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ftopal
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huggingface-models-raw

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text-to-image
diffusers
# SDXL LoRA DreamBooth - aarashfeizi/jean-francois-godbout-batch3-repeats3-rank8-snrNone <Gallery /> ## Model description ### These are aarashfeizi/jean-francois-godbout-batch3-repeats3-rank8-snrNone LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. ## Download model ### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke - **LoRA**: download **[`/home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout-batch3-repeats3-rank8-snrNone.safetensors` here 💾](/aarashfeizi/jean-francois-godbout-batch3-repeats3-rank8-snrNone/blob/main//home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout-batch3-repeats3-rank8-snrNone.safetensors)**. - Place it on your `models/Lora` folder. - On AUTOMATIC1111, load the LoRA by adding `<lora:/home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout-batch3-repeats3-rank8-snrNone:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/). - *Embeddings*: download **[`/home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout-batch3-repeats3-rank8-snrNone_emb.safetensors` here 💾](/aarashfeizi/jean-francois-godbout-batch3-repeats3-rank8-snrNone/blob/main//home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout-batch3-repeats3-rank8-snrNone_emb.safetensors)**. - Place it on it on your `embeddings` folder - Use it by adding `/home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout-batch3-repeats3-rank8-snrNone_emb` to your prompt. For example, `A photo of /home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout-batch3-repeats3-rank8-snrNone_emb` (you need both the LoRA and the embeddings as they were trained together for this LoRA) ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch from huggingface_hub import hf_hub_download from safetensors.torch import load_file pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('aarashfeizi/jean-francois-godbout-batch3-repeats3-rank8-snrNone', weight_name='pytorch_lora_weights.safetensors') embedding_path = hf_hub_download(repo_id='aarashfeizi/jean-francois-godbout-batch3-repeats3-rank8-snrNone', filename='/home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout-batch3-repeats3-rank8-snrNone_emb.safetensors', repo_type="model") state_dict = load_file(embedding_path) pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer) pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2) image = pipeline('A photo of <s0><s1> giving a speech').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Trigger words To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens: to trigger concept `TOK` → use `<s0><s1>` in your prompt ## Details All [Files & versions](/aarashfeizi/jean-francois-godbout-batch3-repeats3-rank8-snrNone/tree/main). The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py). LoRA for the text encoder was enabled. False. Pivotal tuning was enabled: True. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
{"license": "openrail++", "tags": ["stable-diffusion-xl", "stable-diffusion-xl-diffusers", "diffusers-training", "text-to-image", "diffusers", "lora", "template:sd-lora"], "widget": [{"text": "A photo of <s0><s1> giving a speech", "output": {"url": "image_0.png"}}, {"text": "A photo of <s0><s1> giving a speech", "output": {"url": "image_1.png"}}, {"text": "A photo of <s0><s1> giving a speech", "output": {"url": "image_2.png"}}, {"text": "A photo of <s0><s1> giving a speech", "output": {"url": "image_3.png"}}], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "A photo of <s0><s1>"}
aarashfeizi/jean-francois-godbout-batch3-repeats3-rank8-snrNone
null
[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "diffusers-training", "text-to-image", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]
null
2024-04-29T16:36:33+00:00
null
peft
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # T5_lyrics-model This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1808 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 16 - 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: 1 - training_steps: 250 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 6.3144 | 0.01 | 25 | 1.5334 | | 1.2668 | 0.02 | 50 | 0.6688 | | 1.021 | 0.03 | 75 | 0.3675 | | 0.5247 | 0.03 | 100 | 0.3007 | | 0.6415 | 0.04 | 125 | 0.2400 | | 0.3774 | 0.05 | 150 | 0.2170 | | 0.5336 | 0.06 | 175 | 0.1966 | | 0.3201 | 0.07 | 200 | 0.1882 | | 0.5068 | 0.08 | 225 | 0.1824 | | 0.3218 | 0.08 | 250 | 0.1808 | ### Framework versions - PEFT 0.8.2 - Transformers 4.38.1 - Pytorch 1.13.1 - Datasets 2.17.0 - Tokenizers 0.15.2
{"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "google-t5/t5-small", "model-index": [{"name": "T5_lyrics-model", "results": []}]}
anushkat/T5_lyrics-model
null
[ "peft", "safetensors", "generated_from_trainer", "base_model:google-t5/t5-small", "license:apache-2.0", "region:us" ]
null
2024-04-29T16:37:34+00:00
null
null
{}
KamCastle/brkcat
null
[ "region:us" ]
null
2024-04-29T16:37:59+00:00
text-classification
transformers
{"license": "mit"}
moazx/AraBERT-Restaurant-Sentiment
null
[ "transformers", "safetensors", "bert", "text-classification", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us", "has_space" ]
null
2024-04-29T16:38:35+00:00
reinforcement-learning
stable-baselines3
# **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 ThatOneSkyler -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 ThatOneSkyler -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 ThatOneSkyler ``` ## 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'} ```
{"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": "683.00 +/- 196.74", "name": "mean_reward", "verified": false}]}]}]}
ThatOneSkyler/SpaceInvadersNoFrameskip
null
[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
null
2024-04-29T16:38:57+00:00
text-generation
transformers
# output_folder merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) as a base. ### Models Merged The following models were included in the merge: * [PrunaAI/WizardLM-WizardMath-7B-V1.0-bnb-4bit-smashed](https://huggingface.co/PrunaAI/WizardLM-WizardMath-7B-V1.0-bnb-4bit-smashed) * [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: mistralai/Mistral-7B-v0.1 # no parameters necessary for base model - model: PrunaAI/WizardLM-WizardMath-7B-V1.0-bnb-4bit-smashed parameters: density: 0.5 # fraction of weights in differences from the base model to retain weight: # weight gradient - filter: mlp value: 0.5 - value: 0 - model: codellama/CodeLlama-7b-Instruct-hf parameters: density: 0.5 weight: 0.5 merge_method: ties base_model: mistralai/Mistral-7B-v0.1 parameters: normalize: true int8_mask: true dtype: float16 ```
{"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["mistralai/Mistral-7B-v0.1", "PrunaAI/WizardLM-WizardMath-7B-V1.0-bnb-4bit-smashed", "codellama/CodeLlama-7b-Instruct-hf"]}
tanyakansal/botcheck
null
[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "arxiv:2306.01708", "base_model:mistralai/Mistral-7B-v0.1", "base_model:PrunaAI/WizardLM-WizardMath-7B-V1.0-bnb-4bit-smashed", "base_model:codellama/CodeLlama-7b-Instruct-hf", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
null
2024-04-29T16:42:19+00:00
text-generation
transformers
# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
{"library_name": "transformers", "tags": []}
noeloco/loracamel-merged
null
[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
null
2024-04-29T16:42:22+00:00
null
null
{}
kalyan99/Llama-2-7b-hf-finetuned-code
null
[ "region:us" ]
null
2024-04-29T16:42:38+00:00
null
null
{"license": "openrail"}
sherven/hhh
null
[ "license:openrail", "region:us" ]
null
2024-04-29T16:42:47+00:00
reinforcement-learning
null
# **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="AhmedTarek/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
{"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/- 2.71", "name": "mean_reward", "verified": true}]}]}]}
AhmedTarek/q-Taxi-v3
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
null
2024-04-29T16:43:00+00:00
text-generation
transformers
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Meta-Llama-3-8B-Instruct_fictional_arc_Chinese_v2 This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the generator 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: 1 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 36 ### Training results ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2 - Datasets 2.18.0 - Tokenizers 0.15.2
{"license": "other", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "meta-llama/Meta-Llama-3-8B-Instruct", "model-index": [{"name": "Meta-Llama-3-8B-Instruct_fictional_arc_Chinese_v2", "results": []}]}
yzhuang/Meta-Llama-3-8B-Instruct_fictional_arc_Chinese_v2
null
[ "transformers", "tensorboard", "safetensors", "llama", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "dataset:generator", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
null
2024-04-29T16:43:44+00:00
null
null
{}
Birgaiyan/Liberty_Nights
null
[ "region:us" ]
null
2024-04-29T16:43:45+00:00
text-generation
transformers
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 4_bar_lmd_clean_custom_epochs10 This model is a fine-tuned version of [MikeMpapa/4_bar_lmd_clean_custom_epochs10](https://huggingface.co/MikeMpapa/4_bar_lmd_clean_custom_epochs10) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8099 ## 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: 0.005 - train_batch_size: 48 - eval_batch_size: 32 - seed: 1 - gradient_accumulation_steps: 2 - total_train_batch_size: 96 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 81 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.078 | 9.42 | 12000 | 0.9938 | | 0.9861 | 18.84 | 24000 | 0.9498 | | 0.9367 | 28.26 | 36000 | 0.9170 | | 0.8959 | 37.68 | 48000 | 0.8851 | | 0.855 | 47.1 | 60000 | 0.8653 | | 0.8125 | 56.51 | 72000 | 0.8325 | | 0.772 | 65.93 | 84000 | 0.8155 | | 0.7409 | 75.35 | 96000 | 0.8099 | ### Framework versions - Transformers 4.36.0 - Pytorch 2.1.0 - Datasets 2.15.0 - Tokenizers 0.15.1
{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "MikeMpapa/4_bar_lmd_clean_custom_epochs10", "model-index": [{"name": "4_bar_lmd_clean_custom_epochs10", "results": []}]}
MikeMpapa/4_bar_lmd_clean_custom_epochs10
null
[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:MikeMpapa/4_bar_lmd_clean_custom_epochs10", "license:mit", "autotrain_compatible", "endpoints_compatible", "has_space", "text-generation-inference", "region:us" ]
null
2024-04-29T16:46:39+00:00
text2text-generation
transformers
{}
gouravsinha/point_to_descriptionv2.0
null
[ "transformers", "safetensors", "marian", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]
null
2024-04-29T16:46:46+00:00
null
null
{}
shallow6414/1b6h3zh
null
[ "region:us" ]
null
2024-04-29T16:48:23+00:00
feature-extraction
transformers
# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
{"library_name": "transformers", "tags": []}
Ehsanl/e5-base-chnk-ep1
null
[ "transformers", "safetensors", "xlm-roberta", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
null
2024-04-29T16:48:41+00:00
text-generation
transformers
# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
{"library_name": "transformers", "tags": []}
shallow6414/7womgzs
null
[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]
null
2024-04-29T16:48:43+00:00
null
null
{"license": "apache-2.0"}
otmanabs/gcamr
null
[ "safetensors", "license:apache-2.0", "region:us" ]
null
2024-04-29T16:49:05+00:00
text-generation
transformers
# output_folder merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) as a base. ### Models Merged The following models were included in the merge: * [PrunaAI/WizardLM-WizardMath-7B-V1.0-bnb-4bit-smashed](https://huggingface.co/PrunaAI/WizardLM-WizardMath-7B-V1.0-bnb-4bit-smashed) * [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: mistralai/Mistral-7B-v0.1 # no parameters necessary for base model - model: PrunaAI/WizardLM-WizardMath-7B-V1.0-bnb-4bit-smashed parameters: density: 0.5 # fraction of weights in differences from the base model to retain weight: # weight gradient - filter: mlp value: 0.5 - value: 0 - model: codellama/CodeLlama-7b-Instruct-hf parameters: density: 0.5 weight: 0.5 merge_method: ties base_model: mistralai/Mistral-7B-v0.1 parameters: normalize: true int8_mask: true dtype: float16 ```
{"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["mistralai/Mistral-7B-v0.1", "PrunaAI/WizardLM-WizardMath-7B-V1.0-bnb-4bit-smashed", "codellama/CodeLlama-7b-Instruct-hf"]}
tanyakansal/WizardLM-Mistral-CodeLlama-7b
null
[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "arxiv:2306.01708", "base_model:mistralai/Mistral-7B-v0.1", "base_model:PrunaAI/WizardLM-WizardMath-7B-V1.0-bnb-4bit-smashed", "base_model:codellama/CodeLlama-7b-Instruct-hf", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
null
2024-04-29T16:50:57+00:00
null
null
# Llama3-OpenBioLLM-8B-GGUF - Original model: [Llama3-OpenBioLLM-8B](https://huggingface.co/aaditya/Llama3-OpenBioLLM-8B) <!-- description start --> ## Description This repo contains GGUF format model files for [Llama3-OpenBioLLM-8B](https://huggingface.co/aaditya/Llama3-OpenBioLLM-8B). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). This is the source project for GGUF, providing both a Command Line Interface (CLI) and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), Known as the most widely used web UI, this project boasts numerous features and powerful extensions, and supports GPU acceleration. * [Ollama](https://github.com/jmorganca/ollama) Ollama is a lightweight and extensible framework designed for building and running language models locally. It features a simple API for creating, managing, and executing models, along with a library of pre-built models for use in various applications​ * [KoboldCpp](https://github.com/LostRuins/koboldcpp), A comprehensive web UI offering GPU acceleration across all platforms and architectures, particularly renowned for storytelling. * [GPT4All](https://gpt4all.io), This is a free and open source GUI that runs locally, supporting Windows, Linux, and macOS with full GPU acceleration. * [LM Studio](https://lmstudio.ai/) An intuitive and powerful local GUI for Windows and macOS (Silicon), featuring GPU acceleration. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui). A notable web UI with a variety of unique features, including a comprehensive model library for easy model selection. * [Faraday.dev](https://faraday.dev/), An attractive, user-friendly character-based chat GUI for Windows and macOS (both Silicon and Intel), also offering GPU acceleration. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), A Python library equipped with GPU acceleration, LangChain support, and an OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), A Rust-based ML framework focusing on performance, including GPU support, and designed for ease of use. * [ctransformers](https://github.com/marella/ctransformers), A Python library featuring GPU acceleration, LangChain support, and an OpenAI-compatible AI server. * [localGPT](https://github.com/PromtEngineer/localGPT) An open-source initiative enabling private conversations with documents. <!-- README_GGUF.md-about-gguf end --> <!-- compatibility_gguf start --> ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files **Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single folder. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: LiteLLMs/Llama3-OpenBioLLM-8B-GGUF and below it, a specific filename to download, such as: Q4_0/Q4_0-00001-of-00009.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download LiteLLMs/Llama3-OpenBioLLM-8B-GGUF Q4_0/Q4_0-00001-of-00009.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download LiteLLMs/Llama3-OpenBioLLM-8B-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install huggingface_hub[hf_transfer] ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download LiteLLMs/Llama3-OpenBioLLM-8B-GGUF Q4_0/Q4_0-00001-of-00009.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m Q4_0/Q4_0-00001-of-00009.gguf --color -c 8192 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<PROMPT>" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 8192` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./Q4_0/Q4_0-00001-of-00009.gguf", # Download the model file first n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "<PROMPT>", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./Q4_0/Q4_0-00001-of-00009.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer end --> <!-- original-model-card start --> # Original model card: Llama3-OpenBioLLM-8B <div align="center"> <img width="260px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/BrQCb95lmEIFz79QAmoNA.png"></div> ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/2FhDh8NDvMl7iSxbQz9BP.png) <div align="center"> <h1>Advancing Open-source Large Language Models in Medical Domain</h1> </div> <p align="center" style="margin-top: 0px;"> <a href="https://colab.research.google.com/drive/1F5oV20InEYeAJGmBwYF9NM_QhLmjBkKJ?usp=sharing"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="OpenChat Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 10px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style=" margin-right: 5px;">Online Demo</span> </a> | <a href="https://github.com/openlifescience-ai"> <img src="https://github.githubassets.com/assets/GitHub-Mark-ea2971cee799.png" alt="GitHub Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style=" margin-right: 5px;">GitHub</span> </a> | <a href="#"> <img src="https://github.com/alpayariyak/openchat/blob/master/assets/arxiv-logomark-small-square-border.png?raw=true" alt="ArXiv Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style="margin-right: 5px;">Paper</span> </a> | <a href="https://discord.gg/A5Fjf5zC69"> <img src="https://cloud.githubusercontent.com/assets/6291467/26705903/96c2d66e-477c-11e7-9f4e-f3c0efe96c9a.png" alt="Discord Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text">Discord</span> </a> </p> ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/KGmRE5w2sepNtwsEu8t7K.jpeg) Introducing OpenBioLLM-8B: A State-of-the-Art Open Source Biomedical Large Language Model OpenBioLLM-8B is an advanced open source language model designed specifically for the biomedical domain. Developed by Saama AI Labs, this model leverages cutting-edge techniques to achieve state-of-the-art performance on a wide range of biomedical tasks. 🏥 **Biomedical Specialization**: OpenBioLLM-8B is tailored for the unique language and knowledge requirements of the medical and life sciences fields. It was fine-tuned on a vast corpus of high-quality biomedical data, enabling it to understand and generate text with domain-specific accuracy and fluency. 🎓 **Superior Performance**: With 8 billion parameters, OpenBioLLM-8B outperforms other open source biomedical language models of similar scale. It has also demonstrated better results compared to larger proprietary & open-source models like GPT-3.5 and Meditron-70B on biomedical benchmarks. 🧠 **Advanced Training Techniques**: OpenBioLLM-8B builds upon the powerful foundations of the **Meta-Llama-3-8B** and [Meta-Llama-3-8B](meta-llama/Meta-Llama-3-8B) models. It incorporates the DPO dataset and fine-tuning recipe along with a custom diverse medical instruction dataset. Key components of the training pipeline include: <div align="center"> <img width="1200px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/oPchsJsEpQoGcGXVbh7YS.png"> </div> - **Policy Optimization**: [Direct Preference Optimization: Your Language Model is Secretly a Reward Model (DPO)](https://arxiv.org/abs/2305.18290) - **Ranking Dataset**: [berkeley-nest/Nectar](https://huggingface.co/datasets/berkeley-nest/Nectar) - **Fine-tuning dataset**: Custom Medical Instruct dataset (We plan to release a sample training dataset in our upcoming paper; please stay updated) This combination of cutting-edge techniques enables OpenBioLLM-8B to align with key capabilities and preferences for biomedical applications. ⚙️ **Release Details**: - **Model Size**: 8 billion parameters - **Quantization**: Optimized quantized versions available [Here](https://huggingface.co/aaditya/OpenBioLLM-Llama3-8B-GGUF) - **Language(s) (NLP):** en - **Developed By**: [Ankit Pal (Aaditya Ura)](https://aadityaura.github.io/) from Saama AI Labs - **License:** Meta-Llama License - **Fine-tuned from models:** [meta-llama/Meta-Llama-3-8B](meta-llama/Meta-Llama-3-8B) - **Resources for more information:** - Paper: Coming soon The model can be fine-tuned for more specialized tasks and datasets as needed. OpenBioLLM-8B represents an important step forward in democratizing advanced language AI for the biomedical community. By leveraging state-of-the-art architectures and training techniques from leading open source efforts like Llama-3, we have created a powerful tool to accelerate innovation and discovery in healthcare and the life sciences. We are excited to share OpenBioLLM-8B with researchers and developers around the world. ### Use with transformers **Important: Please use the exact chat template provided by Llama-3 instruct version. Otherwise there will be a degradation in the performance. The model output can be verbose in rare cases. Please consider setting temperature = 0 to make this happen less.** See the snippet below for usage with Transformers: ```python import transformers import torch model_id = "aaditya/OpenBioLLM-Llama3-8B" pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device="auto", ) messages = [ {"role": "system", "content": "You are an expert and experienced from the healthcare and biomedical domain with extensive medical knowledge and practical experience. Your name is OpenBioLLM, and you were developed by Saama AI Labs. who's willing to help answer the user's query with explanation. In your explanation, leverage your deep medical expertise such as relevant anatomical structures, physiological processes, diagnostic criteria, treatment guidelines, or other pertinent medical concepts. Use precise medical terminology while still aiming to make the explanation clear and accessible to a general audience."}, {"role": "user", "content": "How can i split a 3mg or 4mg waefin pill so i can get a 2.5mg pill?"}, ] prompt = pipeline.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) terminators = [ pipeline.tokenizer.eos_token_id, pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>") ] outputs = pipeline( prompt, max_new_tokens=256, eos_token_id=terminators, do_sample=True, temperature=0.0, top_p=0.9, ) print(outputs[0]["generated_text"][len(prompt):]) ``` ## **Training procedure** ### **Training hyperparameters** <details> <summary>Click to see details</summary> - learning_rate: 0.0002 - lr_scheduler: cosine - train_batch_size: 12 - eval_batch_size: 8 - GPU: H100 80GB SXM5 - num_devices: 1 - optimizer: adamw_bnb_8bit - lr_scheduler_warmup_steps: 100 - num_epochs: 4 </details> ### **Peft hyperparameters** <details> <summary>Click to see details</summary> - adapter: qlora - lora_r: 128 - lora_alpha: 256 - lora_dropout: 0.05 - lora_target_linear: true -lora_target_modules: - q_proj - v_proj - k_proj - o_proj - gate_proj - down_proj - up_proj </details> ### **Training results** ### **Framework versions** - Transformers 4.39.3 - Pytorch 2.1.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.1 - Axolotl - Lm harness for evaluation # Benchmark Results 🔥 OpenBioLLM-8B demonstrates superior performance compared to larger models, such as GPT-3.5, Meditron-70B across 9 diverse biomedical datasets, achieving state-of-the-art results with an average score of 72.50%, despite having a significantly smaller parameter count. The model's strong performance in domain-specific tasks, such as Clinical KG, Medical Genetics, and PubMedQA, highlights its ability to effectively capture and apply biomedical knowledge. 🚨 The GPT-4, Med-PaLM-1, and Med-PaLM-2 results are taken from their official papers. Since Med-PaLM doesn't provide zero-shot accuracy, we are using 5-shot accuracy from their paper for comparison. All results presented are in the zero-shot setting, except for Med-PaLM-2 and Med-PaLM-1, which use 5-shot accuracy. | | Clinical KG | Medical Genetics | Anatomy | Pro Medicine | College Biology | College Medicine | MedQA 4 opts | PubMedQA | MedMCQA | Avg | | | - | | - | | | **OpenBioLLM-70B** | **92.93** | **93.197** | **83.904** | 93.75 | 93.827 | **85.749** | 78.162 | 78.97 | **74.014** | **86.05588** | | Med-PaLM-2 (5-shot) | 88.3 | 90 | 77.8 | **95.2** | 94.4 | 80.9 | **79.7** | **79.2** | 71.3 | 84.08 | | **GPT-4** | 86.04 | 91 | 80 | 93.01 | **95.14** | 76.88 | 78.87 | 75.2 | 69.52 | 82.85 | | Med-PaLM-1 (Flan-PaLM, 5-shot) | 80.4 | 75 | 63.7 | 83.8 | 88.9 | 76.3 | 67.6 | 79 | 57.6 | 74.7 | | **OpenBioLLM-8B** | 76.101 | 86.1 | 69.829 | 78.21 | 84.213 | 68.042 | 58.993 | 74.12 | 56.913 | 72.502 | | Gemini-1.0 | 76.7 | 75.8 | 66.7 | 77.7 | 88 | 69.2 | 58 | 70.7 | 54.3 | 70.79 | | GPT-3.5 Turbo 1106 | 74.71 | 74 | 72.79 | 72.79 | 72.91 | 64.73 | 57.71 | 72.66 | 53.79 | 66 | | Meditron-70B | 66.79 | 69 | 53.33 | 71.69 | 76.38 | 63 | 57.1 | 76.6 | 46.85 | 64.52 | | gemma-7b | 69.81 | 70 | 59.26 | 66.18 | 79.86 | 60.12 | 47.21 | 76.2 | 48.96 | 64.18 | | Mistral-7B-v0.1 | 68.68 | 71 | 55.56 | 68.38 | 68.06 | 59.54 | 50.82 | 75.4 | 48.2 | 62.85 | | Apollo-7B | 62.26 | 72 | 61.48 | 69.12 | 70.83 | 55.49 | 55.22 | 39.8 | 53.77 | 60 | | MedAlpaca-7b | 57.36 | 69 | 57.04 | 67.28 | 65.28 | 54.34 | 41.71 | 72.8 | 37.51 | 58.03 | | BioMistral-7B | 59.9 | 64 | 56.5 | 60.4 | 59 | 54.7 | 50.6 | 77.5 | 48.1 | 57.3 | | AlpaCare-llama2-7b | 49.81 | 49 | 45.92 | 33.82 | 50 | 43.35 | 29.77 | 72.2 | 34.42 | 45.36 | | ClinicalGPT | 30.56 | 27 | 30.37 | 19.48 | 25 | 24.27 | 26.08 | 63.8 | 28.18 | 30.52 | <div align="center"> <img width="1600px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/_SzdcJSBjZyo8RS1bTEkP.png"> </div> ## Detailed Medical Subjectwise accuracy ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/UXF-V0col0Z0sS6BGPBkE.png) # Use Cases & Examples 🚨 **Below results are from the quantized version of OpenBioLLM-70B** # Summarize Clinical Notes OpenBioLLM-70B can efficiently analyze and summarize complex clinical notes, EHR data, and discharge summaries, extracting key information and generating concise, structured summaries ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/xdwdBgOxNi_TfML0hKlI8.png) # Answer Medical Questions OpenBioLLM-70B can provide answers to a wide range of medical questions. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/zO95GlwOQEZqCKQF69mE6.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/OKBczKw7gWeW5xsuDpc27.png) <details> <summary>Click to see details</summary> ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/eJGHT5khppYvJb8fQ-YW4.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/Cnbwrqa_-ORHRuNRC2P6Y.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/J9DhdcvukAc9mnnW9fj2C.png) </details> # Clinical Entity Recognition OpenBioLLM-70B can perform advanced clinical entity recognition by identifying and extracting key medical concepts, such as diseases, symptoms, medications, procedures, and anatomical structures, from unstructured clinical text. By leveraging its deep understanding of medical terminology and context, the model can accurately annotate and categorize clinical entities, enabling more efficient information retrieval, data analysis, and knowledge discovery from electronic health records, research articles, and other biomedical text sources. This capability can support various downstream applications, such as clinical decision support, pharmacovigilance, and medical research. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/_69BW4k9LVABFwtxixL45.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/DKy5wYCoPhoPPUc1-x8_J.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/7WD9zCCBZT4-4XlfnIQjl.png) # Biomarkers Extraction ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/ZttoM4AiteT7gFYVhjIpN.png) # Classification OpenBioLLM-70B can perform various biomedical classification tasks, such as disease prediction, sentiment analysis, medical document categorization ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/Bf5MW1d75qT-1F_TR_hC0.png) # De-Identification OpenBioLLM-70B can detect and remove personally identifiable information (PII) from medical records, ensuring patient privacy and compliance with data protection regulations like HIPAA. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/hKX4kzm--Tw5bj6K78msy.png) **Advisory Notice!** While OpenBioLLM-70B & 8B leverages high-quality data sources, its outputs may still contain inaccuracies, biases, or misalignments that could pose risks if relied upon for medical decision-making without further testing and refinement. The model's performance has not yet been rigorously evaluated in randomized controlled trials or real-world healthcare environments. Therefore, we strongly advise against using OpenBioLLM-70B & 8B for any direct patient care, clinical decision support, or other professional medical purposes at this time. Its use should be limited to research, development, and exploratory applications by qualified individuals who understand its limitations. OpenBioLLM-70B & 8B are intended solely as a research tool to assist healthcare professionals and should never be considered a replacement for the professional judgment and expertise of a qualified medical doctor. Appropriately adapting and validating OpenBioLLM-70B & 8B for specific medical use cases would require significant additional work, potentially including: - Thorough testing and evaluation in relevant clinical scenarios - Alignment with evidence-based guidelines and best practices - Mitigation of potential biases and failure modes - Integration with human oversight and interpretation - Compliance with regulatory and ethical standards Always consult a qualified healthcare provider for personal medical needs. # Citation If you find OpenBioLLM-70B & 8B useful in your work, please cite the model as follows: ``` @misc{OpenBioLLMs, author = {Ankit Pal, Malaikannan Sankarasubbu}, title = {OpenBioLLMs: Advancing Open-Source Large Language Models for Healthcare and Life Sciences}, year = {2024}, publisher = {Hugging Face}, journal = {Hugging Face repository}, howpublished = {\url{https://huggingface.co/aaditya/OpenBioLLM-Llama3-70B}} } ``` The accompanying paper is currently in progress and will be released soon. <div align="center"> <h2> 💌 Contact </h2> </div> We look forward to hearing you and collaborating on this exciting project! **Contributors:** - [Ankit Pal (Aaditya Ura)](https://aadityaura.github.io/) [aadityaura at gmail dot com] - Saama AI Labs - Note: I am looking for a funded PhD opportunity, especially if it fits my Responsible Generative AI, Multimodal LLMs, Geometric Deep Learning, and Healthcare AI skillset. # References We thank the [Meta Team](meta-llama/Meta-Llama-3-70B-Instruct) for their amazing models! Result sources - [1] GPT-4 [Capabilities of GPT-4 on Medical Challenge Problems] (https://arxiv.org/abs/2303.13375) - [2] Med-PaLM-1 [Large Language Models Encode Clinical Knowledge](https://arxiv.org/abs/2212.13138) - [3] Med-PaLM-2 [Towards Expert-Level Medical Question Answering with Large Language Models](https://arxiv.org/abs/2305.09617) - [4] Gemini-1.0 [Gemini Goes to Med School](https://arxiv.org/abs/2402.07023) <!-- original-model-card end -->
{"language": ["en"], "license": "llama3", "tags": ["llama-3", "llama", "Mixtral", "instruct", "finetune", "chatml", "DPO", "RLHF", "gpt4", "distillation", "GGUF"], "base_model": "meta-llama/Meta-Llama-3-8B", "widget": [{"example_title": "OpenBioLLM-8B", "messages": [{"role": "system", "content": "You are an expert and experienced from the healthcare and biomedical domain with extensive medical knowledge and practical experience."}, {"role": "user", "content": "How long does it take for newborn jaundice to go away?"}], "output": {"text": "Newborn jaundice, also known as neonatal jaundice, is a common condition in newborns where the yellowing of the skin and eyes occurs due to an elevated level of bilirubin in the blood. Bilirubin is a yellow pigment that forms when red blood cells break down. In most cases, newborn jaundice resolves on its own without any specific treatment.\nThe duration of newborn jaundice can vary depending on several factors such as the underlying cause, gestational age at birth, and individual variations in bilirubin metabolism. Here are some general guidelines:\n1. Physiological jaundice: This is the most common type of newborn jaundice and usually appears within 24-72 hours after birth. It tends to peak between the second and fifth day of life and gradually improves over the next week or two. By the time the baby is one week old, the jaundice should have mostly resolved. 2. Breast milk jaundice: This type of jaundice occurs in breastfed babies and may appear later than physiological jaundice, typically between the fifth and fourteenth day of life. It tends to persist for a longer duration but usually resolves within six weeks after birth. 3. Pathological jaundice: This type of jaundice is less common and occurs due to an underlying medical condition that affects bilirubin metabolism or liver function. The duration of pathological jaundice depends on the specific cause and may require treatment.\nIt's important for parents to monitor their newborn's jaundice closely and seek medical advice if the jaundice progresses rapidly, becomes severe, or is accompanied by other symptoms such as poor feeding, lethargy, or excessive sleepiness. In these cases, further evaluation and management may be necessary. Remember that each baby is unique, and the timing of jaundice resolution can vary. If you have concerns about your newborn's jaundice, it's always best to consult with a healthcare professional for personalized advice and guidance."}}], "quantized_by": "andrijdavid", "model-index": [{"name": "OpenBioLLM-8B", "results": []}]}
LiteLLMs/Llama3-OpenBioLLM-8B-GGUF
null
[ "gguf", "llama-3", "llama", "Mixtral", "instruct", "finetune", "chatml", "DPO", "RLHF", "gpt4", "distillation", "GGUF", "en", "arxiv:2305.18290", "arxiv:2303.13375", "arxiv:2212.13138", "arxiv:2305.09617", "arxiv:2402.07023", "base_model:meta-llama/Meta-Llama-3-8B", "license:llama3", "region:us" ]
null
2024-04-29T16:51:27+00:00
null
null
{}
sanchit-gandhi/distil-mixtral-8x1.5B-Instruct-v0.1
null
[ "region:us" ]
null
2024-04-29T16:52:27+00:00
text-generation
null
## Llamacpp imatrix Quantizations of Phi-3-mini-4k-instruct Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> commit <a href="https://github.com/ggerganov/llama.cpp/commit/ffe666572f98a686b17a2cd1dbf4c0a982e5ac0a">ffe6665</a> for quantization. Original model: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct All quants made using imatrix option with dataset provided by Kalomaze [here](https://github.com/ggerganov/llama.cpp/discussions/5263#discussioncomment-8395384) ## Prompt format ``` <s><|user|> {system_prompt}<|end|> <|assistant|> <|user|> {prompt}<|end|> <|assistant|> ``` ## Download a file (not the whole branch) from below: | Filename | Quant type | File Size | Description | | -------- | ---------- | --------- | ----------- | | [Phi-3-mini-4k-instruct-Q8_0.gguf](https://huggingface.co/bartowski/Phi-3-mini-4k-instruct-GGUF/blob/main/Phi-3-mini-4k-instruct-Q8_0.gguf) | Q8_0 | 4.06GB | Extremely high quality, generally unneeded but max available quant. | | [Phi-3-mini-4k-instruct-Q6_K.gguf](https://huggingface.co/bartowski/Phi-3-mini-4k-instruct-GGUF/blob/main/Phi-3-mini-4k-instruct-Q6_K.gguf) | Q6_K | 3.13GB | Very high quality, near perfect, *recommended*. | | [Phi-3-mini-4k-instruct-Q5_K_M.gguf](https://huggingface.co/bartowski/Phi-3-mini-4k-instruct-GGUF/blob/main/Phi-3-mini-4k-instruct-Q5_K_M.gguf) | Q5_K_M | 2.81GB | High quality, *recommended*. | | [Phi-3-mini-4k-instruct-Q5_K_S.gguf](https://huggingface.co/bartowski/Phi-3-mini-4k-instruct-GGUF/blob/main/Phi-3-mini-4k-instruct-Q5_K_S.gguf) | Q5_K_S | 2.64GB | High quality, *recommended*. | | [Phi-3-mini-4k-instruct-Q4_K_M.gguf](https://huggingface.co/bartowski/Phi-3-mini-4k-instruct-GGUF/blob/main/Phi-3-mini-4k-instruct-Q4_K_M.gguf) | Q4_K_M | 2.39GB | Good quality, uses about 4.83 bits per weight, *recommended*. | | [Phi-3-mini-4k-instruct-Q4_K_S.gguf](https://huggingface.co/bartowski/Phi-3-mini-4k-instruct-GGUF/blob/main/Phi-3-mini-4k-instruct-Q4_K_S.gguf) | Q4_K_S | 2.18GB | Slightly lower quality with more space savings, *recommended*. | | [Phi-3-mini-4k-instruct-IQ4_NL.gguf](https://huggingface.co/bartowski/Phi-3-mini-4k-instruct-GGUF/blob/main/Phi-3-mini-4k-instruct-IQ4_NL.gguf) | IQ4_NL | 2.17GB | Decent quality, slightly smaller than Q4_K_S with similar performance *recommended*. | | [Phi-3-mini-4k-instruct-IQ4_XS.gguf](https://huggingface.co/bartowski/Phi-3-mini-4k-instruct-GGUF/blob/main/Phi-3-mini-4k-instruct-IQ4_XS.gguf) | IQ4_XS | 2.05GB | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. | | [Phi-3-mini-4k-instruct-Q3_K_L.gguf](https://huggingface.co/bartowski/Phi-3-mini-4k-instruct-GGUF/blob/main/Phi-3-mini-4k-instruct-Q3_K_L.gguf) | Q3_K_L | 2.08GB | Lower quality but usable, good for low RAM availability. | | [Phi-3-mini-4k-instruct-Q3_K_M.gguf](https://huggingface.co/bartowski/Phi-3-mini-4k-instruct-GGUF/blob/main/Phi-3-mini-4k-instruct-Q3_K_M.gguf) | Q3_K_M | 1.95GB | Even lower quality. | | [Phi-3-mini-4k-instruct-IQ3_M.gguf](https://huggingface.co/bartowski/Phi-3-mini-4k-instruct-GGUF/blob/main/Phi-3-mini-4k-instruct-IQ3_M.gguf) | IQ3_M | 1.85GB | Medium-low quality, new method with decent performance comparable to Q3_K_M. | | [Phi-3-mini-4k-instruct-IQ3_S.gguf](https://huggingface.co/bartowski/Phi-3-mini-4k-instruct-GGUF/blob/main/Phi-3-mini-4k-instruct-IQ3_S.gguf) | IQ3_S | 1.68GB | Lower quality, new method with decent performance, recommended over Q3_K_S quant, same size with better performance. | | [Phi-3-mini-4k-instruct-Q3_K_S.gguf](https://huggingface.co/bartowski/Phi-3-mini-4k-instruct-GGUF/blob/main/Phi-3-mini-4k-instruct-Q3_K_S.gguf) | Q3_K_S | 1.68GB | Low quality, not recommended. | | [Phi-3-mini-4k-instruct-IQ3_XS.gguf](https://huggingface.co/bartowski/Phi-3-mini-4k-instruct-GGUF/blob/main/Phi-3-mini-4k-instruct-IQ3_XS.gguf) | IQ3_XS | 1.62GB | Lower quality, new method with decent performance, slightly better than Q3_K_S. | | [Phi-3-mini-4k-instruct-IQ3_XXS.gguf](https://huggingface.co/bartowski/Phi-3-mini-4k-instruct-GGUF/blob/main/Phi-3-mini-4k-instruct-IQ3_XXS.gguf) | IQ3_XXS | 1.51GB | Lower quality, new method with decent performance, comparable to Q3 quants. | | [Phi-3-mini-4k-instruct-Q2_K.gguf](https://huggingface.co/bartowski/Phi-3-mini-4k-instruct-GGUF/blob/main/Phi-3-mini-4k-instruct-Q2_K.gguf) | Q2_K | 1.41GB | Very low quality but surprisingly usable. | | [Phi-3-mini-4k-instruct-IQ2_M.gguf](https://huggingface.co/bartowski/Phi-3-mini-4k-instruct-GGUF/blob/main/Phi-3-mini-4k-instruct-IQ2_M.gguf) | IQ2_M | 1.31GB | Very low quality, uses SOTA techniques to also be surprisingly usable. | | [Phi-3-mini-4k-instruct-IQ2_S.gguf](https://huggingface.co/bartowski/Phi-3-mini-4k-instruct-GGUF/blob/main/Phi-3-mini-4k-instruct-IQ2_S.gguf) | IQ2_S | 1.21GB | Very low quality, uses SOTA techniques to be usable. | | [Phi-3-mini-4k-instruct-IQ2_XS.gguf](https://huggingface.co/bartowski/Phi-3-mini-4k-instruct-GGUF/blob/main/Phi-3-mini-4k-instruct-IQ2_XS.gguf) | IQ2_XS | 1.15GB | Very low quality, uses SOTA techniques to be usable. | | [Phi-3-mini-4k-instruct-IQ2_XXS.gguf](https://huggingface.co/bartowski/Phi-3-mini-4k-instruct-GGUF/blob/main/Phi-3-mini-4k-instruct-IQ2_XXS.gguf) | IQ2_XXS | 1.04GB | Lower quality, uses SOTA techniques to be usable. | | [Phi-3-mini-4k-instruct-IQ1_M.gguf](https://huggingface.co/bartowski/Phi-3-mini-4k-instruct-GGUF/blob/main/Phi-3-mini-4k-instruct-IQ1_M.gguf) | IQ1_M | .91GB | Extremely low quality, *not* recommended. | | [Phi-3-mini-4k-instruct-IQ1_S.gguf](https://huggingface.co/bartowski/Phi-3-mini-4k-instruct-GGUF/blob/main/Phi-3-mini-4k-instruct-IQ1_S.gguf) | IQ1_S | .84GB | Extremely low quality, *not* recommended. | ## Which file should I choose? A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have. If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM. If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total. Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'. If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M. If you want to get more into the weeds, you can check out this extremely useful feature chart: [llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix) But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size. These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide. The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm. Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
{"language": ["en"], "license": "mit", "tags": ["nlp", "code"], "license_link": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/resolve/main/LICENSE", "pipeline_tag": "text-generation", "widget": [{"messages": [{"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"}]}], "quantized_by": "bartowski"}
bartowski/Phi-3-mini-4k-instruct-GGUF
null
[ "gguf", "nlp", "code", "text-generation", "en", "license:mit", "region:us" ]
null
2024-04-29T16:53:40+00:00
null
null
{"license": "openrail"}
Pedro1230987/MickeyMouse
null
[ "license:openrail", "region:us" ]
null
2024-04-29T16:53:52+00:00
null
null
{}
NAV1515/temple
null
[ "region:us" ]
null
2024-04-29T16:53:55+00:00
sentence-similarity
sentence-transformers
# SentenceTransformer based on distilbert/distilroberta-base This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) on the [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) <!-- at revision fb53ab8802853c8e4fbdbcd0529f21fc6f459b2b --> - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 tokens - **Similarity Function:** Cosine Similarity - **Training Dataset:** - [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) - **Language:** en <!-- - **License:** Unknown --> ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("tomaarsen/distilroberta-base-nli-2d-matryoshka") # Run inference sentences = [ 'A plane in the sky.', 'Two airplanes in the sky.', 'Nelson Mandela undergoes surgery', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> ## Evaluation ### Metrics #### Semantic Similarity * Dataset: `sts-dev` * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.8395 | | **spearman_cosine** | **0.8425** | | pearson_manhattan | 0.8433 | | spearman_manhattan | 0.8436 | | pearson_euclidean | 0.8441 | | spearman_euclidean | 0.8449 | | pearson_dot | 0.7638 | | spearman_dot | 0.757 | | pearson_max | 0.8441 | | spearman_max | 0.8449 | #### Semantic Similarity * Dataset: `sts-test` * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.8187 | | **spearman_cosine** | **0.8171** | | pearson_manhattan | 0.8117 | | spearman_manhattan | 0.8049 | | pearson_euclidean | 0.8127 | | spearman_euclidean | 0.8058 | | pearson_dot | 0.7396 | | spearman_dot | 0.7256 | | pearson_max | 0.8187 | | spearman_max | 0.8171 | <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* --> ## Training Details ### Training Dataset #### sentence-transformers/all-nli * Dataset: [sentence-transformers/all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [65dd388](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/65dd38867b600f42241d2066ba1a35fbd097fcfe) * Size: 557,850 training samples * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 7 tokens</li><li>mean: 10.38 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.8 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.4 tokens</li><li>max: 50 tokens</li></ul> | * Samples: | anchor | positive | negative | |:---------------------------------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------------| | <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is outdoors, on a horse.</code> | <code>A person is at a diner, ordering an omelette.</code> | | <code>Children smiling and waving at camera</code> | <code>There are children present</code> | <code>The kids are frowning</code> | | <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | <code>The boy skates down the sidewalk.</code> | * Loss: [<code>Matryoshka2dLoss</code>](https://sbert.net/docs/package_reference/losses.html#matryoshka2dloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "n_layers_per_step": 1, "last_layer_weight": 1.0, "prior_layers_weight": 1.0, "kl_div_weight": 1.0, "kl_temperature": 0.3, "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": 1 } ``` ### Evaluation Dataset #### sentence-transformers/stsb * Dataset: [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) at [ab7a5ac](https://huggingface.co/datasets/sentence-transformers/stsb/tree/ab7a5ac0e35aa22088bdcf23e7fd99b220e53308) * Size: 1,500 evaluation samples * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | score | |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | <ul><li>min: 5 tokens</li><li>mean: 15.0 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.99 tokens</li><li>max: 61 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.47</li><li>max: 1.0</li></ul> | * Samples: | sentence1 | sentence2 | score | |:--------------------------------------------------|:------------------------------------------------------|:------------------| | <code>A man with a hard hat is dancing.</code> | <code>A man wearing a hard hat is dancing.</code> | <code>1.0</code> | | <code>A young child is riding a horse.</code> | <code>A child is riding a horse.</code> | <code>0.95</code> | | <code>A man is feeding a mouse to a snake.</code> | <code>The man is feeding a mouse to the snake.</code> | <code>1.0</code> | * Loss: [<code>Matryoshka2dLoss</code>](https://sbert.net/docs/package_reference/losses.html#matryoshka2dloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "n_layers_per_step": 1, "last_layer_weight": 1.0, "prior_layers_weight": 1.0, "kl_div_weight": 1.0, "kl_temperature": 0.3, "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": 1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 128 - `per_device_eval_batch_size`: 128 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `fp16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: False - `per_device_train_batch_size`: 128 - `per_device_eval_batch_size`: 128 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: None - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs | Epoch | Step | Training Loss | loss | sts-dev_spearman_cosine | sts-test_spearman_cosine | |:------:|:----:|:-------------:|:------:|:-----------------------:|:------------------------:| | 0.0229 | 100 | 6.2779 | 3.9959 | 0.8008 | - | | 0.0459 | 200 | 4.3212 | 3.5818 | 0.7956 | - | | 0.0688 | 300 | 3.7135 | 3.4422 | 0.7940 | - | | 0.0918 | 400 | 3.5567 | 3.5458 | 0.7951 | - | | 0.1147 | 500 | 3.1297 | 3.1253 | 0.8050 | - | | 0.1376 | 600 | 2.7001 | 3.4366 | 0.7996 | - | | 0.1606 | 700 | 2.8664 | 3.6609 | 0.8033 | - | | 0.1835 | 800 | 2.6656 | 3.3736 | 0.7975 | - | | 0.2065 | 900 | 2.633 | 3.3735 | 0.8076 | - | | 0.2294 | 1000 | 2.4335 | 3.6499 | 0.7996 | - | | 0.2524 | 1100 | 2.4165 | 3.6301 | 0.8015 | - | | 0.2753 | 1200 | 2.2942 | 3.1541 | 0.7994 | - | | 0.2982 | 1300 | 2.2402 | 3.4284 | 0.7977 | - | | 0.3212 | 1400 | 2.2148 | 3.3775 | 0.7988 | - | | 0.3441 | 1500 | 2.2285 | 3.6097 | 0.8016 | - | | 0.3671 | 1600 | 2.0591 | 3.3839 | 0.7926 | - | | 0.3900 | 1700 | 2.0253 | 3.1113 | 0.7981 | - | | 0.4129 | 1800 | 2.0244 | 3.8289 | 0.7954 | - | | 0.4359 | 1900 | 1.8582 | 3.3515 | 0.8000 | - | | 0.4588 | 2000 | 1.977 | 3.3054 | 0.7917 | - | | 0.4818 | 2100 | 1.9028 | 3.2166 | 0.7927 | - | | 0.5047 | 2200 | 1.8316 | 3.6504 | 0.7955 | - | | 0.5276 | 2300 | 1.8404 | 3.2822 | 0.7843 | - | | 0.5506 | 2400 | 1.8455 | 3.2583 | 0.7941 | - | | 0.5735 | 2500 | 1.9488 | 3.3970 | 0.7971 | - | | 0.5965 | 2600 | 1.9403 | 2.8948 | 0.7959 | - | | 0.6194 | 2700 | 1.8884 | 3.2227 | 0.8008 | - | | 0.6423 | 2800 | 1.8655 | 3.1948 | 0.7920 | - | | 0.6653 | 2900 | 1.8567 | 3.4374 | 0.7913 | - | | 0.6882 | 3000 | 1.8423 | 3.1118 | 0.7949 | - | | 0.7112 | 3100 | 1.7475 | 3.1359 | 0.8062 | - | | 0.7341 | 3200 | 1.8166 | 2.9927 | 0.7984 | - | | 0.7571 | 3300 | 1.5626 | 3.5143 | 0.8405 | - | | 0.7800 | 3400 | 1.2038 | 3.3909 | 0.8411 | - | | 0.8029 | 3500 | 1.1579 | 3.2458 | 0.8413 | - | | 0.8259 | 3600 | 1.0978 | 3.1592 | 0.8404 | - | | 0.8488 | 3700 | 1.0283 | 2.9557 | 0.8408 | - | | 0.8718 | 3800 | 0.9993 | 3.4073 | 0.8430 | - | | 0.8947 | 3900 | 0.9727 | 3.0570 | 0.8434 | - | | 0.9176 | 4000 | 0.9692 | 2.9357 | 0.8439 | - | | 0.9406 | 4100 | 0.9412 | 2.9494 | 0.8428 | - | | 0.9635 | 4200 | 1.0063 | 3.4047 | 0.8422 | - | | 0.9865 | 4300 | 0.9678 | 3.4299 | 0.8425 | - | | 1.0 | 4359 | - | - | - | 0.8171 | ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Energy Consumed**: 0.178 kWh - **Carbon Emitted**: 0.069 kg of CO2 - **Hours Used**: 0.626 hours ### Training Hardware - **On Cloud**: No - **GPU Model**: 1 x NVIDIA GeForce RTX 3090 - **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K - **RAM Size**: 31.78 GB ### Framework Versions - Python: 3.11.6 - Sentence Transformers: 3.0.0.dev0 - Transformers: 4.41.0.dev0 - PyTorch: 2.3.0+cu121 - Accelerate: 0.26.1 - Datasets: 2.18.0 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### Matryoshka2dLoss ```bibtex @misc{li20242d, title={2D Matryoshka Sentence Embeddings}, author={Xianming Li and Zongxi Li and Jing Li and Haoran Xie and Qing Li}, year={2024}, eprint={2402.14776}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary *Clearly define terms in order to be accessible across audiences.* --> <!-- ## Model Card Authors *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* --> <!-- ## Model Card Contact *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* -->
{"language": ["en"], "library_name": "sentence-transformers", "tags": ["sentence-transformers", "sentence-similarity", "feature-extraction", "loss:Matryoshka2dLoss", "loss:MatryoshkaLoss", "loss:MultipleNegativesRankingLoss"], "metrics": ["pearson_cosine", "spearman_cosine", "pearson_manhattan", "spearman_manhattan", "pearson_euclidean", "spearman_euclidean", "pearson_dot", "spearman_dot", "pearson_max", "spearman_max"], "base_model": "distilbert/distilroberta-base", "widget": [{"source_sentence": "A woman is reading.", "sentences": ["A woman is writing something.", "A man helps a boy ride a bike.", "A group wading across a ditch"]}, {"source_sentence": "A man shoots a man.", "sentences": ["A man with a pistol shoots another man.", "Suicide bomber strikes in Syria", "China and Taiwan hold historic talks"]}, {"source_sentence": "A boy is vacuuming.", "sentences": ["A little boy is vacuuming the floor.", "Breivik: Jail term 'ridiculous'", "Glorious triple-gold night for Britain"]}, {"source_sentence": "A man is spitting.", "sentences": ["A man is speaking.", "The boy is jumping into a lake.", "10 Things to Know for Thursday"]}, {"source_sentence": "A plane in the sky.", "sentences": ["Two airplanes in the sky.", "Nelson Mandela undergoes surgery", "Nelson Mandela undergoes surgery"]}], "pipeline_tag": "sentence-similarity", "co2_eq_emissions": {"emissions": 69.2573690422145, "energy_consumed": 0.1781760038338226, "source": "codecarbon", "training_type": "fine-tuning", "on_cloud": false, "cpu_model": "13th Gen Intel(R) Core(TM) i7-13700K", "ram_total_size": 31.777088165283203, "hours_used": 0.626, "hardware_used": "1 x NVIDIA GeForce RTX 3090"}, "model-index": [{"name": "SentenceTransformer based on distilbert/distilroberta-base", "results": [{"task": {"type": "semantic-similarity", "name": "Semantic Similarity"}, "dataset": {"name": "sts dev", "type": "sts-dev"}, "metrics": [{"type": "pearson_cosine", "value": 0.8395203447657347, "name": "Pearson Cosine"}, {"type": "spearman_cosine", "value": 0.8424556124488326, "name": "Spearman Cosine"}, {"type": "pearson_manhattan", "value": 0.8432537220190851, "name": "Pearson Manhattan"}, {"type": "spearman_manhattan", "value": 0.8435994230515586, "name": "Spearman Manhattan"}, {"type": "pearson_euclidean", "value": 0.8440900768179745, "name": "Pearson Euclidean"}, {"type": "spearman_euclidean", "value": 0.8449067313707376, "name": "Spearman Euclidean"}, {"type": "pearson_dot", "value": 0.763767029856877, "name": "Pearson Dot"}, {"type": "spearman_dot", "value": 0.7569706383510251, "name": "Spearman Dot"}, {"type": "pearson_max", "value": 0.8440900768179745, "name": "Pearson Max"}, {"type": "spearman_max", "value": 0.8449067313707376, "name": "Spearman Max"}]}, {"task": {"type": "semantic-similarity", "name": "Semantic Similarity"}, "dataset": {"name": "sts test", "type": "sts-test"}, "metrics": [{"type": "pearson_cosine", "value": 0.8186702838538092, "name": "Pearson Cosine"}, {"type": "spearman_cosine", "value": 0.8170686920551, "name": "Spearman Cosine"}, {"type": "pearson_manhattan", "value": 0.8117192659894803, "name": "Pearson Manhattan"}, {"type": "spearman_manhattan", "value": 0.804879002947593, "name": "Spearman Manhattan"}, {"type": "pearson_euclidean", "value": 0.8127154744140831, "name": "Pearson Euclidean"}, {"type": "spearman_euclidean", "value": 0.8058410028545979, "name": "Spearman Euclidean"}, {"type": "pearson_dot", "value": 0.7396245702595934, "name": "Pearson Dot"}, {"type": "spearman_dot", "value": 0.7256120569318246, "name": "Spearman Dot"}, {"type": "pearson_max", "value": 0.8186702838538092, "name": "Pearson Max"}, {"type": "spearman_max", "value": 0.8170686920551, "name": "Spearman Max"}]}]}]}
tomaarsen/distilroberta-base-nli-2d-matryoshka
null
[ "sentence-transformers", "safetensors", "roberta", "sentence-similarity", "feature-extraction", "loss:Matryoshka2dLoss", "loss:MatryoshkaLoss", "loss:MultipleNegativesRankingLoss", "en", "arxiv:1908.10084", "arxiv:2402.14776", "arxiv:2205.13147", "arxiv:1705.00652", "base_model:distilbert/distilroberta-base", "model-index", "co2_eq_emissions", "endpoints_compatible", "region:us" ]
null
2024-04-29T16:56:36+00:00
text-generation
transformers
# Uploaded model - **Developed by:** arthrod - **License:** apache-2.0 - **Finetuned from model :** botbot-ai/CabraLlama3-8b This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl", "orpo"], "base_model": "botbot-ai/CabraLlama3-8b"}
arthrod/cicerocabra
null
[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "orpo", "conversational", "en", "base_model:botbot-ai/CabraLlama3-8b", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
null
2024-04-29T16:57:28+00:00
text-generation
transformers
{"license": "apache-2.0"}
ali77sina/tinyLlamaSEC
null
[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
null
2024-04-29T16:57:58+00:00
text2text-generation
transformers
# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
{"license": "mit", "library_name": "transformers"}
houyu0930/test-demo-t5-qa
null
[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
null
2024-04-29T17:00:11+00:00
text2text-generation
transformers
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # pegasus-samsum This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4833 ## 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: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 1.6599 | 0.5430 | 500 | 1.4833 | ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1
{"tags": ["generated_from_trainer"], "base_model": "google/pegasus-cnn_dailymail", "model-index": [{"name": "pegasus-samsum", "results": []}]}
khwrali/pegasus-samsum
null
[ "transformers", "safetensors", "pegasus", "text2text-generation", "generated_from_trainer", "base_model:google/pegasus-cnn_dailymail", "autotrain_compatible", "endpoints_compatible", "region:us" ]
null
2024-04-29T17:00:12+00:00
text-classification
transformers
{}
YasserSoliman/distilbert-base-uncased-finetuned-emotion
null
[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]
null
2024-04-29T17:00:32+00:00
null
null
{}
WoolyMamooth/sokoban1
null
[ "region:us" ]
null
2024-04-29T17:01:05+00:00
text-to-image
diffusers
# SDXL LoRA DreamBooth - aarashfeizi/jean-francois-godbout-batch3-repeats3-rank32-snrNone <Gallery /> ## Model description ### These are aarashfeizi/jean-francois-godbout-batch3-repeats3-rank32-snrNone LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. ## Download model ### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke - **LoRA**: download **[`/home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout-batch3-repeats3-rank32-snrNone.safetensors` here 💾](/aarashfeizi/jean-francois-godbout-batch3-repeats3-rank32-snrNone/blob/main//home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout-batch3-repeats3-rank32-snrNone.safetensors)**. - Place it on your `models/Lora` folder. - On AUTOMATIC1111, load the LoRA by adding `<lora:/home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout-batch3-repeats3-rank32-snrNone:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/). - *Embeddings*: download **[`/home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout-batch3-repeats3-rank32-snrNone_emb.safetensors` here 💾](/aarashfeizi/jean-francois-godbout-batch3-repeats3-rank32-snrNone/blob/main//home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout-batch3-repeats3-rank32-snrNone_emb.safetensors)**. - Place it on it on your `embeddings` folder - Use it by adding `/home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout-batch3-repeats3-rank32-snrNone_emb` to your prompt. For example, `A photo of /home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout-batch3-repeats3-rank32-snrNone_emb` (you need both the LoRA and the embeddings as they were trained together for this LoRA) ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch from huggingface_hub import hf_hub_download from safetensors.torch import load_file pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('aarashfeizi/jean-francois-godbout-batch3-repeats3-rank32-snrNone', weight_name='pytorch_lora_weights.safetensors') embedding_path = hf_hub_download(repo_id='aarashfeizi/jean-francois-godbout-batch3-repeats3-rank32-snrNone', filename='/home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout-batch3-repeats3-rank32-snrNone_emb.safetensors', repo_type="model") state_dict = load_file(embedding_path) pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer) pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2) image = pipeline('A photo of <s0><s1> giving a speech').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Trigger words To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens: to trigger concept `TOK` → use `<s0><s1>` in your prompt ## Details All [Files & versions](/aarashfeizi/jean-francois-godbout-batch3-repeats3-rank32-snrNone/tree/main). The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py). LoRA for the text encoder was enabled. False. Pivotal tuning was enabled: True. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
{"license": "openrail++", "tags": ["stable-diffusion-xl", "stable-diffusion-xl-diffusers", "diffusers-training", "text-to-image", "diffusers", "lora", "template:sd-lora"], "widget": [{"text": "A photo of <s0><s1> giving a speech", "output": {"url": "image_0.png"}}, {"text": "A photo of <s0><s1> giving a speech", "output": {"url": "image_1.png"}}, {"text": "A photo of <s0><s1> giving a speech", "output": {"url": "image_2.png"}}, {"text": "A photo of <s0><s1> giving a speech", "output": {"url": "image_3.png"}}], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "A photo of <s0><s1>"}
aarashfeizi/jean-francois-godbout-batch3-repeats3-rank32-snrNone
null
[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "diffusers-training", "text-to-image", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]
null
2024-04-29T17:01:40+00:00
sentence-similarity
sentence-transformers
# SentenceTransformer based on distilbert/distilbert-base-uncased This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) <!-- at revision 6cdc0aad91f5ae2e6712e91bc7b65d1cf5c05411 --> - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 tokens - **Similarity Function:** Cosine Similarity - **Training Dataset:** - [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) - **Language:** en <!-- - **License:** Unknown --> ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("tomaarsen/distilbert-base-uncased-sts-2d-matryoshka") # Run inference sentences = [ 'A parrot is talking.', 'A parrot is talking into a microphone.', 'A monkey pratices martial arts.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> ## Evaluation ### Metrics #### Semantic Similarity * Dataset: `sts-dev` * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.8619 | | **spearman_cosine** | **0.8713** | | pearson_manhattan | 0.8611 | | spearman_manhattan | 0.8619 | | pearson_euclidean | 0.8615 | | spearman_euclidean | 0.8624 | | pearson_dot | 0.7716 | | spearman_dot | 0.7816 | | pearson_max | 0.8619 | | spearman_max | 0.8713 | #### Semantic Similarity * Dataset: `sts-test` * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.8282 | | **spearman_cosine** | **0.8373** | | pearson_manhattan | 0.8425 | | spearman_manhattan | 0.8374 | | pearson_euclidean | 0.8425 | | spearman_euclidean | 0.8373 | | pearson_dot | 0.675 | | spearman_dot | 0.6568 | | pearson_max | 0.8425 | | spearman_max | 0.8374 | <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* --> ## Training Details ### Training Dataset #### sentence-transformers/stsb * Dataset: [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) at [ab7a5ac](https://huggingface.co/datasets/sentence-transformers/stsb/tree/ab7a5ac0e35aa22088bdcf23e7fd99b220e53308) * Size: 5,749 training samples * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | score | |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | <ul><li>min: 6 tokens</li><li>mean: 10.0 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 9.95 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.54</li><li>max: 1.0</li></ul> | * Samples: | sentence1 | sentence2 | score | |:-----------------------------------------------------------|:----------------------------------------------------------------------|:------------------| | <code>A plane is taking off.</code> | <code>An air plane is taking off.</code> | <code>1.0</code> | | <code>A man is playing a large flute.</code> | <code>A man is playing a flute.</code> | <code>0.76</code> | | <code>A man is spreading shreded cheese on a pizza.</code> | <code>A man is spreading shredded cheese on an uncooked pizza.</code> | <code>0.76</code> | * Loss: [<code>Matryoshka2dLoss</code>](https://sbert.net/docs/package_reference/losses.html#matryoshka2dloss) with these parameters: ```json { "loss": "CoSENTLoss", "n_layers_per_step": 1, "last_layer_weight": 1.0, "prior_layers_weight": 1.0, "kl_div_weight": 1.0, "kl_temperature": 0.3, "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": 1 } ``` ### Evaluation Dataset #### sentence-transformers/stsb * Dataset: [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) at [ab7a5ac](https://huggingface.co/datasets/sentence-transformers/stsb/tree/ab7a5ac0e35aa22088bdcf23e7fd99b220e53308) * Size: 1,500 evaluation samples * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code> * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | score | |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | <ul><li>min: 5 tokens</li><li>mean: 15.1 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.11 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.47</li><li>max: 1.0</li></ul> | * Samples: | sentence1 | sentence2 | score | |:--------------------------------------------------|:------------------------------------------------------|:------------------| | <code>A man with a hard hat is dancing.</code> | <code>A man wearing a hard hat is dancing.</code> | <code>1.0</code> | | <code>A young child is riding a horse.</code> | <code>A child is riding a horse.</code> | <code>0.95</code> | | <code>A man is feeding a mouse to a snake.</code> | <code>The man is feeding a mouse to the snake.</code> | <code>1.0</code> | * Loss: [<code>Matryoshka2dLoss</code>](https://sbert.net/docs/package_reference/losses.html#matryoshka2dloss) with these parameters: ```json { "loss": "CoSENTLoss", "n_layers_per_step": 1, "last_layer_weight": 1.0, "prior_layers_weight": 1.0, "kl_div_weight": 1.0, "kl_temperature": 0.3, "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": 1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `num_train_epochs`: 4 - `warmup_ratio`: 0.1 - `fp16`: True #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: False - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 4 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: None - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs | Epoch | Step | Training Loss | loss | sts-dev_spearman_cosine | sts-test_spearman_cosine | |:------:|:----:|:-------------:|:------:|:-----------------------:|:------------------------:| | 0.2778 | 100 | 7.1781 | 6.6704 | 0.8345 | - | | 0.5556 | 200 | 6.5316 | 6.7135 | 0.8439 | - | | 0.8333 | 300 | 6.6267 | 6.8697 | 0.8551 | - | | 1.1111 | 400 | 6.5709 | 6.7623 | 0.8568 | - | | 1.3889 | 500 | 6.2898 | 6.4412 | 0.8644 | - | | 1.6667 | 600 | 6.2021 | 6.7711 | 0.8595 | - | | 1.9444 | 700 | 6.201 | 6.5252 | 0.8628 | - | | 2.2222 | 800 | 6.0862 | 6.9795 | 0.8652 | - | | 2.5 | 900 | 6.303 | 6.7339 | 0.8685 | - | | 2.7778 | 1000 | 5.9031 | 6.7249 | 0.8694 | - | | 3.0556 | 1100 | 6.0803 | 6.8350 | 0.8684 | - | | 3.3333 | 1200 | 6.0564 | 6.9703 | 0.8695 | - | | 3.6111 | 1300 | 5.8407 | 7.3822 | 0.8707 | - | | 3.8889 | 1400 | 5.8229 | 7.0442 | 0.8713 | - | | 4.0 | 1440 | - | - | - | 0.8373 | ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Energy Consumed**: 0.014 kWh - **Carbon Emitted**: 0.005 kg of CO2 - **Hours Used**: 0.072 hours ### Training Hardware - **On Cloud**: No - **GPU Model**: 1 x NVIDIA GeForce RTX 3090 - **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K - **RAM Size**: 31.78 GB ### Framework Versions - Python: 3.11.6 - Sentence Transformers: 3.0.0.dev0 - Transformers: 4.41.0.dev0 - PyTorch: 2.3.0+cu121 - Accelerate: 0.26.1 - Datasets: 2.18.0 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### Matryoshka2dLoss ```bibtex @misc{li20242d, title={2D Matryoshka Sentence Embeddings}, author={Xianming Li and Zongxi Li and Jing Li and Haoran Xie and Qing Li}, year={2024}, eprint={2402.14776}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### CoSENTLoss ```bibtex @online{kexuefm-8847, title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT}, author={Su Jianlin}, year={2022}, month={Jan}, url={https://kexue.fm/archives/8847}, } ``` <!-- ## Glossary *Clearly define terms in order to be accessible across audiences.* --> <!-- ## Model Card Authors *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* --> <!-- ## Model Card Contact *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* -->
{"language": ["en"], "library_name": "sentence-transformers", "tags": ["sentence-transformers", "sentence-similarity", "feature-extraction", "loss:Matryoshka2dLoss", "loss:MatryoshkaLoss", "loss:CoSENTLoss"], "metrics": ["pearson_cosine", "spearman_cosine", "pearson_manhattan", "spearman_manhattan", "pearson_euclidean", "spearman_euclidean", "pearson_dot", "spearman_dot", "pearson_max", "spearman_max"], "base_model": "distilbert/distilbert-base-uncased", "widget": [{"source_sentence": "A woman is reading.", "sentences": ["A woman is taking a picture.", "Breivik complains of 'ridicule'", "The small dog protects its owner."]}, {"source_sentence": "A man shoots a man.", "sentences": ["A man is shooting off guns.", "A tiger walks around aimlessly.", "A cat sleeps on purple sheet."]}, {"source_sentence": "A man is speaking.", "sentences": ["A man is talking.", "19 hurt in New Orleans shooting", "The dogs are chasing a black cat."]}, {"source_sentence": "A man is spitting.", "sentences": ["Breivik complains of 'ridicule'", "The man is hiking in the woods.", "Eurozone agrees Greece bail-out"]}, {"source_sentence": "A parrot is talking.", "sentences": ["A parrot is talking into a microphone.", "A monkey pratices martial arts.", "The two men are wearing jeans."]}], "pipeline_tag": "sentence-similarity", "co2_eq_emissions": {"emissions": 5.379215660466108, "energy_consumed": 0.013838919430479152, "source": "codecarbon", "training_type": "fine-tuning", "on_cloud": false, "cpu_model": "13th Gen Intel(R) Core(TM) i7-13700K", "ram_total_size": 31.777088165283203, "hours_used": 0.072, "hardware_used": "1 x NVIDIA GeForce RTX 3090"}, "model-index": [{"name": "SentenceTransformer based on distilbert/distilbert-base-uncased", "results": [{"task": {"type": "semantic-similarity", "name": "Semantic Similarity"}, "dataset": {"name": "sts dev", "type": "sts-dev"}, "metrics": [{"type": "pearson_cosine", "value": 0.861868947947514, "name": "Pearson Cosine"}, {"type": "spearman_cosine", "value": 0.8712617743584893, "name": "Spearman Cosine"}, {"type": "pearson_manhattan", "value": 0.8611484157829896, "name": "Pearson Manhattan"}, {"type": "spearman_manhattan", "value": 0.8619125760745536, "name": "Spearman Manhattan"}, {"type": "pearson_euclidean", "value": 0.8615299857042606, "name": "Pearson Euclidean"}, {"type": "spearman_euclidean", "value": 0.8623855766060573, "name": "Spearman Euclidean"}, {"type": "pearson_dot", "value": 0.7716399182083511, "name": "Pearson Dot"}, {"type": "spearman_dot", "value": 0.781574012832885, "name": "Spearman Dot"}, {"type": "pearson_max", "value": 0.861868947947514, "name": "Pearson Max"}, {"type": "spearman_max", "value": 0.8712617743584893, "name": "Spearman Max"}]}, {"task": {"type": "semantic-similarity", "name": "Semantic Similarity"}, "dataset": {"name": "sts test", "type": "sts-test"}, "metrics": [{"type": "pearson_cosine", "value": 0.8281542233533932, "name": "Pearson Cosine"}, {"type": "spearman_cosine", "value": 0.8373087013752897, "name": "Spearman Cosine"}, {"type": "pearson_manhattan", "value": 0.842468233222574, "name": "Pearson Manhattan"}, {"type": "spearman_manhattan", "value": 0.8374178427964344, "name": "Spearman Manhattan"}, {"type": "pearson_euclidean", "value": 0.8424571958251152, "name": "Pearson Euclidean"}, {"type": "spearman_euclidean", "value": 0.8372826604544046, "name": "Spearman Euclidean"}, {"type": "pearson_dot", "value": 0.6750086731901399, "name": "Pearson Dot"}, {"type": "spearman_dot", "value": 0.656834541089774, "name": "Spearman Dot"}, {"type": "pearson_max", "value": 0.842468233222574, "name": "Pearson Max"}, {"type": "spearman_max", "value": 0.8374178427964344, "name": "Spearman Max"}]}]}]}
tomaarsen/distilbert-base-uncased-sts-2d-matryoshka
null
[ "sentence-transformers", "safetensors", "distilbert", "sentence-similarity", "feature-extraction", "loss:Matryoshka2dLoss", "loss:MatryoshkaLoss", "loss:CoSENTLoss", "en", "arxiv:1908.10084", "arxiv:2402.14776", "arxiv:2205.13147", "base_model:distilbert/distilbert-base-uncased", "model-index", "co2_eq_emissions", "endpoints_compatible", "region:us" ]
null
2024-04-29T17:01:59+00:00
text-generation
transformers
# Finetune Mistral, Gemma, Llama 2-5x faster with 70% less memory via Unsloth! We have a Google Colab Tesla T4 notebook for Phi-3 here: https://colab.research.google.com/drive/1NvkBmkHfucGO3Ve9s1NKZvMNlw5p83ym?usp=sharing [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord%20button.png" width="200"/>](https://discord.gg/u54VK8m8tk) [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/buy%20me%20a%20coffee%20button.png" width="200"/>](https://ko-fi.com/unsloth) [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) ## ✨ Finetune for Free All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face. | Unsloth supports | Free Notebooks | Performance | Memory use | |-----------------|--------------------------------------------------------------------------------------------------------------------------|-------------|----------| | **Llama-3 8b** | [▶️ Start on Colab](https://colab.research.google.com/drive/135ced7oHytdxu3N2DNe1Z0kqjyYIkDXp?usp=sharing) | 2.4x faster | 58% less | | **Gemma 7b** | [▶️ Start on Colab](https://colab.research.google.com/drive/10NbwlsRChbma1v55m8LAPYG15uQv6HLo?usp=sharing) | 2.4x faster | 58% less | | **Mistral 7b** | [▶️ Start on Colab](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing) | 2.2x faster | 62% less | | **Llama-2 7b** | [▶️ Start on Colab](https://colab.research.google.com/drive/1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5?usp=sharing) | 2.2x faster | 43% less | | **TinyLlama** | [▶️ Start on Colab](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing) | 3.9x faster | 74% less | | **CodeLlama 34b** A100 | [▶️ Start on Colab](https://colab.research.google.com/drive/1y7A0AxE3y8gdj4AVkl2aZX47Xu3P1wJT?usp=sharing) | 1.9x faster | 27% less | | **Mistral 7b** 1xT4 | [▶️ Start on Kaggle](https://www.kaggle.com/code/danielhanchen/kaggle-mistral-7b-unsloth-notebook) | 5x faster\* | 62% less | | **DPO - Zephyr** | [▶️ Start on Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) | 1.9x faster | 19% less | - This [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing) is useful for ShareGPT ChatML / Vicuna templates. - This [text completion notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing) is for raw text. This [DPO notebook](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) replicates Zephyr. - \* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.
{"language": ["en"], "license": "llama2", "library_name": "transformers", "tags": ["unsloth", "phi3", "transformers", "phi"]}
unsloth/Phi-3-mini-4k-instruct
null
[ "transformers", "safetensors", "mistral", "text-generation", "unsloth", "phi3", "phi", "conversational", "en", "license:llama2", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
null
2024-04-29T17:02:15+00:00
reinforcement-learning
stable-baselines3
# **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 ImenMasmoudiEm -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 ImenMasmoudiEm -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 ImenMasmoudiEm ``` ## 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'} ```
{"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": "674.00 +/- 133.11", "name": "mean_reward", "verified": false}]}]}]}
ImenMasmoudiEm/dqn-SpaceInvadersNoFrameskip-v4
null
[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
null
2024-04-29T17:02:21+00:00
null
transformers
# Uploaded model - **Developed by:** vonewman - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"}
vonewman/llama-3-8b-finetuned
null
[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
2024-04-29T17:02:45+00:00
text-classification
transformers
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuning-sentiment-model-3000-samples 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: 0.7678 - Accuracy: 0.71 - F1: 0.7100 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": []}]}
kevinwlip/finetuning-sentiment-model-3000-samples
null
[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
null
2024-04-29T17:02:57+00:00
text-generation
transformers
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Meta-Llama-3-8B-Instruct_fictional_arc_Korean_v2 This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the generator 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: 1 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 36 ### Training results ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2 - Datasets 2.18.0 - Tokenizers 0.15.2
{"license": "other", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "meta-llama/Meta-Llama-3-8B-Instruct", "model-index": [{"name": "Meta-Llama-3-8B-Instruct_fictional_arc_Korean_v2", "results": []}]}
yzhuang/Meta-Llama-3-8B-Instruct_fictional_arc_Korean_v2
null
[ "transformers", "tensorboard", "safetensors", "llama", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "dataset:generator", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
null
2024-04-29T17:03:03+00:00
null
fastai
# Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
{"tags": ["fastai"]}
PapiMarkis/futurama
null
[ "fastai", "region:us", "has_space" ]
null
2024-04-29T17:03:48+00:00
text-classification
transformers
# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
{"library_name": "transformers", "tags": []}
OplotNLP/multiclass-sentence-propaganda
null
[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]
null
2024-04-29T17:04:58+00:00
null
null
GGUFs for Blossom v5 32b - https://huggingface.co/Azure99/blossom-v5-32b iMatrix generated using Kalomaze's groups_merged.txt FP16 split with peazip. Recombine with peazip, 7zip, or a simple concatenate command.
{"language": ["zh", "en"], "license": "apache-2.0"}
MarsupialAI/Blossom-v5-32b_iMatrix_GGUF
null
[ "gguf", "zh", "en", "license:apache-2.0", "region:us" ]
null
2024-04-29T17:06:03+00:00
text-classification
transformers
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-cola 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: 0.4603 - Matthews Correlation: 0.5325 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5186 | 1.0 | 535 | 0.4543 | 0.4542 | | 0.3432 | 2.0 | 1070 | 0.4603 | 0.5325 | | 0.2321 | 3.0 | 1605 | 0.6472 | 0.5019 | | 0.1767 | 4.0 | 2140 | 0.7865 | 0.5193 | | 0.1235 | 5.0 | 2675 | 0.8397 | 0.5279 | ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["matthews_correlation"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": []}]}
hanzla107/distilbert-base-uncased-finetuned-cola
null
[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
null
2024-04-29T17:06:20+00:00
null
transformers
# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
{"library_name": "transformers", "tags": []}
HenryCai1129/adapter-llama-adapterhappy2sad-1k-search-3iter-50-0.004
null
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
null
2024-04-29T17:06:23+00:00
null
transformers
# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [Intellectus AI Labs] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [Text Generation] - **Language(s) (NLP):** [Python] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [Mixtral 8x7B] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
{"library_name": "transformers", "tags": []}
IntellectusAI/mixtral_8x7B_law
null
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
null
2024-04-29T17:08:01+00:00
null
null
# rakib72642/Unilever_Dump_Pepsodent # Higgingface : https://huggingface.co/rakib72642/Unilever_Dump_Pepsodent # Setup Global API sudo apt install iproute2 -y && sudo apt install wget -y && sudo apt install nano -y && sudo apt install unzip -y && sudo apt install unzip -y && apt install nvtop -y && sudo apt-get install git-lfs -y && sudo apt-get update && sudo apt-get install libgl1 -y && sudo apt install curl -y && curl -s https://ngrok-agent.s3.amazonaws.com/ngrok.asc | sudo tee /etc/apt/trusted.gpg.d/ngrok.asc >/dev/null && echo "deb https://ngrok-agent.s3.amazonaws.com buster main" | sudo tee /etc/apt/sources.list.d/ngrok.list && sudo apt update && sudo apt install ngrok -y && sudo apt update && sudo apt upgrade -y && ngrok config add-authtoken 2Qm8hS1zPhVXiLjEdlI4738tLzF_2QJwGJMK5oTbQD33QSVXS && sudo apt update && sudo apt upgrade -y && ngrok http --domain=he.ngrok.app 4545 # Setup Local API git clone https://huggingface.co/rakib72642/Unilever_Dump_Pepsodent && cd Unilever_Dump_Pepsodent && pip install -r requirements.txt && sudo apt update && sudo apt upgrade -y && python api.py cd Unilever_Dump_Pepsodent && python api.py hypercorn api:app --bind 127.0.0.1:4545 --workers 4 # ngrok config add-authtoken 2Qm8hS1zPhVXiLjEdlI4738tLzF_2QJwGJMK5oTbQD33QSVXS && sudo apt update && sudo apt upgrade -y && ngrok http --domain=he.ngrok.app 4545
{}
rakib72642/Unilever_Dump_Pepsodent
null
[ "doi:10.57967/hf/2134", "region:us" ]
null
2024-04-29T17:08:33+00:00
null
null
<p><strong>Document Organization: Examining American Article Shaping</strong></p> <p><strong>Introduction:</strong></p> <p>Paper writing in the USA wraps a substitute degree of affiliations changed to meet the moved necessities of understudies and experts the same. From formal enlightening papers to extra great and story centered pieces, the scene offers a ton of choices to figure out individual tendencies and nuts and bolts. In this examination, we bounce into two clear affiliations: the Exceptional Paper Framing Coalition and the Story Piece Making Association. Each help offers a striking framework for managing paper making, assembling various styles, tones, and targets. 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{}
hazelmark/ASDF
null
[ "region:us" ]
null
2024-04-29T17:09:15+00:00
null
null
{"license": "openrail"}
weillon/aquelavoz
null
[ "license:openrail", "region:us" ]
null
2024-04-29T17:09:55+00:00
text-generation
transformers
{}
titanbot/opt-350m-GPTQ-4bit
null
[ "transformers", "opt", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]
null
2024-04-29T17:10:37+00:00
null
null
<p><strong>Document Organization: Examining American Article Shaping</strong></p> <p><strong>Introduction:</strong></p> <p>Paper writing in the USA wraps a substitute degree of affiliations changed to meet the moved necessities of understudies and experts the same. From formal enlightening papers to extra great and story centered pieces, the scene offers a ton of choices to figure out individual tendencies and nuts and bolts. In this examination, we bounce into two clear affiliations: the Exceptional Paper Framing Coalition and the Story Piece Making Association. Each help offers a striking framework for managing paper making, assembling various styles, tones, and targets. Together, they address the singing injury around masterpiece of framing help with opening in the USA, attracting people to convey their considerations, encounters, and stories with clearness, imaginative frontal cortex, and precision.</p> <p><strong>Paper Making Relationship in the USA: Exploring Scholarly Help</strong></p> <p><strong>Different Responsibilities</strong></p> <p>The <a href="https://essaygoat.com/">essay writing service in usa</a> is rich and exceptional, offering a wide exhibit of choices to take astonishing thought of the changing necessities of understudies and worked with informed subject matter experts. From standard speedy undertakings to convey pieces like papers and evaluation papers, these affiliations cover a broad degree of making tries. Moreover, the USA shows a thriving business region for specialty affiliations, including requests pieces for schools and colleges, continue making affiliations, and, especially, starter making help. 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What's more, these relationship twirl around buyer force, offering updates and evaluation, in general, plans to ensure that the unquestionable result limits the client's requesting and nuts and bolts.</p> <p><strong>Responsiveness and Solace</strong></p> <p>Regardless of worth and breaking point, Article Trim Relationship in the USA base on straightforwardness and solace. With the presence of online stages and electronic unequivocal contraptions, individuals could for sure whenever get to these relationship from any spot in the country. This straightforwardness contacts the proposing structure, with different affiliations offering direct partnership works with that grant clients toward put orders, talk with creators, and track progress constantly. Likewise, the serious thought of the market consistently achieves sensible evaluation and adaptable piece decisions, making capable making help open to an exceptional number individuals across different cash related establishments.</p> <p><strong>Reviewing Sublime System: Relaxed Article Making Association</strong></p> <p>Casual Paper Composing Administration stays as an accessory for individuals hoping to place themselves out there in a free and conversational manner. By no means whatsoever, similar to standard edifying works, which consent to crazy guidelines and formal plans, the Easygoing Paper Making Association offers a more flexible method for overseeing making. 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By offering pro heading and sponsorship, the Obliging Paper Making Affiliation draws in individuals to yield their considerations, as a general rule and assist with outing perusers in goliath conversations.</p> <p><strong>Making Creativity and Affiliation</strong></p> <p>Past from an overall perspective conveying works, the Casual Article Making Connection makes inventiveness and relationship by drawing in people to explore their outstanding points of view and voice. Through consistent parties to pass on memorable considerations and data circles, the makers of this assistance with energizing people to extend the limitations of standard making standards and embrace their imaginative psyche. By giving a stage to veritable clarification and affiliation, the Casual Sythesis Framing Connection attracts people to share their records, pieces of data, and encounters with the world, attracting fundamental affiliations and lighting discussions that reverberate long after the words have been made.</p> <p><strong>Story Paper Shaping Association: Making Convincing Stories</strong></p> <p>Making a record structure requires a one of a kind mix of depicting limit and central smart. With a Story Paper Making Connection, people can take advantage of the strength of composed specialists who have some dominance in restoring stories on the page. These affiliations go past standard paper making, zeroing in on reshaping around together stunning records that draw in perusers beginning to end.</p> <p><strong>Star Narrators accessible to You</strong></p> <p>The sign of an account paper composing administration lies in its party of master narrators. These specialists have a sharp knowledge of story procedure, character improvement, and plot parts, permitting them to make accounts that resound by and large with swarms. Whether it's a particular story, a nonexistent story, or a reasonable record, these specialists have the mystery fixing and creative mind to change thoughts into convincing stories that leave a helping through influence.</p> <p><strong>Amazingly organized Records for Each Event</strong></p> <p>From school certifications articles to individual journals,&nbsp;<a href="https://essaygoat.com/narrative-essay-writing-service/">narrative essay writing service</a>&nbsp;offer fitted reactions for people endeavoring to share their records in different settings. Whether it's getting the quintessence of an uncommon encounter or portraying a huge occasion, these affiliations guarantee that records are made with precision and validness. With their prosperity in account plan and portraying frameworks, these affiliations attract people to figure out their encounters and points of view with clearness, feeling, and importance.</p> <p>With everything considered, the different scene of creation illustrating relationship in the USA mirrors the different idea of thinking of itself. From formal vigilant plans to additional satisfying and story driven pieces, these affiliations manage an enormous number of making needs and styles. The Casual Paper Making Connection offers versatility and imaginative psyche, permitting people to convey their contemplations in a free and conversational tone. Obviously, the Story Piece Making Connection works in making convincing stories that draw in and resonate with perusers on a more essential level. Together, these affiliations highlight the lavishness and social occasion of making support accessible in the USA, attracting people to present their thoughts, encounters, and stories with clearness, validness, and effect.</p>
{}
hazelmark/Document_Organization_Examining_American_Article_Shaping
null
[ "region:us" ]
null
2024-04-29T17:10:44+00:00
null
null
{}
diegoqueirozvs/teste1
null
[ "region:us" ]
null
2024-04-29T17:11:33+00:00
text-generation
transformers
# Finetune Mistral, Gemma, Llama 2-5x faster with 70% less memory via Unsloth! Directly quantized 4bit model with `bitsandbytes`. We have a Google Colab Tesla T4 notebook for Phi-3 here: https://colab.research.google.com/drive/1NvkBmkHfucGO3Ve9s1NKZvMNlw5p83ym?usp=sharing [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord%20button.png" width="200"/>](https://discord.gg/u54VK8m8tk) [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/buy%20me%20a%20coffee%20button.png" width="200"/>](https://ko-fi.com/unsloth) [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) ## ✨ Finetune for Free All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face. | Unsloth supports | Free Notebooks | Performance | Memory use | |-----------------|--------------------------------------------------------------------------------------------------------------------------|-------------|----------| | **Llama-3 8b** | [▶️ Start on Colab](https://colab.research.google.com/drive/135ced7oHytdxu3N2DNe1Z0kqjyYIkDXp?usp=sharing) | 2.4x faster | 58% less | | **Gemma 7b** | [▶️ Start on Colab](https://colab.research.google.com/drive/10NbwlsRChbma1v55m8LAPYG15uQv6HLo?usp=sharing) | 2.4x faster | 58% less | | **Mistral 7b** | [▶️ Start on Colab](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing) | 2.2x faster | 62% less | | **Llama-2 7b** | [▶️ Start on Colab](https://colab.research.google.com/drive/1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5?usp=sharing) | 2.2x faster | 43% less | | **TinyLlama** | [▶️ Start on Colab](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing) | 3.9x faster | 74% less | | **CodeLlama 34b** A100 | [▶️ Start on Colab](https://colab.research.google.com/drive/1y7A0AxE3y8gdj4AVkl2aZX47Xu3P1wJT?usp=sharing) | 1.9x faster | 27% less | | **Mistral 7b** 1xT4 | [▶️ Start on Kaggle](https://www.kaggle.com/code/danielhanchen/kaggle-mistral-7b-unsloth-notebook) | 5x faster\* | 62% less | | **DPO - Zephyr** | [▶️ Start on Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) | 1.9x faster | 19% less | - This [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing) is useful for ShareGPT ChatML / Vicuna templates. - This [text completion notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing) is for raw text. This [DPO notebook](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) replicates Zephyr. - \* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.
{"language": ["en"], "license": "llama2", "library_name": "transformers", "tags": ["unsloth", "phi3", "transformers", "phi"]}
unsloth/Phi-3-mini-4k-instruct-bnb-4bit
null
[ "transformers", "safetensors", "mistral", "text-generation", "unsloth", "phi3", "phi", "conversational", "en", "license:llama2", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]
null
2024-04-29T17:11:59+00:00
null
peft
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # nllb-200-distilled-1.3B-ICFOSS-Malayalam_Tamil_Translation1 This model is a fine-tuned version of [facebook/nllb-200-distilled-1.3B](https://huggingface.co/facebook/nllb-200-distilled-1.3B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9585 - Bleu: 27.2186 - Rouge: {'rouge1': 0.24019241472720237, 'rouge2': 0.11743746052802109, 'rougeL': 0.23538895581779812, 'rougeLsum': 0.23566947893424423} - Chrf: {'score': 61.354962127257075, 'char_order': 6, 'word_order': 0, 'beta': 2} ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Rouge | Chrf | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------------------------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------:| | 1.0887 | 1.0 | 3200 | 0.9812 | 26.4453 | {'rouge1': 0.23999162519568762, 'rouge2': 0.11750820308459373, 'rougeL': 0.2354759340604931, 'rougeLsum': 0.23574425317949493} | {'score': 60.78556495445389, 'char_order': 6, 'word_order': 0, 'beta': 2} | | 1.0235 | 2.0 | 6400 | 0.9633 | 27.2057 | {'rouge1': 0.23965959444048868, 'rouge2': 0.11732010332857629, 'rougeL': 0.2348755068042092, 'rougeLsum': 0.2350956429627365} | {'score': 61.143671039500624, 'char_order': 6, 'word_order': 0, 'beta': 2} | | 1.0073 | 3.0 | 9600 | 0.9592 | 27.2471 | {'rouge1': 0.24051300083618463, 'rouge2': 0.11760625620421375, 'rougeL': 0.23594757428338253, 'rougeLsum': 0.23612557860955713} | {'score': 61.30740344086827, 'char_order': 6, 'word_order': 0, 'beta': 2} | | 1.0022 | 4.0 | 12800 | 0.9587 | 27.2024 | {'rouge1': 0.24037345843038344, 'rouge2': 0.1174835459617575, 'rougeL': 0.2356757544571015, 'rougeLsum': 0.23591228047430784} | {'score': 61.34302070824752, 'char_order': 6, 'word_order': 0, 'beta': 2} | | 1.0008 | 5.0 | 16000 | 0.9585 | 27.2186 | {'rouge1': 0.24019241472720237, 'rouge2': 0.11743746052802109, 'rougeL': 0.23538895581779812, 'rougeLsum': 0.23566947893424423} | {'score': 61.354962127257075, 'char_order': 6, 'word_order': 0, 'beta': 2} | ### Framework versions - PEFT 0.10.0 - Transformers 4.39.3 - Pytorch 2.1.0+cu121 - Datasets 2.19.0 - Tokenizers 0.15.0
{"license": "cc-by-nc-4.0", "library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["bleu", "rouge"], "base_model": "facebook/nllb-200-distilled-1.3B", "model-index": [{"name": "nllb-200-distilled-1.3B-ICFOSS-Malayalam_Tamil_Translation1", "results": []}]}
ArunIcfoss/nllb-200-distilled-1.3B-ICFOSS-Malayalam_Tamil_Translation1
null
[ "peft", "safetensors", "generated_from_trainer", "base_model:facebook/nllb-200-distilled-1.3B", "license:cc-by-nc-4.0", "region:us" ]
null
2024-04-29T17:12:14+00:00
null
null
{}
fdsgdsg/gene
null
[ "region:us" ]
null
2024-04-29T17:13:27+00:00
text-to-image
diffusers
# SDXL LoRA DreamBooth - aarashfeizi/jean-francois-godbout-batch2-repeats3-rank16-snrNone <Gallery /> ## Model description ### These are aarashfeizi/jean-francois-godbout-batch2-repeats3-rank16-snrNone LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. ## Download model ### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke - **LoRA**: download **[`/home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout-batch2-repeats3-rank16-snrNone.safetensors` here 💾](/aarashfeizi/jean-francois-godbout-batch2-repeats3-rank16-snrNone/blob/main//home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout-batch2-repeats3-rank16-snrNone.safetensors)**. - Place it on your `models/Lora` folder. - On AUTOMATIC1111, load the LoRA by adding `<lora:/home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout-batch2-repeats3-rank16-snrNone:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/). - *Embeddings*: download **[`/home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout-batch2-repeats3-rank16-snrNone_emb.safetensors` here 💾](/aarashfeizi/jean-francois-godbout-batch2-repeats3-rank16-snrNone/blob/main//home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout-batch2-repeats3-rank16-snrNone_emb.safetensors)**. - Place it on it on your `embeddings` folder - Use it by adding `/home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout-batch2-repeats3-rank16-snrNone_emb` to your prompt. For example, `A photo of /home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout-batch2-repeats3-rank16-snrNone_emb` (you need both the LoRA and the embeddings as they were trained together for this LoRA) ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch from huggingface_hub import hf_hub_download from safetensors.torch import load_file pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('aarashfeizi/jean-francois-godbout-batch2-repeats3-rank16-snrNone', weight_name='pytorch_lora_weights.safetensors') embedding_path = hf_hub_download(repo_id='aarashfeizi/jean-francois-godbout-batch2-repeats3-rank16-snrNone', filename='/home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout-batch2-repeats3-rank16-snrNone_emb.safetensors', repo_type="model") state_dict = load_file(embedding_path) pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer) pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2) image = pipeline('A photo of <s0><s1> giving a speech').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Trigger words To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens: to trigger concept `TOK` → use `<s0><s1>` in your prompt ## Details All [Files & versions](/aarashfeizi/jean-francois-godbout-batch2-repeats3-rank16-snrNone/tree/main). The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py). LoRA for the text encoder was enabled. False. Pivotal tuning was enabled: True. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
{"license": "openrail++", "tags": ["stable-diffusion-xl", "stable-diffusion-xl-diffusers", "diffusers-training", "text-to-image", "diffusers", "lora", "template:sd-lora"], "widget": [{"text": "A photo of <s0><s1> giving a speech", "output": {"url": "image_0.png"}}, {"text": "A photo of <s0><s1> giving a speech", "output": {"url": "image_1.png"}}, {"text": "A photo of <s0><s1> giving a speech", "output": {"url": "image_2.png"}}, {"text": "A photo of <s0><s1> giving a speech", "output": {"url": "image_3.png"}}], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "A photo of <s0><s1>"}
aarashfeizi/jean-francois-godbout-batch2-repeats3-rank16-snrNone
null
[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "diffusers-training", "text-to-image", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]
null
2024-04-29T17:15:06+00:00
text2text-generation
transformers
{}
HariprasathSB/model-finetuned-tamil
null
[ "transformers", "safetensors", "bart", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]
null
2024-04-29T17:15:20+00:00
null
null
{"license": "llama3"}
lsblank/yuanxin
null
[ "license:llama3", "region:us" ]
null
2024-04-29T17:15:21+00:00
text-generation
transformers
# LYNN - AI for Roleplay <img src="./reallynn.png" alt="it's lynn!" width="340"/> > [!TIP] > This version is experimental. The output may unstable. # Soliloquy-L3 Soliloquy-L3 v1.5 is a highly capable roleplaying model designed for immersive, dynamic experiences. Using scale based vector transfer method, Soliloquy-L3 v1.5 has a vast knowledge base, rich literary expression, and support for up to 64k context length. It outperforms existing ~13B models, delivering enhanced roleplaying capabilities. ## Model Info | Context Length | Parameter | Prompt Template | isErp | | --- | --- | --- | --- | | 64k(65536) | 8B | Llama 3 Chat | Partly | ## Prompt Template Use can you following jinja2 template. Which is identical to chat_template in [tokenizer_config](./tokenizer_config.json). ``` {% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% endif %} ``` ## License This model is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License, under [META LLAMA 3 COMMUNITY LICENSE AGREEMENT](https://llama.meta.com/llama3/license/) If you would like to use this model for commercial purposes, please use our proprietary API. (Currently avilable at OpenRouter) For non-commercial use, please adhere to the terms of the CC BY-NC-SA 4.0 license. You are free to share and adapt the model for non-commercial purposes, provided you give appropriate credit, indicate if changes were made, and do not imply endorsement by the licensor. For more information about the CC BY-NC 4.0 license, please visit: https://creativecommons.org/licenses/by-nc-sa/4.0/ If you have any questions or would like to inquire about licensing, please contact us. ## Llama 3 Intended Use **Intended Use Cases** Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. **Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English**. **Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy. [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license) ## Join our Discord [**Join LYNN Discord**](https://discord.gg/xuZVqUyG4Y)
{"language": ["en"], "license": "cc-by-nc-sa-4.0"}
hanzogak/Llama-3-Soliloquy-8B-v1.5-64k-exl2-h8-6.5
null
[ "transformers", "safetensors", "llama", "text-generation", "conversational", "en", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
null
2024-04-29T17:15:31+00:00
text-generation
transformers
# Uploaded model - **Developed by:** vonewman - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl", "sft"], "base_model": "unsloth/llama-3-8b-bnb-4bit"}
vonewman/llama-3-8b-finetuned-16bit
null
[ "transformers", "pytorch", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
null
2024-04-29T17:15:33+00:00
automatic-speech-recognition
transformers
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-tiny This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset. It achieves the following results on the evaluation set: - Loss: 0.5468 - Wer Ortho: 0.2342 - Wer: 0.2295 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:| | 0.0006 | 17.54 | 500 | 0.5468 | 0.2342 | 0.2295 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2 - Datasets 2.18.0 - Tokenizers 0.15.2
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["PolyAI/minds14"], "metrics": ["wer"], "base_model": "openai/whisper-tiny", "model-index": [{"name": "whisper-tiny", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "PolyAI/minds14", "type": "PolyAI/minds14", "config": "en-US", "split": "train", "args": "en-US"}, "metrics": [{"type": "wer", "value": 0.22947872977831035, "name": "Wer"}]}]}]}
mratet/whisper-tiny
null
[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dataset:PolyAI/minds14", "base_model:openai/whisper-tiny", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
null
2024-04-29T17:16:03+00:00
object-detection
transformers
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # tabletransstructrecog_finetuned_pubt1m_lstabletransstrucrecogv1_session1 This model was trained from scratch on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - 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: 1000 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 2.0.1 - Datasets 2.18.0 - Tokenizers 0.19.1
{"tags": ["generated_from_trainer"], "model-index": [{"name": "tabletransstructrecog_finetuned_pubt1m_lstabletransstrucrecogv1_session1", "results": []}]}
nsugianto/tabletransstructrecog_finetuned_pubt1m_lstabletransstrucrecogv1_session1
null
[ "transformers", "tensorboard", "safetensors", "table-transformer", "object-detection", "generated_from_trainer", "endpoints_compatible", "region:us" ]
null
2024-04-29T17:16:31+00:00
null
null
{}
pjaekae/whisper-small-ta
null
[ "region:us" ]
null
2024-04-29T17:16:50+00:00
null
fastai
# Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
{"tags": ["fastai"]}
kylar55/dinos
null
[ "fastai", "region:us" ]
null
2024-04-29T17:16:50+00:00
null
null
{}
Neyto/NeytoModels
null
[ "region:us" ]
null
2024-04-29T17:16:53+00:00
null
null
{}
vincezh2000/ckpt
null
[ "region:us" ]
null
2024-04-29T17:17:22+00:00
null
null
{}
VikumK/Horimiya-Lora
null
[ "region:us" ]
null
2024-04-29T17:18:36+00:00
text-generation
transformers
6.5bpw exl2 quant of https://huggingface.co/Nitral-AI/Poppy_Porpoise-v0.7-L3-8B-4bpw-exl2 # "Poppy Porpoise" is a cutting-edge AI roleplay assistant based on the Llama 3 8B model, specializing in crafting unforgettable narrative experiences. With its advanced language capabilities, Poppy expertly immerses users in an interactive and engaging adventure, tailoring each adventure to their individual preferences. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/642265bc01c62c1e4102dc36/_R1hvMt9_QSBRzlnzo_nY.png) # Recomended ST Presets: [Porpoise Presets](https://huggingface.co/ChaoticNeutrals/Poppy_Porpoise-v0.7-L3-8B/tree/main/Official%20Poppy%20Porpoise%20ST%20Presets) # Quants From the boi: [@Lewdiculus-Poppy-Quants](https://huggingface.co/Lewdiculous/Poppy_Porpoise-v0.7-L3-8B-GGUF-IQ-Imatrix) # 4-bpw-exl2 quant: [here](https://huggingface.co/Nitral-AI/Poppy_Porpoise-v0.7-L3-8B-4bpw-exl2) If you want to use vision functionality: * You must use the latest versions of [Koboldcpp](https://github.com/LostRuins/koboldcpp). # To use the multimodal capabilities of this model and use **vision** you need to load the specified **mmproj** file, this can be found inside this model repo. [Llava MMProj](https://huggingface.co/ChaoticNeutrals/LLaVA-Llama-3-8B-mmproj) * You can load the **mmproj** by using the corresponding section in the interface: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65d4cf2693a0a3744a27536c/UX6Ubss2EPNAT3SKGMLe0.png)
{"license": "other", "library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["Hastagaras/L3-Asbac-8B", "ChaoticNeutrals/Poppy_Porpoise-v0.6-L3-8B"]}
Natkituwu/Poppy_Porpoise-v0.7-L3-8B-6.5bpw-exl2
null
[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "base_model:Hastagaras/L3-Asbac-8B", "base_model:ChaoticNeutrals/Poppy_Porpoise-v0.6-L3-8B", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
null
2024-04-29T17:18:57+00:00
text-generation
transformers
# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
{"library_name": "transformers", "tags": []}
quickstep3621/a83mbk8
null
[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]
null
2024-04-29T17:19:21+00:00
text-generation
transformers
# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
{"library_name": "transformers", "tags": []}
quickstep3621/wn4yijm
null
[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]
null
2024-04-29T17:19:26+00:00
text-generation
transformers
# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
{"library_name": "transformers", "tags": []}
quickstep3621/j8nq10d
null
[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]
null
2024-04-29T17:19:30+00:00
text-generation
transformers
{}
naresh2527/Llama-2-7b-chat-finetune1
null
[ "transformers", "pytorch", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
null
2024-04-29T17:21:11+00:00
text-classification
transformers
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuning-roberta-model This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2530 - Accuracy: 0.45 - F1: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "roberta-large", "model-index": [{"name": "finetuning-roberta-model", "results": []}]}
andrianangg/finetuning-roberta-model
null
[ "transformers", "tensorboard", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:roberta-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
null
2024-04-29T17:22:30+00:00
text-classification
transformers
# Model Trained Using AutoTrain - Problem type: Text Classification ## Validation Metrics loss: 1.0654646158218384 f1_macro: 0.2095479509928179 f1_micro: 0.4584103512014787 f1_weighted: 0.2881768494245037 precision_macro: 0.1528034504004929 precision_micro: 0.4584103512014787 precision_weighted: 0.21014005008866307 recall_macro: 0.3333333333333333 recall_micro: 0.4584103512014787 recall_weighted: 0.4584103512014787 accuracy: 0.4584103512014787
{"tags": ["autotrain", "text-classification"], "datasets": ["ACTSA-CARDIFFNLP/autotrain-data"], "widget": [{"text": "I love AutoTrain"}]}
AnirudhVV/ACTSA-CARDIFFNLP
null
[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "autotrain", "dataset:ACTSA-CARDIFFNLP/autotrain-data", "autotrain_compatible", "endpoints_compatible", "region:us" ]
null
2024-04-29T17:22:37+00:00
text-generation
transformers
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Meta-Llama-3-8B-Instruct_fictional_arc_French_v2 This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the generator 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: 1 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 36 ### Training results ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2 - Datasets 2.18.0 - Tokenizers 0.15.2
{"license": "other", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "meta-llama/Meta-Llama-3-8B-Instruct", "model-index": [{"name": "Meta-Llama-3-8B-Instruct_fictional_arc_French_v2", "results": []}]}
yzhuang/Meta-Llama-3-8B-Instruct_fictional_arc_French_v2
null
[ "transformers", "tensorboard", "safetensors", "llama", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "dataset:generator", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
null
2024-04-29T17:22:50+00:00
text-classification
transformers
{}
SantiagoCorley/multibert
null
[ "transformers", "safetensors", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]
null
2024-04-29T17:24:22+00:00
text-to-image
diffusers
# SDXL LoRA DreamBooth - aarashfeizi/jean-francois-godbout-batch3-repeats3-rank8-snr5.0 <Gallery /> ## Model description ### These are aarashfeizi/jean-francois-godbout-batch3-repeats3-rank8-snr5.0 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. ## Download model ### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke - **LoRA**: download **[`/home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout-batch3-repeats3-rank8-snr5.0.safetensors` here 💾](/aarashfeizi/jean-francois-godbout-batch3-repeats3-rank8-snr5.0/blob/main//home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout-batch3-repeats3-rank8-snr5.0.safetensors)**. - Place it on your `models/Lora` folder. - On AUTOMATIC1111, load the LoRA by adding `<lora:/home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout-batch3-repeats3-rank8-snr5.0:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/). - *Embeddings*: download **[`/home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout-batch3-repeats3-rank8-snr5.0_emb.safetensors` here 💾](/aarashfeizi/jean-francois-godbout-batch3-repeats3-rank8-snr5.0/blob/main//home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout-batch3-repeats3-rank8-snr5.0_emb.safetensors)**. - Place it on it on your `embeddings` folder - Use it by adding `/home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout-batch3-repeats3-rank8-snr5.0_emb` to your prompt. For example, `A photo of /home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout-batch3-repeats3-rank8-snr5.0_emb` (you need both the LoRA and the embeddings as they were trained together for this LoRA) ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch from huggingface_hub import hf_hub_download from safetensors.torch import load_file pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('aarashfeizi/jean-francois-godbout-batch3-repeats3-rank8-snr5.0', weight_name='pytorch_lora_weights.safetensors') embedding_path = hf_hub_download(repo_id='aarashfeizi/jean-francois-godbout-batch3-repeats3-rank8-snr5.0', filename='/home/mila/f/feiziaar/scratch/dreambooth-outputs/jean-francois-godbout-batch3-repeats3-rank8-snr5.0_emb.safetensors', repo_type="model") state_dict = load_file(embedding_path) pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer) pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2) image = pipeline('A photo of <s0><s1> giving a speech').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Trigger words To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens: to trigger concept `TOK` → use `<s0><s1>` in your prompt ## Details All [Files & versions](/aarashfeizi/jean-francois-godbout-batch3-repeats3-rank8-snr5.0/tree/main). The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py). LoRA for the text encoder was enabled. False. Pivotal tuning was enabled: True. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
{"license": "openrail++", "tags": ["stable-diffusion-xl", "stable-diffusion-xl-diffusers", "diffusers-training", "text-to-image", "diffusers", "lora", "template:sd-lora"], "widget": [{"text": "A photo of <s0><s1> giving a speech", "output": {"url": "image_0.png"}}, {"text": "A photo of <s0><s1> giving a speech", "output": {"url": "image_1.png"}}, {"text": "A photo of <s0><s1> giving a speech", "output": {"url": "image_2.png"}}, {"text": "A photo of <s0><s1> giving a speech", "output": {"url": "image_3.png"}}], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "A photo of <s0><s1>"}
aarashfeizi/jean-francois-godbout-batch3-repeats3-rank8-snr5.0
null
[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "diffusers-training", "text-to-image", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]
null
2024-04-29T17:26:54+00:00
text-classification
transformers
# Model Trained Using AutoTrain - Problem type: Text Classification ## Validation Metrics loss: 1.0651801824569702 f1_macro: 0.2095479509928179 f1_micro: 0.4584103512014787 f1_weighted: 0.2881768494245037 precision_macro: 0.1528034504004929 precision_micro: 0.4584103512014787 precision_weighted: 0.21014005008866307 recall_macro: 0.3333333333333333 recall_micro: 0.4584103512014787 recall_weighted: 0.4584103512014787 accuracy: 0.4584103512014787
{"tags": ["autotrain", "text-classification"], "datasets": ["ACTSA-AI4Bharath/autotrain-data"], "widget": [{"text": "I love AutoTrain"}]}
Akhil-9640/ACTSA-AI4Bharath
null
[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "autotrain", "dataset:ACTSA-AI4Bharath/autotrain-data", "autotrain_compatible", "endpoints_compatible", "region:us" ]
null
2024-04-29T17:28:14+00:00
image-to-image
diffusers
# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
{"library_name": "diffusers"}
ShreeKanade07/Real-Image-pipeline
null
[ "diffusers", "safetensors", "arxiv:1910.09700", "diffusers:StableDiffusionInpaintPipeline", "region:us", "has_space" ]
null
2024-04-29T17:28:24+00:00
text-to-image
diffusers
<!-- 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 - krishna4244/lora-KJ-v1 <Gallery /> ## Model description These are krishna4244/lora-KJ-v1 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 sks man to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](krishna4244/lora-KJ-v1/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]
{"license": "openrail++", "library_name": "diffusers", "tags": ["text-to-image", "text-to-image", "diffusers-training", "diffusers", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "a photo of sks man", "widget": [{"text": "A photo of sks man with hat", "output": {"url": "image_0.png"}}, {"text": "A photo of sks man with hat", "output": {"url": "image_1.png"}}, {"text": "A photo of sks man with hat", "output": {"url": "image_2.png"}}, {"text": "A photo of sks man with hat", "output": {"url": "image_3.png"}}]}
krishna4244/lora-KJ-v1
null
[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]
null
2024-04-29T17:28:26+00:00
null
transformers
# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
{"library_name": "transformers", "tags": []}
bdsaglam/llama-3-8b-jerx-peft-hxrqwax0
null
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
null
2024-04-29T17:28:37+00:00
null
transformers
## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/grimulkan/Goliath-longLORA-120b-rope8-32k-fp16 <!-- provided-files --> ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q2_K.gguf) | Q2_K | 43.3 | | | [GGUF](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.IQ3_XS.gguf) | IQ3_XS | 48.2 | | | [PART 1](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q3_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q3_K_S.gguf.part2of2) | Q3_K_S | 50.8 | | | [PART 1](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.IQ3_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.IQ3_S.gguf.part2of2) | IQ3_S | 51.0 | beats Q3_K* | | [PART 1](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.IQ3_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.IQ3_M.gguf.part2of2) | IQ3_M | 52.7 | | | [PART 1](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q3_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q3_K_M.gguf.part2of2) | Q3_K_M | 56.7 | lower quality | | [PART 1](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q3_K_L.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q3_K_L.gguf.part2of2) | Q3_K_L | 61.8 | | | [PART 1](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.IQ4_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.IQ4_XS.gguf.part2of2) | IQ4_XS | 63.5 | | | [PART 1](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q4_K_S.gguf.part2of2) | Q4_K_S | 66.9 | fast, recommended | | [PART 1](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q4_K_M.gguf.part2of2) | Q4_K_M | 70.7 | fast, recommended | | [PART 1](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q5_K_S.gguf.part2of2) | Q5_K_S | 81.1 | | | [PART 1](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q5_K_M.gguf.part2of2) | Q5_K_M | 83.3 | | | [PART 1](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q6_K.gguf.part2of2) | Q6_K | 96.7 | very good quality | | [PART 1](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q8_0.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q8_0.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF/resolve/main/Goliath-longLORA-120b-rope8-32k-fp16.Q8_0.gguf.part3of3) | Q8_0 | 125.2 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
{"language": ["en"], "license": "llama2", "library_name": "transformers", "base_model": "grimulkan/Goliath-longLORA-120b-rope8-32k-fp16", "no_imatrix": "nan", "quantized_by": "mradermacher"}
mradermacher/Goliath-longLORA-120b-rope8-32k-fp16-GGUF
null
[ "transformers", "gguf", "en", "base_model:grimulkan/Goliath-longLORA-120b-rope8-32k-fp16", "license:llama2", "endpoints_compatible", "region:us" ]
null
2024-04-29T17:29:43+00:00
null
peft
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vanilla_dpo_iter_3 This model is a fine-tuned version of [YYYYYYibo/vanilla_dpo_iter_2](https://huggingface.co/YYYYYYibo/vanilla_dpo_iter_2) on the updated and the original datasets. It achieves the following results on the evaluation set: - Loss: 0.5922 - Rewards/chosen: -0.2212 - Rewards/rejected: -0.5103 - Rewards/accuracies: 0.6820 - Rewards/margins: 0.2891 - Logps/rejected: -335.8713 - Logps/chosen: -327.4886 - Logits/rejected: -2.3692 - Logits/chosen: -2.4667 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| | 0.5785 | 0.63 | 100 | 0.5922 | -0.2212 | -0.5103 | 0.6820 | 0.2891 | -335.8713 | -327.4886 | -2.3692 | -2.4667 | ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2
{"license": "apache-2.0", "library_name": "peft", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "dpo"], "datasets": ["updated", "original"], "base_model": "alignment-handbook/zephyr-7b-sft-full", "model-index": [{"name": "vanilla_dpo_iter_3", "results": []}]}
YYYYYYibo/vanilla_dpo_iter_3
null
[ "peft", "safetensors", "mistral", "alignment-handbook", "generated_from_trainer", "trl", "dpo", "dataset:updated", "dataset:original", "base_model:alignment-handbook/zephyr-7b-sft-full", "license:apache-2.0", "region:us" ]
null
2024-04-29T17:29:43+00:00
text-generation
transformers
# tallgemma This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the passthrough merge method. ### Models Merged The following models were included in the merge: * [google/gemma-2b](https://huggingface.co/google/gemma-2b) * [google/gemma-1.1-2b-it](https://huggingface.co/google/gemma-1.1-2b-it) * [google/codegemma-2b](https://huggingface.co/google/codegemma-2b) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: google/gemma-2b layer_range: [0, 1] - sources: - model: google/codegemma-2b layer_range: [0, 1] - sources: - model: google/gemma-1.1-2b-it layer_range: [0, 1] - sources: - model: google/gemma-2b layer_range: [1, 2] - sources: - model: google/codegemma-2b layer_range: [1, 2] - sources: - model: google/gemma-1.1-2b-it layer_range: [1, 2] - sources: - model: google/gemma-2b layer_range: [2, 3] - sources: - model: google/codegemma-2b layer_range: [2, 3] - sources: - model: google/gemma-1.1-2b-it layer_range: [2, 3] - sources: - model: google/gemma-2b layer_range: [3, 4] - sources: - model: google/codegemma-2b layer_range: [3, 4] - sources: - model: google/gemma-1.1-2b-it layer_range: [3, 4] - sources: - model: google/gemma-2b layer_range: [4, 5] - sources: - model: google/codegemma-2b layer_range: [4, 5] - sources: - model: google/gemma-1.1-2b-it layer_range: [4, 5] - sources: - model: google/gemma-2b layer_range: [5, 6] - sources: - model: google/codegemma-2b layer_range: [5, 6] - sources: - model: google/gemma-1.1-2b-it layer_range: [5, 6] - sources: - model: google/gemma-2b layer_range: [6, 7] - sources: - model: google/codegemma-2b layer_range: [6, 7] - sources: - model: google/gemma-1.1-2b-it layer_range: [6, 7] - sources: - model: google/gemma-2b layer_range: [7, 8] - sources: - model: google/codegemma-2b layer_range: [7, 8] - sources: - model: google/gemma-1.1-2b-it layer_range: [7, 8] - sources: - model: google/gemma-2b layer_range: [8, 9] - sources: - model: google/codegemma-2b layer_range: [8, 9] - sources: - model: google/gemma-1.1-2b-it layer_range: [8, 9] - sources: - model: google/gemma-2b layer_range: [9, 10] - sources: - model: google/codegemma-2b layer_range: [9, 10] - sources: - model: google/gemma-1.1-2b-it layer_range: [9, 10] - sources: - model: google/gemma-2b layer_range: [10, 11] - sources: - model: google/codegemma-2b layer_range: [10, 11] - sources: - model: google/gemma-1.1-2b-it layer_range: [10, 11] - sources: - model: google/gemma-2b layer_range: [11, 12] - sources: - model: google/codegemma-2b layer_range: [11, 12] - sources: - model: google/gemma-1.1-2b-it layer_range: [11, 12] - sources: - model: google/gemma-2b layer_range: [12, 13] - sources: - model: google/codegemma-2b layer_range: [12, 13] - sources: - model: google/gemma-1.1-2b-it layer_range: [12, 13] - sources: - model: google/gemma-2b layer_range: [13, 14] - sources: - model: google/codegemma-2b layer_range: [13, 14] - sources: - model: google/gemma-1.1-2b-it layer_range: [13, 14] - sources: - model: google/gemma-2b layer_range: [14, 15] - sources: - model: google/codegemma-2b layer_range: [14, 15] - sources: - model: google/gemma-1.1-2b-it layer_range: [14, 15] - sources: - model: google/gemma-2b layer_range: [15, 16] - sources: - model: google/codegemma-2b layer_range: [15, 16] - sources: - model: google/gemma-1.1-2b-it layer_range: [15, 16] - sources: - model: google/gemma-2b layer_range: [16, 17] - sources: - model: google/codegemma-2b layer_range: [16, 17] - sources: - model: google/gemma-1.1-2b-it layer_range: [16, 17] - sources: - model: google/gemma-2b layer_range: [17, 18] - sources: - model: google/codegemma-2b layer_range: [17, 18] - sources: - model: google/gemma-1.1-2b-it layer_range: [17, 18] merge_method: passthrough dtype: float16 ```
{"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["google/gemma-2b", "google/gemma-1.1-2b-it", "google/codegemma-2b"]}
ScienceArtMagic/TallGemma-Raw
null
[ "transformers", "safetensors", "gemma", "text-generation", "mergekit", "merge", "base_model:google/gemma-2b", "base_model:google/gemma-1.1-2b-it", "base_model:google/codegemma-2b", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
null
2024-04-29T17:30:18+00:00
text-generation
transformers
# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
{"library_name": "transformers", "tags": []}
nmktad/llama-2-7b-dokbot
null
[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
null
2024-04-29T17:30:37+00:00
text-generation
transformers
# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
{"library_name": "transformers", "tags": []}
AI4DS/Llama-3-column-filter
null
[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
null
2024-04-29T17:30:57+00:00
null
null
{}
lewan4/Graphic_proj_CE__Arel__Istanbul_2024
null
[ "region:us" ]
null
2024-04-29T17:31:01+00:00
null
peft
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dpo_pythia1b_hh_rlhf.yml_local_29-04-24_13-31-33_xxxxx This model is a fine-tuned version of [sophiex/pythia-1b-sft_hh_rlhf](https://huggingface.co/sophiex/pythia-1b-sft_hh_rlhf) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6581 - Rewards/chosen: -0.1633 - Rewards/rejected: -0.3103 - Rewards/accuracies: 0.5971 - Rewards/margins: 0.1470 - Logps/rejected: -160.0996 - Logps/chosen: -160.0996 ## 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: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 150 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:| | 0.6931 | 0.0 | 1 | 0.6931 | 0.0 | 0.0 | 0.0 | 0.0 | -158.4665 | -158.4665 | | 0.6701 | 0.2 | 503 | 0.6745 | -0.0602 | -0.1382 | 0.5734 | 0.0779 | -159.0690 | -159.0690 | | 0.6624 | 0.4 | 1006 | 0.6670 | -0.0864 | -0.1939 | 0.5862 | 0.1075 | -159.3303 | -159.3303 | | 0.6587 | 0.6 | 1509 | 0.6612 | -0.1043 | -0.2301 | 0.5891 | 0.1259 | -159.5091 | -159.5091 | | 0.6511 | 0.8 | 2012 | 0.6581 | -0.1633 | -0.3103 | 0.5971 | 0.1470 | -160.0996 | -160.0996 | ### Framework versions - PEFT 0.10.0 - Transformers 4.38.1 - Pytorch 2.1.2+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
{"library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "sophiex/pythia-1b-sft_hh_rlhf", "model-index": [{"name": "dpo_pythia1b_hh_rlhf.yml_local_29-04-24_13-31-33_xxxxx", "results": []}]}
sophiex/dpo_pythia1b_hh_rlhf.yml_local_29-04-24_13-31-33_xxxxx
null
[ "peft", "safetensors", "generated_from_trainer", "base_model:sophiex/pythia-1b-sft_hh_rlhf", "region:us" ]
null
2024-04-29T17:31:54+00:00
null
transformers
# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
{"library_name": "transformers", "tags": []}
HenryCai1129/adapter-llama-adapterhappy2sad-1k-search-3iter-50-0.005
null
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
null
2024-04-29T17:33:18+00:00
null
null
{}
Rujeena/bert_summarization_finetuned
null
[ "region:us" ]
null
2024-04-29T17:34:11+00:00
text-classification
transformers
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_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: 0.2313 - Accuracy: 0.9314 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2224 | 1.0 | 1563 | 0.2692 | 0.9091 | | 0.1565 | 2.0 | 3126 | 0.2313 | 0.9314 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.3.0+cu118 - Tokenizers 0.14.1
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "my_awesome_model", "results": []}]}
miguelactc27/my_awesome_model
null
[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
null
2024-04-29T17:34:38+00:00
text-generation
transformers
Barcenas Llama3 8b ORPO Model trained with the novel new ORPO method, based on the recent Llama 3 8b, specifically: VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct The model was trained with the dataset: reciperesearch/dolphin-sft-v0.1-preference which uses Dolphin data with GPT 4 to improve its conversation sections. Made with ❤️ in Guadalupe, Nuevo Leon, Mexico 🇲🇽
{"language": ["en"], "license": "other", "tags": ["llama", "ORPO"], "license_name": "llama3", "license_link": "LICENSE"}
Danielbrdz/Barcenas-Llama3-8b-ORPO
null
[ "transformers", "safetensors", "llama", "text-generation", "ORPO", "conversational", "en", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
null
2024-04-29T17:34:56+00:00
feature-extraction
transformers
# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
{"library_name": "transformers", "tags": []}
phuongnv/retrosynthesis
null
[ "transformers", "safetensors", "internlm", "feature-extraction", "custom_code", "arxiv:1910.09700", "4-bit", "region:us" ]
null
2024-04-29T17:36:24+00:00
null
null
{}
FumiKuma/results
null
[ "region:us" ]
null
2024-04-29T17:36:25+00:00
null
null
# My Cool Submission This repo contains a submission to the [S23DR Challenge](https://huggingface.co/spaces/usm3d/S23DR) (part of the [USM3D](https://usm3d.github.io/) workshop at CVPR2024). It was prepared by [jacklangerman](https://huggingface.co/jacklangerman).
{}
jacklangerman/my_cool_submission
null
[ "region:us" ]
null
2024-04-29T17:37:21+00:00
text-generation
transformers
# Model Card for Assessment-Llama2-7b Calculate similarity score between expert and student explanation for a given line of code. ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a finetuned model of a Llama2-7b-hf. The model is quantized using 4-bit quantization. - **Developed by:** Jeevan - **Model type:** Llama2-7b-hf - **Language(s) (NLP):** English - **Finetuned from model:** [Llama2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) The prompt should be given in alpaca format ``` Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. ### Instruction: For the given line of code, both the student and expert have provided the explanation for that line of code. Compute the semantic similarity between the student explanation and the expert explanation for the line of code. ### Input: for given line of code int[] values = {5, 8, 4, 78, 95, 12, 1, 0, 6, 35, 46};, the expert explanation is We declare an array of values to hold the numbers. and the student explanation is This line creates the integer array with the values. you need this to achieve the goal bc you need an array to look in ### Response: ``` Dataset The model was trained on [SelfCode 2.0](https://zenodo.org/records/10912669)
{"library_name": "transformers", "tags": []}
xap/assessment-llama2-7b
null
[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]
null
2024-04-29T17:37:59+00:00
text-generation
transformers
# Model Card for Model ID Quantized model of [Trendyol/Trendyol-LLM-7b-chat-v0.1][1] using `bitsandbytes`. [1]: https://huggingface.co/Trendyol/Trendyol-LLM-7b-chat-v0.1 ## 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]
{"language": ["tr"], "license": "apache-2.0", "library_name": "transformers"}
atasoglu/Trendyol-LLM-7b-chat-v0.1-bnb-4bit
null
[ "transformers", "safetensors", "llama", "text-generation", "conversational", "tr", "arxiv:1910.09700", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]
null
2024-04-29T17:38:29+00:00
null
null
{}
kxm1k4m1/WangchanLion7B-text-to-pandas
null
[ "region:us" ]
null
2024-04-29T17:38:49+00:00
reinforcement-learning
stable-baselines3
# **PPO with MlpPolicy** Agent playing **LunarLander-v2** This is a trained model of a **PPO with MlpPolicy** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
{"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO with MlpPolicy", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "248.76 +/- 34.89", "name": "mean_reward", "verified": false}]}]}]}
Hevagog/ppo-LunarLander-v2
null
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
null
2024-04-29T17:39:20+00:00
text-generation
transformers
# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
{"library_name": "transformers", "tags": []}
lunarsylph/stablecell_v52
null
[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]
null
2024-04-29T17:40:08+00:00
text-generation
transformers
{}
w32zhong/s3d-full_finetune_layer11top
null
[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
null
2024-04-29T17:40:24+00:00
null
null
{"license": "apache-2.0"}
celineduerner/Volleyball-Classifier
null
[ "license:apache-2.0", "region:us" ]
null
2024-04-29T17:41:04+00:00