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modelId string	author string	last_modified timestamp[us, tz=UTC]	downloads int64	likes int64	library_name string	tags sequence	pipeline_tag string	createdAt timestamp[us, tz=UTC]	card string
fzzhang/mistral_4bit	fzzhang	2024-02-24T06:52:23Z	76	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-02-24T06:50:21Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
amphion/fastspeech2_ljspeech	amphion	2024-02-24T06:47:22Z	0	0	null	[ "license:mit", "region:us" ]	null	2024-02-23T14:10:43Z	--- license: mit --- # Pretrained Model of Amphion FastSpeech 2 We provide the pre-trained checkpoint of [FastSpeech 2](https://github.com/open-mmlab/Amphion/tree/main/egs/tts/FastSpeech2) trained on LJSpeech, which consists of 13,100 short audio clips of a single speaker and has a total length of approximately 24 hours. ## Quick Start To utilize the pre-trained models, just run the following commands: ### Step1: Download the checkpoint ```bash git lfs install git clone https://huggingface.co/amphion/fastspeech2_ljspeech ``` ### Step2: Clone the Amphion's Source Code of GitHub ```bash git clone https://github.com/open-mmlab/Amphion.git ``` ### Step3: Specify the checkpoint's path Use the soft link to specify the downloaded checkpoint in the first step: ```bash cd Amphion mkdir -p ckpts/tts ln -s ../../../fastspeech2_ljspeech ckpts/tts/ ``` ### Step4: Inference You can follow the inference part of [this recipe](https://github.com/open-mmlab/Amphion/tree/main/egs/tts/FastSpeech2#4-inference) to generate speech from text. For example, if you want to synthesize a clip of speech with the text of "This is a clip of generated speech with the given text from a TTS model.", just, run: ```bash sh egs/tts/FastSpeech2/run.sh --stage 3 \ --config ckpts/tts/fastspeech2_ljspeech/args.json \ --infer_expt_dir ckpts/tts/fastspeech2_ljspeech/ \ --infer_output_dir ckpts/tts/fastspeech2_ljspeech/results \ --infer_mode "single" \ --infer_text "This is a clip of generated speech with the given text from a TTS model." \ --vocoder_dir ckpts/vocoder/hifigan_ljspeech/checkpoints/ \ ``` *\Noted**: Inference FastSpeech 2 requires a vocoder to reconstruct waveform from Mel spectrogram. The pre-trained Amphion HiFi-GAN Vocoder that matches this Amphion FastSpeech 2 can be download [here](https://huggingface.co/amphion/hifigan_ljspeech).
amphion/hifigan_ljspeech	amphion	2024-02-24T06:46:43Z	0	0	null	[ "license:mit", "region:us" ]	null	2024-02-23T13:46:38Z	--- license: mit --- # Pretrained Model of Amphion HiFi-GAN We provide the pre-trained checkpoint of [HiFi-GAN](https://github.com/open-mmlab/Amphion/tree/main/egs/tts/FastSpeech2) trained on LJSpeech, which consists of 13,100 short audio clips of a single speaker and has a total length of approximately 24 hours. ## Quick Start To utilize the pre-trained models, just run the following commands: ### Step1: Download the checkpoint ```bash git lfs install git clone https://huggingface.co/amphion/hifigan_ljspeech ``` ### Step2: Clone the Amphion's Source Code of GitHub ```bash git clone https://github.com/open-mmlab/Amphion.git ``` ### Step3: Specify the checkpoint's path Use the soft link to specify the downloaded checkpoint in the first step: ```bash cd Amphion mkdir -p ckpts/tts ln -s ../../../hifigan_ljspeech ckpts/tts/ ``` ### Step4: Inference This HiFi-GAN Vocoder is pre-trained to support [Amphion FastSpeech 2](https://github.com/open-mmlab/Amphion/tree/main/egs/tts/FastSpeech2) to generate speech waveform from Mel spectrogram. You can follow the inference part of [this recipe](https://github.com/open-mmlab/Amphion/tree/main/egs/tts/FastSpeech2#4-inference) to generate speech. For example, if you want to synthesize a clip of speech with the text of "This is a clip of generated speech with the given text from a TTS model.", just, run: ```bash sh egs/tts/FastSpeech2/run.sh --stage 3 \ --config ckpts/tts/fastspeech2_ljspeech/args.json \ --infer_expt_dir ckpts/tts/fastspeech2_ljspeech/ \ --infer_output_dir ckpts/tts/fastspeech2_ljspeech/results \ --infer_mode "single" \ --infer_text "This is a clip of generated speech with the given text from a TTS model." \ --vocoder_dir ckpts/vocoder/hifigan_ljspeech/checkpoints/ \ ``` *Noted: Pre-trained Amphion FastSpeech 2 can be download [here](https://huggingface.co/amphion/fastspeech2_ljspeech).
ChuckMcSneed/Xwin-LM-70B-V0.1-LORA	ChuckMcSneed	2024-02-24T06:43:15Z	2	1	peft	[ "peft", "safetensors", "lora", "text-generation", "base_model:meta-llama/Llama-2-70b-hf", "base_model:adapter:meta-llama/Llama-2-70b-hf", "region:us" ]	text-generation	2024-02-21T13:29:04Z	--- library_name: peft base_model: meta-llama/Llama-2-70b-hf pipeline_tag: text-generation tags: - lora --- Low-rank decomposition of [Xwin-LM/Xwin-LM-70B-V0.1](https://huggingface.co/Xwin-LM/Xwin-LM-70B-V0.1) using [meta-llama/Llama-2-70b-hf](https://huggingface.co/meta-llama/Llama-2-70b-hf) as base. Created using my fork(script included in files) of [LoRD](https://github.com/thomasgauthier/LoRD).
johnnyluhk/dqn-SpaceInvadersNoFrameskip-v4d	johnnyluhk	2024-02-24T06:37:31Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-02-24T06:37:09Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 550.00 +/- 119.21 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga johnnyluhk -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga johnnyluhk -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga johnnyluhk ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
srikrishnateja/vit-cctv	srikrishnateja	2024-02-24T06:31:35Z	65	0	transformers	[ "transformers", "tf", "tensorboard", "vit", "image-classification", "generated_from_keras_callback", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-02-24T06:12:21Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_keras_callback model-index: - name: srikrishnateja/vit-base-patch16-224-in21k-euroSat results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # srikrishnateja/vit-base-patch16-224-in21k-euroSat This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0403 - Train Accuracy: 0.9952 - Train Top-3-accuracy: 1.0 - Validation Loss: 0.1351 - Validation Accuracy: 0.9645 - Validation Top-3-accuracy: 1.0 - Epoch: 4 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'module': 'transformers.optimization_tf', 'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 425, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.8999999761581421, 'beta_2': 0.9990000128746033, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}, 'registered_name': 'AdamWeightDecay'}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 ### Training results \| Train Loss \| Train Accuracy \| Train Top-3-accuracy \| Validation Loss \| Validation Accuracy \| Validation Top-3-accuracy \| Epoch \| \|:----------:\|:--------------:\|:--------------------:\|:---------------:\|:-------------------:\|:-------------------------:\|:-----:\| \| 0.4326 \| 0.8143 \| 1.0 \| 0.2613 \| 0.9102 \| 1.0 \| 0 \| \| 0.1770 \| 0.9413 \| 1.0 \| 0.1919 \| 0.9332 \| 1.0 \| 1 \| \| 0.0943 \| 0.9760 \| 1.0 \| 0.1654 \| 0.9436 \| 1.0 \| 2 \| \| 0.0576 \| 0.9863 \| 1.0 \| 0.1457 \| 0.9520 \| 1.0 \| 3 \| \| 0.0403 \| 0.9952 \| 1.0 \| 0.1351 \| 0.9645 \| 1.0 \| 4 \| ### Framework versions - Transformers 4.38.1 - TensorFlow 2.15.0 - Datasets 2.17.1 - Tokenizers 0.15.1
Dangurangu/distilbert-base-uncased-finetuned-masakhaneNews	Dangurangu	2024-02-24T06:21:34Z	104	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "fill-mask", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-02-24T05:51:44Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-finetuned-masakhaneNews results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-masakhaneNews This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0001 ## 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: 3.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.0315 \| 1.0 \| 122 \| 0.0002 \| \| 0.0003 \| 2.0 \| 244 \| 0.0001 \| \| 0.0002 \| 3.0 \| 366 \| 0.0001 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
aryachakraborty/GEMMA-2B-NL-SQL	aryachakraborty	2024-02-24T06:11:26Z	108	3	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "code", "text2text", "translate", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-24T05:46:40Z	--- library_name: transformers tags: - code - text2text - translate --- # Model Card for Model ID This Model has been fine tuned on our own curated dataset which is used for generating SQL Queries from natural language. ## Model Details Model is the 2 billion parameter Open Source GEMMA model recently released by GOOGLE. ### 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: [Arya Chakraborty] - Model type: [Text to SQL query Generation] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [GEMMA]: [GEMMA-NL-SQL] ### 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]
gstaff/Mistral-7B-Instruct-v0.1-AWQ	gstaff	2024-02-24T06:09:14Z	0	0	null	[ "region:us" ]	null	2024-02-24T06:00:04Z	# Pre-quantized Weights for Mistral-7B-Instruct This hosts the files from https://catalog.ngc.nvidia.com/orgs/nvidia/models/mistral-7b-int4-chat/files?version=1.1 for easy programmatic access.
GeethikaMeghawarna/my-pet-dog	GeethikaMeghawarna	2024-02-24T06:08:05Z	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	2024-02-24T06:03:24Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### My-Pet-Dog Dreambooth model trained by GeethikaMeghawarna following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: GoX19932gAS Sample pictures of this concept: ![0](https://huggingface.co/GeethikaMeghawarna/my-pet-dog/resolve/main/sample_images/xzg_(1).jpg) ![1](https://huggingface.co/GeethikaMeghawarna/my-pet-dog/resolve/main/sample_images/xzg_(3).jpg) ![2](https://huggingface.co/GeethikaMeghawarna/my-pet-dog/resolve/main/sample_images/xzg_(2).jpg)
christopherlee/christopherlee	christopherlee	2024-02-24T06:02:39Z	0	0	null	[ "arxiv:1910.09700", "region:us" ]	null	2024-02-24T06:02:23Z	--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
glenn2/gemma-7b-lora-distilabel-intel-orca-dpo-pairs	glenn2	2024-02-24T05:50:44Z	1	0	peft	[ "peft", "safetensors", "trl", "dpo", "generated_from_trainer", "base_model:google/gemma-2b", "base_model:adapter:google/gemma-2b", "license:mit", "region:us" ]	null	2024-02-24T05:40:22Z	--- license: mit library_name: peft tags: - trl - dpo - generated_from_trainer base_model: google/gemma-2b model-index: - name: gemma-7b-lora-distilabel-intel-orca-dpo-pairs 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. --> # gemma-7b-lora-distilabel-intel-orca-dpo-pairs This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4641 - Rewards/chosen: -0.2842 - Rewards/rejected: -2.0677 - Rewards/accuracies: 0.8414 - Rewards/margins: 1.7835 - Logps/rejected: -294.6812 - Logps/chosen: -246.1420 - Logits/rejected: -29.7875 - Logits/chosen: -27.6122 ## 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: 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 - lr_scheduler_warmup_steps: 250 - num_epochs: 1 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.6333 \| 0.19 \| 250 \| 0.5221 \| -0.4649 \| -1.1235 \| 0.8196 \| 0.6586 \| -285.2397 \| -247.9492 \| -29.5102 \| -27.3832 \| \| 0.4697 \| 0.39 \| 500 \| 0.4819 \| -0.5572 \| -2.0261 \| 0.8394 \| 1.4689 \| -294.2652 \| -248.8721 \| -29.5979 \| -27.4182 \| \| 0.4471 \| 0.58 \| 750 \| 0.4814 \| -0.5104 \| -2.3183 \| 0.8418 \| 1.8079 \| -297.1878 \| -248.4040 \| -29.6888 \| -27.5182 \| \| 0.4477 \| 0.78 \| 1000 \| 0.4744 \| -0.3874 \| -2.2429 \| 0.8418 \| 1.8555 \| -296.4334 \| -247.1736 \| -29.7387 \| -27.5680 \| \| 0.458 \| 0.97 \| 1250 \| 0.4641 \| -0.2842 \| -2.0677 \| 0.8414 \| 1.7835 \| -294.6812 \| -246.1420 \| -29.7875 \| -27.6122 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.38.0 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2
chu-chu/ppo-LunarLander-v2	chu-chu	2024-02-24T05:43:25Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-02-24T05:43:08Z	--- 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: 294.20 +/- 14.84 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 ... ```
hj279559/bert-finetuned-squad	hj279559	2024-02-24T05:17:13Z	105	0	transformers	[ "transformers", "safetensors", "bert", "question-answering", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2024-02-23T07:08:47Z	--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer model-index: - name: bert-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.38.1 - Pytorch 2.3.0.dev20240222 - Datasets 2.17.1 - Tokenizers 0.15.0
sugatoray/mlx-mistral-7b-v0.1-q4bits	sugatoray	2024-02-24T05:14:35Z	5	0	mlx	[ "mlx", "safetensors", "mistral", "pretrained", "text-generation", "en", "license:apache-2.0", "region:us" ]	text-generation	2024-02-24T05:06:09Z	--- language: - en license: apache-2.0 tags: - pretrained - mlx pipeline_tag: text-generation inference: parameters: temperature: 0.7 --- # sugatoray/mlx-mistral-7b-v0.1-q4bits This model was converted to MLX format from [`mistralai/Mistral-7B-v0.1`](). Refer to the [original model card](https://huggingface.co/mistralai/Mistral-7B-v0.1) for more details on the model. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("sugatoray/mlx-mistral-7b-v0.1-q4bits") response = generate(model, tokenizer, prompt="hello", verbose=True) ```
mofengsi/bert-finetuned-squad	mofengsi	2024-02-24T04:58:17Z	104	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "question-answering", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2024-02-24T02:45:00Z	--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer model-index: - name: bert-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
Chattiori/PeakiFurry	Chattiori	2024-02-24T04:40:24Z	0	1	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-01-29T02:41:27Z	--- license: creativeml-openrail-m ---
liuylhf/mistral-pretty-json	liuylhf	2024-02-24T04:21:47Z	0	0	peft	[ "peft", "mistral", "generated_from_trainer", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2024-02-24T03:58:50Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: mistralai/Mistral-7B-Instruct-v0.2 model-index: - name: text-generation-webui/loras/mistral-instruct-better-formatting-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. --> [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.0` ```yaml base_model: mistralai/Mistral-7B-Instruct-v0.2 model_type: AutoModelForCausalLM tokenizer_type: LlamaTokenizer is_mistral_derived_model: true load_in_8bit: false load_in_4bit: true strict: false datasets: - path: ./data/tool_used_training.jsonl type: sharegpt - path: ./data/tool_not_used_training.jsonl type: sharegpt - path: ./data/no_tools_training.jsonl type: sharegpt dataset_prepared_path: last_run_prepared val_set_size: 0.01 output_dir: ../../text-generation-webui/loras/mistral-instruct-better-formatting-v1 adapter: qlora lora_model_dir: sequence_len: 4096 sample_packing: true pad_to_sequence_len: true lora_r: 32 lora_alpha: 16 lora_dropout: 0.05 lora_target_linear: true lora_fan_in_fan_out: # lora_target_modules: # - gate_proj # - down_proj # - up_proj # - q_proj # - v_proj # - k_proj # - o_proj wandb_project: function-call wandb_name: mixtral-instruct-qlora-v1 wandb_log_model: end gradient_accumulation_steps: 4 micro_batch_size: 2 num_epochs: 1 optimizer: paged_adamw_8bit lr_scheduler: cosine learning_rate: 0.001 adam_beta2: 0.95 adam_epsilon: 0.00001 max_grad_norm: 1.0 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: false gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true # loss_watchdog_threshold: 5.0 # loss_watchdog_patience: 3 warmup_steps: 10 # evals_per_epoch: 20 eval_steps: 0.1 save_steps: 0.1 eval_table_size: eval_max_new_tokens: 256 # saves_per_epoch: 1 debug: deepspeed: weight_decay: 0.0 fsdp: fsdp_config: ``` </details><br> # text-generation-webui/loras/mistral-instruct-better-formatting-v1 This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3566 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - total_eval_batch_size: 4 - optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.9252 \| 0.0 \| 1 \| 0.9944 \| \| 0.3953 \| 0.1 \| 27 \| 0.3981 \| \| 0.3662 \| 0.21 \| 54 \| 0.3824 \| \| 0.3383 \| 0.31 \| 81 \| 0.3778 \| \| 0.3484 \| 0.41 \| 108 \| 0.3730 \| \| 0.4098 \| 0.52 \| 135 \| 0.3686 \| \| 0.3728 \| 0.62 \| 162 \| 0.3642 \| \| 0.3274 \| 0.72 \| 189 \| 0.3602 \| \| 0.3579 \| 0.83 \| 216 \| 0.3576 \| \| 0.3293 \| 0.93 \| 243 \| 0.3566 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.38.0.dev0 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.0
kyryl-georgian/flan-base-sql	kyryl-georgian	2024-02-24T04:17:17Z	1	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:google/flan-t5-small", "base_model:adapter:google/flan-t5-small", "license:apache-2.0", "region:us" ]	null	2024-02-23T20:45:52Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: google/flan-t5-small model-index: - name: flan-base-sql results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # flan-base-sql This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0264 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 0.0001 ### Training results ### Framework versions - PEFT 0.7.1 - Transformers 4.38.0 - Pytorch 2.1.2+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2
fzzhang/roberta_gsm8k_merged	fzzhang	2024-02-24T04:12:41Z	48	0	transformers	[ "transformers", "safetensors", "roberta", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-02-24T04:12:20Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
spookybo/videomae-base-finetuned-workoutfitness-subset	spookybo	2024-02-24T03:57:32Z	13	0	transformers	[ "transformers", "tensorboard", "safetensors", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-base", "base_model:finetune:MCG-NJU/videomae-base", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	video-classification	2024-02-20T02:38:30Z	--- license: cc-by-nc-4.0 tags: - generated_from_trainer base_model: MCG-NJU/videomae-base model-index: - name: videomae-base-finetuned-workoutfitness-subset results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # videomae-base-finetuned-workoutfitness-subset This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 1950 ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0 - Datasets 2.17.1 - Tokenizers 0.15.2
johnnyluhk/q-FrozenLake-v1-4x4-noSlippery	johnnyluhk	2024-02-24T03:55:50Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-02-24T03:55:47Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage model = load_from_hub(repo_id="johnnyluhk/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"])
Ayus077BCT014Bhandari/vartat5-using-100K-plus-13	Ayus077BCT014Bhandari	2024-02-24T03:42:06Z	105	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-24T01:52:36Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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NoamFelix/OneBit	NoamFelix	2024-02-24T03:33:12Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-02-24T03:33:12Z	--- license: creativeml-openrail-m ---
saucam/gemma-7b-4.0bpw-exl2	saucam	2024-02-24T03:28:59Z	4	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-24T02:54:48Z	--- 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" license: other license_name: gemma-terms-of-use license_link: https://ai.google.dev/gemma/terms --- # Gemma-7b-4.0bpw-exl2 Model Card This is 4-bit quantized exl2 version of the base [Google Gemma-7b](https://huggingface.co/google/gemma-7b) model. Using [turboderp's ExLlamaV2 0.0.13.post2](https://github.com/turboderp/exllamav2/releases/tag/0.0.13.post2) for quantization. ### Usage #### Download the model Either clone the repo directly ``` git clone https://huggingface.co/saucam/gemma-7b-4.0bpw-exl2 ``` or use hf cli ``` pip3 install huggingface-hub mkdir gemma-7b-4.0bpw-exl2 huggingface-cli download saucam/gemma-7b-4.0bpw-exl2 --local-dir gemma-7b-4.0bpw-exl2 ``` #### Running the model on a CPU Clone exllamav2 repo for the inference script ``` git clone https://github.com/turboderp/exllamav2.git cd exllamav2 ``` Run the inference script and point to the model dir downloaded in the above step ``` python test_inference.py -m /path/to/gemma-7b-4.0bpw-exl2 -p "I have a dream" ``` Sample run output: ``` python test_inference.py -m /path/to/gemma-7b-4.0bpw-exl2 -p "I have a dream" -- Model: /path/to/gemma-7b-4.0bpw-exl2 -- Options: [] -- Loading model... -- Loaded model in 2.0420 seconds -- Loading tokenizer... -- Warmup... -- Generating... I have a dream of becoming an astronaut and I am going to work hard to achieve my goal. I want you all the best in your life, do what makes u happy! Be strong!! Don't give up on your dreams because they are yours for a reason :) This is my last year at this school...it feels weird but good...because it means that I will be graduating next year!!! It has been a great experience and journey at PS 128....and we still got one last summer together!!!!! woohoo!!!! We had our first class trip to The Metropolitan Museum Of Art today...It was so much fun -- Response generated in 0.96 seconds, 128 tokens, 132.99 tokens/second (includes prompt eval.) ``` As you can see it is quite fast
guilhermebastos96/speecht5_finetuned_sip_ai_v3	guilhermebastos96	2024-02-24T03:19:26Z	77	0	transformers	[ "transformers", "tensorboard", "safetensors", "speecht5", "text-to-audio", "generated_from_trainer", "dataset:multilingual_librispeech", "base_model:guilhermebastos96/speecht5_finetuned_sip_ai_v2", "base_model:finetune:guilhermebastos96/speecht5_finetuned_sip_ai_v2", "license:mit", "endpoints_compatible", "region:us" ]	text-to-audio	2024-02-23T18:46:18Z	--- license: mit base_model: guilhermebastos96/speecht5_finetuned_sip_ai_v2 tags: - generated_from_trainer datasets: - multilingual_librispeech model-index: - name: speecht5_finetuned_sip_ai_v3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # speecht5_finetuned_sip_ai_v3 This model is a fine-tuned version of [guilhermebastos96/speecht5_finetuned_sip_ai_v2](https://huggingface.co/guilhermebastos96/speecht5_finetuned_sip_ai_v2) on the multilingual_librispeech dataset. It achieves the following results on the evaluation set: - Loss: 0.3439 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 10000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 0.3996 \| 1.68 \| 1000 \| 0.3638 \| \| 0.3885 \| 3.35 \| 2000 \| 0.3554 \| \| 0.3785 \| 5.03 \| 3000 \| 0.3508 \| \| 0.3764 \| 6.71 \| 4000 \| 0.3488 \| \| 0.3732 \| 8.38 \| 5000 \| 0.3485 \| \| 0.3806 \| 10.06 \| 6000 \| 0.3460 \| \| 0.3739 \| 11.74 \| 7000 \| 0.3454 \| \| 0.3703 \| 13.41 \| 8000 \| 0.3458 \| \| 0.3732 \| 15.09 \| 9000 \| 0.3445 \| \| 0.3695 \| 16.76 \| 10000 \| 0.3439 \| ### Framework versions - Transformers 4.39.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
ibivibiv/supersciphi-13b-32k	ibivibiv	2024-02-24T03:13:27Z	7	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-24T03:04:20Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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cwtmyd/bert-finetuned-ner	cwtmyd	2024-02-24T03:03:45Z	108	0	transformers	[ "transformers", "safetensors", "bert", "token-classification", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-02-24T02:53:46Z	--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0620 - Precision: 0.9323 - Recall: 0.9505 - F1: 0.9413 - Accuracy: 0.9864 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.0763 \| 1.0 \| 1756 \| 0.0662 \| 0.9028 \| 0.9367 \| 0.9195 \| 0.9808 \| \| 0.0365 \| 2.0 \| 3512 \| 0.0629 \| 0.9312 \| 0.9480 \| 0.9395 \| 0.9854 \| \| 0.0233 \| 3.0 \| 5268 \| 0.0620 \| 0.9323 \| 0.9505 \| 0.9413 \| 0.9864 \| ### Framework versions - Transformers 4.36.2 - Pytorch 2.0.1 - Datasets 2.16.1 - Tokenizers 0.15.0
aws-neuron/Mistral-neuron	aws-neuron	2024-02-24T02:59:44Z	13	0	transformers	[ "transformers", "pytorch", "mistral", "text-generation", "inferentia2", "neuron", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-01-03T15:06:03Z	--- license: apache-2.0 language: - en pipeline_tag: text-generation inference: false tags: - mistral - pytorch - inferentia2 - neuron --- # Please read ## *This repository was based on the transformer implementation of Mistral before Optimum-neuron included support.* *Consider using an Optimum based repository such as [this](https://huggingface.co/aws-neuron/Mistral-7B-Instruct-v0.1-neuron-1x2048-2-cores).* *For running or compiling Mistral-7B-Instruct-v0.2 see [aws-neuron/Mistral-7B-Instruct-v0.2-seqlen-2048-bs-1-cores-2](https://huggingface.co/aws-neuron/Mistral-7B-Instruct-v0.2-seqlen-2048-bs-1-cores-2)* This is especially important if you are changing any paramters that require a recompile because Optimum-neuron will let you take advantage of the compilation cache. # Neuronx model for Mistral This repository contains [AWS Inferentia2](https://aws.amazon.com/ec2/instance-types/inf2/) and [`neuronx`](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/) compatible checkpoints for [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1). However, this file includes an example of how to compile various versions of Mistral. Support isn’t available yet (as of 1/3/2024) in the optimum-neuron framework, so we use the base transformers library. These instructions closely follow the [Developer Guide](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/libraries/transformers-neuronx/transformers-neuronx-developer-guide.html#grouped-query-attention-gqa-support-beta). Look there for more detailed explanations, especially for the GQA settings. This model has been compiled to run on an inf2.xlarge (the smallest Inferentia2 instance). You can run it on a bigger instance, but it will only use two cores no matter how many are available, unless you change the core number available in compilation. Remember that each Neuron processor has two cores. ## Set up the environment First, use the [DLAMI image from Hugging Face](https://aws.amazon.com/marketplace/pp/prodview-gr3e6yiscria2). It has most of the utilities and drivers preinstalled. However, you will need to update transformers-neruonx from the source to get Mistral support. ``` python -m pip install git+https://github.com/aws-neuron/transformers-neuronx.git ``` ## Running inference from this repository If you want to run a quick test or if the exact model you want to use is [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1), you can run it directly using the steps below. Otherwise, jump to the Compilation of other Mistral versions section. First, you will need a local copy of the library. This is because one of the nice things that the Hugging Face optimum library does is abstract local loads from repository loads. However, Mistral inference isn't supported yet. From python: ``` # using python instead of git clone because I know this supports lfs on the DLAMI image from huggingface_hub import Repository repo = Repository(local_dir="Mistral-neuron", clone_from="aws-neuron/Mistral-neuron") ``` This should put a local copy in Mistral-neuron. This process should take a 5-10 minutes. If it completes in a few seconds the first time you run it, you are having problems with git-lfs. You can see this by using ls -al to check the size of the files downloaded. You will also notice it later when you get parsing errors. Next, load the model and neff files from disk into the Neuron processors: ``` import torch from transformers_neuronx import constants from transformers_neuronx.mistral.model import MistralForSampling from transformers_neuronx.module import save_pretrained_split from transformers_neuronx.config import NeuronConfig from transformers import AutoModelForCausalLM, AutoTokenizer # Set sharding strategy for GQA to be shard over heads neuron_config = NeuronConfig( grouped_query_attention=constants.GQA.SHARD_OVER_HEADS ) # define the model. These are the settings used in compilation. # If you want to change these settings, skip to "Compilation of other Mistral versions" model_neuron = MistralForSampling.from_pretrained("Mistral-neuron", batch_size=1, tp_degree=2, n_positions=256, amp='bf16', neuron_config=neuron_config) # load the neff files from the local directory instead of compiling model_neuron.load("Mistral-neuron") # load the neff files into the neuron processors. # you can see this process happening if you run neuron-top from the command line in another console. # if you didn't do the previous load command, this will also compile the neff files model_neuron.to_neuron() ``` ## Inference example This points to the original model for the tokenizer because the tokenizer is the same. If you are compiling your own and want to have a single reference for everything, you can copy the special_tokens_map.json and tokenizer* from the original model to your local copy. ``` # Get a tokenizer and example input. Note that this points to the original model tokenizer = AutoTokenizer.from_pretrained('mistralai/Mistral-7B-Instruct-v0.1') text = "[INST] What is your favourite condiment? [/INST]" encoded_input = tokenizer(text, return_tensors='pt') # Run inference with torch.inference_mode(): generated_sequence = model_neuron.sample(encoded_input.input_ids, sequence_length=256, start_ids=None) print([tokenizer.decode(tok) for tok in generated_sequence]) ``` Example output: (most of the time with amp=‘bf16’, the answer is ketchup. However, if I compiled with amp=f32, the answer was soy sauce. This was for a sample size of one, so let me know what you see —@jburtoft) ``` 2024-Jan-03 15:59:21.0510 1486:2057 [0] nccl_net_ofi_init:1415 CCOM WARN NET/OFI aws-ofi-nccl initialization failed 2024-Jan-03 15:59:21.0510 1486:2057 [0] init.cc:138 CCOM WARN OFI plugin initNet() failed is EFA enabled? ['<s> [INST] What is your favourite condiment? [/INST] My favorite condiment is probably ketchup. It adds a perfect balance of sweet, tangy, and slightly spicy flavor to dishes, and is versatile enough to go with a wide variety of foods.</s>'] ``` ## Compilation of other Mistral versions If you want to use a different version of Mistral from Hugging Face, use the slightly modified code below. It essentially removes the “load” command. When the “to_neuron()” command sees that the model object doesn’t include the neff files, it will kick off the recompile. You can save them at the end so you only have to do the compilation process once. After that, you can use the code above to load a model and the neff files from the local directory. ``` import torch from transformers_neuronx import constants from transformers_neuronx.mistral.model import MistralForSampling from transformers_neuronx.module import save_pretrained_split from transformers_neuronx.config import NeuronConfig from transformers import AutoModelForCausalLM, AutoTokenizer # Load and save the CPU model with bfloat16 casting. This also gives us a local copy # change the Hugging Face model name (mistralai/Mistral-7B-Instruct-v0.1) below to what you want # You can update the other model names if you want, but they just reference a directory on the local disk. model_cpu = AutoModelForCausalLM.from_pretrained('mistralai/Mistral-7B-Instruct-v0.1') save_pretrained_split(model_cpu, 'mistralai/Mistral-7B-Instruct-v0.1-split') # Set sharding strategy for GQA to be shard over heads neuron_config = NeuronConfig( grouped_query_attention=constants.GQA.SHARD_OVER_HEADS ) # Create and compile the Neuron model model_neuron = MistralForSampling.from_pretrained('mistralai/Mistral-7B-Instruct-v0.1-split', batch_size=1, \ tp_degree=2, n_positions=256, amp='bf16', neuron_config=neuron_config) model_neuron.to_neuron() #save compiled neff files out to the same directory model_neuron.save("mistralai/Mistral-7B-Instruct-v0.1-split") ``` ## Arguments passed during compilation The settings use in compilation are the same as shown above in the code. If you want to change these, you will need to recompile. If you don’t want to pass them in each time, you could update the config.json file. This is another nice thing the Hugging Face optimum framework does for us. You can see an example of the format by looking at one of the Llama model config.json files. For [example](https://huggingface.co/aws-neuron/Llama-2-7b-hf-neuron-latency/blob/main/config.json). ``` neuron_config = NeuronConfig( grouped_query_attention=constants.GQA.SHARD_OVER_HEADS ) ("Mistral-neuron", batch_size=1, tp_degree=2, n_positions=256, amp='bf16', neuron_config=neuron_config) ```
nicholasbien/gpt2_finetuned-20k-2_3	nicholasbien	2024-02-24T02:59:12Z	160	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-24T02:18:08Z	--- 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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QUIET-DEVELOPER/my-pet-dog	QUIET-DEVELOPER	2024-02-24T02:42:39Z	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	2024-02-24T02:38:38Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### My-Pet-Dog Dreambooth model trained by QUIET-DEVELOPER following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: GoX19932gAS Sample pictures of this concept: ![0](https://huggingface.co/QUIET-DEVELOPER/my-pet-dog/resolve/main/sample_images/Tiger.jpeg)
ibivibiv/megameditron-120b	ibivibiv	2024-02-24T02:35:53Z	8	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-23T22:01:14Z	--- 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. 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gsstein/model-100-percent-human-llama-og	gsstein	2024-02-24T02:27:12Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-23T17:47:44Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description Fine-tuned llama2 with text summaries (outputted by opt-350m) This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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gsstein/model-100-percent-human-opt	gsstein	2024-02-24T02:26:28Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-09T09:57:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description Fine-tuned opt-350m with text summaries. This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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dspasyuk/paulml_OGNO-7B	dspasyuk	2024-02-24T02:24:43Z	0	1	null	[ "gguf", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	null	2024-02-24T01:19:47Z	--- license: cc-by-nc-4.0 --- Creator of the initial model: Paul Lemaistre Initial Model: paulml/OGNO-7B Overview This repository hosts GGUF format model files designed for paulml/OGNO-7B model. GGUF can be used in: Llama.cpp as of December 13th Llama.cui KoboldCpp 1.52 + LM Studio 0.2.9 + llama-cpp-python 0.2.23 + node-llama
nicholasbien/gpt2_finetuned-20k_ppo_a3	nicholasbien	2024-02-24T02:22:25Z	160	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-24T02:05:10Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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UCLA-AGI/SPIN-Diffusion-iter1	UCLA-AGI	2024-02-24T02:19:54Z	0	2	diffusers	[ "diffusers", "safetensors", "text-to-image", "en", "dataset:yuvalkirstain/pickapic_v2", "arxiv:2402.10210", "license:apache-2.0", "region:us" ]	text-to-image	2024-02-23T05:12:42Z	--- license: apache-2.0 datasets: - yuvalkirstain/pickapic_v2 language: - en pipeline_tag: text-to-image --- Self-Play Fine-Tuning of Diffusion Models for Text-to-Image Generation (https://huggingface.co/papers/2402.10210) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/657be24e8d360b690d5b665f/uzhoFO22ZdQ5XjBxxDA1a.png) # SPIN-Diffusion-iter1 This model is a self-play fine-tuned diffusion model at iteration 1 from [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) using synthetic data based on the winner images of the [yuvalkirstain/pickapic_v2](https://huggingface.co/datasets/yuvalkirstain/pickapic_v2) dataset. We have also made a Gradio Demo at [UCLA-AGI/SPIN-Diffusion-demo-v1](https://huggingface.co/spaces/UCLA-AGI/SPIN-Diffusion-demo-v1). ## Model Details ### Model Description - Model type: An diffusion model with unet fine-tuned, based on the strucure of stable diffusion 1.5 - Language(s) (NLP): Primarily English - License: Apache-2.0 - Finetuned from model: runwayml/stable-diffusion-v1-5 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2.0e-05 - train_batch_size: 8 - distributed_type: multi-GPU - num_devices: 8 - train_gradient_accumulation_steps: 32 - total_train_batch_size: 2048 - optimizer: AdamW - lr_scheduler: "linear" - lr_warmup_steps: 200 - num_training_steps: 500 ## Citation ``` @misc{yuan2024self, title={Self-Play Fine-Tuning of Diffusion Models for Text-to-Image Generation}, author={Yuan, Huizhuo and Chen, Zixiang and Ji, Kaixuan and Gu, Quanquan}, year={2024}, eprint={2402.10210}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```
cti-ttp-18/fine-tune-secureBert	cti-ttp-18	2024-02-24T02:17:13Z	0	0	null	[ "tensorboard", "safetensors", "autotrain", "text-generation", "conversational", "license:other", "endpoints_compatible", "region:us" ]	text-generation	2024-02-24T02:17:08Z	--- tags: - autotrain - text-generation widget: - text: "I love AutoTrain because " license: other --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
nicholasbien/gpt2_finetuned-20k-2_2	nicholasbien	2024-02-24T02:15:38Z	160	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-24T01:34:34Z	--- 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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manusehgal/wine-bottle	manusehgal	2024-02-24T02:04:03Z	1	0	diffusers	[ "diffusers", "text-to-image", "autotrain", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]	text-to-image	2024-02-24T02:03:56Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: wine bottle tags: - text-to-image - diffusers - autotrain inference: true --- # DreamBooth trained by AutoTrain Text encoder was not trained.
mb7419/mistral-7b-legal-dpo-merged	mb7419	2024-02-24T01:54:34Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-24T01:48:54Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Dineth1222/Nova_image_generete	Dineth1222	2024-02-24T01:53:21Z	3	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:stabilityai/stable-cascade", "base_model:adapter:stabilityai/stable-cascade", "license:apache-2.0", "region:us" ]	text-to-image	2024-02-24T01:50:25Z	--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: A boy in scary in green land and blue sky he watching his phone parameters: negative_prompt: Cartoon output: url: images/OIG2..jpeg base_model: stabilityai/stable-cascade instance_prompt: Fuck license: apache-2.0 --- # Nova image creator <Gallery /> ## Model description text to image generete ## Trigger words You should use `Fuck` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/Dineth1222/Nova_image_generete/tree/main) them in the Files & versions tab.
ryusangwon/3564_Llama-2-7b-hf	ryusangwon	2024-02-24T01:38:26Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]	null	2024-02-24T01:38:22Z	--- base_model: meta-llama/Llama-2-7b-hf tags: - generated_from_trainer model-index: - name: 3564_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. --> # 3564_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 an unknown 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
naiftamia/whisper-base-Withaq-V1	naiftamia	2024-02-24T01:37:22Z	71	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "hi", "dataset:SADA", "base_model:openai/whisper-base", "base_model:finetune:openai/whisper-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-02-23T12:18:24Z	--- language: - hi license: apache-2.0 base_model: openai/whisper-base tags: - hf-asr-leaderboard - generated_from_trainer datasets: - SADA model-index: - name: whisper-base-Withaq-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. --> # whisper-base-Withaq-V1 This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the SADA - Arabic dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 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: 4000 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.39.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
mb7419/mistral-7b-legal-dpo	mb7419	2024-02-24T01:36:59Z	4	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-24T01:23:56Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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mi-rei/CT_clinical-longformer_I_efficient_10e	mi-rei	2024-02-24T01:03:28Z	118	0	transformers	[ "transformers", "safetensors", "longformer", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-24T01:02:51Z	--- 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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ferrazzipietro/Llama-2-13b-chat-hf_adapters_en.layer1_4_torch.bfloat16_64_32_0.01_8_0.0008	ferrazzipietro	2024-02-24T00:55:28Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-24T00:54:22Z	--- 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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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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LukeOLuck/llama2-7-dolly-query	LukeOLuck	2024-02-24T00:54:46Z	4	0	peft	[ "peft", "tensorboard", "safetensors", "PyTorch", "Transformers", "trl", "sft", "BitsAndBytes", "PEFT", "QLoRA", "en", "dataset:databricks/databricks-dolly-15k", "arxiv:2205.14135", "arxiv:2305.14314", "base_model:meta-llama/Llama-2-7b-chat", "base_model:adapter:meta-llama/Llama-2-7b-chat", "license:mit", "region:us" ]	null	2024-02-23T21:48:00Z	--- library_name: peft tags: - PyTorch - Transformers - trl - sft - BitsAndBytes - PEFT - QLoRA datasets: - databricks/databricks-dolly-15k base_model: meta-llama/Llama-2-7b-chat model-index: - name: llama2-7-dolly-query results: [] license: mit language: - en --- <!-- 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. --> # llama2-7-dolly-query This model is a fine-tuned version of [Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat) on the generator dataset. Can be used in conjunction with [LukeOLuck/llama2-7-dolly-answer](https://huggingface.co/LukeOLuck/llama2-7-dolly-answer) ## Model description A Fine-Tuned PEFT Adapter for the llama2 7b chat hf model Leverages [FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://arxiv.org/abs/2205.14135), [QLoRA: Efficient Finetuning of Quantized LLMs](https://arxiv.org/abs/2305.14314), and [PEFT](https://huggingface.co/blog/peft) ## Intended uses & limitations Generate a query based on context and input ## Training and evaluation data Used SFTTrainer, [checkout the code](https://colab.research.google.com/drive/1sr0mUF8dwYKo6NNR3tkjk0Z-p5FFr1_6?usp=sharing) ## Training procedure [Checkout the code here](https://colab.research.google.com/drive/1sr0mUF8dwYKo6NNR3tkjk0Z-p5FFr1_6?usp=sharing) ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 3 ### Training results ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65388a56a5ab055cf2d73676/FJ5p_wutu8o1z789Hd93g.png) ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
pabsdsr/bert-finetuned-squad	pabsdsr	2024-02-24T00:53:07Z	105	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "question-answering", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2024-02-20T22:47:14Z	--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer model-index: - name: bert-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
ryusangwon/5841_Llama-2-7b-hf	ryusangwon	2024-02-24T00:39:57Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]	null	2024-02-24T00:39:52Z	--- base_model: meta-llama/Llama-2-7b-hf tags: - generated_from_trainer model-index: - name: 5841_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. --> # 5841_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 an unknown 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: 10 - 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
ferrazzipietro/Llama-2-13b-chat-hf_adapters_en.layer1_4_torch.bfloat16_64_32_0.01_8_0.0002	ferrazzipietro	2024-02-24T00:37:00Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-24T00:35:54Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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argmining-vaccines/longformer-base-4096-reason	argmining-vaccines	2024-02-24T00:20:23Z	119	0	transformers	[ "transformers", "safetensors", "longformer", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-02-24T00:20:01Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ferrazzipietro/Llama-2-13b-chat-hf_adapters_en.layer1_4_torch.bfloat16_64_32_0.01_4_0.0008	ferrazzipietro	2024-02-24T00:18:31Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-24T00:17:23Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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kyryl-georgian/flan-small-sql	kyryl-georgian	2024-02-24T00:08:43Z	0	0	null	[ "tensorboard", "safetensors", "generated_from_trainer", "base_model:google/flan-t5-small", "base_model:finetune:google/flan-t5-small", "license:apache-2.0", "region:us" ]	null	2024-02-24T00:04:47Z	--- license: apache-2.0 base_model: google/flan-t5-small tags: - generated_from_trainer model-index: - name: flan-small-sql results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # flan-small-sql This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0431 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 0.001 ### Training results ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
Tizzzzy/LawLLM_Gemma_v1	Tizzzzy	2024-02-23T23:54:13Z	5	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-23T23:49:45Z	--- 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. 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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ademirweb/disney-pixar	ademirweb	2024-02-23T23:37:36Z	54	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-02-22T00:35:48Z	--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: Post de filme de um personagem homem de animação da Disney Pixar, realista output: url: images/2.jpeg - text: Post de filme de um personagem homem de animação da Disney Pixar, realista output: url: images/neymar-600x600.webp - text: Post de filme de um personagem homem de animação da Disney Pixar, realista output: url: images/André-Fernandes-768x961.webp - text: Post de filme de um personagem homem de animação da Disney Pixar, realista output: url: images/24115723412124.webp - text: Post de filme de uma personagem mulher de animação da Disney Pixar, realista output: url: images/1f1f2325-2b48-44ab-b4ca-c4610f8621f1.jpg - text: Post de filme de uma personagem mulher de animação da Disney Pixar, realista output: url: images/8.jpeg - text: Post de filme de um personagem homem de animação da Disney Pixar, realista output: url: >- images/como-fazer-a-foto-disney-pixar-nova-trend-do-insta-como-fazer-a-foto-disney-pixar-nova-trend-do-insta.webp - text: Post de filme de um personagem homem de animação da Disney Pixar, realista output: url: images/ia-1-2-600x600.webp - text: Post de filme de um personagem homem de animação da Disney Pixar, realista output: url: images/ia-6-600x600.webp - text: Post de filme de uma personagem mulher de animação da Disney Pixar, realista output: url: images/billie-eilish-disney-pixar.webp - text: Post de filme de uma personagem mulher de animação da Disney Pixar, realista output: url: images/pocah-disney-pixar.webp - text: Post de filme de uma personagem mulher de animação da Disney Pixar, realista output: url: images/dua-lipa-disney-pixar.webp - text: Post de filme de uma personagem mulher de animação da Disney Pixar, realista output: url: images/d978d9_53456c2484404798a41426a6b7cd5332~mv2.webp - text: Post de filme de uma personagem mulher de animação da Disney Pixar, realista output: url: images/captura-de-tela-2023-10-25-170159.webp - text: Post de filme de uma personagem mulher de animação da Disney Pixar, realista output: url: images/disney-pixar-famosos.webp - text: Post de filme de uma personagem mulher de animação da Disney Pixar, realista output: url: images/unnamed-3.webp - text: Post de filme de uma personagem mulher de animação da Disney Pixar, realista output: url: images/personagem-768x512.jpg - text: Post de filme de uma personagem mulher de animação da Disney Pixar, realista output: url: >- images/internauta-recriou-cantora-Ariana-Grande-como-personagem-da-Disney-Pixar.jpg base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: null --- # disney-pixar <Gallery /> ## Model description Modelo para criação de personagens estilo disney pixar ## Download model Weights for this model are available in Safetensors format. [Download](/ademirweb/disney-pixar-v3/tree/main) them in the Files & versions tab.
mi-rei/CT_clinical-longformer_II_efficient_10e	mi-rei	2024-02-23T23:35:48Z	118	0	transformers	[ "transformers", "safetensors", "longformer", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-23T23:35:01Z	--- 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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nagyadam0616/zephyr-x-twitter-merged	nagyadam0616	2024-02-23T23:30:32Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-23T23:29:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Radiantloom/radiantloom-llama-70b-instruct	Radiantloom	2024-02-23T23:29:06Z	49	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "SFT", "Llama", "Llama 70B Chat", "arxiv:2306.05685", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-23T05:40:51Z	--- library_name: transformers tags: - SFT - Llama - Llama 70B Chat license: llama2 --- <img src="https://huggingface.co/Radiantloom/radiantloom-llama-70b-instruct/resolve/main/radiantloom-llama-70b-instruct.jpeg" alt="Radiantloom Llama 70B Instruct" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> ## Radiantloom Llama 70B Instruct The Radiantloom Llama 70B Instruct, a large language model (LLM) developed by Radiantloom AI, features approximately 70 billion parameters. From vibes-check evaluations, the Radiantloom Llama 70B Instruct demonstrates great performance in various applications like creative writing, multi-turn conversations, in-context learning through Retrieval Augmented Generation (RAG), and coding tasks. Its out-of-the-box performance already delivers impressive results, particularly in writing, coding, and reasoning tasks. This model produces longer form content and provides detailed explanations of its actions. To maximize its potential, consider implementing instruction tuning and Reinforcement Learning with Human Feedback (RLHF) techniques for further refinement. Alternatively, you can utilize it in its current form. ## Intended Uses & Limitations The Radiantloom Llama 70B Instruct is versatile and can be utilized for various text generation tasks such as summarization, chat, coding, question answering, retrieval augmented generation (RAG), role play, and content generation. It can be considered a state-of-the-art generative language model, it demonstrates competitive performance in general tasks when compared to other open and closed-source large language models such as GPT 3.5 Turbo, and Mixtral 8x7B Instruct. ## Model Usage You can deploy it on a Runpod GPU instance using our [one-click Runpod template](https://www.runpod.io/console/gpu-secure-cloud?ref=80eh3891&template=tbkdia6qdv) (Our Referral Link. Please consider Supporting). This template provides you with an OpenAI-compatible API endpoint that you can integrate into your existing codebase designed for OpenAI APIs. ## Prompt Template We have fine-tuned this model using the ChatML format, and you can achieve optimal performance by utilizing the ChatML format. ``` <s>[INST] <<SYS>>{system_message}<</SYS>> {user_message}[/INST] ``` ## Inference Code ```python ## install dependencies ## !pip install -q -U git+https://github.com/huggingface/transformers.git ## !pip install -q -U git+https://github.com/huggingface/accelerate.git ## !pip install -q -U bitsandbytes sentencepiece ## load model import torch from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextStreamer model_name = "Radiantloom/radiantloom-llama-70b-instruct" bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_use_double_quant=True, bnb_4bit_compute_dtype=torch.bfloat16 ) # load the model and tokenizer model = AutoModelForCausalLM.from_pretrained( model_name, quantization_config=bnb_config, device_map="auto", ) tokenizer = AutoTokenizer.from_pretrained( model_name, trust_remote_code=True ) tokenizer.pad_token = tokenizer.eos_token ## inference prompt = """ <s>[INST]<<SYS>>You are a helpful AI assistant developed by Radiantloom AI.<</SYS>>Take a deep breath and think step-by-step. Write a tale about a time-traveling historian who's determined to witness the most significant events in human history.[/INST] """ # tokenize the input string inputs = tokenizer( prompt, return_tensors="pt", return_attention_mask=False ) # generate text using the model streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) outputs = model.generate(**inputs, max_length=2048, streamer=streamer, do_sample=True, repetition_penalty=1.1")) # decode and print the output text = tokenizer.batch_decode(outputs)[0] ``` Here are a few example use cases. if we query the model with ``` <s>[INST]<<SYS>>You are a helpful AI assistant developed by Radiantloom AI.<</SYS>>Take a deep breath and think step-by-step. Write a tale about a time-traveling historian who's determined to witness the most significant events in human history.[/INST] ``` the model will return ``` Story: The Time Chronicles of Professor Amelia Harcourt Professor Amelia Harcourt was an exceptional historian, renowned for her remarkable contributions to the field of historical studies. However, her passion for understanding the past extended far beyond academics. She had always dreamt of witnessing firsthand the events that shaped the world as we know it. When she stumbled upon an ancient manuscript filled with cryptic symbols while working on a research project, she wasn't aware that it would change her life forever. After translating the mysterious text, Amelia discovered that it contained blueprints for constructing an extraordinary device - a time machine that could transport its user across different eras. Fueled by an all-consuming fervor for exploring pivotal moments in history, Amelia spent her remaining savings on procuring the materials necessary to build the intricate apparatus. The process required tireless dedication and rigorous labor, but through a mixture of intelligence, skill, and unrelenting determination, she finally constructed a functioning time machine three years later. With anticipation surging through her veins, Amelia embarked on a transcendent expedition to experience the most significant occurrences in human history. Her very first stop took place more than two million years ago: the East African Rift Valley, where homo habilis, our early ancestors, were believed to have emerged. In this remote corner of prehistory, Amelia studied their behavior closely and marveled at their resourcefulness. Their innovative use of tools and rudimentary social systems helped shape complex human societies of the future, making the encounter a moving lesson on the importance of adaptability. The second destination brought Amelia even further back in time, landing directly in Earth's arctic region during the late Cretaceous period. This journey held both personal ambition and scientific curiosity; Amelia wished to better understand why dinosaurs went extinct. Upon arrival, the historian felt a wave of unease as she navigated through the treacherous environment filled with towering dinosaur species, including the voracious Tyrannosaurus rex. With immense caution, she conducted her analysis and came to the shocking conclusion that asteroid impacts, not volcanic activity, were primarily responsible for wiping out the majority of dinosaur species. As these creatures faced catastrophic conditions, mammals thrived and eventually evolved into humans. Amelia continued her chronological pilgrimage with an extended stay at Stonehenge, one of the most mystifying historical sites she'd ever visited. After learning from several sources during her academic career, she hypothesized that the magnificent monument was built as part of a larger complex dedicated to worshipping gods tied to celestial bodies and fertility. Upon arriving in Neolithic Britain, Amelia watched in awe as people worked together to erect massive bluestones using ropes made from animal hides, wooden rollers, and levers carved from antlers. Witnessing this collaborative effort revealed a newfound appreciation for the technological ingenuity and social organization present in early human civilizations. Throughout subsequent adventures, Amelia became captivated by the enlightening teachings of Socrates in Ancient Greece and traveled through the bustling metropolis of imperial Rome. These excursions showcased the timeless themes of wisdom, governance, justice, art, and culture. They illuminated various aspects of human nature and the inherent values shared across every epoch. Overwhelmed by curiosity and a newfound sense of wonderment, Amelia decided to explore other transformative eras such as the discovery of the New World and the Industrial Revolution. Eventually, she found herself amidst the burgeoning civil rights movement in America during the 1960s, watching in admiration as advocates for racial equality peacefully rallied against systemic oppression and fought for equal rights. As Professor Amelia Harcourt immersed herself in the diverse expanse of human history, her encounters revealed a profound understanding of progress. Each event she observed had contributed something unique and essential to the development, growth, and evolution of mankind. This revelation reminded her of an old saying: "Only those who look backward can truly see forward." Through her time travels, Amelia began recognizing the patterns and interconnections between different eras, gaining immense insight into the human spirit's resilience and capacity for change. Inspired by her once-in-a-lifetime odyssey, Amelia Harcourt set forth creating The Time Chronicles-an extensive body of work featuring detailed accounts from her journeys through time. This collection transformed academic views regarding the past and offered generations to come priceless knowledge and fascinating perspectives vital to understanding our shared human story. ``` and if we query the model with ``` <s>[INST]<<SYS>>You are an AI assistant. Take a deep breath, think step-by-step and respond to following instructions from the user. <</SYS>>The cafeteria had 23 apples. If they used 20 for lunch and bought 6 more, how many apples do they have?[/INST] ``` the model will return ``` Let's break this down: 1. The cafeteria initially had 23 apples. 2. They used 20 for lunch. To find out how many apples were left after lunch, subtract the number of apples used for lunch (20) from the initial number of apples (23). This gives us 23 - 20 = 3 apples left after lunch. 3. Then they purchased 6 more apples. To find out the total number of apples now, add the number of apples left after lunch (3) to the number of apples bought recently (6). So we have 3 + 6 = 9 apples in total. ``` and if we query the model with ``` <s>[INST]<<SYS>>You are an AI assistant. Take a deep breath, think step-by-step and respond to following instructions from the user. <</SYS>>Write a function in Python that checks if a number is prime.[/INST] ``` the model will return ``` The user will provide you with a positive integer and your task is to check whether it can be divided by any other number (divisors) except for one and itself (the number). Let's say the number given by the user is 2147483647. Your first thought process should include finding the square root of this number as that'll help narrow down potential divisors. Here, sqrt(2147483647) = 46340. Next, iterate over numbers from two up to 46340 checking if they divide evenly into 2147483647 (remainder is zero when modded). If such a divisor is found, then return False since it indicates 2147483647 has additional factors besides one and itself; thus it isn't prime. Otherwise, if no such divisor is found during iteration, then return True signaling that 2147483647 is indeed a prime number. But make sure you also consider edge cases like 2, because some programming languages might use zero-based indexing. Here's a possible solution: ``` ```python import math def is_prime(n): if n <= 1: # Edge case: numbers below 2 are not considered primes return False elif n == 2 or n == 3: # Special cases for small primes return True elif n % 2 == 0: # Quick filter for even numbers return False root = int(math.sqrt(n)) for i in range(3, root + 1, 2): # Only odd divisors needed; start at 3 if n % i == 0: return False return True number = 2147483647 result = is_prime(number) print(f"The result for {number} is:", "Prime" if result else "Not Prime") ``` ## Evaluations We are encouraged by the initial assessments conducted using the [LLM-as-a-Judge method](https://arxiv.org/abs/2306.05685), particularly with the utilization of GPT-4. Our ongoing efforts involve a more comprehensive evaluation of the model. We have also entered this model into the HuggingFace Open Leaderboard for assessment. As we progress, we will continue to provide updates on our findings and share the results on this platform. ## Ethical Considerations and Limitations Radiantloom Llama 70B Instruct, a powerful AI language model, can produce factually incorrect output and content not suitable for work (NSFW). It should not be relied upon to provide factually accurate information and should be used with caution. Due to the limitations of its pre-trained model and the finetuning datasets, it may generate lewd, biased, or otherwise offensive content. Consequently, developers should conduct thorough safety testing prior to implementing any applications of this model. ## About Radiantloom AI Radiantloom AI trains open-source large language models tailored for specific business tasks such as copilots, email assistance, customer support, and database operations. Learn more about Radiantloom by visiting our [website](https://radiantloom.com). Follow us on Twitter at [Radiantloom](https://twitter.com/radiantloom) to gain early access to upcoming Radiantloom AI large language models. ## Want to Support Our Work? We are passionate about contributing open-source AI models to the broader AI community to accelerate AI development and deployment. If you find our work valuable and would like to contribute to its continued success, consider buying us a few coffees! Your support goes a long way in helping us cover training costs, fund development tools, and keep the project thriving. Every coffee you buy is a small but meaningful contribution that fuels our dedication to providing a top-notch experience for users like you. [Buy us a coffee](https://www.buymeacoffee.com/aigeek_) and be a part of our journey towards making this project even more awesome. We appreciate your support!
nagyadam0616/zephyr-x-twitter	nagyadam0616	2024-02-23T23:26:36Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-23T23:25:59Z	--- 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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ferrazzipietro/Llama-2-13b-chat-hf_adapters_en.layer1_4_torch.bfloat16_64_32_0.05_8_0.0008	ferrazzipietro	2024-02-23T23:01:14Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-23T23:00:01Z	--- 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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Eric111/Yarn-Mistral-7b-128k-DPO	Eric111	2024-02-23T23:00:58Z	133	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "custom_code", "arxiv:1910.09700", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-23T22:54:08Z	--- library_name: transformers license: apache-2.0 tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details DPO fine-tuned version of NousResearch/Yarn-Mistral-7b-128k with Intel/orca_dpo_pairs ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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ThomasFG/135-0	ThomasFG	2024-02-23T22:41:41Z	78	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-small.en", "base_model:finetune:openai/whisper-small.en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-02-23T20:18:42Z	--- license: apache-2.0 base_model: openai/whisper-small.en tags: - generated_from_trainer metrics: - wer model-index: - name: 2024-02-23_21-18-37 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 2024-02-23_21-18-37 This model is a fine-tuned version of [openai/whisper-small.en](https://huggingface.co/openai/whisper-small.en) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4033 - Wer: 15.3814 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.1725 \| 1.0 \| 516 \| 0.4033 \| 15.3814 \| ### Framework versions - Transformers 4.37.2 - Pytorch 1.13.1+cu116 - Datasets 2.17.0 - Tokenizers 0.15.2
ferrazzipietro/Llama-2-13b-chat-hf_adapters_en.layer1_4_torch.bfloat16_64_32_0.05_4_0.0008	ferrazzipietro	2024-02-23T22:24:25Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-23T22:23:17Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Arczisan/mitsuri-demonslayer	Arczisan	2024-02-23T22:21:46Z	1	0	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	2024-02-23T22:21:40Z	--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: "UNICODE\0\0m\0a\0s\0t\0e\0r\0p\0i\0e\0c\0e\0,\0b\0e\0s\0t\0 \0q\0u\0a\0l\0i\0t\0y\0,\0h\0i\0g\0h\0r\0e\0s\0,\0u\0l\0t\0r\0a\0-\0d\0e\0t\0a\0i\0l\0e\0d\0,\0a\0a\0m\0i\0t\0s\0u\0r\0i\0,\0(\0m\0o\0l\0e\0 \0u\0n\0d\0e\0r\0 \0e\0y\0e\0:\00\0.\08\0)\0,\0m\0u\0l\0t\0i\0c\0o\0l\0o\0r\0e\0d\0 \0h\0a\0i\0r\0,\0l\0o\0n\0g\0 \0h\0a\0i\0r\0,\0t\0w\0i\0n\0 \0b\0r\0a\0i\0d\0s\0,\0b\0a\0n\0g\0s\0,\0c\0l\0e\0a\0v\0a\0g\0e\0,\0d\0e\0m\0o\0n\0 \0s\0l\0a\0y\0e\0r\0 \0u\0n\0i\0f\0o\0r\0m\0,\0h\0a\0o\0r\0i\0,\0(\0w\0h\0i\0t\0e\0 \0c\0a\0p\0e\0:\01\0.\02\0)\0,\0j\0a\0p\0a\0n\0e\0s\0e\0 \0c\0l\0o\0t\0h\0e\0s\0,\0(\0b\0l\0a\0c\0k\0 \0j\0a\0c\0k\0e\0t\0:\01\0.\02\0)\0,\0s\0h\0i\0r\0t\0,\0p\0a\0r\0t\0i\0a\0l\0l\0y\0 \0u\0n\0b\0u\0t\0t\0o\0n\0e\0d\0,\0l\0o\0n\0g\0 \0s\0l\0e\0e\0v\0e\0s\0,\0b\0e\0l\0t\0,\0(\0b\0l\0a\0c\0k\0 \0s\0k\0i\0r\0t\0:\01\0.\02\0)\0,\0t\0h\0i\0g\0h\0h\0i\0g\0h\0s\0,\0(\0g\0r\0e\0e\0n\0 \0t\0h\0i\0g\0h\0h\0i\0g\0h\0s\0:\01\0.\02\0)\0,\0s\0a\0n\0d\0a\0l\0s\0,\0r\0i\0b\0b\0e\0d\0 \0l\0e\0g\0w\0e\0a\0r\0,\0<\0l\0o\0r\0a\0:\0k\0a\0n\0r\0o\0j\0i\0_\0m\0i\0t\0s\0u\0r\0i\0:\00\0.\08\0>\0,\0o\0u\0t\0d\0o\0o\0r\0s\0,\0c\0o\0w\0b\0o\0y\0 \0s\0h\0o\0t\0,\0s\0m\0i\0l\0e\0,\0s\0t\0a\0n\0d\0i\0n\0g\0,\0" output: url: images/00715-3288883984.jpeg base_model: runwayml/stable-diffusion-v1-5 instance_prompt: null --- # Mitsuri - Demon Slayer <Gallery /> ## Download model Weights for this model are available in Safetensors format. [Download](/Arczisan/mitsuri-demonslayer/tree/main) them in the Files & versions tab.
nisso22/flan1	nisso22	2024-02-23T22:20:29Z	105	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google/flan-t5-base", "base_model:finetune:google/flan-t5-base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-23T21:52:50Z	--- license: apache-2.0 base_model: google/flan-t5-base tags: - generated_from_trainer metrics: - rouge model-index: - name: flan1 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. --> # flan1 This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0792 - Rouge1: 0.8816 - Rouge2: 0.8196 - Rougel: 0.8797 - Rougelsum: 0.8802 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 2 - eval_batch_size: 2 - 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 \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\| \| No log \| 1.0 \| 84 \| 0.0868 \| 0.7926 \| 0.6605 \| 0.7864 \| 0.7886 \| \| No log \| 2.0 \| 168 \| 0.0775 \| 0.9046 \| 0.8065 \| 0.9025 \| 0.9024 \| \| No log \| 3.0 \| 252 \| 0.0792 \| 0.8816 \| 0.8196 \| 0.8797 \| 0.8802 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
thiagolira/CiceroASR	thiagolira	2024-02-23T22:15:41Z	5	1	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2-bert", "automatic-speech-recognition", "generated_from_trainer", "base_model:facebook/w2v-bert-2.0", "base_model:finetune:facebook/w2v-bert-2.0", "license:mit", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-01-31T17:52:27Z	--- license: mit base_model: facebook/w2v-bert-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: CiceroASR 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. --> # CiceroASR This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) for the transcription of Classical Latin! Example from the Aeneid: <video controls src="https://cdn-uploads.huggingface.co/production/uploads/5fc7944e8a82cc0bcf7cc51d/hYNFr2od1EKDlRRdzJmzR.webm"></video> Transcription: arma virumque cano (Of arms and men I sing) Example from Genesis: <video controls src="https://cdn-uploads.huggingface.co/production/uploads/5fc7944e8a82cc0bcf7cc51d/9Q6DfG2h8FkABnl55DLBH.webm"></video> Transcription (little error there): creavit deus chaelum et terram (In the beggining God created the heaven and the earth) It achieves the following results on the evaluation set of my dataset [Latin Youtube](https://huggingface.co/datasets/thiagolira/LatinYoutube): - Loss: 0.5395 - Wer: 0.2220 ## 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: 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: 300 - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 3.6548 \| 0.94 \| 50 \| 2.8634 \| 0.9990 \| \| 2.2055 \| 1.89 \| 100 \| 1.0921 \| 0.9727 \| \| 1.667 \| 2.83 \| 150 \| 0.7201 \| 0.4615 \| \| 1.3148 \| 3.77 \| 200 \| 0.6431 \| 0.3866 \| \| 0.9899 \| 4.72 \| 250 \| 0.5561 \| 0.3116 \| \| 0.9629 \| 5.66 \| 300 \| 0.6027 \| 0.3817 \| \| 0.7557 \| 6.6 \| 350 \| 0.7145 \| 0.3145 \| \| 0.9143 \| 7.55 \| 400 \| 0.4926 \| 0.2610 \| \| 0.5837 \| 8.49 \| 450 \| 0.5396 \| 0.2619 \| \| 0.7037 \| 9.43 \| 500 \| 0.5076 \| 0.2746 \| \| 0.5986 \| 10.38 \| 550 \| 0.5224 \| 0.2415 \| \| 0.5288 \| 11.32 \| 600 \| 0.5332 \| 0.2259 \| \| 0.5034 \| 12.26 \| 650 \| 0.5436 \| 0.2249 \| \| 0.4897 \| 13.21 \| 700 \| 0.5171 \| 0.2162 \| \| 0.4738 \| 14.15 \| 750 \| 0.5395 \| 0.2220 \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
fzzhang/toten_gsm8k_lora_unquantized	fzzhang	2024-02-23T22:11:43Z	4	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:Toten5/Marcoroni-neural-chat-7B-v2", "base_model:adapter:Toten5/Marcoroni-neural-chat-7B-v2", "license:apache-2.0", "region:us" ]	null	2024-02-23T18:29:09Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: Toten5/Marcoroni-neural-chat-7B-v2 model-index: - name: toten_gsm8k_lora_unquantized 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. --> # toten_gsm8k_lora_unquantized This model is a fine-tuned version of [Toten5/Marcoroni-neural-chat-7B-v2](https://huggingface.co/Toten5/Marcoroni-neural-chat-7B-v2) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - PEFT 0.7.2.dev0 - Transformers 4.37.0.dev0 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.0
codeaze/deberta_large_23feb	codeaze	2024-02-23T22:09:41Z	105	0	transformers	[ "transformers", "safetensors", "deberta-v2", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-02-23T22:08:54Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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barissglc/tinyllama-tarot-v1	barissglc	2024-02-23T22:06:45Z	4,149	5	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "text2text-generation", "dataset:barissglc/tarot", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "region:us" ]	text2text-generation	2024-02-23T15:18:13Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 model-index: - name: tinyllama-tarot-v1 results: [] pipeline_tag: text2text-generation widget: - text: >- Give me a one paragraph tarot reading if I pull the cards Nine of Cups, King of Pentacles and Three of Cups. datasets: - barissglc/tarot --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # tinyllama-tarot-v1 This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) . ## Model description This model is a language model capable of making predictions based on tarot cards. Trained to respond to questions related to topics such as love, career, and general life, tarot cards are the foundation of its predictions. The model can make predictions based on the selected tarot cards. You can access the tarot cards from the tarot dataset. ## 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: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 250 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
tybritten/lora-for-starcoder	tybritten	2024-02-23T22:05:13Z	2	0	peft	[ "peft", "pytorch", "gpt_bigcode", "8-bit", "region:us" ]	null	2024-02-23T22:04:58Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False ### Framework versions - PEFT 0.5.0.dev0
Weni/ZeroShot-3.3.6-Mistral-7b-Multilanguage-3.2.0-merged	Weni	2024-02-23T22:00:15Z	4	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-23T19:20:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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mindsurf/Mixtral-8x7B-Instruct-v0.1-small-6-finetuned	mindsurf	2024-02-23T21:58:21Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-23T21:58:20Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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sammyph2000/travel-blogging-phi-v1	sammyph2000	2024-02-23T21:55:10Z	48	0	transformers	[ "transformers", "safetensors", "phi", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-23T21:54:43Z	--- 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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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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ThomasFG/0-10	ThomasFG	2024-02-23T21:47:07Z	79	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-small.en", "base_model:finetune:openai/whisper-small.en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-02-23T20:54:47Z	--- license: apache-2.0 base_model: openai/whisper-small.en tags: - generated_from_trainer metrics: - wer model-index: - name: 2024-02-23_21-54-44 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 2024-02-23_21-54-44 This model is a fine-tuned version of [openai/whisper-small.en](https://huggingface.co/openai/whisper-small.en) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9253 - Wer: 11.1080 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 1.3351 \| 1.0 \| 68 \| 0.9253 \| 11.1080 \| ### Framework versions - Transformers 4.37.2 - Pytorch 1.13.1+cu116 - Datasets 2.17.0 - Tokenizers 0.15.2
ferrazzipietro/Llama-2-13b-chat-hf_adapters_en.layer1_4_torch.bfloat16_64_32_0.05_2_0.0008	ferrazzipietro	2024-02-23T21:45:56Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-23T21:44:48Z	--- 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. 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]
Aea20xd/Hayase-Nagatoro	Aea20xd	2024-02-23T21:41:54Z	1	0	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	2024-02-23T21:41:44Z	--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: >- (masterpiece, best quality:1.2), extremely detailed, soft ambient lighting, sharp focus, 4K, BREAK <lora:Nagatoro_V3:0.8>, 1girl, solo, nagatoro hayase, hair ornament, brown eyes, hairclip, dark skin, black hair, school uniform, white shirt, navy blue skirt, BREAK indoors, school library, looking at viewer, grin, parameters: negative_prompt: Easy Negative, Negative Hand, Bad Hands, (worst quality, low quality:1.2) output: url: images/00003-4269788497.png base_model: runwayml/stable-diffusion-v1-5 instance_prompt: nagatoro hayase, hair ornament, brown eyes, hairclip, dark skin, black hair --- # Hayase-Nagatoro <Gallery /> ## Trigger words You should use `nagatoro hayase` to trigger the image generation. You should use `hair ornament` to trigger the image generation. You should use `brown eyes` to trigger the image generation. You should use `hairclip` to trigger the image generation. You should use `dark skin` to trigger the image generation. You should use `black hair` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/Aea20xd/Hayase-Nagatoro/tree/main) them in the Files & versions tab.
ManuelHettich/acne3_unfiltered_selection_LoRA	ManuelHettich	2024-02-23T21:41:35Z	233	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-02-23T21:41:33Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of TOK acne3 widget: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - ManuelHettich/acne3_LoRA <Gallery /> ## Model description These are ManuelHettich/acne3_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of TOK acne3 to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](ManuelHettich/acne3_LoRA/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
Khadidja22/my_awesome_qa_model	Khadidja22	2024-02-23T21:38:30Z	122	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "question-answering", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2024-02-23T21:29:56Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: my_awesome_qa_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_qa_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4559 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 250 \| 1.9745 \| \| 2.6045 \| 2.0 \| 500 \| 1.5074 \| \| 2.6045 \| 3.0 \| 750 \| 1.4559 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
sambanovasystems/SambaCoder-nsql-llama-2-70b	sambanovasystems	2024-02-23T21:32:28Z	12	6	transformers	[ "transformers", "pytorch", "llama", "text-generation", "en", "dataset:bigcode/the-stack", "dataset:NumbersStation/NSText2SQL", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-02T18:43:18Z	--- license: llama2 datasets: - bigcode/the-stack - NumbersStation/NSText2SQL language: - en --- # SambaCoder-nsql-llama-2-70b ## Model Description NSQL is a family of autoregressive open-source large foundation models (FMs) designed specifically for SQL generation tasks. In this repository we are introducing a new member of NSQL, SambaCoder-nsql-llama-2-70b. It's based on Meta's original [Llama-2 70B model](https://huggingface.co/meta-llama/Llama-2-70b) and further pre-trained on a dataset of general SQL queries and then fine-tuned on a dataset composed of text-to-SQL pairs. Use of this model is governed by the Meta’s Llama 2 Community License Agreement. Please review and accept the license before downloading the model weights and tokenizer ### Basic Information <!-- Provide the basic links for the model. --> - Blog Post: [Link](https://sambanova.ai/blog/sambacoder-nsql-llama-2-70b-model) - HF Hosting: [Chat with me!](https://huggingface.co/spaces/sambanovasystems/nova-nsql-Llama-2-70B-text-to-SQL) ## Training Data The general SQL queries are the SQL subset from [The Stack](https://huggingface.co/datasets/bigcode/the-stack), containing 1M training samples. The labeled text-to-SQL pairs come from the NSText2SQL dataset (https://huggingface.co/datasets/NumbersStation/NSText2SQL). ## Evaluation Data We evaluate our models on three text-to-SQL benchmarks: Spider, Bird, and text2sql. ## Training Procedure SambaCoder-nsql-llama-2-70b was trained using cross-entropy loss to maximize the likelihood of sequential inputs. For finetuning on text-to-SQL pairs, we only compute the loss over the SQL portion of the pair. The model is trained using SambaNova's in-house Reconfigurable Dataflow Unit (RDU), leveraging data and model parallelism. We pre-trained for 2 epochs and fine-tuned for 10 epochs. ### Hyperparameters Continous pretraining on Stack-SQL dataset - Hardware: SambaNova Reconfigurable Dataflow Unit (RDU) - Optimizer: AdamW - Epochs: 2 - Global Batch size: 256 - Batch tokens: 256 * 4096 = 1,048,576 tokens - Learning Rate: 1e-5 - Learning Rate Scheduler: Fixed - Warmup Steps: 0 - Weight decay: 0.1 Finetuning on NSText2SQL dataset - Hardware: SambaNova Reconfigurable Dataflow Unit (RDU) - Optimizer: AdamW - Epochs: 10 - Global Batch size: 64 - Batch tokens: 64 * 4096 = 262,144 tokens - Learning Rate: 1e-5 - Learning Rate Scheduler: Cosine Schedule with Warmup - Warmup Steps: 0 - End Learning Ratio: 0.1 - Weight decay: 0.1 ## Intended Use and Limitations The model was designed for text-to-SQL generation tasks from given table schema and natural language prompts. The model works best with the prompt format defined below and outputting `SELECT` queries. ## How to Use ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("sambanovasystems/SambaCoder-nsql-llama-2-70b") model = AutoModelForCausalLM.from_pretrained("sambanovasystems/SambaCoder-nsql-llama-2-70b", torch_dtype=torch.bfloat16) text = "CREATE TABLE stadium ( stadium_id number, location text, name text, capacity number, highest number, lowest number, average number ) CREATE TABLE singer ( singer_id number, name text, country text, song_name text, song_release_year text, age number, is_male others ) CREATE TABLE concert ( concert_id number, concert_name text, theme text, stadium_id text, year text ) CREATE TABLE singer_in_concert ( concert_id number, singer_id text ) -- Using valid SQLite, answer the following questions for the tables provided above. -- What is the average, minimum, and maximum age of all singers from France? SELECT" input_ids = tokenizer(text, return_tensors="pt").input_ids generated_ids = model.generate(input_ids, max_length=500) print(tokenizer.decode(generated_ids[0], skip_special_tokens=True)) ```
brittlewis12/OpenCodeInterpreter-DS-6.7B-GGUF	brittlewis12	2024-02-23T21:31:37Z	35	14	null	[ "gguf", "code", "text-generation", "en", "base_model:m-a-p/OpenCodeInterpreter-DS-6.7B", "base_model:quantized:m-a-p/OpenCodeInterpreter-DS-6.7B", "license:apache-2.0", "region:us", "conversational" ]	text-generation	2024-02-23T13:41:28Z	--- base_model: m-a-p/OpenCodeInterpreter-DS-6.7B inference: false language: - en license: apache-2.0 license_link: https://github.com/OpenCodeInterpreter/OpenCodeInterpreter/blob/main/LICENSE model_creator: m-a-p model_name: OpenCodeInterpreter-DS-6.7B model_type: llama pipeline_tag: text-generation tags: - code quantized_by: brittlewis12 --- ![HumanEval-pass@1](https://opencodeinterpreter.github.io/static/images/figure1.png) # OpenCodeInterpreter-DS-6.7B GGUF Original model: [OpenCodeInterpreter-DS-6.7B](https://huggingface.co/m-a-p/OpenCodeInterpreter-DS-6.7B) Model creator: [Multimodal Art Projection Research Community](https://huggingface.co/m-a-p) This repo contains GGUF format model files for Multimodal Art Projection Research Community (M-A-P)’s OpenCodeInterpreter-DS-6.7B. > The introduction of large language models has significantly advanced code generation. However, open-source models often lack the execution capabilities and iterative refinement of advanced systems like the GPT-4 Code Interpreter. To address this, we introduce OpenCodeInterpreter, a family of open-source code systems designed for generating, executing, and iteratively refining code. Supported by Code-Feedback, a dataset featuring 68K multi-turn interactions, OpenCodeInterpreter integrates execution and human feedback for dynamic code refinement. Our comprehensive evaluation of OpenCodeInterpreter across key benchmarks such as HumanEval, MBPP, and their enhanced versions from EvalPlus reveals its exceptional performance. Notably, OpenCodeInterpreter-33B achieves an accuracy of 83.2 (76.4) on the average (and plus versions) of HumanEval and MBPP, closely rivaling GPT-4's 84.2 (76.2) and further elevates to 91.6 (84.6) with synthesized human feedback from GPT-4. OpenCodeInterpreter brings the gap between open-source code generation models and proprietary systems like GPT-4 Code Interpreter. Learn more on M-A-P’s [Model page](https://opencodeinterpreter.github.io/). ### What is GGUF? GGUF is a file format for representing AI models. It is the third version of the format, introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Converted using llama.cpp build 2249 (revision [15499eb](https://github.com/ggerganov/llama.cpp/commit/15499eb94227401bdc8875da6eb85c15d37068f7)) ### Prompt template ``` <\|User\|> {{prompt}} <\|Assistant\|> ``` --- ## Download & run with [cnvrs](https://twitter.com/cnvrsai) on iPhone, iPad, and Mac! ![cnvrs.ai](https://pbs.twimg.com/profile_images/1744049151241797632/0mIP-P9e_400x400.jpg) [cnvrs](https://testflight.apple.com/join/sFWReS7K) is the best app for private, local AI on your device: - create & save Characters with custom system prompts & temperature settings - download and experiment with any GGUF model you can [find on HuggingFace](https://huggingface.co/models?library=gguf)! - make it your own with custom Theme colors - powered by Metal ⚡️ & [Llama.cpp](https://github.com/ggerganov/llama.cpp), with haptics during response streaming! - try it out yourself today, on [Testflight](https://testflight.apple.com/join/sFWReS7K)! - follow [cnvrs on twitter](https://twitter.com/cnvrsai) to stay up to date --- ## Original Model Evaluation > The study leverages data from the EvalPlus leaderboard, examining OpenCodeInterpreter's performance against benchmarks such as GPT-3.5/4-Turbo, CodeLlama-Python, WizardCoder, Deepseek-Coder, and CodeT5+ across various scales on the HumanEval and MBPP benchmarks and their advanced versions. For multi-turn code generation, the focus shifts to assessing OpenCodeInterpreter's capability in iterative refinement through a two-round limit, considering execution feedback and human feedback scenarios. The experimental setup aims to highlight OpenCodeInterpreter's adaptability and proficiency in code generation, underscored by its achievements in setting new standards in software development tools through iterative feedback and refinement. For more detail on evaluation process, see [main results](https://opencodeinterpreter.github.io/#mainresults) & eval code [README](https://github.com/OpenCodeInterpreter/OpenCodeInterpreter/blob/f5bcecc42f84b4b789757daf3af476fcfa8b9d79/evaluation/README.md). \| Model \| HumanEval (+) \| MBPP (+) \| Average (+) \| \|----------------------\|---------------\|----------\|-------------\| \| OpenCodeInterpreter-DS-6.7B \| 76.2 (72.0) \| 73.9 (63.7) \| 75.1 (67.9) \| \| --> with Execution Feedback \| 81.1 (78.7) \| 82.7 (72.4) \| 81.9 (75.6) \| \| --> with Synth. Human Feedback \| 87.2 (86.6) \| 86.2 (74.2) \| 86.7 (80.4) \| \| --> with Synth. Human Feedback (Oracle) \| 89.7 (86.6) \| 87.2 (75.2) \| 88.5 (80.9) \| \| — \| — \| — \| — \| \| GPT-4-Turbo \| 85.4 (81.7) \| 83.0 (70.7) \| 84.2 (76.2) \| \| --> with Execution Feedback \| 88.0 (84.2) \| 92.0 (78.2) \| 90.0 (81.2) \| \| — \| — \| — \| — \| \| GPT-3.5-Turbo \| 72.6 (65.9) \| 81.7 (69.4) \| 77.2 (67.7) \| \| --> with Execution Feedback \| 76.8 (70.7) \| 87.0 (73.9) \| 81.9 (72.3) \|
jtatman/Reyna-Mini-1.8B-v0.2-cosmopedia-laser	jtatman	2024-02-23T21:31:35Z	13	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-23T21:21:18Z	--- 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]
traltyaziking/tfjs-mobilenet-549	traltyaziking	2024-02-23T21:30:56Z	0	0	null	[ "image-classification", "tfjs", "license:mit", "region:us" ]	image-classification	2024-02-23T21:29:27Z	--- license: mit tags: - image-classification - tfjs --- ## TensorFlow.js version of Mobilenet Pushed from Web ![](coffee.jpg)
Phearion/bigbrain-v0.0.2	Phearion	2024-02-23T21:27:29Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-23T21:09:47Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Adeptschneider/neuralbeagle-7B-fine-tuned-model	Adeptschneider	2024-02-23T21:15:21Z	4	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "unsloth", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-23T21:12:18Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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mi-rei/CT_clinical-longformer_III_efficient_5e	mi-rei	2024-02-23T21:13:10Z	118	0	transformers	[ "transformers", "safetensors", "longformer", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-23T21:12:28Z	--- 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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Lollitor/MarkedProtein15	Lollitor	2024-02-23T21:07:36Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-23T21:07:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ferrazzipietro/Llama-2-13b-chat-hf_adapters_en.layer1_4_torch.bfloat16_32_32_0.01_8_0.0008	ferrazzipietro	2024-02-23T21:07:10Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-02-23T21:06:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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brittlewis12/gemma-2b-it-GGUF	brittlewis12	2024-02-23T21:02:16Z	32	0	null	[ "gguf", "text-generation", "en", "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:2009.11462", "arxiv:2101.11718", "arxiv:2110.08193", "arxiv:1804.09301", "arxiv:2109.07958", "arxiv:1804.06876", "arxiv:2203.09509", "base_model:google/gemma-2b-it", "base_model:quantized:google/gemma-2b-it", "license:other", "region:us", "conversational" ]	text-generation	2024-02-21T15:35:46Z	--- base_model: google/gemma-2b-it inference: false language: - en model_creator: google model_name: gemma-2b-it model_type: gemma pipeline_tag: text-generation license: other license_name: gemma-terms-of-use license_link: https://ai.google.dev/gemma/terms quantized_by: brittlewis12 --- # Gemma 2B Instruct GGUF Original model: [gemma-2b-it](https://huggingface.co/google/gemma-2b-it) Model creator: [google](https://huggingface.co/google) This repo contains GGUF format model files for Google’s Gemma-2B-it. _Updated 2024-02-23_ > 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. Learn more on Google’s [Model page](https://ai.google.dev/gemma/docs). ### What is GGUF? GGUF is a file format for representing AI models. It is the third version of the format, introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Converted using llama.cpp build 2251 (revision [fd43d66](https://github.com/ggerganov/llama.cpp/commit/fd43d66f46ee3b5345fb8a74a252d86ccd34a409)) ### Prompt template: Gemma Instruct ``` <start_of_turn>user {{prompt}}<end_of_turn> <start_of_turn>model ``` --- ## Download & run with [cnvrs](https://twitter.com/cnvrsai) on iPhone, iPad, and Mac! ![cnvrs.ai](https://pbs.twimg.com/profile_images/1744049151241797632/0mIP-P9e_400x400.jpg) [cnvrs](https://testflight.apple.com/join/sFWReS7K) is the best app for private, local AI on your device: - create & save Characters with custom system prompts & temperature settings - download and experiment with any GGUF model you can [find on HuggingFace](https://huggingface.co/models?library=gguf)! - make it your own with custom Theme colors - powered by Metal ⚡️ & [Llama.cpp](https://github.com/ggerganov/llama.cpp), with haptics during response streaming! - try it out yourself today, on [Testflight](https://testflight.apple.com/join/sFWReS7K)! - follow [cnvrs on twitter](https://twitter.com/cnvrsai) to stay up to date --- ## Original Model Evaluation \| 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 \| \| 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 \|
KevStrider/PixelCopter	KevStrider	2024-02-23T20:57:36Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2024-02-23T20:57:34Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: PixelCopter results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: -2.90 +/- 1.81 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
mucai/vip-llava-7b-base-vcr-ft	mucai	2024-02-23T20:51:26Z	4	0	transformers	[ "transformers", "pytorch", "llava", "text-generation", "arxiv:2312.00784", "license:apache-2.0", "autotrain_compatible", "region:us" ]	text-generation	2024-02-23T20:14:12Z	--- license: apache-2.0 inference: false --- <br> <br> # ViP-LLaVA Model Card ## Model details Model type: ViP-LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on both image level instruction data and region-level instruction data annotated with visual prompts. It is an auto-regressive language model, based on the transformer architecture. Model date: ViP-LLaVA-7B-Base-VCR-FT was trained in November 2023. [Paper](https://arxiv.org/abs/2312.00784) Paper or resources for more information: https://vip-llava.github.io/ ## License Llama 2 is licensed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved. Where to send questions or comments about the model: https://github.com/mu-cai/ViP-LLaVA/issues ## Intended use Primary intended uses: The primary use of ViP-LLaVA is research on large multimodal models and chatbots. Primary intended users: The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence. ## Training dataset - 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP. ## Evaluation dataset ViP-LLaVA achieves state-of-the-art performance in 4 academic region-level benchmarks and our newly proposed RegionBench.
ThomasFG/100-10	ThomasFG	2024-02-23T20:32:10Z	77	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-small.en", "base_model:finetune:openai/whisper-small.en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-02-23T18:43:52Z	--- license: apache-2.0 base_model: openai/whisper-small.en tags: - generated_from_trainer metrics: - wer model-index: - name: 2024-02-23_19-43-47 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 2024-02-23_19-43-47 This model is a fine-tuned version of [openai/whisper-small.en](https://huggingface.co/openai/whisper-small.en) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3055 - Wer: 10.9175 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.2044 \| 1.0 \| 450 \| 0.3055 \| 10.9175 \| ### Framework versions - Transformers 4.37.2 - Pytorch 1.13.1+cu116 - Datasets 2.17.0 - Tokenizers 0.15.2
PoornaChandraVemula/finetuned-bart-base-summarization-samsum	PoornaChandraVemula	2024-02-23T20:25:32Z	105	0	transformers	[ "transformers", "tensorboard", "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	2024-02-22T01:14:52Z	--- license: apache-2.0 base_model: facebook/bart-base tags: - generated_from_trainer metrics: - rouge model-index: - name: finetuned-bart-base-summarization-samsum 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. --> # finetuned-bart-base-summarization-samsum This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on Samsum dataset. It achieves the following results on the evaluation set: - Loss: 0.3267 - Rouge1: 0.4819 - Rouge2: 0.2513 - Rougel: 0.4074 - Rougelsum: 0.4457 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - 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 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\| \| 0.3041 \| 1.0 \| 1841 \| 0.3407 \| 0.4752 \| 0.242 \| 0.3996 \| 0.438 \| \| 0.3184 \| 2.0 \| 3683 \| 0.3274 \| 0.4833 \| 0.2507 \| 0.4081 \| 0.4462 \| \| 0.2993 \| 3.0 \| 5523 \| 0.3267 \| 0.4819 \| 0.2513 \| 0.4074 \| 0.4457 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
wndiros/diva_em_leo_mistral_V07	wndiros	2024-02-23T20:23:19Z	6	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-23T20:18:03Z	--- 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. 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loremipsum3658/AlphaMonarch-laser-DPO	loremipsum3658	2024-02-23T20:16:26Z	6	2	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-23T20:14:01Z	--- 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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TimeSurgeLabs/mistral_sentiment_lora	TimeSurgeLabs	2024-02-23T20:15:33Z	2	0	peft	[ "peft", "safetensors", "mistral", "dataset:FinGPT/fingpt-sentiment-train", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:mit", "region:us" ]	null	2024-02-23T19:55:26Z	--- license: mit library_name: peft base_model: mistralai/Mistral-7B-v0.1 datasets: - FinGPT/fingpt-sentiment-train --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.0` ```yaml base_model: mistralai/Mistral-7B-v0.1 model_type: MistralForCausalLM tokenizer_type: LlamaTokenizergin is_mistral_derived_model: true load_in_8bit: false load_in_4bit: false strict: false datasets: # This will be the path used for the data when it is saved to the Volume in the cloud. - path: data.jsonl ds_type: json type: # JSONL file contains question, context, answer fields per line. # This gets mapped to instruction, input, output axolotl tags. field_instruction: instruction field_input: input field_output: output # Format is used by axolotl to generate the prompt. format: \|- [INST]{input} {instruction} [/INST] dataset_prepared_path: val_set_size: 0.05 output_dir: ./lora-out sequence_len: 4096 sample_packing: false eval_sample_packing: false pad_to_sequence_len: false adapter: lora lora_model_dir: lora_r: 16 lora_alpha: 32 lora_dropout: 0.05 lora_target_linear: true lora_fan_in_fan_out: wandb_project: wandb_entity: wandb_watch: wandb_run_id: gradient_accumulation_steps: 1 micro_batch_size: 32 num_epochs: 4 optimizer: adamw_torch lr_scheduler: cosine learning_rate: 0.0001 bf16: auto fp16: false tf32: false train_on_inputs: false group_by_length: false gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 10 save_steps: debug: deepspeed: /root/axolotl/deepspeed_configs/zero3_bf16.json weight_decay: 0.0 fsdp: fsdp_config: special_tokens: bos_token: "<s>" eos_token: "</s>" unk_token: "<unk>" ``` </details><br> # Mistral Sentiment Analysis This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the [FinGPT Sentiment](https://huggingface.co/datasets/FinGPT/fingpt-sentiment-train) dataset. It is intended to be used for sentiment analysis tasks for financial data. Data was modified to use with Axolotl, see [here](https://github.com/TimeSurgeLabs/llm-finetuning/blob/02fee020a21917d91719da6db25a4f4384ae9a0a/data/fingpt-sentiment.jsonl) for the modified data. See the [FinGPT Project](https://github.com/AI4Finance-Foundation/FinGPT) for more information. It achieves the following results on the evaluation set: * Loss: 0.1598 ## Ollama Example ```bash ollama run chand1012/mistral_sentiment >>> Apple (NASDAQ:AAPL) Up Fractionally despite Rising Vision Pro Returns Please choose an answer from {negative/neutral/positive} positive ``` ## Python Example ```python from transformers import AutoModel, AutoTokenizer, AutoModelForCausalLM, LlamaForCausalLM, LlamaTokenizerFast from peft import PeftModel # 0.8.2 # Load Models base_model = "mistralai/Mistral-7B-v0.1" peft_model = "TimeSurgeLabs/mistral_sentiment_lora" tokenizer = LlamaTokenizerFast.from_pretrained(base_model, trust_remote_code=True) tokenizer.pad_token = tokenizer.eos_token model = LlamaForCausalLM.from_pretrained(base_model, trust_remote_code=True, device_map = "cuda:0", load_in_8bit = True,) model = PeftModel.from_pretrained(model, peft_model) model = model.eval() # Make prompts prompt = [ '''Instruction: What is the sentiment of this news? Please choose an answer from {negative/neutral/positive} Input: FINANCING OF ASPOCOMP 'S GROWTH Aspocomp is aggressively pursuing its growth strategy by increasingly focusing on technologically more demanding HDI printed circuit boards PCBs . Answer: ''', '''Instruction: What is the sentiment of this news? Please choose an answer from {negative/neutral/positive} Input: According to Gran , the company has no plans to move all production to Russia , although that is where the company is growing . Answer: ''', '''Instruction: What is the sentiment of this news? Please choose an answer from {negative/neutral/positive} Input: A tinyurl link takes users to a scamming site promising that users can earn thousands of dollars by becoming a Google ( NASDAQ : GOOG ) Cash advertiser . Answer: ''', ] # Generate results tokens = tokenizer(prompt, return_tensors='pt', padding=True, max_length=512) res = model.generate(*tokens, max_length=512) res_sentences = [tokenizer.decode(i) for i in res] out_text = [o.split("Answer: ")[1] for o in res_sentences] # show results for sentiment in out_text: print(sentiment) # Output: # positive # neutral # negative ``` ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: learning_rate: 0.0001 * train_batch_size: 32 * eval_batch_size: 32 * seed: 42 * distributed_type: multi-GPU * num_devices: 2 * total_train_batch_size: 64 * total_eval_batch_size: 64 * optimizer: Adam with betas=(0.9, 0.999) and epsilon=1e-08 * lr_scheduler_type: cosine * lr_scheduler_warmup_steps: 10 * num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.0678 \| 1.0 \| 1140 \| 0.1124 \| \| 0.1339 \| 2.0 \| 2280 \| 0.1008 \| \| 0.0497 \| 3.0 \| 3420 \| 0.1146 \| \| 0.0016 \| 4.0 \| 4560 \| 0.1598 \| ### Framework versions * PEFT 0.8.2 * Transformers 4.38.0.dev0 * Pytorch 2.1.2+cu121 * Datasets 2.17.0 * Tokenizers 0.15.0
furrutiav/bert_qa_extractor_cockatiel_2022_ulra_by_kmeans_Q_nllf_s_best_ef_signal_it_138	furrutiav	2024-02-23T20:08:49Z	104	0	transformers	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	feature-extraction	2024-02-23T20:08:20Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Ritesh-here/mistral-7b-instruct1	Ritesh-here	2024-02-23T20:05:16Z	66	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-02-23T20:01:30Z	--- 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. 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alpac/azulcentenoxxx	alpac	2024-02-23T20:03:49Z	0	0	null	[ "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "license:creativeml-openrail-m", "region:us" ]	null	2024-02-23T19:32:16Z	--- widget: - text: '-' output: url: images/014.png base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: azul, centeno, nicaragua, adult, creator license: creativeml-openrail-m --- # azulcentenoxxx <Gallery /> ## Trigger words You should use `azul` to trigger the image generation. You should use `centeno` to trigger the image generation. You should use `nicaragua` to trigger the image generation. You should use `adult` to trigger the image generation. You should use `creator` to trigger the image generation. ## Download model [Download](/alpac/azulcentenoxxx/tree/main) them in the Files & versions tab.
kajol/mistral7b_financial_sentiment_cls	kajol	2024-02-23T20:00:37Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2", "region:us" ]	null	2024-02-23T19:58:59Z	--- library_name: peft base_model: mistralai/Mistral-7B-Instruct-v0.2 --- # 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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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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