results on the evaluation set: - Loss: 0.3786 - F1 Score: 0.8327 - Accuracy: 0.8327 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.5136 \| 0.6 \| 200 \| 0.3951 \| 0.8217 \| 0.8217 \| \| 0.4153 \| 1.2 \| 400 \| 0.3880 \| 0.8265 \| 0.8268 \| \| 0.4002 \| 1.81 \| 600 \| 0.3924 \| 0.8262 \| 0.8268 \| \| 0.3984 \| 2.41 \| 800 \| 0.3814 \| 0.8318 \| 0.8321 \| \| 0.3895 \| 3.01 \| 1000 \| 0.3794 \| 0.8325 \| 0.8331 \| \| 0.3846 \| 3.61 \| 1200 \| 0.3729 \| 0.8345 \| 0.8347 \| \| 0.3866 \| 4.22 \| 1400 \| 0.3690 \| 0.8381 \| 0.8381 \| \| 0.3879 \| 4.82 \| 1600 \| 0.3693 \| 0.8370 \| 0.8372 \| \| 0.3746 \| 5.42 \| 1800 \| 0.3728 \| 0.8346 \| 0.8346 \| \| 0.382 \| 6.02 \| 2000 \| 0.3697 \| 0.8375 \| 0.8378 \| \| 0.378 \| 6.63 \| 2200 \| 0.3666 \| 0.8365 \| 0.8366 \| \| 0.3741 \| 7.23 \| 2400 \| 0.3731 \| 0.8346 \| 0.8351 \| \| 0.3749 \| 7.83 \| 2600 \| 0.3636 \| 0.8391 \| 0.8391 \| \| 0.3707 \| 8.43 \| 2800 \| 0.3775 \| 0.8349 \| 0.8357 \| \| 0.3751 \| 9.04 \| 3000 \| 0.3640 \| 0.8409 \| 0.8410 \| \| 0.3674 \| 9.64 \| 3200 \| 0.3633 \| 0.8393 \| 0.8393 \| \| 0.3683 \| 10.24 \| 3400 \| 0.3623 \| 0.8411 \| 0.8412 \| \| 0.3655 \| 10.84 \| 3600 \| 0.3600 \| 0.8419 \| 0.8419 \| \| 0.3654 \| 11.45 \| 3800 \| 0.3603 \| 0.8396 \| 0.8396 \| \| 0.3636 \| 12.05 \| 4000 \| 0.3616 \| 0.8423 \| 0.8423 \| \| 0.3606 \| 12.65 \| 4200 \| 0.3641 \| 0.8406 \| 0.8406 \| \| 0.3643 \| 13.25 \| 4400 \| 0.3632 \| 0.8388 \| 0.8389 \| \| 0.3628 \| 13.86 \| 4600 \| 0.3650 \| 0.8390 \| 0.8391 \| \| 0.3605 \| 14.46 \| 4800 \| 0.3636 \| 0.8388 \| 0.8389 \| \| 0.3612 \| 15.06 \| 5000 \| 0.3580 \| 0.8400 \| 0.8400 \| \| 0.3563 \| 15.66 \| 5200 \| 0.3614 \| 0.8388 \| 0.8389 \| \| 0.3597 \| 16.27 \| 5400 \| 0.3646 \| 0.8402 \| 0.8402 \| \| 0.3565 \| 16.87 \| 5600 \| 0.3689 \| 0.8380 \| 0.8385 \| \| 0.3534 \| 17.47 \| 5800 \| 0.3653 \| 0.8390 \| 0.8393 \| \| 0.3618 \| 18.07 \| 6000 \| 0.3601 \| 0.8410 \| 0.8412 \| \| 0.3549 \| 18.67 \| 6200 \| 0.3577 \| 0.8422 \| 0.8423 \| \| 0.3548 \| 19.28 \| 6400 \| 0.3606 \| 0.8434 \| 0.8434 \| \| 0.3523 \| 19.88 \| 6600 \| 0.3596 \| 0.8404 \| 0.8406 \| \| 0.3461 \| 20.48 \| 6800 \| 0.3600 \| 0.8412 \| 0.8413 \| \| 0.359 \| 21.08 \| 7000 \| 0.3598 \| 0.8411 \| 0.8413 \| \| 0.3558 \| 21.69 \| 7200 \| 0.3595 \| 0.8437 \| 0.8438 \| \| 0.3468 \| 22.29 \| 7400 \| 0.3587 \| 0.8410 \| 0.8412 \| \| 0.3469 \| 22.89 \| 7600 \| 0.3605 \| 0.8402 \| 0.8404 \| \| 0.3479 \| 23.49 \| 7800 \| 0.3592 \| 0.8407 \| 0.8408 \| \| 0.3521 \| 24.1 \| 8000 \| 0.3627 \| 0.8383 \| 0.8385 \| \| 0.3509 \| 24.7 \| 8200 \| 0.3631 \| 0.8395 \| 0.8398 \| \| 0.3451 \| 25.3 \| 8400 \| 0.3639 \| 0.8402 \| 0.8404 \| \| 0.3518 \| 25.9 \| 8600 \| 0.3595 \| 0.8410 \| 0.8412 \| \| 0.3502 \| 26.51 \| 8800 \| 0.3592 \| 0.8413 \| 0.8413 \| \| 0.3503 \| 27.11 \| 9000 \| 0.3583 \| 0.8420 \| 0.8421 \| \| 0.3528 \| 27.71 \| 9200 \| 0.3609 \| 0.8402 \| 0.8404 \| \| 0.3399 \| 28.31 \| 9400 \| 0.3624 \| 0.8392 \| 0.8395 \| \| 0.349 \| 28.92 \| 9600 \| 0.3598 \| 0.8412 \| 0.8413 \| \| 0.3499 \| 29.52 \| 9800 \| 0.3596 \| 0.8403 \| 0.8404 \| \| 0.3414 \| 30.12 \| 10000 \| 0.3604 \| 0.8406 \| 0.8408 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_prom_prom_core_notata-seqsight_32768_512_43M-L8_f", "results": []}]}	mahdibaghbanzadeh/GUE_prom_prom_core_notata-seqsight_32768_512_43M-L8_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]	null	2024-04-30T05:56:16+00:00
null	peft	<!-- 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. --> # GUE_prom_prom_core_notata-seqsight_32768_512_43M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_notata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_notata) dataset. It achieves the following results on the evaluation set: - Loss: 0.3860 - F1 Score: 0.8313 - Accuracy: 0.8314 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.4902 \| 0.6 \| 200 \| 0.3884 \| 0.8259 \| 0.8259 \| \| 0.4053 \| 1.2 \| 400 \| 0.3797 \| 0.8339 \| 0.8342 \| \| 0.3903 \| 1.81 \| 600 \| 0.3945 \| 0.8235 \| 0.8244 \| \| 0.3882 \| 2.41 \| 800 \| 0.3731 \| 0.8377 \| 0.8379 \| \| 0.3811 \| 3.01 \| 1000 \| 0.3734 \| 0.8361 \| 0.8366 \| \| 0.3737 \| 3.61 \| 1200 \| 0.3654 \| 0.8376 \| 0.8378 \| \| 0.3779 \| 4.22 \| 1400 \| 0.3625 \| 0.8389 \| 0.8389 \| \| 0.3767 \| 4.82 \| 1600 \| 0.3628 \| 0.8380 \| 0.8381 \| \| 0.3617 \| 5.42 \| 1800 \| 0.3680 \| 0.8387 \| 0.8387 \| \| 0.37 \| 6.02 \| 2000 \| 0.3670 \| 0.8377 \| 0.8379 \| \| 0.3637 \| 6.63 \| 2200 \| 0.3608 \| 0.8407 \| 0.8408 \| \| 0.3596 \| 7.23 \| 2400 \| 0.3738 \| 0.8340 \| 0.8346 \| \| 0.3578 \| 7.83 \| 2600 \| 0.3667 \| 0.8380 \| 0.8379 \| \| 0.3545 \| 8.43 \| 2800 \| 0.3747 \| 0.8374 \| 0.8379 \| \| 0.3584 \| 9.04 \| 3000 \| 0.3673 \| 0.8394 \| 0.8395 \| \| 0.3481 \| 9.64 \| 3200 \| 0.3652 \| 0.8387 \| 0.8387 \| \| 0.3498 \| 10.24 \| 3400 \| 0.3640 \| 0.8411 \| 0.8412 \| \| 0.3455 \| 10.84 \| 3600 \| 0.3607 \| 0.8394 \| 0.8395 \| \| 0.3435 \| 11.45 \| 3800 \| 0.3607 \| 0.8385 \| 0.8385 \| \| 0.3419 \| 12.05 \| 4000 \| 0.3671 \| 0.8397 \| 0.8396 \| \| 0.335 \| 12.65 \| 4200 \| 0.3724 \| 0.8379 \| 0.8379 \| \| 0.3397 \| 13.25 \| 4400 \| 0.3717 \| 0.8371 \| 0.8372 \| \| 0.3396 \| 13.86 \| 4600 \| 0.3731 \| 0.8393 \| 0.8395 \| \| 0.3337 \| 14.46 \| 4800 \| 0.3753 \| 0.8361 \| 0.8364 \| \| 0.3357 \| 15.06 \| 5000 \| 0.3635 \| 0.8403 \| 0.8404 \| \| 0.3269 \| 15.66 \| 5200 \| 0.3699 \| 0.8403 \| 0.8404 \| \| 0.3319 \| 16.27 \| 5400 \| 0.3785 \| 0.8403 \| 0.8404 \| \| 0.3289 \| 16.87 \| 5600 \| 0.3847 \| 0.8364 \| 0.8370 \| \| 0.3236 \| 17.47 \| 5800 \| 0.3771 \| 0.8395 \| 0.8396 \| \| 0.3314 \| 18.07 \| 6000 \| 0.3719 \| 0.8401 \| 0.8404 \| \| 0.3246 \| 18.67 \| 6200 \| 0.3693 \| 0.8448 \| 0.8449 \| \| 0.3216 \| 19.28 \| 6400 \| 0.3742 \| 0.8404 \| 0.8404 \| \| 0.3206 \| 19.88 \| 6600 \| 0.3721 \| 0.8375 \| 0.8378 \| \| 0.3143 \| 20.48 \| 6800 \| 0.3731 \| 0.8386 \| 0.8387 \| \| 0.3233 \| 21.08 \| 7000 \| 0.3797 \| 0.8370 \| 0.8374 \| \| 0.3197 \| 21.69 \| 7200 \| 0.3799 \| 0.8373 \| 0.8374 \| \| 0.3108 \| 22.29 \| 7400 \| 0.3766 \| 0.8383 \| 0.8385 \| \| 0.3106 \| 22.89 \| 7600 \| 0.3814 \| 0.8365 \| 0.8368 \| \| 0.3089 \| 23.49 \| 7800 \| 0.3778 \| 0.8389 \| 0.8391 \| \| 0.3158 \| 24.1 \| 8000 \| 0.3849 \| 0.8356 \| 0.8359 \| \| 0.3121 \| 24.7 \| 8200 \| 0.3848 \| 0.8352 \| 0.8357 \| \| 0.306 \| 25.3 \| 8400 \| 0.3883 \| 0.8365 \| 0.8368 \| \| 0.3119 \| 25.9 \| 8600 \| 0.3806 \| 0.8370 \| 0.8372 \| \| 0.3095 \| 26.51 \| 8800 \| 0.3817 \| 0.8365 \| 0.8366 \| \| 0.311 \| 27.11 \| 9000 \| 0.3797 \| 0.8392 \| 0.8393 \| \| 0.3079 \| 27.71 \| 9200 \| 0.3860 \| 0.8368 \| 0.8370 \| \| 0.2988 \| 28.31 \| 9400 \| 0.3883 \| 0.8370 \| 0.8374 \| \| 0.3086 \| 28.92 \| 9600 \| 0.3826 \| 0.8380 \| 0.8381 \| \| 0.3066 \| 29.52 \| 9800 \| 0.3831 \| 0.8372 \| 0.8374 \| \| 0.3023 \| 30.12 \| 10000 \| 0.3839 \| 0.8376 \| 0.8378 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_prom_prom_core_notata-seqsight_32768_512_43M-L32_f", "results": []}]}	mahdibaghbanzadeh/GUE_prom_prom_core_notata-seqsight_32768_512_43M-L32_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]	null	2024-04-30T05:56:24+00:00
null	peft	<!-- 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. --> # GUE_prom_prom_core_tata-seqsight_32768_512_43M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_tata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_tata) dataset. It achieves the following results on the evaluation set: - Loss: 0.4468 - F1 Score: 0.8203 - Accuracy: 0.8206 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.6029 \| 5.13 \| 200 \| 0.5832 \| 0.6980 \| 0.7015 \| \| 0.5406 \| 10.26 \| 400 \| 0.5696 \| 0.7163 \| 0.7194 \| \| 0.5176 \| 15.38 \| 600 \| 0.5599 \| 0.7281 \| 0.7308 \| \| 0.4955 \| 20.51 \| 800 \| 0.5382 \| 0.7455 \| 0.7455 \| \| 0.4756 \| 25.64 \| 1000 \| 0.5299 \| 0.7423 \| 0.7423 \| \| 0.465 \| 30.77 \| 1200 \| 0.5255 \| 0.7438 \| 0.7439 \| \| 0.4532 \| 35.9 \| 1400 \| 0.5213 \| 0.7534 \| 0.7537 \| \| 0.4388 \| 41.03 \| 1600 \| 0.5134 \| 0.7548 \| 0.7553 \| \| 0.4319 \| 46.15 \| 1800 \| 0.5187 \| 0.7551 \| 0.7553 \| \| 0.4203 \| 51.28 \| 2000 \| 0.5093 \| 0.7683 \| 0.7684 \| \| 0.4066 \| 56.41 \| 2200 \| 0.5230 \| 0.7714 \| 0.7716 \| \| 0.4086 \| 61.54 \| 2400 \| 0.4994 \| 0.7716 \| 0.7716 \| \| 0.4016 \| 66.67 \| 2600 \| 0.5033 \| 0.7667 \| 0.7667 \| \| 0.391 \| 71.79 \| 2800 \| 0.5018 \| 0.7732 \| 0.7732 \| \| 0.3842 \| 76.92 \| 3000 \| 0.5181 \| 0.7677 \| 0.7684 \| \| 0.3755 \| 82.05 \| 3200 \| 0.4979 \| 0.7732 \| 0.7732 \| \| 0.3695 \| 87.18 \| 3400 \| 0.5117 \| 0.7694 \| 0.7700 \| \| 0.3637 \| 92.31 \| 3600 \| 0.4982 \| 0.7749 \| 0.7749 \| \| 0.3508 \| 97.44 \| 3800 \| 0.5016 \| 0.7748 \| 0.7749 \| \| 0.3503 \| 102.56 \| 4000 \| 0.4929 \| 0.7830 \| 0.7830 \| \| 0.3429 \| 107.69 \| 4200 \| 0.4888 \| 0.7862 \| 0.7863 \| \| 0.3379 \| 112.82 \| 4400 \| 0.4902 \| 0.7797 \| 0.7798 \| \| 0.3324 \| 117.95 \| 4600 \| 0.4944 \| 0.7812 \| 0.7814 \| \| 0.3301 \| 123.08 \| 4800 \| 0.4942 \| 0.7794 \| 0.7798 \| \| 0.3202 \| 128.21 \| 5000 \| 0.4894 \| 0.7862 \| 0.7863 \| \| 0.3263 \| 133.33 \| 5200 \| 0.4753 \| 0.7928 \| 0.7928 \| \| 0.3215 \| 138.46 \| 5400 \| 0.4740 \| 0.7895 \| 0.7896 \| \| 0.3123 \| 143.59 \| 5600 \| 0.4865 \| 0.7845 \| 0.7847 \| \| 0.3151 \| 148.72 \| 5800 \| 0.4858 \| 0.7895 \| 0.7896 \| \| 0.309 \| 153.85 \| 6000 \| 0.4865 \| 0.7845 \| 0.7847 \| \| 0.3092 \| 158.97 \| 6200 \| 0.4841 \| 0.7863 \| 0.7863 \| \| 0.3031 \| 164.1 \| 6400 \| 0.4883 \| 0.7862 \| 0.7863 \| \| 0.3065 \| 169.23 \| 6600 \| 0.4861 \| 0.7895 \| 0.7896 \| \| 0.3016 \| 174.36 \| 6800 \| 0.4825 \| 0.7912 \| 0.7912 \| \| 0.299 \| 179.49 \| 7000 \| 0.4909 \| 0.7974 \| 0.7977 \| \| 0.2988 \| 184.62 \| 7200 \| 0.4942 \| 0.7975 \| 0.7977 \| \| 0.296 \| 189.74 \| 7400 \| 0.4839 \| 0.7976 \| 0.7977 \| \| 0.2923 \| 194.87 \| 7600 \| 0.4837 \| 0.7879 \| 0.7879 \| \| 0.2932 \| 200.0 \| 7800 \| 0.4832 \| 0.7911 \| 0.7912 \| \| 0.2949 \| 205.13 \| 8000 \| 0.4968 \| 0.7909 \| 0.7912 \| \| 0.2924 \| 210.26 \| 8200 \| 0.4875 \| 0.7960 \| 0.7961 \| \| 0.2963 \| 215.38 \| 8400 \| 0.4904 \| 0.7959 \| 0.7961 \| \| 0.2914 \| 220.51 \| 8600 \| 0.5002 \| 0.7925 \| 0.7928 \| \| 0.2892 \| 225.64 \| 8800 \| 0.4993 \| 0.7942 \| 0.7945 \| \| 0.2917 \| 230.77 \| 9000 \| 0.4928 \| 0.7975 \| 0.7977 \| \| 0.2858 \| 235.9 \| 9200 \| 0.4917 \| 0.7959 \| 0.7961 \| \| 0.2924 \| 241.03 \| 9400 \| 0.4853 \| 0.7960 \| 0.7961 \| \| 0.2868 \| 246.15 \| 9600 \| 0.4926 \| 0.7992 \| 0.7993 \| \| 0.2873 \| 251.28 \| 9800 \| 0.4913 \| 0.7976 \| 0.7977 \| \| 0.2875 \| 256.41 \| 10000 \| 0.4899 \| 0.7976 \| 0.7977 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_prom_prom_core_tata-seqsight_32768_512_43M-L1_f", "results": []}]}	mahdibaghbanzadeh/GUE_prom_prom_core_tata-seqsight_32768_512_43M-L1_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]	null	2024-04-30T05:56:29+00:00
null	peft	<!-- 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. --> # GUE_prom_prom_core_tata-seqsight_32768_512_43M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_tata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_tata) dataset. It achieves the following results on the evaluation set: - Loss: 0.6247 - F1 Score: 0.8222 - Accuracy: 0.8222 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.5763 \| 5.13 \| 200 \| 0.5555 \| 0.7217 \| 0.7227 \| \| 0.498 \| 10.26 \| 400 \| 0.5365 \| 0.7505 \| 0.7520 \| \| 0.4604 \| 15.38 \| 600 \| 0.5318 \| 0.7472 \| 0.7488 \| \| 0.4267 \| 20.51 \| 800 \| 0.4895 \| 0.7798 \| 0.7798 \| \| 0.3931 \| 25.64 \| 1000 \| 0.4848 \| 0.7749 \| 0.7749 \| \| 0.362 \| 30.77 \| 1200 \| 0.4607 \| 0.8057 \| 0.8059 \| \| 0.338 \| 35.9 \| 1400 \| 0.4576 \| 0.8026 \| 0.8026 \| \| 0.315 \| 41.03 \| 1600 \| 0.4507 \| 0.8006 \| 0.8010 \| \| 0.2968 \| 46.15 \| 1800 \| 0.4532 \| 0.8140 \| 0.8140 \| \| 0.2813 \| 51.28 \| 2000 \| 0.4684 \| 0.8087 \| 0.8091 \| \| 0.2655 \| 56.41 \| 2200 \| 0.4970 \| 0.8123 \| 0.8124 \| \| 0.2577 \| 61.54 \| 2400 \| 0.4923 \| 0.8007 \| 0.8010 \| \| 0.2449 \| 66.67 \| 2600 \| 0.4722 \| 0.8204 \| 0.8206 \| \| 0.2349 \| 71.79 \| 2800 \| 0.4885 \| 0.8173 \| 0.8173 \| \| 0.2217 \| 76.92 \| 3000 \| 0.5013 \| 0.8172 \| 0.8173 \| \| 0.2111 \| 82.05 \| 3200 \| 0.5198 \| 0.8205 \| 0.8206 \| \| 0.2005 \| 87.18 \| 3400 \| 0.5395 \| 0.8170 \| 0.8173 \| \| 0.1939 \| 92.31 \| 3600 \| 0.5382 \| 0.8123 \| 0.8124 \| \| 0.1867 \| 97.44 \| 3800 \| 0.5531 \| 0.8254 \| 0.8254 \| \| 0.1777 \| 102.56 \| 4000 \| 0.5748 \| 0.8187 \| 0.8189 \| \| 0.171 \| 107.69 \| 4200 \| 0.5901 \| 0.8138 \| 0.8140 \| \| 0.1625 \| 112.82 \| 4400 \| 0.5725 \| 0.8222 \| 0.8222 \| \| 0.1571 \| 117.95 \| 4600 \| 0.5986 \| 0.8157 \| 0.8157 \| \| 0.1574 \| 123.08 \| 4800 \| 0.6007 \| 0.8138 \| 0.8140 \| \| 0.1467 \| 128.21 \| 5000 \| 0.6231 \| 0.8169 \| 0.8173 \| \| 0.1462 \| 133.33 \| 5200 \| 0.5896 \| 0.8204 \| 0.8206 \| \| 0.1371 \| 138.46 \| 5400 \| 0.6265 \| 0.8222 \| 0.8222 \| \| 0.1308 \| 143.59 \| 5600 \| 0.6411 \| 0.8253 \| 0.8254 \| \| 0.1304 \| 148.72 \| 5800 \| 0.6175 \| 0.8254 \| 0.8254 \| \| 0.1274 \| 153.85 \| 6000 \| 0.6336 \| 0.8205 \| 0.8206 \| \| 0.1276 \| 158.97 \| 6200 \| 0.6744 \| 0.8155 \| 0.8157 \| \| 0.1225 \| 164.1 \| 6400 \| 0.6494 \| 0.8220 \| 0.8222 \| \| 0.1239 \| 169.23 \| 6600 \| 0.6373 \| 0.8124 \| 0.8124 \| \| 0.1165 \| 174.36 \| 6800 \| 0.6363 \| 0.8238 \| 0.8238 \| \| 0.1151 \| 179.49 \| 7000 \| 0.6376 \| 0.8302 \| 0.8303 \| \| 0.1117 \| 184.62 \| 7200 \| 0.6631 \| 0.8173 \| 0.8173 \| \| 0.1078 \| 189.74 \| 7400 \| 0.6730 \| 0.8270 \| 0.8271 \| \| 0.1058 \| 194.87 \| 7600 \| 0.6678 \| 0.8271 \| 0.8271 \| \| 0.1015 \| 200.0 \| 7800 \| 0.6791 \| 0.8254 \| 0.8254 \| \| 0.104 \| 205.13 \| 8000 \| 0.6991 \| 0.8186 \| 0.8189 \| \| 0.1034 \| 210.26 \| 8200 \| 0.6741 \| 0.8189 \| 0.8189 \| \| 0.1026 \| 215.38 \| 8400 \| 0.6680 \| 0.8287 \| 0.8287 \| \| 0.1 \| 220.51 \| 8600 \| 0.6933 \| 0.8171 \| 0.8173 \| \| 0.0987 \| 225.64 \| 8800 \| 0.6859 \| 0.8254 \| 0.8254 \| \| 0.0976 \| 230.77 \| 9000 \| 0.6847 \| 0.8254 \| 0.8254 \| \| 0.0966 \| 235.9 \| 9200 \| 0.6927 \| 0.8237 \| 0.8238 \| \| 0.0968 \| 241.03 \| 9400 \| 0.6888 \| 0.8238 \| 0.8238 \| \| 0.0931 \| 246.15 \| 9600 \| 0.6931 \| 0.8253 \| 0.8254 \| \| 0.0906 \| 251.28 \| 9800 \| 0.6998 \| 0.8254 \| 0.8254 \| \| 0.0916 \| 256.41 \| 10000 \| 0.6957 \| 0.8254 \| 0.8254 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_prom_prom_core_tata-seqsight_32768_512_43M-L8_f", "results": []}]}	mahdibaghbanzadeh/GUE_prom_prom_core_tata-seqsight_32768_512_43M-L8_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]	null	2024-04-30T05:57:21+00:00
null	peft	<!-- 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. --> # GUE_prom_prom_core_tata-seqsight_32768_512_43M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_tata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_tata) dataset. It achieves the following results on the evaluation set: - Loss: 0.9752 - F1 Score: 0.8271 - Accuracy: 0.8271 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.5578 \| 5.13 \| 200 \| 0.5322 \| 0.7502 \| 0.7504 \| \| 0.464 \| 10.26 \| 400 \| 0.5083 \| 0.7701 \| 0.7716 \| \| 0.3882 \| 15.38 \| 600 \| 0.4438 \| 0.8074 \| 0.8075 \| \| 0.3241 \| 20.51 \| 800 \| 0.4506 \| 0.8234 \| 0.8238 \| \| 0.2722 \| 25.64 \| 1000 \| 0.4721 \| 0.8303 \| 0.8303 \| \| 0.2338 \| 30.77 \| 1200 \| 0.4767 \| 0.8320 \| 0.8320 \| \| 0.1976 \| 35.9 \| 1400 \| 0.5198 \| 0.8336 \| 0.8336 \| \| 0.1754 \| 41.03 \| 1600 \| 0.4998 \| 0.8303 \| 0.8303 \| \| 0.1428 \| 46.15 \| 1800 \| 0.6118 \| 0.8269 \| 0.8271 \| \| 0.1281 \| 51.28 \| 2000 \| 0.5731 \| 0.8302 \| 0.8303 \| \| 0.1127 \| 56.41 \| 2200 \| 0.6563 \| 0.8319 \| 0.8320 \| \| 0.0994 \| 61.54 \| 2400 \| 0.6877 \| 0.8222 \| 0.8222 \| \| 0.0901 \| 66.67 \| 2600 \| 0.7150 \| 0.8352 \| 0.8352 \| \| 0.0817 \| 71.79 \| 2800 \| 0.7223 \| 0.8254 \| 0.8254 \| \| 0.0725 \| 76.92 \| 3000 \| 0.7396 \| 0.8334 \| 0.8336 \| \| 0.0663 \| 82.05 \| 3200 \| 0.7565 \| 0.8335 \| 0.8336 \| \| 0.0601 \| 87.18 \| 3400 \| 0.7511 \| 0.8418 \| 0.8418 \| \| 0.0589 \| 92.31 \| 3600 \| 0.7803 \| 0.8383 \| 0.8385 \| \| 0.0521 \| 97.44 \| 3800 \| 0.8330 \| 0.8385 \| 0.8385 \| \| 0.0525 \| 102.56 \| 4000 \| 0.8002 \| 0.8434 \| 0.8434 \| \| 0.0466 \| 107.69 \| 4200 \| 0.7893 \| 0.8385 \| 0.8385 \| \| 0.0414 \| 112.82 \| 4400 \| 0.8864 \| 0.8369 \| 0.8369 \| \| 0.0385 \| 117.95 \| 4600 \| 0.8732 \| 0.8335 \| 0.8336 \| \| 0.0402 \| 123.08 \| 4800 \| 0.8392 \| 0.8401 \| 0.8401 \| \| 0.0382 \| 128.21 \| 5000 \| 0.8185 \| 0.8285 \| 0.8287 \| \| 0.0384 \| 133.33 \| 5200 \| 0.8188 \| 0.8401 \| 0.8401 \| \| 0.0334 \| 138.46 \| 5400 \| 0.8668 \| 0.8433 \| 0.8434 \| \| 0.0297 \| 143.59 \| 5600 \| 0.8826 \| 0.8319 \| 0.8320 \| \| 0.033 \| 148.72 \| 5800 \| 0.8982 \| 0.8336 \| 0.8336 \| \| 0.0285 \| 153.85 \| 6000 \| 0.9081 \| 0.8352 \| 0.8352 \| \| 0.0299 \| 158.97 \| 6200 \| 0.8908 \| 0.8384 \| 0.8385 \| \| 0.0296 \| 164.1 \| 6400 \| 0.8685 \| 0.8368 \| 0.8369 \| \| 0.0288 \| 169.23 \| 6600 \| 0.8841 \| 0.8401 \| 0.8401 \| \| 0.0265 \| 174.36 \| 6800 \| 0.8954 \| 0.8336 \| 0.8336 \| \| 0.0277 \| 179.49 \| 7000 \| 0.8666 \| 0.8417 \| 0.8418 \| \| 0.0243 \| 184.62 \| 7200 \| 0.8899 \| 0.8401 \| 0.8401 \| \| 0.023 \| 189.74 \| 7400 \| 0.8804 \| 0.8418 \| 0.8418 \| \| 0.0233 \| 194.87 \| 7600 \| 0.9357 \| 0.8401 \| 0.8401 \| \| 0.0244 \| 200.0 \| 7800 \| 0.8806 \| 0.8401 \| 0.8401 \| \| 0.0212 \| 205.13 \| 8000 \| 0.9329 \| 0.8385 \| 0.8385 \| \| 0.022 \| 210.26 \| 8200 \| 0.9356 \| 0.8434 \| 0.8434 \| \| 0.0212 \| 215.38 \| 8400 \| 0.9286 \| 0.8400 \| 0.8401 \| \| 0.0205 \| 220.51 \| 8600 \| 0.9201 \| 0.8434 \| 0.8434 \| \| 0.0215 \| 225.64 \| 8800 \| 0.9130 \| 0.8434 \| 0.8434 \| \| 0.021 \| 230.77 \| 9000 \| 0.9020 \| 0.8434 \| 0.8434 \| \| 0.0205 \| 235.9 \| 9200 \| 0.9081 \| 0.8385 \| 0.8385 \| \| 0.0194 \| 241.03 \| 9400 \| 0.9260 \| 0.8320 \| 0.8320 \| \| 0.0182 \| 246.15 \| 9600 \| 0.9300 \| 0.8352 \| 0.8352 \| \| 0.0172 \| 251.28 \| 9800 \| 0.9393 \| 0.8352 \| 0.8352 \| \| 0.0167 \| 256.41 \| 10000 \| 0.9422 \| 0.8352 \| 0.8352 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_prom_prom_core_tata-seqsight_32768_512_43M-L32_f", "results": []}]}	mahdibaghbanzadeh/GUE_prom_prom_core_tata-seqsight_32768_512_43M-L32_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]	null	2024-04-30T05:57:21+00:00
text-generation	transformers	<!-- 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-dpo-full-sft-wo-kqa_golden This model is a fine-tuned version of [Minbyul/mistral-7b-wo-kqa_golden-sft](https://huggingface.co/Minbyul/mistral-7b-wo-kqa_golden-sft) on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set: - Loss: 0.0018 - Rewards/chosen: -0.4458 - Rewards/rejected: -10.1099 - Rewards/accuracies: 1.0 - Rewards/margins: 9.6641 - Logps/rejected: -1564.3792 - Logps/chosen: -241.2112 - Logits/rejected: -2.0516 - Logits/chosen: -1.3414 ## 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: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.2478 \| 0.31 \| 100 \| 0.0352 \| -0.1739 \| -4.4264 \| 1.0 \| 4.2525 \| -996.0294 \| -214.0196 \| -2.9200 \| -2.1162 \| \| 0.1385 \| 0.61 \| 200 \| 0.0041 \| -0.3360 \| -8.1997 \| 1.0 \| 7.8637 \| -1373.3590 \| -230.2282 \| -2.3336 \| -1.6287 \| \| 0.0899 \| 0.92 \| 300 \| 0.0019 \| -0.4479 \| -10.0624 \| 1.0 \| 9.6145 \| -1559.6263 \| -241.4165 \| -2.0553 \| -1.3416 \| ### Framework versions - Transformers 4.39.0.dev0 - Pytorch 2.1.2 - Datasets 2.14.6 - Tokenizers 0.15.2	{"license": "apache-2.0", "tags": ["alignment-handbook", "trl", "dpo", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["HuggingFaceH4/ultrafeedback_binarized"], "base_model": "Minbyul/mistral-7b-wo-kqa_golden-sft", "model-index": [{"name": "mistral-7b-dpo-full-sft-wo-kqa_golden", "results": []}]}	Minbyul/mistral-7b-dpo-full-sft-wo-kqa_golden	null	[ "transformers", "safetensors", "mistral", "text-generation", "alignment-handbook", "trl", "dpo", "generated_from_trainer", "dataset:HuggingFaceH4/ultrafeedback_binarized", "base_model:Minbyul/mistral-7b-wo-kqa_golden-sft", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-30T05:57:27+00:00
null	peft	<!-- 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_envs_claim_finetune2 This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 5 - total_train_batch_size: 40 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 100 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.1.0a0+29c30b1 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "mistralai/Mistral-7B-Instruct-v0.2", "model-index": [{"name": "mistral_envs_claim_finetune2", "results": []}]}	Haimee/mistral_envs_claim_finetune2	null	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "region:us" ]	null	2024-04-30T05:58:26+00:00
null	peft	<!-- 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. --> # GUE_prom_prom_300_all-seqsight_32768_512_43M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_prom_prom_300_all](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_300_all) dataset. It achieves the following results on the evaluation set: - Loss: 0.2119 - F1 Score: 0.9145 - Accuracy: 0.9145 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.4346 \| 0.54 \| 200 \| 0.2868 \| 0.8895 \| 0.8895 \| \| 0.2911 \| 1.08 \| 400 \| 0.2578 \| 0.8990 \| 0.8990 \| \| 0.2714 \| 1.62 \| 600 \| 0.2389 \| 0.9039 \| 0.9039 \| \| 0.2514 \| 2.16 \| 800 \| 0.2377 \| 0.9043 \| 0.9044 \| \| 0.2477 \| 2.7 \| 1000 \| 0.2262 \| 0.9061 \| 0.9061 \| \| 0.2379 \| 3.24 \| 1200 \| 0.2297 \| 0.9080 \| 0.9081 \| \| 0.2416 \| 3.78 \| 1400 \| 0.2212 \| 0.9102 \| 0.9103 \| \| 0.2327 \| 4.32 \| 1600 \| 0.2150 \| 0.9111 \| 0.9111 \| \| 0.2277 \| 4.86 \| 1800 \| 0.2154 \| 0.9120 \| 0.9120 \| \| 0.224 \| 5.41 \| 2000 \| 0.2112 \| 0.9142 \| 0.9142 \| \| 0.2231 \| 5.95 \| 2200 \| 0.2120 \| 0.9155 \| 0.9155 \| \| 0.2227 \| 6.49 \| 2400 \| 0.2081 \| 0.9155 \| 0.9155 \| \| 0.2201 \| 7.03 \| 2600 \| 0.2055 \| 0.9164 \| 0.9164 \| \| 0.2153 \| 7.57 \| 2800 \| 0.2038 \| 0.9177 \| 0.9177 \| \| 0.2176 \| 8.11 \| 3000 \| 0.2018 \| 0.9194 \| 0.9194 \| \| 0.2154 \| 8.65 \| 3200 \| 0.2013 \| 0.9193 \| 0.9193 \| \| 0.2099 \| 9.19 \| 3400 \| 0.1997 \| 0.9189 \| 0.9189 \| \| 0.2076 \| 9.73 \| 3600 \| 0.1996 \| 0.9187 \| 0.9187 \| \| 0.2161 \| 10.27 \| 3800 \| 0.1973 \| 0.9206 \| 0.9206 \| \| 0.2091 \| 10.81 \| 4000 \| 0.1972 \| 0.9206 \| 0.9206 \| \| 0.2112 \| 11.35 \| 4200 \| 0.2030 \| 0.9183 \| 0.9184 \| \| 0.2085 \| 11.89 \| 4400 \| 0.1967 \| 0.9208 \| 0.9208 \| \| 0.2041 \| 12.43 \| 4600 \| 0.1979 \| 0.9212 \| 0.9213 \| \| 0.2089 \| 12.97 \| 4800 \| 0.1950 \| 0.9211 \| 0.9211 \| \| 0.2047 \| 13.51 \| 5000 \| 0.1969 \| 0.9208 \| 0.9208 \| \| 0.2065 \| 14.05 \| 5200 \| 0.1946 \| 0.9223 \| 0.9223 \| \| 0.2033 \| 14.59 \| 5400 \| 0.1977 \| 0.9209 \| 0.9209 \| \| 0.2021 \| 15.14 \| 5600 \| 0.1989 \| 0.9212 \| 0.9213 \| \| 0.2004 \| 15.68 \| 5800 \| 0.1977 \| 0.9218 \| 0.9218 \| \| 0.2041 \| 16.22 \| 6000 \| 0.2004 \| 0.9197 \| 0.9198 \| \| 0.2004 \| 16.76 \| 6200 \| 0.1956 \| 0.9219 \| 0.9220 \| \| 0.2002 \| 17.3 \| 6400 \| 0.1943 \| 0.9198 \| 0.9198 \| \| 0.2044 \| 17.84 \| 6600 \| 0.1946 \| 0.9206 \| 0.9206 \| \| 0.1962 \| 18.38 \| 6800 \| 0.1966 \| 0.9221 \| 0.9221 \| \| 0.2041 \| 18.92 \| 7000 \| 0.1957 \| 0.9219 \| 0.9220 \| \| 0.201 \| 19.46 \| 7200 \| 0.1931 \| 0.9235 \| 0.9235 \| \| 0.1972 \| 20.0 \| 7400 \| 0.1928 \| 0.9223 \| 0.9223 \| \| 0.202 \| 20.54 \| 7600 \| 0.1928 \| 0.9240 \| 0.9240 \| \| 0.2 \| 21.08 \| 7800 \| 0.1928 \| 0.9236 \| 0.9236 \| \| 0.1977 \| 21.62 \| 8000 \| 0.1944 \| 0.9233 \| 0.9233 \| \| 0.198 \| 22.16 \| 8200 \| 0.1929 \| 0.9240 \| 0.9240 \| \| 0.1908 \| 22.7 \| 8400 \| 0.1942 \| 0.9241 \| 0.9242 \| \| 0.202 \| 23.24 \| 8600 \| 0.1933 \| 0.9231 \| 0.9231 \| \| 0.1959 \| 23.78 \| 8800 \| 0.1932 \| 0.9231 \| 0.9231 \| \| 0.2012 \| 24.32 \| 9000 \| 0.1924 \| 0.9235 \| 0.9235 \| \| 0.1952 \| 24.86 \| 9200 \| 0.1923 \| 0.9235 \| 0.9235 \| \| 0.195 \| 25.41 \| 9400 \| 0.1928 \| 0.9238 \| 0.9238 \| \| 0.1939 \| 25.95 \| 9600 \| 0.1925 \| 0.9231 \| 0.9231 \| \| 0.1969 \| 26.49 \| 9800 \| 0.1940 \| 0.9233 \| 0.9233 \| \| 0.1955 \| 27.03 \| 10000 \| 0.1931 \| 0.9233 \| 0.9233 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_prom_prom_300_all-seqsight_32768_512_43M-L1_f", "results": []}]}	mahdibaghbanzadeh/GUE_prom_prom_300_all-seqsight_32768_512_43M-L1_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]	null	2024-04-30T05:59:37+00:00
null	peft	<!-- 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. --> # GUE_prom_prom_300_all-seqsight_32768_512_43M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_prom_prom_300_all](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_300_all) dataset. It achieves the following results on the evaluation set: - Loss: 0.2006 - F1 Score: 0.9216 - Accuracy: 0.9216 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.3689 \| 0.54 \| 200 \| 0.2509 \| 0.9032 \| 0.9032 \| \| 0.2545 \| 1.08 \| 400 \| 0.2269 \| 0.9081 \| 0.9081 \| \| 0.2364 \| 1.62 \| 600 \| 0.2112 \| 0.9159 \| 0.9159 \| \| 0.2203 \| 2.16 \| 800 \| 0.2049 \| 0.9203 \| 0.9203 \| \| 0.2183 \| 2.7 \| 1000 \| 0.2038 \| 0.9164 \| 0.9164 \| \| 0.2107 \| 3.24 \| 1200 \| 0.2041 \| 0.9177 \| 0.9177 \| \| 0.2129 \| 3.78 \| 1400 \| 0.2001 \| 0.9182 \| 0.9182 \| \| 0.206 \| 4.32 \| 1600 \| 0.1946 \| 0.9220 \| 0.9220 \| \| 0.2031 \| 4.86 \| 1800 \| 0.1933 \| 0.9230 \| 0.9230 \| \| 0.199 \| 5.41 \| 2000 \| 0.2003 \| 0.9199 \| 0.9199 \| \| 0.1979 \| 5.95 \| 2200 \| 0.1933 \| 0.9231 \| 0.9231 \| \| 0.1985 \| 6.49 \| 2400 \| 0.1892 \| 0.9228 \| 0.9228 \| \| 0.1966 \| 7.03 \| 2600 \| 0.1923 \| 0.9253 \| 0.9253 \| \| 0.1907 \| 7.57 \| 2800 \| 0.1905 \| 0.9248 \| 0.9248 \| \| 0.1936 \| 8.11 \| 3000 \| 0.1867 \| 0.9265 \| 0.9265 \| \| 0.1901 \| 8.65 \| 3200 \| 0.1891 \| 0.9243 \| 0.9243 \| \| 0.1872 \| 9.19 \| 3400 \| 0.1878 \| 0.9247 \| 0.9247 \| \| 0.183 \| 9.73 \| 3600 \| 0.1841 \| 0.9255 \| 0.9255 \| \| 0.1901 \| 10.27 \| 3800 \| 0.1859 \| 0.9236 \| 0.9236 \| \| 0.1842 \| 10.81 \| 4000 \| 0.1845 \| 0.9277 \| 0.9277 \| \| 0.1845 \| 11.35 \| 4200 \| 0.1855 \| 0.9274 \| 0.9274 \| \| 0.1827 \| 11.89 \| 4400 \| 0.1856 \| 0.9262 \| 0.9262 \| \| 0.1807 \| 12.43 \| 4600 \| 0.1813 \| 0.9270 \| 0.9270 \| \| 0.1798 \| 12.97 \| 4800 \| 0.1835 \| 0.9265 \| 0.9265 \| \| 0.178 \| 13.51 \| 5000 \| 0.1861 \| 0.9272 \| 0.9272 \| \| 0.1787 \| 14.05 \| 5200 \| 0.1860 \| 0.9235 \| 0.9235 \| \| 0.1745 \| 14.59 \| 5400 \| 0.1862 \| 0.9275 \| 0.9275 \| \| 0.175 \| 15.14 \| 5600 \| 0.1869 \| 0.9262 \| 0.9262 \| \| 0.1725 \| 15.68 \| 5800 \| 0.1846 \| 0.9231 \| 0.9231 \| \| 0.1746 \| 16.22 \| 6000 \| 0.1852 \| 0.9258 \| 0.9258 \| \| 0.1702 \| 16.76 \| 6200 \| 0.1853 \| 0.9257 \| 0.9257 \| \| 0.1717 \| 17.3 \| 6400 \| 0.1836 \| 0.9260 \| 0.9260 \| \| 0.1738 \| 17.84 \| 6600 \| 0.1820 \| 0.9294 \| 0.9294 \| \| 0.1663 \| 18.38 \| 6800 \| 0.1842 \| 0.9235 \| 0.9235 \| \| 0.1726 \| 18.92 \| 7000 \| 0.1802 \| 0.9279 \| 0.9279 \| \| 0.1699 \| 19.46 \| 7200 \| 0.1822 \| 0.9272 \| 0.9272 \| \| 0.167 \| 20.0 \| 7400 \| 0.1822 \| 0.9289 \| 0.9289 \| \| 0.1712 \| 20.54 \| 7600 \| 0.1813 \| 0.9290 \| 0.9291 \| \| 0.1678 \| 21.08 \| 7800 \| 0.1805 \| 0.9289 \| 0.9289 \| \| 0.1652 \| 21.62 \| 8000 \| 0.1828 \| 0.9299 \| 0.9299 \| \| 0.1651 \| 22.16 \| 8200 \| 0.1817 \| 0.9274 \| 0.9274 \| \| 0.16 \| 22.7 \| 8400 \| 0.1859 \| 0.9258 \| 0.9258 \| \| 0.1684 \| 23.24 \| 8600 \| 0.1830 \| 0.9284 \| 0.9284 \| \| 0.1641 \| 23.78 \| 8800 \| 0.1836 \| 0.9262 \| 0.9262 \| \| 0.1684 \| 24.32 \| 9000 \| 0.1815 \| 0.9269 \| 0.9269 \| \| 0.1609 \| 24.86 \| 9200 \| 0.1823 \| 0.9274 \| 0.9274 \| \| 0.1624 \| 25.41 \| 9400 \| 0.1812 \| 0.9274 \| 0.9274 \| \| 0.1616 \| 25.95 \| 9600 \| 0.1819 \| 0.9277 \| 0.9277 \| \| 0.1634 \| 26.49 \| 9800 \| 0.1821 \| 0.9284 \| 0.9284 \| \| 0.1601 \| 27.03 \| 10000 \| 0.1819 \| 0.9284 \| 0.9284 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_prom_prom_300_all-seqsight_32768_512_43M-L8_f", "results": []}]}	mahdibaghbanzadeh/GUE_prom_prom_300_all-seqsight_32768_512_43M-L8_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]	null	2024-04-30T05:59:48+00:00
null	peft	<!-- 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. --> # GUE_prom_prom_300_all-seqsight_32768_512_43M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_prom_prom_300_all](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_300_all) dataset. It achieves the following results on the evaluation set: - Loss: 0.1981 - F1 Score: 0.9235 - Accuracy: 0.9235 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.3368 \| 0.54 \| 200 \| 0.2353 \| 0.9084 \| 0.9084 \| \| 0.2343 \| 1.08 \| 400 \| 0.2030 \| 0.9176 \| 0.9176 \| \| 0.2205 \| 1.62 \| 600 \| 0.1989 \| 0.9197 \| 0.9198 \| \| 0.209 \| 2.16 \| 800 \| 0.1961 \| 0.9209 \| 0.9209 \| \| 0.207 \| 2.7 \| 1000 \| 0.1989 \| 0.9149 \| 0.9149 \| \| 0.1983 \| 3.24 \| 1200 \| 0.1933 \| 0.9184 \| 0.9184 \| \| 0.1988 \| 3.78 \| 1400 \| 0.1986 \| 0.9192 \| 0.9193 \| \| 0.1943 \| 4.32 \| 1600 \| 0.1880 \| 0.9255 \| 0.9255 \| \| 0.1883 \| 4.86 \| 1800 \| 0.1852 \| 0.9248 \| 0.9248 \| \| 0.182 \| 5.41 \| 2000 \| 0.1877 \| 0.9265 \| 0.9265 \| \| 0.1841 \| 5.95 \| 2200 \| 0.1843 \| 0.9263 \| 0.9264 \| \| 0.1817 \| 6.49 \| 2400 \| 0.1895 \| 0.9239 \| 0.9240 \| \| 0.1795 \| 7.03 \| 2600 \| 0.1829 \| 0.9270 \| 0.9270 \| \| 0.1726 \| 7.57 \| 2800 \| 0.1849 \| 0.9267 \| 0.9267 \| \| 0.1723 \| 8.11 \| 3000 \| 0.1821 \| 0.9287 \| 0.9287 \| \| 0.1686 \| 8.65 \| 3200 \| 0.1881 \| 0.9278 \| 0.9279 \| \| 0.1656 \| 9.19 \| 3400 \| 0.1821 \| 0.9282 \| 0.9282 \| \| 0.1605 \| 9.73 \| 3600 \| 0.1768 \| 0.9291 \| 0.9291 \| \| 0.1656 \| 10.27 \| 3800 \| 0.1778 \| 0.9289 \| 0.9289 \| \| 0.1606 \| 10.81 \| 4000 \| 0.1741 \| 0.9316 \| 0.9316 \| \| 0.1594 \| 11.35 \| 4200 \| 0.1806 \| 0.9309 \| 0.9309 \| \| 0.1563 \| 11.89 \| 4400 \| 0.1826 \| 0.9305 \| 0.9306 \| \| 0.1554 \| 12.43 \| 4600 \| 0.1727 \| 0.9323 \| 0.9323 \| \| 0.1513 \| 12.97 \| 4800 \| 0.1741 \| 0.9285 \| 0.9285 \| \| 0.1481 \| 13.51 \| 5000 \| 0.1776 \| 0.9297 \| 0.9297 \| \| 0.1486 \| 14.05 \| 5200 \| 0.1869 \| 0.9218 \| 0.9218 \| \| 0.1429 \| 14.59 \| 5400 \| 0.1801 \| 0.9304 \| 0.9304 \| \| 0.1445 \| 15.14 \| 5600 \| 0.1792 \| 0.9316 \| 0.9316 \| \| 0.1408 \| 15.68 \| 5800 \| 0.1781 \| 0.9304 \| 0.9304 \| \| 0.1408 \| 16.22 \| 6000 \| 0.1751 \| 0.9301 \| 0.9301 \| \| 0.1352 \| 16.76 \| 6200 \| 0.1871 \| 0.9263 \| 0.9264 \| \| 0.138 \| 17.3 \| 6400 \| 0.1750 \| 0.9294 \| 0.9294 \| \| 0.1358 \| 17.84 \| 6600 \| 0.1777 \| 0.9323 \| 0.9323 \| \| 0.1315 \| 18.38 \| 6800 \| 0.1856 \| 0.9299 \| 0.9299 \| \| 0.1369 \| 18.92 \| 7000 \| 0.1762 \| 0.9316 \| 0.9316 \| \| 0.1321 \| 19.46 \| 7200 \| 0.1793 \| 0.9306 \| 0.9306 \| \| 0.1311 \| 20.0 \| 7400 \| 0.1807 \| 0.9334 \| 0.9334 \| \| 0.1323 \| 20.54 \| 7600 \| 0.1799 \| 0.9306 \| 0.9306 \| \| 0.1272 \| 21.08 \| 7800 \| 0.1808 \| 0.9307 \| 0.9307 \| \| 0.1237 \| 21.62 \| 8000 \| 0.1877 \| 0.9280 \| 0.9280 \| \| 0.1246 \| 22.16 \| 8200 \| 0.1837 \| 0.9302 \| 0.9302 \| \| 0.122 \| 22.7 \| 8400 \| 0.1848 \| 0.9301 \| 0.9301 \| \| 0.1236 \| 23.24 \| 8600 \| 0.1878 \| 0.9299 \| 0.9299 \| \| 0.1224 \| 23.78 \| 8800 \| 0.1875 \| 0.9294 \| 0.9294 \| \| 0.1232 \| 24.32 \| 9000 \| 0.1848 \| 0.9304 \| 0.9304 \| \| 0.1228 \| 24.86 \| 9200 \| 0.1844 \| 0.9307 \| 0.9307 \| \| 0.1188 \| 25.41 \| 9400 \| 0.1856 \| 0.9299 \| 0.9299 \| \| 0.12 \| 25.95 \| 9600 \| 0.1847 \| 0.9316 \| 0.9316 \| \| 0.1195 \| 26.49 \| 9800 \| 0.1859 \| 0.9309 \| 0.9309 \| \| 0.1165 \| 27.03 \| 10000 \| 0.1854 \| 0.9318 \| 0.9318 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_prom_prom_300_all-seqsight_32768_512_43M-L32_f", "results": []}]}	mahdibaghbanzadeh/GUE_prom_prom_300_all-seqsight_32768_512_43M-L32_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]	null	2024-04-30T05:59:58+00:00
null	null	<!-- 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. --> # O0430HMA16 This model is a fine-tuned version of [allenai/OLMo-1B](https://huggingface.co/allenai/OLMo-1B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1386 ## 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 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 100 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.5715 \| 0.09 \| 10 \| 0.2837 \| \| 0.1807 \| 0.18 \| 20 \| 0.1554 \| \| 0.1515 \| 0.27 \| 30 \| 0.1672 \| \| 0.1573 \| 0.36 \| 40 \| 0.1535 \| \| 0.1517 \| 0.45 \| 50 \| 0.1504 \| \| 0.1521 \| 0.54 \| 60 \| 0.1490 \| \| 0.1513 \| 0.63 \| 70 \| 0.1472 \| \| 0.1494 \| 0.73 \| 80 \| 0.1574 \| \| 0.1484 \| 0.82 \| 90 \| 0.1490 \| \| 0.149 \| 0.91 \| 100 \| 0.1494 \| \| 0.1512 \| 1.0 \| 110 \| 0.1499 \| \| 0.1463 \| 1.09 \| 120 \| 0.1482 \| \| 0.1462 \| 1.18 \| 130 \| 0.1522 \| \| 0.1484 \| 1.27 \| 140 \| 0.1487 \| \| 0.1499 \| 1.36 \| 150 \| 0.1501 \| \| 0.1463 \| 1.45 \| 160 \| 0.1478 \| \| 0.146 \| 1.54 \| 170 \| 0.1477 \| \| 0.1472 \| 1.63 \| 180 \| 0.1472 \| \| 0.1461 \| 1.72 \| 190 \| 0.1490 \| \| 0.1443 \| 1.81 \| 200 \| 0.1497 \| \| 0.1494 \| 1.9 \| 210 \| 0.1503 \| \| 0.1456 \| 1.99 \| 220 \| 0.1472 \| \| 0.1429 \| 2.08 \| 230 \| 0.1446 \| \| 0.1383 \| 2.18 \| 240 \| 0.1445 \| \| 0.1401 \| 2.27 \| 250 \| 0.1450 \| \| 0.141 \| 2.36 \| 260 \| 0.1459 \| \| 0.1398 \| 2.45 \| 270 \| 0.1428 \| \| 0.1341 \| 2.54 \| 280 \| 0.1389 \| \| 0.1345 \| 2.63 \| 290 \| 0.1411 \| \| 0.1347 \| 2.72 \| 300 \| 0.1395 \| \| 0.1335 \| 2.81 \| 310 \| 0.1387 \| \| 0.1321 \| 2.9 \| 320 \| 0.1387 \| \| 0.1375 \| 2.99 \| 330 \| 0.1386 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "allenai/OLMo-1B", "model-index": [{"name": "O0430HMA16", "results": []}]}	Litzy619/O0430HMA16	null	[ "safetensors", "generated_from_trainer", "base_model:allenai/OLMo-1B", "license:apache-2.0", "region:us" ]	null	2024-04-30T06:03:10+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3K14ac-seqsight_32768_512_43M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_EMP_H3K14ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K14ac) dataset. It achieves the following results on the evaluation set: - Loss: 0.4907 - F1 Score: 0.7713 - Accuracy: 0.7703 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.6078 \| 0.97 \| 200 \| 0.5696 \| 0.7214 \| 0.7198 \| \| 0.5576 \| 1.93 \| 400 \| 0.5322 \| 0.7501 \| 0.7486 \| \| 0.5381 \| 2.9 \| 600 \| 0.5385 \| 0.7543 \| 0.7528 \| \| 0.5289 \| 3.86 \| 800 \| 0.5084 \| 0.7646 \| 0.7643 \| \| 0.5195 \| 4.83 \| 1000 \| 0.5251 \| 0.7586 \| 0.7570 \| \| 0.5138 \| 5.8 \| 1200 \| 0.5170 \| 0.7626 \| 0.7610 \| \| 0.5131 \| 6.76 \| 1400 \| 0.5057 \| 0.7662 \| 0.7646 \| \| 0.5086 \| 7.73 \| 1600 \| 0.5034 \| 0.7698 \| 0.7682 \| \| 0.5062 \| 8.7 \| 1800 \| 0.5035 \| 0.7668 \| 0.7652 \| \| 0.5012 \| 9.66 \| 2000 \| 0.5088 \| 0.7659 \| 0.7643 \| \| 0.5059 \| 10.63 \| 2200 \| 0.5152 \| 0.7624 \| 0.7610 \| \| 0.4987 \| 11.59 \| 2400 \| 0.4991 \| 0.7686 \| 0.7670 \| \| 0.5029 \| 12.56 \| 2600 \| 0.5098 \| 0.7674 \| 0.7658 \| \| 0.4966 \| 13.53 \| 2800 \| 0.5062 \| 0.7658 \| 0.7643 \| \| 0.4979 \| 14.49 \| 3000 \| 0.5158 \| 0.7632 \| 0.7619 \| \| 0.4895 \| 15.46 \| 3200 \| 0.4918 \| 0.7751 \| 0.7737 \| \| 0.4949 \| 16.43 \| 3400 \| 0.5080 \| 0.7645 \| 0.7631 \| \| 0.4919 \| 17.39 \| 3600 \| 0.4903 \| 0.7742 \| 0.7728 \| \| 0.4882 \| 18.36 \| 3800 \| 0.4883 \| 0.7733 \| 0.7722 \| \| 0.4895 \| 19.32 \| 4000 \| 0.4909 \| 0.7752 \| 0.7737 \| \| 0.4871 \| 20.29 \| 4200 \| 0.4916 \| 0.7761 \| 0.7746 \| \| 0.487 \| 21.26 \| 4400 \| 0.4970 \| 0.7722 \| 0.7707 \| \| 0.4855 \| 22.22 \| 4600 \| 0.5079 \| 0.7702 \| 0.7688 \| \| 0.4866 \| 23.19 \| 4800 \| 0.4903 \| 0.7770 \| 0.7755 \| \| 0.4869 \| 24.15 \| 5000 \| 0.4891 \| 0.7731 \| 0.7716 \| \| 0.4828 \| 25.12 \| 5200 \| 0.5005 \| 0.7713 \| 0.7697 \| \| 0.4815 \| 26.09 \| 5400 \| 0.4942 \| 0.7740 \| 0.7725 \| \| 0.4814 \| 27.05 \| 5600 \| 0.5042 \| 0.7690 \| 0.7676 \| \| 0.4829 \| 28.02 \| 5800 \| 0.4832 \| 0.7760 \| 0.7746 \| \| 0.4815 \| 28.99 \| 6000 \| 0.4999 \| 0.7733 \| 0.7719 \| \| 0.4804 \| 29.95 \| 6200 \| 0.4979 \| 0.7743 \| 0.7728 \| \| 0.4816 \| 30.92 \| 6400 \| 0.4819 \| 0.7778 \| 0.7764 \| \| 0.4798 \| 31.88 \| 6600 \| 0.4874 \| 0.7749 \| 0.7734 \| \| 0.4784 \| 32.85 \| 6800 \| 0.4942 \| 0.7752 \| 0.7737 \| \| 0.483 \| 33.82 \| 7000 \| 0.4982 \| 0.7731 \| 0.7716 \| \| 0.4786 \| 34.78 \| 7200 \| 0.4936 \| 0.7731 \| 0.7716 \| \| 0.4794 \| 35.75 \| 7400 \| 0.4892 \| 0.7770 \| 0.7755 \| \| 0.4748 \| 36.71 \| 7600 \| 0.4904 \| 0.7731 \| 0.7716 \| \| 0.4772 \| 37.68 \| 7800 \| 0.4898 \| 0.7758 \| 0.7743 \| \| 0.4771 \| 38.65 \| 8000 \| 0.4837 \| 0.7770 \| 0.7755 \| \| 0.4826 \| 39.61 \| 8200 \| 0.4880 \| 0.7749 \| 0.7734 \| \| 0.4715 \| 40.58 \| 8400 \| 0.4948 \| 0.7725 \| 0.7710 \| \| 0.4742 \| 41.55 \| 8600 \| 0.4891 \| 0.7734 \| 0.7719 \| \| 0.4721 \| 42.51 \| 8800 \| 0.4891 \| 0.7737 \| 0.7722 \| \| 0.475 \| 43.48 \| 9000 \| 0.4985 \| 0.7743 \| 0.7728 \| \| 0.4741 \| 44.44 \| 9200 \| 0.4925 \| 0.7740 \| 0.7725 \| \| 0.4757 \| 45.41 \| 9400 \| 0.4892 \| 0.7731 \| 0.7716 \| \| 0.469 \| 46.38 \| 9600 \| 0.4934 \| 0.7740 \| 0.7725 \| \| 0.4794 \| 47.34 \| 9800 \| 0.4906 \| 0.7740 \| 0.7725 \| \| 0.474 \| 48.31 \| 10000 \| 0.4891 \| 0.7740 \| 0.7725 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_EMP_H3K14ac-seqsight_32768_512_43M-L1_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K14ac-seqsight_32768_512_43M-L1_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]	null	2024-04-30T06:04:03+00:00
text-generation	transformers	# 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]	{"library_name": "transformers", "tags": []}	pruning/v16o0y7	null	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-30T06:04:38+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3K14ac-seqsight_32768_512_43M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_EMP_H3K14ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K14ac) dataset. It achieves the following results on the evaluation set: - Loss: 0.4984 - F1 Score: 0.7700 - Accuracy: 0.7691 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.5881 \| 0.97 \| 200 \| 0.5331 \| 0.7519 \| 0.7504 \| \| 0.5288 \| 1.93 \| 400 \| 0.5084 \| 0.7643 \| 0.7628 \| \| 0.5108 \| 2.9 \| 600 \| 0.5162 \| 0.7548 \| 0.7534 \| \| 0.5075 \| 3.86 \| 800 \| 0.4914 \| 0.7690 \| 0.7682 \| \| 0.5005 \| 4.83 \| 1000 \| 0.5060 \| 0.7655 \| 0.7640 \| \| 0.4943 \| 5.8 \| 1200 \| 0.4978 \| 0.7701 \| 0.7685 \| \| 0.4904 \| 6.76 \| 1400 \| 0.4867 \| 0.7751 \| 0.7737 \| \| 0.4863 \| 7.73 \| 1600 \| 0.4914 \| 0.7740 \| 0.7725 \| \| 0.4831 \| 8.7 \| 1800 \| 0.4916 \| 0.7698 \| 0.7682 \| \| 0.4792 \| 9.66 \| 2000 \| 0.4948 \| 0.7734 \| 0.7719 \| \| 0.4808 \| 10.63 \| 2200 \| 0.4976 \| 0.7713 \| 0.7697 \| \| 0.4736 \| 11.59 \| 2400 \| 0.4820 \| 0.7721 \| 0.7707 \| \| 0.4753 \| 12.56 \| 2600 \| 0.4928 \| 0.7758 \| 0.7743 \| \| 0.4685 \| 13.53 \| 2800 \| 0.4896 \| 0.7722 \| 0.7707 \| \| 0.469 \| 14.49 \| 3000 \| 0.4958 \| 0.7746 \| 0.7731 \| \| 0.4594 \| 15.46 \| 3200 \| 0.4800 \| 0.7779 \| 0.7767 \| \| 0.4653 \| 16.43 \| 3400 \| 0.4969 \| 0.7736 \| 0.7722 \| \| 0.4602 \| 17.39 \| 3600 \| 0.4808 \| 0.7778 \| 0.7764 \| \| 0.4567 \| 18.36 \| 3800 \| 0.4809 \| 0.7765 \| 0.7761 \| \| 0.4558 \| 19.32 \| 4000 \| 0.4864 \| 0.7802 \| 0.7788 \| \| 0.4537 \| 20.29 \| 4200 \| 0.4880 \| 0.7760 \| 0.7746 \| \| 0.4516 \| 21.26 \| 4400 \| 0.4905 \| 0.7761 \| 0.7746 \| \| 0.4498 \| 22.22 \| 4600 \| 0.5092 \| 0.7702 \| 0.7688 \| \| 0.4484 \| 23.19 \| 4800 \| 0.4872 \| 0.7731 \| 0.7719 \| \| 0.4479 \| 24.15 \| 5000 \| 0.4912 \| 0.7679 \| 0.7664 \| \| 0.4463 \| 25.12 \| 5200 \| 0.5022 \| 0.7737 \| 0.7722 \| \| 0.4407 \| 26.09 \| 5400 \| 0.4960 \| 0.7710 \| 0.7694 \| \| 0.4414 \| 27.05 \| 5600 \| 0.5094 \| 0.7707 \| 0.7691 \| \| 0.4399 \| 28.02 \| 5800 \| 0.4877 \| 0.7719 \| 0.7707 \| \| 0.44 \| 28.99 \| 6000 \| 0.4894 \| 0.7752 \| 0.7737 \| \| 0.4353 \| 29.95 \| 6200 \| 0.4999 \| 0.7692 \| 0.7676 \| \| 0.4355 \| 30.92 \| 6400 \| 0.4850 \| 0.7729 \| 0.7725 \| \| 0.4349 \| 31.88 \| 6600 \| 0.4909 \| 0.7722 \| 0.7710 \| \| 0.432 \| 32.85 \| 6800 \| 0.5072 \| 0.7674 \| 0.7658 \| \| 0.4368 \| 33.82 \| 7000 \| 0.5021 \| 0.7707 \| 0.7691 \| \| 0.4289 \| 34.78 \| 7200 \| 0.5049 \| 0.7716 \| 0.7700 \| \| 0.4296 \| 35.75 \| 7400 \| 0.4976 \| 0.7747 \| 0.7734 \| \| 0.4261 \| 36.71 \| 7600 \| 0.5024 \| 0.7698 \| 0.7682 \| \| 0.425 \| 37.68 \| 7800 \| 0.5051 \| 0.7701 \| 0.7685 \| \| 0.4272 \| 38.65 \| 8000 \| 0.4953 \| 0.7735 \| 0.7722 \| \| 0.432 \| 39.61 \| 8200 \| 0.4941 \| 0.7711 \| 0.7697 \| \| 0.4189 \| 40.58 \| 8400 \| 0.5041 \| 0.7701 \| 0.7685 \| \| 0.421 \| 41.55 \| 8600 \| 0.5030 \| 0.7710 \| 0.7694 \| \| 0.4204 \| 42.51 \| 8800 \| 0.4993 \| 0.7706 \| 0.7691 \| \| 0.421 \| 43.48 \| 9000 \| 0.5108 \| 0.7710 \| 0.7694 \| \| 0.4199 \| 44.44 \| 9200 \| 0.5078 \| 0.7677 \| 0.7661 \| \| 0.4216 \| 45.41 \| 9400 \| 0.5051 \| 0.7692 \| 0.7676 \| \| 0.4155 \| 46.38 \| 9600 \| 0.5062 \| 0.7683 \| 0.7667 \| \| 0.4253 \| 47.34 \| 9800 \| 0.5025 \| 0.7701 \| 0.7685 \| \| 0.4169 \| 48.31 \| 10000 \| 0.5015 \| 0.7724 \| 0.7710 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_EMP_H3K14ac-seqsight_32768_512_43M-L8_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K14ac-seqsight_32768_512_43M-L8_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]	null	2024-04-30T06:04:50+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3K14ac-seqsight_32768_512_43M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_EMP_H3K14ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K14ac) dataset. It achieves the following results on the evaluation set: - Loss: 0.4924 - F1 Score: 0.7762 - Accuracy: 0.7752 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.5719 \| 0.97 \| 200 \| 0.5131 \| 0.7592 \| 0.7576 \| \| 0.516 \| 1.93 \| 400 \| 0.4993 \| 0.7691 \| 0.7676 \| \| 0.5012 \| 2.9 \| 600 \| 0.5039 \| 0.7604 \| 0.7589 \| \| 0.4962 \| 3.86 \| 800 \| 0.4826 \| 0.7744 \| 0.7734 \| \| 0.4878 \| 4.83 \| 1000 \| 0.5088 \| 0.7652 \| 0.7637 \| \| 0.4813 \| 5.8 \| 1200 \| 0.4903 \| 0.7764 \| 0.7749 \| \| 0.4734 \| 6.76 \| 1400 \| 0.4825 \| 0.7806 \| 0.7791 \| \| 0.4678 \| 7.73 \| 1600 \| 0.4871 \| 0.7731 \| 0.7716 \| \| 0.464 \| 8.7 \| 1800 \| 0.4969 \| 0.7730 \| 0.7716 \| \| 0.457 \| 9.66 \| 2000 \| 0.4931 \| 0.7761 \| 0.7746 \| \| 0.4555 \| 10.63 \| 2200 \| 0.5066 \| 0.7755 \| 0.7740 \| \| 0.4445 \| 11.59 \| 2400 \| 0.4927 \| 0.7700 \| 0.7688 \| \| 0.4455 \| 12.56 \| 2600 \| 0.5078 \| 0.7752 \| 0.7737 \| \| 0.4334 \| 13.53 \| 2800 \| 0.5079 \| 0.7677 \| 0.7661 \| \| 0.4316 \| 14.49 \| 3000 \| 0.4904 \| 0.7696 \| 0.7682 \| \| 0.4191 \| 15.46 \| 3200 \| 0.4980 \| 0.7759 \| 0.7749 \| \| 0.4206 \| 16.43 \| 3400 \| 0.4976 \| 0.7710 \| 0.7694 \| \| 0.4119 \| 17.39 \| 3600 \| 0.5108 \| 0.7670 \| 0.7655 \| \| 0.4073 \| 18.36 \| 3800 \| 0.5048 \| 0.7689 \| 0.7691 \| \| 0.3984 \| 19.32 \| 4000 \| 0.5055 \| 0.7800 \| 0.7788 \| \| 0.3956 \| 20.29 \| 4200 \| 0.5051 \| 0.7701 \| 0.7691 \| \| 0.3896 \| 21.26 \| 4400 \| 0.5276 \| 0.7695 \| 0.7679 \| \| 0.3835 \| 22.22 \| 4600 \| 0.5343 \| 0.7647 \| 0.7631 \| \| 0.3797 \| 23.19 \| 4800 \| 0.5330 \| 0.7693 \| 0.7679 \| \| 0.3742 \| 24.15 \| 5000 \| 0.5308 \| 0.7655 \| 0.7643 \| \| 0.3716 \| 25.12 \| 5200 \| 0.5492 \| 0.7650 \| 0.7634 \| \| 0.3631 \| 26.09 \| 5400 \| 0.5351 \| 0.7614 \| 0.7598 \| \| 0.3565 \| 27.05 \| 5600 \| 0.5650 \| 0.7677 \| 0.7661 \| \| 0.3511 \| 28.02 \| 5800 \| 0.5519 \| 0.7723 \| 0.7710 \| \| 0.3508 \| 28.99 \| 6000 \| 0.5461 \| 0.7672 \| 0.7658 \| \| 0.3449 \| 29.95 \| 6200 \| 0.5521 \| 0.7676 \| 0.7664 \| \| 0.3422 \| 30.92 \| 6400 \| 0.5529 \| 0.7701 \| 0.7703 \| \| 0.3384 \| 31.88 \| 6600 \| 0.5605 \| 0.7624 \| 0.7610 \| \| 0.3347 \| 32.85 \| 6800 \| 0.5864 \| 0.7611 \| 0.7595 \| \| 0.3308 \| 33.82 \| 7000 \| 0.5862 \| 0.7644 \| 0.7628 \| \| 0.3215 \| 34.78 \| 7200 \| 0.6019 \| 0.7590 \| 0.7573 \| \| 0.3212 \| 35.75 \| 7400 \| 0.5779 \| 0.7651 \| 0.7637 \| \| 0.3204 \| 36.71 \| 7600 \| 0.5864 \| 0.7660 \| 0.7646 \| \| 0.3105 \| 37.68 \| 7800 \| 0.6002 \| 0.7599 \| 0.7582 \| \| 0.3132 \| 38.65 \| 8000 \| 0.5929 \| 0.7654 \| 0.7640 \| \| 0.317 \| 39.61 \| 8200 \| 0.5880 \| 0.7680 \| 0.7670 \| \| 0.3075 \| 40.58 \| 8400 \| 0.6154 \| 0.7629 \| 0.7613 \| \| 0.3072 \| 41.55 \| 8600 \| 0.6056 \| 0.7673 \| 0.7658 \| \| 0.3029 \| 42.51 \| 8800 \| 0.6055 \| 0.7624 \| 0.7610 \| \| 0.3003 \| 43.48 \| 9000 \| 0.6175 \| 0.7647 \| 0.7631 \| \| 0.3014 \| 44.44 \| 9200 \| 0.6056 \| 0.7622 \| 0.7607 \| \| 0.299 \| 45.41 \| 9400 \| 0.6095 \| 0.7637 \| 0.7622 \| \| 0.2925 \| 46.38 \| 9600 \| 0.6190 \| 0.7637 \| 0.7622 \| \| 0.3016 \| 47.34 \| 9800 \| 0.6069 \| 0.7605 \| 0.7592 \| \| 0.297 \| 48.31 \| 10000 \| 0.6072 \| 0.7626 \| 0.7613 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_EMP_H3K14ac-seqsight_32768_512_43M-L32_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K14ac-seqsight_32768_512_43M-L32_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]	null	2024-04-30T06:04:53+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3K4me2-seqsight_32768_512_43M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me2) dataset. It achieves the following results on the evaluation set: - Loss: 0.5889 - F1 Score: 0.6823 - Accuracy: 0.6859 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.6634 \| 1.04 \| 200 \| 0.6368 \| 0.5949 \| 0.6370 \| \| 0.6269 \| 2.08 \| 400 \| 0.6301 \| 0.6479 \| 0.6478 \| \| 0.6197 \| 3.12 \| 600 \| 0.6218 \| 0.6430 \| 0.6637 \| \| 0.6175 \| 4.17 \| 800 \| 0.6171 \| 0.6532 \| 0.6634 \| \| 0.6135 \| 5.21 \| 1000 \| 0.6189 \| 0.6562 \| 0.6572 \| \| 0.6077 \| 6.25 \| 1200 \| 0.6137 \| 0.6643 \| 0.6699 \| \| 0.6004 \| 7.29 \| 1400 \| 0.6209 \| 0.6650 \| 0.6641 \| \| 0.6018 \| 8.33 \| 1600 \| 0.6177 \| 0.6605 \| 0.6618 \| \| 0.5998 \| 9.38 \| 1800 \| 0.6248 \| 0.6571 \| 0.6546 \| \| 0.5971 \| 10.42 \| 2000 \| 0.6112 \| 0.6675 \| 0.6689 \| \| 0.5978 \| 11.46 \| 2200 \| 0.6064 \| 0.6649 \| 0.6725 \| \| 0.5902 \| 12.5 \| 2400 \| 0.6080 \| 0.6656 \| 0.6709 \| \| 0.5888 \| 13.54 \| 2600 \| 0.6064 \| 0.6657 \| 0.6742 \| \| 0.591 \| 14.58 \| 2800 \| 0.6076 \| 0.6601 \| 0.6712 \| \| 0.5931 \| 15.62 \| 3000 \| 0.6061 \| 0.6685 \| 0.6748 \| \| 0.5876 \| 16.67 \| 3200 \| 0.6108 \| 0.6668 \| 0.6686 \| \| 0.5866 \| 17.71 \| 3400 \| 0.6083 \| 0.6722 \| 0.6764 \| \| 0.587 \| 18.75 \| 3600 \| 0.6062 \| 0.6657 \| 0.6722 \| \| 0.5859 \| 19.79 \| 3800 \| 0.6069 \| 0.6705 \| 0.6751 \| \| 0.5817 \| 20.83 \| 4000 \| 0.6080 \| 0.6707 \| 0.6729 \| \| 0.5844 \| 21.88 \| 4200 \| 0.6106 \| 0.6720 \| 0.6738 \| \| 0.5821 \| 22.92 \| 4400 \| 0.6090 \| 0.6717 \| 0.6748 \| \| 0.5835 \| 23.96 \| 4600 \| 0.6083 \| 0.6711 \| 0.6729 \| \| 0.5788 \| 25.0 \| 4800 \| 0.6077 \| 0.6734 \| 0.6777 \| \| 0.5792 \| 26.04 \| 5000 \| 0.6075 \| 0.6742 \| 0.6777 \| \| 0.5789 \| 27.08 \| 5200 \| 0.6058 \| 0.6730 \| 0.6771 \| \| 0.5787 \| 28.12 \| 5400 \| 0.6047 \| 0.6737 \| 0.6777 \| \| 0.577 \| 29.17 \| 5600 \| 0.6072 \| 0.6742 \| 0.6764 \| \| 0.5749 \| 30.21 \| 5800 \| 0.6089 \| 0.6764 \| 0.6797 \| \| 0.5777 \| 31.25 \| 6000 \| 0.6071 \| 0.6751 \| 0.6787 \| \| 0.5757 \| 32.29 \| 6200 \| 0.6042 \| 0.6748 \| 0.6810 \| \| 0.5751 \| 33.33 \| 6400 \| 0.6049 \| 0.6777 \| 0.6823 \| \| 0.5745 \| 34.38 \| 6600 \| 0.6049 \| 0.6736 \| 0.6804 \| \| 0.5729 \| 35.42 \| 6800 \| 0.6059 \| 0.6732 \| 0.6787 \| \| 0.5747 \| 36.46 \| 7000 \| 0.6046 \| 0.6749 \| 0.6804 \| \| 0.5719 \| 37.5 \| 7200 \| 0.6063 \| 0.6790 \| 0.6830 \| \| 0.5712 \| 38.54 \| 7400 \| 0.6065 \| 0.6757 \| 0.6817 \| \| 0.576 \| 39.58 \| 7600 \| 0.6048 \| 0.6730 \| 0.6790 \| \| 0.5734 \| 40.62 \| 7800 \| 0.6080 \| 0.6770 \| 0.6790 \| \| 0.572 \| 41.67 \| 8000 \| 0.6053 \| 0.6790 \| 0.6826 \| \| 0.5691 \| 42.71 \| 8200 \| 0.6060 \| 0.6743 \| 0.6830 \| \| 0.5714 \| 43.75 \| 8400 \| 0.6064 \| 0.6729 \| 0.6777 \| \| 0.5698 \| 44.79 \| 8600 \| 0.6076 \| 0.6774 \| 0.6807 \| \| 0.5691 \| 45.83 \| 8800 \| 0.6062 \| 0.6757 \| 0.6810 \| \| 0.5708 \| 46.88 \| 9000 \| 0.6077 \| 0.6771 \| 0.6800 \| \| 0.5687 \| 47.92 \| 9200 \| 0.6071 \| 0.6779 \| 0.6813 \| \| 0.57 \| 48.96 \| 9400 \| 0.6062 \| 0.6772 \| 0.6826 \| \| 0.5693 \| 50.0 \| 9600 \| 0.6070 \| 0.6768 \| 0.6810 \| \| 0.5705 \| 51.04 \| 9800 \| 0.6063 \| 0.6778 \| 0.6823 \| \| 0.5675 \| 52.08 \| 10000 \| 0.6066 \| 0.6770 \| 0.6813 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_EMP_H3K4me2-seqsight_32768_512_43M-L1_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K4me2-seqsight_32768_512_43M-L1_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]	null	2024-04-30T06:05:02+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3K4me2-seqsight_32768_512_43M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me2) dataset. It achieves the following results on the evaluation set: - Loss: 0.5958 - F1 Score: 0.6827 - Accuracy: 0.6859 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.6544 \| 1.04 \| 200 \| 0.6235 \| 0.6306 \| 0.6556 \| \| 0.6187 \| 2.08 \| 400 \| 0.6353 \| 0.6397 \| 0.6370 \| \| 0.6082 \| 3.12 \| 600 \| 0.6119 \| 0.6639 \| 0.6670 \| \| 0.6041 \| 4.17 \| 800 \| 0.6275 \| 0.6549 \| 0.6527 \| \| 0.5998 \| 5.21 \| 1000 \| 0.6067 \| 0.6745 \| 0.6807 \| \| 0.5941 \| 6.25 \| 1200 \| 0.6047 \| 0.6746 \| 0.6777 \| \| 0.5862 \| 7.29 \| 1400 \| 0.6132 \| 0.6688 \| 0.6676 \| \| 0.5851 \| 8.33 \| 1600 \| 0.6192 \| 0.6728 \| 0.6712 \| \| 0.583 \| 9.38 \| 1800 \| 0.6262 \| 0.6607 \| 0.6582 \| \| 0.5799 \| 10.42 \| 2000 \| 0.5997 \| 0.6783 \| 0.6843 \| \| 0.58 \| 11.46 \| 2200 \| 0.6031 \| 0.6759 \| 0.6774 \| \| 0.5704 \| 12.5 \| 2400 \| 0.6035 \| 0.6793 \| 0.6820 \| \| 0.569 \| 13.54 \| 2600 \| 0.6077 \| 0.6813 \| 0.6813 \| \| 0.5687 \| 14.58 \| 2800 \| 0.6074 \| 0.6732 \| 0.6777 \| \| 0.5694 \| 15.62 \| 3000 \| 0.6038 \| 0.6775 \| 0.6787 \| \| 0.5639 \| 16.67 \| 3200 \| 0.6062 \| 0.6764 \| 0.6761 \| \| 0.56 \| 17.71 \| 3400 \| 0.6144 \| 0.6696 \| 0.6686 \| \| 0.5615 \| 18.75 \| 3600 \| 0.6066 \| 0.6847 \| 0.6865 \| \| 0.5586 \| 19.79 \| 3800 \| 0.6191 \| 0.6777 \| 0.6764 \| \| 0.5537 \| 20.83 \| 4000 \| 0.6056 \| 0.6795 \| 0.6797 \| \| 0.5519 \| 21.88 \| 4200 \| 0.6202 \| 0.6727 \| 0.6709 \| \| 0.5497 \| 22.92 \| 4400 \| 0.6200 \| 0.6798 \| 0.6787 \| \| 0.5489 \| 23.96 \| 4600 \| 0.6198 \| 0.6710 \| 0.6693 \| \| 0.5436 \| 25.0 \| 4800 \| 0.6249 \| 0.6795 \| 0.6787 \| \| 0.5427 \| 26.04 \| 5000 \| 0.6220 \| 0.6797 \| 0.6790 \| \| 0.5429 \| 27.08 \| 5200 \| 0.6125 \| 0.6775 \| 0.6768 \| \| 0.5397 \| 28.12 \| 5400 \| 0.6088 \| 0.6769 \| 0.6774 \| \| 0.5375 \| 29.17 \| 5600 \| 0.6170 \| 0.6782 \| 0.6790 \| \| 0.5335 \| 30.21 \| 5800 \| 0.6257 \| 0.6752 \| 0.6748 \| \| 0.5343 \| 31.25 \| 6000 \| 0.6239 \| 0.6785 \| 0.6777 \| \| 0.5323 \| 32.29 \| 6200 \| 0.6155 \| 0.6747 \| 0.6755 \| \| 0.5325 \| 33.33 \| 6400 \| 0.6229 \| 0.6756 \| 0.6755 \| \| 0.5274 \| 34.38 \| 6600 \| 0.6185 \| 0.6718 \| 0.6745 \| \| 0.5289 \| 35.42 \| 6800 \| 0.6177 \| 0.6784 \| 0.6790 \| \| 0.5255 \| 36.46 \| 7000 \| 0.6233 \| 0.6782 \| 0.6781 \| \| 0.5242 \| 37.5 \| 7200 \| 0.6262 \| 0.6801 \| 0.6794 \| \| 0.5206 \| 38.54 \| 7400 \| 0.6232 \| 0.6783 \| 0.6790 \| \| 0.5248 \| 39.58 \| 7600 \| 0.6167 \| 0.6799 \| 0.6823 \| \| 0.5231 \| 40.62 \| 7800 \| 0.6301 \| 0.6737 \| 0.6725 \| \| 0.5205 \| 41.67 \| 8000 \| 0.6185 \| 0.6763 \| 0.6771 \| \| 0.515 \| 42.71 \| 8200 \| 0.6307 \| 0.6749 \| 0.6748 \| \| 0.5195 \| 43.75 \| 8400 \| 0.6224 \| 0.6778 \| 0.6777 \| \| 0.5169 \| 44.79 \| 8600 \| 0.6281 \| 0.6767 \| 0.6761 \| \| 0.5146 \| 45.83 \| 8800 \| 0.6279 \| 0.6794 \| 0.6804 \| \| 0.5139 \| 46.88 \| 9000 \| 0.6355 \| 0.6762 \| 0.6748 \| \| 0.5144 \| 47.92 \| 9200 \| 0.6329 \| 0.6781 \| 0.6774 \| \| 0.5148 \| 48.96 \| 9400 \| 0.6308 \| 0.6771 \| 0.6774 \| \| 0.5131 \| 50.0 \| 9600 \| 0.6336 \| 0.6774 \| 0.6768 \| \| 0.5143 \| 51.04 \| 9800 \| 0.6331 \| 0.6783 \| 0.6777 \| \| 0.5076 \| 52.08 \| 10000 \| 0.6350 \| 0.6765 \| 0.6758 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_EMP_H3K4me2-seqsight_32768_512_43M-L8_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K4me2-seqsight_32768_512_43M-L8_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]	null	2024-04-30T06:05:02+00:00
automatic-speech-recognition	transformers	<!-- 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. --> # w2v-bert-2.0-tamil-gpu-custom_preprocessed_v1 This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: inf - eval_wer: 0.4790 - eval_runtime: 231.2694 - eval_samples_per_second: 18.922 - eval_steps_per_second: 2.365 - epoch: 3.17 - step: 3900 ## 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: 4.83567e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - 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_steps: 500 - num_epochs: 10 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2	{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "facebook/w2v-bert-2.0", "model-index": [{"name": "w2v-bert-2.0-tamil-gpu-custom_preprocessed_v1", "results": []}]}	Sajjo/w2v-bert-2.0-tamil-gpu-custom_preprocessed_v1	null	[ "transformers", "tensorboard", "safetensors", "wav2vec2-bert", "automatic-speech-recognition", "generated_from_trainer", "base_model:facebook/w2v-bert-2.0", "license:mit", "endpoints_compatible", "region:us" ]	null	2024-04-30T06:05:24+00:00
token-classification	transformers	<!-- 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. --> # token_classifier This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2720 - Precision: 0.6096 - Recall: 0.3170 - F1: 0.4171 - Accuracy: 0.9426 ## 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 \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 213 \| 0.2820 \| 0.6278 \| 0.2641 \| 0.3718 \| 0.9398 \| \| No log \| 2.0 \| 426 \| 0.2720 \| 0.6096 \| 0.3170 \| 0.4171 \| 0.9426 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.17.1 - Tokenizers 0.15.2	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "distilbert/distilbert-base-uncased", "model-index": [{"name": "token_classifier", "results": []}]}	madanagrawal/token_classifier	null	[ "transformers", "tensorboard", "safetensors", "distilbert", "token-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-30T06:05:38+00:00
null	transformers	# 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]	{"library_name": "transformers", "tags": []}	ryanyeo/kirnect-2-koAlpaca-polyglot-5.8b-remote-5150step-8batch_5epoch	null	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-04-30T06:07:36+00:00
null	null		{}	Kishoar/t5-small-finetuned-xsum	null	[ "region:us" ]	null	2024-04-30T06:08:29+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3K4me2-seqsight_32768_512_43M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me2) dataset. It achieves the following results on the evaluation set: - Loss: 0.5885 - F1 Score: 0.6910 - Accuracy: 0.6960 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.6489 \| 1.04 \| 200 \| 0.6205 \| 0.6282 \| 0.6572 \| \| 0.6141 \| 2.08 \| 400 \| 0.6325 \| 0.6494 \| 0.6468 \| \| 0.6004 \| 3.12 \| 600 \| 0.6101 \| 0.6761 \| 0.6777 \| \| 0.5966 \| 4.17 \| 800 \| 0.6098 \| 0.6706 \| 0.6696 \| \| 0.5871 \| 5.21 \| 1000 \| 0.6038 \| 0.6727 \| 0.6787 \| \| 0.5799 \| 6.25 \| 1200 \| 0.6059 \| 0.6757 \| 0.6748 \| \| 0.5724 \| 7.29 \| 1400 \| 0.6034 \| 0.6771 \| 0.6764 \| \| 0.5654 \| 8.33 \| 1600 \| 0.6109 \| 0.6796 \| 0.6784 \| \| 0.5613 \| 9.38 \| 1800 \| 0.6213 \| 0.6759 \| 0.6735 \| \| 0.554 \| 10.42 \| 2000 \| 0.5952 \| 0.6836 \| 0.6885 \| \| 0.551 \| 11.46 \| 2200 \| 0.6100 \| 0.6832 \| 0.6852 \| \| 0.5368 \| 12.5 \| 2400 \| 0.6070 \| 0.6786 \| 0.6804 \| \| 0.532 \| 13.54 \| 2600 \| 0.6329 \| 0.6777 \| 0.6758 \| \| 0.5253 \| 14.58 \| 2800 \| 0.6159 \| 0.6759 \| 0.6804 \| \| 0.5216 \| 15.62 \| 3000 \| 0.6318 \| 0.6718 \| 0.6703 \| \| 0.5124 \| 16.67 \| 3200 \| 0.6345 \| 0.6771 \| 0.6768 \| \| 0.5005 \| 17.71 \| 3400 \| 0.6745 \| 0.6740 \| 0.6716 \| \| 0.4965 \| 18.75 \| 3600 \| 0.6430 \| 0.6810 \| 0.6804 \| \| 0.4911 \| 19.79 \| 3800 \| 0.6654 \| 0.6789 \| 0.6771 \| \| 0.4822 \| 20.83 \| 4000 \| 0.6607 \| 0.6792 \| 0.6771 \| \| 0.4738 \| 21.88 \| 4200 \| 0.6825 \| 0.6787 \| 0.6768 \| \| 0.466 \| 22.92 \| 4400 \| 0.6785 \| 0.6746 \| 0.6725 \| \| 0.4655 \| 23.96 \| 4600 \| 0.6764 \| 0.6757 \| 0.6745 \| \| 0.455 \| 25.0 \| 4800 \| 0.7236 \| 0.6651 \| 0.6628 \| \| 0.4458 \| 26.04 \| 5000 \| 0.7467 \| 0.6646 \| 0.6621 \| \| 0.4433 \| 27.08 \| 5200 \| 0.7294 \| 0.6622 \| 0.6598 \| \| 0.434 \| 28.12 \| 5400 \| 0.6890 \| 0.6697 \| 0.6693 \| \| 0.4279 \| 29.17 \| 5600 \| 0.7299 \| 0.6700 \| 0.6680 \| \| 0.4234 \| 30.21 \| 5800 \| 0.7531 \| 0.6694 \| 0.6673 \| \| 0.4146 \| 31.25 \| 6000 \| 0.7745 \| 0.6719 \| 0.6696 \| \| 0.4129 \| 32.29 \| 6200 \| 0.7660 \| 0.6646 \| 0.6621 \| \| 0.4072 \| 33.33 \| 6400 \| 0.7582 \| 0.6675 \| 0.6657 \| \| 0.3998 \| 34.38 \| 6600 \| 0.7820 \| 0.6706 \| 0.6693 \| \| 0.3952 \| 35.42 \| 6800 \| 0.8030 \| 0.6623 \| 0.6598 \| \| 0.39 \| 36.46 \| 7000 \| 0.7745 \| 0.6719 \| 0.6696 \| \| 0.387 \| 37.5 \| 7200 \| 0.7637 \| 0.6650 \| 0.6628 \| \| 0.3819 \| 38.54 \| 7400 \| 0.7709 \| 0.6764 \| 0.6764 \| \| 0.3772 \| 39.58 \| 7600 \| 0.7686 \| 0.6702 \| 0.6706 \| \| 0.3793 \| 40.62 \| 7800 \| 0.8079 \| 0.6683 \| 0.6660 \| \| 0.3733 \| 41.67 \| 8000 \| 0.8120 \| 0.6646 \| 0.6621 \| \| 0.3666 \| 42.71 \| 8200 \| 0.8165 \| 0.6693 \| 0.6670 \| \| 0.3671 \| 43.75 \| 8400 \| 0.8185 \| 0.6651 \| 0.6628 \| \| 0.3668 \| 44.79 \| 8600 \| 0.8077 \| 0.6697 \| 0.6676 \| \| 0.362 \| 45.83 \| 8800 \| 0.8043 \| 0.6658 \| 0.6641 \| \| 0.3612 \| 46.88 \| 9000 \| 0.8099 \| 0.6661 \| 0.6637 \| \| 0.3555 \| 47.92 \| 9200 \| 0.8180 \| 0.6710 \| 0.6689 \| \| 0.3501 \| 48.96 \| 9400 \| 0.8214 \| 0.6695 \| 0.6680 \| \| 0.3515 \| 50.0 \| 9600 \| 0.8309 \| 0.6679 \| 0.6657 \| \| 0.3512 \| 51.04 \| 9800 \| 0.8336 \| 0.6694 \| 0.6673 \| \| 0.3464 \| 52.08 \| 10000 \| 0.8380 \| 0.6692 \| 0.6670 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_EMP_H3K4me2-seqsight_32768_512_43M-L32_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K4me2-seqsight_32768_512_43M-L32_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]	null	2024-04-30T06:11:37+00:00
null	null		{"license": "openrail"}	rowlette/Arisu_BlueArchive	null	[ "license:openrail", "region:us" ]	null	2024-04-30T06:11:40+00:00
null	null		{}	LZDXN/bert-base-uncased_ai4privacy_en	null	[ "region:us" ]	null	2024-04-30T06:11:41+00:00
text-generation	transformers	# 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]	{"library_name": "transformers", "tags": []}	lunarsylph/mooncell_v36	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-30T06:12:15+00:00
null	peft	<!-- 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. --> # main This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4148 ## 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 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.0218 \| 0.9032 \| 7 \| 0.8713 \| \| 0.5518 \| 1.9355 \| 15 \| 0.5401 \| \| 0.3373 \| 2.9677 \| 23 \| 0.4473 \| \| 0.3523 \| 4.0 \| 31 \| 0.4159 \| \| 0.3219 \| 4.5161 \| 35 \| 0.4148 \| ### Framework versions - PEFT 0.10.1.dev0 - Transformers 4.41.0.dev0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "llama2", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "meta-llama/Llama-2-7b-chat-hf", "model-index": [{"name": "main", "results": []}]}	Huma97/llama2-EquityAdvisor	null	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-chat-hf", "license:llama2", "region:us" ]	null	2024-04-30T06:13:09+00:00
text-generation	transformers	# 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]	{"library_name": "transformers", "tags": []}	ryanyeo/kirnect-2-koAlpaca-polyglot-5.8B-remote	null	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-30T06:13:23+00:00
text-to-image	diffusers	# 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 🧨 diffusers 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]	{"library_name": "diffusers"}	Niggendar/mightMixes15Ponyxl_pxlBurst	null	[ "diffusers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	null	2024-04-30T06:13:52+00:00
null	null		{"license": "apache-2.0"}	smallscholar/Medical-Llama-3-8B-Chat	null	[ "license:apache-2.0", "region:us" ]	null	2024-04-30T06:14:01+00:00
null	null		{"license": "apache-2.0"}	smallscholar/Medical-Llama-3-8B-Lora	null	[ "license:apache-2.0", "region:us" ]	null	2024-04-30T06:14:30+00:00
null	null	# DerekWolfie/llama-3-8B-Instruct-function-calling-v0.2-Q5_K_M-GGUF This model was converted to GGUF format from [`mzbac/llama-3-8B-Instruct-function-calling-v0.2`](https://huggingface.co/mzbac/llama-3-8B-Instruct-function-calling-v0.2) 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/mzbac/llama-3-8B-Instruct-function-calling-v0.2) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo DerekWolfie/llama-3-8B-Instruct-function-calling-v0.2-Q5_K_M-GGUF --model llama-3-8b-instruct-function-calling-v0.2.Q5_K_M.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo DerekWolfie/llama-3-8B-Instruct-function-calling-v0.2-Q5_K_M-GGUF --model llama-3-8b-instruct-function-calling-v0.2.Q5_K_M.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m llama-3-8b-instruct-function-calling-v0.2.Q5_K_M.gguf -n 128 ```	{"language": ["en"], "license": "llama3", "tags": ["llama-cpp", "gguf-my-repo"], "datasets": ["mzbac/function-calling-llama-3-format-v1.1"]}	DerekWolfie/dereks-llama-3-8B-Instruct-function-calling	null	[ "gguf", "llama-cpp", "gguf-my-repo", "en", "dataset:mzbac/function-calling-llama-3-format-v1.1", "license:llama3", "region:us" ]	null	2024-04-30T06:14:39+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3K9ac-seqsight_32768_512_43M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_EMP_H3K9ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K9ac) dataset. It achieves the following results on the evaluation set: - Loss: 0.4802 - F1 Score: 0.7833 - Accuracy: 0.7827 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.6197 \| 1.15 \| 200 \| 0.5705 \| 0.7183 \| 0.7179 \| \| 0.5503 \| 2.3 \| 400 \| 0.5731 \| 0.7118 \| 0.7125 \| \| 0.5252 \| 3.45 \| 600 \| 0.5792 \| 0.7139 \| 0.7157 \| \| 0.5201 \| 4.6 \| 800 \| 0.5674 \| 0.7232 \| 0.7240 \| \| 0.5124 \| 5.75 \| 1000 \| 0.5417 \| 0.7324 \| 0.7319 \| \| 0.5082 \| 6.9 \| 1200 \| 0.5598 \| 0.7310 \| 0.7308 \| \| 0.5026 \| 8.05 \| 1400 \| 0.5465 \| 0.7388 \| 0.7384 \| \| 0.5014 \| 9.2 \| 1600 \| 0.5725 \| 0.7203 \| 0.7226 \| \| 0.4945 \| 10.34 \| 1800 \| 0.5384 \| 0.7429 \| 0.7424 \| \| 0.4922 \| 11.49 \| 2000 \| 0.5424 \| 0.7436 \| 0.7434 \| \| 0.4867 \| 12.64 \| 2200 \| 0.5651 \| 0.7278 \| 0.7294 \| \| 0.4894 \| 13.79 \| 2400 \| 0.5483 \| 0.7323 \| 0.7334 \| \| 0.4871 \| 14.94 \| 2600 \| 0.5391 \| 0.7400 \| 0.7402 \| \| 0.4809 \| 16.09 \| 2800 \| 0.5321 \| 0.7439 \| 0.7438 \| \| 0.4791 \| 17.24 \| 3000 \| 0.5445 \| 0.7382 \| 0.7384 \| \| 0.4785 \| 18.39 \| 3200 \| 0.5470 \| 0.7407 \| 0.7416 \| \| 0.4804 \| 19.54 \| 3400 \| 0.5253 \| 0.7463 \| 0.7463 \| \| 0.4729 \| 20.69 \| 3600 \| 0.5203 \| 0.7514 \| 0.7510 \| \| 0.4743 \| 21.84 \| 3800 \| 0.5228 \| 0.7468 \| 0.7470 \| \| 0.4701 \| 22.99 \| 4000 \| 0.5275 \| 0.7437 \| 0.7442 \| \| 0.4734 \| 24.14 \| 4200 \| 0.5078 \| 0.7547 \| 0.7542 \| \| 0.4626 \| 25.29 \| 4400 \| 0.5260 \| 0.7533 \| 0.7531 \| \| 0.4698 \| 26.44 \| 4600 \| 0.5283 \| 0.7494 \| 0.7496 \| \| 0.4677 \| 27.59 \| 4800 \| 0.5292 \| 0.7437 \| 0.7445 \| \| 0.4641 \| 28.74 \| 5000 \| 0.5166 \| 0.7538 \| 0.7539 \| \| 0.47 \| 29.89 \| 5200 \| 0.5211 \| 0.7492 \| 0.7492 \| \| 0.4622 \| 31.03 \| 5400 \| 0.5256 \| 0.7467 \| 0.7474 \| \| 0.4644 \| 32.18 \| 5600 \| 0.5069 \| 0.7594 \| 0.7589 \| \| 0.4554 \| 33.33 \| 5800 \| 0.5209 \| 0.7527 \| 0.7528 \| \| 0.4678 \| 34.48 \| 6000 \| 0.5253 \| 0.7440 \| 0.7449 \| \| 0.4559 \| 35.63 \| 6200 \| 0.5153 \| 0.7511 \| 0.7510 \| \| 0.4638 \| 36.78 \| 6400 \| 0.5167 \| 0.7497 \| 0.7499 \| \| 0.4579 \| 37.93 \| 6600 \| 0.5228 \| 0.7478 \| 0.7481 \| \| 0.4589 \| 39.08 \| 6800 \| 0.5101 \| 0.7548 \| 0.7546 \| \| 0.4589 \| 40.23 \| 7000 \| 0.5161 \| 0.7516 \| 0.7517 \| \| 0.4573 \| 41.38 \| 7200 \| 0.5168 \| 0.7512 \| 0.7513 \| \| 0.457 \| 42.53 \| 7400 \| 0.5161 \| 0.7534 \| 0.7535 \| \| 0.4565 \| 43.68 \| 7600 \| 0.5145 \| 0.7564 \| 0.7564 \| \| 0.4535 \| 44.83 \| 7800 \| 0.5226 \| 0.7500 \| 0.7506 \| \| 0.4568 \| 45.98 \| 8000 \| 0.5133 \| 0.7541 \| 0.7542 \| \| 0.4581 \| 47.13 \| 8200 \| 0.5187 \| 0.7503 \| 0.7506 \| \| 0.4531 \| 48.28 \| 8400 \| 0.5167 \| 0.7520 \| 0.7521 \| \| 0.4507 \| 49.43 \| 8600 \| 0.5164 \| 0.7519 \| 0.7521 \| \| 0.4548 \| 50.57 \| 8800 \| 0.5161 \| 0.7528 \| 0.7528 \| \| 0.4545 \| 51.72 \| 9000 \| 0.5210 \| 0.7469 \| 0.7474 \| \| 0.4486 \| 52.87 \| 9200 \| 0.5196 \| 0.7488 \| 0.7492 \| \| 0.4547 \| 54.02 \| 9400 \| 0.5173 \| 0.7503 \| 0.7506 \| \| 0.4513 \| 55.17 \| 9600 \| 0.5190 \| 0.7485 \| 0.7488 \| \| 0.4511 \| 56.32 \| 9800 \| 0.5142 \| 0.7527 \| 0.7528 \| \| 0.4546 \| 57.47 \| 10000 \| 0.5164 \| 0.7504 \| 0.7506 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_EMP_H3K9ac-seqsight_32768_512_43M-L1_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K9ac-seqsight_32768_512_43M-L1_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]	null	2024-04-30T06:14:57+00:00
image-classification	transformers	# Model Trained Using AutoTrain - Problem type: Image Classification ## Validation Metrics loss: 0.3038078248500824 f1_macro: 0.7294036951655769 f1_micro: 0.899283031751451 f1_weighted: 0.8963777407391669 precision_macro: 0.8462013295295603 precision_micro: 0.899283031751451 precision_weighted: 0.9070935900298 recall_macro: 0.6921156764861889 recall_micro: 0.899283031751451 recall_weighted: 0.899283031751451 accuracy: 0.899283031751451	{"tags": ["autotrain", "image-classification"], "datasets": ["autotrain-swin-tiny-patch4-window7-224/autotrain-data"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_title": "Teapot"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg", "example_title": "Palace"}]}	Kushagra07/autotrain-swin-tiny-patch4-window7-224	null	[ "transformers", "tensorboard", "safetensors", "swin", "image-classification", "autotrain", "dataset:autotrain-swin-tiny-patch4-window7-224/autotrain-data", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-30T06:15:18+00:00
text-generation	transformers	# 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]	{"library_name": "transformers", "tags": []}	cilantro9246/ofeq1al	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-30T06:15:36+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3K9ac-seqsight_32768_512_43M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_EMP_H3K9ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K9ac) dataset. It achieves the following results on the evaluation set: - Loss: 0.4635 - F1 Score: 0.7915 - Accuracy: 0.7909 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.5867 \| 1.15 \| 200 \| 0.5738 \| 0.7174 \| 0.7172 \| \| 0.5175 \| 2.3 \| 400 \| 0.5902 \| 0.6854 \| 0.6909 \| \| 0.4952 \| 3.45 \| 600 \| 0.5512 \| 0.7323 \| 0.7330 \| \| 0.4899 \| 4.6 \| 800 \| 0.5397 \| 0.7364 \| 0.7370 \| \| 0.4814 \| 5.75 \| 1000 \| 0.5230 \| 0.7506 \| 0.7503 \| \| 0.4769 \| 6.9 \| 1200 \| 0.5291 \| 0.7465 \| 0.7463 \| \| 0.4718 \| 8.05 \| 1400 \| 0.5302 \| 0.7483 \| 0.7481 \| \| 0.4688 \| 9.2 \| 1600 \| 0.5332 \| 0.7482 \| 0.7488 \| \| 0.4642 \| 10.34 \| 1800 \| 0.5266 \| 0.7500 \| 0.7496 \| \| 0.4591 \| 11.49 \| 2000 \| 0.5179 \| 0.7547 \| 0.7542 \| \| 0.4529 \| 12.64 \| 2200 \| 0.5190 \| 0.7553 \| 0.7549 \| \| 0.4541 \| 13.79 \| 2400 \| 0.5267 \| 0.7575 \| 0.7575 \| \| 0.4482 \| 14.94 \| 2600 \| 0.5170 \| 0.7601 \| 0.7596 \| \| 0.4441 \| 16.09 \| 2800 \| 0.5429 \| 0.7522 \| 0.7531 \| \| 0.441 \| 17.24 \| 3000 \| 0.5347 \| 0.7582 \| 0.7578 \| \| 0.4424 \| 18.39 \| 3200 \| 0.5122 \| 0.7648 \| 0.7643 \| \| 0.4418 \| 19.54 \| 3400 \| 0.5085 \| 0.7645 \| 0.7643 \| \| 0.4304 \| 20.69 \| 3600 \| 0.4982 \| 0.7665 \| 0.7661 \| \| 0.4322 \| 21.84 \| 3800 \| 0.5246 \| 0.7578 \| 0.7582 \| \| 0.4253 \| 22.99 \| 4000 \| 0.5274 \| 0.7545 \| 0.7549 \| \| 0.4304 \| 24.14 \| 4200 \| 0.4977 \| 0.7694 \| 0.7690 \| \| 0.4166 \| 25.29 \| 4400 \| 0.5094 \| 0.7738 \| 0.7733 \| \| 0.4239 \| 26.44 \| 4600 \| 0.5087 \| 0.7705 \| 0.7701 \| \| 0.4218 \| 27.59 \| 4800 \| 0.5072 \| 0.7675 \| 0.7672 \| \| 0.4143 \| 28.74 \| 5000 \| 0.5074 \| 0.7714 \| 0.7711 \| \| 0.4182 \| 29.89 \| 5200 \| 0.5124 \| 0.7705 \| 0.7701 \| \| 0.4117 \| 31.03 \| 5400 \| 0.5165 \| 0.7694 \| 0.7693 \| \| 0.4108 \| 32.18 \| 5600 \| 0.5017 \| 0.7777 \| 0.7773 \| \| 0.4025 \| 33.33 \| 5800 \| 0.5173 \| 0.7698 \| 0.7693 \| \| 0.4101 \| 34.48 \| 6000 \| 0.5022 \| 0.7781 \| 0.7776 \| \| 0.4003 \| 35.63 \| 6200 \| 0.5014 \| 0.7777 \| 0.7773 \| \| 0.4053 \| 36.78 \| 6400 \| 0.5066 \| 0.7756 \| 0.7751 \| \| 0.4024 \| 37.93 \| 6600 \| 0.5323 \| 0.7710 \| 0.7708 \| \| 0.398 \| 39.08 \| 6800 \| 0.5153 \| 0.7737 \| 0.7733 \| \| 0.3991 \| 40.23 \| 7000 \| 0.5225 \| 0.7634 \| 0.7632 \| \| 0.3957 \| 41.38 \| 7200 \| 0.5148 \| 0.7716 \| 0.7711 \| \| 0.3949 \| 42.53 \| 7400 \| 0.5232 \| 0.7682 \| 0.7679 \| \| 0.3934 \| 43.68 \| 7600 \| 0.5160 \| 0.7698 \| 0.7693 \| \| 0.3899 \| 44.83 \| 7800 \| 0.5210 \| 0.7700 \| 0.7697 \| \| 0.3933 \| 45.98 \| 8000 \| 0.5074 \| 0.7737 \| 0.7733 \| \| 0.3914 \| 47.13 \| 8200 \| 0.5191 \| 0.7682 \| 0.7679 \| \| 0.3847 \| 48.28 \| 8400 \| 0.5182 \| 0.7727 \| 0.7722 \| \| 0.3832 \| 49.43 \| 8600 \| 0.5328 \| 0.7643 \| 0.7639 \| \| 0.3883 \| 50.57 \| 8800 \| 0.5249 \| 0.7679 \| 0.7675 \| \| 0.384 \| 51.72 \| 9000 \| 0.5237 \| 0.7712 \| 0.7708 \| \| 0.3826 \| 52.87 \| 9200 \| 0.5268 \| 0.7668 \| 0.7665 \| \| 0.3849 \| 54.02 \| 9400 \| 0.5224 \| 0.7730 \| 0.7726 \| \| 0.3828 \| 55.17 \| 9600 \| 0.5249 \| 0.7694 \| 0.7690 \| \| 0.3827 \| 56.32 \| 9800 \| 0.5188 \| 0.7730 \| 0.7726 \| \| 0.3813 \| 57.47 \| 10000 \| 0.5204 \| 0.7705 \| 0.7701 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_EMP_H3K9ac-seqsight_32768_512_43M-L8_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K9ac-seqsight_32768_512_43M-L8_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]	null	2024-04-30T06:15:54+00:00
null	null		{}	zzunyang/zzu_1_law	null	[ "region:us" ]	null	2024-04-30T06:16:40+00:00
null	null		{"license": "apache-2.0"}	UnicomLLM/Unichat-llama3-Chinese-8B-gguf	null	[ "license:apache-2.0", "region:us" ]	null	2024-04-30T06:16:49+00:00
null	transformers		{}	ravindrakinagi/gen_ai_tool	null	[ "transformers", "gguf", "llama", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-30T06:16:53+00:00
text2text-generation	transformers	# 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]	{"library_name": "transformers", "tags": []}	JD97/bart-typo	null	[ "transformers", "safetensors", "bart", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-30T06:17:21+00:00
text-generation	transformers	<!-- 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. --> # 0.0001_withdpo_4iters_bs256_531lr_iter_2 This model is a fine-tuned version of [ZhangShenao/0.0_ablation_sample1_4iters_bs256_iter_1](https://huggingface.co/ZhangShenao/0.0_ablation_sample1_4iters_bs256_iter_1) 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: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2	{"license": "mit", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["updated", "original"], "base_model": "ZhangShenao/0.0_ablation_sample1_4iters_bs256_iter_1", "model-index": [{"name": "0.0001_withdpo_4iters_bs256_531lr_iter_2", "results": []}]}	ShenaoZ/0.0001_withdpo_4iters_bs256_531lr_iter_2	null	[ "transformers", "safetensors", "mistral", "text-generation", "alignment-handbook", "generated_from_trainer", "trl", "dpo", "conversational", "dataset:updated", "dataset:original", "base_model:ZhangShenao/0.0_ablation_sample1_4iters_bs256_iter_1", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-30T06:18:54+00:00
text-classification	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # robust_llm_pythia-31m_mz-133_WordLength_n-its-10-seed-4 This model is a fine-tuned version of [EleutherAI/pythia-31m](https://huggingface.co/EleutherAI/pythia-31m) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.1 - Datasets 2.18.0 - Tokenizers 0.15.2	{"tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-31m", "model-index": [{"name": "robust_llm_pythia-31m_mz-133_WordLength_n-its-10-seed-4", "results": []}]}	AlignmentResearch/robust_llm_pythia-31m_mz-133_WordLength_n-its-10-seed-4	null	[ "transformers", "tensorboard", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-31m", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-30T06:18:56+00:00
image-classification	transformers		{}	walterg777/oxford-pets-vit-from-scratch	null	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-30T06:19:18+00:00
null	null	# duix.ai ## highlights - 2d数字人推理引擎，可在android/ios等边缘侧设备上一键部署，并且内置了两个形象，可直接使用看到效果。 - 支持二次开发，丰富的sdk接口，用户可根据sdk文档开发自己专属需求。 - 完全开源，底层推理及上层商业化应用逻辑整套流程源码开放。 ### 目录结构 ``` duix-android: android demo GJLocalDigitalDemo: ios demo ``` <p align="center"> <img src="res/女.png" width=200/> <img src="res/男.png" width=200/> </p> 内置的2个模特，模板和AI模型包可以通过公网地址下载。 [女链接地址](https://cdn.guiji.ai/duix/digital/model/1712034391673/bendi1_0329.zip) [男链接地址](https://digital-public.obs.cn-east-3.myhuaweicloud.com/duix/digital/model/1706009711636/liangwei_540s.zip) ### 使用说明 android参考 [README.md](./duix-android/dh_aigc_android/README.md) ios参考 [GJLocalDigitalSDK.md](./GJLocalDigitalDemo/GJLocalDigitalDemo/GJLocalDigitalSDK.md) ### Acknowledgements -音频特征我们借鉴了 [wenet](https://github.com/wenet-e2e/wenet) ### 如果有定制需求或技术支持，请在讨论区留言，更多详细信息请访问 [硅基智能]官网(https://www.guiji.ai)	{}	GuijiAI/duix.ai	null	[ "region:us" ]	null	2024-04-30T06:19:27+00:00
unconditional-image-generation	diffusers	# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('fath2024/sd-class-butterflies-64') image = pipeline().images[0] image ```	{"license": "mit", "tags": ["pytorch", "diffusers", "unconditional-image-generation", "diffusion-models-class"]}	fath2024/sd-class-butterflies-64	null	[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	null	2024-04-30T06:20:04+00:00
text-generation	transformers		{}	Moon-Ahn/kllama_finetune_hyunwook2	null	[ "transformers", "pytorch", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-30T06:20:09+00:00
text-generation	transformers	# Alsebay/Lorge-2x7B AWQ - Model creator: [Alsebay](https://huggingface.co/Alsebay) - Original model: [Lorge-2x7B](https://huggingface.co/Alsebay/Lorge-2x7B) ## How to use ### Install the necessary packages ```bash pip install --upgrade autoawq autoawq-kernels ``` ### Example Python code ```python from awq import AutoAWQForCausalLM from transformers import AutoTokenizer, TextStreamer model_path = "solidrust/Lorge-2x7B-AWQ" system_message = "You are Lorge-2x7B, incarnated as a powerful AI. You were created by Alsebay." # Load model model = AutoAWQForCausalLM.from_quantized(model_path, fuse_layers=True) tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) # Convert prompt to tokens prompt_template = """\ <\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant""" prompt = "You're standing on the surface of the Earth. "\ "You walk one mile south, one mile west and one mile north. "\ "You end up exactly where you started. Where are you?" tokens = tokenizer(prompt_template.format(system_message=system_message,prompt=prompt), return_tensors='pt').input_ids.cuda() # Generate output generation_output = model.generate(tokens, streamer=streamer, max_new_tokens=512) ``` ### 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	{"license": "cc-by-nc-4.0", "library_name": "transformers", "tags": ["4-bit", "AWQ", "text-generation", "autotrain_compatible", "endpoints_compatible"], "pipeline_tag": "text-generation", "inference": false, "quantized_by": "Suparious"}	solidrust/Lorge-2x7B-AWQ	null	[ "transformers", "safetensors", "mixtral", "text-generation", "4-bit", "AWQ", "autotrain_compatible", "endpoints_compatible", "license:cc-by-nc-4.0", "text-generation-inference", "region:us" ]	null	2024-04-30T06:20:11+00:00
null	transformers	# 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]	{"library_name": "transformers", "tags": []}	abhayesian/lat-poisoned-1-hh	null	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-04-30T06:20:52+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3K9ac-seqsight_32768_512_43M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_EMP_H3K9ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K9ac) dataset. It achieves the following results on the evaluation set: - Loss: 0.5028 - F1 Score: 0.7856 - Accuracy: 0.7852 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.5688 \| 1.15 \| 200 \| 0.5832 \| 0.7122 \| 0.7136 \| \| 0.5074 \| 2.3 \| 400 \| 0.5796 \| 0.6942 \| 0.7013 \| \| 0.4833 \| 3.45 \| 600 \| 0.5617 \| 0.7198 \| 0.7233 \| \| 0.4773 \| 4.6 \| 800 \| 0.5231 \| 0.7477 \| 0.7481 \| \| 0.469 \| 5.75 \| 1000 \| 0.5155 \| 0.7546 \| 0.7546 \| \| 0.4592 \| 6.9 \| 1200 \| 0.5154 \| 0.7598 \| 0.7596 \| \| 0.4521 \| 8.05 \| 1400 \| 0.5069 \| 0.7654 \| 0.7650 \| \| 0.4441 \| 9.2 \| 1600 \| 0.5155 \| 0.7576 \| 0.7578 \| \| 0.4386 \| 10.34 \| 1800 \| 0.5178 \| 0.7621 \| 0.7618 \| \| 0.428 \| 11.49 \| 2000 \| 0.5130 \| 0.7610 \| 0.7607 \| \| 0.4204 \| 12.64 \| 2200 \| 0.5044 \| 0.7660 \| 0.7657 \| \| 0.4148 \| 13.79 \| 2400 \| 0.5397 \| 0.7519 \| 0.7528 \| \| 0.4049 \| 14.94 \| 2600 \| 0.5043 \| 0.7687 \| 0.7683 \| \| 0.3952 \| 16.09 \| 2800 \| 0.5817 \| 0.7328 \| 0.7362 \| \| 0.3927 \| 17.24 \| 3000 \| 0.5320 \| 0.7614 \| 0.7614 \| \| 0.3848 \| 18.39 \| 3200 \| 0.5286 \| 0.7667 \| 0.7665 \| \| 0.3843 \| 19.54 \| 3400 \| 0.5311 \| 0.7590 \| 0.7593 \| \| 0.367 \| 20.69 \| 3600 \| 0.5218 \| 0.7695 \| 0.7690 \| \| 0.3629 \| 21.84 \| 3800 \| 0.5338 \| 0.7668 \| 0.7668 \| \| 0.3551 \| 22.99 \| 4000 \| 0.5325 \| 0.7622 \| 0.7621 \| \| 0.3517 \| 24.14 \| 4200 \| 0.5315 \| 0.7705 \| 0.7701 \| \| 0.3384 \| 25.29 \| 4400 \| 0.5510 \| 0.7715 \| 0.7711 \| \| 0.3399 \| 26.44 \| 4600 \| 0.5772 \| 0.7650 \| 0.7650 \| \| 0.3366 \| 27.59 \| 4800 \| 0.5344 \| 0.7680 \| 0.7675 \| \| 0.3234 \| 28.74 \| 5000 \| 0.5506 \| 0.7634 \| 0.7632 \| \| 0.3235 \| 29.89 \| 5200 \| 0.5652 \| 0.7656 \| 0.7654 \| \| 0.3118 \| 31.03 \| 5400 \| 0.5719 \| 0.7569 \| 0.7571 \| \| 0.3092 \| 32.18 \| 5600 \| 0.6078 \| 0.7489 \| 0.7496 \| \| 0.2984 \| 33.33 \| 5800 \| 0.5917 \| 0.7670 \| 0.7668 \| \| 0.3022 \| 34.48 \| 6000 \| 0.5851 \| 0.7687 \| 0.7683 \| \| 0.2887 \| 35.63 \| 6200 \| 0.5829 \| 0.7665 \| 0.7661 \| \| 0.2902 \| 36.78 \| 6400 \| 0.5999 \| 0.7614 \| 0.7611 \| \| 0.2886 \| 37.93 \| 6600 \| 0.5893 \| 0.7662 \| 0.7657 \| \| 0.2761 \| 39.08 \| 6800 \| 0.6140 \| 0.7574 \| 0.7571 \| \| 0.277 \| 40.23 \| 7000 \| 0.6130 \| 0.7615 \| 0.7611 \| \| 0.2745 \| 41.38 \| 7200 \| 0.6231 \| 0.7608 \| 0.7603 \| \| 0.2674 \| 42.53 \| 7400 \| 0.6411 \| 0.7654 \| 0.7650 \| \| 0.2676 \| 43.68 \| 7600 \| 0.6335 \| 0.7640 \| 0.7636 \| \| 0.2632 \| 44.83 \| 7800 \| 0.6251 \| 0.7607 \| 0.7603 \| \| 0.2609 \| 45.98 \| 8000 \| 0.6266 \| 0.7612 \| 0.7607 \| \| 0.2556 \| 47.13 \| 8200 \| 0.6518 \| 0.7614 \| 0.7611 \| \| 0.254 \| 48.28 \| 8400 \| 0.6446 \| 0.7569 \| 0.7564 \| \| 0.2505 \| 49.43 \| 8600 \| 0.6670 \| 0.7522 \| 0.7521 \| \| 0.2483 \| 50.57 \| 8800 \| 0.6745 \| 0.7566 \| 0.7564 \| \| 0.2491 \| 51.72 \| 9000 \| 0.6521 \| 0.7583 \| 0.7578 \| \| 0.2457 \| 52.87 \| 9200 \| 0.6560 \| 0.7608 \| 0.7603 \| \| 0.2446 \| 54.02 \| 9400 \| 0.6666 \| 0.7593 \| 0.7589 \| \| 0.2383 \| 55.17 \| 9600 \| 0.6727 \| 0.7568 \| 0.7564 \| \| 0.2385 \| 56.32 \| 9800 \| 0.6683 \| 0.7601 \| 0.7596 \| \| 0.2362 \| 57.47 \| 10000 \| 0.6676 \| 0.7590 \| 0.7585 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_EMP_H3K9ac-seqsight_32768_512_43M-L32_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K9ac-seqsight_32768_512_43M-L32_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]	null	2024-04-30T06:20:57+00:00
null	null		{}	Daisyyy05/bert-finetuned-ner	null	[ "region:us" ]	null	2024-04-30T06:21:03+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3K4me3-seqsight_32768_512_43M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me3) dataset. It achieves the following results on the evaluation set: - Loss: 0.5776 - F1 Score: 0.6939 - Accuracy: 0.6937 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.6721 \| 0.87 \| 200 \| 0.6563 \| 0.6259 \| 0.6255 \| \| 0.6439 \| 1.74 \| 400 \| 0.6358 \| 0.6492 \| 0.6503 \| \| 0.631 \| 2.61 \| 600 \| 0.6242 \| 0.6694 \| 0.6696 \| \| 0.6158 \| 3.48 \| 800 \| 0.6154 \| 0.6705 \| 0.6704 \| \| 0.6118 \| 4.35 \| 1000 \| 0.6142 \| 0.6628 \| 0.6639 \| \| 0.606 \| 5.22 \| 1200 \| 0.6213 \| 0.6508 \| 0.6554 \| \| 0.5999 \| 6.09 \| 1400 \| 0.6256 \| 0.6514 \| 0.6571 \| \| 0.5947 \| 6.96 \| 1600 \| 0.6122 \| 0.6648 \| 0.6666 \| \| 0.5942 \| 7.83 \| 1800 \| 0.6078 \| 0.6696 \| 0.6698 \| \| 0.5933 \| 8.7 \| 2000 \| 0.6061 \| 0.6707 \| 0.6709 \| \| 0.5886 \| 9.57 \| 2200 \| 0.5988 \| 0.6767 \| 0.6764 \| \| 0.5904 \| 10.43 \| 2400 \| 0.6028 \| 0.6774 \| 0.6774 \| \| 0.5881 \| 11.3 \| 2600 \| 0.6004 \| 0.6756 \| 0.6772 \| \| 0.5874 \| 12.17 \| 2800 \| 0.6003 \| 0.6751 \| 0.675 \| \| 0.5833 \| 13.04 \| 3000 \| 0.5987 \| 0.6797 \| 0.6796 \| \| 0.5807 \| 13.91 \| 3200 \| 0.5954 \| 0.6712 \| 0.6715 \| \| 0.5815 \| 14.78 \| 3400 \| 0.5964 \| 0.6751 \| 0.6761 \| \| 0.5822 \| 15.65 \| 3600 \| 0.5981 \| 0.6794 \| 0.6799 \| \| 0.5788 \| 16.52 \| 3800 \| 0.6010 \| 0.6783 \| 0.6788 \| \| 0.5796 \| 17.39 \| 4000 \| 0.5961 \| 0.6793 \| 0.6802 \| \| 0.5812 \| 18.26 \| 4200 \| 0.5980 \| 0.6804 \| 0.6810 \| \| 0.5738 \| 19.13 \| 4400 \| 0.5980 \| 0.6766 \| 0.6764 \| \| 0.5764 \| 20.0 \| 4600 \| 0.5939 \| 0.6787 \| 0.6793 \| \| 0.5757 \| 20.87 \| 4800 \| 0.5972 \| 0.6838 \| 0.6845 \| \| 0.5747 \| 21.74 \| 5000 \| 0.5963 \| 0.6819 \| 0.6823 \| \| 0.5738 \| 22.61 \| 5200 \| 0.5936 \| 0.6837 \| 0.6840 \| \| 0.5719 \| 23.48 \| 5400 \| 0.5999 \| 0.6754 \| 0.6777 \| \| 0.573 \| 24.35 \| 5600 \| 0.5945 \| 0.6834 \| 0.6834 \| \| 0.5742 \| 25.22 \| 5800 \| 0.5988 \| 0.6792 \| 0.6818 \| \| 0.5692 \| 26.09 \| 6000 \| 0.5962 \| 0.6837 \| 0.6848 \| \| 0.5707 \| 26.96 \| 6200 \| 0.5997 \| 0.6764 \| 0.6785 \| \| 0.5691 \| 27.83 \| 6400 \| 0.6039 \| 0.6752 \| 0.6788 \| \| 0.5693 \| 28.7 \| 6600 \| 0.5951 \| 0.6860 \| 0.6864 \| \| 0.5686 \| 29.57 \| 6800 \| 0.5904 \| 0.6875 \| 0.6875 \| \| 0.5672 \| 30.43 \| 7000 \| 0.5924 \| 0.6859 \| 0.6870 \| \| 0.5719 \| 31.3 \| 7200 \| 0.5921 \| 0.6856 \| 0.6867 \| \| 0.5688 \| 32.17 \| 7400 \| 0.5934 \| 0.6854 \| 0.6867 \| \| 0.5637 \| 33.04 \| 7600 \| 0.5905 \| 0.6888 \| 0.6891 \| \| 0.568 \| 33.91 \| 7800 \| 0.5917 \| 0.6853 \| 0.6859 \| \| 0.5662 \| 34.78 \| 8000 \| 0.5921 \| 0.6863 \| 0.6864 \| \| 0.5671 \| 35.65 \| 8200 \| 0.5908 \| 0.6875 \| 0.6878 \| \| 0.5661 \| 36.52 \| 8400 \| 0.5927 \| 0.6858 \| 0.6864 \| \| 0.5661 \| 37.39 \| 8600 \| 0.5911 \| 0.6874 \| 0.6872 \| \| 0.5632 \| 38.26 \| 8800 \| 0.5947 \| 0.6850 \| 0.6864 \| \| 0.5684 \| 39.13 \| 9000 \| 0.5926 \| 0.6848 \| 0.6861 \| \| 0.5665 \| 40.0 \| 9200 \| 0.5906 \| 0.6879 \| 0.6883 \| \| 0.5647 \| 40.87 \| 9400 \| 0.5906 \| 0.6892 \| 0.6891 \| \| 0.5644 \| 41.74 \| 9600 \| 0.5908 \| 0.6875 \| 0.6878 \| \| 0.5688 \| 42.61 \| 9800 \| 0.5900 \| 0.6872 \| 0.6875 \| \| 0.5613 \| 43.48 \| 10000 \| 0.5903 \| 0.6883 \| 0.6886 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_EMP_H3K4me3-seqsight_32768_512_43M-L1_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K4me3-seqsight_32768_512_43M-L1_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]	null	2024-04-30T06:21:14+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3K4me3-seqsight_32768_512_43M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me3) dataset. It achieves the following results on the evaluation set: - Loss: 0.5802 - F1 Score: 0.7073 - Accuracy: 0.7071 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.6635 \| 0.87 \| 200 \| 0.6406 \| 0.6449 \| 0.6451 \| \| 0.6196 \| 1.74 \| 400 \| 0.6211 \| 0.6601 \| 0.6617 \| \| 0.6025 \| 2.61 \| 600 \| 0.6110 \| 0.6718 \| 0.6715 \| \| 0.5938 \| 3.48 \| 800 \| 0.6061 \| 0.6746 \| 0.6745 \| \| 0.5903 \| 4.35 \| 1000 \| 0.6056 \| 0.6760 \| 0.6758 \| \| 0.587 \| 5.22 \| 1200 \| 0.6109 \| 0.6554 \| 0.6609 \| \| 0.5801 \| 6.09 \| 1400 \| 0.6188 \| 0.6531 \| 0.6609 \| \| 0.5735 \| 6.96 \| 1600 \| 0.5993 \| 0.6771 \| 0.6793 \| \| 0.571 \| 7.83 \| 1800 \| 0.6026 \| 0.6863 \| 0.6861 \| \| 0.5699 \| 8.7 \| 2000 \| 0.6011 \| 0.6841 \| 0.6845 \| \| 0.5639 \| 9.57 \| 2200 \| 0.5849 \| 0.6875 \| 0.6872 \| \| 0.565 \| 10.43 \| 2400 \| 0.5931 \| 0.6867 \| 0.6867 \| \| 0.5591 \| 11.3 \| 2600 \| 0.5862 \| 0.6912 \| 0.6924 \| \| 0.5608 \| 12.17 \| 2800 \| 0.5850 \| 0.6900 \| 0.6897 \| \| 0.5532 \| 13.04 \| 3000 \| 0.5873 \| 0.6931 \| 0.6929 \| \| 0.5508 \| 13.91 \| 3200 \| 0.5834 \| 0.6940 \| 0.6937 \| \| 0.5491 \| 14.78 \| 3400 \| 0.5875 \| 0.6949 \| 0.6954 \| \| 0.5491 \| 15.65 \| 3600 \| 0.5858 \| 0.6960 \| 0.6959 \| \| 0.5424 \| 16.52 \| 3800 \| 0.5915 \| 0.6866 \| 0.6864 \| \| 0.5434 \| 17.39 \| 4000 \| 0.5927 \| 0.6954 \| 0.6962 \| \| 0.5435 \| 18.26 \| 4200 \| 0.5956 \| 0.6889 \| 0.6902 \| \| 0.5361 \| 19.13 \| 4400 \| 0.5902 \| 0.6918 \| 0.6916 \| \| 0.5379 \| 20.0 \| 4600 \| 0.5875 \| 0.6920 \| 0.6927 \| \| 0.5341 \| 20.87 \| 4800 \| 0.5924 \| 0.6955 \| 0.6962 \| \| 0.5343 \| 21.74 \| 5000 \| 0.5925 \| 0.6911 \| 0.6916 \| \| 0.5322 \| 22.61 \| 5200 \| 0.5899 \| 0.6925 \| 0.6929 \| \| 0.5251 \| 23.48 \| 5400 \| 0.6030 \| 0.6896 \| 0.6916 \| \| 0.5271 \| 24.35 \| 5600 \| 0.5900 \| 0.6920 \| 0.6921 \| \| 0.5274 \| 25.22 \| 5800 \| 0.5975 \| 0.6952 \| 0.6965 \| \| 0.5227 \| 26.09 \| 6000 \| 0.6017 \| 0.6941 \| 0.6954 \| \| 0.5239 \| 26.96 \| 6200 \| 0.5954 \| 0.6948 \| 0.6973 \| \| 0.5187 \| 27.83 \| 6400 \| 0.6090 \| 0.6857 \| 0.6891 \| \| 0.5196 \| 28.7 \| 6600 \| 0.5891 \| 0.6966 \| 0.6965 \| \| 0.5176 \| 29.57 \| 6800 \| 0.5873 \| 0.6933 \| 0.6935 \| \| 0.5165 \| 30.43 \| 7000 \| 0.5917 \| 0.6901 \| 0.6908 \| \| 0.5182 \| 31.3 \| 7200 \| 0.5922 \| 0.6897 \| 0.6902 \| \| 0.5151 \| 32.17 \| 7400 \| 0.5929 \| 0.6918 \| 0.6921 \| \| 0.5116 \| 33.04 \| 7600 \| 0.5945 \| 0.6929 \| 0.6932 \| \| 0.5135 \| 33.91 \| 7800 \| 0.5920 \| 0.6946 \| 0.6951 \| \| 0.5123 \| 34.78 \| 8000 \| 0.5963 \| 0.6912 \| 0.6913 \| \| 0.5112 \| 35.65 \| 8200 \| 0.5976 \| 0.6941 \| 0.6943 \| \| 0.512 \| 36.52 \| 8400 \| 0.5934 \| 0.6916 \| 0.6921 \| \| 0.5075 \| 37.39 \| 8600 \| 0.5941 \| 0.6959 \| 0.6959 \| \| 0.506 \| 38.26 \| 8800 \| 0.5992 \| 0.6909 \| 0.6918 \| \| 0.5119 \| 39.13 \| 9000 \| 0.5961 \| 0.6916 \| 0.6921 \| \| 0.5074 \| 40.0 \| 9200 \| 0.5965 \| 0.6949 \| 0.6951 \| \| 0.5056 \| 40.87 \| 9400 \| 0.5974 \| 0.6948 \| 0.6948 \| \| 0.5069 \| 41.74 \| 9600 \| 0.5957 \| 0.6951 \| 0.6954 \| \| 0.5102 \| 42.61 \| 9800 \| 0.5945 \| 0.6950 \| 0.6951 \| \| 0.504 \| 43.48 \| 10000 \| 0.5964 \| 0.6957 \| 0.6959 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_EMP_H3K4me3-seqsight_32768_512_43M-L8_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K4me3-seqsight_32768_512_43M-L8_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]	null	2024-04-30T06:21:34+00:00
fill-mask	transformers		{}	jd445/2019	null	[ "transformers", "safetensors", "roberta", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-30T06:21:40+00:00
null	null		{}	Sumedh1304/mistral-finetuned-alpaca	null	[ "region:us" ]	null	2024-04-30T06:22:04+00:00
reinforcement-learning	sample-factory	A(n) APPO model trained on the doom_health_gathering_supreme environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r aw-infoprojekt/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.	{"library_name": "sample-factory", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "sample-factory"], "model-index": [{"name": "APPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "doom_health_gathering_supreme", "type": "doom_health_gathering_supreme"}, "metrics": [{"type": "mean_reward", "value": "10.86 +/- 3.72", "name": "mean_reward", "verified": false}]}]}]}	aw-infoprojekt/rl_course_vizdoom_health_gathering_supreme	null	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	null	2024-04-30T06:22:42+00:00
null	transformers		{}	Goodarc/TomTestModel2024043001	null	[ "transformers", "pytorch", "tensorboard", "donut", "endpoints_compatible", "has_space", "region:us" ]	null	2024-04-30T06:23:56+00:00
null	null		{}	arjunanand13/Idefics2-8b-multimodal	null	[ "safetensors", "region:us" ]	null	2024-04-30T06:24:26+00:00
null	null		{}	dimson15/sn25-2-2	null	[ "region:us" ]	null	2024-04-30T06:24:52+00:00
text-generation	transformers	# mlx-community/llm-jp-13b-instruct-full-dolly-ichikara_004_001_single-oasst-oasst2-v2.0 This model was converted to MLX format from [`llm-jp/llm-jp-13b-instruct-full-dolly-ichikara_004_001_single-oasst-oasst2-v2.0`]() using mlx-lm version 0.12.0. Refer to the [original model card](https://huggingface.co/llm-jp/llm-jp-13b-instruct-full-dolly-ichikara_004_001_single-oasst-oasst2-v2.0) 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/llm-jp-13b-instruct-full-dolly-ichikara_004_001_single-oasst-oasst2-v2.0") response = generate(model, tokenizer, prompt="hello", verbose=True) ```	{"language": ["en", "ja"], "license": "apache-2.0", "library_name": "transformers", "tags": ["mlx"], "datasets": ["databricks/databricks-dolly-15k", "llm-jp/databricks-dolly-15k-ja", "llm-jp/oasst1-21k-en", "llm-jp/oasst1-21k-ja", "llm-jp/oasst2-33k-en", "llm-jp/oasst2-33k-ja"], "programming_language": ["C", "C++", "C#", "Go", "Java", "JavaScript", "Lua", "PHP", "Python", "Ruby", "Rust", "Scala", "TypeScript"], "pipeline_tag": "text-generation", "inference": false}	mlx-community/llm-jp-13b-instruct-full-dolly-ichikara_004_001_single-oasst-oasst2-v2.0	null	[ "transformers", "safetensors", "llama", "text-generation", "mlx", "conversational", "en", "ja", "dataset:databricks/databricks-dolly-15k", "dataset:llm-jp/databricks-dolly-15k-ja", "dataset:llm-jp/oasst1-21k-en", "dataset:llm-jp/oasst1-21k-ja", "dataset:llm-jp/oasst2-33k-en", "dataset:llm-jp/oasst2-33k-ja", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "region:us" ]	null	2024-04-30T06:24:57+00:00
text-to-image	diffusers	# 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 🧨 diffusers 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]	{"library_name": "diffusers"}	Niggendar/mightMixes15Ponyxl_pxlBlastwrx	null	[ "diffusers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	null	2024-04-30T06:25:17+00:00
text2text-generation	transformers		{}	shenkha/DGSlow_T5-small_BST	null	[ "transformers", "pytorch", "t5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-30T06:25:26+00:00
sentence-similarity	sentence-transformers	# {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 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 113 with parameters: ``` {'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 1000, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 0, "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': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->	{"library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"}	Mihaiii/test16	null	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "transformers", "endpoints_compatible", "region:us" ]	null	2024-04-30T06:25:40+00:00
reinforcement-learning	stable-baselines3	# PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```	{"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "220.28 +/- 85.29", "name": "mean_reward", "verified": false}]}]}]}	Chhabi/PPO-LunarLander-v2	null	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	null	2024-04-30T06:26:59+00:00
null	null		{}	Hyunji0909/logo-model	null	[ "region:us" ]	null	2024-04-30T06:27:55+00:00
null	null		{}	PritamShete/git-base-pokemon	null	[ "region:us" ]	null	2024-04-30T06:27:56+00:00
text2text-generation	transformers		{}	shenkha/DGSlow_T5-small_CV2	null	[ "transformers", "pytorch", "t5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-30T06:27:57+00:00
null	null		{"license": "mit"}	WeiJiang75/dipro1	null	[ "safetensors", "license:mit", "region:us" ]	null	2024-04-30T06:28:49+00:00
text-classification	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # robust_llm_pythia-31m_mz-133_WordLength_n-its-10-seed-3 This model is a fine-tuned version of [EleutherAI/pythia-31m](https://huggingface.co/EleutherAI/pythia-31m) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 3 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.1 - Datasets 2.18.0 - Tokenizers 0.15.2	{"tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-31m", "model-index": [{"name": "robust_llm_pythia-31m_mz-133_WordLength_n-its-10-seed-3", "results": []}]}	AlignmentResearch/robust_llm_pythia-31m_mz-133_WordLength_n-its-10-seed-3	null	[ "transformers", "tensorboard", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-31m", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-30T06:29:09+00:00
null	peft	<!-- 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. --> # taide_llama3_8b_lora_completion_only This model is a fine-tuned version of [taide/Llama3-TAIDE-LX-8B-Chat-Alpha1](https://huggingface.co/taide/Llama3-TAIDE-LX-8B-Chat-Alpha1) on the DandinPower/ZH-Reading-Comprehension-Llama-Instruct dataset. It achieves the following results on the evaluation set: - Loss: 0.0968 ## 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: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - total_eval_batch_size: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 700 - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.1474 \| 0.3690 \| 250 \| 0.1201 \| \| 0.1072 \| 0.7380 \| 500 \| 0.1581 \| \| 0.098 \| 1.1070 \| 750 \| 0.1148 \| \| 0.0963 \| 1.4760 \| 1000 \| 0.1044 \| \| 0.0502 \| 1.8450 \| 1250 \| 0.1064 \| \| 0.05 \| 2.2140 \| 1500 \| 0.1017 \| \| 0.0239 \| 2.5830 \| 1750 \| 0.1015 \| \| 0.0443 \| 2.9520 \| 2000 \| 0.0968 \| ### Framework versions - PEFT 0.10.0 - Transformers 4.40.0 - Pytorch 2.2.2+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"language": ["zh"], "license": "other", "library_name": "peft", "tags": ["trl", "sft", "nycu-112-2-deeplearning-hw2", "generated_from_trainer"], "datasets": ["DandinPower/ZH-Reading-Comprehension-Llama-Instruct"], "base_model": "taide/Llama3-TAIDE-LX-8B-Chat-Alpha1", "model-index": [{"name": "taide_llama3_8b_lora_completion_only", "results": []}]}	DandinPower/taide_llama3_8b_lora_completion_only	null	[ "peft", "safetensors", "trl", "sft", "nycu-112-2-deeplearning-hw2", "generated_from_trainer", "zh", "dataset:DandinPower/ZH-Reading-Comprehension-Llama-Instruct", "base_model:taide/Llama3-TAIDE-LX-8B-Chat-Alpha1", "license:other", "region:us" ]	null	2024-04-30T06:29:55+00:00
null	null		{"license": "llama3"}	yatour/yatourAI	null	[ "license:llama3", "region:us" ]	null	2024-04-30T06:30:13+00:00
text-generation	transformers	# starcoder2-15b-instruct-v0.1-GGUF - Original model: [starcoder2-15b-instruct-v0.1](https://huggingface.co/bigcode/starcoder2-15b-instruct-v0.1) <!-- description start --> ## Description This repo contains GGUF format model files for [starcoder2-15b-instruct-v0.1](https://huggingface.co/bigcode/starcoder2-15b-instruct-v0.1). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). This is the source project for GGUF, providing both a Command Line Interface (CLI) and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), Known as the most widely used web UI, this project boasts numerous features and powerful extensions, and supports GPU acceleration. * [Ollama](https://github.com/jmorganca/ollama) Ollama is a lightweight and extensible framework designed for building and running language models locally. It features a simple API for creating, managing, and executing models, along with a library of pre-built models for use in various applications * [KoboldCpp](https://github.com/LostRuins/koboldcpp), A comprehensive web UI offering GPU acceleration across all platforms and architectures, particularly renowned for storytelling. * [GPT4All](https://gpt4all.io), This is a free and open source GUI that runs locally, supporting Windows, Linux, and macOS with full GPU acceleration. * [LM Studio](https://lmstudio.ai/) An intuitive and powerful local GUI for Windows and macOS (Silicon), featuring GPU acceleration. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui). A notable web UI with a variety of unique features, including a comprehensive model library for easy model selection. * [Faraday.dev](https://faraday.dev/), An attractive, user-friendly character-based chat GUI for Windows and macOS (both Silicon and Intel), also offering GPU acceleration. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), A Python library equipped with GPU acceleration, LangChain support, and an OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), A Rust-based ML framework focusing on performance, including GPU support, and designed for ease of use. * [ctransformers](https://github.com/marella/ctransformers), A Python library featuring GPU acceleration, LangChain support, and an OpenAI-compatible AI server. * [localGPT](https://github.com/PromtEngineer/localGPT) An open-source initiative enabling private conversations with documents. <!-- README_GGUF.md-about-gguf end --> <!-- compatibility_gguf start --> ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single folder. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: LiteLLMs/starcoder2-15b-instruct-v0.1-GGUF and below it, a specific filename to download, such as: Q4_0/Q4_0-00001-of-00009.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download LiteLLMs/starcoder2-15b-instruct-v0.1-GGUF Q4_0/Q4_0-00001-of-00009.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download LiteLLMs/starcoder2-15b-instruct-v0.1-GGUF --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install huggingface_hub[hf_transfer] ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download LiteLLMs/starcoder2-15b-instruct-v0.1-GGUF Q4_0/Q4_0-00001-of-00009.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m Q4_0/Q4_0-00001-of-00009.gguf --color -c 8192 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<PROMPT>" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 8192` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./Q4_0/Q4_0-00001-of-00009.gguf", # Download the model file first n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "<PROMPT>", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./Q4_0/Q4_0-00001-of-00009.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer end --> <!-- original-model-card start --> # Original model card: starcoder2-15b-instruct-v0.1 # StarCoder2-Instruct: Fully Transparent and Permissive Self-Alignment for Code Generation ![Banner](https://huggingface.co/datasets/bigcode/starcoder2-instruct-assets/resolve/main/banner.png) ## Model Summary We introduce StarCoder2-15B-Instruct-v0.1, the very first entirely self-aligned code Large Language Model (LLM) trained with a fully permissive and transparent pipeline. Our open-source pipeline uses StarCoder2-15B to generate thousands of instruction-response pairs, which are then used to fine-tune StarCoder-15B itself without any human annotations or distilled data from huge and proprietary LLMs. - Model: [bigcode/starcoder2-15b-instruct-v0.1](https://huggingface.co/bigcode/starcoder2-instruct-15b-v0.1) - Code: [bigcode-project/starcoder2-self-align](https://github.com/bigcode-project/starcoder2-self-align) - Dataset: [bigcode/self-oss-instruct-sc2-exec-filter-50k](https://huggingface.co/datasets/bigcode/self-oss-instruct-sc2-exec-filter-50k/) - Authors: [Yuxiang Wei](https://yuxiang.cs.illinois.edu), [Federico Cassano](https://federico.codes/), [Jiawei Liu](https://jw-liu.xyz), [Yifeng Ding](https://yifeng-ding.com), [Naman Jain](https://naman-ntc.github.io), [Harm de Vries](https://www.harmdevries.com), [Leandro von Werra](https://twitter.com/lvwerra), [Arjun Guha](https://www.khoury.northeastern.edu/home/arjunguha/main/home/), [Lingming Zhang](https://lingming.cs.illinois.edu). ![self-alignment pipeline](https://huggingface.co/datasets/bigcode/starcoder2-instruct-assets/resolve/main/method.png) ## Use ### Intended use The model is designed to respond to coding-related instructions in a single turn. Instructions in other styles may result in less accurate responses. Here is an example to get started with the model using the [transformers](https://huggingface.co/docs/transformers/index) library: ```python import transformers import torch pipeline = transformers.pipeline( model="bigcode/starcoder2-15b-instruct-v0.1", task="text-generation", torch_dtype=torch.bfloat16, device_map="auto", ) def respond(instruction: str, response_prefix: str) -> str: messages = [{"role": "user", "content": instruction}] prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False) prompt += response_prefix teminators = [ pipeline.tokenizer.eos_token_id, pipeline.tokenizer.convert_tokens_to_ids("###"), ] result = pipeline( prompt, max_length=256, num_return_sequences=1, do_sample=False, eos_token_id=teminators, pad_token_id=pipeline.tokenizer.eos_token_id, truncation=True, ) response = response_prefix + result[0]["generated_text"][len(prompt) :].split("###")[0].rstrip() return response instruction = "Write a quicksort function in Python with type hints and a 'less_than' parameter for custom sorting criteria." response_prefix = "" print(respond(instruction, response_prefix)) ``` Here is the expected output: `````` Here's how you can implement a quicksort function in Python with type hints and a 'less_than' parameter for custom sorting criteria: ```python from typing import TypeVar, Callable T = TypeVar('T') def quicksort(items: list[T], less_than: Callable[[T, T], bool] = lambda x, y: x < y) -> list[T]: if len(items) <= 1: return items pivot = items[0] less = [x for x in items[1:] if less_than(x, pivot)] greater = [x for x in items[1:] if not less_than(x, pivot)] return quicksort(less, less_than) + [pivot] + quicksort(greater, less_than) ``` `````` ### Bias, Risks, and Limitations StarCoder2-15B-Instruct-v0.1 is primarily finetuned for Python code generation tasks that can be verified through execution, which may lead to certain biases and limitations. For example, the model might not adhere strictly to instructions that dictate the output format. In these situations, it's beneficial to provide a response prefix or a one-shot example to steer the model’s output. Additionally, the model may have limitations with other programming languages and out-of-domain coding tasks. The model also inherits the bias, risks, and limitations from its base StarCoder2-15B model. For more information, please refer to the [StarCoder2-15B model card](https://huggingface.co/bigcode/starcoder2-15b). ## Evaluation on EvalPlus, LiveCodeBench, and DS-1000 ![EvalPlus](https://huggingface.co/datasets/bigcode/starcoder2-instruct-assets/resolve/main/evalplus.png) ![LiveCodeBench and DS-1000](https://huggingface.co/datasets/bigcode/starcoder2-instruct-assets/resolve/main/lcb-ds1000.png) ## Training Details ### Hyperparameters - Optimizer: Adafactor - Learning rate: 1e-5 - Epoch: 4 - Batch size: 64 - Warmup ratio: 0.05 - Scheduler: Linear - Sequence length: 1280 - Dropout: Not applied ### Hardware 1 x NVIDIA A100 80GB ## Resources - Model: [bigcode/starCoder2-15b-instruct-v0.1](https://huggingface.co/bigcode/starcoder2-instruct-15b-v0.1) - Code: [bigcode-project/starcoder2-self-align](https://github.com/bigcode-project/starcoder2-self-align) - Dataset: [bigcode/self-oss-instruct-sc2-exec-filter-50k](https://huggingface.co/datasets/bigcode/self-oss-instruct-sc2-exec-filter-50k/) <!-- original-model-card end -->	{"license": "bigcode-openrail-m", "library_name": "transformers", "tags": ["code", "GGUF"], "datasets": ["bigcode/self-oss-instruct-sc2-exec-filter-50k"], "base_model": "bigcode/starcoder2-15b", "pipeline_tag": "text-generation", "quantized_by": "andrijdavid", "model-index": [{"name": "starcoder2-15b-instruct-v0.1", "results": [{"task": {"type": "text-generation"}, "dataset": {"name": "LiveCodeBench (code generation)", "type": "livecodebench-codegeneration"}, "metrics": [{"type": "pass@1", "value": 20.4, "verified": false}]}, {"task": {"type": "text-generation"}, "dataset": {"name": "LiveCodeBench (self repair)", "type": "livecodebench-selfrepair"}, "metrics": [{"type": "pass@1", "value": 20.9, "verified": false}]}, {"task": {"type": "text-generation"}, "dataset": {"name": "LiveCodeBench (test output prediction)", "type": "livecodebench-testoutputprediction"}, "metrics": [{"type": "pass@1", "value": 29.8, "verified": false}]}, {"task": {"type": "text-generation"}, "dataset": {"name": "LiveCodeBench (code execution)", "type": "livecodebench-codeexecution"}, "metrics": [{"type": "pass@1", "value": 28.1, "verified": false}]}, {"task": {"type": "text-generation"}, "dataset": {"name": "HumanEval", "type": "humaneval"}, "metrics": [{"type": "pass@1", "value": 72.6, "verified": false}]}, {"task": {"type": "text-generation"}, "dataset": {"name": "HumanEval+", "type": "humanevalplus"}, "metrics": [{"type": "pass@1", "value": 63.4, "verified": false}]}, {"task": {"type": "text-generation"}, "dataset": {"name": "MBPP", "type": "mbpp"}, "metrics": [{"type": "pass@1", "value": 75.2, "verified": false}]}, {"task": {"type": "text-generation"}, "dataset": {"name": "MBPP+", "type": "mbppplus"}, "metrics": [{"type": "pass@1", "value": 61.2, "verified": false}]}, {"task": {"type": "text-generation"}, "dataset": {"name": "DS-1000", "type": "ds-1000"}, "metrics": [{"type": "pass@1", "value": 40.6, "verified": false}]}]}]}	LiteLLMs/starcoder2-15b-instruct-v0.1-GGUF	null	[ "transformers", "gguf", "code", "GGUF", "text-generation", "dataset:bigcode/self-oss-instruct-sc2-exec-filter-50k", "base_model:bigcode/starcoder2-15b", "license:bigcode-openrail-m", "model-index", "endpoints_compatible", "region:us" ]	null	2024-04-30T06:31:08+00:00
text2text-generation	transformers		{}	shenkha/DGSlow_Bartbase_PC	null	[ "transformers", "pytorch", "bart", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-30T06:32:00+00:00
text-classification	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2222 - Accuracy: 0.927 - F1: 0.9270 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8572 \| 1.0 \| 250 \| 0.3317 \| 0.9015 \| 0.9005 \| \| 0.2552 \| 2.0 \| 500 \| 0.2222 \| 0.927 \| 0.9270 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "config": "split", "split": "validation", "args": "split"}, "metrics": [{"type": "accuracy", "value": 0.927, "name": "Accuracy"}, {"type": "f1", "value": 0.9270352884163217, "name": "F1"}]}]}]}	nanashi999/distilbert-base-uncased-finetuned-emotion	null	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-30T06:32:32+00:00
null	transformers	# 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]	{"library_name": "transformers", "tags": ["unsloth"]}	russgeo/lecw	null	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-04-30T06:33:58+00:00
text-classification	transformers	# Model Trained Using AutoTrain - Problem type: Text Classification ## Validation Metrics loss: 1.0657285451889038 f1_macro: 0.2095479509928179 f1_micro: 0.4584103512014787 f1_weighted: 0.2881768494245037 precision_macro: 0.1528034504004929 precision_micro: 0.4584103512014787 precision_weighted: 0.21014005008866307 recall_macro: 0.3333333333333333 recall_micro: 0.4584103512014787 recall_weighted: 0.4584103512014787 accuracy: 0.4584103512014787	{"tags": ["autotrain", "text-classification"], "datasets": ["actsa-distilbert/autotrain-data"], "widget": [{"text": "I love AutoTrain"}]}	DarkPhantom323/actsa-distilbert	null	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "autotrain", "dataset:actsa-distilbert/autotrain-data", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-30T06:35:24+00:00
question-answering	transformers		{}	stefandi/bert-finetuned-squad	null	[ "transformers", "tensorboard", "safetensors", "bert", "question-answering", "endpoints_compatible", "region:us" ]	null	2024-04-30T06:35:33+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3K4me3-seqsight_32768_512_43M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me3) dataset. It achieves the following results on the evaluation set: - Loss: 0.6628 - F1 Score: 0.7003 - Accuracy: 0.7 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.6509 \| 0.87 \| 200 \| 0.6230 \| 0.6621 \| 0.6625 \| \| 0.6059 \| 1.74 \| 400 \| 0.6135 \| 0.6639 \| 0.6663 \| \| 0.592 \| 2.61 \| 600 \| 0.5994 \| 0.6823 \| 0.6823 \| \| 0.5831 \| 3.48 \| 800 \| 0.5950 \| 0.6831 \| 0.6832 \| \| 0.5769 \| 4.35 \| 1000 \| 0.5927 \| 0.6832 \| 0.6829 \| \| 0.5715 \| 5.22 \| 1200 \| 0.5920 \| 0.6843 \| 0.6856 \| \| 0.5626 \| 6.09 \| 1400 \| 0.6018 \| 0.6851 \| 0.6880 \| \| 0.5546 \| 6.96 \| 1600 \| 0.5913 \| 0.6930 \| 0.6940 \| \| 0.5463 \| 7.83 \| 1800 \| 0.5928 \| 0.6911 \| 0.6910 \| \| 0.5422 \| 8.7 \| 2000 \| 0.5842 \| 0.6886 \| 0.6886 \| \| 0.5318 \| 9.57 \| 2200 \| 0.5834 \| 0.6981 \| 0.6981 \| \| 0.5319 \| 10.43 \| 2400 \| 0.5986 \| 0.6946 \| 0.6946 \| \| 0.5223 \| 11.3 \| 2600 \| 0.5986 \| 0.6917 \| 0.6932 \| \| 0.5222 \| 12.17 \| 2800 \| 0.5934 \| 0.6939 \| 0.6940 \| \| 0.5123 \| 13.04 \| 3000 \| 0.5865 \| 0.6906 \| 0.6910 \| \| 0.5051 \| 13.91 \| 3200 \| 0.5865 \| 0.6982 \| 0.6981 \| \| 0.497 \| 14.78 \| 3400 \| 0.6015 \| 0.6906 \| 0.6927 \| \| 0.4981 \| 15.65 \| 3600 \| 0.5933 \| 0.6932 \| 0.6937 \| \| 0.4854 \| 16.52 \| 3800 \| 0.6061 \| 0.6967 \| 0.6967 \| \| 0.4809 \| 17.39 \| 4000 \| 0.6083 \| 0.6950 \| 0.6965 \| \| 0.4787 \| 18.26 \| 4200 \| 0.6135 \| 0.6979 \| 0.6989 \| \| 0.4718 \| 19.13 \| 4400 \| 0.6113 \| 0.6938 \| 0.6937 \| \| 0.4674 \| 20.0 \| 4600 \| 0.6135 \| 0.6969 \| 0.6986 \| \| 0.4584 \| 20.87 \| 4800 \| 0.6284 \| 0.6975 \| 0.6976 \| \| 0.4547 \| 21.74 \| 5000 \| 0.6107 \| 0.7012 \| 0.7016 \| \| 0.448 \| 22.61 \| 5200 \| 0.6399 \| 0.6990 \| 0.6997 \| \| 0.4411 \| 23.48 \| 5400 \| 0.6365 \| 0.6983 \| 0.6997 \| \| 0.4396 \| 24.35 \| 5600 \| 0.6307 \| 0.6982 \| 0.6986 \| \| 0.4336 \| 25.22 \| 5800 \| 0.6495 \| 0.6961 \| 0.6959 \| \| 0.4294 \| 26.09 \| 6000 \| 0.6630 \| 0.6933 \| 0.6948 \| \| 0.428 \| 26.96 \| 6200 \| 0.6421 \| 0.6955 \| 0.6967 \| \| 0.418 \| 27.83 \| 6400 \| 0.6535 \| 0.7025 \| 0.7033 \| \| 0.4177 \| 28.7 \| 6600 \| 0.6546 \| 0.6955 \| 0.6954 \| \| 0.4142 \| 29.57 \| 6800 \| 0.6534 \| 0.6938 \| 0.6943 \| \| 0.4112 \| 30.43 \| 7000 \| 0.6518 \| 0.7017 \| 0.7016 \| \| 0.4087 \| 31.3 \| 7200 \| 0.6582 \| 0.7031 \| 0.7030 \| \| 0.4011 \| 32.17 \| 7400 \| 0.6718 \| 0.7003 \| 0.7003 \| \| 0.3996 \| 33.04 \| 7600 \| 0.6742 \| 0.6971 \| 0.6970 \| \| 0.3983 \| 33.91 \| 7800 \| 0.6686 \| 0.7005 \| 0.7014 \| \| 0.3922 \| 34.78 \| 8000 \| 0.6739 \| 0.7019 \| 0.7019 \| \| 0.3922 \| 35.65 \| 8200 \| 0.6771 \| 0.7042 \| 0.7041 \| \| 0.3896 \| 36.52 \| 8400 \| 0.6731 \| 0.7005 \| 0.7003 \| \| 0.3892 \| 37.39 \| 8600 \| 0.6700 \| 0.7022 \| 0.7019 \| \| 0.3808 \| 38.26 \| 8800 \| 0.6924 \| 0.7003 \| 0.7005 \| \| 0.388 \| 39.13 \| 9000 \| 0.6855 \| 0.7014 \| 0.7016 \| \| 0.3843 \| 40.0 \| 9200 \| 0.6828 \| 0.7024 \| 0.7024 \| \| 0.3806 \| 40.87 \| 9400 \| 0.6873 \| 0.7009 \| 0.7008 \| \| 0.3827 \| 41.74 \| 9600 \| 0.6855 \| 0.7024 \| 0.7024 \| \| 0.3813 \| 42.61 \| 9800 \| 0.6873 \| 0.7009 \| 0.7008 \| \| 0.3751 \| 43.48 \| 10000 \| 0.6912 \| 0.7000 \| 0.7 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_EMP_H3K4me3-seqsight_32768_512_43M-L32_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3K4me3-seqsight_32768_512_43M-L32_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]	null	2024-04-30T06:35:53+00:00
null	null		{}	yliuhz/FaceEdit-ControlNet-COMP5421	null	[ "region:us" ]	null	2024-04-30T06:35:56+00:00
null	peft	<!-- 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. --> # GUE_EMP_H4-seqsight_32768_512_43M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_EMP_H4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H4) dataset. It achieves the following results on the evaluation set: - Loss: 0.2537 - F1 Score: 0.9048 - Accuracy: 0.9049 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.4138 \| 2.17 \| 200 \| 0.3024 \| 0.8886 \| 0.8884 \| \| 0.2889 \| 4.35 \| 400 \| 0.2914 \| 0.8859 \| 0.8857 \| \| 0.276 \| 6.52 \| 600 \| 0.2811 \| 0.8872 \| 0.8871 \| \| 0.2752 \| 8.7 \| 800 \| 0.2797 \| 0.8845 \| 0.8843 \| \| 0.2645 \| 10.87 \| 1000 \| 0.2767 \| 0.8877 \| 0.8877 \| \| 0.2644 \| 13.04 \| 1200 \| 0.2772 \| 0.8879 \| 0.8877 \| \| 0.259 \| 15.22 \| 1400 \| 0.2717 \| 0.8917 \| 0.8919 \| \| 0.2542 \| 17.39 \| 1600 \| 0.2704 \| 0.8905 \| 0.8905 \| \| 0.2528 \| 19.57 \| 1800 \| 0.2679 \| 0.8937 \| 0.8939 \| \| 0.2524 \| 21.74 \| 2000 \| 0.2727 \| 0.8941 \| 0.8939 \| \| 0.2477 \| 23.91 \| 2200 \| 0.2683 \| 0.8927 \| 0.8925 \| \| 0.2464 \| 26.09 \| 2400 \| 0.2722 \| 0.8961 \| 0.8960 \| \| 0.2452 \| 28.26 \| 2600 \| 0.2672 \| 0.8924 \| 0.8925 \| \| 0.2441 \| 30.43 \| 2800 \| 0.2646 \| 0.8954 \| 0.8953 \| \| 0.2392 \| 32.61 \| 3000 \| 0.2662 \| 0.8960 \| 0.8960 \| \| 0.236 \| 34.78 \| 3200 \| 0.2602 \| 0.8925 \| 0.8925 \| \| 0.2364 \| 36.96 \| 3400 \| 0.2657 \| 0.8968 \| 0.8966 \| \| 0.2351 \| 39.13 \| 3600 \| 0.2631 \| 0.8988 \| 0.8987 \| \| 0.2325 \| 41.3 \| 3800 \| 0.2636 \| 0.8974 \| 0.8973 \| \| 0.2306 \| 43.48 \| 4000 \| 0.2671 \| 0.8967 \| 0.8966 \| \| 0.2334 \| 45.65 \| 4200 \| 0.2600 \| 0.8960 \| 0.8960 \| \| 0.2262 \| 47.83 \| 4400 \| 0.2623 \| 0.8967 \| 0.8966 \| \| 0.231 \| 50.0 \| 4600 \| 0.2588 \| 0.8939 \| 0.8939 \| \| 0.2233 \| 52.17 \| 4800 \| 0.2635 \| 0.8961 \| 0.8960 \| \| 0.2256 \| 54.35 \| 5000 \| 0.2710 \| 0.8941 \| 0.8939 \| \| 0.2223 \| 56.52 \| 5200 \| 0.2700 \| 0.8934 \| 0.8932 \| \| 0.2214 \| 58.7 \| 5400 \| 0.2653 \| 0.8975 \| 0.8973 \| \| 0.2186 \| 60.87 \| 5600 \| 0.2678 \| 0.8942 \| 0.8939 \| \| 0.221 \| 63.04 \| 5800 \| 0.2633 \| 0.9009 \| 0.9008 \| \| 0.2185 \| 65.22 \| 6000 \| 0.2671 \| 0.8954 \| 0.8953 \| \| 0.2184 \| 67.39 \| 6200 \| 0.2688 \| 0.8948 \| 0.8946 \| \| 0.2168 \| 69.57 \| 6400 \| 0.2615 \| 0.8994 \| 0.8994 \| \| 0.2178 \| 71.74 \| 6600 \| 0.2640 \| 0.9002 \| 0.9001 \| \| 0.2162 \| 73.91 \| 6800 \| 0.2676 \| 0.8968 \| 0.8966 \| \| 0.2141 \| 76.09 \| 7000 \| 0.2698 \| 0.8935 \| 0.8932 \| \| 0.2138 \| 78.26 \| 7200 \| 0.2695 \| 0.8934 \| 0.8932 \| \| 0.2113 \| 80.43 \| 7400 \| 0.2642 \| 0.8981 \| 0.8980 \| \| 0.2107 \| 82.61 \| 7600 \| 0.2620 \| 0.8987 \| 0.8987 \| \| 0.2148 \| 84.78 \| 7800 \| 0.2665 \| 0.8989 \| 0.8987 \| \| 0.2109 \| 86.96 \| 8000 \| 0.2640 \| 0.9009 \| 0.9008 \| \| 0.2142 \| 89.13 \| 8200 \| 0.2648 \| 0.8995 \| 0.8994 \| \| 0.2084 \| 91.3 \| 8400 \| 0.2635 \| 0.9015 \| 0.9014 \| \| 0.2093 \| 93.48 \| 8600 \| 0.2636 \| 0.9015 \| 0.9014 \| \| 0.2106 \| 95.65 \| 8800 \| 0.2644 \| 0.9022 \| 0.9021 \| \| 0.2125 \| 97.83 \| 9000 \| 0.2639 \| 0.9022 \| 0.9021 \| \| 0.2079 \| 100.0 \| 9200 \| 0.2666 \| 0.8995 \| 0.8994 \| \| 0.2092 \| 102.17 \| 9400 \| 0.2655 \| 0.8995 \| 0.8994 \| \| 0.2087 \| 104.35 \| 9600 \| 0.2666 \| 0.9002 \| 0.9001 \| \| 0.2061 \| 106.52 \| 9800 \| 0.2648 \| 0.9009 \| 0.9008 \| \| 0.2083 \| 108.7 \| 10000 \| 0.2658 \| 0.8995 \| 0.8994 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_EMP_H4-seqsight_32768_512_43M-L1_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H4-seqsight_32768_512_43M-L1_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]	null	2024-04-30T06:36:38+00:00
null	peft	<!-- 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. --> # GUE_EMP_H4-seqsight_32768_512_43M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_EMP_H4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H4) dataset. It achieves the following results on the evaluation set: - Loss: 0.2507 - F1 Score: 0.9041 - Accuracy: 0.9042 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.3722 \| 2.17 \| 200 \| 0.2879 \| 0.8864 \| 0.8864 \| \| 0.2725 \| 4.35 \| 400 \| 0.2825 \| 0.8920 \| 0.8919 \| \| 0.2595 \| 6.52 \| 600 \| 0.2679 \| 0.8958 \| 0.8960 \| \| 0.2567 \| 8.7 \| 800 \| 0.2810 \| 0.8907 \| 0.8905 \| \| 0.2447 \| 10.87 \| 1000 \| 0.2755 \| 0.8890 \| 0.8891 \| \| 0.2411 \| 13.04 \| 1200 \| 0.2641 \| 0.8959 \| 0.8960 \| \| 0.2304 \| 15.22 \| 1400 \| 0.2797 \| 0.8914 \| 0.8912 \| \| 0.2235 \| 17.39 \| 1600 \| 0.2681 \| 0.8983 \| 0.8980 \| \| 0.2197 \| 19.57 \| 1800 \| 0.2625 \| 0.8989 \| 0.8987 \| \| 0.214 \| 21.74 \| 2000 \| 0.2679 \| 0.8934 \| 0.8932 \| \| 0.2067 \| 23.91 \| 2200 \| 0.2711 \| 0.8919 \| 0.8919 \| \| 0.2026 \| 26.09 \| 2400 \| 0.2663 \| 0.8955 \| 0.8953 \| \| 0.2 \| 28.26 \| 2600 \| 0.2666 \| 0.8954 \| 0.8953 \| \| 0.1983 \| 30.43 \| 2800 \| 0.2663 \| 0.8928 \| 0.8925 \| \| 0.1875 \| 32.61 \| 3000 \| 0.2794 \| 0.8987 \| 0.8987 \| \| 0.1812 \| 34.78 \| 3200 \| 0.2828 \| 0.8960 \| 0.8960 \| \| 0.1795 \| 36.96 \| 3400 \| 0.2861 \| 0.8941 \| 0.8939 \| \| 0.1754 \| 39.13 \| 3600 \| 0.2897 \| 0.8934 \| 0.8932 \| \| 0.1697 \| 41.3 \| 3800 \| 0.2999 \| 0.8932 \| 0.8932 \| \| 0.1616 \| 43.48 \| 4000 \| 0.3106 \| 0.8900 \| 0.8898 \| \| 0.1645 \| 45.65 \| 4200 \| 0.3022 \| 0.8918 \| 0.8919 \| \| 0.1601 \| 47.83 \| 4400 \| 0.3078 \| 0.8940 \| 0.8939 \| \| 0.1581 \| 50.0 \| 4600 \| 0.3147 \| 0.8911 \| 0.8912 \| \| 0.1537 \| 52.17 \| 4800 \| 0.3123 \| 0.8893 \| 0.8891 \| \| 0.1498 \| 54.35 \| 5000 \| 0.3216 \| 0.8818 \| 0.8816 \| \| 0.1452 \| 56.52 \| 5200 \| 0.3378 \| 0.8799 \| 0.8795 \| \| 0.1417 \| 58.7 \| 5400 \| 0.3286 \| 0.8839 \| 0.8836 \| \| 0.1404 \| 60.87 \| 5600 \| 0.3191 \| 0.8899 \| 0.8898 \| \| 0.1355 \| 63.04 \| 5800 \| 0.3498 \| 0.8769 \| 0.8768 \| \| 0.1333 \| 65.22 \| 6000 \| 0.3440 \| 0.8845 \| 0.8843 \| \| 0.1332 \| 67.39 \| 6200 \| 0.3463 \| 0.8852 \| 0.8850 \| \| 0.1295 \| 69.57 \| 6400 \| 0.3534 \| 0.8819 \| 0.8816 \| \| 0.1255 \| 71.74 \| 6600 \| 0.3533 \| 0.8858 \| 0.8857 \| \| 0.1264 \| 73.91 \| 6800 \| 0.3561 \| 0.8819 \| 0.8816 \| \| 0.1232 \| 76.09 \| 7000 \| 0.3631 \| 0.8818 \| 0.8816 \| \| 0.1179 \| 78.26 \| 7200 \| 0.3653 \| 0.8797 \| 0.8795 \| \| 0.1197 \| 80.43 \| 7400 \| 0.3694 \| 0.8831 \| 0.8830 \| \| 0.1127 \| 82.61 \| 7600 \| 0.3778 \| 0.8841 \| 0.8843 \| \| 0.1208 \| 84.78 \| 7800 \| 0.3743 \| 0.8811 \| 0.8809 \| \| 0.1134 \| 86.96 \| 8000 \| 0.3756 \| 0.8782 \| 0.8782 \| \| 0.1158 \| 89.13 \| 8200 \| 0.3737 \| 0.8818 \| 0.8816 \| \| 0.1119 \| 91.3 \| 8400 \| 0.3773 \| 0.8770 \| 0.8768 \| \| 0.1111 \| 93.48 \| 8600 \| 0.3813 \| 0.8816 \| 0.8816 \| \| 0.1108 \| 95.65 \| 8800 \| 0.3786 \| 0.8796 \| 0.8795 \| \| 0.1106 \| 97.83 \| 9000 \| 0.3841 \| 0.8790 \| 0.8789 \| \| 0.1101 \| 100.0 \| 9200 \| 0.3845 \| 0.8805 \| 0.8802 \| \| 0.1106 \| 102.17 \| 9400 \| 0.3841 \| 0.8791 \| 0.8789 \| \| 0.1091 \| 104.35 \| 9600 \| 0.3813 \| 0.8791 \| 0.8789 \| \| 0.105 \| 106.52 \| 9800 \| 0.3847 \| 0.8790 \| 0.8789 \| \| 0.1072 \| 108.7 \| 10000 \| 0.3848 \| 0.8770 \| 0.8768 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_EMP_H4-seqsight_32768_512_43M-L8_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H4-seqsight_32768_512_43M-L8_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]	null	2024-04-30T06:36:39+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3-seqsight_32768_512_43M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_EMP_H3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3) dataset. It achieves the following results on the evaluation set: - Loss: 0.3170 - F1 Score: 0.8730 - Accuracy: 0.8731 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.5063 \| 2.13 \| 200 \| 0.4277 \| 0.8016 \| 0.8029 \| \| 0.3674 \| 4.26 \| 400 \| 0.3869 \| 0.8349 \| 0.8350 \| \| 0.3338 \| 6.38 \| 600 \| 0.3773 \| 0.8415 \| 0.8417 \| \| 0.3189 \| 8.51 \| 800 \| 0.3543 \| 0.8564 \| 0.8564 \| \| 0.3056 \| 10.64 \| 1000 \| 0.3430 \| 0.8597 \| 0.8597 \| \| 0.294 \| 12.77 \| 1200 \| 0.3415 \| 0.8617 \| 0.8617 \| \| 0.2883 \| 14.89 \| 1400 \| 0.3350 \| 0.8677 \| 0.8677 \| \| 0.2803 \| 17.02 \| 1600 \| 0.3305 \| 0.8664 \| 0.8664 \| \| 0.2768 \| 19.15 \| 1800 \| 0.3526 \| 0.8595 \| 0.8597 \| \| 0.2715 \| 21.28 \| 2000 \| 0.3447 \| 0.8654 \| 0.8657 \| \| 0.2709 \| 23.4 \| 2200 \| 0.3240 \| 0.8664 \| 0.8664 \| \| 0.2568 \| 25.53 \| 2400 \| 0.3675 \| 0.8601 \| 0.8604 \| \| 0.2627 \| 27.66 \| 2600 \| 0.3348 \| 0.8703 \| 0.8704 \| \| 0.2611 \| 29.79 \| 2800 \| 0.3316 \| 0.8663 \| 0.8664 \| \| 0.2557 \| 31.91 \| 3000 \| 0.3309 \| 0.8683 \| 0.8684 \| \| 0.2524 \| 34.04 \| 3200 \| 0.3312 \| 0.8670 \| 0.8671 \| \| 0.2512 \| 36.17 \| 3400 \| 0.3520 \| 0.8641 \| 0.8644 \| \| 0.2484 \| 38.3 \| 3600 \| 0.3412 \| 0.8663 \| 0.8664 \| \| 0.2471 \| 40.43 \| 3800 \| 0.3445 \| 0.8608 \| 0.8611 \| \| 0.2468 \| 42.55 \| 4000 \| 0.3551 \| 0.8682 \| 0.8684 \| \| 0.2414 \| 44.68 \| 4200 \| 0.3380 \| 0.8704 \| 0.8704 \| \| 0.2407 \| 46.81 \| 4400 \| 0.3474 \| 0.8681 \| 0.8684 \| \| 0.2421 \| 48.94 \| 4600 \| 0.3840 \| 0.8486 \| 0.8497 \| \| 0.2374 \| 51.06 \| 4800 \| 0.3319 \| 0.8764 \| 0.8764 \| \| 0.2365 \| 53.19 \| 5000 \| 0.3727 \| 0.8605 \| 0.8611 \| \| 0.2352 \| 55.32 \| 5200 \| 0.3354 \| 0.8717 \| 0.8717 \| \| 0.234 \| 57.45 \| 5400 \| 0.3719 \| 0.8608 \| 0.8611 \| \| 0.2322 \| 59.57 \| 5600 \| 0.3533 \| 0.8695 \| 0.8697 \| \| 0.2354 \| 61.7 \| 5800 \| 0.3387 \| 0.8716 \| 0.8717 \| \| 0.2275 \| 63.83 \| 6000 \| 0.3770 \| 0.8599 \| 0.8604 \| \| 0.23 \| 65.96 \| 6200 \| 0.3597 \| 0.8646 \| 0.8651 \| \| 0.2301 \| 68.09 \| 6400 \| 0.3545 \| 0.8708 \| 0.8711 \| \| 0.2303 \| 70.21 \| 6600 \| 0.3620 \| 0.8661 \| 0.8664 \| \| 0.2298 \| 72.34 \| 6800 \| 0.3576 \| 0.8661 \| 0.8664 \| \| 0.2261 \| 74.47 \| 7000 \| 0.4031 \| 0.8480 \| 0.8490 \| \| 0.2229 \| 76.6 \| 7200 \| 0.3632 \| 0.8688 \| 0.8691 \| \| 0.2283 \| 78.72 \| 7400 \| 0.3536 \| 0.8723 \| 0.8724 \| \| 0.2243 \| 80.85 \| 7600 \| 0.3611 \| 0.8688 \| 0.8691 \| \| 0.2245 \| 82.98 \| 7800 \| 0.3722 \| 0.8620 \| 0.8624 \| \| 0.2252 \| 85.11 \| 8000 \| 0.3506 \| 0.8756 \| 0.8758 \| \| 0.2223 \| 87.23 \| 8200 \| 0.3614 \| 0.8688 \| 0.8691 \| \| 0.2214 \| 89.36 \| 8400 \| 0.3702 \| 0.8661 \| 0.8664 \| \| 0.223 \| 91.49 \| 8600 \| 0.3739 \| 0.8620 \| 0.8624 \| \| 0.2197 \| 93.62 \| 8800 \| 0.3719 \| 0.8661 \| 0.8664 \| \| 0.2205 \| 95.74 \| 9000 \| 0.3758 \| 0.8613 \| 0.8617 \| \| 0.2206 \| 97.87 \| 9200 \| 0.3584 \| 0.8736 \| 0.8737 \| \| 0.2208 \| 100.0 \| 9400 \| 0.3588 \| 0.8715 \| 0.8717 \| \| 0.2206 \| 102.13 \| 9600 \| 0.3659 \| 0.8675 \| 0.8677 \| \| 0.2182 \| 104.26 \| 9800 \| 0.3645 \| 0.8708 \| 0.8711 \| \| 0.2198 \| 106.38 \| 10000 \| 0.3647 \| 0.8708 \| 0.8711 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_EMP_H3-seqsight_32768_512_43M-L1_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3-seqsight_32768_512_43M-L1_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]	null	2024-04-30T06:37:05+00:00
null	peft	<!-- 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. --> # GUE_EMP_H4-seqsight_32768_512_43M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_EMP_H4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H4) dataset. It achieves the following results on the evaluation set: - Loss: 0.2456 - F1 Score: 0.9063 - Accuracy: 0.9062 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.3469 \| 2.17 \| 200 \| 0.2894 \| 0.8880 \| 0.8877 \| \| 0.2635 \| 4.35 \| 400 \| 0.2678 \| 0.8973 \| 0.8973 \| \| 0.2476 \| 6.52 \| 600 \| 0.2672 \| 0.8960 \| 0.8960 \| \| 0.2393 \| 8.7 \| 800 \| 0.2811 \| 0.8915 \| 0.8912 \| \| 0.2229 \| 10.87 \| 1000 \| 0.2619 \| 0.8993 \| 0.8994 \| \| 0.212 \| 13.04 \| 1200 \| 0.2620 \| 0.9003 \| 0.9001 \| \| 0.1957 \| 15.22 \| 1400 \| 0.2997 \| 0.8895 \| 0.8891 \| \| 0.1864 \| 17.39 \| 1600 \| 0.2886 \| 0.8915 \| 0.8912 \| \| 0.1764 \| 19.57 \| 1800 \| 0.2986 \| 0.8961 \| 0.8960 \| \| 0.1647 \| 21.74 \| 2000 \| 0.3023 \| 0.8887 \| 0.8884 \| \| 0.154 \| 23.91 \| 2200 \| 0.3210 \| 0.8901 \| 0.8898 \| \| 0.143 \| 26.09 \| 2400 \| 0.3236 \| 0.8915 \| 0.8912 \| \| 0.1354 \| 28.26 \| 2600 \| 0.3311 \| 0.8850 \| 0.8850 \| \| 0.1243 \| 30.43 \| 2800 \| 0.3589 \| 0.8725 \| 0.8720 \| \| 0.1152 \| 32.61 \| 3000 \| 0.3594 \| 0.8791 \| 0.8789 \| \| 0.1002 \| 34.78 \| 3200 \| 0.4006 \| 0.8853 \| 0.8850 \| \| 0.0952 \| 36.96 \| 3400 \| 0.3912 \| 0.8818 \| 0.8816 \| \| 0.0899 \| 39.13 \| 3600 \| 0.4403 \| 0.8809 \| 0.8809 \| \| 0.0816 \| 41.3 \| 3800 \| 0.4618 \| 0.8778 \| 0.8782 \| \| 0.0741 \| 43.48 \| 4000 \| 0.4516 \| 0.8741 \| 0.8741 \| \| 0.0743 \| 45.65 \| 4200 \| 0.4487 \| 0.8780 \| 0.8782 \| \| 0.0673 \| 47.83 \| 4400 \| 0.4597 \| 0.8898 \| 0.8898 \| \| 0.063 \| 50.0 \| 4600 \| 0.4948 \| 0.8817 \| 0.8816 \| \| 0.06 \| 52.17 \| 4800 \| 0.5218 \| 0.8749 \| 0.8747 \| \| 0.0529 \| 54.35 \| 5000 \| 0.5205 \| 0.8811 \| 0.8809 \| \| 0.0501 \| 56.52 \| 5200 \| 0.5313 \| 0.8845 \| 0.8843 \| \| 0.0473 \| 58.7 \| 5400 \| 0.5863 \| 0.8757 \| 0.8754 \| \| 0.0438 \| 60.87 \| 5600 \| 0.5475 \| 0.8763 \| 0.8761 \| \| 0.0432 \| 63.04 \| 5800 \| 0.5901 \| 0.8791 \| 0.8789 \| \| 0.0387 \| 65.22 \| 6000 \| 0.6309 \| 0.8669 \| 0.8665 \| \| 0.0361 \| 67.39 \| 6200 \| 0.6609 \| 0.8785 \| 0.8782 \| \| 0.0349 \| 69.57 \| 6400 \| 0.6233 \| 0.8754 \| 0.8754 \| \| 0.0331 \| 71.74 \| 6600 \| 0.6171 \| 0.8797 \| 0.8795 \| \| 0.0351 \| 73.91 \| 6800 \| 0.6380 \| 0.8852 \| 0.8850 \| \| 0.0288 \| 76.09 \| 7000 \| 0.6467 \| 0.8824 \| 0.8823 \| \| 0.0295 \| 78.26 \| 7200 \| 0.6264 \| 0.8776 \| 0.8775 \| \| 0.0277 \| 80.43 \| 7400 \| 0.6538 \| 0.8824 \| 0.8823 \| \| 0.0247 \| 82.61 \| 7600 \| 0.6973 \| 0.8809 \| 0.8809 \| \| 0.0278 \| 84.78 \| 7800 \| 0.7178 \| 0.8797 \| 0.8795 \| \| 0.0247 \| 86.96 \| 8000 \| 0.6858 \| 0.8843 \| 0.8843 \| \| 0.0237 \| 89.13 \| 8200 \| 0.7218 \| 0.8792 \| 0.8789 \| \| 0.022 \| 91.3 \| 8400 \| 0.6885 \| 0.8809 \| 0.8809 \| \| 0.0213 \| 93.48 \| 8600 \| 0.7192 \| 0.8831 \| 0.8830 \| \| 0.0214 \| 95.65 \| 8800 \| 0.7241 \| 0.8803 \| 0.8802 \| \| 0.0214 \| 97.83 \| 9000 \| 0.7257 \| 0.8790 \| 0.8789 \| \| 0.0184 \| 100.0 \| 9200 \| 0.7460 \| 0.8778 \| 0.8775 \| \| 0.0201 \| 102.17 \| 9400 \| 0.7567 \| 0.8770 \| 0.8768 \| \| 0.0191 \| 104.35 \| 9600 \| 0.7382 \| 0.8816 \| 0.8816 \| \| 0.0185 \| 106.52 \| 9800 \| 0.7424 \| 0.8803 \| 0.8802 \| \| 0.0185 \| 108.7 \| 10000 \| 0.7438 \| 0.8810 \| 0.8809 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_EMP_H4-seqsight_32768_512_43M-L32_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H4-seqsight_32768_512_43M-L32_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]	null	2024-04-30T06:37:07+00:00
text-generation	transformers		{}	liuyuxiang/wiki_cs_imitator	null	[ "transformers", "safetensors", "gpt2", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-30T06:38:29+00:00
null	null		{}	rasasa/Mistral-7B-text-to-sql-flash-attention-2	null	[ "region:us" ]	null	2024-04-30T06:38:36+00:00
null	peft	<!-- 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. --> # phi3nedtuned-ner This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 0.6568 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 1 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.2 - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.10.1.dev0 - Transformers 4.41.0.dev0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1 ### License The model is licensed under the MIT license.	{"license": "mit", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "microsoft/Phi-3-mini-4k-instruct", "model-index": [{"name": "checkpoint_dir", "results": []}]}	shujatoor/phi3nedtuned-ner	null	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:microsoft/Phi-3-mini-4k-instruct", "license:mit", "region:us" ]	null	2024-04-30T06:41:58+00:00
text2text-generation	transformers		{}	shenkha/DGSlow_Bartbase_ED	null	[ "transformers", "pytorch", "bart", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-04-30T06:42:56+00:00
null	null		{}	Xrunner/hive-m	null	[ "region:us" ]	null	2024-04-30T06:43:19+00:00
null	peft	<!-- 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. --> # GUE_EMP_H3-seqsight_32768_512_43M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_EMP_H3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3) dataset. It achieves the following results on the evaluation set: - Loss: 0.3077 - F1 Score: 0.8791 - Accuracy: 0.8791 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Score \| Accuracy \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 0.4475 \| 2.13 \| 200 \| 0.3711 \| 0.8427 \| 0.8430 \| \| 0.3173 \| 4.26 \| 400 \| 0.3464 \| 0.8557 \| 0.8557 \| \| 0.291 \| 6.38 \| 600 \| 0.3571 \| 0.8615 \| 0.8617 \| \| 0.277 \| 8.51 \| 800 \| 0.3289 \| 0.8630 \| 0.8631 \| \| 0.2649 \| 10.64 \| 1000 \| 0.3380 \| 0.8650 \| 0.8651 \| \| 0.2537 \| 12.77 \| 1200 \| 0.3459 \| 0.8676 \| 0.8677 \| \| 0.2497 \| 14.89 \| 1400 \| 0.3562 \| 0.8621 \| 0.8624 \| \| 0.24 \| 17.02 \| 1600 \| 0.3300 \| 0.8757 \| 0.8758 \| \| 0.2347 \| 19.15 \| 1800 \| 0.3622 \| 0.8627 \| 0.8631 \| \| 0.2272 \| 21.28 \| 2000 \| 0.3581 \| 0.8695 \| 0.8697 \| \| 0.2244 \| 23.4 \| 2200 \| 0.3776 \| 0.8599 \| 0.8604 \| \| 0.207 \| 25.53 \| 2400 \| 0.4066 \| 0.8547 \| 0.8550 \| \| 0.2113 \| 27.66 \| 2600 \| 0.3849 \| 0.8633 \| 0.8637 \| \| 0.2094 \| 29.79 \| 2800 \| 0.3830 \| 0.8660 \| 0.8664 \| \| 0.2012 \| 31.91 \| 3000 \| 0.3522 \| 0.8696 \| 0.8697 \| \| 0.197 \| 34.04 \| 3200 \| 0.3700 \| 0.8715 \| 0.8717 \| \| 0.1945 \| 36.17 \| 3400 \| 0.4030 \| 0.8578 \| 0.8584 \| \| 0.1872 \| 38.3 \| 3600 \| 0.4093 \| 0.8661 \| 0.8664 \| \| 0.1861 \| 40.43 \| 3800 \| 0.4181 \| 0.8592 \| 0.8597 \| \| 0.1786 \| 42.55 \| 4000 \| 0.4381 \| 0.8599 \| 0.8604 \| \| 0.1745 \| 44.68 \| 4200 \| 0.4421 \| 0.8544 \| 0.8550 \| \| 0.1721 \| 46.81 \| 4400 \| 0.3950 \| 0.8654 \| 0.8657 \| \| 0.172 \| 48.94 \| 4600 \| 0.4968 \| 0.8457 \| 0.8470 \| \| 0.1635 \| 51.06 \| 4800 \| 0.3863 \| 0.8729 \| 0.8731 \| \| 0.1619 \| 53.19 \| 5000 \| 0.4594 \| 0.8585 \| 0.8591 \| \| 0.1593 \| 55.32 \| 5200 \| 0.4623 \| 0.8551 \| 0.8557 \| \| 0.1591 \| 57.45 \| 5400 \| 0.4254 \| 0.8622 \| 0.8624 \| \| 0.1557 \| 59.57 \| 5600 \| 0.4582 \| 0.8540 \| 0.8544 \| \| 0.1532 \| 61.7 \| 5800 \| 0.4197 \| 0.8663 \| 0.8664 \| \| 0.1485 \| 63.83 \| 6000 \| 0.4785 \| 0.8564 \| 0.8570 \| \| 0.1456 \| 65.96 \| 6200 \| 0.4841 \| 0.8578 \| 0.8584 \| \| 0.1444 \| 68.09 \| 6400 \| 0.5085 \| 0.8516 \| 0.8524 \| \| 0.1432 \| 70.21 \| 6600 \| 0.4829 \| 0.8626 \| 0.8631 \| \| 0.1426 \| 72.34 \| 6800 \| 0.4582 \| 0.8642 \| 0.8644 \| \| 0.1391 \| 74.47 \| 7000 \| 0.5618 \| 0.8461 \| 0.8470 \| \| 0.1348 \| 76.6 \| 7200 \| 0.4947 \| 0.8647 \| 0.8651 \| \| 0.1383 \| 78.72 \| 7400 \| 0.4901 \| 0.8593 \| 0.8597 \| \| 0.1317 \| 80.85 \| 7600 \| 0.5457 \| 0.8492 \| 0.8497 \| \| 0.1312 \| 82.98 \| 7800 \| 0.5402 \| 0.8484 \| 0.8490 \| \| 0.1311 \| 85.11 \| 8000 \| 0.5053 \| 0.8572 \| 0.8577 \| \| 0.1303 \| 87.23 \| 8200 \| 0.5300 \| 0.8544 \| 0.8550 \| \| 0.128 \| 89.36 \| 8400 \| 0.5192 \| 0.8572 \| 0.8577 \| \| 0.1281 \| 91.49 \| 8600 \| 0.5447 \| 0.8524 \| 0.8530 \| \| 0.1214 \| 93.62 \| 8800 \| 0.5264 \| 0.8553 \| 0.8557 \| \| 0.1244 \| 95.74 \| 9000 \| 0.5569 \| 0.8504 \| 0.8510 \| \| 0.1197 \| 97.87 \| 9200 \| 0.5364 \| 0.8572 \| 0.8577 \| \| 0.1241 \| 100.0 \| 9400 \| 0.5406 \| 0.8532 \| 0.8537 \| \| 0.1216 \| 102.13 \| 9600 \| 0.5441 \| 0.8511 \| 0.8517 \| \| 0.1177 \| 104.26 \| 9800 \| 0.5631 \| 0.8490 \| 0.8497 \| \| 0.1205 \| 106.38 \| 10000 \| 0.5507 \| 0.8504 \| 0.8510 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2	{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_EMP_H3-seqsight_32768_512_43M-L8_f", "results": []}]}	mahdibaghbanzadeh/GUE_EMP_H3-seqsight_32768_512_43M-L8_f	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]	null	2024-04-30T06:43:48+00:00
text-generation	transformers		{}	NEGI007/Llama-2-7b-chat-finetune	null	[ "transformers", "pytorch", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-04-30T06:43:50+00:00

null

{}

AImonkeys/mistralXdocker

null

[ "region:us" ]

null

2024-04-30T05:47:21+00:00

null

# kat33/Mixtral-8x7B-Instruct-v0.1-Q6_K-GGUF This model was converted to GGUF format from [`mistralai/Mixtral-8x7B-Instruct-v0.1`](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) 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/mistralai/Mixtral-8x7B-Instruct-v0.1) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo kat33/Mixtral-8x7B-Instruct-v0.1-Q6_K-GGUF --model mixtral-8x7b-instruct-v0.1.Q6_K.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo kat33/Mixtral-8x7B-Instruct-v0.1-Q6_K-GGUF --model mixtral-8x7b-instruct-v0.1.Q6_K.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. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m mixtral-8x7b-instruct-v0.1.Q6_K.gguf -n 128 ```

{"language": ["fr", "it", "de", "es", "en"], "license": "apache-2.0", "tags": ["llama-cpp", "gguf-my-repo"], "inference": {"parameters": {"temperature": 0.5}}, "widget": [{"messages": [{"role": "user", "content": "What is your favorite condiment?"}]}]}

kat33/Mixtral-8x7B-Instruct-v0.1-Q6_K-GGUF

null

[ "gguf", "llama-cpp", "gguf-my-repo", "fr", "it", "de", "es", "en", "license:apache-2.0", "region:us" ]

null

2024-04-30T05:47:29+00:00

null

peft

# GUE_prom_prom_core_notata-seqsight_32768_512_43M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_notata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_notata) dataset. It achieves the following results on the evaluation set: - Loss: 0.3840 - F1 Score: 0.8338 - Accuracy: 0.8338 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.5472 | 0.6 | 200 | 0.4181 | 0.8117 | 0.8119 | | 0.4381 | 1.2 | 400 | 0.4003 | 0.8190 | 0.8191 | | 0.4205 | 1.81 | 600 | 0.3911 | 0.8243 | 0.8244 | | 0.4179 | 2.41 | 800 | 0.3876 | 0.8264 | 0.8266 | | 0.4072 | 3.01 | 1000 | 0.3833 | 0.8287 | 0.8289 | | 0.4051 | 3.61 | 1200 | 0.3853 | 0.8272 | 0.8276 | | 0.4021 | 4.22 | 1400 | 0.3797 | 0.8318 | 0.8319 | | 0.4066 | 4.82 | 1600 | 0.3777 | 0.8310 | 0.8312 | | 0.3943 | 5.42 | 1800 | 0.3787 | 0.8297 | 0.8297 | | 0.3998 | 6.02 | 2000 | 0.3801 | 0.8315 | 0.8319 | | 0.3971 | 6.63 | 2200 | 0.3780 | 0.8335 | 0.8336 | | 0.392 | 7.23 | 2400 | 0.3841 | 0.8294 | 0.8300 | | 0.3939 | 7.83 | 2600 | 0.3736 | 0.8331 | 0.8332 | | 0.3904 | 8.43 | 2800 | 0.3861 | 0.8293 | 0.8300 | | 0.3951 | 9.04 | 3000 | 0.3779 | 0.8299 | 0.8302 | | 0.387 | 9.64 | 3200 | 0.3752 | 0.8328 | 0.8329 | | 0.3886 | 10.24 | 3400 | 0.3737 | 0.8326 | 0.8327 | | 0.3848 | 10.84 | 3600 | 0.3716 | 0.8332 | 0.8332 | | 0.3857 | 11.45 | 3800 | 0.3736 | 0.8307 | 0.8308 | | 0.3849 | 12.05 | 4000 | 0.3704 | 0.8332 | 0.8332 | | 0.3814 | 12.65 | 4200 | 0.3767 | 0.8328 | 0.8331 | | 0.3859 | 13.25 | 4400 | 0.3726 | 0.8339 | 0.8340 | | 0.3851 | 13.86 | 4600 | 0.3712 | 0.8315 | 0.8315 | | 0.383 | 14.46 | 4800 | 0.3728 | 0.8327 | 0.8329 | | 0.3822 | 15.06 | 5000 | 0.3713 | 0.8318 | 0.8319 | | 0.3802 | 15.66 | 5200 | 0.3708 | 0.8330 | 0.8331 | | 0.3821 | 16.27 | 5400 | 0.3712 | 0.8321 | 0.8321 | | 0.3788 | 16.87 | 5600 | 0.3812 | 0.8313 | 0.8319 | | 0.375 | 17.47 | 5800 | 0.3789 | 0.8334 | 0.8338 | | 0.385 | 18.07 | 6000 | 0.3745 | 0.8341 | 0.8346 | | 0.3775 | 18.67 | 6200 | 0.3698 | 0.8334 | 0.8336 | | 0.379 | 19.28 | 6400 | 0.3706 | 0.8330 | 0.8331 | | 0.3764 | 19.88 | 6600 | 0.3706 | 0.8324 | 0.8327 | | 0.3714 | 20.48 | 6800 | 0.3743 | 0.8340 | 0.8344 | | 0.3842 | 21.08 | 7000 | 0.3683 | 0.8345 | 0.8347 | | 0.3801 | 21.69 | 7200 | 0.3683 | 0.8347 | 0.8347 | | 0.3727 | 22.29 | 7400 | 0.3686 | 0.8348 | 0.8349 | | 0.3725 | 22.89 | 7600 | 0.3691 | 0.8333 | 0.8334 | | 0.3754 | 23.49 | 7800 | 0.3689 | 0.8342 | 0.8344 | | 0.3772 | 24.1 | 8000 | 0.3725 | 0.8335 | 0.8338 | | 0.3773 | 24.7 | 8200 | 0.3736 | 0.8335 | 0.8340 | | 0.371 | 25.3 | 8400 | 0.3721 | 0.8337 | 0.8340 | | 0.379 | 25.9 | 8600 | 0.3688 | 0.8335 | 0.8336 | | 0.3786 | 26.51 | 8800 | 0.3682 | 0.8347 | 0.8347 | | 0.3773 | 27.11 | 9000 | 0.3680 | 0.8329 | 0.8331 | | 0.3799 | 27.71 | 9200 | 0.3692 | 0.8329 | 0.8331 | | 0.3689 | 28.31 | 9400 | 0.3715 | 0.8326 | 0.8329 | | 0.3744 | 28.92 | 9600 | 0.3692 | 0.8334 | 0.8336 | | 0.3783 | 29.52 | 9800 | 0.3690 | 0.8334 | 0.8336 | | 0.3679 | 30.12 | 10000 | 0.3695 | 0.8334 | 0.8336 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_prom_prom_core_notata-seqsight_32768_512_43M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_prom_prom_core_notata-seqsight_32768_512_43M-L1_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]

null

2024-04-30T05:48:08+00:00

text-generation

transformers

# Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```

{"license": "other", "library_name": "transformers", "tags": ["autotrain", "text-generation-inference", "text-generation", "peft"], "widget": [{"messages": [{"role": "user", "content": "What is your favorite condiment?"}]}]}

nanxiz/autotrain-h731u-jdfg6

null

[ "transformers", "tensorboard", "safetensors", "autotrain", "text-generation-inference", "text-generation", "peft", "conversational", "license:other", "endpoints_compatible", "region:us" ]

null

2024-04-30T05:48:39+00:00

null

# O0430HMA14 This model is a fine-tuned version of [allenai/OLMo-1B](https://huggingface.co/allenai/OLMo-1B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0186 ## 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 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 100 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.558 | 0.09 | 10 | 0.2938 | | 0.1782 | 0.18 | 20 | 0.1518 | | 0.1488 | 0.27 | 30 | 0.1634 | | 0.1562 | 0.36 | 40 | 0.1549 | | 0.1523 | 0.45 | 50 | 0.1528 | | 0.1532 | 0.54 | 60 | 0.1495 | | 0.1487 | 0.63 | 70 | 0.1476 | | 0.1493 | 0.73 | 80 | 0.1547 | | 0.148 | 0.82 | 90 | 0.1499 | | 0.1487 | 0.91 | 100 | 0.1516 | | 0.1516 | 1.0 | 110 | 0.1509 | | 0.1464 | 1.09 | 120 | 0.1491 | | 0.2792 | 1.18 | 130 | 2.5830 | | 1.2568 | 1.27 | 140 | 0.1547 | | 0.1824 | 1.36 | 150 | 0.1368 | | 0.341 | 1.45 | 160 | 0.3759 | | 0.1732 | 1.54 | 170 | 0.0789 | | 0.444 | 1.63 | 180 | 0.0761 | | 0.0692 | 1.72 | 190 | 0.0591 | | 0.0553 | 1.81 | 200 | 0.0601 | | 0.0576 | 1.9 | 210 | 0.0560 | | 0.0578 | 1.99 | 220 | 0.0525 | | 0.0498 | 2.08 | 230 | 0.0459 | | 0.0412 | 2.18 | 240 | 0.0334 | | 0.0359 | 2.27 | 250 | 0.0302 | | 0.0315 | 2.36 | 260 | 0.0261 | | 0.0254 | 2.45 | 270 | 0.0243 | | 0.0179 | 2.54 | 280 | 0.0219 | | 0.0251 | 2.63 | 290 | 0.0211 | | 0.0226 | 2.72 | 300 | 0.0195 | | 0.0216 | 2.81 | 310 | 0.0197 | | 0.0231 | 2.9 | 320 | 0.0186 | | 0.0224 | 2.99 | 330 | 0.0186 | ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "allenai/OLMo-1B", "model-index": [{"name": "O0430HMA14", "results": []}]}

Litzy619/O0430HMA14

null

[ "safetensors", "generated_from_trainer", "base_model:allenai/OLMo-1B", "license:apache-2.0", "region:us" ]

null

2024-04-30T05:49:17+00:00

null

{}

oguzkurt/layoutparser-onnx

null

[ "onnx", "region:us" ]

null

2024-04-30T05:49:28+00:00

null

# O0430HMA15 This model is a fine-tuned version of [allenai/OLMo-1B](https://huggingface.co/allenai/OLMo-1B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0266 ## 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 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 100 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.5644 | 0.09 | 10 | 0.2800 | | 0.178 | 0.18 | 20 | 0.1523 | | 0.1487 | 0.27 | 30 | 0.1618 | | 0.1564 | 0.36 | 40 | 0.1585 | | 0.1535 | 0.45 | 50 | 0.1523 | | 0.1531 | 0.54 | 60 | 0.1488 | | 0.1503 | 0.63 | 70 | 0.1486 | | 0.1497 | 0.73 | 80 | 0.1558 | | 0.147 | 0.82 | 90 | 0.1492 | | 0.1496 | 0.91 | 100 | 0.1499 | | 0.1507 | 1.0 | 110 | 0.1486 | | 0.1469 | 1.09 | 120 | 0.1510 | | 0.1478 | 1.18 | 130 | 0.1494 | | 0.1483 | 1.27 | 140 | 0.1481 | | 0.1499 | 1.36 | 150 | 0.1506 | | 0.146 | 1.45 | 160 | 0.1442 | | 0.3204 | 1.54 | 170 | 2.2831 | | 0.367 | 1.63 | 180 | 0.2210 | | 0.0994 | 1.72 | 190 | 0.0781 | | 0.0734 | 1.81 | 200 | 0.0705 | | 0.0635 | 1.9 | 210 | 0.0575 | | 0.0585 | 1.99 | 220 | 0.0566 | | 0.0659 | 2.08 | 230 | 0.0568 | | 0.0521 | 2.18 | 240 | 0.0482 | | 0.0439 | 2.27 | 250 | 0.0367 | | 0.0508 | 2.36 | 260 | 0.0361 | | 0.037 | 2.45 | 270 | 0.0350 | | 0.0269 | 2.54 | 280 | 0.0289 | | 0.0326 | 2.63 | 290 | 0.0277 | | 0.0316 | 2.72 | 300 | 0.0298 | | 0.0286 | 2.81 | 310 | 0.0278 | | 0.028 | 2.9 | 320 | 0.0270 | | 0.0307 | 2.99 | 330 | 0.0266 | ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "allenai/OLMo-1B", "model-index": [{"name": "O0430HMA15", "results": []}]}

Litzy619/O0430HMA15

null

[ "safetensors", "generated_from_trainer", "base_model:allenai/OLMo-1B", "license:apache-2.0", "region:us" ]

null

2024-04-30T05:50:59+00:00

text-to-image

diffusers

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

{"library_name": "diffusers"}

Niggendar/mugenmalumixSDXL_v30

null

[ "diffusers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]

null

2024-04-30T05:51:08+00:00

null

# NikolayKozloff/tweety-tatar-base-7b-2024-v1-Q8_0-GGUF This model was converted to GGUF format from [`Tweeties/tweety-tatar-base-7b-2024-v1`](https://huggingface.co/Tweeties/tweety-tatar-base-7b-2024-v1) 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/Tweeties/tweety-tatar-base-7b-2024-v1) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo NikolayKozloff/tweety-tatar-base-7b-2024-v1-Q8_0-GGUF --model tweety-tatar-base-7b-2024-v1.Q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo NikolayKozloff/tweety-tatar-base-7b-2024-v1-Q8_0-GGUF --model tweety-tatar-base-7b-2024-v1.Q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m tweety-tatar-base-7b-2024-v1.Q8_0.gguf -n 128 ```

{"language": ["tt"], "license": "apache-2.0", "tags": ["tweety", "llama-cpp", "gguf-my-repo"], "datasets": ["oscar-corpus/OSCAR-2301"], "base_model": "mistralai/Mistral-7B-Instruct-v0.2"}

NikolayKozloff/tweety-tatar-base-7b-2024-v1-GGUF

null

[ "gguf", "tweety", "llama-cpp", "gguf-my-repo", "tt", "dataset:oscar-corpus/OSCAR-2301", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "region:us" ]

null

2024-04-30T05:51:28+00:00

null

{"license": "afl-3.0"}

agknoows/OppapaJesus

null

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

null

2024-04-30T05:53:45+00:00

text2text-generation

transformers

{}

shenkha/DGSlow_Bartbase_BST

null

[ "transformers", "pytorch", "bart", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-04-30T05:54:55+00:00

text-generation

transformers

{}

arctic126/hospital_mm4-3b

null

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

null

2024-04-30T05:55:25+00:00

null

peft

# GUE_prom_prom_core_notata-seqsight_32768_512_43M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_notata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_notata) dataset. It achieves the following results on the evaluation set: - Loss: 0.3786 - F1 Score: 0.8327 - Accuracy: 0.8327 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.5136 | 0.6 | 200 | 0.3951 | 0.8217 | 0.8217 | | 0.4153 | 1.2 | 400 | 0.3880 | 0.8265 | 0.8268 | | 0.4002 | 1.81 | 600 | 0.3924 | 0.8262 | 0.8268 | | 0.3984 | 2.41 | 800 | 0.3814 | 0.8318 | 0.8321 | | 0.3895 | 3.01 | 1000 | 0.3794 | 0.8325 | 0.8331 | | 0.3846 | 3.61 | 1200 | 0.3729 | 0.8345 | 0.8347 | | 0.3866 | 4.22 | 1400 | 0.3690 | 0.8381 | 0.8381 | | 0.3879 | 4.82 | 1600 | 0.3693 | 0.8370 | 0.8372 | | 0.3746 | 5.42 | 1800 | 0.3728 | 0.8346 | 0.8346 | | 0.382 | 6.02 | 2000 | 0.3697 | 0.8375 | 0.8378 | | 0.378 | 6.63 | 2200 | 0.3666 | 0.8365 | 0.8366 | | 0.3741 | 7.23 | 2400 | 0.3731 | 0.8346 | 0.8351 | | 0.3749 | 7.83 | 2600 | 0.3636 | 0.8391 | 0.8391 | | 0.3707 | 8.43 | 2800 | 0.3775 | 0.8349 | 0.8357 | | 0.3751 | 9.04 | 3000 | 0.3640 | 0.8409 | 0.8410 | | 0.3674 | 9.64 | 3200 | 0.3633 | 0.8393 | 0.8393 | | 0.3683 | 10.24 | 3400 | 0.3623 | 0.8411 | 0.8412 | | 0.3655 | 10.84 | 3600 | 0.3600 | 0.8419 | 0.8419 | | 0.3654 | 11.45 | 3800 | 0.3603 | 0.8396 | 0.8396 | | 0.3636 | 12.05 | 4000 | 0.3616 | 0.8423 | 0.8423 | | 0.3606 | 12.65 | 4200 | 0.3641 | 0.8406 | 0.8406 | | 0.3643 | 13.25 | 4400 | 0.3632 | 0.8388 | 0.8389 | | 0.3628 | 13.86 | 4600 | 0.3650 | 0.8390 | 0.8391 | | 0.3605 | 14.46 | 4800 | 0.3636 | 0.8388 | 0.8389 | | 0.3612 | 15.06 | 5000 | 0.3580 | 0.8400 | 0.8400 | | 0.3563 | 15.66 | 5200 | 0.3614 | 0.8388 | 0.8389 | | 0.3597 | 16.27 | 5400 | 0.3646 | 0.8402 | 0.8402 | | 0.3565 | 16.87 | 5600 | 0.3689 | 0.8380 | 0.8385 | | 0.3534 | 17.47 | 5800 | 0.3653 | 0.8390 | 0.8393 | | 0.3618 | 18.07 | 6000 | 0.3601 | 0.8410 | 0.8412 | | 0.3549 | 18.67 | 6200 | 0.3577 | 0.8422 | 0.8423 | | 0.3548 | 19.28 | 6400 | 0.3606 | 0.8434 | 0.8434 | | 0.3523 | 19.88 | 6600 | 0.3596 | 0.8404 | 0.8406 | | 0.3461 | 20.48 | 6800 | 0.3600 | 0.8412 | 0.8413 | | 0.359 | 21.08 | 7000 | 0.3598 | 0.8411 | 0.8413 | | 0.3558 | 21.69 | 7200 | 0.3595 | 0.8437 | 0.8438 | | 0.3468 | 22.29 | 7400 | 0.3587 | 0.8410 | 0.8412 | | 0.3469 | 22.89 | 7600 | 0.3605 | 0.8402 | 0.8404 | | 0.3479 | 23.49 | 7800 | 0.3592 | 0.8407 | 0.8408 | | 0.3521 | 24.1 | 8000 | 0.3627 | 0.8383 | 0.8385 | | 0.3509 | 24.7 | 8200 | 0.3631 | 0.8395 | 0.8398 | | 0.3451 | 25.3 | 8400 | 0.3639 | 0.8402 | 0.8404 | | 0.3518 | 25.9 | 8600 | 0.3595 | 0.8410 | 0.8412 | | 0.3502 | 26.51 | 8800 | 0.3592 | 0.8413 | 0.8413 | | 0.3503 | 27.11 | 9000 | 0.3583 | 0.8420 | 0.8421 | | 0.3528 | 27.71 | 9200 | 0.3609 | 0.8402 | 0.8404 | | 0.3399 | 28.31 | 9400 | 0.3624 | 0.8392 | 0.8395 | | 0.349 | 28.92 | 9600 | 0.3598 | 0.8412 | 0.8413 | | 0.3499 | 29.52 | 9800 | 0.3596 | 0.8403 | 0.8404 | | 0.3414 | 30.12 | 10000 | 0.3604 | 0.8406 | 0.8408 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_prom_prom_core_notata-seqsight_32768_512_43M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_prom_prom_core_notata-seqsight_32768_512_43M-L8_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]

null

2024-04-30T05:56:16+00:00

null

peft

# GUE_prom_prom_core_notata-seqsight_32768_512_43M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_notata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_notata) dataset. It achieves the following results on the evaluation set: - Loss: 0.3860 - F1 Score: 0.8313 - Accuracy: 0.8314 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.4902 | 0.6 | 200 | 0.3884 | 0.8259 | 0.8259 | | 0.4053 | 1.2 | 400 | 0.3797 | 0.8339 | 0.8342 | | 0.3903 | 1.81 | 600 | 0.3945 | 0.8235 | 0.8244 | | 0.3882 | 2.41 | 800 | 0.3731 | 0.8377 | 0.8379 | | 0.3811 | 3.01 | 1000 | 0.3734 | 0.8361 | 0.8366 | | 0.3737 | 3.61 | 1200 | 0.3654 | 0.8376 | 0.8378 | | 0.3779 | 4.22 | 1400 | 0.3625 | 0.8389 | 0.8389 | | 0.3767 | 4.82 | 1600 | 0.3628 | 0.8380 | 0.8381 | | 0.3617 | 5.42 | 1800 | 0.3680 | 0.8387 | 0.8387 | | 0.37 | 6.02 | 2000 | 0.3670 | 0.8377 | 0.8379 | | 0.3637 | 6.63 | 2200 | 0.3608 | 0.8407 | 0.8408 | | 0.3596 | 7.23 | 2400 | 0.3738 | 0.8340 | 0.8346 | | 0.3578 | 7.83 | 2600 | 0.3667 | 0.8380 | 0.8379 | | 0.3545 | 8.43 | 2800 | 0.3747 | 0.8374 | 0.8379 | | 0.3584 | 9.04 | 3000 | 0.3673 | 0.8394 | 0.8395 | | 0.3481 | 9.64 | 3200 | 0.3652 | 0.8387 | 0.8387 | | 0.3498 | 10.24 | 3400 | 0.3640 | 0.8411 | 0.8412 | | 0.3455 | 10.84 | 3600 | 0.3607 | 0.8394 | 0.8395 | | 0.3435 | 11.45 | 3800 | 0.3607 | 0.8385 | 0.8385 | | 0.3419 | 12.05 | 4000 | 0.3671 | 0.8397 | 0.8396 | | 0.335 | 12.65 | 4200 | 0.3724 | 0.8379 | 0.8379 | | 0.3397 | 13.25 | 4400 | 0.3717 | 0.8371 | 0.8372 | | 0.3396 | 13.86 | 4600 | 0.3731 | 0.8393 | 0.8395 | | 0.3337 | 14.46 | 4800 | 0.3753 | 0.8361 | 0.8364 | | 0.3357 | 15.06 | 5000 | 0.3635 | 0.8403 | 0.8404 | | 0.3269 | 15.66 | 5200 | 0.3699 | 0.8403 | 0.8404 | | 0.3319 | 16.27 | 5400 | 0.3785 | 0.8403 | 0.8404 | | 0.3289 | 16.87 | 5600 | 0.3847 | 0.8364 | 0.8370 | | 0.3236 | 17.47 | 5800 | 0.3771 | 0.8395 | 0.8396 | | 0.3314 | 18.07 | 6000 | 0.3719 | 0.8401 | 0.8404 | | 0.3246 | 18.67 | 6200 | 0.3693 | 0.8448 | 0.8449 | | 0.3216 | 19.28 | 6400 | 0.3742 | 0.8404 | 0.8404 | | 0.3206 | 19.88 | 6600 | 0.3721 | 0.8375 | 0.8378 | | 0.3143 | 20.48 | 6800 | 0.3731 | 0.8386 | 0.8387 | | 0.3233 | 21.08 | 7000 | 0.3797 | 0.8370 | 0.8374 | | 0.3197 | 21.69 | 7200 | 0.3799 | 0.8373 | 0.8374 | | 0.3108 | 22.29 | 7400 | 0.3766 | 0.8383 | 0.8385 | | 0.3106 | 22.89 | 7600 | 0.3814 | 0.8365 | 0.8368 | | 0.3089 | 23.49 | 7800 | 0.3778 | 0.8389 | 0.8391 | | 0.3158 | 24.1 | 8000 | 0.3849 | 0.8356 | 0.8359 | | 0.3121 | 24.7 | 8200 | 0.3848 | 0.8352 | 0.8357 | | 0.306 | 25.3 | 8400 | 0.3883 | 0.8365 | 0.8368 | | 0.3119 | 25.9 | 8600 | 0.3806 | 0.8370 | 0.8372 | | 0.3095 | 26.51 | 8800 | 0.3817 | 0.8365 | 0.8366 | | 0.311 | 27.11 | 9000 | 0.3797 | 0.8392 | 0.8393 | | 0.3079 | 27.71 | 9200 | 0.3860 | 0.8368 | 0.8370 | | 0.2988 | 28.31 | 9400 | 0.3883 | 0.8370 | 0.8374 | | 0.3086 | 28.92 | 9600 | 0.3826 | 0.8380 | 0.8381 | | 0.3066 | 29.52 | 9800 | 0.3831 | 0.8372 | 0.8374 | | 0.3023 | 30.12 | 10000 | 0.3839 | 0.8376 | 0.8378 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_prom_prom_core_notata-seqsight_32768_512_43M-L32_f", "results": []}]}

mahdibaghbanzadeh/GUE_prom_prom_core_notata-seqsight_32768_512_43M-L32_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]

null

2024-04-30T05:56:24+00:00

null

peft

# GUE_prom_prom_core_tata-seqsight_32768_512_43M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_tata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_tata) dataset. It achieves the following results on the evaluation set: - Loss: 0.4468 - F1 Score: 0.8203 - Accuracy: 0.8206 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:| | 0.6029 | 5.13 | 200 | 0.5832 | 0.6980 | 0.7015 | | 0.5406 | 10.26 | 400 | 0.5696 | 0.7163 | 0.7194 | | 0.5176 | 15.38 | 600 | 0.5599 | 0.7281 | 0.7308 | | 0.4955 | 20.51 | 800 | 0.5382 | 0.7455 | 0.7455 | | 0.4756 | 25.64 | 1000 | 0.5299 | 0.7423 | 0.7423 | | 0.465 | 30.77 | 1200 | 0.5255 | 0.7438 | 0.7439 | | 0.4532 | 35.9 | 1400 | 0.5213 | 0.7534 | 0.7537 | | 0.4388 | 41.03 | 1600 | 0.5134 | 0.7548 | 0.7553 | | 0.4319 | 46.15 | 1800 | 0.5187 | 0.7551 | 0.7553 | | 0.4203 | 51.28 | 2000 | 0.5093 | 0.7683 | 0.7684 | | 0.4066 | 56.41 | 2200 | 0.5230 | 0.7714 | 0.7716 | | 0.4086 | 61.54 | 2400 | 0.4994 | 0.7716 | 0.7716 | | 0.4016 | 66.67 | 2600 | 0.5033 | 0.7667 | 0.7667 | | 0.391 | 71.79 | 2800 | 0.5018 | 0.7732 | 0.7732 | | 0.3842 | 76.92 | 3000 | 0.5181 | 0.7677 | 0.7684 | | 0.3755 | 82.05 | 3200 | 0.4979 | 0.7732 | 0.7732 | | 0.3695 | 87.18 | 3400 | 0.5117 | 0.7694 | 0.7700 | | 0.3637 | 92.31 | 3600 | 0.4982 | 0.7749 | 0.7749 | | 0.3508 | 97.44 | 3800 | 0.5016 | 0.7748 | 0.7749 | | 0.3503 | 102.56 | 4000 | 0.4929 | 0.7830 | 0.7830 | | 0.3429 | 107.69 | 4200 | 0.4888 | 0.7862 | 0.7863 | | 0.3379 | 112.82 | 4400 | 0.4902 | 0.7797 | 0.7798 | | 0.3324 | 117.95 | 4600 | 0.4944 | 0.7812 | 0.7814 | | 0.3301 | 123.08 | 4800 | 0.4942 | 0.7794 | 0.7798 | | 0.3202 | 128.21 | 5000 | 0.4894 | 0.7862 | 0.7863 | | 0.3263 | 133.33 | 5200 | 0.4753 | 0.7928 | 0.7928 | | 0.3215 | 138.46 | 5400 | 0.4740 | 0.7895 | 0.7896 | | 0.3123 | 143.59 | 5600 | 0.4865 | 0.7845 | 0.7847 | | 0.3151 | 148.72 | 5800 | 0.4858 | 0.7895 | 0.7896 | | 0.309 | 153.85 | 6000 | 0.4865 | 0.7845 | 0.7847 | | 0.3092 | 158.97 | 6200 | 0.4841 | 0.7863 | 0.7863 | | 0.3031 | 164.1 | 6400 | 0.4883 | 0.7862 | 0.7863 | | 0.3065 | 169.23 | 6600 | 0.4861 | 0.7895 | 0.7896 | | 0.3016 | 174.36 | 6800 | 0.4825 | 0.7912 | 0.7912 | | 0.299 | 179.49 | 7000 | 0.4909 | 0.7974 | 0.7977 | | 0.2988 | 184.62 | 7200 | 0.4942 | 0.7975 | 0.7977 | | 0.296 | 189.74 | 7400 | 0.4839 | 0.7976 | 0.7977 | | 0.2923 | 194.87 | 7600 | 0.4837 | 0.7879 | 0.7879 | | 0.2932 | 200.0 | 7800 | 0.4832 | 0.7911 | 0.7912 | | 0.2949 | 205.13 | 8000 | 0.4968 | 0.7909 | 0.7912 | | 0.2924 | 210.26 | 8200 | 0.4875 | 0.7960 | 0.7961 | | 0.2963 | 215.38 | 8400 | 0.4904 | 0.7959 | 0.7961 | | 0.2914 | 220.51 | 8600 | 0.5002 | 0.7925 | 0.7928 | | 0.2892 | 225.64 | 8800 | 0.4993 | 0.7942 | 0.7945 | | 0.2917 | 230.77 | 9000 | 0.4928 | 0.7975 | 0.7977 | | 0.2858 | 235.9 | 9200 | 0.4917 | 0.7959 | 0.7961 | | 0.2924 | 241.03 | 9400 | 0.4853 | 0.7960 | 0.7961 | | 0.2868 | 246.15 | 9600 | 0.4926 | 0.7992 | 0.7993 | | 0.2873 | 251.28 | 9800 | 0.4913 | 0.7976 | 0.7977 | | 0.2875 | 256.41 | 10000 | 0.4899 | 0.7976 | 0.7977 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_prom_prom_core_tata-seqsight_32768_512_43M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_prom_prom_core_tata-seqsight_32768_512_43M-L1_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]

null

2024-04-30T05:56:29+00:00

null

peft

# GUE_prom_prom_core_tata-seqsight_32768_512_43M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_tata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_tata) dataset. It achieves the following results on the evaluation set: - Loss: 0.6247 - F1 Score: 0.8222 - Accuracy: 0.8222 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:| | 0.5763 | 5.13 | 200 | 0.5555 | 0.7217 | 0.7227 | | 0.498 | 10.26 | 400 | 0.5365 | 0.7505 | 0.7520 | | 0.4604 | 15.38 | 600 | 0.5318 | 0.7472 | 0.7488 | | 0.4267 | 20.51 | 800 | 0.4895 | 0.7798 | 0.7798 | | 0.3931 | 25.64 | 1000 | 0.4848 | 0.7749 | 0.7749 | | 0.362 | 30.77 | 1200 | 0.4607 | 0.8057 | 0.8059 | | 0.338 | 35.9 | 1400 | 0.4576 | 0.8026 | 0.8026 | | 0.315 | 41.03 | 1600 | 0.4507 | 0.8006 | 0.8010 | | 0.2968 | 46.15 | 1800 | 0.4532 | 0.8140 | 0.8140 | | 0.2813 | 51.28 | 2000 | 0.4684 | 0.8087 | 0.8091 | | 0.2655 | 56.41 | 2200 | 0.4970 | 0.8123 | 0.8124 | | 0.2577 | 61.54 | 2400 | 0.4923 | 0.8007 | 0.8010 | | 0.2449 | 66.67 | 2600 | 0.4722 | 0.8204 | 0.8206 | | 0.2349 | 71.79 | 2800 | 0.4885 | 0.8173 | 0.8173 | | 0.2217 | 76.92 | 3000 | 0.5013 | 0.8172 | 0.8173 | | 0.2111 | 82.05 | 3200 | 0.5198 | 0.8205 | 0.8206 | | 0.2005 | 87.18 | 3400 | 0.5395 | 0.8170 | 0.8173 | | 0.1939 | 92.31 | 3600 | 0.5382 | 0.8123 | 0.8124 | | 0.1867 | 97.44 | 3800 | 0.5531 | 0.8254 | 0.8254 | | 0.1777 | 102.56 | 4000 | 0.5748 | 0.8187 | 0.8189 | | 0.171 | 107.69 | 4200 | 0.5901 | 0.8138 | 0.8140 | | 0.1625 | 112.82 | 4400 | 0.5725 | 0.8222 | 0.8222 | | 0.1571 | 117.95 | 4600 | 0.5986 | 0.8157 | 0.8157 | | 0.1574 | 123.08 | 4800 | 0.6007 | 0.8138 | 0.8140 | | 0.1467 | 128.21 | 5000 | 0.6231 | 0.8169 | 0.8173 | | 0.1462 | 133.33 | 5200 | 0.5896 | 0.8204 | 0.8206 | | 0.1371 | 138.46 | 5400 | 0.6265 | 0.8222 | 0.8222 | | 0.1308 | 143.59 | 5600 | 0.6411 | 0.8253 | 0.8254 | | 0.1304 | 148.72 | 5800 | 0.6175 | 0.8254 | 0.8254 | | 0.1274 | 153.85 | 6000 | 0.6336 | 0.8205 | 0.8206 | | 0.1276 | 158.97 | 6200 | 0.6744 | 0.8155 | 0.8157 | | 0.1225 | 164.1 | 6400 | 0.6494 | 0.8220 | 0.8222 | | 0.1239 | 169.23 | 6600 | 0.6373 | 0.8124 | 0.8124 | | 0.1165 | 174.36 | 6800 | 0.6363 | 0.8238 | 0.8238 | | 0.1151 | 179.49 | 7000 | 0.6376 | 0.8302 | 0.8303 | | 0.1117 | 184.62 | 7200 | 0.6631 | 0.8173 | 0.8173 | | 0.1078 | 189.74 | 7400 | 0.6730 | 0.8270 | 0.8271 | | 0.1058 | 194.87 | 7600 | 0.6678 | 0.8271 | 0.8271 | | 0.1015 | 200.0 | 7800 | 0.6791 | 0.8254 | 0.8254 | | 0.104 | 205.13 | 8000 | 0.6991 | 0.8186 | 0.8189 | | 0.1034 | 210.26 | 8200 | 0.6741 | 0.8189 | 0.8189 | | 0.1026 | 215.38 | 8400 | 0.6680 | 0.8287 | 0.8287 | | 0.1 | 220.51 | 8600 | 0.6933 | 0.8171 | 0.8173 | | 0.0987 | 225.64 | 8800 | 0.6859 | 0.8254 | 0.8254 | | 0.0976 | 230.77 | 9000 | 0.6847 | 0.8254 | 0.8254 | | 0.0966 | 235.9 | 9200 | 0.6927 | 0.8237 | 0.8238 | | 0.0968 | 241.03 | 9400 | 0.6888 | 0.8238 | 0.8238 | | 0.0931 | 246.15 | 9600 | 0.6931 | 0.8253 | 0.8254 | | 0.0906 | 251.28 | 9800 | 0.6998 | 0.8254 | 0.8254 | | 0.0916 | 256.41 | 10000 | 0.6957 | 0.8254 | 0.8254 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_prom_prom_core_tata-seqsight_32768_512_43M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_prom_prom_core_tata-seqsight_32768_512_43M-L8_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]

null

2024-04-30T05:57:21+00:00

null

peft

# GUE_prom_prom_core_tata-seqsight_32768_512_43M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_tata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_tata) dataset. It achieves the following results on the evaluation set: - Loss: 0.9752 - F1 Score: 0.8271 - Accuracy: 0.8271 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:| | 0.5578 | 5.13 | 200 | 0.5322 | 0.7502 | 0.7504 | | 0.464 | 10.26 | 400 | 0.5083 | 0.7701 | 0.7716 | | 0.3882 | 15.38 | 600 | 0.4438 | 0.8074 | 0.8075 | | 0.3241 | 20.51 | 800 | 0.4506 | 0.8234 | 0.8238 | | 0.2722 | 25.64 | 1000 | 0.4721 | 0.8303 | 0.8303 | | 0.2338 | 30.77 | 1200 | 0.4767 | 0.8320 | 0.8320 | | 0.1976 | 35.9 | 1400 | 0.5198 | 0.8336 | 0.8336 | | 0.1754 | 41.03 | 1600 | 0.4998 | 0.8303 | 0.8303 | | 0.1428 | 46.15 | 1800 | 0.6118 | 0.8269 | 0.8271 | | 0.1281 | 51.28 | 2000 | 0.5731 | 0.8302 | 0.8303 | | 0.1127 | 56.41 | 2200 | 0.6563 | 0.8319 | 0.8320 | | 0.0994 | 61.54 | 2400 | 0.6877 | 0.8222 | 0.8222 | | 0.0901 | 66.67 | 2600 | 0.7150 | 0.8352 | 0.8352 | | 0.0817 | 71.79 | 2800 | 0.7223 | 0.8254 | 0.8254 | | 0.0725 | 76.92 | 3000 | 0.7396 | 0.8334 | 0.8336 | | 0.0663 | 82.05 | 3200 | 0.7565 | 0.8335 | 0.8336 | | 0.0601 | 87.18 | 3400 | 0.7511 | 0.8418 | 0.8418 | | 0.0589 | 92.31 | 3600 | 0.7803 | 0.8383 | 0.8385 | | 0.0521 | 97.44 | 3800 | 0.8330 | 0.8385 | 0.8385 | | 0.0525 | 102.56 | 4000 | 0.8002 | 0.8434 | 0.8434 | | 0.0466 | 107.69 | 4200 | 0.7893 | 0.8385 | 0.8385 | | 0.0414 | 112.82 | 4400 | 0.8864 | 0.8369 | 0.8369 | | 0.0385 | 117.95 | 4600 | 0.8732 | 0.8335 | 0.8336 | | 0.0402 | 123.08 | 4800 | 0.8392 | 0.8401 | 0.8401 | | 0.0382 | 128.21 | 5000 | 0.8185 | 0.8285 | 0.8287 | | 0.0384 | 133.33 | 5200 | 0.8188 | 0.8401 | 0.8401 | | 0.0334 | 138.46 | 5400 | 0.8668 | 0.8433 | 0.8434 | | 0.0297 | 143.59 | 5600 | 0.8826 | 0.8319 | 0.8320 | | 0.033 | 148.72 | 5800 | 0.8982 | 0.8336 | 0.8336 | | 0.0285 | 153.85 | 6000 | 0.9081 | 0.8352 | 0.8352 | | 0.0299 | 158.97 | 6200 | 0.8908 | 0.8384 | 0.8385 | | 0.0296 | 164.1 | 6400 | 0.8685 | 0.8368 | 0.8369 | | 0.0288 | 169.23 | 6600 | 0.8841 | 0.8401 | 0.8401 | | 0.0265 | 174.36 | 6800 | 0.8954 | 0.8336 | 0.8336 | | 0.0277 | 179.49 | 7000 | 0.8666 | 0.8417 | 0.8418 | | 0.0243 | 184.62 | 7200 | 0.8899 | 0.8401 | 0.8401 | | 0.023 | 189.74 | 7400 | 0.8804 | 0.8418 | 0.8418 | | 0.0233 | 194.87 | 7600 | 0.9357 | 0.8401 | 0.8401 | | 0.0244 | 200.0 | 7800 | 0.8806 | 0.8401 | 0.8401 | | 0.0212 | 205.13 | 8000 | 0.9329 | 0.8385 | 0.8385 | | 0.022 | 210.26 | 8200 | 0.9356 | 0.8434 | 0.8434 | | 0.0212 | 215.38 | 8400 | 0.9286 | 0.8400 | 0.8401 | | 0.0205 | 220.51 | 8600 | 0.9201 | 0.8434 | 0.8434 | | 0.0215 | 225.64 | 8800 | 0.9130 | 0.8434 | 0.8434 | | 0.021 | 230.77 | 9000 | 0.9020 | 0.8434 | 0.8434 | | 0.0205 | 235.9 | 9200 | 0.9081 | 0.8385 | 0.8385 | | 0.0194 | 241.03 | 9400 | 0.9260 | 0.8320 | 0.8320 | | 0.0182 | 246.15 | 9600 | 0.9300 | 0.8352 | 0.8352 | | 0.0172 | 251.28 | 9800 | 0.9393 | 0.8352 | 0.8352 | | 0.0167 | 256.41 | 10000 | 0.9422 | 0.8352 | 0.8352 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_prom_prom_core_tata-seqsight_32768_512_43M-L32_f", "results": []}]}

mahdibaghbanzadeh/GUE_prom_prom_core_tata-seqsight_32768_512_43M-L32_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]

null

2024-04-30T05:57:21+00:00

text-generation

transformers

# mistral-7b-dpo-full-sft-wo-kqa_golden This model is a fine-tuned version of [Minbyul/mistral-7b-wo-kqa_golden-sft](https://huggingface.co/Minbyul/mistral-7b-wo-kqa_golden-sft) on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set: - Loss: 0.0018 - Rewards/chosen: -0.4458 - Rewards/rejected: -10.1099 - Rewards/accuracies: 1.0 - Rewards/margins: 9.6641 - Logps/rejected: -1564.3792 - Logps/chosen: -241.2112 - Logits/rejected: -2.0516 - Logits/chosen: -1.3414 ## 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: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| | 0.2478 | 0.31 | 100 | 0.0352 | -0.1739 | -4.4264 | 1.0 | 4.2525 | -996.0294 | -214.0196 | -2.9200 | -2.1162 | | 0.1385 | 0.61 | 200 | 0.0041 | -0.3360 | -8.1997 | 1.0 | 7.8637 | -1373.3590 | -230.2282 | -2.3336 | -1.6287 | | 0.0899 | 0.92 | 300 | 0.0019 | -0.4479 | -10.0624 | 1.0 | 9.6145 | -1559.6263 | -241.4165 | -2.0553 | -1.3416 | ### Framework versions - Transformers 4.39.0.dev0 - Pytorch 2.1.2 - Datasets 2.14.6 - Tokenizers 0.15.2

{"license": "apache-2.0", "tags": ["alignment-handbook", "trl", "dpo", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["HuggingFaceH4/ultrafeedback_binarized"], "base_model": "Minbyul/mistral-7b-wo-kqa_golden-sft", "model-index": [{"name": "mistral-7b-dpo-full-sft-wo-kqa_golden", "results": []}]}

Minbyul/mistral-7b-dpo-full-sft-wo-kqa_golden

null

[ "transformers", "safetensors", "mistral", "text-generation", "alignment-handbook", "trl", "dpo", "generated_from_trainer", "dataset:HuggingFaceH4/ultrafeedback_binarized", "base_model:Minbyul/mistral-7b-wo-kqa_golden-sft", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-04-30T05:57:27+00:00

null

peft

# mistral_envs_claim_finetune2 This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 5 - total_train_batch_size: 40 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 100 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.1.0a0+29c30b1 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "mistralai/Mistral-7B-Instruct-v0.2", "model-index": [{"name": "mistral_envs_claim_finetune2", "results": []}]}

Haimee/mistral_envs_claim_finetune2

null

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

null

2024-04-30T05:58:26+00:00

null

peft

# GUE_prom_prom_300_all-seqsight_32768_512_43M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_prom_prom_300_all](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_300_all) dataset. It achieves the following results on the evaluation set: - Loss: 0.2119 - F1 Score: 0.9145 - Accuracy: 0.9145 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.4346 | 0.54 | 200 | 0.2868 | 0.8895 | 0.8895 | | 0.2911 | 1.08 | 400 | 0.2578 | 0.8990 | 0.8990 | | 0.2714 | 1.62 | 600 | 0.2389 | 0.9039 | 0.9039 | | 0.2514 | 2.16 | 800 | 0.2377 | 0.9043 | 0.9044 | | 0.2477 | 2.7 | 1000 | 0.2262 | 0.9061 | 0.9061 | | 0.2379 | 3.24 | 1200 | 0.2297 | 0.9080 | 0.9081 | | 0.2416 | 3.78 | 1400 | 0.2212 | 0.9102 | 0.9103 | | 0.2327 | 4.32 | 1600 | 0.2150 | 0.9111 | 0.9111 | | 0.2277 | 4.86 | 1800 | 0.2154 | 0.9120 | 0.9120 | | 0.224 | 5.41 | 2000 | 0.2112 | 0.9142 | 0.9142 | | 0.2231 | 5.95 | 2200 | 0.2120 | 0.9155 | 0.9155 | | 0.2227 | 6.49 | 2400 | 0.2081 | 0.9155 | 0.9155 | | 0.2201 | 7.03 | 2600 | 0.2055 | 0.9164 | 0.9164 | | 0.2153 | 7.57 | 2800 | 0.2038 | 0.9177 | 0.9177 | | 0.2176 | 8.11 | 3000 | 0.2018 | 0.9194 | 0.9194 | | 0.2154 | 8.65 | 3200 | 0.2013 | 0.9193 | 0.9193 | | 0.2099 | 9.19 | 3400 | 0.1997 | 0.9189 | 0.9189 | | 0.2076 | 9.73 | 3600 | 0.1996 | 0.9187 | 0.9187 | | 0.2161 | 10.27 | 3800 | 0.1973 | 0.9206 | 0.9206 | | 0.2091 | 10.81 | 4000 | 0.1972 | 0.9206 | 0.9206 | | 0.2112 | 11.35 | 4200 | 0.2030 | 0.9183 | 0.9184 | | 0.2085 | 11.89 | 4400 | 0.1967 | 0.9208 | 0.9208 | | 0.2041 | 12.43 | 4600 | 0.1979 | 0.9212 | 0.9213 | | 0.2089 | 12.97 | 4800 | 0.1950 | 0.9211 | 0.9211 | | 0.2047 | 13.51 | 5000 | 0.1969 | 0.9208 | 0.9208 | | 0.2065 | 14.05 | 5200 | 0.1946 | 0.9223 | 0.9223 | | 0.2033 | 14.59 | 5400 | 0.1977 | 0.9209 | 0.9209 | | 0.2021 | 15.14 | 5600 | 0.1989 | 0.9212 | 0.9213 | | 0.2004 | 15.68 | 5800 | 0.1977 | 0.9218 | 0.9218 | | 0.2041 | 16.22 | 6000 | 0.2004 | 0.9197 | 0.9198 | | 0.2004 | 16.76 | 6200 | 0.1956 | 0.9219 | 0.9220 | | 0.2002 | 17.3 | 6400 | 0.1943 | 0.9198 | 0.9198 | | 0.2044 | 17.84 | 6600 | 0.1946 | 0.9206 | 0.9206 | | 0.1962 | 18.38 | 6800 | 0.1966 | 0.9221 | 0.9221 | | 0.2041 | 18.92 | 7000 | 0.1957 | 0.9219 | 0.9220 | | 0.201 | 19.46 | 7200 | 0.1931 | 0.9235 | 0.9235 | | 0.1972 | 20.0 | 7400 | 0.1928 | 0.9223 | 0.9223 | | 0.202 | 20.54 | 7600 | 0.1928 | 0.9240 | 0.9240 | | 0.2 | 21.08 | 7800 | 0.1928 | 0.9236 | 0.9236 | | 0.1977 | 21.62 | 8000 | 0.1944 | 0.9233 | 0.9233 | | 0.198 | 22.16 | 8200 | 0.1929 | 0.9240 | 0.9240 | | 0.1908 | 22.7 | 8400 | 0.1942 | 0.9241 | 0.9242 | | 0.202 | 23.24 | 8600 | 0.1933 | 0.9231 | 0.9231 | | 0.1959 | 23.78 | 8800 | 0.1932 | 0.9231 | 0.9231 | | 0.2012 | 24.32 | 9000 | 0.1924 | 0.9235 | 0.9235 | | 0.1952 | 24.86 | 9200 | 0.1923 | 0.9235 | 0.9235 | | 0.195 | 25.41 | 9400 | 0.1928 | 0.9238 | 0.9238 | | 0.1939 | 25.95 | 9600 | 0.1925 | 0.9231 | 0.9231 | | 0.1969 | 26.49 | 9800 | 0.1940 | 0.9233 | 0.9233 | | 0.1955 | 27.03 | 10000 | 0.1931 | 0.9233 | 0.9233 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_prom_prom_300_all-seqsight_32768_512_43M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_prom_prom_300_all-seqsight_32768_512_43M-L1_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]

null

2024-04-30T05:59:37+00:00

null

peft

# GUE_prom_prom_300_all-seqsight_32768_512_43M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_prom_prom_300_all](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_300_all) dataset. It achieves the following results on the evaluation set: - Loss: 0.2006 - F1 Score: 0.9216 - Accuracy: 0.9216 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.3689 | 0.54 | 200 | 0.2509 | 0.9032 | 0.9032 | | 0.2545 | 1.08 | 400 | 0.2269 | 0.9081 | 0.9081 | | 0.2364 | 1.62 | 600 | 0.2112 | 0.9159 | 0.9159 | | 0.2203 | 2.16 | 800 | 0.2049 | 0.9203 | 0.9203 | | 0.2183 | 2.7 | 1000 | 0.2038 | 0.9164 | 0.9164 | | 0.2107 | 3.24 | 1200 | 0.2041 | 0.9177 | 0.9177 | | 0.2129 | 3.78 | 1400 | 0.2001 | 0.9182 | 0.9182 | | 0.206 | 4.32 | 1600 | 0.1946 | 0.9220 | 0.9220 | | 0.2031 | 4.86 | 1800 | 0.1933 | 0.9230 | 0.9230 | | 0.199 | 5.41 | 2000 | 0.2003 | 0.9199 | 0.9199 | | 0.1979 | 5.95 | 2200 | 0.1933 | 0.9231 | 0.9231 | | 0.1985 | 6.49 | 2400 | 0.1892 | 0.9228 | 0.9228 | | 0.1966 | 7.03 | 2600 | 0.1923 | 0.9253 | 0.9253 | | 0.1907 | 7.57 | 2800 | 0.1905 | 0.9248 | 0.9248 | | 0.1936 | 8.11 | 3000 | 0.1867 | 0.9265 | 0.9265 | | 0.1901 | 8.65 | 3200 | 0.1891 | 0.9243 | 0.9243 | | 0.1872 | 9.19 | 3400 | 0.1878 | 0.9247 | 0.9247 | | 0.183 | 9.73 | 3600 | 0.1841 | 0.9255 | 0.9255 | | 0.1901 | 10.27 | 3800 | 0.1859 | 0.9236 | 0.9236 | | 0.1842 | 10.81 | 4000 | 0.1845 | 0.9277 | 0.9277 | | 0.1845 | 11.35 | 4200 | 0.1855 | 0.9274 | 0.9274 | | 0.1827 | 11.89 | 4400 | 0.1856 | 0.9262 | 0.9262 | | 0.1807 | 12.43 | 4600 | 0.1813 | 0.9270 | 0.9270 | | 0.1798 | 12.97 | 4800 | 0.1835 | 0.9265 | 0.9265 | | 0.178 | 13.51 | 5000 | 0.1861 | 0.9272 | 0.9272 | | 0.1787 | 14.05 | 5200 | 0.1860 | 0.9235 | 0.9235 | | 0.1745 | 14.59 | 5400 | 0.1862 | 0.9275 | 0.9275 | | 0.175 | 15.14 | 5600 | 0.1869 | 0.9262 | 0.9262 | | 0.1725 | 15.68 | 5800 | 0.1846 | 0.9231 | 0.9231 | | 0.1746 | 16.22 | 6000 | 0.1852 | 0.9258 | 0.9258 | | 0.1702 | 16.76 | 6200 | 0.1853 | 0.9257 | 0.9257 | | 0.1717 | 17.3 | 6400 | 0.1836 | 0.9260 | 0.9260 | | 0.1738 | 17.84 | 6600 | 0.1820 | 0.9294 | 0.9294 | | 0.1663 | 18.38 | 6800 | 0.1842 | 0.9235 | 0.9235 | | 0.1726 | 18.92 | 7000 | 0.1802 | 0.9279 | 0.9279 | | 0.1699 | 19.46 | 7200 | 0.1822 | 0.9272 | 0.9272 | | 0.167 | 20.0 | 7400 | 0.1822 | 0.9289 | 0.9289 | | 0.1712 | 20.54 | 7600 | 0.1813 | 0.9290 | 0.9291 | | 0.1678 | 21.08 | 7800 | 0.1805 | 0.9289 | 0.9289 | | 0.1652 | 21.62 | 8000 | 0.1828 | 0.9299 | 0.9299 | | 0.1651 | 22.16 | 8200 | 0.1817 | 0.9274 | 0.9274 | | 0.16 | 22.7 | 8400 | 0.1859 | 0.9258 | 0.9258 | | 0.1684 | 23.24 | 8600 | 0.1830 | 0.9284 | 0.9284 | | 0.1641 | 23.78 | 8800 | 0.1836 | 0.9262 | 0.9262 | | 0.1684 | 24.32 | 9000 | 0.1815 | 0.9269 | 0.9269 | | 0.1609 | 24.86 | 9200 | 0.1823 | 0.9274 | 0.9274 | | 0.1624 | 25.41 | 9400 | 0.1812 | 0.9274 | 0.9274 | | 0.1616 | 25.95 | 9600 | 0.1819 | 0.9277 | 0.9277 | | 0.1634 | 26.49 | 9800 | 0.1821 | 0.9284 | 0.9284 | | 0.1601 | 27.03 | 10000 | 0.1819 | 0.9284 | 0.9284 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_prom_prom_300_all-seqsight_32768_512_43M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_prom_prom_300_all-seqsight_32768_512_43M-L8_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]

null

2024-04-30T05:59:48+00:00

null

peft

# GUE_prom_prom_300_all-seqsight_32768_512_43M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_prom_prom_300_all](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_300_all) dataset. It achieves the following results on the evaluation set: - Loss: 0.1981 - F1 Score: 0.9235 - Accuracy: 0.9235 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.3368 | 0.54 | 200 | 0.2353 | 0.9084 | 0.9084 | | 0.2343 | 1.08 | 400 | 0.2030 | 0.9176 | 0.9176 | | 0.2205 | 1.62 | 600 | 0.1989 | 0.9197 | 0.9198 | | 0.209 | 2.16 | 800 | 0.1961 | 0.9209 | 0.9209 | | 0.207 | 2.7 | 1000 | 0.1989 | 0.9149 | 0.9149 | | 0.1983 | 3.24 | 1200 | 0.1933 | 0.9184 | 0.9184 | | 0.1988 | 3.78 | 1400 | 0.1986 | 0.9192 | 0.9193 | | 0.1943 | 4.32 | 1600 | 0.1880 | 0.9255 | 0.9255 | | 0.1883 | 4.86 | 1800 | 0.1852 | 0.9248 | 0.9248 | | 0.182 | 5.41 | 2000 | 0.1877 | 0.9265 | 0.9265 | | 0.1841 | 5.95 | 2200 | 0.1843 | 0.9263 | 0.9264 | | 0.1817 | 6.49 | 2400 | 0.1895 | 0.9239 | 0.9240 | | 0.1795 | 7.03 | 2600 | 0.1829 | 0.9270 | 0.9270 | | 0.1726 | 7.57 | 2800 | 0.1849 | 0.9267 | 0.9267 | | 0.1723 | 8.11 | 3000 | 0.1821 | 0.9287 | 0.9287 | | 0.1686 | 8.65 | 3200 | 0.1881 | 0.9278 | 0.9279 | | 0.1656 | 9.19 | 3400 | 0.1821 | 0.9282 | 0.9282 | | 0.1605 | 9.73 | 3600 | 0.1768 | 0.9291 | 0.9291 | | 0.1656 | 10.27 | 3800 | 0.1778 | 0.9289 | 0.9289 | | 0.1606 | 10.81 | 4000 | 0.1741 | 0.9316 | 0.9316 | | 0.1594 | 11.35 | 4200 | 0.1806 | 0.9309 | 0.9309 | | 0.1563 | 11.89 | 4400 | 0.1826 | 0.9305 | 0.9306 | | 0.1554 | 12.43 | 4600 | 0.1727 | 0.9323 | 0.9323 | | 0.1513 | 12.97 | 4800 | 0.1741 | 0.9285 | 0.9285 | | 0.1481 | 13.51 | 5000 | 0.1776 | 0.9297 | 0.9297 | | 0.1486 | 14.05 | 5200 | 0.1869 | 0.9218 | 0.9218 | | 0.1429 | 14.59 | 5400 | 0.1801 | 0.9304 | 0.9304 | | 0.1445 | 15.14 | 5600 | 0.1792 | 0.9316 | 0.9316 | | 0.1408 | 15.68 | 5800 | 0.1781 | 0.9304 | 0.9304 | | 0.1408 | 16.22 | 6000 | 0.1751 | 0.9301 | 0.9301 | | 0.1352 | 16.76 | 6200 | 0.1871 | 0.9263 | 0.9264 | | 0.138 | 17.3 | 6400 | 0.1750 | 0.9294 | 0.9294 | | 0.1358 | 17.84 | 6600 | 0.1777 | 0.9323 | 0.9323 | | 0.1315 | 18.38 | 6800 | 0.1856 | 0.9299 | 0.9299 | | 0.1369 | 18.92 | 7000 | 0.1762 | 0.9316 | 0.9316 | | 0.1321 | 19.46 | 7200 | 0.1793 | 0.9306 | 0.9306 | | 0.1311 | 20.0 | 7400 | 0.1807 | 0.9334 | 0.9334 | | 0.1323 | 20.54 | 7600 | 0.1799 | 0.9306 | 0.9306 | | 0.1272 | 21.08 | 7800 | 0.1808 | 0.9307 | 0.9307 | | 0.1237 | 21.62 | 8000 | 0.1877 | 0.9280 | 0.9280 | | 0.1246 | 22.16 | 8200 | 0.1837 | 0.9302 | 0.9302 | | 0.122 | 22.7 | 8400 | 0.1848 | 0.9301 | 0.9301 | | 0.1236 | 23.24 | 8600 | 0.1878 | 0.9299 | 0.9299 | | 0.1224 | 23.78 | 8800 | 0.1875 | 0.9294 | 0.9294 | | 0.1232 | 24.32 | 9000 | 0.1848 | 0.9304 | 0.9304 | | 0.1228 | 24.86 | 9200 | 0.1844 | 0.9307 | 0.9307 | | 0.1188 | 25.41 | 9400 | 0.1856 | 0.9299 | 0.9299 | | 0.12 | 25.95 | 9600 | 0.1847 | 0.9316 | 0.9316 | | 0.1195 | 26.49 | 9800 | 0.1859 | 0.9309 | 0.9309 | | 0.1165 | 27.03 | 10000 | 0.1854 | 0.9318 | 0.9318 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_prom_prom_300_all-seqsight_32768_512_43M-L32_f", "results": []}]}

mahdibaghbanzadeh/GUE_prom_prom_300_all-seqsight_32768_512_43M-L32_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]

null

2024-04-30T05:59:58+00:00

null

# O0430HMA16 This model is a fine-tuned version of [allenai/OLMo-1B](https://huggingface.co/allenai/OLMo-1B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1386 ## 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 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 100 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.5715 | 0.09 | 10 | 0.2837 | | 0.1807 | 0.18 | 20 | 0.1554 | | 0.1515 | 0.27 | 30 | 0.1672 | | 0.1573 | 0.36 | 40 | 0.1535 | | 0.1517 | 0.45 | 50 | 0.1504 | | 0.1521 | 0.54 | 60 | 0.1490 | | 0.1513 | 0.63 | 70 | 0.1472 | | 0.1494 | 0.73 | 80 | 0.1574 | | 0.1484 | 0.82 | 90 | 0.1490 | | 0.149 | 0.91 | 100 | 0.1494 | | 0.1512 | 1.0 | 110 | 0.1499 | | 0.1463 | 1.09 | 120 | 0.1482 | | 0.1462 | 1.18 | 130 | 0.1522 | | 0.1484 | 1.27 | 140 | 0.1487 | | 0.1499 | 1.36 | 150 | 0.1501 | | 0.1463 | 1.45 | 160 | 0.1478 | | 0.146 | 1.54 | 170 | 0.1477 | | 0.1472 | 1.63 | 180 | 0.1472 | | 0.1461 | 1.72 | 190 | 0.1490 | | 0.1443 | 1.81 | 200 | 0.1497 | | 0.1494 | 1.9 | 210 | 0.1503 | | 0.1456 | 1.99 | 220 | 0.1472 | | 0.1429 | 2.08 | 230 | 0.1446 | | 0.1383 | 2.18 | 240 | 0.1445 | | 0.1401 | 2.27 | 250 | 0.1450 | | 0.141 | 2.36 | 260 | 0.1459 | | 0.1398 | 2.45 | 270 | 0.1428 | | 0.1341 | 2.54 | 280 | 0.1389 | | 0.1345 | 2.63 | 290 | 0.1411 | | 0.1347 | 2.72 | 300 | 0.1395 | | 0.1335 | 2.81 | 310 | 0.1387 | | 0.1321 | 2.9 | 320 | 0.1387 | | 0.1375 | 2.99 | 330 | 0.1386 | ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "allenai/OLMo-1B", "model-index": [{"name": "O0430HMA16", "results": []}]}

Litzy619/O0430HMA16

null

[ "safetensors", "generated_from_trainer", "base_model:allenai/OLMo-1B", "license:apache-2.0", "region:us" ]

null

2024-04-30T06:03:10+00:00

null

peft

# GUE_EMP_H3K14ac-seqsight_32768_512_43M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_EMP_H3K14ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K14ac) dataset. It achieves the following results on the evaluation set: - Loss: 0.4907 - F1 Score: 0.7713 - Accuracy: 0.7703 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.6078 | 0.97 | 200 | 0.5696 | 0.7214 | 0.7198 | | 0.5576 | 1.93 | 400 | 0.5322 | 0.7501 | 0.7486 | | 0.5381 | 2.9 | 600 | 0.5385 | 0.7543 | 0.7528 | | 0.5289 | 3.86 | 800 | 0.5084 | 0.7646 | 0.7643 | | 0.5195 | 4.83 | 1000 | 0.5251 | 0.7586 | 0.7570 | | 0.5138 | 5.8 | 1200 | 0.5170 | 0.7626 | 0.7610 | | 0.5131 | 6.76 | 1400 | 0.5057 | 0.7662 | 0.7646 | | 0.5086 | 7.73 | 1600 | 0.5034 | 0.7698 | 0.7682 | | 0.5062 | 8.7 | 1800 | 0.5035 | 0.7668 | 0.7652 | | 0.5012 | 9.66 | 2000 | 0.5088 | 0.7659 | 0.7643 | | 0.5059 | 10.63 | 2200 | 0.5152 | 0.7624 | 0.7610 | | 0.4987 | 11.59 | 2400 | 0.4991 | 0.7686 | 0.7670 | | 0.5029 | 12.56 | 2600 | 0.5098 | 0.7674 | 0.7658 | | 0.4966 | 13.53 | 2800 | 0.5062 | 0.7658 | 0.7643 | | 0.4979 | 14.49 | 3000 | 0.5158 | 0.7632 | 0.7619 | | 0.4895 | 15.46 | 3200 | 0.4918 | 0.7751 | 0.7737 | | 0.4949 | 16.43 | 3400 | 0.5080 | 0.7645 | 0.7631 | | 0.4919 | 17.39 | 3600 | 0.4903 | 0.7742 | 0.7728 | | 0.4882 | 18.36 | 3800 | 0.4883 | 0.7733 | 0.7722 | | 0.4895 | 19.32 | 4000 | 0.4909 | 0.7752 | 0.7737 | | 0.4871 | 20.29 | 4200 | 0.4916 | 0.7761 | 0.7746 | | 0.487 | 21.26 | 4400 | 0.4970 | 0.7722 | 0.7707 | | 0.4855 | 22.22 | 4600 | 0.5079 | 0.7702 | 0.7688 | | 0.4866 | 23.19 | 4800 | 0.4903 | 0.7770 | 0.7755 | | 0.4869 | 24.15 | 5000 | 0.4891 | 0.7731 | 0.7716 | | 0.4828 | 25.12 | 5200 | 0.5005 | 0.7713 | 0.7697 | | 0.4815 | 26.09 | 5400 | 0.4942 | 0.7740 | 0.7725 | | 0.4814 | 27.05 | 5600 | 0.5042 | 0.7690 | 0.7676 | | 0.4829 | 28.02 | 5800 | 0.4832 | 0.7760 | 0.7746 | | 0.4815 | 28.99 | 6000 | 0.4999 | 0.7733 | 0.7719 | | 0.4804 | 29.95 | 6200 | 0.4979 | 0.7743 | 0.7728 | | 0.4816 | 30.92 | 6400 | 0.4819 | 0.7778 | 0.7764 | | 0.4798 | 31.88 | 6600 | 0.4874 | 0.7749 | 0.7734 | | 0.4784 | 32.85 | 6800 | 0.4942 | 0.7752 | 0.7737 | | 0.483 | 33.82 | 7000 | 0.4982 | 0.7731 | 0.7716 | | 0.4786 | 34.78 | 7200 | 0.4936 | 0.7731 | 0.7716 | | 0.4794 | 35.75 | 7400 | 0.4892 | 0.7770 | 0.7755 | | 0.4748 | 36.71 | 7600 | 0.4904 | 0.7731 | 0.7716 | | 0.4772 | 37.68 | 7800 | 0.4898 | 0.7758 | 0.7743 | | 0.4771 | 38.65 | 8000 | 0.4837 | 0.7770 | 0.7755 | | 0.4826 | 39.61 | 8200 | 0.4880 | 0.7749 | 0.7734 | | 0.4715 | 40.58 | 8400 | 0.4948 | 0.7725 | 0.7710 | | 0.4742 | 41.55 | 8600 | 0.4891 | 0.7734 | 0.7719 | | 0.4721 | 42.51 | 8800 | 0.4891 | 0.7737 | 0.7722 | | 0.475 | 43.48 | 9000 | 0.4985 | 0.7743 | 0.7728 | | 0.4741 | 44.44 | 9200 | 0.4925 | 0.7740 | 0.7725 | | 0.4757 | 45.41 | 9400 | 0.4892 | 0.7731 | 0.7716 | | 0.469 | 46.38 | 9600 | 0.4934 | 0.7740 | 0.7725 | | 0.4794 | 47.34 | 9800 | 0.4906 | 0.7740 | 0.7725 | | 0.474 | 48.31 | 10000 | 0.4891 | 0.7740 | 0.7725 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_EMP_H3K14ac-seqsight_32768_512_43M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3K14ac-seqsight_32768_512_43M-L1_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]

null

2024-04-30T06:04:03+00:00

text-generation

transformers

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

{"library_name": "transformers", "tags": []}

pruning/v16o0y7

null

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

null

2024-04-30T06:04:38+00:00

null

peft

# GUE_EMP_H3K14ac-seqsight_32768_512_43M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_EMP_H3K14ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K14ac) dataset. It achieves the following results on the evaluation set: - Loss: 0.4984 - F1 Score: 0.7700 - Accuracy: 0.7691 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.5881 | 0.97 | 200 | 0.5331 | 0.7519 | 0.7504 | | 0.5288 | 1.93 | 400 | 0.5084 | 0.7643 | 0.7628 | | 0.5108 | 2.9 | 600 | 0.5162 | 0.7548 | 0.7534 | | 0.5075 | 3.86 | 800 | 0.4914 | 0.7690 | 0.7682 | | 0.5005 | 4.83 | 1000 | 0.5060 | 0.7655 | 0.7640 | | 0.4943 | 5.8 | 1200 | 0.4978 | 0.7701 | 0.7685 | | 0.4904 | 6.76 | 1400 | 0.4867 | 0.7751 | 0.7737 | | 0.4863 | 7.73 | 1600 | 0.4914 | 0.7740 | 0.7725 | | 0.4831 | 8.7 | 1800 | 0.4916 | 0.7698 | 0.7682 | | 0.4792 | 9.66 | 2000 | 0.4948 | 0.7734 | 0.7719 | | 0.4808 | 10.63 | 2200 | 0.4976 | 0.7713 | 0.7697 | | 0.4736 | 11.59 | 2400 | 0.4820 | 0.7721 | 0.7707 | | 0.4753 | 12.56 | 2600 | 0.4928 | 0.7758 | 0.7743 | | 0.4685 | 13.53 | 2800 | 0.4896 | 0.7722 | 0.7707 | | 0.469 | 14.49 | 3000 | 0.4958 | 0.7746 | 0.7731 | | 0.4594 | 15.46 | 3200 | 0.4800 | 0.7779 | 0.7767 | | 0.4653 | 16.43 | 3400 | 0.4969 | 0.7736 | 0.7722 | | 0.4602 | 17.39 | 3600 | 0.4808 | 0.7778 | 0.7764 | | 0.4567 | 18.36 | 3800 | 0.4809 | 0.7765 | 0.7761 | | 0.4558 | 19.32 | 4000 | 0.4864 | 0.7802 | 0.7788 | | 0.4537 | 20.29 | 4200 | 0.4880 | 0.7760 | 0.7746 | | 0.4516 | 21.26 | 4400 | 0.4905 | 0.7761 | 0.7746 | | 0.4498 | 22.22 | 4600 | 0.5092 | 0.7702 | 0.7688 | | 0.4484 | 23.19 | 4800 | 0.4872 | 0.7731 | 0.7719 | | 0.4479 | 24.15 | 5000 | 0.4912 | 0.7679 | 0.7664 | | 0.4463 | 25.12 | 5200 | 0.5022 | 0.7737 | 0.7722 | | 0.4407 | 26.09 | 5400 | 0.4960 | 0.7710 | 0.7694 | | 0.4414 | 27.05 | 5600 | 0.5094 | 0.7707 | 0.7691 | | 0.4399 | 28.02 | 5800 | 0.4877 | 0.7719 | 0.7707 | | 0.44 | 28.99 | 6000 | 0.4894 | 0.7752 | 0.7737 | | 0.4353 | 29.95 | 6200 | 0.4999 | 0.7692 | 0.7676 | | 0.4355 | 30.92 | 6400 | 0.4850 | 0.7729 | 0.7725 | | 0.4349 | 31.88 | 6600 | 0.4909 | 0.7722 | 0.7710 | | 0.432 | 32.85 | 6800 | 0.5072 | 0.7674 | 0.7658 | | 0.4368 | 33.82 | 7000 | 0.5021 | 0.7707 | 0.7691 | | 0.4289 | 34.78 | 7200 | 0.5049 | 0.7716 | 0.7700 | | 0.4296 | 35.75 | 7400 | 0.4976 | 0.7747 | 0.7734 | | 0.4261 | 36.71 | 7600 | 0.5024 | 0.7698 | 0.7682 | | 0.425 | 37.68 | 7800 | 0.5051 | 0.7701 | 0.7685 | | 0.4272 | 38.65 | 8000 | 0.4953 | 0.7735 | 0.7722 | | 0.432 | 39.61 | 8200 | 0.4941 | 0.7711 | 0.7697 | | 0.4189 | 40.58 | 8400 | 0.5041 | 0.7701 | 0.7685 | | 0.421 | 41.55 | 8600 | 0.5030 | 0.7710 | 0.7694 | | 0.4204 | 42.51 | 8800 | 0.4993 | 0.7706 | 0.7691 | | 0.421 | 43.48 | 9000 | 0.5108 | 0.7710 | 0.7694 | | 0.4199 | 44.44 | 9200 | 0.5078 | 0.7677 | 0.7661 | | 0.4216 | 45.41 | 9400 | 0.5051 | 0.7692 | 0.7676 | | 0.4155 | 46.38 | 9600 | 0.5062 | 0.7683 | 0.7667 | | 0.4253 | 47.34 | 9800 | 0.5025 | 0.7701 | 0.7685 | | 0.4169 | 48.31 | 10000 | 0.5015 | 0.7724 | 0.7710 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_EMP_H3K14ac-seqsight_32768_512_43M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3K14ac-seqsight_32768_512_43M-L8_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]

null

2024-04-30T06:04:50+00:00

null

peft

# GUE_EMP_H3K14ac-seqsight_32768_512_43M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_EMP_H3K14ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K14ac) dataset. It achieves the following results on the evaluation set: - Loss: 0.4924 - F1 Score: 0.7762 - Accuracy: 0.7752 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.5719 | 0.97 | 200 | 0.5131 | 0.7592 | 0.7576 | | 0.516 | 1.93 | 400 | 0.4993 | 0.7691 | 0.7676 | | 0.5012 | 2.9 | 600 | 0.5039 | 0.7604 | 0.7589 | | 0.4962 | 3.86 | 800 | 0.4826 | 0.7744 | 0.7734 | | 0.4878 | 4.83 | 1000 | 0.5088 | 0.7652 | 0.7637 | | 0.4813 | 5.8 | 1200 | 0.4903 | 0.7764 | 0.7749 | | 0.4734 | 6.76 | 1400 | 0.4825 | 0.7806 | 0.7791 | | 0.4678 | 7.73 | 1600 | 0.4871 | 0.7731 | 0.7716 | | 0.464 | 8.7 | 1800 | 0.4969 | 0.7730 | 0.7716 | | 0.457 | 9.66 | 2000 | 0.4931 | 0.7761 | 0.7746 | | 0.4555 | 10.63 | 2200 | 0.5066 | 0.7755 | 0.7740 | | 0.4445 | 11.59 | 2400 | 0.4927 | 0.7700 | 0.7688 | | 0.4455 | 12.56 | 2600 | 0.5078 | 0.7752 | 0.7737 | | 0.4334 | 13.53 | 2800 | 0.5079 | 0.7677 | 0.7661 | | 0.4316 | 14.49 | 3000 | 0.4904 | 0.7696 | 0.7682 | | 0.4191 | 15.46 | 3200 | 0.4980 | 0.7759 | 0.7749 | | 0.4206 | 16.43 | 3400 | 0.4976 | 0.7710 | 0.7694 | | 0.4119 | 17.39 | 3600 | 0.5108 | 0.7670 | 0.7655 | | 0.4073 | 18.36 | 3800 | 0.5048 | 0.7689 | 0.7691 | | 0.3984 | 19.32 | 4000 | 0.5055 | 0.7800 | 0.7788 | | 0.3956 | 20.29 | 4200 | 0.5051 | 0.7701 | 0.7691 | | 0.3896 | 21.26 | 4400 | 0.5276 | 0.7695 | 0.7679 | | 0.3835 | 22.22 | 4600 | 0.5343 | 0.7647 | 0.7631 | | 0.3797 | 23.19 | 4800 | 0.5330 | 0.7693 | 0.7679 | | 0.3742 | 24.15 | 5000 | 0.5308 | 0.7655 | 0.7643 | | 0.3716 | 25.12 | 5200 | 0.5492 | 0.7650 | 0.7634 | | 0.3631 | 26.09 | 5400 | 0.5351 | 0.7614 | 0.7598 | | 0.3565 | 27.05 | 5600 | 0.5650 | 0.7677 | 0.7661 | | 0.3511 | 28.02 | 5800 | 0.5519 | 0.7723 | 0.7710 | | 0.3508 | 28.99 | 6000 | 0.5461 | 0.7672 | 0.7658 | | 0.3449 | 29.95 | 6200 | 0.5521 | 0.7676 | 0.7664 | | 0.3422 | 30.92 | 6400 | 0.5529 | 0.7701 | 0.7703 | | 0.3384 | 31.88 | 6600 | 0.5605 | 0.7624 | 0.7610 | | 0.3347 | 32.85 | 6800 | 0.5864 | 0.7611 | 0.7595 | | 0.3308 | 33.82 | 7000 | 0.5862 | 0.7644 | 0.7628 | | 0.3215 | 34.78 | 7200 | 0.6019 | 0.7590 | 0.7573 | | 0.3212 | 35.75 | 7400 | 0.5779 | 0.7651 | 0.7637 | | 0.3204 | 36.71 | 7600 | 0.5864 | 0.7660 | 0.7646 | | 0.3105 | 37.68 | 7800 | 0.6002 | 0.7599 | 0.7582 | | 0.3132 | 38.65 | 8000 | 0.5929 | 0.7654 | 0.7640 | | 0.317 | 39.61 | 8200 | 0.5880 | 0.7680 | 0.7670 | | 0.3075 | 40.58 | 8400 | 0.6154 | 0.7629 | 0.7613 | | 0.3072 | 41.55 | 8600 | 0.6056 | 0.7673 | 0.7658 | | 0.3029 | 42.51 | 8800 | 0.6055 | 0.7624 | 0.7610 | | 0.3003 | 43.48 | 9000 | 0.6175 | 0.7647 | 0.7631 | | 0.3014 | 44.44 | 9200 | 0.6056 | 0.7622 | 0.7607 | | 0.299 | 45.41 | 9400 | 0.6095 | 0.7637 | 0.7622 | | 0.2925 | 46.38 | 9600 | 0.6190 | 0.7637 | 0.7622 | | 0.3016 | 47.34 | 9800 | 0.6069 | 0.7605 | 0.7592 | | 0.297 | 48.31 | 10000 | 0.6072 | 0.7626 | 0.7613 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_EMP_H3K14ac-seqsight_32768_512_43M-L32_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3K14ac-seqsight_32768_512_43M-L32_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]

null

2024-04-30T06:04:53+00:00

null

peft

# GUE_EMP_H3K4me2-seqsight_32768_512_43M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me2) dataset. It achieves the following results on the evaluation set: - Loss: 0.5889 - F1 Score: 0.6823 - Accuracy: 0.6859 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.6634 | 1.04 | 200 | 0.6368 | 0.5949 | 0.6370 | | 0.6269 | 2.08 | 400 | 0.6301 | 0.6479 | 0.6478 | | 0.6197 | 3.12 | 600 | 0.6218 | 0.6430 | 0.6637 | | 0.6175 | 4.17 | 800 | 0.6171 | 0.6532 | 0.6634 | | 0.6135 | 5.21 | 1000 | 0.6189 | 0.6562 | 0.6572 | | 0.6077 | 6.25 | 1200 | 0.6137 | 0.6643 | 0.6699 | | 0.6004 | 7.29 | 1400 | 0.6209 | 0.6650 | 0.6641 | | 0.6018 | 8.33 | 1600 | 0.6177 | 0.6605 | 0.6618 | | 0.5998 | 9.38 | 1800 | 0.6248 | 0.6571 | 0.6546 | | 0.5971 | 10.42 | 2000 | 0.6112 | 0.6675 | 0.6689 | | 0.5978 | 11.46 | 2200 | 0.6064 | 0.6649 | 0.6725 | | 0.5902 | 12.5 | 2400 | 0.6080 | 0.6656 | 0.6709 | | 0.5888 | 13.54 | 2600 | 0.6064 | 0.6657 | 0.6742 | | 0.591 | 14.58 | 2800 | 0.6076 | 0.6601 | 0.6712 | | 0.5931 | 15.62 | 3000 | 0.6061 | 0.6685 | 0.6748 | | 0.5876 | 16.67 | 3200 | 0.6108 | 0.6668 | 0.6686 | | 0.5866 | 17.71 | 3400 | 0.6083 | 0.6722 | 0.6764 | | 0.587 | 18.75 | 3600 | 0.6062 | 0.6657 | 0.6722 | | 0.5859 | 19.79 | 3800 | 0.6069 | 0.6705 | 0.6751 | | 0.5817 | 20.83 | 4000 | 0.6080 | 0.6707 | 0.6729 | | 0.5844 | 21.88 | 4200 | 0.6106 | 0.6720 | 0.6738 | | 0.5821 | 22.92 | 4400 | 0.6090 | 0.6717 | 0.6748 | | 0.5835 | 23.96 | 4600 | 0.6083 | 0.6711 | 0.6729 | | 0.5788 | 25.0 | 4800 | 0.6077 | 0.6734 | 0.6777 | | 0.5792 | 26.04 | 5000 | 0.6075 | 0.6742 | 0.6777 | | 0.5789 | 27.08 | 5200 | 0.6058 | 0.6730 | 0.6771 | | 0.5787 | 28.12 | 5400 | 0.6047 | 0.6737 | 0.6777 | | 0.577 | 29.17 | 5600 | 0.6072 | 0.6742 | 0.6764 | | 0.5749 | 30.21 | 5800 | 0.6089 | 0.6764 | 0.6797 | | 0.5777 | 31.25 | 6000 | 0.6071 | 0.6751 | 0.6787 | | 0.5757 | 32.29 | 6200 | 0.6042 | 0.6748 | 0.6810 | | 0.5751 | 33.33 | 6400 | 0.6049 | 0.6777 | 0.6823 | | 0.5745 | 34.38 | 6600 | 0.6049 | 0.6736 | 0.6804 | | 0.5729 | 35.42 | 6800 | 0.6059 | 0.6732 | 0.6787 | | 0.5747 | 36.46 | 7000 | 0.6046 | 0.6749 | 0.6804 | | 0.5719 | 37.5 | 7200 | 0.6063 | 0.6790 | 0.6830 | | 0.5712 | 38.54 | 7400 | 0.6065 | 0.6757 | 0.6817 | | 0.576 | 39.58 | 7600 | 0.6048 | 0.6730 | 0.6790 | | 0.5734 | 40.62 | 7800 | 0.6080 | 0.6770 | 0.6790 | | 0.572 | 41.67 | 8000 | 0.6053 | 0.6790 | 0.6826 | | 0.5691 | 42.71 | 8200 | 0.6060 | 0.6743 | 0.6830 | | 0.5714 | 43.75 | 8400 | 0.6064 | 0.6729 | 0.6777 | | 0.5698 | 44.79 | 8600 | 0.6076 | 0.6774 | 0.6807 | | 0.5691 | 45.83 | 8800 | 0.6062 | 0.6757 | 0.6810 | | 0.5708 | 46.88 | 9000 | 0.6077 | 0.6771 | 0.6800 | | 0.5687 | 47.92 | 9200 | 0.6071 | 0.6779 | 0.6813 | | 0.57 | 48.96 | 9400 | 0.6062 | 0.6772 | 0.6826 | | 0.5693 | 50.0 | 9600 | 0.6070 | 0.6768 | 0.6810 | | 0.5705 | 51.04 | 9800 | 0.6063 | 0.6778 | 0.6823 | | 0.5675 | 52.08 | 10000 | 0.6066 | 0.6770 | 0.6813 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_EMP_H3K4me2-seqsight_32768_512_43M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3K4me2-seqsight_32768_512_43M-L1_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]

null

2024-04-30T06:05:02+00:00

null

peft

# GUE_EMP_H3K4me2-seqsight_32768_512_43M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me2) dataset. It achieves the following results on the evaluation set: - Loss: 0.5958 - F1 Score: 0.6827 - Accuracy: 0.6859 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.6544 | 1.04 | 200 | 0.6235 | 0.6306 | 0.6556 | | 0.6187 | 2.08 | 400 | 0.6353 | 0.6397 | 0.6370 | | 0.6082 | 3.12 | 600 | 0.6119 | 0.6639 | 0.6670 | | 0.6041 | 4.17 | 800 | 0.6275 | 0.6549 | 0.6527 | | 0.5998 | 5.21 | 1000 | 0.6067 | 0.6745 | 0.6807 | | 0.5941 | 6.25 | 1200 | 0.6047 | 0.6746 | 0.6777 | | 0.5862 | 7.29 | 1400 | 0.6132 | 0.6688 | 0.6676 | | 0.5851 | 8.33 | 1600 | 0.6192 | 0.6728 | 0.6712 | | 0.583 | 9.38 | 1800 | 0.6262 | 0.6607 | 0.6582 | | 0.5799 | 10.42 | 2000 | 0.5997 | 0.6783 | 0.6843 | | 0.58 | 11.46 | 2200 | 0.6031 | 0.6759 | 0.6774 | | 0.5704 | 12.5 | 2400 | 0.6035 | 0.6793 | 0.6820 | | 0.569 | 13.54 | 2600 | 0.6077 | 0.6813 | 0.6813 | | 0.5687 | 14.58 | 2800 | 0.6074 | 0.6732 | 0.6777 | | 0.5694 | 15.62 | 3000 | 0.6038 | 0.6775 | 0.6787 | | 0.5639 | 16.67 | 3200 | 0.6062 | 0.6764 | 0.6761 | | 0.56 | 17.71 | 3400 | 0.6144 | 0.6696 | 0.6686 | | 0.5615 | 18.75 | 3600 | 0.6066 | 0.6847 | 0.6865 | | 0.5586 | 19.79 | 3800 | 0.6191 | 0.6777 | 0.6764 | | 0.5537 | 20.83 | 4000 | 0.6056 | 0.6795 | 0.6797 | | 0.5519 | 21.88 | 4200 | 0.6202 | 0.6727 | 0.6709 | | 0.5497 | 22.92 | 4400 | 0.6200 | 0.6798 | 0.6787 | | 0.5489 | 23.96 | 4600 | 0.6198 | 0.6710 | 0.6693 | | 0.5436 | 25.0 | 4800 | 0.6249 | 0.6795 | 0.6787 | | 0.5427 | 26.04 | 5000 | 0.6220 | 0.6797 | 0.6790 | | 0.5429 | 27.08 | 5200 | 0.6125 | 0.6775 | 0.6768 | | 0.5397 | 28.12 | 5400 | 0.6088 | 0.6769 | 0.6774 | | 0.5375 | 29.17 | 5600 | 0.6170 | 0.6782 | 0.6790 | | 0.5335 | 30.21 | 5800 | 0.6257 | 0.6752 | 0.6748 | | 0.5343 | 31.25 | 6000 | 0.6239 | 0.6785 | 0.6777 | | 0.5323 | 32.29 | 6200 | 0.6155 | 0.6747 | 0.6755 | | 0.5325 | 33.33 | 6400 | 0.6229 | 0.6756 | 0.6755 | | 0.5274 | 34.38 | 6600 | 0.6185 | 0.6718 | 0.6745 | | 0.5289 | 35.42 | 6800 | 0.6177 | 0.6784 | 0.6790 | | 0.5255 | 36.46 | 7000 | 0.6233 | 0.6782 | 0.6781 | | 0.5242 | 37.5 | 7200 | 0.6262 | 0.6801 | 0.6794 | | 0.5206 | 38.54 | 7400 | 0.6232 | 0.6783 | 0.6790 | | 0.5248 | 39.58 | 7600 | 0.6167 | 0.6799 | 0.6823 | | 0.5231 | 40.62 | 7800 | 0.6301 | 0.6737 | 0.6725 | | 0.5205 | 41.67 | 8000 | 0.6185 | 0.6763 | 0.6771 | | 0.515 | 42.71 | 8200 | 0.6307 | 0.6749 | 0.6748 | | 0.5195 | 43.75 | 8400 | 0.6224 | 0.6778 | 0.6777 | | 0.5169 | 44.79 | 8600 | 0.6281 | 0.6767 | 0.6761 | | 0.5146 | 45.83 | 8800 | 0.6279 | 0.6794 | 0.6804 | | 0.5139 | 46.88 | 9000 | 0.6355 | 0.6762 | 0.6748 | | 0.5144 | 47.92 | 9200 | 0.6329 | 0.6781 | 0.6774 | | 0.5148 | 48.96 | 9400 | 0.6308 | 0.6771 | 0.6774 | | 0.5131 | 50.0 | 9600 | 0.6336 | 0.6774 | 0.6768 | | 0.5143 | 51.04 | 9800 | 0.6331 | 0.6783 | 0.6777 | | 0.5076 | 52.08 | 10000 | 0.6350 | 0.6765 | 0.6758 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_EMP_H3K4me2-seqsight_32768_512_43M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3K4me2-seqsight_32768_512_43M-L8_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]

null

2024-04-30T06:05:02+00:00

automatic-speech-recognition

transformers

# w2v-bert-2.0-tamil-gpu-custom_preprocessed_v1 This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: inf - eval_wer: 0.4790 - eval_runtime: 231.2694 - eval_samples_per_second: 18.922 - eval_steps_per_second: 2.365 - epoch: 3.17 - step: 3900 ## 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: 4.83567e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - 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_steps: 500 - num_epochs: 10 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "facebook/w2v-bert-2.0", "model-index": [{"name": "w2v-bert-2.0-tamil-gpu-custom_preprocessed_v1", "results": []}]}

Sajjo/w2v-bert-2.0-tamil-gpu-custom_preprocessed_v1

null

[ "transformers", "tensorboard", "safetensors", "wav2vec2-bert", "automatic-speech-recognition", "generated_from_trainer", "base_model:facebook/w2v-bert-2.0", "license:mit", "endpoints_compatible", "region:us" ]

null

2024-04-30T06:05:24+00:00

token-classification

transformers

# token_classifier This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2720 - Precision: 0.6096 - Recall: 0.3170 - F1: 0.4171 - Accuracy: 0.9426 ## 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 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 213 | 0.2820 | 0.6278 | 0.2641 | 0.3718 | 0.9398 | | No log | 2.0 | 426 | 0.2720 | 0.6096 | 0.3170 | 0.4171 | 0.9426 | ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.17.1 - Tokenizers 0.15.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "distilbert/distilbert-base-uncased", "model-index": [{"name": "token_classifier", "results": []}]}

madanagrawal/token_classifier

null

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

null

2024-04-30T06:05:38+00:00

null

transformers

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

{"library_name": "transformers", "tags": []}

ryanyeo/kirnect-2-koAlpaca-polyglot-5.8b-remote-5150step-8batch_5epoch

null

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

null

2024-04-30T06:07:36+00:00

null

{}

Kishoar/t5-small-finetuned-xsum

null

[ "region:us" ]

null

2024-04-30T06:08:29+00:00

null

peft

# GUE_EMP_H3K4me2-seqsight_32768_512_43M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me2) dataset. It achieves the following results on the evaluation set: - Loss: 0.5885 - F1 Score: 0.6910 - Accuracy: 0.6960 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.6489 | 1.04 | 200 | 0.6205 | 0.6282 | 0.6572 | | 0.6141 | 2.08 | 400 | 0.6325 | 0.6494 | 0.6468 | | 0.6004 | 3.12 | 600 | 0.6101 | 0.6761 | 0.6777 | | 0.5966 | 4.17 | 800 | 0.6098 | 0.6706 | 0.6696 | | 0.5871 | 5.21 | 1000 | 0.6038 | 0.6727 | 0.6787 | | 0.5799 | 6.25 | 1200 | 0.6059 | 0.6757 | 0.6748 | | 0.5724 | 7.29 | 1400 | 0.6034 | 0.6771 | 0.6764 | | 0.5654 | 8.33 | 1600 | 0.6109 | 0.6796 | 0.6784 | | 0.5613 | 9.38 | 1800 | 0.6213 | 0.6759 | 0.6735 | | 0.554 | 10.42 | 2000 | 0.5952 | 0.6836 | 0.6885 | | 0.551 | 11.46 | 2200 | 0.6100 | 0.6832 | 0.6852 | | 0.5368 | 12.5 | 2400 | 0.6070 | 0.6786 | 0.6804 | | 0.532 | 13.54 | 2600 | 0.6329 | 0.6777 | 0.6758 | | 0.5253 | 14.58 | 2800 | 0.6159 | 0.6759 | 0.6804 | | 0.5216 | 15.62 | 3000 | 0.6318 | 0.6718 | 0.6703 | | 0.5124 | 16.67 | 3200 | 0.6345 | 0.6771 | 0.6768 | | 0.5005 | 17.71 | 3400 | 0.6745 | 0.6740 | 0.6716 | | 0.4965 | 18.75 | 3600 | 0.6430 | 0.6810 | 0.6804 | | 0.4911 | 19.79 | 3800 | 0.6654 | 0.6789 | 0.6771 | | 0.4822 | 20.83 | 4000 | 0.6607 | 0.6792 | 0.6771 | | 0.4738 | 21.88 | 4200 | 0.6825 | 0.6787 | 0.6768 | | 0.466 | 22.92 | 4400 | 0.6785 | 0.6746 | 0.6725 | | 0.4655 | 23.96 | 4600 | 0.6764 | 0.6757 | 0.6745 | | 0.455 | 25.0 | 4800 | 0.7236 | 0.6651 | 0.6628 | | 0.4458 | 26.04 | 5000 | 0.7467 | 0.6646 | 0.6621 | | 0.4433 | 27.08 | 5200 | 0.7294 | 0.6622 | 0.6598 | | 0.434 | 28.12 | 5400 | 0.6890 | 0.6697 | 0.6693 | | 0.4279 | 29.17 | 5600 | 0.7299 | 0.6700 | 0.6680 | | 0.4234 | 30.21 | 5800 | 0.7531 | 0.6694 | 0.6673 | | 0.4146 | 31.25 | 6000 | 0.7745 | 0.6719 | 0.6696 | | 0.4129 | 32.29 | 6200 | 0.7660 | 0.6646 | 0.6621 | | 0.4072 | 33.33 | 6400 | 0.7582 | 0.6675 | 0.6657 | | 0.3998 | 34.38 | 6600 | 0.7820 | 0.6706 | 0.6693 | | 0.3952 | 35.42 | 6800 | 0.8030 | 0.6623 | 0.6598 | | 0.39 | 36.46 | 7000 | 0.7745 | 0.6719 | 0.6696 | | 0.387 | 37.5 | 7200 | 0.7637 | 0.6650 | 0.6628 | | 0.3819 | 38.54 | 7400 | 0.7709 | 0.6764 | 0.6764 | | 0.3772 | 39.58 | 7600 | 0.7686 | 0.6702 | 0.6706 | | 0.3793 | 40.62 | 7800 | 0.8079 | 0.6683 | 0.6660 | | 0.3733 | 41.67 | 8000 | 0.8120 | 0.6646 | 0.6621 | | 0.3666 | 42.71 | 8200 | 0.8165 | 0.6693 | 0.6670 | | 0.3671 | 43.75 | 8400 | 0.8185 | 0.6651 | 0.6628 | | 0.3668 | 44.79 | 8600 | 0.8077 | 0.6697 | 0.6676 | | 0.362 | 45.83 | 8800 | 0.8043 | 0.6658 | 0.6641 | | 0.3612 | 46.88 | 9000 | 0.8099 | 0.6661 | 0.6637 | | 0.3555 | 47.92 | 9200 | 0.8180 | 0.6710 | 0.6689 | | 0.3501 | 48.96 | 9400 | 0.8214 | 0.6695 | 0.6680 | | 0.3515 | 50.0 | 9600 | 0.8309 | 0.6679 | 0.6657 | | 0.3512 | 51.04 | 9800 | 0.8336 | 0.6694 | 0.6673 | | 0.3464 | 52.08 | 10000 | 0.8380 | 0.6692 | 0.6670 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_EMP_H3K4me2-seqsight_32768_512_43M-L32_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3K4me2-seqsight_32768_512_43M-L32_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]

null

2024-04-30T06:11:37+00:00

null

{"license": "openrail"}

rowlette/Arisu_BlueArchive

null

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

null

2024-04-30T06:11:40+00:00

null

{}

LZDXN/bert-base-uncased_ai4privacy_en

null

[ "region:us" ]

null

2024-04-30T06:11:41+00:00

text-generation

transformers

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

{"library_name": "transformers", "tags": []}

lunarsylph/mooncell_v36

null

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

null

2024-04-30T06:12:15+00:00

null

peft

# main This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4148 ## 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 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 1.0218 | 0.9032 | 7 | 0.8713 | | 0.5518 | 1.9355 | 15 | 0.5401 | | 0.3373 | 2.9677 | 23 | 0.4473 | | 0.3523 | 4.0 | 31 | 0.4159 | | 0.3219 | 4.5161 | 35 | 0.4148 | ### Framework versions - PEFT 0.10.1.dev0 - Transformers 4.41.0.dev0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "llama2", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "meta-llama/Llama-2-7b-chat-hf", "model-index": [{"name": "main", "results": []}]}

Huma97/llama2-EquityAdvisor

null

[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-chat-hf", "license:llama2", "region:us" ]

null

2024-04-30T06:13:09+00:00

text-generation

transformers

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

{"library_name": "transformers", "tags": []}

ryanyeo/kirnect-2-koAlpaca-polyglot-5.8B-remote

null

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

null

2024-04-30T06:13:23+00:00

text-to-image

diffusers

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

{"library_name": "diffusers"}

Niggendar/mightMixes15Ponyxl_pxlBurst

null

[ "diffusers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]

null

2024-04-30T06:13:52+00:00

null

{"license": "apache-2.0"}

smallscholar/Medical-Llama-3-8B-Chat

null

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

null

2024-04-30T06:14:01+00:00

null

{"license": "apache-2.0"}

smallscholar/Medical-Llama-3-8B-Lora

null

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

null

2024-04-30T06:14:30+00:00

null

# DerekWolfie/llama-3-8B-Instruct-function-calling-v0.2-Q5_K_M-GGUF This model was converted to GGUF format from [`mzbac/llama-3-8B-Instruct-function-calling-v0.2`](https://huggingface.co/mzbac/llama-3-8B-Instruct-function-calling-v0.2) 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/mzbac/llama-3-8B-Instruct-function-calling-v0.2) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo DerekWolfie/llama-3-8B-Instruct-function-calling-v0.2-Q5_K_M-GGUF --model llama-3-8b-instruct-function-calling-v0.2.Q5_K_M.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo DerekWolfie/llama-3-8B-Instruct-function-calling-v0.2-Q5_K_M-GGUF --model llama-3-8b-instruct-function-calling-v0.2.Q5_K_M.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m llama-3-8b-instruct-function-calling-v0.2.Q5_K_M.gguf -n 128 ```

{"language": ["en"], "license": "llama3", "tags": ["llama-cpp", "gguf-my-repo"], "datasets": ["mzbac/function-calling-llama-3-format-v1.1"]}

DerekWolfie/dereks-llama-3-8B-Instruct-function-calling

null

[ "gguf", "llama-cpp", "gguf-my-repo", "en", "dataset:mzbac/function-calling-llama-3-format-v1.1", "license:llama3", "region:us" ]

null

2024-04-30T06:14:39+00:00

null

peft

# GUE_EMP_H3K9ac-seqsight_32768_512_43M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_EMP_H3K9ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K9ac) dataset. It achieves the following results on the evaluation set: - Loss: 0.4802 - F1 Score: 0.7833 - Accuracy: 0.7827 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.6197 | 1.15 | 200 | 0.5705 | 0.7183 | 0.7179 | | 0.5503 | 2.3 | 400 | 0.5731 | 0.7118 | 0.7125 | | 0.5252 | 3.45 | 600 | 0.5792 | 0.7139 | 0.7157 | | 0.5201 | 4.6 | 800 | 0.5674 | 0.7232 | 0.7240 | | 0.5124 | 5.75 | 1000 | 0.5417 | 0.7324 | 0.7319 | | 0.5082 | 6.9 | 1200 | 0.5598 | 0.7310 | 0.7308 | | 0.5026 | 8.05 | 1400 | 0.5465 | 0.7388 | 0.7384 | | 0.5014 | 9.2 | 1600 | 0.5725 | 0.7203 | 0.7226 | | 0.4945 | 10.34 | 1800 | 0.5384 | 0.7429 | 0.7424 | | 0.4922 | 11.49 | 2000 | 0.5424 | 0.7436 | 0.7434 | | 0.4867 | 12.64 | 2200 | 0.5651 | 0.7278 | 0.7294 | | 0.4894 | 13.79 | 2400 | 0.5483 | 0.7323 | 0.7334 | | 0.4871 | 14.94 | 2600 | 0.5391 | 0.7400 | 0.7402 | | 0.4809 | 16.09 | 2800 | 0.5321 | 0.7439 | 0.7438 | | 0.4791 | 17.24 | 3000 | 0.5445 | 0.7382 | 0.7384 | | 0.4785 | 18.39 | 3200 | 0.5470 | 0.7407 | 0.7416 | | 0.4804 | 19.54 | 3400 | 0.5253 | 0.7463 | 0.7463 | | 0.4729 | 20.69 | 3600 | 0.5203 | 0.7514 | 0.7510 | | 0.4743 | 21.84 | 3800 | 0.5228 | 0.7468 | 0.7470 | | 0.4701 | 22.99 | 4000 | 0.5275 | 0.7437 | 0.7442 | | 0.4734 | 24.14 | 4200 | 0.5078 | 0.7547 | 0.7542 | | 0.4626 | 25.29 | 4400 | 0.5260 | 0.7533 | 0.7531 | | 0.4698 | 26.44 | 4600 | 0.5283 | 0.7494 | 0.7496 | | 0.4677 | 27.59 | 4800 | 0.5292 | 0.7437 | 0.7445 | | 0.4641 | 28.74 | 5000 | 0.5166 | 0.7538 | 0.7539 | | 0.47 | 29.89 | 5200 | 0.5211 | 0.7492 | 0.7492 | | 0.4622 | 31.03 | 5400 | 0.5256 | 0.7467 | 0.7474 | | 0.4644 | 32.18 | 5600 | 0.5069 | 0.7594 | 0.7589 | | 0.4554 | 33.33 | 5800 | 0.5209 | 0.7527 | 0.7528 | | 0.4678 | 34.48 | 6000 | 0.5253 | 0.7440 | 0.7449 | | 0.4559 | 35.63 | 6200 | 0.5153 | 0.7511 | 0.7510 | | 0.4638 | 36.78 | 6400 | 0.5167 | 0.7497 | 0.7499 | | 0.4579 | 37.93 | 6600 | 0.5228 | 0.7478 | 0.7481 | | 0.4589 | 39.08 | 6800 | 0.5101 | 0.7548 | 0.7546 | | 0.4589 | 40.23 | 7000 | 0.5161 | 0.7516 | 0.7517 | | 0.4573 | 41.38 | 7200 | 0.5168 | 0.7512 | 0.7513 | | 0.457 | 42.53 | 7400 | 0.5161 | 0.7534 | 0.7535 | | 0.4565 | 43.68 | 7600 | 0.5145 | 0.7564 | 0.7564 | | 0.4535 | 44.83 | 7800 | 0.5226 | 0.7500 | 0.7506 | | 0.4568 | 45.98 | 8000 | 0.5133 | 0.7541 | 0.7542 | | 0.4581 | 47.13 | 8200 | 0.5187 | 0.7503 | 0.7506 | | 0.4531 | 48.28 | 8400 | 0.5167 | 0.7520 | 0.7521 | | 0.4507 | 49.43 | 8600 | 0.5164 | 0.7519 | 0.7521 | | 0.4548 | 50.57 | 8800 | 0.5161 | 0.7528 | 0.7528 | | 0.4545 | 51.72 | 9000 | 0.5210 | 0.7469 | 0.7474 | | 0.4486 | 52.87 | 9200 | 0.5196 | 0.7488 | 0.7492 | | 0.4547 | 54.02 | 9400 | 0.5173 | 0.7503 | 0.7506 | | 0.4513 | 55.17 | 9600 | 0.5190 | 0.7485 | 0.7488 | | 0.4511 | 56.32 | 9800 | 0.5142 | 0.7527 | 0.7528 | | 0.4546 | 57.47 | 10000 | 0.5164 | 0.7504 | 0.7506 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_EMP_H3K9ac-seqsight_32768_512_43M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3K9ac-seqsight_32768_512_43M-L1_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]

null

2024-04-30T06:14:57+00:00

image-classification

transformers

# Model Trained Using AutoTrain - Problem type: Image Classification ## Validation Metrics loss: 0.3038078248500824 f1_macro: 0.7294036951655769 f1_micro: 0.899283031751451 f1_weighted: 0.8963777407391669 precision_macro: 0.8462013295295603 precision_micro: 0.899283031751451 precision_weighted: 0.9070935900298 recall_macro: 0.6921156764861889 recall_micro: 0.899283031751451 recall_weighted: 0.899283031751451 accuracy: 0.899283031751451

{"tags": ["autotrain", "image-classification"], "datasets": ["autotrain-swin-tiny-patch4-window7-224/autotrain-data"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_title": "Teapot"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg", "example_title": "Palace"}]}

Kushagra07/autotrain-swin-tiny-patch4-window7-224

null

[ "transformers", "tensorboard", "safetensors", "swin", "image-classification", "autotrain", "dataset:autotrain-swin-tiny-patch4-window7-224/autotrain-data", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-04-30T06:15:18+00:00

text-generation

transformers

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

{"library_name": "transformers", "tags": []}

cilantro9246/ofeq1al

null

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

null

2024-04-30T06:15:36+00:00

null

peft

# GUE_EMP_H3K9ac-seqsight_32768_512_43M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_EMP_H3K9ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K9ac) dataset. It achieves the following results on the evaluation set: - Loss: 0.4635 - F1 Score: 0.7915 - Accuracy: 0.7909 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.5867 | 1.15 | 200 | 0.5738 | 0.7174 | 0.7172 | | 0.5175 | 2.3 | 400 | 0.5902 | 0.6854 | 0.6909 | | 0.4952 | 3.45 | 600 | 0.5512 | 0.7323 | 0.7330 | | 0.4899 | 4.6 | 800 | 0.5397 | 0.7364 | 0.7370 | | 0.4814 | 5.75 | 1000 | 0.5230 | 0.7506 | 0.7503 | | 0.4769 | 6.9 | 1200 | 0.5291 | 0.7465 | 0.7463 | | 0.4718 | 8.05 | 1400 | 0.5302 | 0.7483 | 0.7481 | | 0.4688 | 9.2 | 1600 | 0.5332 | 0.7482 | 0.7488 | | 0.4642 | 10.34 | 1800 | 0.5266 | 0.7500 | 0.7496 | | 0.4591 | 11.49 | 2000 | 0.5179 | 0.7547 | 0.7542 | | 0.4529 | 12.64 | 2200 | 0.5190 | 0.7553 | 0.7549 | | 0.4541 | 13.79 | 2400 | 0.5267 | 0.7575 | 0.7575 | | 0.4482 | 14.94 | 2600 | 0.5170 | 0.7601 | 0.7596 | | 0.4441 | 16.09 | 2800 | 0.5429 | 0.7522 | 0.7531 | | 0.441 | 17.24 | 3000 | 0.5347 | 0.7582 | 0.7578 | | 0.4424 | 18.39 | 3200 | 0.5122 | 0.7648 | 0.7643 | | 0.4418 | 19.54 | 3400 | 0.5085 | 0.7645 | 0.7643 | | 0.4304 | 20.69 | 3600 | 0.4982 | 0.7665 | 0.7661 | | 0.4322 | 21.84 | 3800 | 0.5246 | 0.7578 | 0.7582 | | 0.4253 | 22.99 | 4000 | 0.5274 | 0.7545 | 0.7549 | | 0.4304 | 24.14 | 4200 | 0.4977 | 0.7694 | 0.7690 | | 0.4166 | 25.29 | 4400 | 0.5094 | 0.7738 | 0.7733 | | 0.4239 | 26.44 | 4600 | 0.5087 | 0.7705 | 0.7701 | | 0.4218 | 27.59 | 4800 | 0.5072 | 0.7675 | 0.7672 | | 0.4143 | 28.74 | 5000 | 0.5074 | 0.7714 | 0.7711 | | 0.4182 | 29.89 | 5200 | 0.5124 | 0.7705 | 0.7701 | | 0.4117 | 31.03 | 5400 | 0.5165 | 0.7694 | 0.7693 | | 0.4108 | 32.18 | 5600 | 0.5017 | 0.7777 | 0.7773 | | 0.4025 | 33.33 | 5800 | 0.5173 | 0.7698 | 0.7693 | | 0.4101 | 34.48 | 6000 | 0.5022 | 0.7781 | 0.7776 | | 0.4003 | 35.63 | 6200 | 0.5014 | 0.7777 | 0.7773 | | 0.4053 | 36.78 | 6400 | 0.5066 | 0.7756 | 0.7751 | | 0.4024 | 37.93 | 6600 | 0.5323 | 0.7710 | 0.7708 | | 0.398 | 39.08 | 6800 | 0.5153 | 0.7737 | 0.7733 | | 0.3991 | 40.23 | 7000 | 0.5225 | 0.7634 | 0.7632 | | 0.3957 | 41.38 | 7200 | 0.5148 | 0.7716 | 0.7711 | | 0.3949 | 42.53 | 7400 | 0.5232 | 0.7682 | 0.7679 | | 0.3934 | 43.68 | 7600 | 0.5160 | 0.7698 | 0.7693 | | 0.3899 | 44.83 | 7800 | 0.5210 | 0.7700 | 0.7697 | | 0.3933 | 45.98 | 8000 | 0.5074 | 0.7737 | 0.7733 | | 0.3914 | 47.13 | 8200 | 0.5191 | 0.7682 | 0.7679 | | 0.3847 | 48.28 | 8400 | 0.5182 | 0.7727 | 0.7722 | | 0.3832 | 49.43 | 8600 | 0.5328 | 0.7643 | 0.7639 | | 0.3883 | 50.57 | 8800 | 0.5249 | 0.7679 | 0.7675 | | 0.384 | 51.72 | 9000 | 0.5237 | 0.7712 | 0.7708 | | 0.3826 | 52.87 | 9200 | 0.5268 | 0.7668 | 0.7665 | | 0.3849 | 54.02 | 9400 | 0.5224 | 0.7730 | 0.7726 | | 0.3828 | 55.17 | 9600 | 0.5249 | 0.7694 | 0.7690 | | 0.3827 | 56.32 | 9800 | 0.5188 | 0.7730 | 0.7726 | | 0.3813 | 57.47 | 10000 | 0.5204 | 0.7705 | 0.7701 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_EMP_H3K9ac-seqsight_32768_512_43M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3K9ac-seqsight_32768_512_43M-L8_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]

null

2024-04-30T06:15:54+00:00

null

{}

zzunyang/zzu_1_law

null

[ "region:us" ]

null

2024-04-30T06:16:40+00:00

null

{"license": "apache-2.0"}

UnicomLLM/Unichat-llama3-Chinese-8B-gguf

null

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

null

2024-04-30T06:16:49+00:00

null

transformers

{}

ravindrakinagi/gen_ai_tool

null

[ "transformers", "gguf", "llama", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-04-30T06:16:53+00:00

text2text-generation

transformers

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

{"library_name": "transformers", "tags": []}

JD97/bart-typo

null

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

null

2024-04-30T06:17:21+00:00

text-generation

transformers

# 0.0001_withdpo_4iters_bs256_531lr_iter_2 This model is a fine-tuned version of [ZhangShenao/0.0_ablation_sample1_4iters_bs256_iter_1](https://huggingface.co/ZhangShenao/0.0_ablation_sample1_4iters_bs256_iter_1) 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: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2

{"license": "mit", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["updated", "original"], "base_model": "ZhangShenao/0.0_ablation_sample1_4iters_bs256_iter_1", "model-index": [{"name": "0.0001_withdpo_4iters_bs256_531lr_iter_2", "results": []}]}

ShenaoZ/0.0001_withdpo_4iters_bs256_531lr_iter_2

null

[ "transformers", "safetensors", "mistral", "text-generation", "alignment-handbook", "generated_from_trainer", "trl", "dpo", "conversational", "dataset:updated", "dataset:original", "base_model:ZhangShenao/0.0_ablation_sample1_4iters_bs256_iter_1", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-04-30T06:18:54+00:00

text-classification

transformers

# robust_llm_pythia-31m_mz-133_WordLength_n-its-10-seed-4 This model is a fine-tuned version of [EleutherAI/pythia-31m](https://huggingface.co/EleutherAI/pythia-31m) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.1 - Datasets 2.18.0 - Tokenizers 0.15.2

{"tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-31m", "model-index": [{"name": "robust_llm_pythia-31m_mz-133_WordLength_n-its-10-seed-4", "results": []}]}

AlignmentResearch/robust_llm_pythia-31m_mz-133_WordLength_n-its-10-seed-4

null

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

null

2024-04-30T06:18:56+00:00

image-classification

transformers

{}

walterg777/oxford-pets-vit-from-scratch

null

[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-04-30T06:19:18+00:00

null

# duix.ai ## highlights - 2d数字人推理引擎，可在android/ios等边缘侧设备上一键部署，并且内置了两个形象，可直接使用看到效果。 - 支持二次开发，丰富的sdk接口，用户可根据sdk文档开发自己专属需求。 - 完全开源，底层推理及上层商业化应用逻辑整套流程源码开放。 ### 目录结构 ``` duix-android: android demo GJLocalDigitalDemo: ios demo ``` <p align="center"> <img src="res/女.png" width=200/> <img src="res/男.png" width=200/> </p> 内置的2个模特，模板和AI模型包可以通过公网地址下载。 [女链接地址](https://cdn.guiji.ai/duix/digital/model/1712034391673/bendi1_0329.zip) [男链接地址](https://digital-public.obs.cn-east-3.myhuaweicloud.com/duix/digital/model/1706009711636/liangwei_540s.zip) ### 使用说明 android参考 [README.md](./duix-android/dh_aigc_android/README.md) ios参考 [GJLocalDigitalSDK.md](./GJLocalDigitalDemo/GJLocalDigitalDemo/GJLocalDigitalSDK.md) ### Acknowledgements -音频特征我们借鉴了 [wenet](https://github.com/wenet-e2e/wenet) ### 如果有定制需求或技术支持，请在讨论区留言，更多详细信息请访问 [**硅基智能**]官网(https://www.guiji.ai)

{}

GuijiAI/duix.ai

null

[ "region:us" ]

null

2024-04-30T06:19:27+00:00

unconditional-image-generation

diffusers

# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('fath2024/sd-class-butterflies-64') image = pipeline().images[0] image ```

{"license": "mit", "tags": ["pytorch", "diffusers", "unconditional-image-generation", "diffusion-models-class"]}

fath2024/sd-class-butterflies-64

null

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

null

2024-04-30T06:20:04+00:00

text-generation

transformers

{}

Moon-Ahn/kllama_finetune_hyunwook2

null

[ "transformers", "pytorch", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-04-30T06:20:09+00:00

text-generation

transformers

# Alsebay/Lorge-2x7B AWQ - Model creator: [Alsebay](https://huggingface.co/Alsebay) - Original model: [Lorge-2x7B](https://huggingface.co/Alsebay/Lorge-2x7B) ## How to use ### Install the necessary packages ```bash pip install --upgrade autoawq autoawq-kernels ``` ### Example Python code ```python from awq import AutoAWQForCausalLM from transformers import AutoTokenizer, TextStreamer model_path = "solidrust/Lorge-2x7B-AWQ" system_message = "You are Lorge-2x7B, incarnated as a powerful AI. You were created by Alsebay." # Load model model = AutoAWQForCausalLM.from_quantized(model_path, fuse_layers=True) tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) # Convert prompt to tokens prompt_template = """\ <|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant""" prompt = "You're standing on the surface of the Earth. "\ "You walk one mile south, one mile west and one mile north. "\ "You end up exactly where you started. Where are you?" tokens = tokenizer(prompt_template.format(system_message=system_message,prompt=prompt), return_tensors='pt').input_ids.cuda() # Generate output generation_output = model.generate(tokens, streamer=streamer, max_new_tokens=512) ``` ### 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

{"license": "cc-by-nc-4.0", "library_name": "transformers", "tags": ["4-bit", "AWQ", "text-generation", "autotrain_compatible", "endpoints_compatible"], "pipeline_tag": "text-generation", "inference": false, "quantized_by": "Suparious"}

solidrust/Lorge-2x7B-AWQ

null

[ "transformers", "safetensors", "mixtral", "text-generation", "4-bit", "AWQ", "autotrain_compatible", "endpoints_compatible", "license:cc-by-nc-4.0", "text-generation-inference", "region:us" ]

null

2024-04-30T06:20:11+00:00

null

transformers

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

{"library_name": "transformers", "tags": []}

abhayesian/lat-poisoned-1-hh

null

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

null

2024-04-30T06:20:52+00:00

null

peft

# GUE_EMP_H3K9ac-seqsight_32768_512_43M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_EMP_H3K9ac](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K9ac) dataset. It achieves the following results on the evaluation set: - Loss: 0.5028 - F1 Score: 0.7856 - Accuracy: 0.7852 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.5688 | 1.15 | 200 | 0.5832 | 0.7122 | 0.7136 | | 0.5074 | 2.3 | 400 | 0.5796 | 0.6942 | 0.7013 | | 0.4833 | 3.45 | 600 | 0.5617 | 0.7198 | 0.7233 | | 0.4773 | 4.6 | 800 | 0.5231 | 0.7477 | 0.7481 | | 0.469 | 5.75 | 1000 | 0.5155 | 0.7546 | 0.7546 | | 0.4592 | 6.9 | 1200 | 0.5154 | 0.7598 | 0.7596 | | 0.4521 | 8.05 | 1400 | 0.5069 | 0.7654 | 0.7650 | | 0.4441 | 9.2 | 1600 | 0.5155 | 0.7576 | 0.7578 | | 0.4386 | 10.34 | 1800 | 0.5178 | 0.7621 | 0.7618 | | 0.428 | 11.49 | 2000 | 0.5130 | 0.7610 | 0.7607 | | 0.4204 | 12.64 | 2200 | 0.5044 | 0.7660 | 0.7657 | | 0.4148 | 13.79 | 2400 | 0.5397 | 0.7519 | 0.7528 | | 0.4049 | 14.94 | 2600 | 0.5043 | 0.7687 | 0.7683 | | 0.3952 | 16.09 | 2800 | 0.5817 | 0.7328 | 0.7362 | | 0.3927 | 17.24 | 3000 | 0.5320 | 0.7614 | 0.7614 | | 0.3848 | 18.39 | 3200 | 0.5286 | 0.7667 | 0.7665 | | 0.3843 | 19.54 | 3400 | 0.5311 | 0.7590 | 0.7593 | | 0.367 | 20.69 | 3600 | 0.5218 | 0.7695 | 0.7690 | | 0.3629 | 21.84 | 3800 | 0.5338 | 0.7668 | 0.7668 | | 0.3551 | 22.99 | 4000 | 0.5325 | 0.7622 | 0.7621 | | 0.3517 | 24.14 | 4200 | 0.5315 | 0.7705 | 0.7701 | | 0.3384 | 25.29 | 4400 | 0.5510 | 0.7715 | 0.7711 | | 0.3399 | 26.44 | 4600 | 0.5772 | 0.7650 | 0.7650 | | 0.3366 | 27.59 | 4800 | 0.5344 | 0.7680 | 0.7675 | | 0.3234 | 28.74 | 5000 | 0.5506 | 0.7634 | 0.7632 | | 0.3235 | 29.89 | 5200 | 0.5652 | 0.7656 | 0.7654 | | 0.3118 | 31.03 | 5400 | 0.5719 | 0.7569 | 0.7571 | | 0.3092 | 32.18 | 5600 | 0.6078 | 0.7489 | 0.7496 | | 0.2984 | 33.33 | 5800 | 0.5917 | 0.7670 | 0.7668 | | 0.3022 | 34.48 | 6000 | 0.5851 | 0.7687 | 0.7683 | | 0.2887 | 35.63 | 6200 | 0.5829 | 0.7665 | 0.7661 | | 0.2902 | 36.78 | 6400 | 0.5999 | 0.7614 | 0.7611 | | 0.2886 | 37.93 | 6600 | 0.5893 | 0.7662 | 0.7657 | | 0.2761 | 39.08 | 6800 | 0.6140 | 0.7574 | 0.7571 | | 0.277 | 40.23 | 7000 | 0.6130 | 0.7615 | 0.7611 | | 0.2745 | 41.38 | 7200 | 0.6231 | 0.7608 | 0.7603 | | 0.2674 | 42.53 | 7400 | 0.6411 | 0.7654 | 0.7650 | | 0.2676 | 43.68 | 7600 | 0.6335 | 0.7640 | 0.7636 | | 0.2632 | 44.83 | 7800 | 0.6251 | 0.7607 | 0.7603 | | 0.2609 | 45.98 | 8000 | 0.6266 | 0.7612 | 0.7607 | | 0.2556 | 47.13 | 8200 | 0.6518 | 0.7614 | 0.7611 | | 0.254 | 48.28 | 8400 | 0.6446 | 0.7569 | 0.7564 | | 0.2505 | 49.43 | 8600 | 0.6670 | 0.7522 | 0.7521 | | 0.2483 | 50.57 | 8800 | 0.6745 | 0.7566 | 0.7564 | | 0.2491 | 51.72 | 9000 | 0.6521 | 0.7583 | 0.7578 | | 0.2457 | 52.87 | 9200 | 0.6560 | 0.7608 | 0.7603 | | 0.2446 | 54.02 | 9400 | 0.6666 | 0.7593 | 0.7589 | | 0.2383 | 55.17 | 9600 | 0.6727 | 0.7568 | 0.7564 | | 0.2385 | 56.32 | 9800 | 0.6683 | 0.7601 | 0.7596 | | 0.2362 | 57.47 | 10000 | 0.6676 | 0.7590 | 0.7585 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_EMP_H3K9ac-seqsight_32768_512_43M-L32_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3K9ac-seqsight_32768_512_43M-L32_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]

null

2024-04-30T06:20:57+00:00

null

{}

Daisyyy05/bert-finetuned-ner

null

[ "region:us" ]

null

2024-04-30T06:21:03+00:00

null

peft

# GUE_EMP_H3K4me3-seqsight_32768_512_43M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me3) dataset. It achieves the following results on the evaluation set: - Loss: 0.5776 - F1 Score: 0.6939 - Accuracy: 0.6937 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.6721 | 0.87 | 200 | 0.6563 | 0.6259 | 0.6255 | | 0.6439 | 1.74 | 400 | 0.6358 | 0.6492 | 0.6503 | | 0.631 | 2.61 | 600 | 0.6242 | 0.6694 | 0.6696 | | 0.6158 | 3.48 | 800 | 0.6154 | 0.6705 | 0.6704 | | 0.6118 | 4.35 | 1000 | 0.6142 | 0.6628 | 0.6639 | | 0.606 | 5.22 | 1200 | 0.6213 | 0.6508 | 0.6554 | | 0.5999 | 6.09 | 1400 | 0.6256 | 0.6514 | 0.6571 | | 0.5947 | 6.96 | 1600 | 0.6122 | 0.6648 | 0.6666 | | 0.5942 | 7.83 | 1800 | 0.6078 | 0.6696 | 0.6698 | | 0.5933 | 8.7 | 2000 | 0.6061 | 0.6707 | 0.6709 | | 0.5886 | 9.57 | 2200 | 0.5988 | 0.6767 | 0.6764 | | 0.5904 | 10.43 | 2400 | 0.6028 | 0.6774 | 0.6774 | | 0.5881 | 11.3 | 2600 | 0.6004 | 0.6756 | 0.6772 | | 0.5874 | 12.17 | 2800 | 0.6003 | 0.6751 | 0.675 | | 0.5833 | 13.04 | 3000 | 0.5987 | 0.6797 | 0.6796 | | 0.5807 | 13.91 | 3200 | 0.5954 | 0.6712 | 0.6715 | | 0.5815 | 14.78 | 3400 | 0.5964 | 0.6751 | 0.6761 | | 0.5822 | 15.65 | 3600 | 0.5981 | 0.6794 | 0.6799 | | 0.5788 | 16.52 | 3800 | 0.6010 | 0.6783 | 0.6788 | | 0.5796 | 17.39 | 4000 | 0.5961 | 0.6793 | 0.6802 | | 0.5812 | 18.26 | 4200 | 0.5980 | 0.6804 | 0.6810 | | 0.5738 | 19.13 | 4400 | 0.5980 | 0.6766 | 0.6764 | | 0.5764 | 20.0 | 4600 | 0.5939 | 0.6787 | 0.6793 | | 0.5757 | 20.87 | 4800 | 0.5972 | 0.6838 | 0.6845 | | 0.5747 | 21.74 | 5000 | 0.5963 | 0.6819 | 0.6823 | | 0.5738 | 22.61 | 5200 | 0.5936 | 0.6837 | 0.6840 | | 0.5719 | 23.48 | 5400 | 0.5999 | 0.6754 | 0.6777 | | 0.573 | 24.35 | 5600 | 0.5945 | 0.6834 | 0.6834 | | 0.5742 | 25.22 | 5800 | 0.5988 | 0.6792 | 0.6818 | | 0.5692 | 26.09 | 6000 | 0.5962 | 0.6837 | 0.6848 | | 0.5707 | 26.96 | 6200 | 0.5997 | 0.6764 | 0.6785 | | 0.5691 | 27.83 | 6400 | 0.6039 | 0.6752 | 0.6788 | | 0.5693 | 28.7 | 6600 | 0.5951 | 0.6860 | 0.6864 | | 0.5686 | 29.57 | 6800 | 0.5904 | 0.6875 | 0.6875 | | 0.5672 | 30.43 | 7000 | 0.5924 | 0.6859 | 0.6870 | | 0.5719 | 31.3 | 7200 | 0.5921 | 0.6856 | 0.6867 | | 0.5688 | 32.17 | 7400 | 0.5934 | 0.6854 | 0.6867 | | 0.5637 | 33.04 | 7600 | 0.5905 | 0.6888 | 0.6891 | | 0.568 | 33.91 | 7800 | 0.5917 | 0.6853 | 0.6859 | | 0.5662 | 34.78 | 8000 | 0.5921 | 0.6863 | 0.6864 | | 0.5671 | 35.65 | 8200 | 0.5908 | 0.6875 | 0.6878 | | 0.5661 | 36.52 | 8400 | 0.5927 | 0.6858 | 0.6864 | | 0.5661 | 37.39 | 8600 | 0.5911 | 0.6874 | 0.6872 | | 0.5632 | 38.26 | 8800 | 0.5947 | 0.6850 | 0.6864 | | 0.5684 | 39.13 | 9000 | 0.5926 | 0.6848 | 0.6861 | | 0.5665 | 40.0 | 9200 | 0.5906 | 0.6879 | 0.6883 | | 0.5647 | 40.87 | 9400 | 0.5906 | 0.6892 | 0.6891 | | 0.5644 | 41.74 | 9600 | 0.5908 | 0.6875 | 0.6878 | | 0.5688 | 42.61 | 9800 | 0.5900 | 0.6872 | 0.6875 | | 0.5613 | 43.48 | 10000 | 0.5903 | 0.6883 | 0.6886 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_EMP_H3K4me3-seqsight_32768_512_43M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3K4me3-seqsight_32768_512_43M-L1_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]

null

2024-04-30T06:21:14+00:00

null

peft

# GUE_EMP_H3K4me3-seqsight_32768_512_43M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me3) dataset. It achieves the following results on the evaluation set: - Loss: 0.5802 - F1 Score: 0.7073 - Accuracy: 0.7071 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.6635 | 0.87 | 200 | 0.6406 | 0.6449 | 0.6451 | | 0.6196 | 1.74 | 400 | 0.6211 | 0.6601 | 0.6617 | | 0.6025 | 2.61 | 600 | 0.6110 | 0.6718 | 0.6715 | | 0.5938 | 3.48 | 800 | 0.6061 | 0.6746 | 0.6745 | | 0.5903 | 4.35 | 1000 | 0.6056 | 0.6760 | 0.6758 | | 0.587 | 5.22 | 1200 | 0.6109 | 0.6554 | 0.6609 | | 0.5801 | 6.09 | 1400 | 0.6188 | 0.6531 | 0.6609 | | 0.5735 | 6.96 | 1600 | 0.5993 | 0.6771 | 0.6793 | | 0.571 | 7.83 | 1800 | 0.6026 | 0.6863 | 0.6861 | | 0.5699 | 8.7 | 2000 | 0.6011 | 0.6841 | 0.6845 | | 0.5639 | 9.57 | 2200 | 0.5849 | 0.6875 | 0.6872 | | 0.565 | 10.43 | 2400 | 0.5931 | 0.6867 | 0.6867 | | 0.5591 | 11.3 | 2600 | 0.5862 | 0.6912 | 0.6924 | | 0.5608 | 12.17 | 2800 | 0.5850 | 0.6900 | 0.6897 | | 0.5532 | 13.04 | 3000 | 0.5873 | 0.6931 | 0.6929 | | 0.5508 | 13.91 | 3200 | 0.5834 | 0.6940 | 0.6937 | | 0.5491 | 14.78 | 3400 | 0.5875 | 0.6949 | 0.6954 | | 0.5491 | 15.65 | 3600 | 0.5858 | 0.6960 | 0.6959 | | 0.5424 | 16.52 | 3800 | 0.5915 | 0.6866 | 0.6864 | | 0.5434 | 17.39 | 4000 | 0.5927 | 0.6954 | 0.6962 | | 0.5435 | 18.26 | 4200 | 0.5956 | 0.6889 | 0.6902 | | 0.5361 | 19.13 | 4400 | 0.5902 | 0.6918 | 0.6916 | | 0.5379 | 20.0 | 4600 | 0.5875 | 0.6920 | 0.6927 | | 0.5341 | 20.87 | 4800 | 0.5924 | 0.6955 | 0.6962 | | 0.5343 | 21.74 | 5000 | 0.5925 | 0.6911 | 0.6916 | | 0.5322 | 22.61 | 5200 | 0.5899 | 0.6925 | 0.6929 | | 0.5251 | 23.48 | 5400 | 0.6030 | 0.6896 | 0.6916 | | 0.5271 | 24.35 | 5600 | 0.5900 | 0.6920 | 0.6921 | | 0.5274 | 25.22 | 5800 | 0.5975 | 0.6952 | 0.6965 | | 0.5227 | 26.09 | 6000 | 0.6017 | 0.6941 | 0.6954 | | 0.5239 | 26.96 | 6200 | 0.5954 | 0.6948 | 0.6973 | | 0.5187 | 27.83 | 6400 | 0.6090 | 0.6857 | 0.6891 | | 0.5196 | 28.7 | 6600 | 0.5891 | 0.6966 | 0.6965 | | 0.5176 | 29.57 | 6800 | 0.5873 | 0.6933 | 0.6935 | | 0.5165 | 30.43 | 7000 | 0.5917 | 0.6901 | 0.6908 | | 0.5182 | 31.3 | 7200 | 0.5922 | 0.6897 | 0.6902 | | 0.5151 | 32.17 | 7400 | 0.5929 | 0.6918 | 0.6921 | | 0.5116 | 33.04 | 7600 | 0.5945 | 0.6929 | 0.6932 | | 0.5135 | 33.91 | 7800 | 0.5920 | 0.6946 | 0.6951 | | 0.5123 | 34.78 | 8000 | 0.5963 | 0.6912 | 0.6913 | | 0.5112 | 35.65 | 8200 | 0.5976 | 0.6941 | 0.6943 | | 0.512 | 36.52 | 8400 | 0.5934 | 0.6916 | 0.6921 | | 0.5075 | 37.39 | 8600 | 0.5941 | 0.6959 | 0.6959 | | 0.506 | 38.26 | 8800 | 0.5992 | 0.6909 | 0.6918 | | 0.5119 | 39.13 | 9000 | 0.5961 | 0.6916 | 0.6921 | | 0.5074 | 40.0 | 9200 | 0.5965 | 0.6949 | 0.6951 | | 0.5056 | 40.87 | 9400 | 0.5974 | 0.6948 | 0.6948 | | 0.5069 | 41.74 | 9600 | 0.5957 | 0.6951 | 0.6954 | | 0.5102 | 42.61 | 9800 | 0.5945 | 0.6950 | 0.6951 | | 0.504 | 43.48 | 10000 | 0.5964 | 0.6957 | 0.6959 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_EMP_H3K4me3-seqsight_32768_512_43M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3K4me3-seqsight_32768_512_43M-L8_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]

null

2024-04-30T06:21:34+00:00

fill-mask

transformers

{}

jd445/2019

null

[ "transformers", "safetensors", "roberta", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-04-30T06:21:40+00:00

null

{}

Sumedh1304/mistral-finetuned-alpaca

null

[ "region:us" ]

null

2024-04-30T06:22:04+00:00

reinforcement-learning

sample-factory

A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r aw-infoprojekt/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

{"library_name": "sample-factory", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "sample-factory"], "model-index": [{"name": "APPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "doom_health_gathering_supreme", "type": "doom_health_gathering_supreme"}, "metrics": [{"type": "mean_reward", "value": "10.86 +/- 3.72", "name": "mean_reward", "verified": false}]}]}]}

aw-infoprojekt/rl_course_vizdoom_health_gathering_supreme

null

[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

null

2024-04-30T06:22:42+00:00

null

transformers

{}

Goodarc/TomTestModel2024043001

null

[ "transformers", "pytorch", "tensorboard", "donut", "endpoints_compatible", "has_space", "region:us" ]

null

2024-04-30T06:23:56+00:00

null

{}

arjunanand13/Idefics2-8b-multimodal

null

[ "safetensors", "region:us" ]

null

2024-04-30T06:24:26+00:00

null

{}

dimson15/sn25-2-2

null

[ "region:us" ]

null

2024-04-30T06:24:52+00:00

text-generation

transformers

# mlx-community/llm-jp-13b-instruct-full-dolly-ichikara_004_001_single-oasst-oasst2-v2.0 This model was converted to MLX format from [`llm-jp/llm-jp-13b-instruct-full-dolly-ichikara_004_001_single-oasst-oasst2-v2.0`]() using mlx-lm version **0.12.0**. Refer to the [original model card](https://huggingface.co/llm-jp/llm-jp-13b-instruct-full-dolly-ichikara_004_001_single-oasst-oasst2-v2.0) 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/llm-jp-13b-instruct-full-dolly-ichikara_004_001_single-oasst-oasst2-v2.0") response = generate(model, tokenizer, prompt="hello", verbose=True) ```

{"language": ["en", "ja"], "license": "apache-2.0", "library_name": "transformers", "tags": ["mlx"], "datasets": ["databricks/databricks-dolly-15k", "llm-jp/databricks-dolly-15k-ja", "llm-jp/oasst1-21k-en", "llm-jp/oasst1-21k-ja", "llm-jp/oasst2-33k-en", "llm-jp/oasst2-33k-ja"], "programming_language": ["C", "C++", "C#", "Go", "Java", "JavaScript", "Lua", "PHP", "Python", "Ruby", "Rust", "Scala", "TypeScript"], "pipeline_tag": "text-generation", "inference": false}

mlx-community/llm-jp-13b-instruct-full-dolly-ichikara_004_001_single-oasst-oasst2-v2.0

null

[ "transformers", "safetensors", "llama", "text-generation", "mlx", "conversational", "en", "ja", "dataset:databricks/databricks-dolly-15k", "dataset:llm-jp/databricks-dolly-15k-ja", "dataset:llm-jp/oasst1-21k-en", "dataset:llm-jp/oasst1-21k-ja", "dataset:llm-jp/oasst2-33k-en", "dataset:llm-jp/oasst2-33k-ja", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "region:us" ]

null

2024-04-30T06:24:57+00:00

text-to-image

diffusers

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

{"library_name": "diffusers"}

Niggendar/mightMixes15Ponyxl_pxlBlastwrx

null

[ "diffusers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]

null

2024-04-30T06:25:17+00:00

text2text-generation

transformers

{}

shenkha/DGSlow_T5-small_BST

null

[ "transformers", "pytorch", "t5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-04-30T06:25:26+00:00

sentence-similarity

sentence-transformers

# {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{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  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 113 with parameters: ``` {'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 1000, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 0, "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': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Citing & Authors

{"library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"}

Mihaiii/test16

null

[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "transformers", "endpoints_compatible", "region:us" ]

null

2024-04-30T06:25:40+00:00

reinforcement-learning

stable-baselines3

# **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

{"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "220.28 +/- 85.29", "name": "mean_reward", "verified": false}]}]}]}

Chhabi/PPO-LunarLander-v2

null

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

null

2024-04-30T06:26:59+00:00

null

{}

Hyunji0909/logo-model

null

[ "region:us" ]

null

2024-04-30T06:27:55+00:00

null

{}

PritamShete/git-base-pokemon

null

[ "region:us" ]

null

2024-04-30T06:27:56+00:00

text2text-generation

transformers

{}

shenkha/DGSlow_T5-small_CV2

null

[ "transformers", "pytorch", "t5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-04-30T06:27:57+00:00

null

{"license": "mit"}

WeiJiang75/dipro1

null

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

null

2024-04-30T06:28:49+00:00

text-classification

transformers

# robust_llm_pythia-31m_mz-133_WordLength_n-its-10-seed-3 This model is a fine-tuned version of [EleutherAI/pythia-31m](https://huggingface.co/EleutherAI/pythia-31m) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 3 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.1 - Datasets 2.18.0 - Tokenizers 0.15.2

{"tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-31m", "model-index": [{"name": "robust_llm_pythia-31m_mz-133_WordLength_n-its-10-seed-3", "results": []}]}

AlignmentResearch/robust_llm_pythia-31m_mz-133_WordLength_n-its-10-seed-3

null

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

null

2024-04-30T06:29:09+00:00

null

peft

# taide_llama3_8b_lora_completion_only This model is a fine-tuned version of [taide/Llama3-TAIDE-LX-8B-Chat-Alpha1](https://huggingface.co/taide/Llama3-TAIDE-LX-8B-Chat-Alpha1) on the DandinPower/ZH-Reading-Comprehension-Llama-Instruct dataset. It achieves the following results on the evaluation set: - Loss: 0.0968 ## 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: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - total_eval_batch_size: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 700 - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 0.1474 | 0.3690 | 250 | 0.1201 | | 0.1072 | 0.7380 | 500 | 0.1581 | | 0.098 | 1.1070 | 750 | 0.1148 | | 0.0963 | 1.4760 | 1000 | 0.1044 | | 0.0502 | 1.8450 | 1250 | 0.1064 | | 0.05 | 2.2140 | 1500 | 0.1017 | | 0.0239 | 2.5830 | 1750 | 0.1015 | | 0.0443 | 2.9520 | 2000 | 0.0968 | ### Framework versions - PEFT 0.10.0 - Transformers 4.40.0 - Pytorch 2.2.2+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"language": ["zh"], "license": "other", "library_name": "peft", "tags": ["trl", "sft", "nycu-112-2-deeplearning-hw2", "generated_from_trainer"], "datasets": ["DandinPower/ZH-Reading-Comprehension-Llama-Instruct"], "base_model": "taide/Llama3-TAIDE-LX-8B-Chat-Alpha1", "model-index": [{"name": "taide_llama3_8b_lora_completion_only", "results": []}]}

DandinPower/taide_llama3_8b_lora_completion_only

null

[ "peft", "safetensors", "trl", "sft", "nycu-112-2-deeplearning-hw2", "generated_from_trainer", "zh", "dataset:DandinPower/ZH-Reading-Comprehension-Llama-Instruct", "base_model:taide/Llama3-TAIDE-LX-8B-Chat-Alpha1", "license:other", "region:us" ]

null

2024-04-30T06:29:55+00:00

null

{"license": "llama3"}

yatour/yatourAI

null

[ "license:llama3", "region:us" ]

null

2024-04-30T06:30:13+00:00

text-generation

transformers

# starcoder2-15b-instruct-v0.1-GGUF - Original model: [starcoder2-15b-instruct-v0.1](https://huggingface.co/bigcode/starcoder2-15b-instruct-v0.1)  ## Description This repo contains GGUF format model files for [starcoder2-15b-instruct-v0.1](https://huggingface.co/bigcode/starcoder2-15b-instruct-v0.1).   ### 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). This is the source project for GGUF, providing both a Command Line Interface (CLI) and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), Known as the most widely used web UI, this project boasts numerous features and powerful extensions, and supports GPU acceleration. * [Ollama](https://github.com/jmorganca/ollama) Ollama is a lightweight and extensible framework designed for building and running language models locally. It features a simple API for creating, managing, and executing models, along with a library of pre-built models for use in various applications * [KoboldCpp](https://github.com/LostRuins/koboldcpp), A comprehensive web UI offering GPU acceleration across all platforms and architectures, particularly renowned for storytelling. * [GPT4All](https://gpt4all.io), This is a free and open source GUI that runs locally, supporting Windows, Linux, and macOS with full GPU acceleration. * [LM Studio](https://lmstudio.ai/) An intuitive and powerful local GUI for Windows and macOS (Silicon), featuring GPU acceleration. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui). A notable web UI with a variety of unique features, including a comprehensive model library for easy model selection. * [Faraday.dev](https://faraday.dev/), An attractive, user-friendly character-based chat GUI for Windows and macOS (both Silicon and Intel), also offering GPU acceleration. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), A Python library equipped with GPU acceleration, LangChain support, and an OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), A Rust-based ML framework focusing on performance, including GPU support, and designed for ease of use. * [ctransformers](https://github.com/marella/ctransformers), A Python library featuring GPU acceleration, LangChain support, and an OpenAI-compatible AI server. * [localGPT](https://github.com/PromtEngineer/localGPT) An open-source initiative enabling private conversations with documents.   ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw. </details>   ## How to download GGUF files **Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single folder. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: LiteLLMs/starcoder2-15b-instruct-v0.1-GGUF and below it, a specific filename to download, such as: Q4_0/Q4_0-00001-of-00009.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download LiteLLMs/starcoder2-15b-instruct-v0.1-GGUF Q4_0/Q4_0-00001-of-00009.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download LiteLLMs/starcoder2-15b-instruct-v0.1-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install huggingface_hub[hf_transfer] ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download LiteLLMs/starcoder2-15b-instruct-v0.1-GGUF Q4_0/Q4_0-00001-of-00009.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details>   ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m Q4_0/Q4_0-00001-of-00009.gguf --color -c 8192 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<PROMPT>" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 8192` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./Q4_0/Q4_0-00001-of-00009.gguf", # Download the model file first n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "<PROMPT>", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./Q4_0/Q4_0-00001-of-00009.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)    # Original model card: starcoder2-15b-instruct-v0.1 # StarCoder2-Instruct: Fully Transparent and Permissive Self-Alignment for Code Generation ![Banner](https://huggingface.co/datasets/bigcode/starcoder2-instruct-assets/resolve/main/banner.png) ## Model Summary We introduce StarCoder2-15B-Instruct-v0.1, the very first entirely self-aligned code Large Language Model (LLM) trained with a fully permissive and transparent pipeline. Our open-source pipeline uses StarCoder2-15B to generate thousands of instruction-response pairs, which are then used to fine-tune StarCoder-15B itself without any human annotations or distilled data from huge and proprietary LLMs. - **Model:** [bigcode/starcoder2-15b-instruct-v0.1](https://huggingface.co/bigcode/starcoder2-instruct-15b-v0.1) - **Code:** [bigcode-project/starcoder2-self-align](https://github.com/bigcode-project/starcoder2-self-align) - **Dataset:** [bigcode/self-oss-instruct-sc2-exec-filter-50k](https://huggingface.co/datasets/bigcode/self-oss-instruct-sc2-exec-filter-50k/) - **Authors:** [Yuxiang Wei](https://yuxiang.cs.illinois.edu), [Federico Cassano](https://federico.codes/), [Jiawei Liu](https://jw-liu.xyz), [Yifeng Ding](https://yifeng-ding.com), [Naman Jain](https://naman-ntc.github.io), [Harm de Vries](https://www.harmdevries.com), [Leandro von Werra](https://twitter.com/lvwerra), [Arjun Guha](https://www.khoury.northeastern.edu/home/arjunguha/main/home/), [Lingming Zhang](https://lingming.cs.illinois.edu). ![self-alignment pipeline](https://huggingface.co/datasets/bigcode/starcoder2-instruct-assets/resolve/main/method.png) ## Use ### Intended use The model is designed to respond to **coding-related instructions in a single turn**. Instructions in other styles may result in less accurate responses. Here is an example to get started with the model using the [transformers](https://huggingface.co/docs/transformers/index) library: ```python import transformers import torch pipeline = transformers.pipeline( model="bigcode/starcoder2-15b-instruct-v0.1", task="text-generation", torch_dtype=torch.bfloat16, device_map="auto", ) def respond(instruction: str, response_prefix: str) -> str: messages = [{"role": "user", "content": instruction}] prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False) prompt += response_prefix teminators = [ pipeline.tokenizer.eos_token_id, pipeline.tokenizer.convert_tokens_to_ids("###"), ] result = pipeline( prompt, max_length=256, num_return_sequences=1, do_sample=False, eos_token_id=teminators, pad_token_id=pipeline.tokenizer.eos_token_id, truncation=True, ) response = response_prefix + result[0]["generated_text"][len(prompt) :].split("###")[0].rstrip() return response instruction = "Write a quicksort function in Python with type hints and a 'less_than' parameter for custom sorting criteria." response_prefix = "" print(respond(instruction, response_prefix)) ``` Here is the expected output: `````` Here's how you can implement a quicksort function in Python with type hints and a 'less_than' parameter for custom sorting criteria: ```python from typing import TypeVar, Callable T = TypeVar('T') def quicksort(items: list[T], less_than: Callable[[T, T], bool] = lambda x, y: x < y) -> list[T]: if len(items) <= 1: return items pivot = items[0] less = [x for x in items[1:] if less_than(x, pivot)] greater = [x for x in items[1:] if not less_than(x, pivot)] return quicksort(less, less_than) + [pivot] + quicksort(greater, less_than) ``` `````` ### Bias, Risks, and Limitations StarCoder2-15B-Instruct-v0.1 is primarily finetuned for Python code generation tasks that can be verified through execution, which may lead to certain biases and limitations. For example, the model might not adhere strictly to instructions that dictate the output format. In these situations, it's beneficial to provide a **response prefix** or a **one-shot example** to steer the model’s output. Additionally, the model may have limitations with other programming languages and out-of-domain coding tasks. The model also inherits the bias, risks, and limitations from its base StarCoder2-15B model. For more information, please refer to the [StarCoder2-15B model card](https://huggingface.co/bigcode/starcoder2-15b). ## Evaluation on EvalPlus, LiveCodeBench, and DS-1000 ![EvalPlus](https://huggingface.co/datasets/bigcode/starcoder2-instruct-assets/resolve/main/evalplus.png) ![LiveCodeBench and DS-1000](https://huggingface.co/datasets/bigcode/starcoder2-instruct-assets/resolve/main/lcb-ds1000.png) ## Training Details ### Hyperparameters - **Optimizer:** Adafactor - **Learning rate:** 1e-5 - **Epoch:** 4 - **Batch size:** 64 - **Warmup ratio:** 0.05 - **Scheduler:** Linear - **Sequence length:** 1280 - **Dropout**: Not applied ### Hardware 1 x NVIDIA A100 80GB ## Resources - **Model:** [bigcode/starCoder2-15b-instruct-v0.1](https://huggingface.co/bigcode/starcoder2-instruct-15b-v0.1) - **Code:** [bigcode-project/starcoder2-self-align](https://github.com/bigcode-project/starcoder2-self-align) - **Dataset:** [bigcode/self-oss-instruct-sc2-exec-filter-50k](https://huggingface.co/datasets/bigcode/self-oss-instruct-sc2-exec-filter-50k/)

{"license": "bigcode-openrail-m", "library_name": "transformers", "tags": ["code", "GGUF"], "datasets": ["bigcode/self-oss-instruct-sc2-exec-filter-50k"], "base_model": "bigcode/starcoder2-15b", "pipeline_tag": "text-generation", "quantized_by": "andrijdavid", "model-index": [{"name": "starcoder2-15b-instruct-v0.1", "results": [{"task": {"type": "text-generation"}, "dataset": {"name": "LiveCodeBench (code generation)", "type": "livecodebench-codegeneration"}, "metrics": [{"type": "pass@1", "value": 20.4, "verified": false}]}, {"task": {"type": "text-generation"}, "dataset": {"name": "LiveCodeBench (self repair)", "type": "livecodebench-selfrepair"}, "metrics": [{"type": "pass@1", "value": 20.9, "verified": false}]}, {"task": {"type": "text-generation"}, "dataset": {"name": "LiveCodeBench (test output prediction)", "type": "livecodebench-testoutputprediction"}, "metrics": [{"type": "pass@1", "value": 29.8, "verified": false}]}, {"task": {"type": "text-generation"}, "dataset": {"name": "LiveCodeBench (code execution)", "type": "livecodebench-codeexecution"}, "metrics": [{"type": "pass@1", "value": 28.1, "verified": false}]}, {"task": {"type": "text-generation"}, "dataset": {"name": "HumanEval", "type": "humaneval"}, "metrics": [{"type": "pass@1", "value": 72.6, "verified": false}]}, {"task": {"type": "text-generation"}, "dataset": {"name": "HumanEval+", "type": "humanevalplus"}, "metrics": [{"type": "pass@1", "value": 63.4, "verified": false}]}, {"task": {"type": "text-generation"}, "dataset": {"name": "MBPP", "type": "mbpp"}, "metrics": [{"type": "pass@1", "value": 75.2, "verified": false}]}, {"task": {"type": "text-generation"}, "dataset": {"name": "MBPP+", "type": "mbppplus"}, "metrics": [{"type": "pass@1", "value": 61.2, "verified": false}]}, {"task": {"type": "text-generation"}, "dataset": {"name": "DS-1000", "type": "ds-1000"}, "metrics": [{"type": "pass@1", "value": 40.6, "verified": false}]}]}]}

LiteLLMs/starcoder2-15b-instruct-v0.1-GGUF

null

[ "transformers", "gguf", "code", "GGUF", "text-generation", "dataset:bigcode/self-oss-instruct-sc2-exec-filter-50k", "base_model:bigcode/starcoder2-15b", "license:bigcode-openrail-m", "model-index", "endpoints_compatible", "region:us" ]

null

2024-04-30T06:31:08+00:00

text2text-generation

transformers

{}

shenkha/DGSlow_Bartbase_PC

null

[ "transformers", "pytorch", "bart", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-04-30T06:32:00+00:00

text-classification

transformers

# distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2222 - Accuracy: 0.927 - F1: 0.9270 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8572 | 1.0 | 250 | 0.3317 | 0.9015 | 0.9005 | | 0.2552 | 2.0 | 500 | 0.2222 | 0.927 | 0.9270 | ### Framework versions - Transformers 4.40.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "config": "split", "split": "validation", "args": "split"}, "metrics": [{"type": "accuracy", "value": 0.927, "name": "Accuracy"}, {"type": "f1", "value": 0.9270352884163217, "name": "F1"}]}]}]}

nanashi999/distilbert-base-uncased-finetuned-emotion

null

[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-04-30T06:32:32+00:00

null

transformers

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

{"library_name": "transformers", "tags": ["unsloth"]}

russgeo/lecw

null

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

null

2024-04-30T06:33:58+00:00

text-classification

transformers

# Model Trained Using AutoTrain - Problem type: Text Classification ## Validation Metrics loss: 1.0657285451889038 f1_macro: 0.2095479509928179 f1_micro: 0.4584103512014787 f1_weighted: 0.2881768494245037 precision_macro: 0.1528034504004929 precision_micro: 0.4584103512014787 precision_weighted: 0.21014005008866307 recall_macro: 0.3333333333333333 recall_micro: 0.4584103512014787 recall_weighted: 0.4584103512014787 accuracy: 0.4584103512014787

{"tags": ["autotrain", "text-classification"], "datasets": ["actsa-distilbert/autotrain-data"], "widget": [{"text": "I love AutoTrain"}]}

DarkPhantom323/actsa-distilbert

null

[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "autotrain", "dataset:actsa-distilbert/autotrain-data", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-04-30T06:35:24+00:00

question-answering

transformers

{}

stefandi/bert-finetuned-squad

null

[ "transformers", "tensorboard", "safetensors", "bert", "question-answering", "endpoints_compatible", "region:us" ]

null

2024-04-30T06:35:33+00:00

null

peft

# GUE_EMP_H3K4me3-seqsight_32768_512_43M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me3) dataset. It achieves the following results on the evaluation set: - Loss: 0.6628 - F1 Score: 0.7003 - Accuracy: 0.7 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 0.6509 | 0.87 | 200 | 0.6230 | 0.6621 | 0.6625 | | 0.6059 | 1.74 | 400 | 0.6135 | 0.6639 | 0.6663 | | 0.592 | 2.61 | 600 | 0.5994 | 0.6823 | 0.6823 | | 0.5831 | 3.48 | 800 | 0.5950 | 0.6831 | 0.6832 | | 0.5769 | 4.35 | 1000 | 0.5927 | 0.6832 | 0.6829 | | 0.5715 | 5.22 | 1200 | 0.5920 | 0.6843 | 0.6856 | | 0.5626 | 6.09 | 1400 | 0.6018 | 0.6851 | 0.6880 | | 0.5546 | 6.96 | 1600 | 0.5913 | 0.6930 | 0.6940 | | 0.5463 | 7.83 | 1800 | 0.5928 | 0.6911 | 0.6910 | | 0.5422 | 8.7 | 2000 | 0.5842 | 0.6886 | 0.6886 | | 0.5318 | 9.57 | 2200 | 0.5834 | 0.6981 | 0.6981 | | 0.5319 | 10.43 | 2400 | 0.5986 | 0.6946 | 0.6946 | | 0.5223 | 11.3 | 2600 | 0.5986 | 0.6917 | 0.6932 | | 0.5222 | 12.17 | 2800 | 0.5934 | 0.6939 | 0.6940 | | 0.5123 | 13.04 | 3000 | 0.5865 | 0.6906 | 0.6910 | | 0.5051 | 13.91 | 3200 | 0.5865 | 0.6982 | 0.6981 | | 0.497 | 14.78 | 3400 | 0.6015 | 0.6906 | 0.6927 | | 0.4981 | 15.65 | 3600 | 0.5933 | 0.6932 | 0.6937 | | 0.4854 | 16.52 | 3800 | 0.6061 | 0.6967 | 0.6967 | | 0.4809 | 17.39 | 4000 | 0.6083 | 0.6950 | 0.6965 | | 0.4787 | 18.26 | 4200 | 0.6135 | 0.6979 | 0.6989 | | 0.4718 | 19.13 | 4400 | 0.6113 | 0.6938 | 0.6937 | | 0.4674 | 20.0 | 4600 | 0.6135 | 0.6969 | 0.6986 | | 0.4584 | 20.87 | 4800 | 0.6284 | 0.6975 | 0.6976 | | 0.4547 | 21.74 | 5000 | 0.6107 | 0.7012 | 0.7016 | | 0.448 | 22.61 | 5200 | 0.6399 | 0.6990 | 0.6997 | | 0.4411 | 23.48 | 5400 | 0.6365 | 0.6983 | 0.6997 | | 0.4396 | 24.35 | 5600 | 0.6307 | 0.6982 | 0.6986 | | 0.4336 | 25.22 | 5800 | 0.6495 | 0.6961 | 0.6959 | | 0.4294 | 26.09 | 6000 | 0.6630 | 0.6933 | 0.6948 | | 0.428 | 26.96 | 6200 | 0.6421 | 0.6955 | 0.6967 | | 0.418 | 27.83 | 6400 | 0.6535 | 0.7025 | 0.7033 | | 0.4177 | 28.7 | 6600 | 0.6546 | 0.6955 | 0.6954 | | 0.4142 | 29.57 | 6800 | 0.6534 | 0.6938 | 0.6943 | | 0.4112 | 30.43 | 7000 | 0.6518 | 0.7017 | 0.7016 | | 0.4087 | 31.3 | 7200 | 0.6582 | 0.7031 | 0.7030 | | 0.4011 | 32.17 | 7400 | 0.6718 | 0.7003 | 0.7003 | | 0.3996 | 33.04 | 7600 | 0.6742 | 0.6971 | 0.6970 | | 0.3983 | 33.91 | 7800 | 0.6686 | 0.7005 | 0.7014 | | 0.3922 | 34.78 | 8000 | 0.6739 | 0.7019 | 0.7019 | | 0.3922 | 35.65 | 8200 | 0.6771 | 0.7042 | 0.7041 | | 0.3896 | 36.52 | 8400 | 0.6731 | 0.7005 | 0.7003 | | 0.3892 | 37.39 | 8600 | 0.6700 | 0.7022 | 0.7019 | | 0.3808 | 38.26 | 8800 | 0.6924 | 0.7003 | 0.7005 | | 0.388 | 39.13 | 9000 | 0.6855 | 0.7014 | 0.7016 | | 0.3843 | 40.0 | 9200 | 0.6828 | 0.7024 | 0.7024 | | 0.3806 | 40.87 | 9400 | 0.6873 | 0.7009 | 0.7008 | | 0.3827 | 41.74 | 9600 | 0.6855 | 0.7024 | 0.7024 | | 0.3813 | 42.61 | 9800 | 0.6873 | 0.7009 | 0.7008 | | 0.3751 | 43.48 | 10000 | 0.6912 | 0.7000 | 0.7 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_EMP_H3K4me3-seqsight_32768_512_43M-L32_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3K4me3-seqsight_32768_512_43M-L32_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]

null

2024-04-30T06:35:53+00:00

null

{}

yliuhz/FaceEdit-ControlNet-COMP5421

null

[ "region:us" ]

null

2024-04-30T06:35:56+00:00

null

peft

# GUE_EMP_H4-seqsight_32768_512_43M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_EMP_H4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H4) dataset. It achieves the following results on the evaluation set: - Loss: 0.2537 - F1 Score: 0.9048 - Accuracy: 0.9049 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:| | 0.4138 | 2.17 | 200 | 0.3024 | 0.8886 | 0.8884 | | 0.2889 | 4.35 | 400 | 0.2914 | 0.8859 | 0.8857 | | 0.276 | 6.52 | 600 | 0.2811 | 0.8872 | 0.8871 | | 0.2752 | 8.7 | 800 | 0.2797 | 0.8845 | 0.8843 | | 0.2645 | 10.87 | 1000 | 0.2767 | 0.8877 | 0.8877 | | 0.2644 | 13.04 | 1200 | 0.2772 | 0.8879 | 0.8877 | | 0.259 | 15.22 | 1400 | 0.2717 | 0.8917 | 0.8919 | | 0.2542 | 17.39 | 1600 | 0.2704 | 0.8905 | 0.8905 | | 0.2528 | 19.57 | 1800 | 0.2679 | 0.8937 | 0.8939 | | 0.2524 | 21.74 | 2000 | 0.2727 | 0.8941 | 0.8939 | | 0.2477 | 23.91 | 2200 | 0.2683 | 0.8927 | 0.8925 | | 0.2464 | 26.09 | 2400 | 0.2722 | 0.8961 | 0.8960 | | 0.2452 | 28.26 | 2600 | 0.2672 | 0.8924 | 0.8925 | | 0.2441 | 30.43 | 2800 | 0.2646 | 0.8954 | 0.8953 | | 0.2392 | 32.61 | 3000 | 0.2662 | 0.8960 | 0.8960 | | 0.236 | 34.78 | 3200 | 0.2602 | 0.8925 | 0.8925 | | 0.2364 | 36.96 | 3400 | 0.2657 | 0.8968 | 0.8966 | | 0.2351 | 39.13 | 3600 | 0.2631 | 0.8988 | 0.8987 | | 0.2325 | 41.3 | 3800 | 0.2636 | 0.8974 | 0.8973 | | 0.2306 | 43.48 | 4000 | 0.2671 | 0.8967 | 0.8966 | | 0.2334 | 45.65 | 4200 | 0.2600 | 0.8960 | 0.8960 | | 0.2262 | 47.83 | 4400 | 0.2623 | 0.8967 | 0.8966 | | 0.231 | 50.0 | 4600 | 0.2588 | 0.8939 | 0.8939 | | 0.2233 | 52.17 | 4800 | 0.2635 | 0.8961 | 0.8960 | | 0.2256 | 54.35 | 5000 | 0.2710 | 0.8941 | 0.8939 | | 0.2223 | 56.52 | 5200 | 0.2700 | 0.8934 | 0.8932 | | 0.2214 | 58.7 | 5400 | 0.2653 | 0.8975 | 0.8973 | | 0.2186 | 60.87 | 5600 | 0.2678 | 0.8942 | 0.8939 | | 0.221 | 63.04 | 5800 | 0.2633 | 0.9009 | 0.9008 | | 0.2185 | 65.22 | 6000 | 0.2671 | 0.8954 | 0.8953 | | 0.2184 | 67.39 | 6200 | 0.2688 | 0.8948 | 0.8946 | | 0.2168 | 69.57 | 6400 | 0.2615 | 0.8994 | 0.8994 | | 0.2178 | 71.74 | 6600 | 0.2640 | 0.9002 | 0.9001 | | 0.2162 | 73.91 | 6800 | 0.2676 | 0.8968 | 0.8966 | | 0.2141 | 76.09 | 7000 | 0.2698 | 0.8935 | 0.8932 | | 0.2138 | 78.26 | 7200 | 0.2695 | 0.8934 | 0.8932 | | 0.2113 | 80.43 | 7400 | 0.2642 | 0.8981 | 0.8980 | | 0.2107 | 82.61 | 7600 | 0.2620 | 0.8987 | 0.8987 | | 0.2148 | 84.78 | 7800 | 0.2665 | 0.8989 | 0.8987 | | 0.2109 | 86.96 | 8000 | 0.2640 | 0.9009 | 0.9008 | | 0.2142 | 89.13 | 8200 | 0.2648 | 0.8995 | 0.8994 | | 0.2084 | 91.3 | 8400 | 0.2635 | 0.9015 | 0.9014 | | 0.2093 | 93.48 | 8600 | 0.2636 | 0.9015 | 0.9014 | | 0.2106 | 95.65 | 8800 | 0.2644 | 0.9022 | 0.9021 | | 0.2125 | 97.83 | 9000 | 0.2639 | 0.9022 | 0.9021 | | 0.2079 | 100.0 | 9200 | 0.2666 | 0.8995 | 0.8994 | | 0.2092 | 102.17 | 9400 | 0.2655 | 0.8995 | 0.8994 | | 0.2087 | 104.35 | 9600 | 0.2666 | 0.9002 | 0.9001 | | 0.2061 | 106.52 | 9800 | 0.2648 | 0.9009 | 0.9008 | | 0.2083 | 108.7 | 10000 | 0.2658 | 0.8995 | 0.8994 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_EMP_H4-seqsight_32768_512_43M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H4-seqsight_32768_512_43M-L1_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]

null

2024-04-30T06:36:38+00:00

null

peft

# GUE_EMP_H4-seqsight_32768_512_43M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_EMP_H4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H4) dataset. It achieves the following results on the evaluation set: - Loss: 0.2507 - F1 Score: 0.9041 - Accuracy: 0.9042 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:| | 0.3722 | 2.17 | 200 | 0.2879 | 0.8864 | 0.8864 | | 0.2725 | 4.35 | 400 | 0.2825 | 0.8920 | 0.8919 | | 0.2595 | 6.52 | 600 | 0.2679 | 0.8958 | 0.8960 | | 0.2567 | 8.7 | 800 | 0.2810 | 0.8907 | 0.8905 | | 0.2447 | 10.87 | 1000 | 0.2755 | 0.8890 | 0.8891 | | 0.2411 | 13.04 | 1200 | 0.2641 | 0.8959 | 0.8960 | | 0.2304 | 15.22 | 1400 | 0.2797 | 0.8914 | 0.8912 | | 0.2235 | 17.39 | 1600 | 0.2681 | 0.8983 | 0.8980 | | 0.2197 | 19.57 | 1800 | 0.2625 | 0.8989 | 0.8987 | | 0.214 | 21.74 | 2000 | 0.2679 | 0.8934 | 0.8932 | | 0.2067 | 23.91 | 2200 | 0.2711 | 0.8919 | 0.8919 | | 0.2026 | 26.09 | 2400 | 0.2663 | 0.8955 | 0.8953 | | 0.2 | 28.26 | 2600 | 0.2666 | 0.8954 | 0.8953 | | 0.1983 | 30.43 | 2800 | 0.2663 | 0.8928 | 0.8925 | | 0.1875 | 32.61 | 3000 | 0.2794 | 0.8987 | 0.8987 | | 0.1812 | 34.78 | 3200 | 0.2828 | 0.8960 | 0.8960 | | 0.1795 | 36.96 | 3400 | 0.2861 | 0.8941 | 0.8939 | | 0.1754 | 39.13 | 3600 | 0.2897 | 0.8934 | 0.8932 | | 0.1697 | 41.3 | 3800 | 0.2999 | 0.8932 | 0.8932 | | 0.1616 | 43.48 | 4000 | 0.3106 | 0.8900 | 0.8898 | | 0.1645 | 45.65 | 4200 | 0.3022 | 0.8918 | 0.8919 | | 0.1601 | 47.83 | 4400 | 0.3078 | 0.8940 | 0.8939 | | 0.1581 | 50.0 | 4600 | 0.3147 | 0.8911 | 0.8912 | | 0.1537 | 52.17 | 4800 | 0.3123 | 0.8893 | 0.8891 | | 0.1498 | 54.35 | 5000 | 0.3216 | 0.8818 | 0.8816 | | 0.1452 | 56.52 | 5200 | 0.3378 | 0.8799 | 0.8795 | | 0.1417 | 58.7 | 5400 | 0.3286 | 0.8839 | 0.8836 | | 0.1404 | 60.87 | 5600 | 0.3191 | 0.8899 | 0.8898 | | 0.1355 | 63.04 | 5800 | 0.3498 | 0.8769 | 0.8768 | | 0.1333 | 65.22 | 6000 | 0.3440 | 0.8845 | 0.8843 | | 0.1332 | 67.39 | 6200 | 0.3463 | 0.8852 | 0.8850 | | 0.1295 | 69.57 | 6400 | 0.3534 | 0.8819 | 0.8816 | | 0.1255 | 71.74 | 6600 | 0.3533 | 0.8858 | 0.8857 | | 0.1264 | 73.91 | 6800 | 0.3561 | 0.8819 | 0.8816 | | 0.1232 | 76.09 | 7000 | 0.3631 | 0.8818 | 0.8816 | | 0.1179 | 78.26 | 7200 | 0.3653 | 0.8797 | 0.8795 | | 0.1197 | 80.43 | 7400 | 0.3694 | 0.8831 | 0.8830 | | 0.1127 | 82.61 | 7600 | 0.3778 | 0.8841 | 0.8843 | | 0.1208 | 84.78 | 7800 | 0.3743 | 0.8811 | 0.8809 | | 0.1134 | 86.96 | 8000 | 0.3756 | 0.8782 | 0.8782 | | 0.1158 | 89.13 | 8200 | 0.3737 | 0.8818 | 0.8816 | | 0.1119 | 91.3 | 8400 | 0.3773 | 0.8770 | 0.8768 | | 0.1111 | 93.48 | 8600 | 0.3813 | 0.8816 | 0.8816 | | 0.1108 | 95.65 | 8800 | 0.3786 | 0.8796 | 0.8795 | | 0.1106 | 97.83 | 9000 | 0.3841 | 0.8790 | 0.8789 | | 0.1101 | 100.0 | 9200 | 0.3845 | 0.8805 | 0.8802 | | 0.1106 | 102.17 | 9400 | 0.3841 | 0.8791 | 0.8789 | | 0.1091 | 104.35 | 9600 | 0.3813 | 0.8791 | 0.8789 | | 0.105 | 106.52 | 9800 | 0.3847 | 0.8790 | 0.8789 | | 0.1072 | 108.7 | 10000 | 0.3848 | 0.8770 | 0.8768 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_EMP_H4-seqsight_32768_512_43M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H4-seqsight_32768_512_43M-L8_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]

null

2024-04-30T06:36:39+00:00

null

peft

# GUE_EMP_H3-seqsight_32768_512_43M-L1_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_EMP_H3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3) dataset. It achieves the following results on the evaluation set: - Loss: 0.3170 - F1 Score: 0.8730 - Accuracy: 0.8731 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:| | 0.5063 | 2.13 | 200 | 0.4277 | 0.8016 | 0.8029 | | 0.3674 | 4.26 | 400 | 0.3869 | 0.8349 | 0.8350 | | 0.3338 | 6.38 | 600 | 0.3773 | 0.8415 | 0.8417 | | 0.3189 | 8.51 | 800 | 0.3543 | 0.8564 | 0.8564 | | 0.3056 | 10.64 | 1000 | 0.3430 | 0.8597 | 0.8597 | | 0.294 | 12.77 | 1200 | 0.3415 | 0.8617 | 0.8617 | | 0.2883 | 14.89 | 1400 | 0.3350 | 0.8677 | 0.8677 | | 0.2803 | 17.02 | 1600 | 0.3305 | 0.8664 | 0.8664 | | 0.2768 | 19.15 | 1800 | 0.3526 | 0.8595 | 0.8597 | | 0.2715 | 21.28 | 2000 | 0.3447 | 0.8654 | 0.8657 | | 0.2709 | 23.4 | 2200 | 0.3240 | 0.8664 | 0.8664 | | 0.2568 | 25.53 | 2400 | 0.3675 | 0.8601 | 0.8604 | | 0.2627 | 27.66 | 2600 | 0.3348 | 0.8703 | 0.8704 | | 0.2611 | 29.79 | 2800 | 0.3316 | 0.8663 | 0.8664 | | 0.2557 | 31.91 | 3000 | 0.3309 | 0.8683 | 0.8684 | | 0.2524 | 34.04 | 3200 | 0.3312 | 0.8670 | 0.8671 | | 0.2512 | 36.17 | 3400 | 0.3520 | 0.8641 | 0.8644 | | 0.2484 | 38.3 | 3600 | 0.3412 | 0.8663 | 0.8664 | | 0.2471 | 40.43 | 3800 | 0.3445 | 0.8608 | 0.8611 | | 0.2468 | 42.55 | 4000 | 0.3551 | 0.8682 | 0.8684 | | 0.2414 | 44.68 | 4200 | 0.3380 | 0.8704 | 0.8704 | | 0.2407 | 46.81 | 4400 | 0.3474 | 0.8681 | 0.8684 | | 0.2421 | 48.94 | 4600 | 0.3840 | 0.8486 | 0.8497 | | 0.2374 | 51.06 | 4800 | 0.3319 | 0.8764 | 0.8764 | | 0.2365 | 53.19 | 5000 | 0.3727 | 0.8605 | 0.8611 | | 0.2352 | 55.32 | 5200 | 0.3354 | 0.8717 | 0.8717 | | 0.234 | 57.45 | 5400 | 0.3719 | 0.8608 | 0.8611 | | 0.2322 | 59.57 | 5600 | 0.3533 | 0.8695 | 0.8697 | | 0.2354 | 61.7 | 5800 | 0.3387 | 0.8716 | 0.8717 | | 0.2275 | 63.83 | 6000 | 0.3770 | 0.8599 | 0.8604 | | 0.23 | 65.96 | 6200 | 0.3597 | 0.8646 | 0.8651 | | 0.2301 | 68.09 | 6400 | 0.3545 | 0.8708 | 0.8711 | | 0.2303 | 70.21 | 6600 | 0.3620 | 0.8661 | 0.8664 | | 0.2298 | 72.34 | 6800 | 0.3576 | 0.8661 | 0.8664 | | 0.2261 | 74.47 | 7000 | 0.4031 | 0.8480 | 0.8490 | | 0.2229 | 76.6 | 7200 | 0.3632 | 0.8688 | 0.8691 | | 0.2283 | 78.72 | 7400 | 0.3536 | 0.8723 | 0.8724 | | 0.2243 | 80.85 | 7600 | 0.3611 | 0.8688 | 0.8691 | | 0.2245 | 82.98 | 7800 | 0.3722 | 0.8620 | 0.8624 | | 0.2252 | 85.11 | 8000 | 0.3506 | 0.8756 | 0.8758 | | 0.2223 | 87.23 | 8200 | 0.3614 | 0.8688 | 0.8691 | | 0.2214 | 89.36 | 8400 | 0.3702 | 0.8661 | 0.8664 | | 0.223 | 91.49 | 8600 | 0.3739 | 0.8620 | 0.8624 | | 0.2197 | 93.62 | 8800 | 0.3719 | 0.8661 | 0.8664 | | 0.2205 | 95.74 | 9000 | 0.3758 | 0.8613 | 0.8617 | | 0.2206 | 97.87 | 9200 | 0.3584 | 0.8736 | 0.8737 | | 0.2208 | 100.0 | 9400 | 0.3588 | 0.8715 | 0.8717 | | 0.2206 | 102.13 | 9600 | 0.3659 | 0.8675 | 0.8677 | | 0.2182 | 104.26 | 9800 | 0.3645 | 0.8708 | 0.8711 | | 0.2198 | 106.38 | 10000 | 0.3647 | 0.8708 | 0.8711 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_EMP_H3-seqsight_32768_512_43M-L1_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3-seqsight_32768_512_43M-L1_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]

null

2024-04-30T06:37:05+00:00

null

peft

# GUE_EMP_H4-seqsight_32768_512_43M-L32_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_EMP_H4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H4) dataset. It achieves the following results on the evaluation set: - Loss: 0.2456 - F1 Score: 0.9063 - Accuracy: 0.9062 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:| | 0.3469 | 2.17 | 200 | 0.2894 | 0.8880 | 0.8877 | | 0.2635 | 4.35 | 400 | 0.2678 | 0.8973 | 0.8973 | | 0.2476 | 6.52 | 600 | 0.2672 | 0.8960 | 0.8960 | | 0.2393 | 8.7 | 800 | 0.2811 | 0.8915 | 0.8912 | | 0.2229 | 10.87 | 1000 | 0.2619 | 0.8993 | 0.8994 | | 0.212 | 13.04 | 1200 | 0.2620 | 0.9003 | 0.9001 | | 0.1957 | 15.22 | 1400 | 0.2997 | 0.8895 | 0.8891 | | 0.1864 | 17.39 | 1600 | 0.2886 | 0.8915 | 0.8912 | | 0.1764 | 19.57 | 1800 | 0.2986 | 0.8961 | 0.8960 | | 0.1647 | 21.74 | 2000 | 0.3023 | 0.8887 | 0.8884 | | 0.154 | 23.91 | 2200 | 0.3210 | 0.8901 | 0.8898 | | 0.143 | 26.09 | 2400 | 0.3236 | 0.8915 | 0.8912 | | 0.1354 | 28.26 | 2600 | 0.3311 | 0.8850 | 0.8850 | | 0.1243 | 30.43 | 2800 | 0.3589 | 0.8725 | 0.8720 | | 0.1152 | 32.61 | 3000 | 0.3594 | 0.8791 | 0.8789 | | 0.1002 | 34.78 | 3200 | 0.4006 | 0.8853 | 0.8850 | | 0.0952 | 36.96 | 3400 | 0.3912 | 0.8818 | 0.8816 | | 0.0899 | 39.13 | 3600 | 0.4403 | 0.8809 | 0.8809 | | 0.0816 | 41.3 | 3800 | 0.4618 | 0.8778 | 0.8782 | | 0.0741 | 43.48 | 4000 | 0.4516 | 0.8741 | 0.8741 | | 0.0743 | 45.65 | 4200 | 0.4487 | 0.8780 | 0.8782 | | 0.0673 | 47.83 | 4400 | 0.4597 | 0.8898 | 0.8898 | | 0.063 | 50.0 | 4600 | 0.4948 | 0.8817 | 0.8816 | | 0.06 | 52.17 | 4800 | 0.5218 | 0.8749 | 0.8747 | | 0.0529 | 54.35 | 5000 | 0.5205 | 0.8811 | 0.8809 | | 0.0501 | 56.52 | 5200 | 0.5313 | 0.8845 | 0.8843 | | 0.0473 | 58.7 | 5400 | 0.5863 | 0.8757 | 0.8754 | | 0.0438 | 60.87 | 5600 | 0.5475 | 0.8763 | 0.8761 | | 0.0432 | 63.04 | 5800 | 0.5901 | 0.8791 | 0.8789 | | 0.0387 | 65.22 | 6000 | 0.6309 | 0.8669 | 0.8665 | | 0.0361 | 67.39 | 6200 | 0.6609 | 0.8785 | 0.8782 | | 0.0349 | 69.57 | 6400 | 0.6233 | 0.8754 | 0.8754 | | 0.0331 | 71.74 | 6600 | 0.6171 | 0.8797 | 0.8795 | | 0.0351 | 73.91 | 6800 | 0.6380 | 0.8852 | 0.8850 | | 0.0288 | 76.09 | 7000 | 0.6467 | 0.8824 | 0.8823 | | 0.0295 | 78.26 | 7200 | 0.6264 | 0.8776 | 0.8775 | | 0.0277 | 80.43 | 7400 | 0.6538 | 0.8824 | 0.8823 | | 0.0247 | 82.61 | 7600 | 0.6973 | 0.8809 | 0.8809 | | 0.0278 | 84.78 | 7800 | 0.7178 | 0.8797 | 0.8795 | | 0.0247 | 86.96 | 8000 | 0.6858 | 0.8843 | 0.8843 | | 0.0237 | 89.13 | 8200 | 0.7218 | 0.8792 | 0.8789 | | 0.022 | 91.3 | 8400 | 0.6885 | 0.8809 | 0.8809 | | 0.0213 | 93.48 | 8600 | 0.7192 | 0.8831 | 0.8830 | | 0.0214 | 95.65 | 8800 | 0.7241 | 0.8803 | 0.8802 | | 0.0214 | 97.83 | 9000 | 0.7257 | 0.8790 | 0.8789 | | 0.0184 | 100.0 | 9200 | 0.7460 | 0.8778 | 0.8775 | | 0.0201 | 102.17 | 9400 | 0.7567 | 0.8770 | 0.8768 | | 0.0191 | 104.35 | 9600 | 0.7382 | 0.8816 | 0.8816 | | 0.0185 | 106.52 | 9800 | 0.7424 | 0.8803 | 0.8802 | | 0.0185 | 108.7 | 10000 | 0.7438 | 0.8810 | 0.8809 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_EMP_H4-seqsight_32768_512_43M-L32_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H4-seqsight_32768_512_43M-L32_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]

null

2024-04-30T06:37:07+00:00

text-generation

transformers

{}

liuyuxiang/wiki_cs_imitator

null

[ "transformers", "safetensors", "gpt2", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-04-30T06:38:29+00:00

null

{}

rasasa/Mistral-7B-text-to-sql-flash-attention-2

null

[ "region:us" ]

null

2024-04-30T06:38:36+00:00

null

peft

# phi3nedtuned-ner This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 0.6568 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 1 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.2 - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.10.1.dev0 - Transformers 4.41.0.dev0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1 ### License The model is licensed under the MIT license.

{"license": "mit", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "microsoft/Phi-3-mini-4k-instruct", "model-index": [{"name": "checkpoint_dir", "results": []}]}

shujatoor/phi3nedtuned-ner

null

[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:microsoft/Phi-3-mini-4k-instruct", "license:mit", "region:us" ]

null

2024-04-30T06:41:58+00:00

text2text-generation

transformers

{}

shenkha/DGSlow_Bartbase_ED

null

[ "transformers", "pytorch", "bart", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

null

2024-04-30T06:42:56+00:00

null

{}

Xrunner/hive-m

null

[ "region:us" ]

null

2024-04-30T06:43:19+00:00

null

peft

# GUE_EMP_H3-seqsight_32768_512_43M-L8_f This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_43M](https://huggingface.co/mahdibaghbanzadeh/seqsight_32768_512_43M) on the [mahdibaghbanzadeh/GUE_EMP_H3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3) dataset. It achieves the following results on the evaluation set: - Loss: 0.3077 - F1 Score: 0.8791 - Accuracy: 0.8791 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 10000 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy | |:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:| | 0.4475 | 2.13 | 200 | 0.3711 | 0.8427 | 0.8430 | | 0.3173 | 4.26 | 400 | 0.3464 | 0.8557 | 0.8557 | | 0.291 | 6.38 | 600 | 0.3571 | 0.8615 | 0.8617 | | 0.277 | 8.51 | 800 | 0.3289 | 0.8630 | 0.8631 | | 0.2649 | 10.64 | 1000 | 0.3380 | 0.8650 | 0.8651 | | 0.2537 | 12.77 | 1200 | 0.3459 | 0.8676 | 0.8677 | | 0.2497 | 14.89 | 1400 | 0.3562 | 0.8621 | 0.8624 | | 0.24 | 17.02 | 1600 | 0.3300 | 0.8757 | 0.8758 | | 0.2347 | 19.15 | 1800 | 0.3622 | 0.8627 | 0.8631 | | 0.2272 | 21.28 | 2000 | 0.3581 | 0.8695 | 0.8697 | | 0.2244 | 23.4 | 2200 | 0.3776 | 0.8599 | 0.8604 | | 0.207 | 25.53 | 2400 | 0.4066 | 0.8547 | 0.8550 | | 0.2113 | 27.66 | 2600 | 0.3849 | 0.8633 | 0.8637 | | 0.2094 | 29.79 | 2800 | 0.3830 | 0.8660 | 0.8664 | | 0.2012 | 31.91 | 3000 | 0.3522 | 0.8696 | 0.8697 | | 0.197 | 34.04 | 3200 | 0.3700 | 0.8715 | 0.8717 | | 0.1945 | 36.17 | 3400 | 0.4030 | 0.8578 | 0.8584 | | 0.1872 | 38.3 | 3600 | 0.4093 | 0.8661 | 0.8664 | | 0.1861 | 40.43 | 3800 | 0.4181 | 0.8592 | 0.8597 | | 0.1786 | 42.55 | 4000 | 0.4381 | 0.8599 | 0.8604 | | 0.1745 | 44.68 | 4200 | 0.4421 | 0.8544 | 0.8550 | | 0.1721 | 46.81 | 4400 | 0.3950 | 0.8654 | 0.8657 | | 0.172 | 48.94 | 4600 | 0.4968 | 0.8457 | 0.8470 | | 0.1635 | 51.06 | 4800 | 0.3863 | 0.8729 | 0.8731 | | 0.1619 | 53.19 | 5000 | 0.4594 | 0.8585 | 0.8591 | | 0.1593 | 55.32 | 5200 | 0.4623 | 0.8551 | 0.8557 | | 0.1591 | 57.45 | 5400 | 0.4254 | 0.8622 | 0.8624 | | 0.1557 | 59.57 | 5600 | 0.4582 | 0.8540 | 0.8544 | | 0.1532 | 61.7 | 5800 | 0.4197 | 0.8663 | 0.8664 | | 0.1485 | 63.83 | 6000 | 0.4785 | 0.8564 | 0.8570 | | 0.1456 | 65.96 | 6200 | 0.4841 | 0.8578 | 0.8584 | | 0.1444 | 68.09 | 6400 | 0.5085 | 0.8516 | 0.8524 | | 0.1432 | 70.21 | 6600 | 0.4829 | 0.8626 | 0.8631 | | 0.1426 | 72.34 | 6800 | 0.4582 | 0.8642 | 0.8644 | | 0.1391 | 74.47 | 7000 | 0.5618 | 0.8461 | 0.8470 | | 0.1348 | 76.6 | 7200 | 0.4947 | 0.8647 | 0.8651 | | 0.1383 | 78.72 | 7400 | 0.4901 | 0.8593 | 0.8597 | | 0.1317 | 80.85 | 7600 | 0.5457 | 0.8492 | 0.8497 | | 0.1312 | 82.98 | 7800 | 0.5402 | 0.8484 | 0.8490 | | 0.1311 | 85.11 | 8000 | 0.5053 | 0.8572 | 0.8577 | | 0.1303 | 87.23 | 8200 | 0.5300 | 0.8544 | 0.8550 | | 0.128 | 89.36 | 8400 | 0.5192 | 0.8572 | 0.8577 | | 0.1281 | 91.49 | 8600 | 0.5447 | 0.8524 | 0.8530 | | 0.1214 | 93.62 | 8800 | 0.5264 | 0.8553 | 0.8557 | | 0.1244 | 95.74 | 9000 | 0.5569 | 0.8504 | 0.8510 | | 0.1197 | 97.87 | 9200 | 0.5364 | 0.8572 | 0.8577 | | 0.1241 | 100.0 | 9400 | 0.5406 | 0.8532 | 0.8537 | | 0.1216 | 102.13 | 9600 | 0.5441 | 0.8511 | 0.8517 | | 0.1177 | 104.26 | 9800 | 0.5631 | 0.8490 | 0.8497 | | 0.1205 | 106.38 | 10000 | 0.5507 | 0.8504 | 0.8510 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2

{"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_43M", "model-index": [{"name": "GUE_EMP_H3-seqsight_32768_512_43M-L8_f", "results": []}]}

mahdibaghbanzadeh/GUE_EMP_H3-seqsight_32768_512_43M-L8_f

null

[ "peft", "safetensors", "generated_from_trainer", "base_model:mahdibaghbanzadeh/seqsight_32768_512_43M", "region:us" ]

null

2024-04-30T06:43:48+00:00

text-generation

transformers

{}

NEGI007/Llama-2-7b-chat-finetune

null

[ "transformers", "pytorch", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

null

2024-04-30T06:43:50+00:00