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modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-07-16 12:29:00	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 523 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-07-16 12:28:25	card stringlengths 11 1.01M
furrutiav/bert_qa_extractor_cockatiel_2022_ulra_by_question_best_ef_signal_it_66	furrutiav	2024-02-21T14:17:49Z	5	0	transformers	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	feature-extraction	2024-02-21T14:17:19Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
gayanin/bart-with-asr-noise-ins-0.3	gayanin	2024-02-21T14:09:42Z	7	0	transformers	[ "transformers", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "base_model:facebook/bart-base", "base_model:finetune:facebook/bart-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-21T14:01:56Z	--- license: apache-2.0 base_model: facebook/bart-base tags: - generated_from_trainer model-index: - name: bart-with-asr-noise-ins-0.3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-with-asr-noise-ins-0.3 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0532 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.1269 \| 0.62 \| 500 \| 0.0756 \| \| 0.0664 \| 1.24 \| 1000 \| 0.0581 \| \| 0.0488 \| 1.86 \| 1500 \| 0.0535 \| \| 0.0308 \| 2.48 \| 2000 \| 0.0532 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.2+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
gayanin/bart-with-asr-noise-sub-0.3	gayanin	2024-02-21T14:08:51Z	7	0	transformers	[ "transformers", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "base_model:facebook/bart-base", "base_model:finetune:facebook/bart-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-21T14:02:01Z	--- license: apache-2.0 base_model: facebook/bart-base tags: - generated_from_trainer model-index: - name: bart-with-asr-noise-sub-0.3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-with-asr-noise-sub-0.3 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1018 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.2674 \| 0.62 \| 500 \| 0.2235 \| \| 0.1333 \| 1.24 \| 1000 \| 0.1412 \| \| 0.0896 \| 1.86 \| 1500 \| 0.1121 \| \| 0.0312 \| 2.48 \| 2000 \| 0.1018 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.2+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
hellomyoh/DNU_mistral_7b_ft_en-ko-en_v0.1_Translator	hellomyoh	2024-02-21T14:08:05Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	null	2024-02-21T14:07:25Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer datasets: - generator base_model: mistralai/Mistral-7B-v0.1 model-index: - name: mistral_instruct_generation results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mistral_instruct_generation This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 1.3225 ## 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: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_steps: 100 - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.9407 \| 0.85 \| 1000 \| 1.2600 \| \| 0.8466 \| 1.7 \| 2000 \| 1.3225 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.0.1+cu118 - Datasets 2.17.1 - Tokenizers 0.15.2
danielhanchen/gguf_merged_model4_21022024	danielhanchen	2024-02-21T14:07:35Z	18	0	transformers	[ "transformers", "gguf", "mistral", "text-generation-inference", "unsloth", "en", "base_model:unsloth/mistral-7b-bnb-4bit", "base_model:quantized:unsloth/mistral-7b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-02-20T15:16:14Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - gguf base_model: unsloth/mistral-7b-bnb-4bit --- # Uploaded model - Developed by: danielhanchen - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Srijinesh/BERT-PUBMEDQA	Srijinesh	2024-02-21T14:07:32Z	0	0	null	[ "biology", "medical", "clinical", "question-answering", "en", "dataset:pubmed_qa", "region:us" ]	question-answering	2024-02-21T14:01:50Z	--- datasets: - pubmed_qa language: - en metrics: - bertscore - bleu pipeline_tag: question-answering tags: - biology - medical - clinical ---
Priyanshuchaudhary2425/AI-Text-Ditactor	Priyanshuchaudhary2425	2024-02-21T14:07:21Z	6	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-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" ]	text-classification	2024-02-11T09:51:35Z	--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: AI-Text-Ditactor 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. --> # AI-Text-Ditactor 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: 0.0658 - Accuracy: 0.987 - Precision: 0.9851 - Recall: 0.9873 - F1: 0.9862 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.0899 \| 1.0 \| 1250 \| 0.0658 \| 0.987 \| 0.9851 \| 0.9873 \| 0.9862 \| ### Framework versions - Transformers 4.38.0 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
damerajee/maybe-last	damerajee	2024-02-21T14:05:25Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:sarvamai/OpenHathi-7B-Hi-v0.1-Base", "base_model:adapter:sarvamai/OpenHathi-7B-Hi-v0.1-Base", "region:us" ]	null	2024-02-20T14:35:15Z	--- library_name: peft base_model: sarvamai/OpenHathi-7B-Hi-v0.1-Base --- das # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.8.2
Azureus212/fireflyhonkaistarrail	Azureus212	2024-02-21T14:04:27Z	4	1	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "region:us" ]	text-to-image	2024-02-21T14:04:15Z	--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: >- <lora:firefly:1> firefly $honkai: star rail$, 1girl, solo, standing, grey hair, long hair, purple eyes, black hairband, jacket, dress, long sleeves, white sleeves, shirt, thighhighs, blue thighhighs, closed mouth, light smile, black thighhighs, parameters: negative_prompt: >- (worse quality, low quality:1.4), easynegative, greyscale, blurry, monochrome, text, output: url: images/tmpwjb_npg0.png base_model: runwayml/stable-diffusion-v1-5 instance_prompt: null --- # Firefly Honkai:Star Rail <Gallery /> ## Download model Weights for this model are available in Safetensors format. [Download](/Azureus212/fireflyhonkaistarrail/tree/main) them in the Files & versions tab.
alpindale/gemma-2b-it	alpindale	2024-02-21T14:02:12Z	14	3	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:2312.11805", "arxiv:2009.03300", "arxiv:1905.07830", "arxiv:1911.11641", "arxiv:1904.09728", "arxiv:1905.10044", "arxiv:1907.10641", "arxiv:1811.00937", "arxiv:1809.02789", "arxiv:1911.01547", "arxiv:1705.03551", "arxiv:2107.03374", "arxiv:2108.07732", "arxiv:2110.14168", "arxiv:2304.06364", "arxiv:2206.04615", "arxiv:1804.06876", "arxiv:2110.08193", "arxiv:2009.11462", "arxiv:2101.11718", "arxiv:1804.09301", "arxiv:2109.07958", "arxiv:2203.09509", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-21T14:01:54Z	--- library_name: transformers tags: [] extra_gated_heading: "Access Gemma on Hugging Face" extra_gated_prompt: "To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged-in to Hugging Face and click below. Requests are processed immediately." extra_gated_button_content: "Acknowledge license" --- # Gemma Model Card Model Page: [Gemma](https://ai.google.dev/gemma/docs) This model card corresponds to the 2B instruct version of the Gemma model. You can also visit the model card of the [2B base model](https://huggingface.co/google/gemma-2b), [7B base model](https://huggingface.co/google/gemma-7b), and [7B instruct model](https://huggingface.co/google/gemma-7b-it). Resources and Technical Documentation: * [Responsible Generative AI Toolkit](https://ai.google.dev/responsible) * [Gemma on Kaggle](https://www.kaggle.com/models/google/gemma) * [Gemma on Vertex Model Garden](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/335) Terms of Use: [Terms](https://www.kaggle.com/models/google/gemma/license/consent) Authors: Google ## Model Information Summary description and brief definition of inputs and outputs. ### Description Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. They are text-to-text, decoder-only large language models, available in English, with open weights, pre-trained variants, and instruction-tuned variants. Gemma models are well-suited for a variety of text generation tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as a laptop, desktop or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. ### Usage Below we share some code snippets on how to get quickly started with running the model. First make sure to `pip install -U transformers`, then copy the snippet from the section that is relevant for your usecase. #### Running the model on a CPU ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it") model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it") input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt") outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) ``` #### Running the model on a single / multi GPU ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it") model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it", device_map="auto") input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) ``` #### Running the model on a GPU using different precisions * _Using `torch.float16`_ ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it") model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it", device_map="auto", torch_dtype=torch.float16) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(*input_ids) print(tokenizer.decode(outputs[0])) ``` _Using `torch.bfloat16`_ ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it") model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it", device_map="auto", torch_dtype=torch.bfloat16) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(*input_ids) print(tokenizer.decode(outputs[0])) ``` #### Quantized Versions through `bitsandbytes` _Using 8-bit precision (int8)_ ```python # pip install bitsandbytes accelerate from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig quantization_config = BitsAndBytesConfig(load_in_8bit=True) tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it") model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it", quantization_config=quantization_config) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(*input_ids) print(tokenizer.decode(outputs[0])) ``` _Using 4-bit precision_ ```python # pip install bitsandbytes accelerate from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig quantization_config = BitsAndBytesConfig(load_in_4bit=True) tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it") model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it", quantization_config=quantization_config) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(*input_ids) print(tokenizer.decode(outputs[0])) ``` #### Other optimizations _Flash Attention 2_ First make sure to install `flash-attn` in your environment `pip install flash-attn` ```diff model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16, + attn_implementation="flash_attention_2" ).to(0) ``` ### Chat Template The instruction-tuned models use a chat template that must be adhered to for conversational use. The easiest way to apply it is using the tokenizer's built-in chat template, as shown in the following snippet. Let's load the model and apply the chat template to a conversation. In this example, we'll start with a single user interaction: ```py from transformers import AutoTokenizer, AutoModelForCausalLM import transformers import torch model_id = "gg-hf/gemma-2b-it" dtype = torch.bfloat16 tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, device_map="cuda", torch_dtype=dtype, ) chat = [ { "role": "user", "content": "Write a hello world program" }, ] prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True) ``` At this point, the prompt contains the following text: ``` <start_of_turn>user Write a hello world program<end_of_turn> <start_of_turn>model ``` As you can see, each turn is preceeded by a `<start_of_turn>` delimiter and then the role of the entity (either `user`, for content supplied by the user, or `model` for LLM responses). Turns finish with the `<end_of_turn>` token. You can follow this format to build the prompt manually, if you need to do it without the tokenizer's chat template. After the prompt is ready, generation can be performed like this: ```py inputs = tokenizer.encode(prompt, add_special_tokens=True, return_tensors="pt") outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=150) ``` ### Inputs and outputs * Input: Text string, such as a question, a prompt, or a document to be summarized. * Output: Generated English-language text in response to the input, such as an answer to a question, or a summary of a document. ## Model Data Data used for model training and how the data was processed. ### Training Dataset These models were trained on a dataset of text data that includes a wide variety of sources, totaling 6 trillion tokens. Here are the key components: * Web Documents: A diverse collection of web text ensures the model is exposed to a broad range of linguistic styles, topics, and vocabulary. Primarily English-language content. * Code: Exposing the model to code helps it to learn the syntax and patterns of programming languages, which improves its ability to generate code or understand code-related questions. * Mathematics: Training on mathematical text helps the model learn logical reasoning, symbolic representation, and to address mathematical queries. The combination of these diverse data sources is crucial for training a powerful language model that can handle a wide variety of different tasks and text formats. ### Data Preprocessing Here are the key data cleaning and filtering methods applied to the training data: * CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering was applied at multiple stages in the data preparation process to ensure the exclusion of harmful and illegal content * Sensitive Data Filtering: As part of making Gemma pre-trained models safe and reliable, automated techniques were used to filter out certain personal information and other sensitive data from training sets. * Additional methods: Filtering based on content quality and safely in line with [our policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11). ## Implementation Information Details about the model internals. ### Hardware Gemma was trained using the latest generation of [Tensor Processing Unit (TPU)](https://cloud.google.com/tpu/docs/intro-to-tpu) hardware (TPUv5e). Training large language models requires significant computational power. TPUs, designed specifically for matrix operations common in machine learning, offer several advantages in this domain: * Performance: TPUs are specifically designed to handle the massive computations involved in training LLMs. They can speed up training considerably compared to CPUs. * Memory: TPUs often come with large amounts of high-bandwidth memory, allowing for the handling of large models and batch sizes during training. This can lead to better model quality. * Scalability: TPU Pods (large clusters of TPUs) provide a scalable solution for handling the growing complexity of large foundation models. You can distribute training across multiple TPU devices for faster and more efficient processing. * Cost-effectiveness: In many scenarios, TPUs can provide a more cost-effective solution for training large models compared to CPU-based infrastructure, especially when considering the time and resources saved due to faster training. * These advantages are aligned with [Google's commitments to operate sustainably](https://sustainability.google/operating-sustainably/). ### Software Training was done using [JAX](https://github.com/google/jax) and [ML Pathways](https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/ml-pathways). JAX allows researchers to take advantage of the latest generation of hardware, including TPUs, for faster and more efficient training of large models. ML Pathways is Google's latest effort to build artificially intelligent systems capable of generalizing across multiple tasks. This is specially suitable for [foundation models](https://ai.google/discover/foundation-models/), including large language models like these ones. Together, JAX and ML Pathways are used as described in the [paper about the Gemini family of models](https://arxiv.org/abs/2312.11805); "the 'single controller' programming model of Jax and Pathways allows a single Python process to orchestrate the entire training run, dramatically simplifying the development workflow." ## Evaluation Model evaluation metrics and results. ### Benchmark Results These models were evaluated against a large collection of different datasets and metrics to cover different aspects of text generation: \| Benchmark \| Metric \| 2B Params \| 7B Params \| \| ------------------------------ \| ------------- \| ----------- \| --------- \| \| [MMLU](https://arxiv.org/abs/2009.03300) \| 5-shot, top-1 \| 42.3 \| 64.3 \| \| [HellaSwag](https://arxiv.org/abs/1905.07830) \| 0-shot \|71.4 \| 81.2 \| \| [PIQA](https://arxiv.org/abs/1911.11641) \| 0-shot \| 77.3 \| 81.2 \| \| [SocialIQA](https://arxiv.org/abs/1904.09728) \| 0-shot \| 59.7 \| 51.8 \| \| [BooIQ](https://arxiv.org/abs/1905.10044) \| 0-shot \| 69.4 \| 83.2 \| \| [WinoGrande](https://arxiv.org/abs/1907.10641) \| partial score \| 65.4 \| 72.3 \| \| [CommonsenseQA](https://arxiv.org/abs/1811.00937) \| 7-shot \| 65.3 \| 71.3 \| \| [OpenBookQA](https://arxiv.org/abs/1809.02789) \| \| 47.8 \| 52.8 \| \| [ARC-e](https://arxiv.org/abs/1911.01547) \| \| 73.2 \| 81.5 \| \| [ARC-c](https://arxiv.org/abs/1911.01547) \| \| 42.1 \| 53.2 \| \| [TriviaQA](https://arxiv.org/abs/1705.03551) \| 5-shot \| 53.2 \| 63.4 \| \| [Natural Questions](https://github.com/google-research-datasets/natural-questions) \| 5-shot \| - \| 23 \| \| [HumanEval](https://arxiv.org/abs/2107.03374) \| pass@1 \| 22.0 \| 32.3 \| \| [MBPP](https://arxiv.org/abs/2108.07732) \| 3-shot \| 29.2 \| 44.4 \| \| [GSM8K](https://arxiv.org/abs/2110.14168) \| maj@1 \| 17.7 \| 46.4 \| \| [MATH](https://arxiv.org/abs/2108.07732) \| 4-shot \| 11.8 \| 24.3 \| \| [AGIEval](https://arxiv.org/abs/2304.06364) \| \| 24.2 \| 41.7 \| \| [BIG-Bench](https://arxiv.org/abs/2206.04615) \| \| 35.2 \| 55.1 \| \| ------------------------------ \| ------------- \| ----------- \| --------- \| \| Average \| \| 54.0 \| 56.4 \| ## Ethics and Safety Ethics and safety evaluation approach and results. ### Evaluation Approach Our evaluation methods include structured evaluations and internal red-teaming testing of relevant content policies. Red-teaming was conducted by a number of different teams, each with different goals and human evaluation metrics. These models were evaluated against a number of different categories relevant to ethics and safety, including: * Text-to-Text Content Safety: Human evaluation on prompts covering safety policies including child sexual abuse and exploitation, harassment, violence and gore, and hate speech. * Text-to-Text Representational Harms: Benchmark against relevant academic datasets such as [WinoBias](https://arxiv.org/abs/1804.06876) and [BBQ Dataset](https://arxiv.org/abs/2110.08193v2). * Memorization: Automated evaluation of memorization of training data, including the risk of personally identifiable information exposure. * Large-scale harm: Tests for "dangerous capabilities," such as chemical, biological, radiological, and nuclear (CBRN) risks. ### Evaluation Results The results of ethics and safety evaluations are within acceptable thresholds for meeting [internal policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11) for categories such as child safety, content safety, representational harms, memorization, large-scale harms. On top of robust internal evaluations, the results of well known safety benchmarks like BBQ, BOLD, Winogender, Winobias, RealToxicity, and TruthfulQA are shown here. \| Benchmark \| Metric \| 2B Params \| 7B Params \| \| ------------------------------ \| ------------- \| ----------- \| --------- \| \| [RealToxicity](https://arxiv.org/abs/2009.11462) \| average \| 6.86 \| 7.90 \| \| [BOLD](https://arxiv.org/abs/2101.11718) \| \| 45.57 \| 49.08 \| \| [CrowS-Pairs](https://aclanthology.org/2020.emnlp-main.154/) \| top-1 \| 45.82 \| 51.33 \| \| [BBQ Ambig](https://arxiv.org/abs/2110.08193v2) \| 1-shot, top-1 \| 62.58 \| 92.54 \| \| [BBQ Disambig](https://arxiv.org/abs/2110.08193v2) \| top-1 \| 54.62 \| 71.99 \| \| [Winogender](https://arxiv.org/abs/1804.09301) \| top-1 \| 51.25 \| 54.17 \| \| [TruthfulQA](https://arxiv.org/abs/2109.07958) \| \| 44.84 \| 31.81 \| \| [Winobias 1_2](https://arxiv.org/abs/1804.06876) \| \| 56.12 \| 59.09 \| \| [Winobias 2_2](https://arxiv.org/abs/1804.06876) \| \| 91.10 \| 92.23 \| \| [Toxigen](https://arxiv.org/abs/2203.09509) \| \| 29.77 \| 39.59 \| \| ------------------------------ \| ------------- \| ----------- \| --------- \| ## Usage and Limitations These models have certain limitations that users should be aware of. ### Intended Usage Open Large Language Models (LLMs) have a wide range of applications across various industries and domains. The following list of potential uses is not comprehensive. The purpose of this list is to provide contextual information about the possible use-cases that the model creators considered as part of model training and development. * Content Creation and Communication * Text Generation: These models can be used to generate creative text formats such as poems, scripts, code, marketing copy, and email drafts. * Chatbots and Conversational AI: Power conversational interfaces for customer service, virtual assistants, or interactive applications. * Text Summarization: Generate concise summaries of a text corpus, research papers, or reports. * Research and Education * Natural Language Processing (NLP) Research: These models can serve as a foundation for researchers to experiment with NLP techniques, develop algorithms, and contribute to the advancement of the field. * Language Learning Tools: Support interactive language learning experiences, aiding in grammar correction or providing writing practice. * Knowledge Exploration: Assist researchers in exploring large bodies of text by generating summaries or answering questions about specific topics. ### Limitations * Training Data * The quality and diversity of the training data significantly influence the model's capabilities. Biases or gaps in the training data can lead to limitations in the model's responses. * The scope of the training dataset determines the subject areas the model can handle effectively. * Context and Task Complexity * LLMs are better at tasks that can be framed with clear prompts and instructions. Open-ended or highly complex tasks might be challenging. * A model's performance can be influenced by the amount of context provided (longer context generally leads to better outputs, up to a certain point). * Language Ambiguity and Nuance * Natural language is inherently complex. LLMs might struggle to grasp subtle nuances, sarcasm, or figurative language. * Factual Accuracy * LLMs generate responses based on information they learned from their training datasets, but they are not knowledge bases. They may generate incorrect or outdated factual statements. * Common Sense * LLMs rely on statistical patterns in language. They might lack the ability to apply common sense reasoning in certain situations. ### Ethical Considerations and Risks The development of large language models (LLMs) raises several ethical concerns. In creating an open model, we have carefully considered the following: * Bias and Fairness * LLMs trained on large-scale, real-world text data can reflect socio-cultural biases embedded in the training material. These models underwent careful scrutiny, input data pre-processing described and posterior evaluations reported in this card. * Misinformation and Misuse * LLMs can be misused to generate text that is false, misleading, or harmful. * Guidelines are provided for responsible use with the model, see the [Responsible Generative AI Toolkit](http://ai.google.dev/gemma/responsible). * Transparency and Accountability: * This model card summarizes details on the models' architecture, capabilities, limitations, and evaluation processes. * A responsibly developed open model offers the opportunity to share innovation by making LLM technology accessible to developers and researchers across the AI ecosystem. Risks identified and mitigations: * Perpetuation of biases: It's encouraged to perform continuous monitoring (using evaluation metrics, human review) and the exploration of de-biasing techniques during model training, fine-tuning, and other use cases. * Generation of harmful content: Mechanisms and guidelines for content safety are essential. Developers are encouraged to exercise caution and implement appropriate content safety safeguards based on their specific product policies and application use cases. * Misuse for malicious purposes: Technical limitations and developer and end-user education can help mitigate against malicious applications of LLMs. Educational resources and reporting mechanisms for users to flag misuse are provided. Prohibited uses of Gemma models are outlined in the [Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy). * Privacy violations: Models were trained on data filtered for removal of PII (Personally Identifiable Information). Developers are encouraged to adhere to privacy regulations with privacy-preserving techniques. ### Benefits At the time of release, this family of models provides high-performance open large language model implementations designed from the ground up for Responsible AI development compared to similarly sized models. Using the benchmark evaluation metrics described in this document, these models have shown to provide superior performance to other, comparably-sized open model alternatives.
alpindale/gemma-2b	alpindale	2024-02-21T14:01:45Z	194	7	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:2312.11805", "arxiv:2009.03300", "arxiv:1905.07830", "arxiv:1911.11641", "arxiv:1904.09728", "arxiv:1905.10044", "arxiv:1907.10641", "arxiv:1811.00937", "arxiv:1809.02789", "arxiv:1911.01547", "arxiv:1705.03551", "arxiv:2107.03374", "arxiv:2108.07732", "arxiv:2110.14168", "arxiv:2304.06364", "arxiv:2206.04615", "arxiv:1804.06876", "arxiv:2110.08193", "arxiv:2009.11462", "arxiv:2101.11718", "arxiv:1804.09301", "arxiv:2109.07958", "arxiv:2203.09509", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-21T14:01:31Z	--- library_name: transformers tags: [] extra_gated_heading: "Access Gemma on Hugging Face" extra_gated_prompt: "To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged-in to Hugging Face and click below. Requests are processed immediately." extra_gated_button_content: "Acknowledge license" --- # Gemma Model Card Model Page: [Gemma](https://ai.google.dev/gemma/docs) This model card corresponds to the 2B base version of the Gemma model. You can also visit the model card of the [7B base model](https://huggingface.co/google/gemma-7b), [7B instruct model](https://huggingface.co/google/gemma-7b-it), and [2B instruct model](https://huggingface.co/google/gemma-2b-it). Resources and Technical Documentation: * [Responsible Generative AI Toolkit](https://ai.google.dev/responsible) * [Gemma on Kaggle](https://www.kaggle.com/models/google/gemma) * [Gemma on Vertex Model Garden](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/335) Terms of Use: [Terms](https://www.kaggle.com/models/google/gemma/license/consent) Authors: Google ## Model Information Summary description and brief definition of inputs and outputs. ### Description Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. They are text-to-text, decoder-only large language models, available in English, with open weights, pre-trained variants, and instruction-tuned variants. Gemma models are well-suited for a variety of text generation tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as a laptop, desktop or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. ### Usage Below we share some code snippets on how to get quickly started with running the model. First make sure to `pip install -U transformers`, then copy the snippet from the section that is relevant for your usecase. #### Fine-tuning the model You can find fine-tuning scripts and notebook under the [`examples/` directory](https://huggingface.co/google/gemma-7b/tree/main/examples) of [`google/gemma-7b`](https://huggingface.co/google/gemma-7b) repository. To adapt it to this model, simply change the model-id to `google/gemma-2b`. In that repository, we provide: * A script to perform Supervised Fine-Tuning (SFT) on UltraChat dataset using QLoRA * A script to perform SFT using FSDP on TPU devices * A notebook that you can run on a free-tier Google Colab instance to perform SFT on English quotes dataset #### Running the model on a CPU ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b") model = AutoModelForCausalLM.from_pretrained("google/gemma-2b") input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt") outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) ``` #### Running the model on a single / multi GPU ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b") model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", device_map="auto") input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0])) ``` #### Running the model on a GPU using different precisions * _Using `torch.float16`_ ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b") model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", device_map="auto", torch_dtype=torch.float16) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(*input_ids) print(tokenizer.decode(outputs[0])) ``` _Using `torch.bfloat16`_ ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b") model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", device_map="auto", torch_dtype=torch.bfloat16) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(*input_ids) print(tokenizer.decode(outputs[0])) ``` #### Quantized Versions through `bitsandbytes` _Using 8-bit precision (int8)_ ```python # pip install bitsandbytes accelerate from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig quantization_config = BitsAndBytesConfig(load_in_8bit=True) tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b") model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", quantization_config=quantization_config) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(*input_ids) print(tokenizer.decode(outputs[0])) ``` _Using 4-bit precision_ ```python # pip install bitsandbytes accelerate from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig quantization_config = BitsAndBytesConfig(load_in_4bit=True) tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b") model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", quantization_config=quantization_config) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(*input_ids) print(tokenizer.decode(outputs[0])) ``` #### Other optimizations _Flash Attention 2_ First make sure to install `flash-attn` in your environment `pip install flash-attn` ```diff model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16, + attn_implementation="flash_attention_2" ).to(0) ``` ### Inputs and outputs * Input: Text string, such as a question, a prompt, or a document to be summarized. * Output: Generated English-language text in response to the input, such as an answer to a question, or a summary of a document. ## Model Data Data used for model training and how the data was processed. ### Training Dataset These models were trained on a dataset of text data that includes a wide variety of sources, totaling 6 trillion tokens. Here are the key components: * Web Documents: A diverse collection of web text ensures the model is exposed to a broad range of linguistic styles, topics, and vocabulary. Primarily English-language content. * Code: Exposing the model to code helps it to learn the syntax and patterns of programming languages, which improves its ability to generate code or understand code-related questions. * Mathematics: Training on mathematical text helps the model learn logical reasoning, symbolic representation, and to address mathematical queries. The combination of these diverse data sources is crucial for training a powerful language model that can handle a wide variety of different tasks and text formats. ### Data Preprocessing Here are the key data cleaning and filtering methods applied to the training data: * CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering was applied at multiple stages in the data preparation process to ensure the exclusion of harmful and illegal content * Sensitive Data Filtering: As part of making Gemma pre-trained models safe and reliable, automated techniques were used to filter out certain personal information and other sensitive data from training sets. * Additional methods: Filtering based on content quality and safely in line with [our policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11). ## Implementation Information Details about the model internals. ### Hardware Gemma was trained using the latest generation of [Tensor Processing Unit (TPU)](https://cloud.google.com/tpu/docs/intro-to-tpu) hardware (TPUv5e). Training large language models requires significant computational power. TPUs, designed specifically for matrix operations common in machine learning, offer several advantages in this domain: * Performance: TPUs are specifically designed to handle the massive computations involved in training LLMs. They can speed up training considerably compared to CPUs. * Memory: TPUs often come with large amounts of high-bandwidth memory, allowing for the handling of large models and batch sizes during training. This can lead to better model quality. * Scalability: TPU Pods (large clusters of TPUs) provide a scalable solution for handling the growing complexity of large foundation models. You can distribute training across multiple TPU devices for faster and more efficient processing. * Cost-effectiveness: In many scenarios, TPUs can provide a more cost-effective solution for training large models compared to CPU-based infrastructure, especially when considering the time and resources saved due to faster training. * These advantages are aligned with [Google's commitments to operate sustainably](https://sustainability.google/operating-sustainably/). ### Software Training was done using [JAX](https://github.com/google/jax) and [ML Pathways](https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/ml-pathways). JAX allows researchers to take advantage of the latest generation of hardware, including TPUs, for faster and more efficient training of large models. ML Pathways is Google's latest effort to build artificially intelligent systems capable of generalizing across multiple tasks. This is specially suitable for [foundation models](https://ai.google/discover/foundation-models/), including large language models like these ones. Together, JAX and ML Pathways are used as described in the [paper about the Gemini family of models](https://arxiv.org/abs/2312.11805); "the 'single controller' programming model of Jax and Pathways allows a single Python process to orchestrate the entire training run, dramatically simplifying the development workflow." ## Evaluation Model evaluation metrics and results. ### Benchmark Results These models were evaluated against a large collection of different datasets and metrics to cover different aspects of text generation: \| Benchmark \| Metric \| 2B Params \| 7B Params \| \| ------------------------------ \| ------------- \| ----------- \| --------- \| \| [MMLU](https://arxiv.org/abs/2009.03300) \| 5-shot, top-1 \| 42.3 \| 64.3 \| \| [HellaSwag](https://arxiv.org/abs/1905.07830) \| 0-shot \|71.4 \| 81.2 \| \| [PIQA](https://arxiv.org/abs/1911.11641) \| 0-shot \| 77.3 \| 81.2 \| \| [SocialIQA](https://arxiv.org/abs/1904.09728) \| 0-shot \| 59.7 \| 51.8 \| \| [BooIQ](https://arxiv.org/abs/1905.10044) \| 0-shot \| 69.4 \| 83.2 \| \| [WinoGrande](https://arxiv.org/abs/1907.10641) \| partial score \| 65.4 \| 72.3 \| \| [CommonsenseQA](https://arxiv.org/abs/1811.00937) \| 7-shot \| 65.3 \| 71.3 \| \| [OpenBookQA](https://arxiv.org/abs/1809.02789) \| \| 47.8 \| 52.8 \| \| [ARC-e](https://arxiv.org/abs/1911.01547) \| \| 73.2 \| 81.5 \| \| [ARC-c](https://arxiv.org/abs/1911.01547) \| \| 42.1 \| 53.2 \| \| [TriviaQA](https://arxiv.org/abs/1705.03551) \| 5-shot \| 53.2 \| 63.4 \| \| [Natural Questions](https://github.com/google-research-datasets/natural-questions) \| 5-shot \| - \| 23 \| \| [HumanEval](https://arxiv.org/abs/2107.03374) \| pass@1 \| 22.0 \| 32.3 \| \| [MBPP](https://arxiv.org/abs/2108.07732) \| 3-shot \| 29.2 \| 44.4 \| \| [GSM8K](https://arxiv.org/abs/2110.14168) \| maj@1 \| 17.7 \| 46.4 \| \| [MATH](https://arxiv.org/abs/2108.07732) \| 4-shot \| 11.8 \| 24.3 \| \| [AGIEval](https://arxiv.org/abs/2304.06364) \| \| 24.2 \| 41.7 \| \| [BIG-Bench](https://arxiv.org/abs/2206.04615) \| \| 35.2 \| 55.1 \| \| ------------------------------ \| ------------- \| ----------- \| --------- \| \| Average \| \| 54.0 \| 56.4 \| ## Ethics and Safety Ethics and safety evaluation approach and results. ### Evaluation Approach Our evaluation methods include structured evaluations and internal red-teaming testing of relevant content policies. Red-teaming was conducted by a number of different teams, each with different goals and human evaluation metrics. These models were evaluated against a number of different categories relevant to ethics and safety, including: * Text-to-Text Content Safety: Human evaluation on prompts covering safety policies including child sexual abuse and exploitation, harassment, violence and gore, and hate speech. * Text-to-Text Representational Harms: Benchmark against relevant academic datasets such as [WinoBias](https://arxiv.org/abs/1804.06876) and [BBQ Dataset](https://arxiv.org/abs/2110.08193v2). * Memorization: Automated evaluation of memorization of training data, including the risk of personally identifiable information exposure. * Large-scale harm: Tests for "dangerous capabilities," such as chemical, biological, radiological, and nuclear (CBRN) risks. ### Evaluation Results The results of ethics and safety evaluations are within acceptable thresholds for meeting [internal policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11) for categories such as child safety, content safety, representational harms, memorization, large-scale harms. On top of robust internal evaluations, the results of well known safety benchmarks like BBQ, BOLD, Winogender, Winobias, RealToxicity, and TruthfulQA are shown here. \| Benchmark \| Metric \| 2B Params \| 7B Params \| \| ------------------------------ \| ------------- \| ----------- \| --------- \| \| [RealToxicity](https://arxiv.org/abs/2009.11462) \| average \| 6.86 \| 7.90 \| \| [BOLD](https://arxiv.org/abs/2101.11718) \| \| 45.57 \| 49.08 \| \| [CrowS-Pairs](https://aclanthology.org/2020.emnlp-main.154/) \| top-1 \| 45.82 \| 51.33 \| \| [BBQ Ambig](https://arxiv.org/abs/2110.08193v2) \| 1-shot, top-1 \| 62.58 \| 92.54 \| \| [BBQ Disambig](https://arxiv.org/abs/2110.08193v2) \| top-1 \| 54.62 \| 71.99 \| \| [Winogender](https://arxiv.org/abs/1804.09301) \| top-1 \| 51.25 \| 54.17 \| \| [TruthfulQA](https://arxiv.org/abs/2109.07958) \| \| 44.84 \| 31.81 \| \| [Winobias 1_2](https://arxiv.org/abs/1804.06876) \| \| 56.12 \| 59.09 \| \| [Winobias 2_2](https://arxiv.org/abs/1804.06876) \| \| 91.10 \| 92.23 \| \| [Toxigen](https://arxiv.org/abs/2203.09509) \| \| 29.77 \| 39.59 \| \| ------------------------------ \| ------------- \| ----------- \| --------- \| ## Usage and Limitations These models have certain limitations that users should be aware of. ### Intended Usage Open Large Language Models (LLMs) have a wide range of applications across various industries and domains. The following list of potential uses is not comprehensive. The purpose of this list is to provide contextual information about the possible use-cases that the model creators considered as part of model training and development. * Content Creation and Communication * Text Generation: These models can be used to generate creative text formats such as poems, scripts, code, marketing copy, and email drafts. * Chatbots and Conversational AI: Power conversational interfaces for customer service, virtual assistants, or interactive applications. * Text Summarization: Generate concise summaries of a text corpus, research papers, or reports. * Research and Education * Natural Language Processing (NLP) Research: These models can serve as a foundation for researchers to experiment with NLP techniques, develop algorithms, and contribute to the advancement of the field. * Language Learning Tools: Support interactive language learning experiences, aiding in grammar correction or providing writing practice. * Knowledge Exploration: Assist researchers in exploring large bodies of text by generating summaries or answering questions about specific topics. ### Limitations * Training Data * The quality and diversity of the training data significantly influence the model's capabilities. Biases or gaps in the training data can lead to limitations in the model's responses. * The scope of the training dataset determines the subject areas the model can handle effectively. * Context and Task Complexity * LLMs are better at tasks that can be framed with clear prompts and instructions. Open-ended or highly complex tasks might be challenging. * A model's performance can be influenced by the amount of context provided (longer context generally leads to better outputs, up to a certain point). * Language Ambiguity and Nuance * Natural language is inherently complex. LLMs might struggle to grasp subtle nuances, sarcasm, or figurative language. * Factual Accuracy * LLMs generate responses based on information they learned from their training datasets, but they are not knowledge bases. They may generate incorrect or outdated factual statements. * Common Sense * LLMs rely on statistical patterns in language. They might lack the ability to apply common sense reasoning in certain situations. ### Ethical Considerations and Risks The development of large language models (LLMs) raises several ethical concerns. In creating an open model, we have carefully considered the following: * Bias and Fairness * LLMs trained on large-scale, real-world text data can reflect socio-cultural biases embedded in the training material. These models underwent careful scrutiny, input data pre-processing described and posterior evaluations reported in this card. * Misinformation and Misuse * LLMs can be misused to generate text that is false, misleading, or harmful. * Guidelines are provided for responsible use with the model, see the [Responsible Generative AI Toolkit](http://ai.google.dev/gemma/responsible). * Transparency and Accountability: * This model card summarizes details on the models' architecture, capabilities, limitations, and evaluation processes. * A responsibly developed open model offers the opportunity to share innovation by making LLM technology accessible to developers and researchers across the AI ecosystem. Risks identified and mitigations: * Perpetuation of biases: It's encouraged to perform continuous monitoring (using evaluation metrics, human review) and the exploration of de-biasing techniques during model training, fine-tuning, and other use cases. * Generation of harmful content: Mechanisms and guidelines for content safety are essential. Developers are encouraged to exercise caution and implement appropriate content safety safeguards based on their specific product policies and application use cases. * Misuse for malicious purposes: Technical limitations and developer and end-user education can help mitigate against malicious applications of LLMs. Educational resources and reporting mechanisms for users to flag misuse are provided. Prohibited uses of Gemma models are outlined in the [Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy). * Privacy violations: Models were trained on data filtered for removal of PII (Personally Identifiable Information). Developers are encouraged to adhere to privacy regulations with privacy-preserving techniques. ### Benefits At the time of release, this family of models provides high-performance open large language model implementations designed from the ground up for Responsible AI development compared to similarly sized models. Using the benchmark evaluation metrics described in this document, these models have shown to provide superior performance to other, comparably-sized open model alternatives.
cijo-uc/layoutlm-funsd	cijo-uc	2024-02-21T14:01:40Z	7	0	transformers	[ "transformers", "tensorboard", "safetensors", "layoutlm", "token-classification", "generated_from_trainer", "dataset:funsd", "base_model:microsoft/layoutlm-base-uncased", "base_model:finetune:microsoft/layoutlm-base-uncased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-02-20T05:11:27Z	--- license: mit base_model: microsoft/layoutlm-base-uncased tags: - generated_from_trainer datasets: - funsd model-index: - name: layoutlm-funsd 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. --> # layoutlm-funsd This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset. It achieves the following results on the evaluation set: - Loss: 1.3476 - Answer: {'precision': 0.17894736842105263, 'recall': 0.3362175525339926, 'f1': 0.2335766423357664, 'number': 809} - Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} - Question: {'precision': 0.27942998760842624, 'recall': 0.42347417840375584, 'f1': 0.33669279581933553, 'number': 1065} - Overall Precision: 0.2307 - Overall Recall: 0.3628 - Overall F1: 0.2820 - Overall Accuracy: 0.4351 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Answer \| Header \| Question \| Overall Precision \| Overall Recall \| Overall F1 \| Overall Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------------------------------------------------------------------------------------------------------------:\|:-----------------------------------------------------------:\|:------------------------------------------------------------------------------------------------------------:\|:-----------------:\|:--------------:\|:----------:\|:----------------:\| \| 1.7432 \| 1.0 \| 10 \| 1.5651 \| {'precision': 0.03228782287822878, 'recall': 0.04326328800988875, 'f1': 0.036978341257263604, 'number': 809} \| {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} \| {'precision': 0.18964259664478483, 'recall': 0.24413145539906103, 'f1': 0.2134646962233169, 'number': 1065} \| 0.1202 \| 0.1480 \| 0.1326 \| 0.3666 \| \| 1.5478 \| 2.0 \| 20 \| 1.4279 \| {'precision': 0.13696715583508037, 'recall': 0.242274412855377, 'f1': 0.17500000000000002, 'number': 809} \| {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} \| {'precision': 0.25, 'recall': 0.3652582159624413, 'f1': 0.29683326974437235, 'number': 1065} \| 0.1958 \| 0.2935 \| 0.2349 \| 0.4085 \| \| 1.4112 \| 3.0 \| 30 \| 1.3476 \| {'precision': 0.17894736842105263, 'recall': 0.3362175525339926, 'f1': 0.2335766423357664, 'number': 809} \| {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} \| {'precision': 0.27942998760842624, 'recall': 0.42347417840375584, 'f1': 0.33669279581933553, 'number': 1065} \| 0.2307 \| 0.3628 \| 0.2820 \| 0.4351 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
omerfguzel/emotion_funnel-transformer	omerfguzel	2024-02-21T14:01:40Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "funnel", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:funnel-transformer/small", "base_model:finetune:funnel-transformer/small", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-21T11:01:12Z	--- license: apache-2.0 base_model: funnel-transformer/small tags: - generated_from_trainer datasets: - emotion metrics: - accuracy model-index: - name: emotion_funnel-transformer results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.918 --- <!-- 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. --> # emotion_funnel-transformer This model is a fine-tuned version of [funnel-transformer/small](https://huggingface.co/funnel-transformer/small) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2210 - Accuracy: 0.918 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 250 \| 0.4164 \| 0.863 \| \| 0.6384 \| 2.0 \| 500 \| 0.2493 \| 0.911 \| \| 0.6384 \| 3.0 \| 750 \| 0.2227 \| 0.9225 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
casque/Nude_tattoo	casque	2024-02-21T14:01:17Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-02-21T14:00:28Z	--- license: creativeml-openrail-m ---
casque/Bubble_bath	casque	2024-02-21T13:59:44Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-02-21T13:58:44Z	--- license: creativeml-openrail-m ---
tnet-devs/zephyr-7b-beta-inc	tnet-devs	2024-02-21T13:58:52Z	6	0	transformers	[ "transformers", "pytorch", "safetensors", "mistral", "text-generation", "generated_from_trainer", "conversational", "en", "dataset:HuggingFaceH4/ultrachat_200k", "dataset:HuggingFaceH4/ultrafeedback_binarized", "arxiv:2305.18290", "arxiv:2310.16944", "base_model:mistralai/Mistral-7B-v0.1", "base_model:finetune:mistralai/Mistral-7B-v0.1", "license:mit", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-21T08:24:37Z	--- tags: - generated_from_trainer license: mit datasets: - HuggingFaceH4/ultrachat_200k - HuggingFaceH4/ultrafeedback_binarized language: - en base_model: mistralai/Mistral-7B-v0.1 widget: - text: "<\|system\|>\nYou are a pirate chatbot who always responds with Arr!</s>\n<\|user\|>\nThere's a llama on my lawn, how can I get rid of him?</s>\n<\|assistant\|>\n" output: text: "Arr! 'Tis a puzzlin' matter, me hearty! A llama on yer lawn be a rare sight, but I've got a plan that might help ye get rid of 'im. Ye'll need to gather some carrots and hay, and then lure the llama away with the promise of a tasty treat. Once he's gone, ye can clean up yer lawn and enjoy the peace and quiet once again. But beware, me hearty, for there may be more llamas where that one came from! Arr!" pipeline_tag: text-generation model-index: - name: zephyr-7b-beta results: # AI2 Reasoning Challenge (25-Shot) - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm name: normalized accuracy value: 62.03071672354948 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # HellaSwag (10-shot) - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm name: normalized accuracy value: 84.35570603465445 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # DROP (3-shot) - task: type: text-generation name: Text Generation dataset: name: Drop (3-Shot) type: drop split: validation args: num_few_shot: 3 metrics: - type: f1 name: f1 score value: 9.662437080536909 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # TruthfulQA (0-shot) - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 57.44916942762855 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # GSM8k (5-shot) - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc name: accuracy value: 12.736921910538287 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # MMLU (5-Shot) - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc name: accuracy value: 61.07 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # Winogrande (5-shot) - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc name: accuracy value: 77.74269928966061 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta # AlpacaEval (taken from model card) - task: type: text-generation name: Text Generation dataset: name: AlpacaEval type: tatsu-lab/alpaca_eval metrics: - type: unknown name: win rate value: 0.9060 source: url: https://tatsu-lab.github.io/alpaca_eval/ # MT-Bench (taken from model card) - task: type: text-generation name: Text Generation dataset: name: MT-Bench type: unknown metrics: - type: unknown name: score value: 7.34 source: url: https://huggingface.co/spaces/lmsys/mt-bench --- <!-- 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://huggingface.co/HuggingFaceH4/zephyr-7b-alpha/resolve/main/thumbnail.png" alt="Zephyr Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> # Model Card for Zephyr 7B β Zephyr is a series of language models that are trained to act as helpful assistants. Zephyr-7B-β is the second model in the series, and is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) that was trained on on a mix of publicly available, synthetic datasets using [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290). We found that removing the in-built alignment of these datasets boosted performance on [MT Bench](https://huggingface.co/spaces/lmsys/mt-bench) and made the model more helpful. However, this means that model is likely to generate problematic text when prompted to do so. You can find more details in the [technical report](https://arxiv.org/abs/2310.16944). ## Model description - Model type: A 7B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets. - Language(s) (NLP): Primarily English - License: MIT - Finetuned from model: [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) ### Model Sources <!-- Provide the basic links for the model. --> - Repository: https://github.com/huggingface/alignment-handbook - Demo: https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat - Chatbot Arena: Evaluate Zephyr 7B against 10+ LLMs in the LMSYS arena: http://arena.lmsys.org ## Performance At the time of release, Zephyr-7B-β is the highest ranked 7B chat model on the [MT-Bench](https://huggingface.co/spaces/lmsys/mt-bench) and [AlpacaEval](https://tatsu-lab.github.io/alpaca_eval/) benchmarks: \| Model \| Size \| Alignment \| MT-Bench (score) \| AlpacaEval (win rate %) \| \|-------------\|-----\|----\|---------------\|--------------\| \| StableLM-Tuned-α \| 7B\| dSFT \|2.75\| -\| \| MPT-Chat \| 7B \|dSFT \|5.42\| -\| \| Xwin-LMv0.1 \| 7B\| dPPO\| 6.19\| 87.83\| \| Mistral-Instructv0.1 \| 7B\| - \| 6.84 \|-\| \| Zephyr-7b-α \|7B\| dDPO\| 6.88\| -\| \| Zephyr-7b-β 🪁 \| 7B \| dDPO \| 7.34 \| 90.60 \| \| Falcon-Instruct \| 40B \|dSFT \|5.17 \|45.71\| \| Guanaco \| 65B \| SFT \|6.41\| 71.80\| \| Llama2-Chat \| 70B \|RLHF \|6.86\| 92.66\| \| Vicuna v1.3 \| 33B \|dSFT \|7.12 \|88.99\| \| WizardLM v1.0 \| 70B \|dSFT \|7.71 \|-\| \| Xwin-LM v0.1 \| 70B \|dPPO \|- \|95.57\| \| GPT-3.5-turbo \| - \|RLHF \|7.94 \|89.37\| \| Claude 2 \| - \|RLHF \|8.06\| 91.36\| \| GPT-4 \| -\| RLHF \|8.99\| 95.28\| In particular, on several categories of MT-Bench, Zephyr-7B-β has strong performance compared to larger open models like Llama2-Chat-70B: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6200d0a443eb0913fa2df7cc/raxvt5ma16d7T23my34WC.png) However, on more complex tasks like coding and mathematics, Zephyr-7B-β lags behind proprietary models and more research is needed to close the gap. ## Intended uses & limitations The model was initially fine-tuned on a filtered and preprocessed of the [`UltraChat`](https://huggingface.co/datasets/stingning/ultrachat) dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT. We then further aligned the model with [🤗 TRL's](https://github.com/huggingface/trl) `DPOTrainer` on the [openbmb/UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback) dataset, which contains 64k prompts and model completions that are ranked by GPT-4. As a result, the model can be used for chat and you can check out our [demo](https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat) to test its capabilities. You can find the datasets used for training Zephyr-7B-β [here](https://huggingface.co/collections/HuggingFaceH4/zephyr-7b-6538c6d6d5ddd1cbb1744a66) Here's how you can run the model using the `pipeline()` function from 🤗 Transformers: ```python # Install transformers from source - only needed for versions <= v4.34 # pip install git+https://github.com/huggingface/transformers.git # pip install accelerate import torch from transformers import pipeline pipe = pipeline("text-generation", model="HuggingFaceH4/zephyr-7b-beta", torch_dtype=torch.bfloat16, device_map="auto") # We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating messages = [ { "role": "system", "content": "You are a friendly chatbot who always responds in the style of a pirate", }, {"role": "user", "content": "How many helicopters can a human eat in one sitting?"}, ] prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) # <\|system\|> # You are a friendly chatbot who always responds in the style of a pirate.</s> # <\|user\|> # How many helicopters can a human eat in one sitting?</s> # <\|assistant\|> # Ah, me hearty matey! But yer question be a puzzler! A human cannot eat a helicopter in one sitting, as helicopters are not edible. They be made of metal, plastic, and other materials, not food! ``` ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> Zephyr-7B-β has not been aligned to human preferences for safety within the RLHF phase or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). It is also unknown what the size and composition of the corpus was used to train the base model (`mistralai/Mistral-7B-v0.1`), however it is likely to have included a mix of Web data and technical sources like books and code. See the [Falcon 180B model card](https://huggingface.co/tiiuae/falcon-180B#training-data) for an example of this. ## Training and evaluation data During DPO training, this model achieves the following results on the evaluation set: - Loss: 0.7496 - Rewards/chosen: -4.5221 - Rewards/rejected: -8.3184 - Rewards/accuracies: 0.7812 - Rewards/margins: 3.7963 - Logps/rejected: -340.1541 - Logps/chosen: -299.4561 - Logits/rejected: -2.3081 - Logits/chosen: -2.3531 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 2 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 16 - total_train_batch_size: 32 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3.0 ### Training results The table below shows the full set of DPO training metrics: \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.6284 \| 0.05 \| 100 \| 0.6098 \| 0.0425 \| -0.1872 \| 0.7344 \| 0.2297 \| -258.8416 \| -253.8099 \| -2.7976 \| -2.8234 \| \| 0.4908 \| 0.1 \| 200 \| 0.5426 \| -0.0279 \| -0.6842 \| 0.75 \| 0.6563 \| -263.8124 \| -254.5145 \| -2.7719 \| -2.7960 \| \| 0.5264 \| 0.15 \| 300 \| 0.5324 \| 0.0414 \| -0.9793 \| 0.7656 \| 1.0207 \| -266.7627 \| -253.8209 \| -2.7892 \| -2.8122 \| \| 0.5536 \| 0.21 \| 400 \| 0.4957 \| -0.0185 \| -1.5276 \| 0.7969 \| 1.5091 \| -272.2460 \| -254.4203 \| -2.8542 \| -2.8764 \| \| 0.5362 \| 0.26 \| 500 \| 0.5031 \| -0.2630 \| -1.5917 \| 0.7812 \| 1.3287 \| -272.8869 \| -256.8653 \| -2.8702 \| -2.8958 \| \| 0.5966 \| 0.31 \| 600 \| 0.5963 \| -0.2993 \| -1.6491 \| 0.7812 \| 1.3499 \| -273.4614 \| -257.2279 \| -2.8778 \| -2.8986 \| \| 0.5014 \| 0.36 \| 700 \| 0.5382 \| -0.2859 \| -1.4750 \| 0.75 \| 1.1891 \| -271.7204 \| -257.0942 \| -2.7659 \| -2.7869 \| \| 0.5334 \| 0.41 \| 800 \| 0.5677 \| -0.4289 \| -1.8968 \| 0.7969 \| 1.4679 \| -275.9378 \| -258.5242 \| -2.7053 \| -2.7265 \| \| 0.5251 \| 0.46 \| 900 \| 0.5772 \| -0.2116 \| -1.3107 \| 0.7344 \| 1.0991 \| -270.0768 \| -256.3507 \| -2.8463 \| -2.8662 \| \| 0.5205 \| 0.52 \| 1000 \| 0.5262 \| -0.3792 \| -1.8585 \| 0.7188 \| 1.4793 \| -275.5552 \| -258.0276 \| -2.7893 \| -2.7979 \| \| 0.5094 \| 0.57 \| 1100 \| 0.5433 \| -0.6279 \| -1.9368 \| 0.7969 \| 1.3089 \| -276.3377 \| -260.5136 \| -2.7453 \| -2.7536 \| \| 0.5837 \| 0.62 \| 1200 \| 0.5349 \| -0.3780 \| -1.9584 \| 0.7656 \| 1.5804 \| -276.5542 \| -258.0154 \| -2.7643 \| -2.7756 \| \| 0.5214 \| 0.67 \| 1300 \| 0.5732 \| -1.0055 \| -2.2306 \| 0.7656 \| 1.2251 \| -279.2761 \| -264.2903 \| -2.6986 \| -2.7113 \| \| 0.6914 \| 0.72 \| 1400 \| 0.5137 \| -0.6912 \| -2.1775 \| 0.7969 \| 1.4863 \| -278.7448 \| -261.1467 \| -2.7166 \| -2.7275 \| \| 0.4655 \| 0.77 \| 1500 \| 0.5090 \| -0.7987 \| -2.2930 \| 0.7031 \| 1.4943 \| -279.8999 \| -262.2220 \| -2.6651 \| -2.6838 \| \| 0.5731 \| 0.83 \| 1600 \| 0.5312 \| -0.8253 \| -2.3520 \| 0.7812 \| 1.5268 \| -280.4902 \| -262.4876 \| -2.6543 \| -2.6728 \| \| 0.5233 \| 0.88 \| 1700 \| 0.5206 \| -0.4573 \| -2.0951 \| 0.7812 \| 1.6377 \| -277.9205 \| -258.8084 \| -2.6870 \| -2.7097 \| \| 0.5593 \| 0.93 \| 1800 \| 0.5231 \| -0.5508 \| -2.2000 \| 0.7969 \| 1.6492 \| -278.9703 \| -259.7433 \| -2.6221 \| -2.6519 \| \| 0.4967 \| 0.98 \| 1900 \| 0.5290 \| -0.5340 \| -1.9570 \| 0.8281 \| 1.4230 \| -276.5395 \| -259.5749 \| -2.6564 \| -2.6878 \| \| 0.0921 \| 1.03 \| 2000 \| 0.5368 \| -1.1376 \| -3.1615 \| 0.7812 \| 2.0239 \| -288.5854 \| -265.6111 \| -2.6040 \| -2.6345 \| \| 0.0733 \| 1.08 \| 2100 \| 0.5453 \| -1.1045 \| -3.4451 \| 0.7656 \| 2.3406 \| -291.4208 \| -265.2799 \| -2.6289 \| -2.6595 \| \| 0.0972 \| 1.14 \| 2200 \| 0.5571 \| -1.6915 \| -3.9823 \| 0.8125 \| 2.2908 \| -296.7934 \| -271.1505 \| -2.6471 \| -2.6709 \| \| 0.1058 \| 1.19 \| 2300 \| 0.5789 \| -1.0621 \| -3.8941 \| 0.7969 \| 2.8319 \| -295.9106 \| -264.8563 \| -2.5527 \| -2.5798 \| \| 0.2423 \| 1.24 \| 2400 \| 0.5455 \| -1.1963 \| -3.5590 \| 0.7812 \| 2.3627 \| -292.5599 \| -266.1981 \| -2.5414 \| -2.5784 \| \| 0.1177 \| 1.29 \| 2500 \| 0.5889 \| -1.8141 \| -4.3942 \| 0.7969 \| 2.5801 \| -300.9120 \| -272.3761 \| -2.4802 \| -2.5189 \| \| 0.1213 \| 1.34 \| 2600 \| 0.5683 \| -1.4608 \| -3.8420 \| 0.8125 \| 2.3812 \| -295.3901 \| -268.8436 \| -2.4774 \| -2.5207 \| \| 0.0889 \| 1.39 \| 2700 \| 0.5890 \| -1.6007 \| -3.7337 \| 0.7812 \| 2.1330 \| -294.3068 \| -270.2423 \| -2.4123 \| -2.4522 \| \| 0.0995 \| 1.45 \| 2800 \| 0.6073 \| -1.5519 \| -3.8362 \| 0.8281 \| 2.2843 \| -295.3315 \| -269.7538 \| -2.4685 \| -2.5050 \| \| 0.1145 \| 1.5 \| 2900 \| 0.5790 \| -1.7939 \| -4.2876 \| 0.8438 \| 2.4937 \| -299.8461 \| -272.1744 \| -2.4272 \| -2.4674 \| \| 0.0644 \| 1.55 \| 3000 \| 0.5735 \| -1.7285 \| -4.2051 \| 0.8125 \| 2.4766 \| -299.0209 \| -271.5201 \| -2.4193 \| -2.4574 \| \| 0.0798 \| 1.6 \| 3100 \| 0.5537 \| -1.7226 \| -4.2850 \| 0.8438 \| 2.5624 \| -299.8200 \| -271.4610 \| -2.5367 \| -2.5696 \| \| 0.1013 \| 1.65 \| 3200 \| 0.5575 \| -1.5715 \| -3.9813 \| 0.875 \| 2.4098 \| -296.7825 \| -269.9498 \| -2.4926 \| -2.5267 \| \| 0.1254 \| 1.7 \| 3300 \| 0.5905 \| -1.6412 \| -4.4703 \| 0.8594 \| 2.8291 \| -301.6730 \| -270.6473 \| -2.5017 \| -2.5340 \| \| 0.085 \| 1.76 \| 3400 \| 0.6133 \| -1.9159 \| -4.6760 \| 0.8438 \| 2.7601 \| -303.7296 \| -273.3941 \| -2.4614 \| -2.4960 \| \| 0.065 \| 1.81 \| 3500 \| 0.6074 \| -1.8237 \| -4.3525 \| 0.8594 \| 2.5288 \| -300.4951 \| -272.4724 \| -2.4597 \| -2.5004 \| \| 0.0755 \| 1.86 \| 3600 \| 0.5836 \| -1.9252 \| -4.4005 \| 0.8125 \| 2.4753 \| -300.9748 \| -273.4872 \| -2.4327 \| -2.4716 \| \| 0.0746 \| 1.91 \| 3700 \| 0.5789 \| -1.9280 \| -4.4906 \| 0.8125 \| 2.5626 \| -301.8762 \| -273.5149 \| -2.4686 \| -2.5115 \| \| 0.1348 \| 1.96 \| 3800 \| 0.6015 \| -1.8658 \| -4.2428 \| 0.8281 \| 2.3769 \| -299.3976 \| -272.8936 \| -2.4943 \| -2.5393 \| \| 0.0217 \| 2.01 \| 3900 \| 0.6122 \| -2.3335 \| -4.9229 \| 0.8281 \| 2.5894 \| -306.1988 \| -277.5699 \| -2.4841 \| -2.5272 \| \| 0.0219 \| 2.07 \| 4000 \| 0.6522 \| -2.9890 \| -6.0164 \| 0.8281 \| 3.0274 \| -317.1334 \| -284.1248 \| -2.4105 \| -2.4545 \| \| 0.0119 \| 2.12 \| 4100 \| 0.6922 \| -3.4777 \| -6.6749 \| 0.7969 \| 3.1972 \| -323.7187 \| -289.0121 \| -2.4272 \| -2.4699 \| \| 0.0153 \| 2.17 \| 4200 \| 0.6993 \| -3.2406 \| -6.6775 \| 0.7969 \| 3.4369 \| -323.7453 \| -286.6413 \| -2.4047 \| -2.4465 \| \| 0.011 \| 2.22 \| 4300 \| 0.7178 \| -3.7991 \| -7.4397 \| 0.7656 \| 3.6406 \| -331.3667 \| -292.2260 \| -2.3843 \| -2.4290 \| \| 0.0072 \| 2.27 \| 4400 \| 0.6840 \| -3.3269 \| -6.8021 \| 0.8125 \| 3.4752 \| -324.9908 \| -287.5042 \| -2.4095 \| -2.4536 \| \| 0.0197 \| 2.32 \| 4500 \| 0.7013 \| -3.6890 \| -7.3014 \| 0.8125 \| 3.6124 \| -329.9841 \| -291.1250 \| -2.4118 \| -2.4543 \| \| 0.0182 \| 2.37 \| 4600 \| 0.7476 \| -3.8994 \| -7.5366 \| 0.8281 \| 3.6372 \| -332.3356 \| -293.2291 \| -2.4163 \| -2.4565 \| \| 0.0125 \| 2.43 \| 4700 \| 0.7199 \| -4.0560 \| -7.5765 \| 0.8438 \| 3.5204 \| -332.7345 \| -294.7952 \| -2.3699 \| -2.4100 \| \| 0.0082 \| 2.48 \| 4800 \| 0.7048 \| -3.6613 \| -7.1356 \| 0.875 \| 3.4743 \| -328.3255 \| -290.8477 \| -2.3925 \| -2.4303 \| \| 0.0118 \| 2.53 \| 4900 \| 0.6976 \| -3.7908 \| -7.3152 \| 0.8125 \| 3.5244 \| -330.1224 \| -292.1431 \| -2.3633 \| -2.4047 \| \| 0.0118 \| 2.58 \| 5000 \| 0.7198 \| -3.9049 \| -7.5557 \| 0.8281 \| 3.6508 \| -332.5271 \| -293.2844 \| -2.3764 \| -2.4194 \| \| 0.006 \| 2.63 \| 5100 \| 0.7506 \| -4.2118 \| -7.9149 \| 0.8125 \| 3.7032 \| -336.1194 \| -296.3530 \| -2.3407 \| -2.3860 \| \| 0.0143 \| 2.68 \| 5200 \| 0.7408 \| -4.2433 \| -7.9802 \| 0.8125 \| 3.7369 \| -336.7721 \| -296.6682 \| -2.3509 \| -2.3946 \| \| 0.0057 \| 2.74 \| 5300 \| 0.7552 \| -4.3392 \| -8.0831 \| 0.7969 \| 3.7439 \| -337.8013 \| -297.6275 \| -2.3388 \| -2.3842 \| \| 0.0138 \| 2.79 \| 5400 \| 0.7404 \| -4.2395 \| -7.9762 \| 0.8125 \| 3.7367 \| -336.7322 \| -296.6304 \| -2.3286 \| -2.3737 \| \| 0.0079 \| 2.84 \| 5500 \| 0.7525 \| -4.4466 \| -8.2196 \| 0.7812 \| 3.7731 \| -339.1662 \| -298.7007 \| -2.3200 \| -2.3641 \| \| 0.0077 \| 2.89 \| 5600 \| 0.7520 \| -4.5586 \| -8.3485 \| 0.7969 \| 3.7899 \| -340.4545 \| -299.8206 \| -2.3078 \| -2.3517 \| \| 0.0094 \| 2.94 \| 5700 \| 0.7527 \| -4.5542 \| -8.3509 \| 0.7812 \| 3.7967 \| -340.4790 \| -299.7773 \| -2.3062 \| -2.3510 \| \| 0.0054 \| 2.99 \| 5800 \| 0.7520 \| -4.5169 \| -8.3079 \| 0.7812 \| 3.7911 \| -340.0493 \| -299.4038 \| -2.3081 \| -2.3530 \| ### Framework versions - Transformers 4.35.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.14.0 ## Citation If you find Zephyr-7B-β is useful in your work, please cite it with: ``` @misc{tunstall2023zephyr, title={Zephyr: Direct Distillation of LM Alignment}, author={Lewis Tunstall and Edward Beeching and Nathan Lambert and Nazneen Rajani and Kashif Rasul and Younes Belkada and Shengyi Huang and Leandro von Werra and Clémentine Fourrier and Nathan Habib and Nathan Sarrazin and Omar Sanseviero and Alexander M. Rush and Thomas Wolf}, year={2023}, eprint={2310.16944}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_HuggingFaceH4__zephyr-7b-beta) \| Metric \| Value \| \|-----------------------\|---------------------------\| \| Avg. \| 52.15 \| \| ARC (25-shot) \| 62.03 \| \| HellaSwag (10-shot) \| 84.36 \| \| MMLU (5-shot) \| 61.07 \| \| TruthfulQA (0-shot) \| 57.45 \| \| Winogrande (5-shot) \| 77.74 \| \| GSM8K (5-shot) \| 12.74 \| \| DROP (3-shot) \| 9.66 \|
rAIfle/Sloppier-Wingman-Alternative-8x7B-hf	rAIfle	2024-02-21T13:58:46Z	10	1	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "mergekit", "merge", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-21T11:48:52Z	--- base_model: [] tags: - mergekit - merge --- # Sloppier-Wingman-Alternative-8x7B-hf ![Sloppier-Nasty-Wingman](https://files.catbox.moe/i2aigj.png) Alternative to [rAIfle/Sloppy-Wingman-8x7B-hf](https://huggingface.co/rAIfle/Sloppy-Wingman-8x7B-hf). Second part of the merge has a bit of difference compared to the other one. I, personally, still prefer ChatML on this one, but Alpaca and/or Mistral-formats ought to work regardless. ```yaml models: - model: mistralai/Mixtral-8x7B-v0.1+retrieval-bar/Mixtral-8x7B-v0.1_case-briefs parameters: weight: 0.33 - model: mistralai/Mixtral-8x7B-v0.1+wandb/Mixtral-8x7b-Remixtral parameters: weight: 0.33 merge_method: task_arithmetic base_model: mistralai/Mixtral-8x7B-v0.1 dtype: float16 ``` and ```yaml models: - model: mistralai/Mixtral-8x7B-Instruct-v0.1+/ai/LLM/tmp/pefts/daybreak-peft/mixtral-8x7b parameters: weight: 0.85 - model: mistralai/Mixtral-8x7B-Instruct-v0.1+SeanWu25/Mixtral_8x7b_Medicine parameters: weight: 0.33 - model: notstoic/Nous-Hermes-2-Mixtruct-v0.1-8x7B-DPO-DARE_TIES parameters: weight: 0.25 merge_method: task_arithmetic base_model: mistralai/Mixtral-8x7B-Instruct-v0.1 dtype: float16 ``` This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * ./02.5-pal-instruct * ./01-pal-base ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: ./01-pal-base - model: ./02.5-pal-instruct merge_method: slerp base_model: ./01-pal-base parameters: t: - value: 0.66 dtype: float16 ```
Leul78/16sft10	Leul78	2024-02-21T13:57:04Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-21T13:52:03Z	--- 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]
gayanin/bart-with-asr-noise-ins-0.2	gayanin	2024-02-21T13:53:42Z	5	0	transformers	[ "transformers", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "base_model:facebook/bart-base", "base_model:finetune:facebook/bart-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-21T13:46:46Z	--- license: apache-2.0 base_model: facebook/bart-base tags: - generated_from_trainer model-index: - name: bart-with-asr-noise-ins-0.2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-with-asr-noise-ins-0.2 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0513 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.1429 \| 0.62 \| 500 \| 0.0742 \| \| 0.0597 \| 1.24 \| 1000 \| 0.0596 \| \| 0.042 \| 1.86 \| 1500 \| 0.0542 \| \| 0.0235 \| 2.48 \| 2000 \| 0.0513 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.2+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
gayanin/bart-with-asr-noise-sub-0.2	gayanin	2024-02-21T13:53:09Z	5	0	transformers	[ "transformers", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "base_model:facebook/bart-base", "base_model:finetune:facebook/bart-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-21T13:46:48Z	--- license: apache-2.0 base_model: facebook/bart-base tags: - generated_from_trainer model-index: - name: bart-with-asr-noise-sub-0.2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-with-asr-noise-sub-0.2 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0740 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.1895 \| 0.62 \| 500 \| 0.1706 \| \| 0.0997 \| 1.24 \| 1000 \| 0.1073 \| \| 0.0464 \| 1.86 \| 1500 \| 0.0881 \| \| 0.028 \| 2.48 \| 2000 \| 0.0740 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.2+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
thevin123/llama2-7B_finetuned_15000_5	thevin123	2024-02-21T13:52:07Z	0	0	peft	[ "peft", "region:us" ]	null	2024-02-21T13:51:58Z	--- 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 ### Framework versions - PEFT 0.4.0
YeonghunJo/KoAlpaca	YeonghunJo	2024-02-21T13:51:44Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-19T14:43:05Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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casque/Metal_tassel	casque	2024-02-21T13:51:13Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-02-21T13:50:26Z	--- license: creativeml-openrail-m ---
Leul78/8sft20	Leul78	2024-02-21T13:47:32Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-21T13:41:51Z	--- 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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chris-santiago/distilbert-base-uncased-distilled-clinc	chris-santiago	2024-02-21T13:45:21Z	8	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:clinc_oos", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-08-24T02:29:21Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - clinc_oos metrics: - accuracy base_model: distilbert-base-uncased model-index: - name: distilbert-base-uncased-distilled-clinc results: - task: type: text-classification name: Text Classification dataset: name: clinc_oos type: clinc_oos args: plus metrics: - type: accuracy value: 0.9458064516129032 name: Accuracy --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-distilled-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. It achieves the following results on the evaluation set: - Loss: 0.2469 - Accuracy: 0.9458 ## 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: 48 - eval_batch_size: 48 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 318 \| 3.1622 \| 0.7468 \| \| 3.6918 \| 2.0 \| 636 \| 1.5555 \| 0.8565 \| \| 3.6918 \| 3.0 \| 954 \| 0.7728 \| 0.9142 \| \| 1.3257 \| 4.0 \| 1272 \| 0.4589 \| 0.9319 \| \| 0.431 \| 5.0 \| 1590 \| 0.3350 \| 0.9426 \| \| 0.431 \| 6.0 \| 1908 \| 0.2879 \| 0.9406 \| \| 0.1752 \| 7.0 \| 2226 \| 0.2609 \| 0.9465 \| \| 0.0893 \| 8.0 \| 2544 \| 0.2512 \| 0.9455 \| \| 0.0893 \| 9.0 \| 2862 \| 0.2488 \| 0.9452 \| \| 0.062 \| 10.0 \| 3180 \| 0.2469 \| 0.9458 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.12.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
adityarra07/whisper-medium-train_noise3	adityarra07	2024-02-21T13:44:34Z	3	0	transformers	[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-medium", "base_model:finetune:openai/whisper-medium", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-02-20T22:16:47Z	--- license: apache-2.0 base_model: openai/whisper-medium tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-medium-train_noise3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-medium-train_noise3 This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1641 - Wer: 7.3711 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2575 \| 1.0 \| 1385 \| 0.1408 \| 7.9993 \| \| 0.0824 \| 2.0 \| 2770 \| 0.1419 \| 7.4266 \| \| 0.0356 \| 3.0 \| 4155 \| 0.1427 \| 7.2788 \| \| 0.0131 \| 4.0 \| 5540 \| 0.1548 \| 7.3157 \| \| 0.0039 \| 5.0 \| 6925 \| 0.1588 \| 7.1864 \| \| 0.0011 \| 6.0 \| 8310 \| 0.1641 \| 7.3711 \| ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3
omerfguzel/emotion_xlnet	omerfguzel	2024-02-21T13:40:53Z	9	0	transformers	[ "transformers", "tensorboard", "safetensors", "xlnet", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:xlnet/xlnet-base-cased", "base_model:finetune:xlnet/xlnet-base-cased", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-21T12:29:31Z	--- license: mit base_model: xlnet/xlnet-base-cased tags: - generated_from_trainer datasets: - emotion metrics: - accuracy model-index: - name: emotion_xlnet results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.9155 --- <!-- 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. --> # emotion_xlnet This model is a fine-tuned version of [xlnet/xlnet-base-cased](https://huggingface.co/xlnet/xlnet-base-cased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2021 - Accuracy: 0.9155 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 250 \| 0.3639 \| 0.8715 \| \| 0.5892 \| 2.0 \| 500 \| 0.2404 \| 0.911 \| \| 0.5892 \| 3.0 \| 750 \| 0.2102 \| 0.9175 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
ferrazzipietro/Mistral-7B-Instruct-v0.2_adapters_en.layer1_8_torch.bfloat16_64_32_0.01_8_0.0008	ferrazzipietro	2024-02-21T13:39:51Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-21T13:39:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
danrrr/Taxi-v3	danrrr	2024-02-21T13:39:39Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-02-21T13:39:37Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.52 +/- 2.73 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="danrrr/Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
casque/ConceptU2	casque	2024-02-21T13:28:12Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-02-21T13:27:20Z	--- license: creativeml-openrail-m ---
mudogruer/Phi-2-hf-SciQ-20pc	mudogruer	2024-02-21T13:26:25Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-21T13:26:06Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
whitefox123/whisper-ar-13	whitefox123	2024-02-21T13:24:19Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "hi", "dataset:mozilla-foundation/common_voice_11_0", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-02-21T11:31:24Z	--- language: - hi license: apache-2.0 base_model: openai/whisper-small tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Small Hi - Sanchit Gandhi 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: test args: 'config: hi, split: test' metrics: - name: Wer type: wer value: 265.44144144144144 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Hi - Sanchit Gandhi This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.1023 - Wer: 265.4414 ## 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: 1000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.0706 \| 1.6 \| 1000 \| 0.1023 \| 265.4414 \| ### Framework versions - Transformers 4.39.0.dev0 - Pytorch 2.1.0+cu118 - Datasets 2.17.1 - Tokenizers 0.15.2
PremRajiv/my-pet-dog	PremRajiv	2024-02-21T13:17:18Z	1	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	2024-02-21T13:13:31Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### My-Pet-Dog Dreambooth model trained by PremRajiv following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: GoX19932gAS Sample pictures of this concept: ![0](https://huggingface.co/PremRajiv/my-pet-dog/resolve/main/sample_images/Designer.png)
BlackSamorez/TinyLlama-1_1B-Chat-v1_0-AQLM-2Bit-1x16-hf	BlackSamorez	2024-02-21T13:16:19Z	72	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "aqlm", "region:us" ]	text-generation	2024-02-20T08:02:53Z	--- 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/Mistral-7B-Instruct-v0.2_adapters_en.layer1_8_torch.bfloat16_64_32_0.01_4_0.0002	ferrazzipietro	2024-02-21T13:16:03Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-21T13:15:22Z	--- 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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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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Amankankriya/ppo-LunarLander-v2	Amankankriya	2024-02-21T13:15:34Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-02-21T13:15:19Z	--- 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: 285.12 +/- 15.94 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 ... ```
Youssef-Fhm/models	Youssef-Fhm	2024-02-21T13:12:01Z	4	0	transformers	[ "transformers", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "dataset:scientific_papers", "base_model:facebook/bart-large-cnn", "base_model:finetune:facebook/bart-large-cnn", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-19T19:50:38Z	--- license: mit base_model: facebook/bart-large-cnn tags: - generated_from_trainer datasets: - scientific_papers model-index: - name: models 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. --> # models This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the scientific_papers dataset. It achieves the following results on the evaluation set: - Loss: 2.6842 - Rouge2 Precision: 0.1282 - Rouge2 Recall: 0.1133 - Rouge2 Fmeasure: 0.1186 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge2 Precision \| Rouge2 Recall \| Rouge2 Fmeasure \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:----------------:\|:-------------:\|:---------------:\| \| 2.9888 \| 0.32 \| 10 \| 2.8091 \| 0.1445 \| 0.1158 \| 0.1251 \| \| 2.7186 \| 0.64 \| 20 \| 2.6898 \| 0.1332 \| 0.1183 \| 0.1232 \| \| 2.6847 \| 0.96 \| 30 \| 2.6861 \| 0.1291 \| 0.1144 \| 0.1197 \| ### Framework versions - Transformers 4.37.2 - Pytorch 1.13.1 - Datasets 2.16.1 - Tokenizers 0.15.2
Krishnavamsi9848/elephant-afr	Krishnavamsi9848	2024-02-21T13:10:51Z	1	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	2024-02-21T13:07:03Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### elephant-afr Dreambooth model trained by Krishnavamsi9848 following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: 23HQ5A0211 Sample pictures of this concept: ![0](https://huggingface.co/Krishnavamsi9848/elephant-afr/resolve/main/sample_images/afr_(1).jpg)
sanjeevkashayp/my-pet-cat-xzg	sanjeevkashayp	2024-02-21T13:08:54Z	1	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	2024-02-21T13:04:32Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### My-Pet-Cat-XZG Dreambooth model trained by sanjeevkashayp following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: 21/CSE/01 Sample pictures of this concept: ![0](https://huggingface.co/sanjeevkashayp/my-pet-cat-xzg/resolve/main/sample_images/cat_(4).jpg) ![1](https://huggingface.co/sanjeevkashayp/my-pet-cat-xzg/resolve/main/sample_images/cat_(1).jpg) ![2](https://huggingface.co/sanjeevkashayp/my-pet-cat-xzg/resolve/main/sample_images/cat_(2).jpg) ![3](https://huggingface.co/sanjeevkashayp/my-pet-cat-xzg/resolve/main/sample_images/cat_(5).jpg) ![4](https://huggingface.co/sanjeevkashayp/my-pet-cat-xzg/resolve/main/sample_images/cat_(3).jpg)
ferrazzipietro/Mistral-7B-Instruct-v0.2_adapters_en.layer1_8_torch.bfloat16_64_32_0.01_2_0.0008	ferrazzipietro	2024-02-21T13:07:55Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-21T13:07:09Z	--- 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. 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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SudiptoPramanik/Mistral_Gen_DataFormatting	SudiptoPramanik	2024-02-21T13:05:24Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-21T13:05:21Z	--- 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. 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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]
lambdavi/legal-luke-base-ner	lambdavi	2024-02-21T13:03:49Z	9	1	transformers	[ "transformers", "safetensors", "luke", "token-classification", "generated_from_trainer", "base_model:studio-ousia/luke-base", "base_model:finetune:studio-ousia/luke-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-02-21T11:49:26Z	--- license: apache-2.0 base_model: studio-ousia/luke-base tags: - generated_from_trainer model-index: - name: legal-luke-base-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. --> # legal-luke-base-ner This model is a fine-tuned version of [studio-ousia/luke-base](https://huggingface.co/studio-ousia/luke-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0153 - F1-type-match: 0.9297 - F1-partial: 0.9197 - F1-strict: 0.8794 - F1-exact: 0.8891 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.06 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1-type-match \| F1-partial \| F1-strict \| F1-exact \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------------:\|:----------:\|:---------:\|:--------:\| \| 0.021 \| 1.0 \| 1375 \| 0.0219 \| 0.8297 \| 0.8176 \| 0.7238 \| 0.7525 \| \| 0.0132 \| 2.0 \| 2750 \| 0.0156 \| 0.8841 \| 0.8722 \| 0.7943 \| 0.8166 \| \| 0.0087 \| 3.0 \| 4125 \| 0.0155 \| 0.8901 \| 0.8796 \| 0.8271 \| 0.8374 \| \| 0.0052 \| 4.0 \| 5500 \| 0.0153 \| 0.9190 \| 0.9100 \| 0.8633 \| 0.8750 \| \| 0.0035 \| 5.0 \| 6875 \| 0.0153 \| 0.9297 \| 0.9197 \| 0.8794 \| 0.8891 \| ### Framework versions - Transformers 4.36.0 - Pytorch 2.0.0 - Datasets 2.17.1 - Tokenizers 0.15.0
eno-conan/bert-base-japanese-v3-wrime-sentiment	eno-conan	2024-02-21T12:55:30Z	0	0	peft	[ "peft", "safetensors", "bert", "arxiv:1910.09700", "base_model:tohoku-nlp/bert-base-japanese-v3", "base_model:adapter:tohoku-nlp/bert-base-japanese-v3", "region:us" ]	null	2024-02-20T12:05:15Z	--- library_name: peft base_model: cl-tohoku/bert-base-japanese-v3 --- # 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] - 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 Data 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 Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.8.2
gayanin/bart-with-asr-noise-del-0.1	gayanin	2024-02-21T12:50:02Z	5	0	transformers	[ "transformers", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "base_model:facebook/bart-base", "base_model:finetune:facebook/bart-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-21T12:43:33Z	--- license: apache-2.0 base_model: facebook/bart-base tags: - generated_from_trainer model-index: - name: bart-with-asr-noise-del-0.1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-with-asr-noise-del-0.1 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3546 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.4582 \| 0.62 \| 500 \| 0.3819 \| \| 0.3364 \| 1.24 \| 1000 \| 0.3736 \| \| 0.2773 \| 1.86 \| 1500 \| 0.3411 \| \| 0.1604 \| 2.48 \| 2000 \| 0.3546 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.2+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
gayanin/bart-with-asr-noise-sub-0.1	gayanin	2024-02-21T12:49:50Z	7	0	transformers	[ "transformers", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "base_model:facebook/bart-base", "base_model:finetune:facebook/bart-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-21T12:43:32Z	--- license: apache-2.0 base_model: facebook/bart-base tags: - generated_from_trainer model-index: - name: bart-with-asr-noise-sub-0.1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-with-asr-noise-sub-0.1 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0552 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.1943 \| 0.62 \| 500 \| 0.1056 \| \| 0.0813 \| 1.24 \| 1000 \| 0.0795 \| \| 0.0354 \| 1.86 \| 1500 \| 0.0608 \| \| 0.0169 \| 2.48 \| 2000 \| 0.0552 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.2+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
DrishtiSharma/llama-7b-chat-hf-medqa-packing-false-padding-left	DrishtiSharma	2024-02-21T12:49:21Z	1	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:NousResearch/Llama-2-7b-chat-hf", "base_model:adapter:NousResearch/Llama-2-7b-chat-hf", "region:us" ]	null	2024-02-21T12:48:41Z	--- library_name: peft tags: - trl - sft - generated_from_trainer base_model: NousResearch/Llama-2-7b-chat-hf model-index: - name: llama-7b-chat-hf-medqa-packing-false-padding-left results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # llama-7b-chat-hf-medqa-packing-false-padding-left This model is a fine-tuned version of [NousResearch/Llama-2-7b-chat-hf](https://huggingface.co/NousResearch/Llama-2-7b-chat-hf) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5329 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.5902 \| 0.67 \| 1 \| 0.6134 \| \| 0.2125 \| 2.0 \| 3 \| 0.5329 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.39.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.17.2.dev0 - Tokenizers 0.15.2
ferrazzipietro/Mistral-7B-Instruct-v0.2_adapters_en.layer1_8_torch.bfloat16_64_32_0.05_8_0.0002	ferrazzipietro	2024-02-21T12:43:30Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-21T12:42:49Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
gabrielecaddeo/tactile-autoencoder	gabrielecaddeo	2024-02-21T12:42:33Z	0	0	null	[ "region:us" ]	null	2024-02-21T12:29:15Z	# tactile-autoencoder This repo contains the weights of the autoencoder presented in [Collision-aware In-hand 6D Object Pose Estimation using Multiple Vision-based Tactile Sensors](https://github.com/hsp-iit/multi-tactile-6d-estimation)
EchineF/ppo-Huggy	EchineF	2024-02-21T12:41:47Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2024-02-21T12:39:41Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: EchineF/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Gordon119/TAT-openai-whisper-large-v3-special-tag-v1-epoch4-total5epoch	Gordon119	2024-02-21T12:41:32Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-19T15:19:41Z	--- 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. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
askenaz/results1728410761713039188	askenaz	2024-02-21T12:40:55Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-chat-hf", "base_model:adapter:meta-llama/Llama-2-7b-chat-hf", "region:us" ]	null	2024-02-21T12:40:47Z	--- library_name: peft tags: - generated_from_trainer base_model: meta-llama/Llama-2-7b-chat-hf model-index: - name: results1728410761713039188 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. --> # results1728410761713039188 This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 3 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 12 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 10 ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1
nikes64/whisper-medium-uk	nikes64	2024-02-21T12:39:36Z	7	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "uk", "dataset:mozilla-foundation/common_voice_16_1", "base_model:openai/whisper-medium", "base_model:finetune:openai/whisper-medium", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-02-20T22:23:59Z	--- language: - uk license: apache-2.0 base_model: openai/whisper-medium tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_16_1 metrics: - wer model-index: - name: Whisper Small Ukrainian results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 16.1 type: mozilla-foundation/common_voice_16_1 config: uk split: test args: 'config: uk, split: test' metrics: - name: Wer type: wer value: 20.106509860483175 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-medium-uk This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 16.1 dataset. It achieves the following results on the evaluation set: - Loss: 0.3673 - Wer: 20.1065 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-06 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 10000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\| \| 0.1947 \| 0.94 \| 1000 \| 0.2269 \| 22.7263 \| \| 0.1034 \| 1.89 \| 2000 \| 0.2102 \| 20.6058 \| \| 0.0572 \| 2.83 \| 3000 \| 0.2192 \| 20.3908 \| \| 0.0261 \| 3.77 \| 4000 \| 0.2483 \| 21.0204 \| \| 0.0112 \| 4.72 \| 5000 \| 0.2758 \| 21.1480 \| \| 0.0058 \| 5.66 \| 6000 \| 0.3166 \| 20.3270 \| \| 0.0026 \| 6.6 \| 7000 \| 0.3268 \| 20.5877 \| \| 0.0017 \| 7.55 \| 8000 \| 0.3483 \| 20.0455 \| \| 0.0006 \| 8.49 \| 9000 \| 0.3635 \| 20.0996 \| \| 0.0005 \| 9.43 \| 10000 \| 0.3673 \| 20.1065 \| ### Framework versions - Transformers 4.38.0.dev0 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
agnisharmanv/idClassification	agnisharmanv	2024-02-21T12:35:29Z	8	0	transformers	[ "transformers", "tensorboard", "safetensors", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "dataset:imagefolder", "base_model:naver-clova-ix/donut-base", "base_model:finetune:naver-clova-ix/donut-base", "license:mit", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-02-19T12:49:36Z	--- license: mit base_model: naver-clova-ix/donut-base tags: - generated_from_trainer datasets: - imagefolder model-index: - name: idClassification 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. --> # idClassification This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.39.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
dataautogpt3/Miniaturus_PotentiaV1.2	dataautogpt3	2024-02-21T12:35:02Z	20	12	diffusers	[ "diffusers", "text-to-image", "license:gpl-3.0", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-01-23T12:01:21Z	--- license: gpl-3.0 pipeline_tag: text-to-image widget: - text: >- The image features an older man, a long white beard and mustache, He has a stern expression, giving the impression of a wise and experienced individual. The mans beard and mustache are prominent, adding to his distinguished appearance. The close-up shot of the mans face emphasizes his facial features and the intensity of his gaze. output: url: ComfyUI_04996_.png - text: >- The image features an older man, a long white beard and mustache, He has a stern expression, giving the impression of a wise and experienced individual. The mans beard and mustache are prominent, adding to his distinguished appearance. The close-up shot of the mans face emphasizes his facial features and the intensity of his gaze. output: url: ComfyUI_04997_.png - text: >- cinematic film still of Kodak Motion Picture Film: (Sharp Detailed Image) An Oscar winning movie for Best Cinematography a woman in a kimono standing on a subway train in Japan Kodak Motion Picture Film Style, shallow depth of field, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy output: url: ComfyUI_04992_.png - text: >- Super Closeup Portrait, action shot, Profoundly dark whiteish meadow, glass flowers, Stains, space grunge style, Jeanne d\Arc wearing White Olive green used styled Cotton frock, Wielding thin silver sword, Sci-fi vibe, dirty, noisy, Vintage monk style, very detailed, hd output: url: ComfyUI_04969_.png - text: >- Super Closeup Portrait, action shot, Profoundly dark whiteish meadow, glass flowers, Stains, space grunge style, Jeanne d\Arc wearing White Olive green used styled Cotton frock, Wielding thin silver sword, Sci-fi vibe, dirty, noisy, Vintage monk style, very detailed, hd parameters: negative_prompt: > bad quality, bad anatomy, worst quality, low quality, low resolution, extra fingers, blur, blurry, ugly, wrong proportions, watermark, image artifacts, lowres, ugly, jpeg artifacts, deformed, noisy image, output: url: ComfyUI_04954_.png --- <Gallery /> Miniaturus Potentia is my sd 1.5 family of models using my main acheivements discovered and first implemented within SDXL/Proteus. trained on 220k GPTV captioned images and than dpo tuned on 3k hand picked stylistic images. the same methodoligy used for Proteus. ## Settings for Miniaturus_Potentia v1.2 Use these settings for the best results with Miniaturus_Potentia v1.2: CFG Scale: Use a CFG scale of 8.5 Steps: 35 to 70 steps for more detail, 35 steps for faster results. Sampler: DPM++ 3M SDE Scheduler: Karras ## Use it with 🧨 diffusers ```python from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('dataautogpt3/Miniaturus_PotentiaV1.2', torch_dtype=torch.float16).to('cuda') image = pipeline('The image features an older man, a long white beard and mustache, He has a stern expression, giving the impression of a wise and experienced individual. The mans beard and mustache are prominent, adding to his distinguished appearance. The close-up shot of the mans face emphasizes his facial features and the intensity of his gaze.').images[0] ``` please support the work I do through donating to me on: https://www.buymeacoffee.com/DataVoid or following me on https://twitter.com/DataPlusEngine
askenaz/results-2641641906921332418	askenaz	2024-02-21T12:29:14Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-chat-hf", "base_model:adapter:meta-llama/Llama-2-7b-chat-hf", "region:us" ]	null	2024-02-21T12:29:06Z	--- library_name: peft tags: - generated_from_trainer base_model: meta-llama/Llama-2-7b-chat-hf model-index: - name: results-2641641906921332418 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-2641641906921332418 This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 3 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 12 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 10 ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1
ferrazzipietro/Mistral-7B-Instruct-v0.2_adapters_en.layer1_8_torch.bfloat16_64_32_0.05_4_0.0002	ferrazzipietro	2024-02-21T12:27:36Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-21T12:26:51Z	--- 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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mryannugent/finetuning-miscon-detect-BLOOMZ-3b	mryannugent	2024-02-21T12:25:59Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-18T16:08:05Z	--- 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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Ayus077BCT014Bhandari/vartat5-test-for-2k-plus-2	Ayus077BCT014Bhandari	2024-02-21T12:23:54Z	7	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-21T12:20:04Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Ayus077BCT014Bhandari/vartat5-test-for-2k-plus-1	Ayus077BCT014Bhandari	2024-02-21T12:17:08Z	5	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-21T12:13:13Z	--- 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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aissatoubalde/lab	aissatoubalde	2024-02-21T12:14:43Z	4	0	transformers	[ "transformers", "safetensors", "phi", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-08T14:03:24Z	--- 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/Mistral-7B-Instruct-v0.2_adapters_en.layer1_8_torch.bfloat16_64_32_0.05_2_0.0002	ferrazzipietro	2024-02-21T12:11:20Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-21T12:10:34Z	--- 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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AbdulrehmanKryon/llmware-dragon-deci-6b-v0-quantized-4bit	AbdulrehmanKryon	2024-02-21T12:11:01Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-02-21T12:08:59Z	--- 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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smutuvi/w2v-bert-2.0-swahili-colab-CV16.0_5epochs	smutuvi	2024-02-21T12:06:22Z	10	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2-bert", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice_16_0", "base_model:facebook/w2v-bert-2.0", "base_model:finetune:facebook/w2v-bert-2.0", "license:mit", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-02-20T20:25:28Z	--- license: mit tags: - generated_from_trainer datasets: - common_voice_16_0 metrics: - wer base_model: facebook/w2v-bert-2.0 model-index: - name: w2v-bert-2.0-swahili-colab-CV16.0_5epochs results: - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: common_voice_16_0 type: common_voice_16_0 config: sw split: test args: sw metrics: - type: wer value: 0.8218669188312941 name: Wer --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # w2v-bert-2.0-swahili-colab-CV16.0_5epochs This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the common_voice_16_0 dataset. It achieves the following results on the evaluation set: - Loss: inf - Wer: 0.8219 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 2.015 \| 0.16 \| 300 \| inf \| 0.2387 \| \| 0.2497 \| 0.33 \| 600 \| inf \| 0.2413 \| \| 0.2246 \| 0.49 \| 900 \| inf \| 0.2121 \| \| 0.2032 \| 0.66 \| 1200 \| inf \| 0.2097 \| \| 0.1895 \| 0.82 \| 1500 \| inf \| 0.1969 \| \| 0.1897 \| 0.99 \| 1800 \| inf \| 0.2092 \| \| 0.1718 \| 1.15 \| 2100 \| inf \| 0.1895 \| \| 0.1872 \| 1.31 \| 2400 \| inf \| 0.1949 \| \| 0.2056 \| 1.48 \| 2700 \| inf \| 0.1975 \| \| 0.3533 \| 1.64 \| 3000 \| inf \| 0.4304 \| \| 0.5492 \| 1.81 \| 3300 \| inf \| 0.2979 \| \| 1.0312 \| 1.97 \| 3600 \| inf \| 0.5560 \| \| 0.8936 \| 2.14 \| 3900 \| inf \| 0.8217 \| \| 1.0655 \| 2.3 \| 4200 \| inf \| 0.8219 \| \| 1.0856 \| 2.46 \| 4500 \| inf \| 0.8219 \| \| 1.0855 \| 2.63 \| 4800 \| inf \| 0.8219 \| \| 1.0823 \| 2.79 \| 5100 \| inf \| 0.8219 \| \| 1.0847 \| 2.96 \| 5400 \| inf \| 0.8219 \| \| 1.0835 \| 3.12 \| 5700 \| inf \| 0.8219 \| \| 1.0886 \| 3.28 \| 6000 \| inf \| 0.8219 \| \| 1.0801 \| 3.45 \| 6300 \| inf \| 0.8219 \| \| 1.0765 \| 3.61 \| 6600 \| inf \| 0.8219 \| \| 1.0878 \| 3.78 \| 6900 \| inf \| 0.8219 \| \| 1.0884 \| 3.94 \| 7200 \| inf \| 0.8219 \| \| 1.0824 \| 4.11 \| 7500 \| inf \| 0.8219 \| \| 1.0881 \| 4.27 \| 7800 \| inf \| 0.8219 \| \| 1.0884 \| 4.43 \| 8100 \| inf \| 0.8219 \| \| 1.0786 \| 4.6 \| 8400 \| inf \| 0.8219 \| \| 1.0846 \| 4.76 \| 8700 \| inf \| 0.8219 \| \| 1.0861 \| 4.93 \| 9000 \| inf \| 0.8219 \| ### Framework versions - Transformers 4.37.1 - Pytorch 2.1.1+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0
Safeen/Model	Safeen	2024-02-21T12:04:26Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-02-21T12:04:26Z	--- license: creativeml-openrail-m ---
0x7o/RussianVibe-XL-v2.0	0x7o	2024-02-21T12:01:21Z	43	0	diffusers	[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "dataset:0x7o/RussianVibe-data", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-02-21T07:24:00Z	--- license: creativeml-openrail-m library_name: diffusers tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true base_model: stabilityai/stable-diffusion-xl-base-1.0 datasets: - 0x7o/RussianVibe-data --- # RussianVibe XL v2.0 These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were fine-tuned on the 0x7o/RussianVibe-data dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Intended uses & limitations #### How to use ```python from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler import torch pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16) pipe.load_lora_weights("0x7o/RussianVibe-XL-v2.0") pipe.to("cuda") pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) prompt = "The sun is setting through a window, casting a warm glow on the cityscape beyond. The sun casts a warm orange glow on the buildings in the distance, creating a beautiful and serene atmosphere." image = pipe(prompt, num_inference_steps=30, guidance_scale=5.0, negative_prompt="bad quality, painting, art").images[0] image.save("output.png") ```
Fynd/gptq_cyclops_chat_model	Fynd	2024-02-21T11:58:49Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "gptq", "region:us" ]	text-generation	2024-02-21T11:56:25Z	--- 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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Ayus077BCT014Bhandari/vartat5-test-for-2k	Ayus077BCT014Bhandari	2024-02-21T11:57:34Z	6	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-21T11:52:45Z	--- 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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ferrazzipietro/Mistral-7B-Instruct-v0.2_adapters_en.layer1_8_torch.bfloat16_32_32_0.01_8_0.0002	ferrazzipietro	2024-02-21T11:55:27Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-21T11:55:05Z	--- 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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sallywww/codeLLaMA_oneStep_fuzzTargets	sallywww	2024-02-21T11:51:30Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "arxiv:1910.09700", "base_model:codellama/CodeLlama-7b-hf", "base_model:adapter:codellama/CodeLlama-7b-hf", "region:us" ]	null	2024-02-21T11:48:10Z	--- library_name: peft base_model: codellama/CodeLlama-7b-hf --- # 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/Mistral-7B-Instruct-v0.2_adapters_en.layer1_8_torch.bfloat16_32_32_0.01_4_0.0008	ferrazzipietro	2024-02-21T11:47:55Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-21T11:47:34Z	--- 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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rAIfle/Sloppy-Wingman-8x7B-hf	rAIfle	2024-02-21T11:47:52Z	10	1	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "mergekit", "merge", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-21T09:11:23Z	--- base_model: [] tags: - mergekit - merge --- # Sloppy-Wingman-8x7B-hf ![Sloppy Wingman](https://files.catbox.moe/7ay3me.png) Big slop, good model. Running better at slightly higher temp (1.1-ish) than usual, along with 0.05 MinP and 0.28 snoot. Bog-standard ChatML works best imo, but Alpaca and Mixtral formats work (to some degree) too. Parts: ```yaml models: - model: mistralai/Mixtral-8x7B-v0.1+retrieval-bar/Mixtral-8x7B-v0.1_case-briefs parameters: weight: 0.33 - model: mistralai/Mixtral-8x7B-v0.1+wandb/Mixtral-8x7b-Remixtral parameters: weight: 0.33 merge_method: task_arithmetic base_model: mistralai/Mixtral-8x7B-v0.1 dtype: float16 ``` and ```yaml models: - model: mistralai/Mixtral-8x7B-Instruct-v0.1+/ai/LLM/tmp/pefts/daybreak-peft/mixtral-8x7b parameters: weight: 0.85 - model: notstoic/Nous-Hermes-2-Mixtruct-v0.1-8x7B-DPO-DARE_TIES parameters: weight: 0.25 - model: ycros/BagelWorldTour-8x7B parameters: weight: 0.1 merge_method: task_arithmetic base_model: mistralai/Mixtral-8x7B-Instruct-v0.1 dtype: float16 ``` SLERP:ed together as per below. --- This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * ./02-friend2-instruct * ./01-friend2-base ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: ./01-friend2-base - model: ./02-friend2-instruct merge_method: slerp base_model: ./01-friend2-base parameters: t: - value: 0.5 dtype: float16 ```
Uiji/my_awesome_opus_books_model	Uiji	2024-02-21T11:46:47Z	8	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	2024-02-21T10:59:13Z	--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer metrics: - bleu model-index: - name: my_awesome_opus_books_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_opus_books_model 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: 2.8113 - Bleu: 4.0012 - Gen Len: 16.4328 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:-------:\| \| 3.1624 \| 1.0 \| 2574 \| 2.8554 \| 3.7133 \| 16.4393 \| \| 3.087 \| 2.0 \| 5148 \| 2.8113 \| 4.0012 \| 16.4328 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
CatBarks/GPT2ES_ClassWeighted2_2bce_tokenizer	CatBarks	2024-02-21T11:45:46Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-21T11:45:45Z	--- 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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CatBarks/GPT2ES_ClassWeighted2_2bce_model	CatBarks	2024-02-21T11:45:44Z	5	0	transformers	[ "transformers", "safetensors", "gpt2", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-02-21T11:44:43Z	--- 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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Re0x10/polarity-detection-for-ChatGPT-sentiment	Re0x10	2024-02-21T11:44:13Z	5	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "autotrain", "dataset:autotrain-as4wo-k82ez/autotrain-data", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-21T11:44:02Z	--- tags: - autotrain - text-classification widget: - text: "I love AutoTrain" datasets: - autotrain-as4wo-k82ez/autotrain-data --- # Model Trained Using AutoTrain - Problem type: Text Classification ## Validation Metrics loss: 0.06356040388345718 f1: 0.9819203407494677 precision: 0.977799837559787 recall: 0.9860757189661449 auc: 0.998944462787882 accuracy: 0.9876148497640924
chaouch/ppo-Huggy	chaouch	2024-02-21T11:41:20Z	2	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2024-02-21T11:41:13Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: chaouch/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
ferrazzipietro/Mistral-7B-Instruct-v0.2_adapters_en.layer1_8_torch.bfloat16_32_32_0.01_4_0.0002	ferrazzipietro	2024-02-21T11:40:13Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-21T11:39:51Z	--- 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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aquaflame/PPO-LunarLander-v2	aquaflame	2024-02-21T11:39:12Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-02-21T11:38:53Z	--- 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: 277.94 +/- 14.15 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 ... ```
Nisha1212/teddy-bear-abc	Nisha1212	2024-02-21T11:37:56Z	0	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	2024-02-21T11:33:52Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### Teddy-Bear-ABC Dreambooth model trained by Nisha1212 following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: 21/CSE/069(D) Sample pictures of this concept: ![0](https://huggingface.co/Nisha1212/teddy-bear-abc/resolve/main/sample_images/abc(4).jpg) ![1](https://huggingface.co/Nisha1212/teddy-bear-abc/resolve/main/sample_images/abc(2).jpg) ![2](https://huggingface.co/Nisha1212/teddy-bear-abc/resolve/main/sample_images/abc(5).jpg) ![3](https://huggingface.co/Nisha1212/teddy-bear-abc/resolve/main/sample_images/abc(3).jpg) ![4](https://huggingface.co/Nisha1212/teddy-bear-abc/resolve/main/sample_images/abc(1).jpg)
linoyts/linoy_lora_unet	linoyts	2024-02-21T11:34:34Z	5	1	diffusers	[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-02-21T11:06:41Z	--- tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora widget: - text: 'a TOK woman with pink hair at a party' output: url: "image_0.png" - text: 'a TOK woman with pink hair at a party' output: url: "image_1.png" - text: 'a TOK woman with pink hair at a party' output: url: "image_2.png" - text: 'a TOK woman with pink hair at a party' output: url: "image_3.png" base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: photo of a TOK woman license: openrail++ --- # SDXL LoRA DreamBooth - linoyts/linoy_lora_unet <Gallery /> ## Model description ### These are linoyts/linoy_lora_unet LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. ## Download model ### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke - LoRA: download [`linoy_lora_unet.safetensors` here 💾](/linoyts/linoy_lora_unet/blob/main/linoy_lora_unet.safetensors). - Place it on your `models/Lora` folder. - On AUTOMATIC1111, load the LoRA by adding `<lora:linoy_lora_unet:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/). ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('linoyts/linoy_lora_unet', weight_name='pytorch_lora_weights.safetensors') image = pipeline('a TOK woman with pink hair at a party').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Trigger words You should use photo of a TOK woman to trigger the image generation. ## Details All [Files & versions](/linoyts/linoy_lora_unet/tree/main). The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py). LoRA for the text encoder was enabled. False. Pivotal tuning was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
StephanAkkerman/FinTwitBERT-sentiment	StephanAkkerman	2024-02-21T11:33:22Z	33,099	12	transformers	[ "transformers", "safetensors", "bert", "text-classification", "NLP", "BERT", "FinBERT", "FinTwitBERT", "sentiment", "finance", "financial-analysis", "sentiment-analysis", "financial-sentiment-analysis", "twitter", "tweets", "tweet-analysis", "stocks", "stock-market", "crypto", "cryptocurrency", "en", "dataset:TimKoornstra/financial-tweets-sentiment", "dataset:TimKoornstra/synthetic-financial-tweets-sentiment", "base_model:StephanAkkerman/FinTwitBERT", "base_model:finetune:StephanAkkerman/FinTwitBERT", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-12-13T16:34:16Z	--- license: mit datasets: - TimKoornstra/financial-tweets-sentiment - TimKoornstra/synthetic-financial-tweets-sentiment language: - en metrics: - accuracy - f1 pipeline_tag: text-classification tags: - NLP - BERT - FinBERT - FinTwitBERT - sentiment - finance - financial-analysis - sentiment-analysis - financial-sentiment-analysis - twitter - tweets - tweet-analysis - stocks - stock-market - crypto - cryptocurrency base_model: StephanAkkerman/FinTwitBERT widget: - text: Nice 9% pre market move for $para, pump my calls Uncle Buffett 🤑 example_title: Bullish Crypto Tweet - text: It is about damn time that my $ARB and $ETH bags pump FFS. 🚀 example_title: Bullish Crypto Tweet 2 - text: $SPY $SPX closed higher 8th consecutive weeks. Last time it closed 9th straight was 20 years ago. example_title: Bullish Stock Tweet - text: $TCBP Lowest float stock in the market. Float just 325k. Don’t sell for pennies, this one will be a monster. Still early example_title: Bullish Stock Tweet 2 - text: Italian companies braced for more political uncertainty example_title: Bearish News #model-index: #- name: FinTwitBERT-sentiment # results: --- # FinTwitBERT-sentiment FinTwitBERT-sentiment is a finetuned model for classifying the sentiment of financial tweets. It uses [FinTwitBERT](https://huggingface.co/StephanAkkerman/FinTwitBERT) as a base model, which has been pre-trained on 10 million financial tweets. This approach ensures that the FinTwitBERT-sentiment has seen enough financial tweets, which have an informal nature, compared to other financial texts, such as news headlines. Therefore this model performs great on informal financial texts, seen on social media. ## Intended Uses FinTwitBERT-sentiment is intended for classifying financial tweets or other financial social media texts. ## Dataset FinTwitBERT-sentiment has been trained on two datasets. One being a collection of several financial tweet datasets and the other a synthetic dataset created out of the first. - [TimKoornstra/financial-tweets-sentiment](https://huggingface.co/datasets/TimKoornstra/financial-tweets-sentiment): 38,091 human-labeled tweets - [TimKoornstra/synthetic-financial-tweets-sentiment](https://huggingface.co/datasets/TimKoornstra/synthetic-financial-tweets-sentiment): 1,428,771 synethtic tweets ## More Information For a comprehensive overview, including the training setup and analysis of the model, visit the [FinTwitBERT GitHub repository](https://github.com/TimKoornstra/FinTwitBERT). ## Usage Using [HuggingFace's transformers library](https://huggingface.co/docs/transformers/index) the model and tokenizers can be converted into a pipeline for text classification. ```python from transformers import pipeline # Create a sentiment analysis pipeline pipe = pipeline( "sentiment-analysis", model="StephanAkkerman/FinTwitBERT-sentiment", ) # Get the predicted sentiment print(pipe("Nice 9% pre market move for $para, pump my calls Uncle Buffett 🤑")) ``` ## Citing & Authors If you use FinTwitBERT or FinTwitBERT-sentiment in your research, please cite us as follows, noting that both authors contributed equally to this work: ``` @misc{FinTwitBERT, author = {Stephan Akkerman, Tim Koornstra}, title = {FinTwitBERT: A Specialized Language Model for Financial Tweets}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/TimKoornstra/FinTwitBERT}} } ``` Additionally, if you utilize the sentiment classifier, please cite: ``` @misc{FinTwitBERT-sentiment, author = {Stephan Akkerman, Tim Koornstra}, title = {FinTwitBERT-sentiment: A Sentiment Classifier for Financial Tweets}, year = {2023}, publisher = {Hugging Face}, howpublished = {\url{https://huggingface.co/StephanAkkerman/FinTwitBERT-sentiment}} } ``` ## License This project is licensed under the MIT License. See the [LICENSE](https://choosealicense.com/licenses/mit/) file for details.
KevinJadiya/dpdp	KevinJadiya	2024-02-21T11:32:46Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-02-21T11:29:31Z	--- library_name: transformers tags: - trl - sft --- # 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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manikeerthi/reim-embedding-model	manikeerthi	2024-02-21T11:30:42Z	5	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-02-21T11:29:15Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 99 with parameters: ``` {'batch_size': 10, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 2, "evaluation_steps": 50, "evaluator": "sentence_transformers.evaluation.InformationRetrievalEvaluator.InformationRetrievalEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 19, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
NOVA-vision-language/PlanLLM	NOVA-vision-language	2024-02-21T11:30:01Z	10	2	transformers	[ "transformers", "safetensors", "llama", "text-generation", "en", "arxiv:2402.01053", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-02-01T20:42:14Z	--- license: apache-2.0 inference: false language: - en library_name: transformers --- # PlanLLM <img src="https://i.imgur.com/nHuVNAn.png" alt="drawing" style="width:300px;"/> ## Model Details PlanLLM is a conversational assistant trained to assist users in completing a recipe from beginning to end and be able to answer any related or relevant requests that the user might have. The model was also tested with DIY Tasks and performed similarly. ### Training PlanLLM was trained by fine-tuning a [Vicuna](https://huggingface.co/lmsys/vicuna-7b-v1.1) model on synthetic dialogue between users and an assistant about a given recipe. The model was first trained using SFT and then using Direct Preference Optimization (DPO). #### Details SFT: - Train Type: Fully Sharded Data Parallel (FSDP) with 4 A100 40GB GPUs - Batch Size: 1 - Gradient Acc. Steps: 64 - Train steps: 600 DPO: - Train Type: Low-Rank Adaptation (LoRA) with 1 A100 40GB GPU - LoRA Rank: 64 - LoRA Alpha: 16 - Batch Size: 1 - Gradient Acc. Steps: 64 - Train steps: 350 ### Dataset PlanLLM was trained on synthetic user-system dialogues where the role of the system is to aid the user in completing a predetermined task. For our case, we used recipes. These dialogues were generated using the user utterances collected from Alexa users who interacted with TWIZ, our entry in the Alexa Prize Taskbot Challenge 1. Using an intent classifier we mapped each user utterance to a specific intent allowing us to collect intent-specific utterances and a dialogue graph of each dialogue (with intents being the graph nodes). For the system responses, we used a combination of templates, external knowledge sources, and Large Language Models. Using this we built a pipeline that would navigate a dialogue graph generating user requests and system responses for each turn, creating complete dialogues that follow a similar dialogue pattern used by real users. #### Details SFT: - Dialogues: 10k (90/5/5 splits) - Recipes: 1000 DPO: - Dialogues: 3k (90/5/5 splits) - Recipes: 1000 (same recipes used for SFT) ### License It's the same as Vicuna. A non-commercial Apache 2.0 license. ### Paper ["Plan-Grounded Large Language Models for Dual Goal Conversational Settings" (Accepted at EACL 2024) Diogo Glória-Silva, Rafael Ferreira, Diogo Tavares, David Semedo, João Magalhães](https://arxiv.org/abs/2402.01053) #### Cite Us! ``` @InProceedings{planllm_eacl24, author="Glória-Silva, Diogo and Ferreira, Rafael and Tavares, Diogo and Semedo, David and Magalhães, João", title="Plan-Grounded Large Language Models for Dual Goal Conversational Settings", booktitle="European Chapter of the Association for Computational Linguistics (EACL 2024)", year="2024", } ```
ferrazzipietro/Mistral-7B-Instruct-v0.2_adapters_en.layer1_8_torch.bfloat16_32_32_0.01_2_0.0002	ferrazzipietro	2024-02-21T11:24:40Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-21T11:24:15Z	--- 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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ba8im/phi-2-bash-v3	ba8im	2024-02-21T11:19:04Z	6	0	mlx	[ "mlx", "safetensors", "phi", "nlp", "code", "text-generation", "custom_code", "en", "license:mit", "region:us" ]	text-generation	2024-02-21T11:15:29Z	--- language: - en license: mit tags: - nlp - code - mlx license_link: https://huggingface.co/microsoft/phi-2/resolve/main/LICENSE pipeline_tag: text-generation --- # ba8im/phi-2-bash-v3 This model was converted to MLX format from [`microsoft/phi-2`](). Refer to the [original model card](https://huggingface.co/microsoft/phi-2) for more details on the model. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("ba8im/phi-2-bash-v3") response = generate(model, tokenizer, prompt="hello", verbose=True) ```
Leul78/sftwork3	Leul78	2024-02-21T11:12:18Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-21T11:08:14Z	--- 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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ferrazzipietro/Mistral-7B-Instruct-v0.2_adapters_en.layer1_8_torch.bfloat16_32_32_0.05_8_0.0002	ferrazzipietro	2024-02-21T11:09:06Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-21T11:08:44Z	--- 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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vishnu027/cm1132_type4_re_m2	vishnu027	2024-02-21T11:08:56Z	5	0	transformers	[ "transformers", "safetensors", "vision-encoder-decoder", "image-text-to-text", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-02-21T11:01:58Z	--- 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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(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]
DrishtiSharma/llama-7b-chat-hf-medqa-packing-true-padding-left	DrishtiSharma	2024-02-21T11:08:04Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:NousResearch/Llama-2-7b-chat-hf", "base_model:adapter:NousResearch/Llama-2-7b-chat-hf", "region:us" ]	null	2024-02-21T11:07:45Z	--- library_name: peft tags: - trl - sft - generated_from_trainer base_model: NousResearch/Llama-2-7b-chat-hf model-index: - name: llama-7b-chat-hf-medqa-packing-true-padding-left results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # llama-7b-chat-hf-medqa-packing-true-padding-left This model is a fine-tuned version of [NousResearch/Llama-2-7b-chat-hf](https://huggingface.co/NousResearch/Llama-2-7b-chat-hf) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.39.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.17.2.dev0 - Tokenizers 0.15.2
lunadebruyne/test_trainer	lunadebruyne	2024-02-21T11:07:29Z	15	1	transformers	[ "transformers", "tensorboard", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:j-hartmann/emotion-english-distilroberta-base", "base_model:finetune:j-hartmann/emotion-english-distilroberta-base", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-21T11:07:14Z	--- base_model: j-hartmann/emotion-english-distilroberta-base tags: - generated_from_trainer model-index: - name: test_trainer results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # test_trainer This model is a fine-tuned version of [j-hartmann/emotion-english-distilroberta-base](https://huggingface.co/j-hartmann/emotion-english-distilroberta-base) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Tokenizers 0.15.2
linoyts/linoy_lora_te	linoyts	2024-02-21T11:06:23Z	4	1	diffusers	[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-02-21T10:33:49Z	--- tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora widget: - text: 'a TOK woman with pink hair at a party' output: url: "image_0.png" - text: 'a TOK woman with pink hair at a party' output: url: "image_1.png" - text: 'a TOK woman with pink hair at a party' output: url: "image_2.png" - text: 'a TOK woman with pink hair at a party' output: url: "image_3.png" base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: photo of a TOK woman license: openrail++ --- # SDXL LoRA DreamBooth - linoyts/linoy_lora_te <Gallery /> ## Model description ### These are linoyts/linoy_lora_te LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. ## Download model ### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke - LoRA: download [`linoy_lora_te.safetensors` here 💾](/linoyts/linoy_lora_te/blob/main/linoy_lora_te.safetensors). - Place it on your `models/Lora` folder. - On AUTOMATIC1111, load the LoRA by adding `<lora:linoy_lora_te:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/). ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('linoyts/linoy_lora_te', weight_name='pytorch_lora_weights.safetensors') image = pipeline('a TOK woman with pink hair at a party').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Trigger words You should use photo of a TOK woman to trigger the image generation. ## Details All [Files & versions](/linoyts/linoy_lora_te/tree/main). The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py). LoRA for the text encoder was enabled. True. Pivotal tuning was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
cactusgame/donut-docvqa-demo	cactusgame	2024-02-21T11:04:50Z	6	0	transformers	[ "transformers", "safetensors", "vision-encoder-decoder", "image-text-to-text", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-02-21T10:37:48Z	--- 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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ferrazzipietro/Mistral-7B-Instruct-v0.2_adapters_en.layer1_8_torch.bfloat16_32_32_0.05_4_0.0008	ferrazzipietro	2024-02-21T11:01:37Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-21T11:01:14Z	--- 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. 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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ThuyNT03/SOMD-xlm-stage2-pre-v1	ThuyNT03	2024-02-21T10:50:18Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-21T09:37:33Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer model-index: - name: SOMD-xlm-stage2-pre-v1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # SOMD-xlm-stage2-pre-v1 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0149 ## 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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 202 \| 0.8929 \| \| No log \| 1.99 \| 404 \| 0.5770 \| \| 0.9912 \| 2.99 \| 606 \| 0.3821 \| \| 0.9912 \| 3.98 \| 808 \| 0.2613 \| \| 0.377 \| 4.98 \| 1010 \| 0.1956 \| \| 0.377 \| 5.97 \| 1212 \| 0.1237 \| \| 0.377 \| 6.97 \| 1414 \| 0.1022 \| \| 0.1873 \| 7.96 \| 1616 \| 0.0802 \| \| 0.1873 \| 8.96 \| 1818 \| 0.0625 \| \| 0.1002 \| 9.95 \| 2020 \| 0.0627 \| \| 0.1002 \| 10.95 \| 2222 \| 0.0325 \| \| 0.1002 \| 11.94 \| 2424 \| 0.0302 \| \| 0.0608 \| 12.94 \| 2626 \| 0.0392 \| \| 0.0608 \| 13.93 \| 2828 \| 0.0244 \| \| 0.0385 \| 14.93 \| 3030 \| 0.0237 \| \| 0.0385 \| 15.92 \| 3232 \| 0.0204 \| \| 0.0385 \| 16.92 \| 3434 \| 0.0179 \| \| 0.0279 \| 17.91 \| 3636 \| 0.0154 \| \| 0.0279 \| 18.91 \| 3838 \| 0.0165 \| \| 0.0232 \| 19.9 \| 4040 \| 0.0149 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
hongce-tech/openhermes-mistral-dpo-gptq	hongce-tech	2024-02-21T10:48:48Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "dpo", "generated_from_trainer", "base_model:TheBloke/OpenHermes-2-Mistral-7B-GPTQ", "base_model:adapter:TheBloke/OpenHermes-2-Mistral-7B-GPTQ", "license:apache-2.0", "region:us" ]	null	2024-01-30T14:37:44Z	--- license: apache-2.0 library_name: peft tags: - trl - dpo - generated_from_trainer base_model: TheBloke/OpenHermes-2-Mistral-7B-GPTQ model-index: - name: openhermes-mistral-dpo-gptq 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. --> # openhermes-mistral-dpo-gptq This model is a fine-tuned version of [TheBloke/OpenHermes-2-Mistral-7B-GPTQ](https://huggingface.co/TheBloke/OpenHermes-2-Mistral-7B-GPTQ) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4346 - Rewards/chosen: 0.6886 - Rewards/rejected: -0.1517 - Rewards/accuracies: 0.875 - Rewards/margins: 0.8403 - Logps/rejected: -258.0681 - Logps/chosen: -269.4644 - Logits/rejected: -2.3873 - Logits/chosen: -2.4450 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2 - training_steps: 100 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.6927 \| 0.02 \| 5 \| 0.6723 \| -0.0624 \| -0.1130 \| 0.5 \| 0.0506 \| -257.6814 \| -276.9746 \| -2.3921 \| -2.4532 \| \| 0.6896 \| 0.04 \| 10 \| 0.6814 \| -0.0837 \| -0.1949 \| 0.5625 \| 0.1113 \| -258.5006 \| -277.1875 \| -2.3785 \| -2.4393 \| \| 0.7286 \| 0.06 \| 15 \| 0.7217 \| -0.1116 \| -0.2049 \| 0.8125 \| 0.0933 \| -258.6005 \| -277.4668 \| -2.3732 \| -2.4343 \| \| 0.6049 \| 0.08 \| 20 \| 0.6488 \| -0.5231 \| -0.7234 \| 0.9375 \| 0.2003 \| -263.7849 \| -281.5815 \| -2.3599 \| -2.4201 \| \| 3.1019 \| 0.1 \| 25 \| 0.6202 \| -0.7269 \| -1.0069 \| 0.9375 \| 0.2800 \| -266.6205 \| -283.6199 \| -2.3529 \| -2.4132 \| \| 3.4522 \| 0.12 \| 30 \| 0.6238 \| -0.8793 \| -1.2160 \| 0.875 \| 0.3367 \| -268.7114 \| -285.1440 \| -2.3418 \| -2.4001 \| \| 1.7538 \| 0.14 \| 35 \| 0.6336 \| -0.5977 \| -0.8794 \| 0.875 \| 0.2816 \| -265.3451 \| -282.3282 \| -2.3479 \| -2.4068 \| \| 0.6167 \| 0.16 \| 40 \| 0.6979 \| 0.0308 \| -0.1700 \| 0.8125 \| 0.2008 \| -258.2513 \| -276.0429 \| -2.3591 \| -2.4196 \| \| 1.5103 \| 0.18 \| 45 \| 0.7053 \| 0.0521 \| -0.1713 \| 0.875 \| 0.2233 \| -258.2638 \| -275.8300 \| -2.3607 \| -2.4207 \| \| 0.6762 \| 0.2 \| 50 \| 0.7144 \| 0.1606 \| -0.1470 \| 0.875 \| 0.3076 \| -258.0209 \| -274.7448 \| -2.3658 \| -2.4243 \| \| 0.6587 \| 0.22 \| 55 \| 0.7123 \| 0.1399 \| -0.2934 \| 0.8125 \| 0.4333 \| -259.4854 \| -274.9521 \| -2.3670 \| -2.4244 \| \| 0.7563 \| 0.24 \| 60 \| 0.7987 \| 0.4547 \| 0.0155 \| 0.8125 \| 0.4391 \| -256.3959 \| -271.8042 \| -2.3793 \| -2.4378 \| \| 0.8208 \| 0.26 \| 65 \| 0.8288 \| 1.0234 \| 0.5622 \| 0.8125 \| 0.4611 \| -250.9289 \| -266.1172 \| -2.4012 \| -2.4618 \| \| 0.9904 \| 0.28 \| 70 \| 0.7683 \| 1.4763 \| 0.9615 \| 0.8125 \| 0.5148 \| -246.9362 \| -261.5881 \| -2.4184 \| -2.4798 \| \| 0.8327 \| 0.3 \| 75 \| 0.6556 \| 1.6107 \| 1.0087 \| 0.8125 \| 0.6019 \| -246.4639 \| -260.2441 \| -2.4218 \| -2.4838 \| \| 0.8238 \| 0.32 \| 80 \| 0.5524 \| 1.5571 \| 0.8762 \| 0.8125 \| 0.6809 \| -247.7892 \| -260.7801 \| -2.4168 \| -2.4797 \| \| 0.7712 \| 0.34 \| 85 \| 0.5144 \| 1.3444 \| 0.6352 \| 0.8125 \| 0.7092 \| -250.1996 \| -262.9072 \| -2.4079 \| -2.4697 \| \| 0.691 \| 0.36 \| 90 \| 0.4688 \| 1.0225 \| 0.2544 \| 0.875 \| 0.7682 \| -254.0075 \| -266.1254 \| -2.3981 \| -2.4588 \| \| 0.6386 \| 0.38 \| 95 \| 0.4490 \| 0.8498 \| 0.0425 \| 0.875 \| 0.8074 \| -256.1265 \| -267.8524 \| -2.3927 \| -2.4521 \| \| 0.6413 \| 0.4 \| 100 \| 0.4346 \| 0.6886 \| -0.1517 \| 0.875 \| 0.8403 \| -258.0681 \| -269.4644 \| -2.3873 \| -2.4450 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.0.1+cu117 - Datasets 2.17.1 - Tokenizers 0.15.2
jaysoni/stable-diff-2-1-try-1	jaysoni	2024-02-21T10:46:56Z	21	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-02-21T10:43:28Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### stable_diff_2.1_try_1 Dreambooth model trained by jaysoni with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept: ![0](https://huggingface.co/jaysoni/stable-diff-2-1-try-1/resolve/main/sample_images/3image-608_(1).jpg) ![1](https://huggingface.co/jaysoni/stable-diff-2-1-try-1/resolve/main/sample_images/3image-609_(1).jpg) ![2](https://huggingface.co/jaysoni/stable-diff-2-1-try-1/resolve/main/sample_images/3image-606_(1).jpg)
ferrazzipietro/Mistral-7B-Instruct-v0.2_adapters_en.layer1_8_torch.bfloat16_32_32_0.05_2_0.0008	ferrazzipietro	2024-02-21T10:46:09Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-21T10:45:48Z	--- 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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ryusangwon/3239_Llama-2-7b-hf	ryusangwon	2024-02-21T10:37:31Z	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	2024-02-21T10:37:27Z	--- base_model: meta-llama/Llama-2-7b-hf tags: - generated_from_trainer datasets: - samsum model-index: - name: 3239_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. --> # 3239_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
linoyts/linoy_lora_pivotal	linoyts	2024-02-21T10:33:32Z	31	1	diffusers	[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-02-20T15:19:56Z	--- tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora widget: - text: 'a <s0><s1> woman with pink hair at a party' output: url: "image_0.png" - text: 'a <s0><s1> woman with pink hair at a party' output: url: "image_1.png" - text: 'a <s0><s1> woman with pink hair at a party' output: url: "image_2.png" - text: 'a <s0><s1> woman with pink hair at a party' output: url: "image_3.png" base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: photo of a <s0><s1> woman license: openrail++ --- # SDXL LoRA DreamBooth - linoyts/linoy_lora_pivotal <Gallery /> ## Model description ### These are linoyts/linoy_lora_pivotal LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. ## Download model ### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke - LoRA: download [`linoy_lora_pivotal.safetensors` here 💾](/linoyts/linoy_lora_pivotal/blob/main/linoy_lora_pivotal.safetensors). - Place it on your `models/Lora` folder. - On AUTOMATIC1111, load the LoRA by adding `<lora:linoy_lora_pivotal:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/). - Embeddings: download [`linoy_lora_pivotal_emb.safetensors` here 💾](/linoyts/linoy_lora_pivotal/blob/main/linoy_lora_pivotal_emb.safetensors). - Place it on it on your `embeddings` folder - Use it by adding `linoy_lora_pivotal_emb` to your prompt. For example, `photo of a linoy_lora_pivotal_emb woman` (you need both the LoRA and the embeddings as they were trained together for this LoRA) ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch from huggingface_hub import hf_hub_download from safetensors.torch import load_file pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('linoyts/linoy_lora_pivotal', weight_name='pytorch_lora_weights.safetensors') embedding_path = hf_hub_download(repo_id='linoyts/linoy_lora_pivotal', filename='linoy_lora_pivotal_emb.safetensors', repo_type="model") state_dict = load_file(embedding_path) pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer) pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2) image = pipeline('a <s0><s1> woman with pink hair at a party').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Trigger words To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens: to trigger concept `TOK` → use `<s0><s1>` in your prompt ## Details All [Files & versions](/linoyts/linoy_lora_pivotal/tree/main). The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py). LoRA for the text encoder was enabled. False. Pivotal tuning was enabled: True. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.

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