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modelId string	author string	last_modified timestamp[us, tz=UTC]	downloads int64	likes int64	library_name string	tags sequence	pipeline_tag string	createdAt timestamp[us, tz=UTC]	card string
rachid16/bloomfinetunned	rachid16	2024-02-23T19:53:38Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-23T19:53:36Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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sanjayuzu/gpssohi-distilbart-qgen-3-3-pretrained_text_summarization_samsum	sanjayuzu	2024-02-23T19:44:09Z	106	0	transformers	[ "transformers", "safetensors", "bart", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-23T19:43:22Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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lvcalucioli/phi2_linear_multiple-choice_merged	lvcalucioli	2024-02-23T19:41:54Z	4	0	transformers	[ "transformers", "safetensors", "phi", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-02-23T19:10:55Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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mi-rei/CT_longformer_III_efficient_5e	mi-rei	2024-02-23T19:39:20Z	118	0	transformers	[ "transformers", "safetensors", "longformer", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-23T19:38:47Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ferrazzipietro/Llama-2-13b-chat-hf_adapters_en.layer1_4_torch.bfloat16_32_32_0.01_2_0.0002	ferrazzipietro	2024-02-23T19:36:17Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-23T19:35:41Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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adityasihag/math_QA-Mistral-7B-QLoRA-adapter	adityasihag	2024-02-23T19:33:16Z	9	2	peft	[ "peft", "safetensors", "Mathematical Reasoning", "en", "dataset:adityasihag/math_QAaugP", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:mit", "region:us" ]	null	2024-02-04T16:50:55Z	--- library_name: peft base_model: mistralai/Mistral-7B-v0.1 license: mit tags: - Mathematical Reasoning language: - en datasets: - adityasihag/math_QAaugP --- This repo contains LoRA adapter weights. ### Model Description - Project GitHub Page: https://github.com/adityasihag1996/math_QA.git - Developed by: [Aditya Sihag](https://www.linkedin.com/in/aditya-sihag-ab29681a9/) - Model type: fine-tuned using QLoRA on 1x RTX 4090 - Finetuned from model: mistralai/Mistral-7B-v0.1 ## Results <table> <thead> <tr> <th>Prompt Approach</th> <th>GSM8k</th> <th>MATH</th> </tr> </thead> <tbody> <tr> <td>Zero-Shot CoT</td> <td><b>75.81</b></td> <td><b>-</b></td> </tr> </tbody> </table> ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 `LoraConfig` params: - r: 128 - lora_alpha: lora_r * 2 - lora_dropout: 0.05 - bias: "none" - task_type: "CAUSAL_LM" - target_modules: ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"] The hyperparameters for the LoRA fine-tuning are listed below: - epochs: 3 - learning_rate: 5e-5 - batch_size: 256 - max_grad_norm: 1.0 - weight_decay: 0.001 - lr_scheduler_type: "cosine" - warmup_ratio: 0.03 ## Dataset math_QA dataset is prepared as combination of [MetaMathQA](https://huggingface.co/datasets/meta-math/MetaMathQA) and [MathInstruct](https://huggingface.co/datasets/TIGER-Lab/MathInstruct), and some internal data. Refer [math_QAaugP](https://huggingface.co/datasets/adityasihag/math_QAaugP) ## Model Usage ``` import torch from transformers import ( AutoModelForCausalLM, AutoTokenizer ) from peft import PeftModel model_path = "mistralai/Mistral-7B-v0.1" model = AutoModelForCausalLM.from_pretrained( model_path, torch_dtype = torch.float16, device_map = {"": 0}, ) # Load LoRA and merge model = PeftModel.from_pretrained(model, "adityasihag/math_QA-Mistral-7B-QLoRA-adapter") model = model.merge_and_unload() tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) tokenizer.pad_token = tokenizer.eos_token question = """Solve the linear equations. $3(x+2)-x=x + 9$. Find the value of x.""" sample_input = f"""Question: {question} \n Answer: """ sample_input_tokenised = tokenizer(sample_input, return_tensors = "pt").to("cuda") generated_ids = model.generate( sample_input_tokenised, max_new_tokens = 1024, temperature = 0.3 ) output = tokenizer.decode(generated_ids[0], skip_special_tokens = True) print(output) ``` ##### Sample Input: ``` Question: Solve the linear equations. $3(x+2)-x=x + 9$. Find the value of x. \n Answer: ``` ##### Model Output: ``` Given the linear equation 3(x+2)-x=x+9. First, distribute the 3 in the brackets to get 3x + 6 - x = x + 9. Simplify the equation to get 2x + 6 = x + 9. Next, transpose x from the right side to the left side and from the left side to the right side to get x = 9 - 6. Finally, solve for x to get x = 3. ``` #### Prompt Template: ``` Question: <question> Answer: ``` ## Comparing math_QA models with other SFT LLM models \| Model \| GSM8k Pass@1 \| MATH Pass@1 \| \|---------------------\|--------------\|-------------\| \| LLaMA-2-7B \| 14.6 \| 2.5 \| \| gemma-2b \| 17.7 \| \| \| LLaMA-2-13B \| 28.7 \| 3.9 \| \| LLaMA-2-34B \| 42.2 \| 6.24 \| \| math_QA-gemma-2B \| 43.66 \| \| \| gemma-7b \| 46.4 \| \| \| WizardMath-7B \| 54.9 \| 10.7 \| \| Mistral-7B \| 35.4 \| \| \| WizardMath-13B \| 63.9 \| 14.0 \| \| MetaMath-7B \| 66.5 \| 19.8 \| \| MetaMath-13B \| 72.3 \| 22.4 \| \| math_QA-Mistral-7B \| 75.81** \| \| \| Arithmo2-Mistral-7B \| 76.4 \| 27.2 \| \| MetaMath-Mistral-7B \| 77.7 \| 28.2 \| \| DeepSeekMath-Instruct-7B \| 82.9 \| 46.8 \| \| GPT4 \| 92.0 \| 52.9 \|
vapogore/mbart-neutralization	vapogore	2024-02-23T19:28:57Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "mbart", "text2text-generation", "simplification", "generated_from_trainer", "base_model:facebook/mbart-large-50", "base_model:finetune:facebook/mbart-large-50", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-15T15:49:39Z	--- license: mit base_model: facebook/mbart-large-50 tags: - simplification - generated_from_trainer metrics: - bleu model-index: - name: mbart-neutralization results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mbart-neutralization This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the Spanish Gender Neutralization dataset (hackathon-pln-es/neutral-es ). It achieves the following results on the evaluation set: - Loss: 0.0220 - Bleu: 98.2132 - Gen Len: 18.5417 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\| \| No log \| 1.0 \| 440 \| 0.0490 \| 96.2659 \| 19.0104 \| \| 0.2462 \| 2.0 \| 880 \| 0.0220 \| 98.2132 \| 18.5417 \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
furrutiav/bert_qa_extractor_cockatiel_2022_ulra_by_question_type_s_ef_signal_it_60	furrutiav	2024-02-23T19:26:48Z	104	0	transformers	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	feature-extraction	2024-02-23T19:26:22Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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frluquba/pegasus_paraphrase	frluquba	2024-02-23T19:26:29Z	85	1	transformers	[ "transformers", "tensorboard", "safetensors", "pegasus", "text2text-generation", "simplification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-23T09:09:07Z	--- tags: - simplification - generated_from_trainer metrics: - bleu model-index: - name: pegasus_paraphrase results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # pegasus_paraphrase This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.8274 - Bleu: 18.1989 - Gen Len: 10.1252 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\| \| No log \| 1.0 \| 316 \| 2.8784 \| 15.735 \| 10.3708 \| \| 2.814 \| 2.0 \| 632 \| 2.8274 \| 18.1989 \| 10.1252 \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
fzzhang/mistralv1_gsm8k_s	fzzhang	2024-02-23T19:23:09Z	6	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:microsoft/phi-2", "base_model:adapter:microsoft/phi-2", "license:mit", "region:us" ]	null	2024-02-16T08:27:46Z	--- license: mit library_name: peft tags: - generated_from_trainer base_model: microsoft/phi-2 model-index: - name: mistralv1_gsm8k_s results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mistralv1_gsm8k_s This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-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: 1e-05 - train_batch_size: 8 - eval_batch_size: 16 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - PEFT 0.7.2.dev0 - Transformers 4.36.2 - Pytorch 2.1.2 - Datasets 2.16.1 - Tokenizers 0.15.1
ferrazzipietro/Llama-2-13b-chat-hf_adapters_en.layer1_4_torch.bfloat16_32_32_0.05_8_0.0008	ferrazzipietro	2024-02-23T19:17:32Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-23T19:16:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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extraltodeus/llamachill_13b	extraltodeus	2024-02-23T19:13:27Z	119	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "en", "fr", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "gptq", "region:us" ]	text-generation	2024-02-23T17:47:01Z	--- license: llama2 language: - en - fr --- This model is the selection of the values having the smallest sum of euclidean distances in between: - TheBloke_WizardLM-1.0-Uncensored-Llama2-13B - TheBloke_Spicyboros-13B-2.2-GPTQ - TheBloke_MythoMax-L2-13B-GPTQ - TheBloke_Llama-2-13B-GPTQ Overall it seems to be the most resilient to "bad settings" that I have tried so far. It is surprisingly efficient at french too.
rwang5688/distilbert-base-uncased-finetuned-sst2-pt	rwang5688	2024-02-23T19:12:59Z	31	1	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-04-10T04:35:09Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-sst2-pt results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-sst2-pt This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5249 - Accuracy: 0.9071 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.1906 \| 1.0 \| 4210 \| 0.3109 \| 0.8979 \| \| 0.141 \| 2.0 \| 8420 \| 0.4039 \| 0.8922 \| \| 0.0925 \| 3.0 \| 12630 \| 0.3897 \| 0.9002 \| \| 0.0627 \| 4.0 \| 16840 \| 0.4421 \| 0.9048 \| \| 0.0422 \| 5.0 \| 21050 \| 0.5249 \| 0.9071 \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
lvcalucioli/phi2_linear_multiple-choice	lvcalucioli	2024-02-23T19:10:46Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:microsoft/phi-2", "base_model:adapter:microsoft/phi-2", "license:mit", "region:us" ]	null	2024-02-23T19:10:33Z	--- license: mit library_name: peft tags: - trl - sft - generated_from_trainer base_model: microsoft/phi-2 model-index: - name: phi2_linear_multiple-choice results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # phi2_linear_multiple-choice This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-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: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 10 ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.38.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.16.1 - Tokenizers 0.15.2
sanjayuzu/facebook-bart-large-cnn-pretrained_text_summarization_samsum	sanjayuzu	2024-02-23T19:06:44Z	113	0	transformers	[ "transformers", "safetensors", "bart", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-23T19:05:15Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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ferrazzipietro/Llama-2-13b-chat-hf_adapters_en.layer1_4_torch.bfloat16_32_32_0.05_4_0.0008	ferrazzipietro	2024-02-23T18:41:55Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-23T18:41:20Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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lvcalucioli/phi2_okq_multiple-choice_merged	lvcalucioli	2024-02-23T18:39:26Z	5	0	transformers	[ "transformers", "safetensors", "phi", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-02-23T18:32:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ryusangwon/9588_Llama-2-7b-hf	ryusangwon	2024-02-23T18:36:11Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "dataset:samsum", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]	null	2024-02-23T18:36:06Z	--- base_model: meta-llama/Llama-2-7b-hf tags: - generated_from_trainer datasets: - samsum model-index: - name: 9588_Llama-2-7b-hf results: [] library_name: 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. --> # 9588_Llama-2-7b-hf This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the samsum dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 100 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - PEFT 0.4.0 - Transformers 4.36.2 - Pytorch 2.0.1+cu117 - Datasets 2.15.0 - Tokenizers 0.15.0
Sumanth-0413/my-pet-dog	Sumanth-0413	2024-02-23T18:32:49Z	2	0	diffusers	[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-02-23T18:28:48Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### My-Pet-Dog Dreambooth model trained by Sumanth-0413 following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: GoX19932gAS Sample pictures of this concept: ![0](https://huggingface.co/Sumanth-0413/my-pet-dog/resolve/main/sample_images/pic.jpg)
whitefox123/whisper-ar-16	whitefox123	2024-02-23T18:27:21Z	62	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "ar", "dataset:mozilla-foundation/common_voice_11_0", "base_model:openai/whisper-large-v3", "base_model:finetune:openai/whisper-large-v3", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-02-23T18:00:39Z	--- language: - ar license: apache-2.0 base_model: openai/whisper-large-v3 tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Large Ar - Rami results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: default split: None args: 'config: hi, split: test' metrics: - name: Wer type: wer value: 185.9099099099099 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Large Ar - Rami This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.1139 - Wer: 185.9099 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 100 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.1065 \| 0.16 \| 100 \| 0.1139 \| 185.9099 \| ### Framework versions - Transformers 4.39.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
ferrazzipietro/Llama-2-13b-chat-hf_adapters_en.layer1_4_torch.bfloat16_32_32_0.05_4_0.0002	ferrazzipietro	2024-02-23T18:23:27Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-23T18:22:52Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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maritaca-ai/sabia-2-tokenizer-medium	maritaca-ai	2024-02-23T18:21:54Z	0	2	null	[ "region:us" ]	null	2024-01-27T14:37:59Z	This is the tokenizer used by the Sabiá-2 Medium model. Sabiá2 Medium is a proprietary LLM that can be used through an API endpoint, which we refer to as the "MariTalk API", or a downloadable version that can be used locally and is encrypted, known as "MariTalk Local". The purpose of including this tokenizer is to allow you to estimate the number of tokens in your prompts and, therefore, the cost of using the model. ```python import transformers tokenizer = transformers.AutoTokenizer.from_pretrained("maritaca-ai/sabia-2-tokenizer-medium") prompt = "Com quantos paus se faz uma canoa?" tokens = tokenizer.encode(prompt) print(f'O prompt "{prompt}" contém {len(tokens)} tokens.') # It should print 12 tokens. ``` For more information on how to use the model, please refer to our documentation at [this link](https://maritaca-ai.github.io/maritalk-api/maritalk.html).
sosancn/flan-t5-base	sosancn	2024-02-23T18:14:55Z	105	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "simplification", "generated_from_trainer", "base_model:google/flan-t5-base", "base_model:finetune:google/flan-t5-base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-23T12:27:15Z	--- license: apache-2.0 base_model: google/flan-t5-base tags: - simplification - generated_from_trainer metrics: - bleu model-index: - name: flan-t5-base results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # flan-t5-base This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0068 - Bleu: 31.623 - Gen Len: 17.8806 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\| \| No log \| 1.0 \| 34 \| 1.0331 \| 31.1917 \| 17.8806 \| \| No log \| 2.0 \| 68 \| 1.0068 \| 31.623 \| 17.8806 \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
Leul78/70bfinetune2	Leul78	2024-02-23T18:12:34Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-23T17:43:41Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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protoml/mistral_7b_guanaco	protoml	2024-02-23T18:09:43Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "mistral", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-Instruct-v0.1", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.1", "region:us" ]	null	2023-12-14T23:12:51Z	--- library_name: peft base_model: mistralai/Mistral-7B-Instruct-v0.1 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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ferrazzipietro/Llama-2-13b-chat-hf_adapters_en.layer1_4_torch.bfloat16_32_32_0.05_2_0.0008	ferrazzipietro	2024-02-23T18:05:06Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-23T18:04:30Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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serhii-korobchenko/mt5-small_poetry_Helsinki-2024-02-23-17-44-34	serhii-korobchenko	2024-02-23T17:55:29Z	61	0	transformers	[ "transformers", "tf", "marian", "text2text-generation", "generated_from_keras_callback", "base_model:Helsinki-NLP/opus-mt-ru-uk", "base_model:finetune:Helsinki-NLP/opus-mt-ru-uk", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-23T17:44:44Z	--- license: apache-2.0 base_model: Helsinki-NLP/opus-mt-ru-uk tags: - generated_from_keras_callback model-index: - name: serhii-korobchenko/mt5-small_poetry_Helsinki-2024-02-23-17-44-34 results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # serhii-korobchenko/mt5-small_poetry_Helsinki-2024-02-23-17-44-34 This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ru-uk](https://huggingface.co/Helsinki-NLP/opus-mt-ru-uk) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0194 - Validation Loss: 2.6788 - Epoch: 19 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-06, 'decay_steps': 2740, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 1e-06} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 0.0892 \| 2.5969 \| 0 \| \| 0.0658 \| 2.6050 \| 1 \| \| 0.0609 \| 2.6026 \| 2 \| \| 0.0519 \| 2.6134 \| 3 \| \| 0.0411 \| 2.6232 \| 4 \| \| 0.0392 \| 2.6326 \| 5 \| \| 0.0333 \| 2.6448 \| 6 \| \| 0.0311 \| 2.6494 \| 7 \| \| 0.0277 \| 2.6555 \| 8 \| \| 0.0245 \| 2.6601 \| 9 \| \| 0.0249 \| 2.6607 \| 10 \| \| 0.0234 \| 2.6678 \| 11 \| \| 0.0213 \| 2.6720 \| 12 \| \| 0.0210 \| 2.6713 \| 13 \| \| 0.0192 \| 2.6732 \| 14 \| \| 0.0203 \| 2.6741 \| 15 \| \| 0.0173 \| 2.6764 \| 16 \| \| 0.0170 \| 2.6778 \| 17 \| \| 0.0170 \| 2.6787 \| 18 \| \| 0.0194 \| 2.6788 \| 19 \| ### Framework versions - Transformers 4.37.2 - TensorFlow 2.15.0 - Datasets 2.17.1 - Tokenizers 0.15.2
ryusangwon/4605_Llama-2-7b-hf	ryusangwon	2024-02-23T17:55:02Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "dataset:samsum", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]	null	2024-02-23T17:54:57Z	--- base_model: meta-llama/Llama-2-7b-hf tags: - generated_from_trainer datasets: - samsum model-index: - name: 4605_Llama-2-7b-hf results: [] library_name: 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. --> # 4605_Llama-2-7b-hf This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the samsum dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 1 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - PEFT 0.4.0 - Transformers 4.36.2 - Pytorch 2.0.1+cu117 - Datasets 2.15.0 - Tokenizers 0.15.0
femboysLover/vikhr-0.1-otvetmailru-lora-128	femboysLover	2024-02-23T17:39:31Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-23T17:38:31Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
danrrr/dqn-SpaceInvadersNoFrameskip-v4	danrrr	2024-02-23T17:34:56Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-02-23T17:34:22Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 568.00 +/- 173.45 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga danrrr -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga danrrr -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga danrrr ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
ThomasFG/50-10	ThomasFG	2024-02-23T17:29:50Z	76	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-small.en", "base_model:finetune:openai/whisper-small.en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-02-23T15:59:40Z	--- license: apache-2.0 base_model: openai/whisper-small.en tags: - generated_from_trainer metrics: - wer model-index: - name: 2024-02-23_16-59-38 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 2024-02-23_16-59-38 This model is a fine-tuned version of [openai/whisper-small.en](https://huggingface.co/openai/whisper-small.en) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3279 - Wer: 11.2833 ## 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: 64 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.3162 \| 1.0 \| 261 \| 0.3279 \| 11.2833 \| ### Framework versions - Transformers 4.37.2 - Pytorch 1.13.1+cu116 - Datasets 2.17.0 - Tokenizers 0.15.2
duraad/nep-spell-mt5-small-final-100k-1-4	duraad	2024-02-23T17:25:04Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-23T16:12:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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ryusangwon/6872_Llama-2-7b-hf	ryusangwon	2024-02-23T17:22:32Z	1	0	peft	[ "peft", "safetensors", "generated_from_trainer", "dataset:samsum", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]	null	2024-02-23T17:22:27Z	--- base_model: meta-llama/Llama-2-7b-hf tags: - generated_from_trainer datasets: - samsum model-index: - name: 6872_Llama-2-7b-hf results: [] library_name: 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. --> # 6872_Llama-2-7b-hf This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the samsum dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 1 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - PEFT 0.4.0 - Transformers 4.36.2 - Pytorch 2.0.1+cu117 - Datasets 2.15.0 - Tokenizers 0.15.0
Boyem/a2c-2-PandaReachDense-v3	Boyem	2024-02-23T17:21:48Z	0	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-02-23T17:21:11Z	--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -0.61 +/- 1.32 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v3 This is a trained model of a A2C agent playing PandaReachDense-v3 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 ... ```
Boyem/a2c-PandaReachDense-v3	Boyem	2024-02-23T17:20:39Z	0	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-02-23T17:15:53Z	--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -1.16 +/- 1.88 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v3 This is a trained model of a A2C agent playing PandaReachDense-v3 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 ... ```
lmaccarini/mutual-full-v2-preproc	lmaccarini	2024-02-23T17:07:41Z	163	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:dccuchile/tulio-chilean-spanish-bert", "base_model:finetune:dccuchile/tulio-chilean-spanish-bert", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-23T17:06:58Z	--- license: cc-by-4.0 base_model: dccuchile/tulio-chilean-spanish-bert tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: mutual-full-v2-preproc results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mutual-full-v2-preproc This model is a fine-tuned version of [dccuchile/tulio-chilean-spanish-bert](https://huggingface.co/dccuchile/tulio-chilean-spanish-bert) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3775 - Accuracy: 0.9020 - Precision: 0.9022 - Recall: 0.9020 - F1: 0.9020 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 10 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.4642 \| 0.59 \| 500 \| 0.3463 \| 0.8608 \| 0.8610 \| 0.8608 \| 0.8607 \| \| 0.3661 \| 1.17 \| 1000 \| 0.3150 \| 0.8861 \| 0.8903 \| 0.8861 \| 0.8858 \| \| 0.2999 \| 1.76 \| 1500 \| 0.3704 \| 0.8861 \| 0.8883 \| 0.8861 \| 0.8859 \| \| 0.2378 \| 2.34 \| 2000 \| 0.3593 \| 0.9034 \| 0.9036 \| 0.9034 \| 0.9034 \| \| 0.2031 \| 2.93 \| 2500 \| 0.3775 \| 0.9020 \| 0.9022 \| 0.9020 \| 0.9020 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
Boyem/LunarLander-v2	Boyem	2024-02-23T17:07:33Z	0	0	null	[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2024-02-23T17:07:29Z	--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -176.35 +/- 97.72 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 50000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'Boyem/LunarLander-v2' 'batch_size': 512 'minibatch_size': 128} ```
gimmi45/sentiment-analyzer	gimmi45	2024-02-23T17:07:27Z	0	1	null	[ "license:mit", "region:us" ]	null	2024-02-23T16:49:11Z	--- license: mit --- Ultra-Lightweight Sentiment Analyzer Welcome to the repository for the ultra-lightweight, real-time sentiment analysis model designed specifically for mobile applications. This model brings the power of BERT's transformer architecture to a form that's optimized for performance on mobile devices. Model Details Architecture: DistilBERT, a distilled version of the BERT model known for its balance between performance and efficiency. Training Data: The model has been trained on a diverse semantic dataset, ensuring a robust understanding of language nuances. Quantization: Post-training dynamic quantization is applied to reduce model size without a significant reduction in accuracy, making it ideal for on-device deployment. Size: Approximately 130 MB, optimized for mobile environments. Latency: Achieves real-time inference on mobile devices with reduced latency. Usage This model can be used directly for sentiment analysis tasks or further fine-tuned on domain-specific datasets. It's perfect for applications where quick, on-the-fly text analysis is needed, such as live customer feedback analysis or on-device content moderation. python Copy code from transformers import pipeline # Load the sentiment analysis pipeline sentiment_pipeline = pipeline("sentiment-analysis", model="YOUR_MODEL_NAME") # Analyze sentiment result = sentiment_pipeline("I love using this mobile app, it's so fast and easy!") print(result) Applications The sentiment analyzer is suited for a variety of real-world applications: Customer Feedback Analysis: Quickly determine sentiment in user feedback or reviews. Content Moderation: Maintain community guidelines by filtering inappropriate content in real-time. Market Research: Analyze survey responses or social media posts to extract actionable insights. Compliance Monitoring: Ensure communications adhere to industry regulations. Performance Metrics Refer to the attached performance graphs to understand the model's efficiency gains through quantization. The graphs illustrate latency improvements and training versus validation loss, showcasing the model's reliability and speed. How to Contribute Contributions to improve the model or expand its use cases are welcome. Please follow the contribution guidelines outlined in CONTRIBUTING.md. License This model is open-source and available under the MIT license, allowing for commercial and private use, modification, and distribution. Acknowledgements example usecase.... # Install required libraries !pip install transformers torch import torch from transformers import DistilBertTokenizer, DistilBertForSequenceClassification from google.colab import files uploaded = files.upload() tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased') import torch from transformers import DistilBertTokenizer, DistilBertForSequenceClassification from torch.quantization import quantize_dynamic # Load the tokenizer tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased') # Initialize the model structure model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased', return_dict=True) # Apply dynamic quantization to match the saved model structure quantized_model = quantize_dynamic(model, {torch.nn.Linear}, dtype=torch.qint8) # Now load your quantized model state_dict quantized_model.load_state_dict(torch.load('brain.pt', map_location=torch.device('cpu'))) # If you're using a GPU, move the model to GPU quantized_model.to("cuda" if torch.cuda.is_available() else "cpu") def ask_model(question): inputs = tokenizer(question, return_tensors="pt", padding=True, truncation=True, max_length=512) inputs = inputs.to("cuda" if torch.cuda.is_available() else "cpu") with torch.no_grad(): outputs = quantized_model(**inputs) predictions = torch.nn.functional.softmax(outputs.logits, dim=-1) return predictions # Return the raw tensor of predictions import torch def simple_chatbot(input_text): # Classify the input text using your model predictions = ask_model(input_text) # This should now return a tensor probabilities = torch.nn.functional.softmax(predictions, dim=-1) class_index = probabilities.argmax().item() # Extract the predicted class index confidence = probabilities[0, class_index].item() # Get the confidence of the prediction # Define your response logic based on the class index if class_index == 1: # Assuming class 1 is positive sentiment response = f"I'm glad to hear that! How can I assist you further? (Confidence: {confidence:.2f})" elif class_index == 0: # Assuming class 0 is negative sentiment response = f"I'm sorry to hear that. How can I help make things better? (Confidence: {confidence:.2f})" else: response = f"Could you tell me more about it? (Confidence: {confidence:.2f})" # Return both the input and the response including confidence return f"User said: \"{input_text}\"\nChatbot response: {response}\n" # Example usage print(simple_chatbot("I love this service!")) print(simple_chatbot("This is terrible.")) print(simple_chatbot("Can you help me with my account?")) print(simple_chatbot("Your app is amazing, saved me so much time!")) print(simple_chatbot("I'm not happy with the service.")) print(simple_chatbot("How does the subscription model work?"))
hieunguyenminh/model-v3-merged	hieunguyenminh	2024-02-23T17:07:16Z	6	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "en", "dataset:hieunguyenminh/roleplay", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-23T16:54:37Z	--- license: mit datasets: - hieunguyenminh/roleplay language: - en --- ## Model is merged from [hieuminhnguyen/v3](https://huggingface.co/hieunguyenminh/v3) Model architecture : [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
fxmarty/tiny-random-GemmaForCausalLM	fxmarty	2024-02-23T17:06:41Z	311,544	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-23T14:44:32Z	--- license: mit --- This one with a custom `config.head_dim` as allowed by the architecture (see 7b model).
dataequity/dataequity-opus-mt-tl-de	dataequity	2024-02-23T17:06:28Z	105	0	transformers	[ "transformers", "safetensors", "marian", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-23T17:06:20Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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dataequity/dataequity-opus-mt-tl-en	dataequity	2024-02-23T17:05:37Z	107	0	transformers	[ "transformers", "safetensors", "marian", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-23T17:05:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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dataequity/dataequity-opus-mt-ar-es	dataequity	2024-02-23T17:04:46Z	104	0	transformers	[ "transformers", "safetensors", "marian", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-23T17:04:38Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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dataequity/dataequity-opus-mt-ar-en	dataequity	2024-02-23T17:04:18Z	105	0	transformers	[ "transformers", "safetensors", "marian", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-23T17:04:10Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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dataequity/dataequity-opus-mt-de-ar	dataequity	2024-02-23T17:03:38Z	106	0	transformers	[ "transformers", "safetensors", "marian", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-23T17:03:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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dataequity/dataequity-opus-mt-de-es	dataequity	2024-02-23T17:02:52Z	104	0	transformers	[ "transformers", "safetensors", "marian", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-23T17:02:45Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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KhosrojerdiA/bert-finetuned-ner	KhosrojerdiA	2024-02-23T17:02:45Z	104	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-02-23T16:51:30Z	--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0564 - Precision: 0.9360 - Recall: 0.9520 - F1: 0.9439 - Accuracy: 0.9871 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.0769 \| 1.0 \| 1756 \| 0.0747 \| 0.9029 \| 0.9291 \| 0.9158 \| 0.9794 \| \| 0.04 \| 2.0 \| 3512 \| 0.0586 \| 0.9289 \| 0.9492 \| 0.9389 \| 0.9862 \| \| 0.0248 \| 3.0 \| 5268 \| 0.0564 \| 0.9360 \| 0.9520 \| 0.9439 \| 0.9871 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
dataequity/dataequity-opus-mt-es-tl	dataequity	2024-02-23T17:02:01Z	104	0	transformers	[ "transformers", "safetensors", "marian", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-23T17:01:52Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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dataequity/dataequity-opus-mt-es-ar	dataequity	2024-02-23T17:01:25Z	104	0	transformers	[ "transformers", "safetensors", "marian", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-23T17:01:15Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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dataequity/dataequity-opus-mt-es-de	dataequity	2024-02-23T17:00:46Z	104	0	transformers	[ "transformers", "safetensors", "marian", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-23T17:00:37Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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dataequity/dataequity-opus-mt-en-tl	dataequity	2024-02-23T16:59:13Z	104	0	transformers	[ "transformers", "safetensors", "marian", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-23T16:59:05Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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dataequity/dataequity-opus-mt-en-ar	dataequity	2024-02-23T16:58:06Z	104	0	transformers	[ "transformers", "safetensors", "marian", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-23T16:57:58Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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zzzAI19/ChromaFT	zzzAI19	2024-02-23T16:57:45Z	0	31	null	[ "region:us" ]	null	2023-07-15T11:47:54Z	2/24/2024 v4 released. 10/24/2023 v3 released. 8/14/2023 v2 released. This model was created through fine tuning. There are two versions: an all-ages depiction-oriented version and an NSFW-enhanced version. All-ages depiction-oriented version：ChromaFT_v3.safetensors NSFW-enhanced version：ChromaNeoFT_v3.safetensors (NEO：Nsfw Erotic Option) For sample illustrations, please visit my blog. https://ai-drawing.net/en/2024/02/24/image-sampleschromaft-v4-chromasparkle-v2/ I am using the following prompts. You may get better results with other prompts. Positive prompt ((masterpiece)) (best quality) (beautiful detailed eyes) (ultra-detailed) (finely detailed) (highres) Negative prompt lowres bad anatomy bad hands text error ugly duplicate morbid bokeh limb incorrect limb fusion finger lose finger multiple finger multiple digit fusion hand lose leg fused leg multiple leg bad feet fewer digits cropped normal quality simple background white background abstract portrait jpeg ((worst quality)) ((low quality)) bad-hands-5 2024/2/24 v4公開しました。 2023/10/24 v3公開しました。 2023/8/14 v2公開しました。追加学習により作成したモデルです。全年齢描写重視版とNSFW強化版があります。全年齢描写重視版：ChromaFT_v3.safetensors NSFW強化版：ChromaNeoFT_v3.safetensors (NEO：Nsfw Erotic Option) サンプルイラストはブログをご覧ください。 https://ai-drawing.net/2024/02/24/%e7%94%bb%e5%83%8f%e3%82%b5%e3%83%b3%e3%83%97%e3%83%abchromaft-v4-chromasparkle-v2/ 私は以下のプロンプトを用いています。他のプロンプトでも良い結果が得られるかもしれません。ポジティブプロンプト ((masterpiece)) (best quality) (beautiful detailed eyes) (ultra-detailed) (finely detail) (highres) ネガティブプロンプト lowres bad anatomy bad hands text error ugly duplicate morbid bokeh limb incorrect limb fusion finger lose finger multiple finger multiple digit fusion hand lose leg fused leg multiple leg bad feet fewer digits cropped normal quality simple background white background abstract portrait jpeg ((worst quality)) ((low quality)) bad-hands-5 --- license: creativeml-openrail-m ---
manusehgal/autotrain-07985-2fqgn	manusehgal	2024-02-23T16:45:31Z	1	0	diffusers	[ "diffusers", "text-to-image", "autotrain", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]	text-to-image	2024-02-23T16:45:27Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: <enter your prompt here> tags: - text-to-image - diffusers - autotrain inference: true --- # DreamBooth trained by AutoTrain Text encoder was not trained.
ibunescu/Phi-2_GDPR_4_10e_adapter	ibunescu	2024-02-23T16:42:45Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-23T16:42:17Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ibunescu/Phi-2_GDPR_4_7e	ibunescu	2024-02-23T16:33:28Z	48	0	transformers	[ "transformers", "safetensors", "phi", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-23T16:29:35Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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isom5240ust/bert-base-uncased-emotion	isom5240ust	2024-02-23T16:32:02Z	136	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-23T16:31:45Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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serhii-korobchenko/mt5-small_poetry_test-2024-02-23-15-43-02	serhii-korobchenko	2024-02-23T16:30:46Z	61	0	transformers	[ "transformers", "tf", "mt5", "text2text-generation", "generated_from_keras_callback", "base_model:serhii-korobchenko/mt5-small_poetry_test-2024-02-23-11-37-04", "base_model:finetune:serhii-korobchenko/mt5-small_poetry_test-2024-02-23-11-37-04", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-23T16:05:35Z	--- license: apache-2.0 tags: - generated_from_keras_callback base_model: serhii-korobchenko/mt5-small_poetry_test-2024-02-23-11-37-04 model-index: - name: mt5-small_poetry_test-2024-02-23-15-43-02 results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # mt5-small_poetry_test-2024-02-23-15-43-02 This model is a fine-tuned version of [serhii-korobchenko/mt5-small_poetry_test-2024-02-23-11-37-04](https://huggingface.co/serhii-korobchenko/mt5-small_poetry_test-2024-02-23-11-37-04) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.0719 - Validation Loss: 8.8056 - Epoch: 49 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.0056, 'decay_steps': 750, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 1e-06} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 10.6051 \| 8.0186 \| 0 \| \| 7.2838 \| 7.5097 \| 1 \| \| 6.1079 \| 7.5227 \| 2 \| \| 5.8520 \| 7.6991 \| 3 \| \| 5.7403 \| 7.6098 \| 4 \| \| 5.7371 \| 7.6277 \| 5 \| \| 5.6356 \| 7.6047 \| 6 \| \| 5.6156 \| 7.7156 \| 7 \| \| 5.5722 \| 7.5735 \| 8 \| \| 5.5118 \| 7.5908 \| 9 \| \| 5.4539 \| 7.6017 \| 10 \| \| 5.3815 \| 7.6174 \| 11 \| \| 5.3763 \| 7.6022 \| 12 \| \| 5.2895 \| 7.4830 \| 13 \| \| 5.2140 \| 7.5455 \| 14 \| \| 5.1843 \| 7.4243 \| 15 \| \| 5.1056 \| 7.3897 \| 16 \| \| 4.9740 \| 7.2854 \| 17 \| \| 4.9361 \| 7.2887 \| 18 \| \| 4.8234 \| 7.4561 \| 19 \| \| 4.9462 \| 7.3764 \| 20 \| \| 4.8029 \| 7.2209 \| 21 \| \| 4.6122 \| 7.1327 \| 22 \| \| 4.4010 \| 7.3362 \| 23 \| \| 4.2291 \| 7.0549 \| 24 \| \| 4.0323 \| 7.2076 \| 25 \| \| 3.8655 \| 7.2932 \| 26 \| \| 3.6406 \| 7.3575 \| 27 \| \| 3.4665 \| 7.2689 \| 28 \| \| 3.4070 \| 7.1520 \| 29 \| \| 3.3049 \| 7.4382 \| 30 \| \| 3.0354 \| 7.5552 \| 31 \| \| 2.7136 \| 7.2149 \| 32 \| \| 2.5568 \| 7.8140 \| 33 \| \| 2.2594 \| 7.7701 \| 34 \| \| 2.1743 \| 7.9400 \| 35 \| \| 2.0776 \| 8.1060 \| 36 \| \| 1.8686 \| 7.7733 \| 37 \| \| 1.8453 \| 8.1850 \| 38 \| \| 1.7281 \| 7.8816 \| 39 \| \| 1.5912 \| 7.8918 \| 40 \| \| 1.4447 \| 8.4160 \| 41 \| \| 1.4090 \| 8.5857 \| 42 \| \| 1.2143 \| 8.5367 \| 43 \| \| 1.2254 \| 8.3491 \| 44 \| \| 1.0937 \| 8.8601 \| 45 \| \| 1.1357 \| 8.4994 \| 46 \| \| 1.0708 \| 8.9421 \| 47 \| \| 1.0830 \| 8.9817 \| 48 \| \| 1.0719 \| 8.8056 \| 49 \| ### Framework versions - Transformers 4.37.2 - TensorFlow 2.15.0 - Datasets 2.17.1 - Tokenizers 0.15.2
ibunescu/Phi-2_GDPR_4_6e	ibunescu	2024-02-23T16:29:07Z	48	0	transformers	[ "transformers", "safetensors", "phi", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-23T16:25:23Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ibunescu/Phi-2_GDPR_4_5e	ibunescu	2024-02-23T16:24:46Z	48	0	transformers	[ "transformers", "safetensors", "phi", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-23T16:20:46Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Hari11/LunarLanderv2	Hari11	2024-02-23T16:22:22Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-02-23T16:21:56Z	--- 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: 212.06 +/- 52.11 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
kowalsky/reddit-financial_wisdom	kowalsky	2024-02-23T16:21:05Z	76	1	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "question-answering", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	question-answering	2024-02-21T16:38:09Z	--- pipeline_tag: question-answering --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ibunescu/Phi-2_GDPR_4_3e	ibunescu	2024-02-23T16:14:20Z	4	0	transformers	[ "transformers", "safetensors", "phi", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-23T12:43:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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dhruviljhala/t5-small-finetuned-samsun-10epoch-32	dhruviljhala	2024-02-23T16:14:01Z	104	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-23T16:13:40Z	--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-small-finetuned-samsun-10epoch-32 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-samsun-10epoch-32 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.7427 - Rouge1: 41.259 - Rouge2: 18.6668 - Rougel: 34.8705 - Rougelsum: 38.2147 - Gen Len: 16.6051 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 2.2715 \| 1.0 \| 921 \| 1.8796 \| 39.1544 \| 16.6193 \| 32.6399 \| 36.2408 \| 16.3888 \| \| 2.042 \| 2.0 \| 1842 \| 1.8251 \| 40.2761 \| 17.3143 \| 33.5239 \| 37.3139 \| 16.4218 \| \| 1.987 \| 3.0 \| 2763 \| 1.7981 \| 40.3499 \| 17.8091 \| 34.0216 \| 37.6063 \| 16.4095 \| \| 1.9529 \| 4.0 \| 3684 \| 1.7779 \| 40.9741 \| 18.1594 \| 34.3685 \| 37.8782 \| 16.4401 \| \| 1.9298 \| 5.0 \| 4605 \| 1.7657 \| 41.3166 \| 18.6125 \| 34.8556 \| 38.2854 \| 16.4914 \| \| 1.9105 \| 6.0 \| 5526 \| 1.7566 \| 41.4267 \| 18.5259 \| 34.7167 \| 38.2684 \| 16.6711 \| \| 1.8945 \| 7.0 \| 6447 \| 1.7508 \| 41.075 \| 18.4725 \| 34.6262 \| 38.043 \| 16.5037 \| \| 1.8815 \| 8.0 \| 7368 \| 1.7474 \| 41.4103 \| 18.5046 \| 34.8252 \| 38.2475 \| 16.5513 \| \| 1.8805 \| 9.0 \| 8289 \| 1.7422 \| 41.4699 \| 18.7027 \| 34.921 \| 38.3892 \| 16.6467 \| \| 1.8786 \| 10.0 \| 9210 \| 1.7427 \| 41.259 \| 18.6668 \| 34.8705 \| 38.2147 \| 16.6051 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
psk/W2V2-BERT-KA-Georgian	psk	2024-02-23T16:13:35Z	76	0	transformers	[ "transformers", "safetensors", "wav2vec2-bert", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-02-23T15:59:36Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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VATSAL1729/q-FrozenLake-v1-4x4-noSlippery	VATSAL1729	2024-02-23T16:04:21Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-02-23T16:04:19Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="VATSAL1729/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
DewiBrynJones/tts	DewiBrynJones	2024-02-23T16:03:52Z	5	0	transformers	[ "transformers", "text-to-speech", "license:cc0-1.0", "endpoints_compatible", "region:us" ]	text-to-speech	2024-01-23T07:39:14Z	--- license: cc0-1.0 pipeline_tag: text-to-speech ---
Geerath/mistral_7b_guanaco	Geerath	2024-02-23T15:58:28Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-23T15:57:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ferrazzipietro/Llama-2-13b-chat-hf_adapters_en.layer1_4_torch.bfloat16_16_32_0.01_2_0.0002	ferrazzipietro	2024-02-23T15:57:55Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-23T15:57:35Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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ThomasFG/10-10	ThomasFG	2024-02-23T15:57:20Z	76	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-small.en", "base_model:finetune:openai/whisper-small.en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-02-23T14:50:12Z	--- license: apache-2.0 base_model: openai/whisper-small.en tags: - generated_from_trainer metrics: - wer model-index: - name: 2024-02-23_15-50-07 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 2024-02-23_15-50-07 This model is a fine-tuned version of [openai/whisper-small.en](https://huggingface.co/openai/whisper-small.en) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7395 - Wer: 11.3293 ## 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: 64 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.7855 \| 1.0 \| 110 \| 0.7395 \| 11.3293 \| ### Framework versions - Transformers 4.37.2 - Pytorch 1.13.1+cu116 - Datasets 2.17.0 - Tokenizers 0.15.2
MaziyarPanahi/Mistral-7B-Alpaca-52k-v0.2	MaziyarPanahi	2024-02-23T15:54:00Z	0	2	peft	[ "peft", "pytorch", "tensorboard", "safetensors", "mistral", "text-generation", "axolotl", "generated_from_trainer", "transformers", "dataset:tatsu-lab/alpaca", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-19T12:01:40Z	--- license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - axolotl - generated_from_trainer - peft - transformers model-index: - name: Mistral-7B-Alpaca-52k-v0.2 results: [] datasets: - tatsu-lab/alpaca pipeline_tag: text-generation --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.0` ```yaml base_model: mistralai/Mistral-7B-v0.1 model_type: MistralForCausalLM tokenizer_type: LlamaTokenizer is_mistral_derived_model: true hub_model_id: MaziyarPanahi/Mistral-7B-Alpaca-52k-v0.2 hf_use_auth_token: true load_in_8bit: false load_in_4bit: false strict: false datasets: - path: tatsu-lab/alpaca type: alpaca - path: mhenrichsen/alpaca_2k_test type: alpaca dataset_prepared_path: val_set_size: 0.05 output_dir: ./MaziyarPanahi/Mistral-7B-Alpaca-52k-v0.2 sequence_len: 8192 sample_packing: true pad_to_sequence_len: true eval_sample_packing: false wandb_project: wandb_entity: wandb_watch: wandb_name: wandb_log_model: gradient_accumulation_steps: 4 micro_batch_size: 2 num_epochs: 4 optimizer: adamw_bnb_8bit lr_scheduler: cosine learning_rate: 0.000005 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: false gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 10 evals_per_epoch: 4 eval_table_size: eval_max_new_tokens: 128 saves_per_epoch: 1 debug: deepspeed: weight_decay: 0.0 fsdp: fsdp_config: special_tokens: bos_token: "<s>" eos_token: "</s>" unk_token: "<unk>" ``` </details><br> # Mistral-7B-Alpaca-52k-v0.2 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9730 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.3017 \| 0.04 \| 1 \| 1.4067 \| \| 1.1285 \| 0.25 \| 6 \| 1.0677 \| \| 1.0586 \| 0.5 \| 12 \| 0.9915 \| \| 1.0515 \| 0.75 \| 18 \| 0.9769 \| \| 1.0608 \| 1.0 \| 24 \| 0.9700 \| \| 1.0003 \| 1.23 \| 30 \| 0.9689 \| \| 0.9761 \| 1.48 \| 36 \| 0.9679 \| \| 0.9783 \| 1.73 \| 42 \| 0.9659 \| \| 0.9631 \| 1.98 \| 48 \| 0.9663 \| \| 0.9273 \| 2.21 \| 54 \| 0.9724 \| \| 0.9093 \| 2.46 \| 60 \| 0.9720 \| \| 0.9038 \| 2.71 \| 66 \| 0.9729 \| \| 0.903 \| 2.96 \| 72 \| 0.9724 \| \| 0.9231 \| 3.19 \| 78 \| 0.9725 \| \| 0.9017 \| 3.44 \| 84 \| 0.9729 \| \| 0.9279 \| 3.69 \| 90 \| 0.9730 \| \| 0.9069 \| 3.94 \| 96 \| 0.9730 \| ### Framework versions - Transformers 4.38.0.dev0 - Pytorch 2.2.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.0
MaziyarPanahi/Nous-Hermes-2-Mixtral-8x7B-SFT-Alpaca	MaziyarPanahi	2024-02-23T15:53:21Z	10	3	peft	[ "peft", "pytorch", "tensorboard", "safetensors", "mixtral", "text-generation", "axolotl", "generated_from_trainer", "alpaca", "nous_hermes", "lora", "qlora", "adapter", "finetune", "transformers", "conversational", "base_model:NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT", "base_model:adapter:NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-20T22:54:31Z	--- license: apache-2.0 tags: - axolotl - generated_from_trainer - alpaca - mixtral - nous_hermes - peft - lora - qlora - adapter - finetune - transformers base_model: NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT model-index: - name: Nous-Hermes-2-Mixtral-8x7B-SFT-Alpaca results: [] pipeline_tag: text-generation --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.0` ```yaml base_model: NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT model_type: MixtralForCausalLM tokenizer_type: LlamaTokenizer trust_remote_code: true hub_model_id: MaziyarPanahi/Nous-Hermes-2-Mixtral-8x7B-SFT-Alpaca hf_use_auth_token: true load_in_4bit: true strict: false datasets: - path: tatsu-lab/alpaca type: alpaca dataset_prepared_path: last_run_prepared val_set_size: 0.1 output_dir: ./qlora-out # save_safetensors: true adapter: qlora lora_model_dir: sequence_len: 1024 sample_packing: true pad_to_sequence_len: true lora_r: 32 lora_alpha: 16 lora_dropout: 0.05 lora_target_linear: true lora_fan_in_fan_out: lora_target_modules: # - gate - q_proj # - k_proj - v_proj # - o_proj # - w1 # - w2 # - w3 wandb_project: wandb_entity: wandb_watch: wandb_name: wandb_log_model: gradient_accumulation_steps: 4 micro_batch_size: 2 num_epochs: 1 optimizer: adamw_bnb_8bit lr_scheduler: cosine learning_rate: 0.0002 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: false gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true loss_watchdog_threshold: 5.0 loss_watchdog_patience: 3 warmup_steps: 10 evals_per_epoch: 4 eval_table_size: eval_max_new_tokens: 128 saves_per_epoch: 1 debug: deepspeed: weight_decay: 0.0 fsdp: fsdp_config: special_tokens: bos_token: "<s>" eos_token: "</s>" unk_token: "<unk>" ``` </details><br> # Nous-Hermes-2-Mixtral-8x7B-SFT-Alpaca This model is a fine-tuned version of [NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT](https://huggingface.co/NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0276 ## How to use PEFT ```python from peft import PeftModel, PeftConfig from transformers import AutoModelForCausalLM config = PeftConfig.from_pretrained("MaziyarPanahi/Nous-Hermes-2-Mixtral-8x7B-SFT-Alpaca") model = AutoModelForCausalLM.from_pretrained("NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT") model = PeftModel.from_pretrained(model, "MaziyarPanahi/Nous-Hermes-2-Mixtral-8x7B-SFT-Alpaca") ``` Transformers ```python # Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MaziyarPanahi/Nous-Hermes-2-Mixtral-8x7B-SFT-Alpaca") # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("MaziyarPanahi/Nous-Hermes-2-Mixtral-8x7B-SFT-Alpaca") model = AutoModelForCausalLM.from_pretrained("MaziyarPanahi/Nous-Hermes-2-Mixtral-8x7B-SFT-Alpaca") ``` ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.3912 \| 0.01 \| 1 \| 1.3714 \| \| 1.0321 \| 0.25 \| 45 \| 1.0427 \| \| 1.0312 \| 0.51 \| 90 \| 1.0327 \| \| 0.9917 \| 0.76 \| 135 \| 1.0276 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.38.0.dev0 - Pytorch 2.2.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.0
ibunescu/Phi-2_GDPR_4_10e	ibunescu	2024-02-23T15:53:19Z	48	0	transformers	[ "transformers", "safetensors", "phi", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-23T15:50:14Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MaziyarPanahi/Nous-Hermes-2-Mixtral-8x7B-SFT-Wikihow	MaziyarPanahi	2024-02-23T15:52:56Z	8	3	peft	[ "peft", "pytorch", "tensorboard", "safetensors", "mixtral", "text-generation", "axolotl", "generated_from_trainer", "nous_hermes", "wikihow", "lora", "qlora", "adapter", "finetune", "transformers", "conversational", "dataset:HuggingFaceTB/cosmopedia", "base_model:NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT", "base_model:adapter:NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-21T08:51:36Z	--- license: apache-2.0 tags: - axolotl - generated_from_trainer - mixtral - nous_hermes - wikihow - peft - lora - qlora - adapter - finetune - transformers base_model: NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT model-index: - name: Nous-Hermes-2-Mixtral-8x7B-SFT-Wikihow results: [] datasets: - HuggingFaceTB/cosmopedia pipeline_tag: text-generation --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.0` ```yaml base_model: NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT model_type: MixtralForCausalLM tokenizer_type: LlamaTokenizer trust_remote_code: true hub_model_id: MaziyarPanahi/Nous-Hermes-2-Mixtral-8x7B-SFT-Wikihow hf_use_auth_token: true load_in_4bit: true strict: false datasets: - path: HuggingFaceTB/cosmopedia name: - wikihow type: system_prompt: "" field_instruction: prompt field_output: text format: "[INST] {instruction} [/INST]" no_input_format: "[INST] {instruction} [/INST]" dataset_prepared_path: last_run_prepared val_set_size: 0.1 output_dir: ./qlora-out-wikihow # save_safetensors: true adapter: qlora lora_model_dir: sequence_len: 1024 sample_packing: true pad_to_sequence_len: true lora_r: 32 lora_alpha: 16 lora_dropout: 0.05 lora_target_linear: true lora_fan_in_fan_out: lora_target_modules: # - gate - q_proj # - k_proj - v_proj # - o_proj # - w1 # - w2 # - w3 wandb_project: wandb_entity: wandb_watch: wandb_name: wandb_log_model: gradient_accumulation_steps: 4 micro_batch_size: 2 num_epochs: 1 optimizer: adamw_bnb_8bit lr_scheduler: cosine learning_rate: 0.0002 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: false gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true loss_watchdog_threshold: 5.0 loss_watchdog_patience: 3 warmup_steps: 10 evals_per_epoch: 4 eval_table_size: eval_max_new_tokens: 128 saves_per_epoch: 1 debug: deepspeed: weight_decay: 0.0 fsdp: fsdp_config: special_tokens: bos_token: "<s>" eos_token: "</s>" unk_token: "<unk>" ``` </details><br> # Nous-Hermes-2-Mixtral-8x7B-SFT-Wikihow This model is a fine-tuned version of [NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT](https://huggingface.co/NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4559 ## GGUF and Quantized models - https://huggingface.co/MaziyarPanahi/Nous-Hermes-2-Mixtral-8x7B-SFT-Wikihow-GGUF ## How to use PEFT ```python from peft import PeftModel, PeftConfig from transformers import AutoModelForCausalLM model_id = "MaziyarPanahi/Nous-Hermes-2-Mixtral-8x7B-SFT-Wikihow" config = PeftConfig.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained("NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT") model = PeftModel.from_pretrained(model, model_id) ``` Transformers ```python # Use a pipeline as a high-level helper from transformers import pipeline model_id = "MaziyarPanahi/Nous-Hermes-2-Mixtral-8x7B-SFT-Wikihow" pipe = pipeline("text-generation", model=model_id) # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) ``` ## Examples ``` <\|im_start\|> system You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.<\|im_end\|> <\|im_start\|> user Write a short story about Goku discovering kirby has teamed up with Majin Buu to destroy the world.<\|im_end\|> <\|im_start\|> assistant Once upon a time in Universe Seven, there lived two powerful beings - Son Goku, Earth's greatest hero; and Kirby, an interdimensional pink puffball known for his insatiable appetite and ability to copy abilities from those he consumes. One fateful day, these unlikely adversaries would cross paths when they discovered that their once-rivalized worlds were now under threat by none other than...a collaboration between Kirby himself and one of Dragon Ball Z’s most feared villains – Majin Buu! It began as any ordinary morning might have started out: breakfast at Capsule Corp., followed by rigorous training exercises led personally by Vegeta (who had since become something like family). However, midway through sparring practice against Cell Jr., Gohan suddenly interrupted them via telepathy—his voice filled with urgency yet tainted slightly with disbelief—"Dad…Kakarot!" He hesitated briefly before continuing gravely, "I just received word from King Kai regarding some disturbingly odd activity happening back home on Popstar." Goku raised an eyebrow curiously but didn't stop practicing until completing his current set. Afterward, wiping sweat off his brow, he asked casually while heading towards Bulma's lab where she was working diligently alongside Trunks & Goten, “So what kind of trouble does my old buddy say we should look into?” Bulma glanced away momentarily from whatever gadgetry held her attention captive long enough to respond dryly without looking up, “Well apparently our favorite little vacuum cleaner seems hellbent on destroying everything again.” She paused dramatically then added sarcastically, “But this time around? Get ready because guess who decided tagging along sounded fun?" With great flair, she finally revealed the answer written clearly across multiple screens surrounding her workspace, displaying images captured directly from Planet Namek itself showing both Kirby AND MAJIN BUU causing widespread chaos hand-in-hand!! For several seconds following confirmation of such preposterous news, silence reigned supreme within the usually bustling confines of Capsule Corporation HQ. Finally breaking free from shock induced paralysis, Goku managed only three words initially, spoken softly almost reverentially, “Buu...and...Kirby?” Then louder, more forcefully demanding clarification, “How could THAT possibly happen?! They don't even exist within same universe let alone work together willingly!” Vegeta scoffed derisively beside him muttering dismissively beneath breath, 'Impossible!' Yet despite initial skepticism evident throughout entire room moments ago, everyone present couldn't deny mounting evidence suggesting otherwise right before eyes... And thus begins epic tale chronicling adventures undertaken joint forces comprised primarily Super Saiyan God Blue empowered protagonist hailing originally from distant planet Vegeta accompanied unexpected albeit reluctant antihero originating entirely separate reality altogether bent solely upon saving existence itself rather than dominion over it unlike certain counterparts sharing similar circumstances elsewhere far removed spatially speaking....<\|im_end\|> ``` ``` <\|im_start\|> system You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.<\|im_end\|> <\|im_start\|> user If I lay 2 wet shirts out in the sun flat on the ground to dry and it takes 5 hours until they are dry, how long would it take to dry 4 shirts of the same type that way simultanously?<\|im_end\|> <\|im_start\|> assistant The time required for drying multiple shirts simultaneously remains the same as when you dried them one at a time because there is no dependency between the individual drying processes. The factors affecting drying speed include sunlight intensity, air circulation, humidity levels, and fabric properties – none of which change based on whether you're drying two or four shirts concurrently. Thus, if laying out four wet shirts still permits ample exposure to direct sunlight without overlapping too much (which could hinder efficient evaporation), then those additional garments should also be completely dried within approximately 5 hours under identical environmental conditions.<\|im_end\|> ``` ``` <\|im_start\|> system You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.<\|im_end\|> <\|im_start\|> user explain step by step 25-42+3=?<\|im_end\|> <\|im_start\|> assistant Sure! Let's break down the equation into simpler steps: Given expression: 25 - 4 2 + 3 = ? Step 1: Calculate the multiplication inside parentheses (if any). Since there aren't any in this case, move on to Step 2 directly. Step 2: Perform operations from left to right according to order of precedence – Parentheses > Exponents > Multiplication & Division > Addition & Subtraction. Here we have only addition (+) and subtraction (-), so perform them from left to right. First operation is subtraction: 25 - 8 (since 4 times 2 equals 8), resulting in: 25 - 8 = 17 Next operation is addition; add 3 to our current result: 17 + 3 = 20 So, the final answer after evaluating all parts of the given expression would be 20.<\|im_end\|> ``` ``` <\|im_start\|> system You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.<\|im_end\|> <\|im_start\|> user Mark is faster than Mary , Mary is faster than Joe. Is Joe faster than Mark? Let's think step by step<\|im_end\|> <\|im_start\|> assistant Step 1: Understand the given information We know that Mark is faster than Mary, which we can represent as Mark > Mary (using '>' symbol for comparison). Similarly, Mary is faster than Joe, so we have Mary > Joe. Step 2: Compare the speeds of Mark and Joe using transitive property The transitive property states that if A > B and B > C, then A > C. Applying this concept here - since we know Mark > Mary and Mary > Joe, we should be able to determine whether Mark > Joe or not. Step 3: Analyze results By applying the transitive property from Step 2, it becomes evident that because Mark is faster than Mary, who in turn is faster than Joe; therefore logically, yes – Mark would indeed be faster than Joe. So our final statement would look like this - Mark > Joe.<\|im_end\|> ``` ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.8589 \| 0.0 \| 1 \| 0.8705 \| \| 0.5094 \| 0.25 \| 483 \| 0.5009 \| \| 0.4503 \| 0.5 \| 966 \| 0.4734 \| \| 0.4569 \| 0.75 \| 1449 \| 0.4559 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.38.0.dev0 - Pytorch 2.2.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.0
mfidabel/Modelo_1_Whisper_Large_V3	mfidabel	2024-02-23T15:49:40Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:openai/whisper-large-v3", "base_model:adapter:openai/whisper-large-v3", "license:apache-2.0", "region:us" ]	null	2024-02-23T13:36:52Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: openai/whisper-large-v3 model-index: - name: Modelo_1_Whisper_Large_V3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Modelo_1_Whisper_Large_V3 This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5410 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.0693 \| 1.0 \| 121 \| 0.7802 \| \| 0.5056 \| 2.0 \| 242 \| 0.5917 \| \| 0.2957 \| 3.0 \| 363 \| 0.5428 \| \| 0.1679 \| 4.0 \| 484 \| 0.5250 \| \| 0.0889 \| 5.0 \| 605 \| 0.5410 \| ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.0+cu118 - Datasets 2.16.1 - Tokenizers 0.15.2
Boyem/dqn3-SpaceInvadersNoFrameskip-v4	Boyem	2024-02-23T15:47:29Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-02-23T15:46:45Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 853.50 +/- 273.59 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Boyem -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Boyem -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Boyem ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
ferrazzipietro/Llama-2-13b-chat-hf_adapters_en.layer1_4_torch.bfloat16_16_32_0.05_8_0.0008	ferrazzipietro	2024-02-23T15:39:36Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-23T15:39:17Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Gopal2002/donut_finetune_vqa	Gopal2002	2024-02-23T15:39:03Z	48	0	transformers	[ "transformers", "safetensors", "vision-encoder-decoder", "image-text-to-text", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-02-15T07:38:56Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Boyem/dqn2-SpaceInvadersNoFrameskip-v4	Boyem	2024-02-23T15:30:26Z	2	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-02-23T15:30:01Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 329.00 +/- 157.97 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Boyem -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Boyem -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Boyem ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 10000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
Reihaneh/wav2vec2_frisian_common_voice_1	Reihaneh	2024-02-23T15:28:52Z	7	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-05-09T13:59:17Z	--- {} --- Fine-tuning wav2vec2-xls-r-1b on Common Voice 13 for Frisian. Here are the training parameters: learning Rate: 0.00008, per device train batch size:8, gradient accumulation steps:8, num_train_epochs: 50, max_steps: 3600
CamiloVega/Tweet-Classifier	CamiloVega	2024-02-23T15:28:13Z	105	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "autotrain", "dataset:autotrain-Tweet-Classifier/autotrain-data", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-23T15:27:53Z	--- tags: - autotrain - text-classification widget: - text: "I love AutoTrain" datasets: - autotrain-Tweet-Classifier/autotrain-data --- # Model Trained Using AutoTrain - Problem type: Text Classification ## Validation Metrics loss: 1.1234140396118164 f1_macro: 0.17777777777777778 f1_micro: 0.36363636363636365 f1_weighted: 0.19393939393939394 precision_macro: 0.12121212121212122 precision_micro: 0.36363636363636365 precision_weighted: 0.1322314049586777 recall_macro: 0.3333333333333333 recall_micro: 0.36363636363636365 recall_weighted: 0.36363636363636365 accuracy: 0.36363636363636365
simeneide/berit-instruct	simeneide	2024-02-23T15:27:03Z	7	1	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-23T15:24:02Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ferrazzipietro/Llama-2-13b-chat-hf_adapters_en.layer1_4_torch.bfloat16_16_32_0.05_8_0.0002	ferrazzipietro	2024-02-23T15:22:00Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-23T15:21:40Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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nickprock/sentence-bert-base-italian-uncased-sts-matryoshka	nickprock	2024-02-23T15:17:53Z	6	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-02-23T14:48:04Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # nickprock/sentence-bert-base-italian-uncased-sts-matryoshka This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["Una ragazza si acconcia i capelli.", "Una ragazza si sta spazzolando i capelli."] matryoshka_dim = 64 model = SentenceTransformer('nickprock/sentence-bert-base-italian-uncased-sts-matryoshka') embeddings = model.encode(sentences) embeddings = embeddings[..., :matryoshka_dim] # Shrink the embedding dimensions print(embeddings.shape) # => (2, 64) ``` ## 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('nickprock/sentence-bert-base-italian-uncased-sts-matryoshka') model = AutoModel.from_pretrained('nickprock/sentence-bert-base-italian-uncased-sts-matryoshka') # 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=nickprock/sentence-bert-base-italian-uncased-sts-matryoshka) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 360 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.MatryoshkaLoss.MatryoshkaLoss` with parameters: ``` {'loss': 'CoSENTLoss', 'matryoshka_dims': [768, 512, 256, 128, 64], 'matryoshka_weights': [1, 1, 1, 1, 1]} ``` Parameters of the fit()-Method: ``` { "epochs": 10, "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": 360, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
Liamdu/ppo-LunarLander-v2	Liamdu	2024-02-23T15:16:01Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-02-23T15:15:40Z	--- 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: -335.26 +/- 150.14 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
Pointcept/PointTransformerV3	Pointcept	2024-02-23T15:13:28Z	0	12	null	[ "tensorboard", "license:mit", "region:us" ]	null	2023-12-20T17:26:51Z	--- license: mit --- ## Model Zoo ### 1. Indoor semantic segmentation \| Model \| Benchmark \| Additional Data \| Num GPUs \| Val mIoU \| Config \| Tensorboard \| Exp Record \| \| :---: \| :---: \|:---------------:\| :---: \| :---: \| :---: \| :---: \| :---: \| \| PTv3 \| ScanNet \| &cross; \| 4 \| 77.6% \| [link](https://github.com/Pointcept/Pointcept/blob/main/configs/scannet/semseg-pt-v3m1-0-base.py) \| [link](https://huggingface.co/Pointcept/PointTransformerV3/tensorboard) \| [link](https://huggingface.co/Pointcept/PointTransformerV3/tree/main/scannet-semseg-pt-v3m1-0-base) \| \| PTv3 + PPT \| ScanNet \| &check; \| 8 \| 78.5% \| [link](https://github.com/Pointcept/Pointcept/blob/main/configs/scannet/semseg-pt-v3m1-1-ppt-extreme.py) \| [link](https://huggingface.co/Pointcept/PointTransformerV3/tensorboard) \| [link](https://huggingface.co/Pointcept/PointTransformerV3/tree/main/scannet-semseg-pt-v3m1-1-ppt-extreme) \| \| PTv3 \| ScanNet200 \| &cross; \| 4 \| 35.3% \| [link](https://github.com/Pointcept/Pointcept/blob/main/configs/scannet200/semseg-pt-v3m1-0-base.py) \| [link](https://huggingface.co/Pointcept/PointTransformerV3/tensorboard) \|[link](https://huggingface.co/Pointcept/PointTransformerV3/tree/main/scannet200-semseg-pt-v3m1-0-base)\| \| PTv3 + PPT \| ScanNet200 \| &check; (f.t.) \| 4 \| \| \| \| \| \| PTv3 \| S3DIS (Area5) \| &cross; \| 4 \| 73.6% \| [link](https://github.com/Pointcept/Pointcept/blob/main/configs/s3dis/semseg-pt-v3m1-0-rpe.py) \| [link](https://huggingface.co/Pointcept/PointTransformerV3/tensorboard) \| [link](https://huggingface.co/Pointcept/PointTransformerV3/tree/main/s3dis-semseg-pt-v3m1-0-rpe) \| \| PTv3 + PPT \| S3DIS (Area5) \| &check; \| 8 \| 75.4% \| [link](https://github.com/Pointcept/Pointcept/blob/main/configs/s3dis/semseg-pt-v3m1-1-ppt-extreme.py) \| [link](https://huggingface.co/Pointcept/PointTransformerV3/tensorboard) \| [link](https://huggingface.co/Pointcept/PointTransformerV3/tree/main/s3dis-semseg-pt-v3m1-1-ppt-extreme) \| ### 2. Outdoor semantic segmentation \| Model \| Benchmark \| Additional Data \| Num GPUs \| Val mIoU \| Config \| Tensorboard \| Exp Record \| \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| \| PTv3 \| nuScenes \| &cross; \| 4 \| 80.3 \| [link](https://github.com/Pointcept/Pointcept/blob/main/configs/nuscenes/semseg-pt-v3m1-0-base.py) \| [link](https://huggingface.co/Pointcept/PointTransformerV3/tensorboard)\|[link](https://huggingface.co/Pointcept/PointTransformerV3/tree/main/nuscenes-semseg-pt-v3m1-0-base) \| \| PTv3 + PPT \| nuScenes \| &check; \| 8 \| \| \| \| \| \| PTv3 \| SemanticKITTI \| &cross; \| 4 \| \| \| \| \| \| PTv3 + PPT \| SemanticKITTI \| &check; \| 8 \| \| \| \| \| \| PTv3 \| Waymo \| &cross; \| 4 \| 71.2 \| [link](https://github.com/Pointcept/Pointcept/blob/main/configs/waymo/semseg-pt-v3m1-0-base.py) \| [link](https://huggingface.co/Pointcept/PointTransformerV3/tensorboard) \| [link](https://huggingface.co/Pointcept/PointTransformerV3/tree/main/waymo-semseg-pt-v3m1-0-base) (log only) \| \| PTv3 + PPT \| Waymo \| &check; \| 8 \| \| \| \| \| * Model weights trained with Waymo Open Dataset cannot be released due to the regulations.
leonardo580/funetuned_mistral	leonardo580	2024-02-23T15:08:14Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-23T15:04:21Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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mohsin-aslam/results_t5	mohsin-aslam	2024-02-23T15:04:10Z	113	1	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:gaussalgo/T5-LM-Large-text2sql-spider", "base_model:finetune:gaussalgo/T5-LM-Large-text2sql-spider", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-23T15:02:55Z	--- base_model: gaussalgo/T5-LM-Large-text2sql-spider tags: - generated_from_trainer model-index: - name: results_t5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # results_t5 This model is a fine-tuned version of [gaussalgo/T5-LM-Large-text2sql-spider](https://huggingface.co/gaussalgo/T5-LM-Large-text2sql-spider) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0175 ## 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: 4 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.3879 \| 0.53 \| 500 \| 0.0261 \| \| 0.027 \| 1.05 \| 1000 \| 0.0203 \| \| 0.0183 \| 1.58 \| 1500 \| 0.0175 \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.2.1+cu121 - Datasets 2.4.0 - Tokenizers 0.15.2
wjworld/chaoyang_df_3_2_2_colon_slide	wjworld	2024-02-23T14:52:40Z	27	1	diffusers	[ "diffusers", "tensorboard", "safetensors", "text-to-image", "dreambooth", "stable-diffusion", "stable-diffusion-diffusers", "base_model:CompVis/stable-diffusion-v1-4", "base_model:finetune:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-02-23T14:48:12Z	--- license: creativeml-openrail-m library_name: diffusers tags: - text-to-image - dreambooth - stable-diffusion - stable-diffusion-diffusers inference: true base_model: CompVis/stable-diffusion-v1-4 instance_prompt: 'A Photo of a colon section: the expert 1 labels it as adenoma, expert 2 labels it as adenocarcinoma, expert 3 labels it as adenocarcinoma' --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # DreamBooth - wjworld/chaoyang_df_3_2_2_colon_slide This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on A Photo of a colon section: the expert 1 labels it as adenoma, expert 2 labels it as adenocarcinoma, expert 3 labels it as adenocarcinoma using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
Sachin7/llama-2-7b-storychat	Sachin7	2024-02-23T14:49:52Z	1	0	peft	[ "peft", "llama", "4-bit", "region:us" ]	null	2024-02-23T14:47:28Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0
wjworld/chaoyang_df_3_1_1_colon_slide	wjworld	2024-02-23T14:48:01Z	27	1	diffusers	[ "diffusers", "tensorboard", "safetensors", "text-to-image", "dreambooth", "stable-diffusion", "stable-diffusion-diffusers", "base_model:CompVis/stable-diffusion-v1-4", "base_model:finetune:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-02-19T09:49:07Z	--- license: creativeml-openrail-m library_name: diffusers tags: - text-to-image - dreambooth - stable-diffusion - stable-diffusion-diffusers - text-to-image - dreambooth - stable-diffusion - stable-diffusion-diffusers inference: true base_model: CompVis/stable-diffusion-v1-4 instance_prompt: 'A Photo of a colon section: one expert labels it as ''adenoma'', two others suggest it''s an ''serrated''.' --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # DreamBooth - wjworld/chaoyang_df_3_1_1_colon_slide This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on A Photo of a colon section: one expert labels it as 'adenoma', two others suggest it's an 'serrated'. using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
khalilmas9/Carnet_LoRA	khalilmas9	2024-02-23T14:44:56Z	1	1	diffusers	[ "diffusers", "tensorboard", "text-to-image", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-02-23T14:44:53Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: carnet style widget: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - khalilmas9/Carnet_LoRA <Gallery /> ## Model description These are khalilmas9/Carnet_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use carnet style to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](khalilmas9/Carnet_LoRA/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
FINNUMBER/Yi-Ko-6B-Finch-QA-300-PER100-NEW-epoch3	FINNUMBER	2024-02-23T14:43:45Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-23T07:00:09Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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wjworld/chaoyang_df_2_3_3_colon_slide	wjworld	2024-02-23T14:43:29Z	27	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "text-to-image", "dreambooth", "stable-diffusion", "stable-diffusion-diffusers", "base_model:CompVis/stable-diffusion-v1-4", "base_model:finetune:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-02-23T14:39:02Z	--- license: creativeml-openrail-m library_name: diffusers tags: - text-to-image - dreambooth - stable-diffusion - stable-diffusion-diffusers inference: true base_model: CompVis/stable-diffusion-v1-4 instance_prompt: 'A Photo of a colon section: the expert 1 labels it as adenocarcinoma, expert 2 labels it as adenoma, expert 3 labels it as adenoma' --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # DreamBooth - wjworld/chaoyang_df_2_3_3_colon_slide This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on A Photo of a colon section: the expert 1 labels it as adenocarcinoma, expert 2 labels it as adenoma, expert 3 labels it as adenoma using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
EchineF/Taxi-v3-qlearning	EchineF	2024-02-23T14:43:25Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-02-23T14:43:21Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3-qlearning results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="EchineF/Taxi-v3-qlearning", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
wjworld/chaoyang_df_1_3_3_colon_slide	wjworld	2024-02-23T14:38:51Z	27	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "text-to-image", "dreambooth", "stable-diffusion", "stable-diffusion-diffusers", "base_model:CompVis/stable-diffusion-v1-4", "base_model:finetune:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-02-23T14:34:25Z	--- license: creativeml-openrail-m library_name: diffusers tags: - text-to-image - dreambooth - stable-diffusion - stable-diffusion-diffusers inference: true base_model: CompVis/stable-diffusion-v1-4 instance_prompt: 'A Photo of a colon section: the expert 1 labels it as serrated, expert 2 labels it as adenoma, expert 3 labels it as adenoma' --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # DreamBooth - wjworld/chaoyang_df_1_3_3_colon_slide This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on A Photo of a colon section: the expert 1 labels it as serrated, expert 2 labels it as adenoma, expert 3 labels it as adenoma using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
nickprock/sentence-BERTino-sts-matryoshka	nickprock	2024-02-23T14:36:28Z	47	2	sentence-transformers	[ "sentence-transformers", "safetensors", "distilbert", "feature-extraction", "sentence-similarity", "transformers", "it", "dataset:stsb_multi_mt", "license:mit", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-02-23T14:34:15Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers license: mit datasets: - stsb_multi_mt language: - it library_name: sentence-transformers --- # nickprock/sentence-BERTino-sts-matryoshka This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["Una ragazza si acconcia i capelli.", "Una ragazza si sta spazzolando i capelli."] matryoshka_dim = 64 model = SentenceTransformer('nickprock/sentence-BERTino-sts-matryoshka') embeddings = model.encode(sentences) embeddings = embeddings[..., :matryoshka_dim] # Shrink the embedding dimensions print(embeddings.shape) # => (2, 64) ``` ## 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('nickprock/sentence-BERTino-sts-matryoshka') model = AutoModel.from_pretrained('nickprock/sentence-BERTino-sts-matryoshka') # 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=nickprock/sentence-BERTino-sts-matryoshka) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 360 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.MatryoshkaLoss.MatryoshkaLoss` with parameters: ``` {'loss': 'CoSENTLoss', 'matryoshka_dims': [768, 512, 256, 128, 64], 'matryoshka_weights': [1, 1, 1, 1, 1]} ``` Parameters of the fit()-Method: ``` { "epochs": 10, "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": 360, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
manusehgal/autotrain-r2tdw-43802	manusehgal	2024-02-23T14:36:27Z	3	0	diffusers	[ "diffusers", "text-to-image", "autotrain", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]	text-to-image	2024-02-23T14:36:24Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: <enter your prompt here> tags: - text-to-image - diffusers - autotrain inference: true --- # DreamBooth trained by AutoTrain Text encoder was not trained.
nickprock/sentence-bert-base-italian-xxl-uncased-sts-matryoshka	nickprock	2024-02-23T14:35:31Z	13	2	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "transformers", "it", "dataset:stsb_multi_mt", "license:mit", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-02-23T14:27:01Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers license: mit datasets: - stsb_multi_mt language: - it library_name: sentence-transformers --- # nickprock/sentence-bert-base-italian-xxl-uncased-sts-matryoshka This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["Una ragazza si acconcia i capelli.", "Una ragazza si sta spazzolando i capelli."] matryoshka_dim = 64 model = SentenceTransformer('nickprock/sentence-bert-base-italian-xxl-uncased-sts-matryoshka') embeddings = model.encode(sentences) embeddings = embeddings[..., :matryoshka_dim] # Shrink the embedding dimensions print(embeddings.shape) # => (2, 64) ``` ## 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 = ["Una ragazza si acconcia i capelli.", "Una ragazza si sta spazzolando i capelli."] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('nickprock/sentence-bert-base-italian-xxl-uncased-sts-matryoshka') model = AutoModel.from_pretrained('nickprock/sentence-bert-base-italian-xxl-uncased-sts-matryoshka') # 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=nickprock/sentence-bert-base-italian-xxl-uncased-sts-matryoshka) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 360 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.MatryoshkaLoss.MatryoshkaLoss` with parameters: ``` {'loss': 'CoSENTLoss', 'matryoshka_dims': [768, 512, 256, 128, 64], 'matryoshka_weights': [1, 1, 1, 1, 1]} ``` Parameters of the fit()-Method: ``` { "epochs": 10, "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": 360, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
wjworld/chaoyang_df_0_3_3_colon_slide	wjworld	2024-02-23T14:29:40Z	27	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "text-to-image", "dreambooth", "stable-diffusion", "stable-diffusion-diffusers", "base_model:CompVis/stable-diffusion-v1-4", "base_model:finetune:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-02-23T13:54:26Z	--- license: creativeml-openrail-m library_name: diffusers tags: - text-to-image - dreambooth - stable-diffusion - stable-diffusion-diffusers - text-to-image - dreambooth - stable-diffusion - stable-diffusion-diffusers inference: true base_model: CompVis/stable-diffusion-v1-4 instance_prompt: 'A Photo of a colon section: the expert 1 labels it as normal, expert 2 labels it as adenoma, expert 3 labels it as adenoma' --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # DreamBooth - wjworld/chaoyang_df_0_3_3_colon_slide This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on A Photo of a colon section: the expert 1 labels it as normal, expert 2 labels it as adenoma, expert 3 labels it as adenoma using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]

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