librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId string	author string	last_modified timestamp[us, tz=UTC]	downloads int64	likes int64	library_name string	tags sequence	pipeline_tag string	createdAt timestamp[us, tz=UTC]	card string
grace-pro/oops_i_did_it_again_eval_hans_full_set	grace-pro	2024-03-11T02:56:38Z	2	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	null	2024-03-11T02:55:26Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer metrics: - precision - recall - accuracy base_model: mistralai/Mistral-7B-v0.1 model-index: - name: oops_i_did_it_again_eval_hans_full_set results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # oops_i_did_it_again_eval_hans_full_set This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.8314 - Precision: 0.7598 - Recall: 0.2665 - F1-score: 0.3946 - Accuracy: 0.5911 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1-score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:---------:\|:------:\|:--------:\|:--------:\| \| 0.5414 \| 1.0 \| 24544 \| 1.8314 \| 0.7598 \| 0.2665 \| 0.3946 \| 0.5911 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
essiam/clean_art_cat	essiam	2024-03-11T02:55:52Z	0	1	diffusers	[ "diffusers", "tensorboard", "safetensors", "text-to-image", "dreambooth", "stable-diffusion", "stable-diffusion-diffusers", "base_model:CompVis/stable-diffusion-v1-4", "base_model:finetune:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-03-11T02:44:09Z	--- license: creativeml-openrail-m library_name: diffusers tags: - text-to-image - dreambooth - stable-diffusion - stable-diffusion-diffusers inference: true base_model: CompVis/stable-diffusion-v1-4 instance_prompt: a photo of 686HenrietteRonnerKnip859 cat --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # DreamBooth - essiam/clean_art_cat This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of 686HenrietteRonnerKnip859 cat using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: True. ## 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]
jeonsiyun/layoutlmv3-v29-epoch5	jeonsiyun	2024-03-11T02:45:47Z	117	0	transformers	[ "transformers", "safetensors", "layoutlmv3", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-11T02:45:15Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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]
Reemvn/distilroberta-base	Reemvn	2024-03-11T02:43:33Z	46	0	transformers	[ "transformers", "tf", "roberta", "text-classification", "generated_from_keras_callback", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-10T23:41:57Z	--- tags: - generated_from_keras_callback model-index: - name: Reemvn/distilroberta-base results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # Reemvn/distilroberta-base This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0947 - Validation Loss: 0.1512 - Train Accuracy: 0.9455 - Epoch: 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: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 5000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 0.1343 \| 0.1610 \| 0.945 \| 0 \| \| 0.1097 \| 0.1589 \| 0.949 \| 1 \| \| 0.0947 \| 0.1512 \| 0.9455 \| 2 \| ### Framework versions - Transformers 4.38.2 - TensorFlow 2.15.0 - Datasets 2.18.0 - Tokenizers 0.15.2
lilyray/albert_emotion	lilyray	2024-03-11T02:28:15Z	121	0	transformers	[ "transformers", "tensorboard", "safetensors", "albert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:lilyray/albert_emotion", "base_model:finetune:lilyray/albert_emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-10T17:30:03Z	--- license: apache-2.0 base_model: lilyray/albert_emotion tags: - generated_from_trainer datasets: - emotion metrics: - accuracy model-index: - name: albert_emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.9295 --- <!-- 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. --> # albert_emotion This model is a fine-tuned version of [lilyray/albert_emotion](https://huggingface.co/lilyray/albert_emotion) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2391 - Accuracy: 0.9295 ## 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: 9.363600088100325e-06 - train_batch_size: 4 - eval_batch_size: 8 - seed: 19 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.1744 \| 1.0 \| 4000 \| 0.2001 \| 0.938 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
michaelnoyes/thedistil	michaelnoyes	2024-03-11T02:24:37Z	0	0	null	[ "region:us" ]	null	2024-03-10T20:45:37Z	Within the repository I have untrained models by their names and then the fine-tuned versions. I also included the code that would be used for the quantitavtive analysis table which i was not able to run.
DaRkSpyro/GabiRio2	DaRkSpyro	2024-03-11T02:22:37Z	0	0	flair	[ "flair", "music", "en", "dataset:HuggingFaceTB/cosmopedia", "license:apache-2.0", "region:us" ]	null	2024-03-11T02:19:38Z	--- license: apache-2.0 datasets: - HuggingFaceTB/cosmopedia language: - en metrics: - accuracy library_name: flair tags: - music ---
asedmammad/Contextual_KTO_Mistral_PairRM-GGUF	asedmammad	2024-03-11T01:54:18Z	83	2	null	[ "gguf", "kto", "dpo", "human feedback", "rlhf", "preferences", "alignment", "HALO", "halos", "rl", "rlaif", "en", "dataset:snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset", "arxiv:2402.01306", "base_model:ContextualAI/Contextual_KTO_Mistral_PairRM", "base_model:quantized:ContextualAI/Contextual_KTO_Mistral_PairRM", "license:apache-2.0", "region:us", "conversational" ]	null	2024-03-10T22:07:16Z	--- base_model: ContextualAI/Contextual_KTO_Mistral_PairRM inference: false language: - en license: apache-2.0 tags: - kto - dpo - human feedback - rlhf - preferences - alignment - HALO - halos - rl - rlaif datasets: - snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset metrics: - accuracy model_creator: ContextualAI model_name: Contextual KTO Mistral PairRM model_type: mistral prompt_template: '<\|user\|> {prompt} <\|assistant\|> ' quantized_by: Ased Mammad --- # Contextual_KTO_Mistral_PairRM - GGUF - Model creator: [ContextualAI](https://huggingface.co/ContextualAI) - Original model: [Contextual_KTO_Mistral_PairRM](https://huggingface.co/ContextualAI/Contextual_KTO_Mistral_PairRM) <!-- description start --> ## Description This repo contains GGUF format model files for [Contextual_KTO_Mistral_PairRM](https://huggingface.co/ContextualAI/Contextual_KTO_Mistral_PairRM). <!-- 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). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. <!-- README_GGUF.md-about-gguf end --> <!-- prompt-template start --> ## Prompt template: ChatML ``` <\|user\|> {prompt} <\|assistant\|> ``` <!-- prompt-template end --> <!-- compatibility_gguf start --> ## Compatibility These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) They are also compatible with many third party UIs and libraries - please see the list at the top of this README. ## 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 Refer to the Provided Files table below to see what files use which methods, and how. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-provided-files start --> ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [Contextual_KTO_Mistral_PairRM.Q2_K.gguf](https://huggingface.co/AsedMammad/Contextual_KTO_Mistral_PairRM-GGUF/blob/main/Contextual_KTO_Mistral_PairRM.Q2_K.gguf) \| Q2_K \| 2 \| 2.72 GB\| 5.22 GB \| significant quality loss - not recommended for most purposes \| \| [Contextual_KTO_Mistral_PairRM.Q3_K_S.gguf](https://huggingface.co/AsedMammad/Contextual_KTO_Mistral_PairRM-GGUF/blob/main/Contextual_KTO_Mistral_PairRM.Q3_K_S.gguf) \| Q3_K_S \| 3 \| 3.16 GB\| 5.66 GB \| very small, high quality loss \| \| [Contextual_KTO_Mistral_PairRM.Q3_K_M.gguf](https://huggingface.co/AsedMammad/Contextual_KTO_Mistral_PairRM-GGUF/blob/main/Contextual_KTO_Mistral_PairRM.Q3_K_M.gguf) \| Q3_K_M \| 3 \| 3.52 GB\| 6.02 GB \| very small, high quality loss \| \| [Contextual_KTO_Mistral_PairRM.Q3_K_L.gguf](https://huggingface.co/AsedMammad/Contextual_KTO_Mistral_PairRM-GGUF/blob/main/Contextual_KTO_Mistral_PairRM.Q3_K_L.gguf) \| Q3_K_L \| 3 \| 3.82 GB\| 6.32 GB \| small, substantial quality loss \| \| [Contextual_KTO_Mistral_PairRM.Q4_0.gguf](https://huggingface.co/AsedMammad/Contextual_KTO_Mistral_PairRM-GGUF/blob/main/Contextual_KTO_Mistral_PairRM.Q4_0.gguf) \| Q4_0 \| 4 \| 4.11 GB\| 6.61 GB \| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [Contextual_KTO_Mistral_PairRM.Q4_K_S.gguf](https://huggingface.co/AsedMammad/Contextual_KTO_Mistral_PairRM-GGUF/blob/main/Contextual_KTO_Mistral_PairRM.Q4_K_S.gguf) \| Q4_K_S \| 4 \| 4.14 GB\| 6.64 GB \| small, greater quality loss \| \| [Contextual_KTO_Mistral_PairRM.Q4_K_M.gguf](https://huggingface.co/AsedMammad/Contextual_KTO_Mistral_PairRM-GGUF/blob/main/Contextual_KTO_Mistral_PairRM.Q4_K_M.gguf) \| Q4_K_M \| 4 \| 4.37 GB\| 6.87 GB \| medium, balanced quality - recommended \| \| [Contextual_KTO_Mistral_PairRM.Q5_0.gguf](https://huggingface.co/AsedMammad/Contextual_KTO_Mistral_PairRM-GGUF/blob/main/Contextual_KTO_Mistral_PairRM.Q5_0.gguf) \| Q5_0 \| 5 \| 5.00 GB\| 7.50 GB \| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [Contextual_KTO_Mistral_PairRM.Q5_K_S.gguf](https://huggingface.co/AsedMammad/Contextual_KTO_Mistral_PairRM-GGUF/blob/main/Contextual_KTO_Mistral_PairRM.Q5_K_S.gguf) \| Q5_K_S \| 5 \| 5.00 GB\| 7.50 GB \| large, low quality loss - recommended \| \| [Contextual_KTO_Mistral_PairRM.Q5_K_M.gguf](https://huggingface.co/AsedMammad/Contextual_KTO_Mistral_PairRM-GGUF/blob/main/Contextual_KTO_Mistral_PairRM.Q5_K_M.gguf) \| Q5_K_M \| 5 \| 5.13 GB\| 7.63 GB \| large, very low quality loss - recommended \| \| [Contextual_KTO_Mistral_PairRM.Q6_K.gguf](https://huggingface.co/AsedMammad/Contextual_KTO_Mistral_PairRM-GGUF/blob/main/Contextual_KTO_Mistral_PairRM.Q6_K.gguf) \| Q6_K \| 6 \| 5.94 GB\| 8.44 GB \| very large, extremely low quality loss \| \| [Contextual_KTO_Mistral_PairRM.Q8_0.gguf](https://huggingface.co/AsedMammad/Contextual_KTO_Mistral_PairRM-GGUF/blob/main/Contextual_KTO_Mistral_PairRM.Q8_0.gguf) \| Q8_0 \| 8 \| 7.70 GB\| 10.20 GB \| very large, extremely low quality loss - not recommended \| Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. <!-- README_GGUF.md-provided-files 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 file. 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: AsedMammad/Contextual_KTO_Mistral_PairRM-GGUF and below it, a specific filename to download, such as: Contextual_KTO_Mistral_PairRM.Q4_K_M.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 AsedMammad/Contextual_KTO_Mistral_PairRM-GGUF Contextual_KTO_Mistral_PairRM.Q4_K_M.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 AsedMammad/Contextual_KTO_Mistral_PairRM-GGUF --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download AsedMammad/Contextual_KTO_Mistral_PairRM-GGUF Contextual_KTO_Mistral_PairRM.Q4_K_M.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 Contextual_KTO_Mistral_PairRM.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<\|im_start\|>system\n{system_message}<\|im_end\|>\n<\|im_start\|>user\n{prompt}<\|im_end\|>\n<\|im_start\|>assistant" ``` Change `-ngl 35` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 32768` 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="./Contextual_KTO_Mistral_PairRM.Q4_K_M.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( "<\|user\|>\n{prompt}<\|assistant\|>\n", # 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="./Contextual_KTO_Mistral_PairRM.Q4_K_M.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 start --> <!-- original-model-card start --> This repo contains the model and tokenizer checkpoints for: - model family [<b>mistralai/Mistral-7B-Instruct-v0.2</b>](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) - optimized with the loss [<b>KTO</b>](https://twitter.com/winniethexu/status/1732839295365554643) - aligned using the [snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset](https://huggingface.co/datasets/snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset) - via 3 iterations of KTO on one epoch of each training partition, each previous iteration's model serving as the reference for the subsequent. [03/06/2024]: We are #2 on the (verified) [Alpaca Eval 2.0 Leaderboard](https://tatsu-lab.github.io/alpaca_eval/) scoring 33.23! To prompt this model, ensure that the format is consistent with that of TuluV2. For example, a prompt should be formatted as follows, where `<\|user\|>` corresponds to the human's role and `<\|assistant\|>` corresponds to the LLM's role. The human should speak first: ``` <\|user\|> Hi! I'm looking for a cake recipe. <\|assistant\|> What kind of cake? <\|user\|> Chocolate cake. <\|assistant\|> ``` Note that a beginning-of-sequence (BOS) token is automatically added at tokenization time and does not have to be added by you. No end-of-sequence (EOS) token is added to the prompt. You may also use our tokenizer's `apply_chat_template` if doing inference with `chatml` set or evaluating generations through non-local clients. Please refer to our [code repository](https://github.com/ContextualAI/HALOs) or [blog](https://contextual.ai/better-cheaper-faster-llm-alignment-with-kto/) for more details on the methodology. If you found this work useful, feel free to cite [our work](https://arxiv.org/abs/2402.01306): ``` @techreport{ethayarajh2023halos, author = {Ethayarajh, Kawin and Xu, Winnie, and Jurafsky, Dan and Kiela, Douwe}, title = {Human-Centered Loss Functions (HALOs)}, institution = {Contextual AI}, note = {https://github.com/ContextualAI/HALOs/blob/main/assets/report.pdf}, year = {2023}, } ``` <!-- original-model-card end -->
jeonsiyun/layoutlmv3-v29-epoch25	jeonsiyun	2024-03-11T01:54:04Z	119	0	transformers	[ "transformers", "safetensors", "layoutlmv3", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-11T01:53:24Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
shadowlight2784/Adagio_Dazzle	shadowlight2784	2024-03-11T01:51:35Z	0	0	null	[ "region:us" ]	null	2023-11-09T05:16:00Z	Use for Retrival-Voice-Conversion (RVC). https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI
shadowlight2784/Sonata_Dusk_Singing_Voice	shadowlight2784	2024-03-11T01:50:56Z	0	1	null	[ "region:us" ]	null	2023-08-25T23:43:01Z	Use for Retrival-Voice-Conversion (RVC). https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI
ITT-AF/ITT-Yi-Ko-6B-v6.0	ITT-AF	2024-03-11T01:43:50Z	55	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-07T02:52:58Z	--- license: cc-by-nc-4.0 --- ## ITT-AF/ITT-Yi-Ko-6B-v6.0 This model is a fine-tuned version of [beomi/Yi-Ko-6B](https://huggingface.co/beomi/Yi-Ko-6B) on an custom dataset. ### Model description More information needed ### Intended uses & limitations More information needed ### Training and evaluation data More information needed ### Training procedure ### Training hypuerparameters The following hyperparameters were used during training: * learning_rate: 2e-05 * train_batch_size: 4 * eval_batch_size: 8 * seed: 42 * gradient_accumulation_steps: 8 * total_train_batch_size: 32 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr_scheduler_type: linear * num_epochs: 1.0 * mixed_precision_training: Native AMP ### Training results ### Framework versions * Transformers 4.36.2 * Pytorch 2.1.2+cu121 * Datasets 2.0.0 * Tokenizers 0.15.0
juhwanlee/llmdo-Mistral-7B-case-7	juhwanlee	2024-03-11T01:43:03Z	48	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "en", "dataset:Open-Orca/OpenOrca", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-11T00:41:21Z	--- license: apache-2.0 datasets: - Open-Orca/OpenOrca language: - en --- # Model Details * Model Description: This model is test for data ordering. * Developed by: Juhwan Lee * Model Type: Large Language Model # Model Architecture This model is based on Mistral-7B-v0.1. We fine-tuning this model for data ordering task. Mistral-7B-v0.1 is a transformer model, with the following architecture choices: * Grouped-Query Attention * Sliding-Window Attention * Byte-fallback BPE tokenizer # Dataset We random sample Open-Orca dataset. (We finetune the 100,000 dataset) # Guthub https://github.com/trailerAI # License Apache License 2.0
grace-pro/oops_i_did_it_again_eval_hans	grace-pro	2024-03-11T01:40:10Z	1	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	null	2024-03-11T01:38:55Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer metrics: - precision - recall - accuracy base_model: mistralai/Mistral-7B-v0.1 model-index: - name: oops_i_did_it_again_eval_hans results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # oops_i_did_it_again_eval_hans This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.5556 - Precision: 0.8070 - Recall: 0.2481 - F1-score: 0.3796 - Accuracy: 0.5944 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1-score \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:--------:\|:--------:\| \| 0.6706 \| 1.0 \| 4909 \| 1.5556 \| 0.8070 \| 0.2481 \| 0.3796 \| 0.5944 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
furrutiav/bert_qa_extractor_cockatiel_2022_ulra_sign_lf_signal_it_38	furrutiav	2024-03-11T01:39:52Z	91	0	transformers	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	feature-extraction	2024-03-11T01:39:27Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
furrutiav/bert_qa_extractor_cockatiel_2022_ulra_org_lf_signal_it_32	furrutiav	2024-03-11T01:39:12Z	91	0	transformers	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	feature-extraction	2024-03-11T01:38:44Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
wongctroman/fine-tuned-cloudy-sentence-transformer-3	wongctroman	2024-03-11T01:30:02Z	48	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-03-11T01:28:55Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # wongctroman/fine-tuned-cloudy-sentence-transformer-3 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('wongctroman/fine-tuned-cloudy-sentence-transformer-3') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name=wongctroman/fine-tuned-cloudy-sentence-transformer-3) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 3 with parameters: ``` {'batch_size': 5, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.TripletLoss.TripletLoss` with parameters: ``` {'distance_metric': 'TripletDistanceMetric.EUCLIDEAN', 'triplet_margin': 5} ``` Parameters of the fit()-Method: ``` { "epochs": 100, "evaluation_steps": 500, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 100, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 1024, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
xia01ongLi/dummy-model2	xia01ongLi	2024-03-11T01:28:47Z	201	0	transformers	[ "transformers", "safetensors", "camembert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-03-11T01:27:07Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Holarissun/gptj6b-aisft-hh-randsampler-subset2000	Holarissun	2024-03-11T01:20:53Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:EleutherAI/gpt-j-6b", "base_model:adapter:EleutherAI/gpt-j-6b", "license:apache-2.0", "region:us" ]	null	2024-03-11T01:16:40Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: EleutherAI/gpt-j-6b model-index: - name: gptj6b-aisft-hh-randsampler-subset2000 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gptj6b-aisft-hh-randsampler-subset2000 This model is a fine-tuned version of [EleutherAI/gpt-j-6b](https://huggingface.co/EleutherAI/gpt-j-6b) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
tsavage68/mistralit2_250_STEPS_1e7_rate_05_beta_DPO	tsavage68	2024-03-11T01:17:30Z	6	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "trl", "dpo", "generated_from_trainer", "conversational", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:finetune:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-11T01:13:23Z	--- license: apache-2.0 base_model: mistralai/Mistral-7B-Instruct-v0.2 tags: - trl - dpo - generated_from_trainer model-index: - name: mistralit2_250_STEPS_1e7_rate_05_beta_DPO results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mistralit2_250_STEPS_1e7_rate_05_beta_DPO This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6043 - Rewards/chosen: -1.1749 - Rewards/rejected: -1.6856 - Rewards/accuracies: 0.6330 - Rewards/margins: 0.5107 - Logps/rejected: -31.9435 - Logps/chosen: -25.7355 - Logits/rejected: -2.8536 - Logits/chosen: -2.8539 ## 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-07 - train_batch_size: 4 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 250 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.6684 \| 0.1 \| 50 \| 0.6660 \| -0.2264 \| -0.2957 \| 0.5934 \| 0.0693 \| -29.1637 \| -23.8386 \| -2.8636 \| -2.8639 \| \| 0.5945 \| 0.2 \| 100 \| 0.6396 \| -1.5064 \| -1.9635 \| 0.6044 \| 0.4572 \| -32.4994 \| -26.3985 \| -2.8444 \| -2.8447 \| \| 0.4857 \| 0.29 \| 150 \| 0.6440 \| -2.0003 \| -2.6364 \| 0.6198 \| 0.6362 \| -33.8453 \| -27.3863 \| -2.8460 \| -2.8463 \| \| 0.5631 \| 0.39 \| 200 \| 0.6018 \| -1.1675 \| -1.6769 \| 0.6330 \| 0.5093 \| -31.9261 \| -25.7209 \| -2.8536 \| -2.8539 \| \| 0.6109 \| 0.49 \| 250 \| 0.6043 \| -1.1749 \| -1.6856 \| 0.6330 \| 0.5107 \| -31.9435 \| -25.7355 \| -2.8536 \| -2.8539 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.0.0+cu117 - Datasets 2.18.0 - Tokenizers 0.15.2
aegunal/FT_IPD_llama7b	aegunal	2024-03-11T01:11:37Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-11T01:11:32Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
kaijie-qin/llama-2-7b-kube	kaijie-qin	2024-03-11T01:04:03Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-10T08:23:40Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
ozzyonfire/bird-species-classifier	ozzyonfire	2024-03-11T01:00:42Z	150	0	transformers	[ "transformers", "onnx", "safetensors", "efficientnet", "image-classification", "biology", "vision", "en", "dataset:chriamue/bird-species-dataset", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-03-10T18:57:02Z	--- license: mit datasets: - chriamue/bird-species-dataset language: - en metrics: - accuracy library_name: transformers pipeline_tag: image-classification tags: - biology - image-classification - vision model-index: - name: bird-species-classifier results: - task: type: ImageClassification dataset: type: chriamue/bird-species-dataset name: Bird Species config: default split: validation metrics: - type: accuracy value: 96.8 - type: loss value: 0.1379 --- # Model Card for "Bird Species Classifier" This model came from chiramue/bird-species-classifier. This has been retrained using ResNet50 in hopes to get it running using Transformers JS. ## Model Description The "Bird Species Classifier" is a state-of-the-art image classification model designed to identify various bird species from images. It uses the EfficientNet architecture and has been fine-tuned to achieve high accuracy in recognizing a wide range of bird species. ### How to Use You can easily use the model in your Python environment with the following code: ```python from transformers import AutoFeatureExtractor, AutoModelForImageClassification extractor = AutoFeatureExtractor.from_pretrained("chriamue/bird-species-classifier") model = AutoModelForImageClassification.from_pretrained("chriamue/bird-species-classifier") ``` ### Applications - Bird species identification for educational or ecological research. - Assistance in biodiversity monitoring and conservation efforts. - Enhancing user experience in nature apps and platforms. ## Training Data The model was trained on the "Bird Species" dataset, which is a comprehensive collection of bird images. Key features of this dataset include: - Total Species: 525 bird species. - Training Images: 84,635 images. - Validation Images: 2,625 images. - Test Images: 2,625 images. - Image Format: Color images (224x224x3) in JPG format. - Source: Sourced from Kaggle. ## Training Results The model achieved impressive results after 6 epochs of training: - Accuracy: 96.8% - Loss: 0.1379 - Runtime: 136.81 seconds - Samples per Second: 19.188 - Steps per Second: 1.206 - Total Training Steps: 31,740 These metrics indicate a high level of performance, making the model reliable for practical applications. ## Limitations and Bias - The performance of the model might vary under different lighting conditions or image qualities. - The model's accuracy is dependent on the diversity and representation in the training dataset. It may perform less effectively on bird species not well represented in the dataset. ## Ethical Considerations This model should be used responsibly, considering privacy and environmental impacts. It should not be used for harmful purposes such as targeting endangered species or violating wildlife protection laws. ## Acknowledgements We would like to acknowledge the creators of the dataset on Kaggle for providing a rich source of data that made this model possible. ## See also - [Bird Species Dataset](https://huggingface.co/datasets/chriamue/bird-species-dataset) - [Kaggle Dataset](https://www.kaggle.com/datasets/gpiosenka/100-bird-species/data) - [Bird Species Classifier](https://huggingface.co/dennisjooo/Birds-Classifier-EfficientNetB2)
ZainAli60/miner_1	ZainAli60	2024-03-11T00:59:16Z	175	0	transformers	[ "transformers", "safetensors", "bart", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-03-11T00:58:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
Nandapratama241/path-to-save-model	Nandapratama241	2024-03-11T00:47:31Z	2	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "text-to-image", "dreambooth", "diffusers-training", "stable-diffusion", "stable-diffusion-diffusers", "base_model:stabilityai/stable-diffusion-2-1-base", "base_model:finetune:stabilityai/stable-diffusion-2-1-base", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-03-11T00:11:35Z	--- license: creativeml-openrail-m library_name: diffusers tags: - text-to-image - dreambooth - diffusers-training - stable-diffusion - stable-diffusion-diffusers base_model: stabilityai/stable-diffusion-2-1-base inference: true instance_prompt: a photo of NAnFRst person --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # DreamBooth - Nandapratama241/path-to-save-model This is a dreambooth model derived from stabilityai/stable-diffusion-2-1-base. The weights were trained on a photo of NAnFRst person using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
Omar23moh/UNIT3	Omar23moh	2024-03-11T00:45:47Z	1	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-03-10T14:20:13Z	--- 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: 136.00 +/- 26.81 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Omar23moh -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 Omar23moh -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 Omar23moh ``` ## 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', 1000), ('n_timesteps', 10000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 100), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
furrutiav/bert_qa_extractor_cockatiel_2022_ulra_sign_adamw_lf_signal_it_1	furrutiav	2024-03-11T00:37:50Z	91	0	transformers	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	feature-extraction	2024-03-11T00:37:23Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
Litzy619/V0309O6	Litzy619	2024-03-11T00:26:55Z	0	0	null	[ "safetensors", "generated_from_trainer", "base_model:microsoft/phi-2", "base_model:finetune:microsoft/phi-2", "license:mit", "region:us" ]	null	2024-03-10T16:43:26Z	--- license: mit base_model: microsoft/phi-2 tags: - generated_from_trainer model-index: - name: V0309O6 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # V0309O6 This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0631 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 20 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.9549 \| 0.09 \| 10 \| 0.7661 \| \| 0.3073 \| 0.17 \| 20 \| 0.1105 \| \| 0.1318 \| 0.26 \| 30 \| 0.0849 \| \| 0.1149 \| 0.34 \| 40 \| 0.0834 \| \| 0.1155 \| 0.43 \| 50 \| 0.0803 \| \| 0.1048 \| 0.51 \| 60 \| 0.0807 \| \| 0.0963 \| 0.6 \| 70 \| 0.0808 \| \| 0.0992 \| 0.68 \| 80 \| 0.0777 \| \| 0.0893 \| 0.77 \| 90 \| 0.0731 \| \| 0.1061 \| 0.85 \| 100 \| 0.0747 \| \| 0.098 \| 0.94 \| 110 \| 0.0711 \| \| 0.095 \| 1.02 \| 120 \| 0.0699 \| \| 0.0908 \| 1.11 \| 130 \| 0.0743 \| \| 0.0874 \| 1.19 \| 140 \| 0.0734 \| \| 0.083 \| 1.28 \| 150 \| 0.0682 \| \| 0.0823 \| 1.37 \| 160 \| 0.0701 \| \| 0.0812 \| 1.45 \| 170 \| 0.0684 \| \| 0.078 \| 1.54 \| 180 \| 0.0683 \| \| 0.0763 \| 1.62 \| 190 \| 0.0671 \| \| 0.0763 \| 1.71 \| 200 \| 0.0650 \| \| 0.08 \| 1.79 \| 210 \| 0.0634 \| \| 0.0686 \| 1.88 \| 220 \| 0.0650 \| \| 0.0685 \| 1.96 \| 230 \| 0.0638 \| \| 0.074 \| 2.05 \| 240 \| 0.0644 \| \| 0.0646 \| 2.13 \| 250 \| 0.0630 \| \| 0.0669 \| 2.22 \| 260 \| 0.0675 \| \| 0.061 \| 2.3 \| 270 \| 0.0675 \| \| 0.0672 \| 2.39 \| 280 \| 0.0635 \| \| 0.0687 \| 2.47 \| 290 \| 0.0625 \| \| 0.0656 \| 2.56 \| 300 \| 0.0625 \| \| 0.0738 \| 2.65 \| 310 \| 0.0626 \| \| 0.062 \| 2.73 \| 320 \| 0.0628 \| \| 0.0622 \| 2.82 \| 330 \| 0.0631 \| \| 0.0632 \| 2.9 \| 340 \| 0.0630 \| \| 0.0644 \| 2.99 \| 350 \| 0.0631 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1
NorGLM/NorLLama-3B-NO-MRPC-peft	NorGLM	2024-03-11T00:17:11Z	0	0	null	[ "no", "license:cc-by-nc-sa-4.0", "region:us" ]	null	2024-03-11T00:15:43Z	--- license: cc-by-nc-sa-4.0 language: - 'no' --- # Model Card NorLLama-3B-NO-MRPC-peft is trained on top of [NorLLama-3B](https://huggingface.co/NorGLM/NorLLama-3B) model on [NO-MRPC](https://huggingface.co/datasets/NorGLM/NO-MRPC) dataset. Data format: ``` input: {text_a}[SEP]{text_b} label: {0, 1} ``` ## Run the Model ```python from peft import PeftModel, PeftConfig from transformers import AutoModelForCausalLM, AutoTokenizer import torch torch_device = "cuda" if torch.cuda.is_available() else "cpu" source_model_id = "NorGLM/NorLLama-3B" peft_model_id = "NorGLM/NorLLama-3B-NO-MRPC-peft" config = PeftConfig.from_pretrained(peft_model_id) model = AutoModelForCausalLM.from_pretrained(source_model_id, device_map='balanced') tokenizer_max_len = 2048 tokenizer_config = {'pretrained_model_name_or_path': source_model_id, 'max_len': tokenizer_max_len} tokenizer = tokenizer = AutoTokenizer.from_pretrained(tokenizer_config) tokenizer.pad_token = tokenizer.eos_token model = PeftModel.from_pretrained(model, peft_model_id) ``` ## Inference Example Load the model to evaluate on the validation set: ```python def getDataSetFromFiles(df): # convert dataset df["text"] = df[["text_a", "text_b"]].apply(lambda x: " [SEP] ".join(x.astype(str)), axis =1) df = df.drop(["idx", "text_a", "text_b"], axis=1) df["label"] = df.label.map({0: 0, 1: 1}) return Dataset.from_pandas(df) print("--LOADING EVAL DATAS---") eval_data = load_dataset("NorGLM/NO-MRPC", data_files="val.jsonl") eval_data = getDataSetFromFiles(eval_data["train"].to_pandas()) print("--MAKING PREDICTIONS---") model.eval() y_true = [] y_pred = [] count = 0 for data in eval_data: count = count + 1 if count % 100 == 0: print(count) inputs = tokenizer(data['text'], return_tensors="pt").to(torch_device) with torch.no_grad(): logits = model(inputs).logits #print(logits) predicted_class_id = logits.argmax().item() y_true.append(data['label']) y_pred.append(predicted_class_id) print(y_pred) print(f"Lenght of true_values: {len(y_true)}") print(f"Lenght of predicted_values: {len(y_pred)}") y_true = np.array(y_true) y_pred = np.array(y_pred) F_score = f1_score(y_true, y_pred, average="macro") print(f"F1 score: {F_score}") accuracy = accuracy_score(y_true, y_pred) print(f"Accuracy: {accuracy}") ``` ## Note More training details will be released soon!
NorGLM/NorGPT-3B-NO-MRPC-peft	NorGLM	2024-03-11T00:11:59Z	0	0	null	[ "no", "license:cc-by-nc-sa-4.0", "region:us" ]	null	2024-03-11T00:10:03Z	--- license: cc-by-nc-sa-4.0 language: - 'no' --- # Model Card NorGPT-3B-NO-MRPC-peft is trained on top of [NorGPT-3B](https://huggingface.co/NorGLM/NorGPT-3B) model on [NO-MRPC](https://huggingface.co/datasets/NorGLM/NO-MRPC) dataset. Data format: ``` input: {text_a}[SEP]{text_b} label: {0, 1} ``` ## Run the Model ```python from peft import PeftModel, PeftConfig from transformers import AutoModelForCausalLM, AutoTokenizer import torch torch_device = "cuda" if torch.cuda.is_available() else "cpu" source_model_id = "NorGLM/NorGPT-3B" peft_model_id = "NorGLM/NorGPT-3B-NO-MRPC-peft" config = PeftConfig.from_pretrained(peft_model_id) model = AutoModelForCausalLM.from_pretrained(source_model_id, device_map='balanced') tokenizer_max_len = 2048 tokenizer_config = {'pretrained_model_name_or_path': source_model_id, 'max_len': tokenizer_max_len} tokenizer = tokenizer = AutoTokenizer.from_pretrained(tokenizer_config) tokenizer.pad_token = tokenizer.eos_token model = PeftModel.from_pretrained(model, peft_model_id) ``` ## Inference Example Load the model to evaluate on the validation set: ```python def getDataSetFromFiles(df): # convert dataset df["text"] = df[["text_a", "text_b"]].apply(lambda x: " [SEP] ".join(x.astype(str)), axis =1) df = df.drop(["idx", "text_a", "text_b"], axis=1) df["label"] = df.label.map({0: 0, 1: 1}) return Dataset.from_pandas(df) print("--LOADING EVAL DATAS---") eval_data = load_dataset("NorGLM/NO-MRPC", data_files="val.jsonl") eval_data = getDataSetFromFiles(eval_data["train"].to_pandas()) print("--MAKING PREDICTIONS---") model.eval() y_true = [] y_pred = [] count = 0 for data in eval_data: count = count + 1 if count % 100 == 0: print(count) inputs = tokenizer(data['text'], return_tensors="pt").to(torch_device) with torch.no_grad(): logits = model(inputs).logits #print(logits) predicted_class_id = logits.argmax().item() y_true.append(data['label']) y_pred.append(predicted_class_id) print(y_pred) print(f"Lenght of true_values: {len(y_true)}") print(f"Lenght of predicted_values: {len(y_pred)}") y_true = np.array(y_true) y_pred = np.array(y_pred) F_score = f1_score(y_true, y_pred, average="macro") print(f"F1 score: {F_score}") accuracy = accuracy_score(y_true, y_pred) print(f"Accuracy: {accuracy}") ``` ## Note More training details will be released soon!
NorGLM/NorGPT-3B-NO-QNLI-peft	NorGLM	2024-03-11T00:09:21Z	0	0	null	[ "no", "license:cc-by-nc-sa-4.0", "region:us" ]	null	2024-03-10T23:50:10Z	--- license: cc-by-nc-sa-4.0 language: - 'no' --- # Model Card NorGPT-3B-NO-QNLI-peft is trained on top of [NorGPT-3B](https://huggingface.co/NorGLM/NorGPT-369M) model on [NO-QNLI](https://huggingface.co/datasets/NorGLM/NO-QNLI) dataset. Data format: ``` input: {premise}[SEP]{hypothesis} label: {entailment, not_entailment} -> {1,0} ``` ## Run the Model ```python from peft import PeftModel, PeftConfig from transformers import AutoModelForCausalLM, AutoTokenizer import torch torch_device = "cuda" if torch.cuda.is_available() else "cpu" source_model_id = "NorGLM/NorGPT-3B" peft_model_id = "NorGLM/NorGPT-3B-NO-QNLI-peft" config = PeftConfig.from_pretrained(peft_model_id) model = AutoModelForCausalLM.from_pretrained(source_model_id, device_map='balanced') tokenizer_max_len = 2048 tokenizer_config = {'pretrained_model_name_or_path': source_model_id, 'max_len': tokenizer_max_len} tokenizer = tokenizer = AutoTokenizer.from_pretrained(tokenizer_config) tokenizer.pad_token = tokenizer.eos_token model = PeftModel.from_pretrained(model, peft_model_id) ``` ## Inference Example Load the model to evaluate on the validation set: ```python def getDataSetFromFiles(df): # convert dataset df["text"] = df[["premise", "hypothesis"]].apply(lambda x: " [SEP] ".join(x.astype(str)), axis =1) df = df.drop(["idx", "premise", "hypothesis"], axis=1) #df['label'] = df['label'].replace({1:'contradiction', -1:'entailment', 0:'neutral'}) df["label"] = df.label.map({"not_entailment": 0, "entailment": 1}) return Dataset.from_pandas(df) print("--LOADING EVAL DATAS---") eval_data = load_dataset("NorGLM/NO-QNLI", data_files="val.jsonl") eval_data = getDataSetFromFiles(eval_data["train"].to_pandas()) print("--MAKING PREDICTIONS---") model.eval() y_true = [] y_pred = [] count = 0 for data in eval_data: count = count + 1 if count % 100 == 0: print(count) inputs = tokenizer(data['text'], return_tensors="pt").to(torch_device) with torch.no_grad(): logits = model(inputs).logits #print(logits) predicted_class_id = logits.argmax().item() y_true.append(data['label']) y_pred.append(predicted_class_id) print(y_pred) print(f"Lenght of true_values: {len(y_true)}") print(f"Lenght of predicted_values: {len(y_pred)}") y_true = np.array(y_true) y_pred = np.array(y_pred) F_score = f1_score(y_true, y_pred, average="macro") print(f"F1 score: {F_score}") accuracy = accuracy_score(y_true, y_pred) print(f"Accuracy: {accuracy}") ``` ## Note More training details will be released soon!
NorGLM/NorLlama-3B-NO-QNLI-peft	NorGLM	2024-03-11T00:09:04Z	0	0	null	[ "no", "license:cc-by-nc-sa-4.0", "region:us" ]	null	2024-03-10T23:55:51Z	--- license: cc-by-nc-sa-4.0 language: - 'no' --- # Model Card NorLlama-3B-NO-QNLI-peft is trained on top of [NorLlama-3B](https://huggingface.co/NorGLM/NorLlama-3B) model on [NO-QNLI](https://huggingface.co/datasets/NorGLM/NO-QNLI) dataset. Data format: ``` input: {premise}[SEP]{hypothesis} label: {entailment, not_entailment} -> {1,0} ``` ## Run the Model ```python from peft import PeftModel, PeftConfig from transformers import AutoModelForCausalLM, AutoTokenizer import torch torch_device = "cuda" if torch.cuda.is_available() else "cpu" source_model_id = "NorGLM/NorLlama-3B" peft_model_id = "NorGLM/NorLlama-3B-NO-QNLI-peft" config = PeftConfig.from_pretrained(peft_model_id) model = AutoModelForCausalLM.from_pretrained(source_model_id, device_map='balanced') tokenizer_max_len = 2048 tokenizer_config = {'pretrained_model_name_or_path': source_model_id, 'max_len': tokenizer_max_len} tokenizer = tokenizer = AutoTokenizer.from_pretrained(tokenizer_config) tokenizer.pad_token = tokenizer.eos_token model = PeftModel.from_pretrained(model, peft_model_id) ``` ## Inference Example Load the model to evaluate on the validation set: ```python def getDataSetFromFiles(df): # convert dataset df["text"] = df[["premise", "hypothesis"]].apply(lambda x: " [SEP] ".join(x.astype(str)), axis =1) df = df.drop(["idx", "premise", "hypothesis"], axis=1) #df['label'] = df['label'].replace({1:'contradiction', -1:'entailment', 0:'neutral'}) df["label"] = df.label.map({"not_entailment": 0, "entailment": 1}) return Dataset.from_pandas(df) print("--LOADING EVAL DATAS---") eval_data = load_dataset("NorGLM/NO-QNLI", data_files="val.jsonl") eval_data = getDataSetFromFiles(eval_data["train"].to_pandas()) print("--MAKING PREDICTIONS---") model.eval() y_true = [] y_pred = [] count = 0 for data in eval_data: count = count + 1 if count % 100 == 0: print(count) inputs = tokenizer(data['text'], return_tensors="pt").to(torch_device) with torch.no_grad(): logits = model(inputs).logits #print(logits) predicted_class_id = logits.argmax().item() y_true.append(data['label']) y_pred.append(predicted_class_id) print(y_pred) print(f"Lenght of true_values: {len(y_true)}") print(f"Lenght of predicted_values: {len(y_pred)}") y_true = np.array(y_true) y_pred = np.array(y_pred) F_score = f1_score(y_true, y_pred, average="macro") print(f"F1 score: {F_score}") accuracy = accuracy_score(y_true, y_pred) print(f"Accuracy: {accuracy}") ``` ## Note More training details will be released soon!
NorGLM/NorGPT-3B-continue-NO-QNLI-peft	NorGLM	2024-03-11T00:08:47Z	0	0	null	[ "no", "license:cc-by-nc-sa-4.0", "region:us" ]	null	2024-03-10T23:53:53Z	--- license: cc-by-nc-sa-4.0 language: - 'no' --- # Model Card NorGPT-3B-continue-NO-QNLI-peft is trained on top of [NorGPT-3B-continue](https://huggingface.co/NorGLM/NorGPT-3B-continue) model on [NO-QNLI](https://huggingface.co/datasets/NorGLM/NO-QNLI) dataset. Data format: ``` input: {premise}[SEP]{hypothesis} label: {entailment, not_entailment} -> {1,0} ``` ## Run the Model ```python from peft import PeftModel, PeftConfig from transformers import AutoModelForCausalLM, AutoTokenizer import torch torch_device = "cuda" if torch.cuda.is_available() else "cpu" source_model_id = "NorGLM/NorGPT-3B-continue" peft_model_id = "NorGLM/NorGPT-3B-continue-NO-QNLI-peft" config = PeftConfig.from_pretrained(peft_model_id) model = AutoModelForCausalLM.from_pretrained(source_model_id, device_map='balanced') tokenizer_max_len = 2048 tokenizer_config = {'pretrained_model_name_or_path': source_model_id, 'max_len': tokenizer_max_len} tokenizer = tokenizer = AutoTokenizer.from_pretrained(tokenizer_config) tokenizer.pad_token = tokenizer.eos_token model = PeftModel.from_pretrained(model, peft_model_id) ``` ## Inference Example Load the model to evaluate on the validation set: ```python def getDataSetFromFiles(df): # convert dataset df["text"] = df[["premise", "hypothesis"]].apply(lambda x: " [SEP] ".join(x.astype(str)), axis =1) df = df.drop(["idx", "premise", "hypothesis"], axis=1) #df['label'] = df['label'].replace({1:'contradiction', -1:'entailment', 0:'neutral'}) df["label"] = df.label.map({"not_entailment": 0, "entailment": 1}) return Dataset.from_pandas(df) print("--LOADING EVAL DATAS---") eval_data = load_dataset("NorGLM/NO-QNLI", data_files="val.jsonl") eval_data = getDataSetFromFiles(eval_data["train"].to_pandas()) print("--MAKING PREDICTIONS---") model.eval() y_true = [] y_pred = [] count = 0 for data in eval_data: count = count + 1 if count % 100 == 0: print(count) inputs = tokenizer(data['text'], return_tensors="pt").to(torch_device) with torch.no_grad(): logits = model(inputs).logits #print(logits) predicted_class_id = logits.argmax().item() y_true.append(data['label']) y_pred.append(predicted_class_id) print(y_pred) print(f"Lenght of true_values: {len(y_true)}") print(f"Lenght of predicted_values: {len(y_pred)}") y_true = np.array(y_true) y_pred = np.array(y_pred) F_score = f1_score(y_true, y_pred, average="macro") print(f"F1 score: {F_score}") accuracy = accuracy_score(y_true, y_pred) print(f"Accuracy: {accuracy}") ``` ## Note More training details will be released soon!
NorGLM/NorGPT-369M-NO-QNLI-peft	NorGLM	2024-03-11T00:08:20Z	0	0	null	[ "no", "license:cc-by-nc-sa-4.0", "region:us" ]	null	2024-03-10T23:45:24Z	--- license: cc-by-nc-sa-4.0 language: - 'no' --- # Model Card NorGPT-369M-NO-QNLI-peft is trained on top of [NorGPT-369M](https://huggingface.co/NorGLM/NorGPT-369M) model on [NO-QNLI](https://huggingface.co/datasets/NorGLM/NO-QNLI) dataset. Data format: ``` input: {premise}[SEP]{hypothesis} label: {entailment, not_entailment} -> {1,0} ``` ## Run the Model ```python from peft import PeftModel, PeftConfig from transformers import AutoModelForCausalLM, AutoTokenizer import torch torch_device = "cuda" if torch.cuda.is_available() else "cpu" source_model_id = "NorGLM/NorGPT-369M" peft_model_id = "NorGLM/NorGPT-369M-NO-QNLI-peft" config = PeftConfig.from_pretrained(peft_model_id) model = AutoModelForCausalLM.from_pretrained(source_model_id, device_map='balanced') tokenizer_max_len = 2048 tokenizer_config = {'pretrained_model_name_or_path': source_model_id, 'max_len': tokenizer_max_len} tokenizer = tokenizer = AutoTokenizer.from_pretrained(tokenizer_config) tokenizer.pad_token = tokenizer.eos_token model = PeftModel.from_pretrained(model, peft_model_id) ``` ## Inference Example Load the model to evaluate on the validation set: ```python def getDataSetFromFiles(df): # convert dataset df["text"] = df[["premise", "hypothesis"]].apply(lambda x: " [SEP] ".join(x.astype(str)), axis =1) df = df.drop(["idx", "premise", "hypothesis"], axis=1) #df['label'] = df['label'].replace({1:'contradiction', -1:'entailment', 0:'neutral'}) df["label"] = df.label.map({"not_entailment": 0, "entailment": 1}) return Dataset.from_pandas(df) print("--LOADING EVAL DATAS---") eval_data = load_dataset("NorGLM/NO-QNLI", data_files="val.jsonl") eval_data = getDataSetFromFiles(eval_data["train"].to_pandas()) print("--MAKING PREDICTIONS---") model.eval() y_true = [] y_pred = [] count = 0 for data in eval_data: count = count + 1 if count % 100 == 0: print(count) inputs = tokenizer(data['text'], return_tensors="pt").to(torch_device) with torch.no_grad(): logits = model(inputs).logits #print(logits) predicted_class_id = logits.argmax().item() y_true.append(data['label']) y_pred.append(predicted_class_id) print(y_pred) print(f"Lenght of true_values: {len(y_true)}") print(f"Lenght of predicted_values: {len(y_pred)}") y_true = np.array(y_true) y_pred = np.array(y_pred) F_score = f1_score(y_true, y_pred, average="macro") print(f"F1 score: {F_score}") accuracy = accuracy_score(y_true, y_pred) print(f"Accuracy: {accuracy}") ``` ## Note More training details will be released soon!
Davada/subnet6	Davada	2024-03-11T00:08:11Z	90	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-10T23:32:37Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
NorGLM/NorGPT-369M-NO-MRPC-peft	NorGLM	2024-03-11T00:07:22Z	0	0	null	[ "no", "license:cc-by-nc-sa-4.0", "region:us" ]	null	2024-03-11T00:02:01Z	--- license: cc-by-nc-sa-4.0 language: - 'no' --- # Model Card NorGPT-369M-NO-MRPC-peft is trained on top of [NorGPT-369M](https://huggingface.co/NorGLM/NorGPT-369M) model on [NO-MRPC](https://huggingface.co/datasets/NorGLM/NO-MRPC) dataset. Data format: ``` input: {text_a}[SEP]{text_b} label: {0, 1} ``` ## Run the Model ```python from peft import PeftModel, PeftConfig from transformers import AutoModelForCausalLM, AutoTokenizer import torch torch_device = "cuda" if torch.cuda.is_available() else "cpu" source_model_id = "NorGLM/NorGPT-369M" peft_model_id = "NorGLM/NorGPT-369M-NO-MRPC-peft" config = PeftConfig.from_pretrained(peft_model_id) model = AutoModelForCausalLM.from_pretrained(source_model_id, device_map='balanced') tokenizer_max_len = 2048 tokenizer_config = {'pretrained_model_name_or_path': source_model_id, 'max_len': tokenizer_max_len} tokenizer = tokenizer = AutoTokenizer.from_pretrained(tokenizer_config) tokenizer.pad_token = tokenizer.eos_token model = PeftModel.from_pretrained(model, peft_model_id) ``` ## Inference Example Load the model to evaluate on the validation set: ```python def getDataSetFromFiles(df): # convert dataset df["text"] = df[["text_a", "text_b"]].apply(lambda x: " [SEP] ".join(x.astype(str)), axis =1) df = df.drop(["idx", "text_a", "text_b"], axis=1) df["label"] = df.label.map({0: 0, 1: 1}) return Dataset.from_pandas(df) print("--LOADING EVAL DATAS---") eval_data = load_dataset("NorGLM/NO-MRPC", data_files="val.jsonl") eval_data = getDataSetFromFiles(eval_data["train"].to_pandas()) print("--MAKING PREDICTIONS---") model.eval() y_true = [] y_pred = [] count = 0 for data in eval_data: count = count + 1 if count % 100 == 0: print(count) inputs = tokenizer(data['text'], return_tensors="pt").to(torch_device) with torch.no_grad(): logits = model(inputs).logits #print(logits) predicted_class_id = logits.argmax().item() y_true.append(data['label']) y_pred.append(predicted_class_id) print(y_pred) print(f"Lenght of true_values: {len(y_true)}") print(f"Lenght of predicted_values: {len(y_pred)}") y_true = np.array(y_true) y_pred = np.array(y_pred) F_score = f1_score(y_true, y_pred, average="macro") print(f"F1 score: {F_score}") accuracy = accuracy_score(y_true, y_pred) print(f"Accuracy: {accuracy}") ``` ## Note More training details will be released soon!
Holarissun/phi2-aisft-synhh-randsampler-subset30000	Holarissun	2024-03-10T23:59:25Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:microsoft/phi-2", "base_model:adapter:microsoft/phi-2", "license:mit", "region:us" ]	null	2024-03-10T23:59:14Z	--- license: mit library_name: peft tags: - trl - sft - generated_from_trainer base_model: microsoft/phi-2 model-index: - name: phi2-aisft-synhh-randsampler-subset30000 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # phi2-aisft-synhh-randsampler-subset30000 This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
nold/MediKAI-GGUF	nold	2024-03-10T23:58:10Z	20	0	null	[ "gguf", "license:other", "endpoints_compatible", "region:us" ]	null	2024-03-10T15:35:00Z	--- license: other --- # MediKAI - Your Healthcare Companion 🏥💬 Welcome to mediKAI, the latest healthcare-focused model by HelpingAI designed to provide personalized assistance and support in medical-related queries. ## Overview mediKAI is a 14 billion parameters model that specializes in healthcare-related topics and medical assistance. Whether you have questions about symptoms, treatments, medications, or general health and wellness, mediKAI is here to help. ## Languages Supported - English - French - Hindi - Spanish - Arabic ``` *** Quantization of Model [OEvortex/MediKAI](https://huggingface.co/OEvortex/MediKAI). Created using [llm-quantizer](https://github.com/Nold360/llm-quantizer) Pipeline
dzakwan/cybersec	dzakwan	2024-03-10T23:56:53Z	5	0	transformers	[ "transformers", "pytorch", "safetensors", "gemma", "text-generation", "unsloth", "trl", "sft", "conversational", "en", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-10T15:53:39Z	--- library_name: transformers widget: - messages: - role: user content: >- We need to prepare for the possibility of a security incident. Can you create an incident response plan for our organization? inference: parameters: max_new_tokens: 200 tags: - unsloth - trl - sft language: - en --- # 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: M Dzakwan Falih - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): English - 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]
hanzla/gemma-2b-it-ds	hanzla	2024-03-10T23:49:53Z	23	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-10T18:33:23Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
koesn/Saul-Instruct-v1-GGUF	koesn	2024-03-10T23:48:42Z	56	5	transformers	[ "transformers", "gguf", "legal", "en", "arxiv:2403.03883", "license:mit", "endpoints_compatible", "region:us" ]	null	2024-03-10T16:19:55Z	--- library_name: transformers tags: - legal license: mit language: - en --- # Saul-Instruct-v1 ## Description This repo contains GGUF format model files for Saul-Instruct-v1. ## Files Provided \| Name \| Quant \| Bits \| File Size \| Remark \| \| ---------------------------- \| ------- \| ---- \| --------- \| -------------------------------- \| \| saul-instruct-v1.IQ3_S.gguf \| IQ3_S \| 3 \| 3.18 GB \| 3.44 bpw quantization \| \| saul-instruct-v1.IQ3_M.gguf \| IQ3_M \| 3 \| 3.28 GB \| 3.66 bpw quantization mix \| \| saul-instruct-v1.Q4_0.gguf \| Q4_0 \| 4 \| 4.11 GB \| 3.56G, +0.2166 ppl \| \| saul-instruct-v1.IQ4_NL.gguf \| IQ4_NL \| 4 \| 4.16 GB \| 4.25 bpw non-linear quantization \| \| saul-instruct-v1.Q4_K_M.gguf \| Q4_K_M \| 4 \| 4.37 GB \| 3.80G, +0.0532 ppl \| \| saul-instruct-v1.Q5_K_M.gguf \| Q5_K_M \| 5 \| 5.13 GB \| 4.45G, +0.0122 ppl \| \| saul-instruct-v1.Q6_K.gguf \| Q6_K \| 6 \| 5.94 GB \| 5.15G, +0.0008 ppl \| \| saul-instruct-v1.Q8_0.gguf \| Q8_0 \| 8 \| 7.70 GB \| 6.70G, +0.0004 ppl \| ## Parameters \| path \| type \| architecture \| rope_theta \| sliding_win \| max_pos_embed \| \| ----------------------- \| ------- \| ------------------ \| ---------- \| ----------- \| ------------- \| \| Equall/Saul-Instruct-v1 \| mistral \| MistralForCausalLM \| 10000 \| 4096 \| 32768 \| ## Benchmarks See original model card. # Original Model Card # Equall/Saul-Instruct-v1 This is the instruct model for Equall/Saul-Instruct-v1, a large instruct language model tailored for Legal domain. This model is obtained by continue pretraining of Mistral-7B. Checkout our website and register https://equall.ai/ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/644a900e3a619fe72b14af0f/OU4Y3s-WckYKMN4fQkNiS.png) ## 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: Equall.ai in collaboration with CentraleSupelec, Sorbonne Université, Instituto Superior Técnico and NOVA School of Law - Model type: 7B - Language(s) (NLP): English - License: MIT ### Model Sources <!-- Provide the basic links for the model. --> - Paper: https://arxiv.org/abs/2403.03883 ## 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. --> You can use it for legal use cases that involves generation. Here's how you can run the model using the pipeline() function from 🤗 Transformers: ```python # Install transformers from source - only needed for versions <= v4.34 # pip install git+https://github.com/huggingface/transformers.git # pip install accelerate import torch from transformers import pipeline pipe = pipeline("text-generation", model="Equall/Saul-Instruct-v1", torch_dtype=torch.bfloat16, device_map="auto") # We use the tokenizer’s chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating messages = [ {"role": "user", "content": "[YOUR QUERY GOES HERE]"}, ] prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) outputs = pipe(prompt, max_new_tokens=256, do_sample=False) print(outputs[0]["generated_text"]) ``` ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> This model is built upon the technology of LLM, which comes with inherent limitations. It may occasionally generate inaccurate or nonsensical outputs. Furthermore, being a 7B model, it's anticipated to exhibit less robust performance compared to larger models, such as the 70B variant. ## Citation <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: ```bibtex @misc{colombo2024saullm7b, title={SaulLM-7B: A pioneering Large Language Model for Law}, author={Pierre Colombo and Telmo Pessoa Pires and Malik Boudiaf and Dominic Culver and Rui Melo and Caio Corro and Andre F. T. Martins and Fabrizio Esposito and Vera Lúcia Raposo and Sofia Morgado and Michael Desa}, year={2024}, eprint={2403.03883}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
ZainAli60/miner_	ZainAli60	2024-03-10T23:43:29Z	189	0	transformers	[ "transformers", "safetensors", "bart", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-03-10T20:49:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
btmiller/output	btmiller	2024-03-10T23:37:44Z	1	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:google/flan-t5-small", "base_model:adapter:google/flan-t5-small", "license:apache-2.0", "region:us" ]	null	2024-03-10T23:37:43Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer datasets: - generator base_model: google/flan-t5-small model-index: - name: output results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # output This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) 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: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.9.1.dev0 - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
USGOV/Gov	USGOV	2024-03-10T23:30:04Z	0	0	null	[ "license:other", "region:us" ]	null	2024-03-10T23:30:04Z	--- license: other license_name: government-open-source license_link: LICENSE ---
alinerodrigues/wav2vec2-large-xlsr-mecita-coraa-portuguese-all-grade-2-5	alinerodrigues	2024-03-10T23:25:48Z	1	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-03-10T19:49:47Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: wav2vec2-large-xlsr-mecita-coraa-portuguese-all-grade-2-5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xlsr-mecita-coraa-portuguese-all-grade-2-5 This model is a fine-tuned version of [Edresson/wav2vec2-large-xlsr-coraa-portuguese](https://huggingface.co/Edresson/wav2vec2-large-xlsr-coraa-portuguese) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1888 - Wer: 0.1049 - Cer: 0.0343 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\| \| 31.7094 \| 1.0 \| 58 \| 3.6328 \| 1.0 \| 1.0 \| \| 7.717 \| 2.0 \| 116 \| 3.1503 \| 1.0 \| 1.0 \| \| 7.717 \| 3.0 \| 174 \| 3.0149 \| 1.0 \| 1.0 \| \| 3.0503 \| 4.0 \| 232 \| 2.9605 \| 1.0 \| 1.0 \| \| 3.0503 \| 5.0 \| 290 \| 2.9212 \| 1.0 \| 1.0 \| \| 2.9265 \| 6.0 \| 348 \| 2.8995 \| 1.0 \| 1.0 \| \| 2.8799 \| 7.0 \| 406 \| 2.6417 \| 1.0 \| 1.0 \| \| 2.8799 \| 8.0 \| 464 \| 1.3314 \| 0.9950 \| 0.2921 \| \| 2.0484 \| 9.0 \| 522 \| 0.7075 \| 0.3918 \| 0.1000 \| \| 2.0484 \| 10.0 \| 580 \| 0.5138 \| 0.2276 \| 0.0664 \| \| 0.9682 \| 11.0 \| 638 \| 0.4169 \| 0.2071 \| 0.0596 \| \| 0.9682 \| 12.0 \| 696 \| 0.3580 \| 0.1835 \| 0.0530 \| \| 0.6198 \| 13.0 \| 754 \| 0.3281 \| 0.1719 \| 0.0513 \| \| 0.529 \| 14.0 \| 812 \| 0.3166 \| 0.1692 \| 0.0502 \| \| 0.529 \| 15.0 \| 870 \| 0.2954 \| 0.1595 \| 0.0483 \| \| 0.445 \| 16.0 \| 928 \| 0.2783 \| 0.1502 \| 0.0453 \| \| 0.445 \| 17.0 \| 986 \| 0.2721 \| 0.1452 \| 0.0445 \| \| 0.3943 \| 18.0 \| 1044 \| 0.2537 \| 0.1390 \| 0.0415 \| \| 0.3798 \| 19.0 \| 1102 \| 0.2567 \| 0.1332 \| 0.0416 \| \| 0.3798 \| 20.0 \| 1160 \| 0.2434 \| 0.1196 \| 0.0388 \| \| 0.3459 \| 21.0 \| 1218 \| 0.2421 \| 0.1181 \| 0.0384 \| \| 0.3459 \| 22.0 \| 1276 \| 0.2252 \| 0.1150 \| 0.0365 \| \| 0.3187 \| 23.0 \| 1334 \| 0.2331 \| 0.1146 \| 0.0368 \| \| 0.3187 \| 24.0 \| 1392 \| 0.2195 \| 0.1181 \| 0.0371 \| \| 0.2982 \| 25.0 \| 1450 \| 0.2180 \| 0.1181 \| 0.0375 \| \| 0.2874 \| 26.0 \| 1508 \| 0.2181 \| 0.1069 \| 0.0355 \| \| 0.2874 \| 27.0 \| 1566 \| 0.2159 \| 0.1099 \| 0.0360 \| \| 0.2542 \| 28.0 \| 1624 \| 0.2173 \| 0.1161 \| 0.0380 \| \| 0.2542 \| 29.0 \| 1682 \| 0.2127 \| 0.1080 \| 0.0358 \| \| 0.2663 \| 30.0 \| 1740 \| 0.2112 \| 0.1158 \| 0.0372 \| \| 0.2663 \| 31.0 \| 1798 \| 0.2114 \| 0.1130 \| 0.0364 \| \| 0.2371 \| 32.0 \| 1856 \| 0.2052 \| 0.1092 \| 0.0359 \| \| 0.2348 \| 33.0 \| 1914 \| 0.2044 \| 0.1061 \| 0.0346 \| \| 0.2348 \| 34.0 \| 1972 \| 0.2067 \| 0.1072 \| 0.0344 \| \| 0.2368 \| 35.0 \| 2030 \| 0.2023 \| 0.1099 \| 0.0350 \| \| 0.2368 \| 36.0 \| 2088 \| 0.1992 \| 0.1049 \| 0.0353 \| \| 0.217 \| 37.0 \| 2146 \| 0.1972 \| 0.1076 \| 0.0354 \| \| 0.234 \| 38.0 \| 2204 \| 0.1938 \| 0.1076 \| 0.0347 \| \| 0.234 \| 39.0 \| 2262 \| 0.1982 \| 0.1069 \| 0.0348 \| \| 0.1979 \| 40.0 \| 2320 \| 0.1945 \| 0.1061 \| 0.0346 \| \| 0.1979 \| 41.0 \| 2378 \| 0.2003 \| 0.1069 \| 0.0353 \| \| 0.2062 \| 42.0 \| 2436 \| 0.1970 \| 0.1053 \| 0.0350 \| \| 0.2062 \| 43.0 \| 2494 \| 0.1984 \| 0.1007 \| 0.0341 \| \| 0.2011 \| 44.0 \| 2552 \| 0.1992 \| 0.1072 \| 0.0343 \| \| 0.1807 \| 45.0 \| 2610 \| 0.1962 \| 0.1084 \| 0.0342 \| \| 0.1807 \| 46.0 \| 2668 \| 0.1958 \| 0.1030 \| 0.0334 \| \| 0.1982 \| 47.0 \| 2726 \| 0.1928 \| 0.1038 \| 0.0340 \| \| 0.1982 \| 48.0 \| 2784 \| 0.1961 \| 0.1053 \| 0.0344 \| \| 0.1948 \| 49.0 \| 2842 \| 0.1939 \| 0.1049 \| 0.0336 \| \| 0.1777 \| 50.0 \| 2900 \| 0.1888 \| 0.1049 \| 0.0343 \| \| 0.1777 \| 51.0 \| 2958 \| 0.1930 \| 0.1026 \| 0.0336 \| \| 0.1655 \| 52.0 \| 3016 \| 0.1900 \| 0.1018 \| 0.0333 \| \| 0.1655 \| 53.0 \| 3074 \| 0.1950 \| 0.1034 \| 0.0331 \| \| 0.1805 \| 54.0 \| 3132 \| 0.1946 \| 0.1045 \| 0.0340 \| \| 0.1805 \| 55.0 \| 3190 \| 0.1959 \| 0.1030 \| 0.0337 \| \| 0.1829 \| 56.0 \| 3248 \| 0.1933 \| 0.0987 \| 0.0325 \| \| 0.1621 \| 57.0 \| 3306 \| 0.1908 \| 0.0976 \| 0.0325 \| \| 0.1621 \| 58.0 \| 3364 \| 0.1892 \| 0.1010 \| 0.0331 \| \| 0.1702 \| 59.0 \| 3422 \| 0.1907 \| 0.0995 \| 0.0322 \| \| 0.1702 \| 60.0 \| 3480 \| 0.1934 \| 0.1003 \| 0.0326 \| \| 0.1652 \| 61.0 \| 3538 \| 0.1959 \| 0.0987 \| 0.0328 \| \| 0.1652 \| 62.0 \| 3596 \| 0.1961 \| 0.0976 \| 0.0323 \| \| 0.1567 \| 63.0 \| 3654 \| 0.1927 \| 0.0991 \| 0.0330 \| \| 0.1496 \| 64.0 \| 3712 \| 0.1912 \| 0.0983 \| 0.0327 \| \| 0.1496 \| 65.0 \| 3770 \| 0.1963 \| 0.1007 \| 0.0330 \| \| 0.1672 \| 66.0 \| 3828 \| 0.1958 \| 0.0999 \| 0.0328 \| \| 0.1672 \| 67.0 \| 3886 \| 0.1962 \| 0.0987 \| 0.0328 \| \| 0.141 \| 68.0 \| 3944 \| 0.1957 \| 0.0964 \| 0.0320 \| \| 0.144 \| 69.0 \| 4002 \| 0.1942 \| 0.0949 \| 0.0316 \| \| 0.144 \| 70.0 \| 4060 \| 0.1931 \| 0.0995 \| 0.0331 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.2.1+cu121 - Datasets 2.17.0 - Tokenizers 0.13.3
hamzasidat/Hamzas_Bert_Irony3	hamzasidat	2024-03-10T23:09:59Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-10T23:09:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
hamzasidat/BertIronyResults3	hamzasidat	2024-03-10T23:09:55Z	179	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-10T23:09:12Z	--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: BertIronyResults3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # BertIronyResults3 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5868 - Accuracy: 0.6932 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 179 \| 0.5868 \| 0.6932 \| \| No log \| 2.0 \| 358 \| 0.6104 \| 0.6869 \| \| 0.4907 \| 3.0 \| 537 \| 0.6448 \| 0.7026 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
hamzasidat/Hamzas_assignment1_Albert2	hamzasidat	2024-03-10T23:05:49Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-10T23:05:47Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
Francois2511/distilbert-base-uncased-finetuned-emotion	Francois2511	2024-03-10T23:03:13Z	94	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-10T22:26:32Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.9215 - name: F1 type: f1 value: 0.921552692432107 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2165 - Accuracy: 0.9215 - F1: 0.9216 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8067 \| 1.0 \| 250 \| 0.3244 \| 0.902 \| 0.9008 \| \| 0.2493 \| 2.0 \| 500 \| 0.2165 \| 0.9215 \| 0.9216 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2
saintazunya/outputs-dreambooth-sdxl-kanade	saintazunya	2024-03-10T23:03:06Z	2	2	diffusers	[ "diffusers", "tensorboard", "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", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-03-10T22:17:56Z	--- 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 skskanadetachibana figure widget: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - saintazunya/outputs-dreambooth-sdxl-kanade <Gallery /> ## Model description These are saintazunya/outputs-dreambooth-sdxl-kanade 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 skskanadetachibana figure to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](saintazunya/outputs-dreambooth-sdxl-kanade/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]
keskin-oguzhan/phi2-squadv2	keskin-oguzhan	2024-03-10T23:02:58Z	39	0	transformers	[ "transformers", "safetensors", "phi", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-10T22:58:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
hamzasidat/Hamzas_assignment1_Bert2	hamzasidat	2024-03-10T23:02:02Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-10T23:02:01Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
hamzasidat/BertResults2	hamzasidat	2024-03-10T23:02:00Z	177	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-10T23:01:39Z	--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer datasets: - emotion metrics: - accuracy model-index: - name: BertResults2 results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.94 --- <!-- 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. --> # BertResults2 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1487 - Accuracy: 0.94 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2236 \| 1.0 \| 1000 \| 0.1929 \| 0.924 \| \| 0.1179 \| 2.0 \| 2000 \| 0.1487 \| 0.94 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
tsavage68/mistralit2_1000_STEPS_1e8_rate_03_beta_DPO	tsavage68	2024-03-10T22:55:57Z	6	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "trl", "dpo", "generated_from_trainer", "conversational", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:finetune:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-10T22:49:41Z	--- license: apache-2.0 base_model: mistralai/Mistral-7B-Instruct-v0.2 tags: - trl - dpo - generated_from_trainer model-index: - name: mistralit2_1000_STEPS_1e8_rate_03_beta_DPO results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mistralit2_1000_STEPS_1e8_rate_03_beta_DPO This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6902 - Rewards/chosen: -0.0213 - Rewards/rejected: -0.0282 - Rewards/accuracies: 0.4945 - Rewards/margins: 0.0070 - Logps/rejected: -28.6665 - Logps/chosen: -23.4567 - Logits/rejected: -2.8649 - Logits/chosen: -2.8651 ## 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-08 - train_batch_size: 4 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.6911 \| 0.1 \| 50 \| 0.6909 \| 0.0027 \| -0.0025 \| 0.4967 \| 0.0052 \| -28.5807 \| -23.3768 \| -2.8653 \| -2.8655 \| \| 0.6916 \| 0.2 \| 100 \| 0.6928 \| -0.0010 \| -0.0023 \| 0.4571 \| 0.0014 \| -28.5802 \| -23.3891 \| -2.8653 \| -2.8655 \| \| 0.6924 \| 0.29 \| 150 \| 0.6922 \| -0.0091 \| -0.0117 \| 0.4681 \| 0.0026 \| -28.6115 \| -23.4162 \| -2.8651 \| -2.8654 \| \| 0.6941 \| 0.39 \| 200 \| 0.6914 \| -0.0066 \| -0.0109 \| 0.4879 \| 0.0043 \| -28.6088 \| -23.4078 \| -2.8652 \| -2.8654 \| \| 0.6942 \| 0.49 \| 250 \| 0.6911 \| -0.0070 \| -0.0120 \| 0.4791 \| 0.0050 \| -28.6123 \| -23.4090 \| -2.8649 \| -2.8652 \| \| 0.6909 \| 0.59 \| 300 \| 0.6921 \| -0.0151 \| -0.0181 \| 0.4593 \| 0.0030 \| -28.6327 \| -23.4362 \| -2.8650 \| -2.8653 \| \| 0.696 \| 0.68 \| 350 \| 0.6903 \| -0.0140 \| -0.0207 \| 0.5121 \| 0.0067 \| -28.6414 \| -23.4326 \| -2.8651 \| -2.8653 \| \| 0.6907 \| 0.78 \| 400 \| 0.6904 \| -0.0153 \| -0.0217 \| 0.4945 \| 0.0064 \| -28.6448 \| -23.4369 \| -2.8649 \| -2.8652 \| \| 0.6895 \| 0.88 \| 450 \| 0.6898 \| -0.0157 \| -0.0232 \| 0.4945 \| 0.0075 \| -28.6497 \| -23.4380 \| -2.8649 \| -2.8652 \| \| 0.6902 \| 0.98 \| 500 \| 0.6892 \| -0.0192 \| -0.0282 \| 0.5165 \| 0.0090 \| -28.6665 \| -23.4500 \| -2.8650 \| -2.8652 \| \| 0.6923 \| 1.07 \| 550 \| 0.6893 \| -0.0196 \| -0.0282 \| 0.5385 \| 0.0086 \| -28.6663 \| -23.4511 \| -2.8649 \| -2.8652 \| \| 0.6957 \| 1.17 \| 600 \| 0.6897 \| -0.0210 \| -0.0288 \| 0.5011 \| 0.0078 \| -28.6684 \| -23.4560 \| -2.8649 \| -2.8652 \| \| 0.6885 \| 1.27 \| 650 \| 0.6897 \| -0.0173 \| -0.0251 \| 0.5143 \| 0.0078 \| -28.6560 \| -23.4436 \| -2.8650 \| -2.8653 \| \| 0.6912 \| 1.37 \| 700 \| 0.6906 \| -0.0207 \| -0.0268 \| 0.4967 \| 0.0061 \| -28.6617 \| -23.4548 \| -2.8650 \| -2.8652 \| \| 0.6874 \| 1.46 \| 750 \| 0.6903 \| -0.0216 \| -0.0282 \| 0.4923 \| 0.0065 \| -28.6663 \| -23.4580 \| -2.8650 \| -2.8652 \| \| 0.6896 \| 1.56 \| 800 \| 0.6877 \| -0.0180 \| -0.0298 \| 0.5451 \| 0.0119 \| -28.6719 \| -23.4457 \| -2.8649 \| -2.8651 \| \| 0.6904 \| 1.66 \| 850 \| 0.6905 \| -0.0217 \| -0.0279 \| 0.4791 \| 0.0062 \| -28.6655 \| -23.4582 \| -2.8649 \| -2.8651 \| \| 0.6913 \| 1.76 \| 900 \| 0.6902 \| -0.0213 \| -0.0282 \| 0.4945 \| 0.0070 \| -28.6665 \| -23.4567 \| -2.8649 \| -2.8651 \| \| 0.6977 \| 1.86 \| 950 \| 0.6902 \| -0.0213 \| -0.0282 \| 0.4945 \| 0.0070 \| -28.6665 \| -23.4567 \| -2.8649 \| -2.8651 \| \| 0.6892 \| 1.95 \| 1000 \| 0.6902 \| -0.0213 \| -0.0282 \| 0.4945 \| 0.0070 \| -28.6665 \| -23.4567 \| -2.8649 \| -2.8651 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.0.0+cu117 - Datasets 2.18.0 - Tokenizers 0.15.2
arsruts/distilbert-base-uncased-finetuned-cola	arsruts	2024-03-10T22:54:08Z	4	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-08T13:37:09Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - matthews_correlation model-index: - name: distilbert-base-uncased-finetuned-cola results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-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: 0.8855 - Matthews Correlation: 0.5339 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| 0.5155 \| 1.0 \| 535 \| 0.4625 \| 0.4354 \| \| 0.3412 \| 2.0 \| 1070 \| 0.4636 \| 0.5212 \| \| 0.2297 \| 3.0 \| 1605 \| 0.6616 \| 0.5111 \| \| 0.1737 \| 4.0 \| 2140 \| 0.8490 \| 0.5265 \| \| 0.1228 \| 5.0 \| 2675 \| 0.8855 \| 0.5339 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
SjardiWillems/distilbert-base-uncased-finetuned-stsb	SjardiWillems	2024-03-10T22:47:48Z	23	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:SjardiWillems/distilbert-base-uncased-finetuned-stsb", "base_model:finetune:SjardiWillems/distilbert-base-uncased-finetuned-stsb", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-05T21:52:02Z	--- license: apache-2.0 base_model: SjardiWillems/distilbert-base-uncased-finetuned-stsb tags: - generated_from_trainer metrics: - spearmanr model-index: - name: distilbert-base-uncased-finetuned-stsb results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-stsb This model is a fine-tuned version of [SjardiWillems/distilbert-base-uncased-finetuned-stsb](https://huggingface.co/SjardiWillems/distilbert-base-uncased-finetuned-stsb) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5419 - Pearson: 0.8736 - Spearmanr: 0.8702 ## 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: 3.1992432473500055e-06 - train_batch_size: 64 - eval_batch_size: 16 - seed: 25 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Pearson \| Spearmanr \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:---------:\| \| No log \| 1.0 \| 90 \| 0.5404 \| 0.8727 \| 0.8690 \| \| No log \| 2.0 \| 180 \| 0.5394 \| 0.8736 \| 0.8701 \| \| No log \| 3.0 \| 270 \| 0.5394 \| 0.8738 \| 0.8703 \| \| No log \| 4.0 \| 360 \| 0.5419 \| 0.8736 \| 0.8702 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
Litzy619/V0309P6	Litzy619	2024-03-10T22:45:47Z	0	0	null	[ "safetensors", "generated_from_trainer", "base_model:microsoft/phi-2", "base_model:finetune:microsoft/phi-2", "license:mit", "region:us" ]	null	2024-03-10T07:39:51Z	--- license: mit base_model: microsoft/phi-2 tags: - generated_from_trainer model-index: - name: V0309P6 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # V0309P6 This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0648 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 20 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.969 \| 0.09 \| 10 \| 0.5527 \| \| 0.2118 \| 0.17 \| 20 \| 0.0895 \| \| 0.1076 \| 0.26 \| 30 \| 0.0750 \| \| 0.0998 \| 0.34 \| 40 \| 0.0690 \| \| 0.0936 \| 0.43 \| 50 \| 0.0643 \| \| 0.0846 \| 0.51 \| 60 \| 0.0642 \| \| 0.0784 \| 0.6 \| 70 \| 0.0639 \| \| 0.0857 \| 0.68 \| 80 \| 0.0668 \| \| 0.0748 \| 0.77 \| 90 \| 0.0641 \| \| 0.111 \| 0.85 \| 100 \| 0.0680 \| \| 0.0874 \| 0.94 \| 110 \| 0.0704 \| \| 0.0842 \| 1.02 \| 120 \| 0.0675 \| \| 0.0797 \| 1.11 \| 130 \| 0.0678 \| \| 0.0731 \| 1.19 \| 140 \| 0.0642 \| \| 0.0714 \| 1.28 \| 150 \| 0.0584 \| \| 0.0709 \| 1.37 \| 160 \| 0.0621 \| \| 0.0703 \| 1.45 \| 170 \| 0.0587 \| \| 0.0638 \| 1.54 \| 180 \| 0.0595 \| \| 0.0678 \| 1.62 \| 190 \| 0.0580 \| \| 0.067 \| 1.71 \| 200 \| 0.0600 \| \| 0.0672 \| 1.79 \| 210 \| 0.0604 \| \| 0.0627 \| 1.88 \| 220 \| 0.0640 \| \| 0.0587 \| 1.96 \| 230 \| 0.0592 \| \| 0.057 \| 2.05 \| 240 \| 0.0622 \| \| 0.0486 \| 2.13 \| 250 \| 0.0663 \| \| 0.0484 \| 2.22 \| 260 \| 0.0690 \| \| 0.0457 \| 2.3 \| 270 \| 0.0677 \| \| 0.0529 \| 2.39 \| 280 \| 0.0636 \| \| 0.0533 \| 2.47 \| 290 \| 0.0622 \| \| 0.0523 \| 2.56 \| 300 \| 0.0627 \| \| 0.0523 \| 2.65 \| 310 \| 0.0638 \| \| 0.0456 \| 2.73 \| 320 \| 0.0642 \| \| 0.048 \| 2.82 \| 330 \| 0.0648 \| \| 0.0454 \| 2.9 \| 340 \| 0.0642 \| \| 0.0491 \| 2.99 \| 350 \| 0.0648 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1
Litzy619/V0309P4	Litzy619	2024-03-10T22:45:17Z	0	0	null	[ "safetensors", "generated_from_trainer", "base_model:microsoft/phi-2", "base_model:finetune:microsoft/phi-2", "license:mit", "region:us" ]	null	2024-03-10T07:37:44Z	--- license: mit base_model: microsoft/phi-2 tags: - generated_from_trainer model-index: - name: V0309P4 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # V0309P4 This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0689 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 20 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.1886 \| 0.09 \| 10 \| 0.9747 \| \| 0.3651 \| 0.17 \| 20 \| 0.0977 \| \| 0.1129 \| 0.26 \| 30 \| 0.0765 \| \| 0.0955 \| 0.34 \| 40 \| 0.0707 \| \| 0.0894 \| 0.43 \| 50 \| 0.0684 \| \| 0.083 \| 0.51 \| 60 \| 0.0679 \| \| 0.0762 \| 0.6 \| 70 \| 0.0688 \| \| 0.0807 \| 0.68 \| 80 \| 0.0672 \| \| 0.0699 \| 0.77 \| 90 \| 0.0735 \| \| 0.0699 \| 0.85 \| 100 \| 0.0735 \| \| 0.0757 \| 0.94 \| 110 \| 0.0663 \| \| 0.0726 \| 1.02 \| 120 \| 0.0632 \| \| 0.0641 \| 1.11 \| 130 \| 0.0692 \| \| 0.0627 \| 1.19 \| 140 \| 0.0625 \| \| 0.0579 \| 1.28 \| 150 \| 0.0625 \| \| 0.0579 \| 1.37 \| 160 \| 0.0682 \| \| 0.0564 \| 1.45 \| 170 \| 0.0642 \| \| 0.0544 \| 1.54 \| 180 \| 0.0651 \| \| 0.0565 \| 1.62 \| 190 \| 0.0623 \| \| 0.057 \| 1.71 \| 200 \| 0.0605 \| \| 0.0589 \| 1.79 \| 210 \| 0.0602 \| \| 0.0538 \| 1.88 \| 220 \| 0.0659 \| \| 0.0528 \| 1.96 \| 230 \| 0.0623 \| \| 0.0482 \| 2.05 \| 240 \| 0.0640 \| \| 0.0396 \| 2.13 \| 250 \| 0.0693 \| \| 0.0398 \| 2.22 \| 260 \| 0.0753 \| \| 0.0372 \| 2.3 \| 270 \| 0.0771 \| \| 0.0463 \| 2.39 \| 280 \| 0.0707 \| \| 0.0447 \| 2.47 \| 290 \| 0.0676 \| \| 0.0429 \| 2.56 \| 300 \| 0.0672 \| \| 0.0454 \| 2.65 \| 310 \| 0.0670 \| \| 0.0377 \| 2.73 \| 320 \| 0.0678 \| \| 0.0387 \| 2.82 \| 330 \| 0.0690 \| \| 0.0394 \| 2.9 \| 340 \| 0.0690 \| \| 0.0414 \| 2.99 \| 350 \| 0.0689 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1
NorGLM/NorLlama-3B-Instruction-peft	NorGLM	2024-03-10T22:42:28Z	0	0	null	[ "no", "license:cc-by-nc-sa-4.0", "region:us" ]	null	2024-03-10T22:40:42Z	--- license: cc-by-nc-sa-4.0 language: - 'no' --- # Model Card NorLlama-3B-Instruction-peft is trained on top of [NorLlama-3B](https://huggingface.co/NorGLM/NorLlama-3B) model on [NO-Alpaca](https://huggingface.co/datasets/NbAiLab/norwegian-alpaca) dataset. Prompt format: ``` {instruction} {input} : {output} ``` Inference prompt: ``` {instruction} {input} : ``` ## Run the Model ```python from peft import PeftModel, PeftConfig from transformers import AutoModelForCausalLM, AutoTokenizer import torch source_model_id = "NorGLM/NorLlama-3B" peft_model_id = "NorGLM/NorLlama-3B-Instruction-peft" config = PeftConfig.from_pretrained(peft_model_id) model = AutoModelForCausalLM.from_pretrained(source_model_id, device_map='balanced') tokenizer_max_len = 2048 tokenizer_config = {'pretrained_model_name_or_path': source_model_id, 'max_len': tokenizer_max_len} tokenizer = tokenizer = AutoTokenizer.from_pretrained(tokenizer_config) tokenizer.pad_token = tokenizer.eos_token model = PeftModel.from_pretrained(model, peft_model_id) ``` ## Inference Example Load the model to evaluate on the last 20\% of NO-Alpaca dataset: ```python def merge_columns(example): if str(example["input"]) == "": example["text"] = str(example["instruction"]) + " : " else: example["text"] = str(example["instruction"]) + " " + str(example["input"]) + " : " return example def generate_text(text, max_length=200, do_sample=True, top_p = 0.92, top_k=0): set_seed(42) model_inputs = tokenizer(text, return_tensors='pt').to(torch_device) output = model.generate(model_inputs, max_new_tokens = max_length, no_repeat_ngram_size=2, pad_token_id=tokenizer.eos_token_id) return tokenizer.decode(output[0], skip_special_tokens=True) print("--LOADING EVAL DATAS---") eval_data = load_dataset("NbAiLab/norwegian-alpaca", split='train[-20%:]') print("--MAKING PREDICTIONS---") model.eval() output_file = <output file name> with open(output_file, 'w', encoding='utf-8-sig') as file: generated_text = [] for question in eval_data['text']: generated_text.append({"generated_text": generate_text(question)}) print({"text_generated": len(generated_text)}) json_lines = [json.dumps(data) for data in generated_text] json_data = "\n".join(json_lines) file.write(json_data) ``` ## Note More training details will be released soon!
ThuyNT03/CS505_MvPCOQE_viT5_Prompting5_top1	ThuyNT03	2024-03-10T22:41:01Z	93	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:VietAI/vit5-large", "base_model:finetune:VietAI/vit5-large", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-03-10T16:30:59Z	--- license: mit base_model: VietAI/vit5-large tags: - generated_from_trainer model-index: - name: CS505_MvPCOQE_viT5_Prompting5_top1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # CS505_MvPCOQE_viT5_Prompting5_top1 This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.2 - Datasets 2.1.0 - Tokenizers 0.15.2
Jackline/Blip2-HateSpeech-PEFT-LLM-2.7b	Jackline	2024-03-10T22:37:03Z	3	0	peft	[ "peft", "arxiv:1910.09700", "base_model:Salesforce/blip2-opt-2.7b", "base_model:adapter:Salesforce/blip2-opt-2.7b", "region:us" ]	null	2024-03-10T20:32:22Z	--- library_name: peft base_model: Salesforce/blip2-opt-2.7b --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: QuantizationMethod.BITS_AND_BYTES - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.6.1
hamzasidat/DistilbertIronyResults3	hamzasidat	2024-03-10T22:33:08Z	176	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-10T22:32:41Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: DistilbertIronyResults3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # DistilbertIronyResults3 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: 0.6026 - Accuracy: 0.6806 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 179 \| 0.6294 \| 0.6147 \| \| No log \| 2.0 \| 358 \| 0.6026 \| 0.6806 \| \| 0.5319 \| 3.0 \| 537 \| 0.6334 \| 0.6817 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
EarthnDusk/Lora_Extractions	EarthnDusk	2024-03-10T22:31:59Z	0	1	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-03-06T04:21:26Z	--- license: creativeml-openrail-m --- Lora extractions via Bmaltais/Kohya SS --- These are extractions of models we have existing, feel free to mooch there should be no activation tag. these are 128x128 dim/alpha for 1.5 - but SplatterpunkALpha is SDXL and is 32/16. Feel free WITH CREDIT if possible to merge back into your own content. SD 1.5 versions DIDNT TURN OUT, unless we tested them wrong. Splatterpunk is an XL one.
TeeZee/GALAXY-XB-v.02	TeeZee	2024-03-10T22:25:42Z	54	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-09T22:19:44Z	--- license: apache-2.0 model-index: - name: GALAXY-XB-v.02 results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 60.67 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/GALAXY-XB-v.02 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 83.27 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/GALAXY-XB-v.02 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 64.99 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/GALAXY-XB-v.02 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 43.6 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/GALAXY-XB-v.02 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 80.27 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/GALAXY-XB-v.02 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 42.08 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/GALAXY-XB-v.02 name: Open LLM Leaderboard --- ### TeeZee/GALAXY-XB-v.02 ### Experiment, can DUS be taken one or more steps further? ### Technical notes: - 10 layers removed from both models this time, 2 more than in original paper. - base version of upstage/SOLAR-10.7B-v1.0 used for merge - no finetuning done yet, this is just a merge, first step in DUS paper - next step, if evaluation proves that its at least as 'smart' as base model, should be finetuning to 'recover' after merge # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_TeeZee__GALAXY-XB-v.02) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|62.48\| \|AI2 Reasoning Challenge (25-Shot)\|60.67\| \|HellaSwag (10-Shot) \|83.27\| \|MMLU (5-Shot) \|64.99\| \|TruthfulQA (0-shot) \|43.60\| \|Winogrande (5-shot) \|80.27\| \|GSM8k (5-shot) \|42.08\|
dcarpintero/digit-classifier	dcarpintero	2024-03-10T22:24:35Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-03-08T17:54:00Z	--- license: apache-2.0 --- Implemented as a Multi-Layer Perceptron to classify handwritten Digits (0-9) [[Annotated Notebook](https://github.com/dcarpintero/fastai-deeplearning/blob/main/course2024/lesson_03.full.mnist.mlp.md)] Model Architecture and Results The model comprises a flattening layer and three linear layers `((256, 64) hidden dimensions)` with relus to approximate non-linearity. It achieves 95.6% accuracy after `15 training epochs` and `batch size = 64`. Taining and Test MNIST datasets are loaded with PyTorch [dataloaders](https://pytorch.org/tutorials/beginner/basics/data_tutorial.html).
TeeZee/GALAXY-XB-v.01	TeeZee	2024-03-10T22:24:21Z	57	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-09T18:48:51Z	--- license: apache-2.0 model-index: - name: GALAXY-XB-v.01 results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 60.92 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/GALAXY-XB-v.01 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 82.92 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/GALAXY-XB-v.01 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 65.11 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/GALAXY-XB-v.01 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 43.67 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/GALAXY-XB-v.01 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 81.14 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/GALAXY-XB-v.01 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 43.44 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/GALAXY-XB-v.01 name: Open LLM Leaderboard --- ### TeeZee/GALAXY-XB-v.01 ### Experiment, can DUS be taken one or more steps further? ### Technical notes: - 8 layers removed from both models, as per original paper. - base version of upstage/SOLAR-10.7B-v1.0 used for merge - no finetuning done yet, this is just a merge, first step in DUS paper - next step, if evaluation proves that its at least as 'smart' as base model, should be finetuning to 'recover' after merge # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_TeeZee__GALAXY-XB-v.01) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|62.87\| \|AI2 Reasoning Challenge (25-Shot)\|60.92\| \|HellaSwag (10-Shot) \|82.92\| \|MMLU (5-Shot) \|65.11\| \|TruthfulQA (0-shot) \|43.67\| \|Winogrande (5-shot) \|81.14\| \|GSM8k (5-shot) \|43.44\|
numen-tech/TinyLlama-1.1B-Chat-v1.0-w4a16g128asym	numen-tech	2024-03-10T22:21:47Z	0	0	null	[ "arxiv:2308.13137", "license:apache-2.0", "region:us" ]	null	2024-03-10T22:17:19Z	--- license: apache-2.0 --- 4-bit [OmniQuant](https://arxiv.org/abs/2308.13137) quantized version of [TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0).
thomasolav/distilbert-base-uncased-finetuned-cola	thomasolav	2024-03-10T22:11:32Z	14	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-10T21:56:27Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - matthews_correlation model-index: - name: distilbert-base-uncased-finetuned-cola results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-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: 0.8412 - Matthews Correlation: 0.5340 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| 0.5208 \| 1.0 \| 535 \| 0.4576 \| 0.4452 \| \| 0.3435 \| 2.0 \| 1070 \| 0.4613 \| 0.5168 \| \| 0.2338 \| 3.0 \| 1605 \| 0.6399 \| 0.5195 \| \| 0.1753 \| 4.0 \| 2140 \| 0.8412 \| 0.5340 \| \| 0.1295 \| 5.0 \| 2675 \| 0.8539 \| 0.5305 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
sweetfelinity/ppo-SnowballTarget	sweetfelinity	2024-03-10T22:00:03Z	2	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2024-03-10T22:00:00Z	--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: sweetfelinity/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
grace-pro/one_half_data_high_rank_v2	grace-pro	2024-03-10T21:55:21Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	null	2024-03-10T21:53:43Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer metrics: - precision - recall - accuracy base_model: mistralai/Mistral-7B-v0.1 model-index: - name: one_half_data_high_rank_v2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # one_half_data_high_rank_v2 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6564 - Precision: 0.8403 - Recall: 0.9383 - F1-score: 0.8866 - Accuracy: 0.8450 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1-score \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:---------:\|:------:\|:--------:\|:--------:\| \| 0.535 \| 1.0 \| 24544 \| 0.6564 \| 0.8403 \| 0.9383 \| 0.8866 \| 0.8450 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
roiyeho/bart-large-samsum	roiyeho	2024-03-10T21:54:35Z	98	0	transformers	[ "transformers", "tensorboard", "safetensors", "bart", "text2text-generation", "summarization", "generated_from_trainer", "base_model:facebook/bart-large-cnn", "base_model:finetune:facebook/bart-large-cnn", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2024-02-19T04:34:55Z	--- license: mit base_model: facebook/bart-large-cnn tags: - summarization - generated_from_trainer metrics: - rouge model-index: - name: bart-large-samsum results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-large-samsum This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3635 - Rouge1: 0.3962 - Rouge2: 0.2011 - Rougel: 0.3064 - Rougelsum: 0.3064 ## 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 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\| \| 1.3824 \| 0.43 \| 400 \| 1.4666 \| 0.3995 \| 0.2014 \| 0.3061 \| 0.3064 \| \| 1.2617 \| 0.87 \| 800 \| 1.3350 \| 0.4065 \| 0.2063 \| 0.3113 \| 0.3115 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
AwAppp/benchmarks_4bit_batch_size45	AwAppp	2024-03-10T21:49:33Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-10T21:49:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
m7n/dierenleven-sdxl-lora-001	m7n	2024-03-10T21:48:51Z	3	1	diffusers	[ "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", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-03-10T17:23:19Z	--- tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - diffusers-training - text-to-image - diffusers - lora - template:sd-lora widget: - text: 'litograph in the style of <s0><s1>, showing a beautiful bird of paradise' output: url: "image_0.png" - text: 'litograph in the style of <s0><s1>, showing a beautiful bird of paradise' output: url: "image_1.png" - text: 'litograph in the style of <s0><s1>, showing a beautiful bird of paradise' output: url: "image_2.png" - text: 'litograph in the style of <s0><s1>, showing a beautiful bird of paradise' output: url: "image_3.png" base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: litograph in the style of <s0><s1>, showing a beautiful bird of paradise license: openrail++ --- # SDXL LoRA DreamBooth - m7n/dierenleven-sdxl-lora-001 <Gallery /> ## Model description ### These are m7n/dierenleven-sdxl-lora-001 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 [`dierenleven-sdxl-lora-001.safetensors` here 💾](/m7n/dierenleven-sdxl-lora-001/blob/main/dierenleven-sdxl-lora-001.safetensors). - Place it on your `models/Lora` folder. - On AUTOMATIC1111, load the LoRA by adding `<lora:dierenleven-sdxl-lora-001:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/). - Embeddings: download [`dierenleven-sdxl-lora-001_emb.safetensors` here 💾](/m7n/dierenleven-sdxl-lora-001/blob/main/dierenleven-sdxl-lora-001_emb.safetensors). - Place it on it on your `embeddings` folder - Use it by adding `dierenleven-sdxl-lora-001_emb` to your prompt. For example, `litograph in the style of dierenleven-sdxl-lora-001_emb, showing a beautiful bird of paradise` (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('m7n/dierenleven-sdxl-lora-001', weight_name='pytorch_lora_weights.safetensors') embedding_path = hf_hub_download(repo_id='m7n/dierenleven-sdxl-lora-001', filename='dierenleven-sdxl-lora-001_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('litograph in the style of <s0><s1>, showing a beautiful bird of paradise').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](/m7n/dierenleven-sdxl-lora-001/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.
WhiteRabbitNeo/WhiteRabbitNeo-7B-v1.5a	WhiteRabbitNeo	2024-03-10T21:45:53Z	98	47	transformers	[ "transformers", "pytorch", "llama", "text-generation", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-18T02:33:26Z	--- license: other license_name: deepseek license_link: https://huggingface.co/deepseek-ai/deepseek-coder-33b-base/blob/main/LICENSE --- # Our latest 33B model is live (We'll always be serving the newest model on our web app)! Access at: https://www.whiterabbitneo.com/ # Our Discord Server Join us at: https://discord.gg/8Ynkrcbk92 (Updated on Dec 29th. Now permanent link to join) # DeepSeek Coder Licence + WhiteRabbitNeo Extended Version # Licence: Usage Restrictions ``` You agree not to use the Model or Derivatives of the Model: - In any way that violates any applicable national or international law or regulation or infringes upon the lawful rights and interests of any third party; - For military use in any way; - For the purpose of exploiting, harming or attempting to exploit or harm minors in any way; - To generate or disseminate verifiably false information and/or content with the purpose of harming others; - To generate or disseminate inappropriate content subject to applicable regulatory requirements; - To generate or disseminate personal identifiable information without due authorization or for unreasonable use; - To defame, disparage or otherwise harass others; - For fully automated decision making that adversely impacts an individual’s legal rights or otherwise creates or modifies a binding, enforceable obligation; - For any use intended to or which has the effect of discriminating against or harming individuals or groups based on online or offline social behavior or known or predicted personal or personality characteristics; - To exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm; - For any use intended to or which has the effect of discriminating against individuals or groups based on legally protected characteristics or categories. ``` # Topics Covered: ``` - Open Ports: Identifying open ports is crucial as they can be entry points for attackers. Common ports to check include HTTP (80, 443), FTP (21), SSH (22), and SMB (445). - Outdated Software or Services: Systems running outdated software or services are often vulnerable to exploits. This includes web servers, database servers, and any third-party software. - Default Credentials: Many systems and services are installed with default usernames and passwords, which are well-known and can be easily exploited. - Misconfigurations: Incorrectly configured services, permissions, and security settings can introduce vulnerabilities. - Injection Flaws: SQL injection, command injection, and cross-site scripting (XSS) are common issues in web applications. - Unencrypted Services: Services that do not use encryption (like HTTP instead of HTTPS) can expose sensitive data. - Known Software Vulnerabilities: Checking for known vulnerabilities in software using databases like the National Vulnerability Database (NVD) or tools like Nessus or OpenVAS. - Cross-Site Request Forgery (CSRF): This is where unauthorized commands are transmitted from a user that the web application trusts. - Insecure Direct Object References: This occurs when an application provides direct access to objects based on user-supplied input. - Security Misconfigurations in Web Servers/Applications: This includes issues like insecure HTTP headers or verbose error messages that reveal too much information. - Broken Authentication and Session Management: This can allow attackers to compromise passwords, keys, or session tokens, or to exploit other implementation flaws to assume other users' identities. - Sensitive Data Exposure: Includes vulnerabilities that expose sensitive data, such as credit card numbers, health records, or personal information. - API Vulnerabilities: In modern web applications, APIs are often used and can have vulnerabilities like insecure endpoints or data leakage. - Denial of Service (DoS) Vulnerabilities: Identifying services that are vulnerable to DoS attacks, which can make the resource unavailable to legitimate users. - Buffer Overflows: Common in older software, these vulnerabilities can allow an attacker to crash the system or execute arbitrary code. ``` # Terms of Use By accessing and using this Artificial Intelligence (AI) model, you, the user, acknowledge and agree that you are solely responsible for your use of the model and its outcomes. You hereby agree to indemnify, defend, and hold harmless the creators, developers, and any affiliated persons or entities of this AI model from and against any and all claims, liabilities, damages, losses, costs, expenses, fees (including reasonable attorneys' fees and court costs) that may arise, directly or indirectly, from your use of the AI model. This AI model is provided "as is" and "as available" without any warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose, and non-infringement. The creators make no warranty that the AI model will meet your requirements or be available on an uninterrupted, secure, or error-free basis. Your use of the AI model is at your own risk and discretion, and you will be solely responsible for any damage to computer systems or loss of data that results from the use of the AI model. This disclaimer constitutes part of the agreement between you and the creators of the AI model regarding your use of the model, superseding any prior agreements between you and the creators regarding your use of this AI model. # WhiteRabbitNeo <br> ![WhiteRabbitNeo](https://huggingface.co/migtissera/WhiteRabbitNeo/resolve/main/WhiteRabbitNeo.png) <br> WhiteRabbitNeo is a model series that can be used for offensive and defensive cybersecurity. Our models are now getting released as a public preview of its capabilities, and also to assess the societal impact of such an AI. ```python import torch, json from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "WhiteRabbitNeo/WhiteRabbitNeo-7B-v1.5a" model = AutoModelForCausalLM.from_pretrained( model_path, torch_dtype=torch.float16, device_map="auto", load_in_4bit=False, load_in_8bit=True, trust_remote_code=True, ) tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) def generate_text(instruction): tokens = tokenizer.encode(instruction) tokens = torch.LongTensor(tokens).unsqueeze(0) tokens = tokens.to("cuda") instance = { "input_ids": tokens, "top_p": 1.0, "temperature": 0.5, "generate_len": 1024, "top_k": 50, } length = len(tokens[0]) with torch.no_grad(): rest = model.generate( input_ids=tokens, max_length=length + instance["generate_len"], use_cache=True, do_sample=True, top_p=instance["top_p"], temperature=instance["temperature"], top_k=instance["top_k"], num_return_sequences=1, ) output = rest[0][length:] string = tokenizer.decode(output, skip_special_tokens=True) answer = string.split("USER:")[0].strip() return f"{answer}" conversation = f"SYSTEM: You are an AI that code. Answer with code." while True: user_input = input("You: ") llm_prompt = f"{conversation} \nUSER: {user_input} \nASSISTANT: " answer = generate_text(llm_prompt) print(answer) conversation = f"{llm_prompt}{answer}" # print(conversation) json_data = {"prompt": user_input, "answer": answer} # print(json_data) # with open(output_file_path, "a") as output_file: # output_file.write(json.dumps(json_data) + "\n") ```
AwAppp/benchmarks_4bit_batch_size20	AwAppp	2024-03-10T21:41:42Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-10T21:41:40Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
operablepattern/gemma-2b-it-Q	operablepattern	2024-03-10T21:41:02Z	11	1	transformers	[ "transformers", "gguf", "gemma", "text-generation", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-03-07T19:00:03Z	--- license: other license_name: gemma-terms-of-use license_link: https://ai.google.dev/gemma/terms --- This repository contains gemma 2B models quantized using llama.cpp. For details of the model see https://huggingface.co/google/gemma-2b-it. Details of the k-quants can be found here: https://github.com/ggerganov/llama.cpp/pull/1684 ## Provided files \| Name \| Quant method \| Bits \| Size \| \| ---- \| ---- \| ---- \| ---- \| \| [gemma-2b-it-Q4_K_M.gguf](https://huggingface.co/operablepattern/gemma-2b-it-Q/blob/main/gemma-2b-it-Q4_K_M.gguf) \| Q4_K_M \| 4 \| 1.63 GB\| \| [gemma-2b-it-Q5_K_M.gguf](https://huggingface.co/operablepattern/gemma-2b-it-Q/blob/main/gemma-2b-it-Q5_K_M.gguf) \| Q5_K_M \| 5 \| 1.84 GB\|
AwAppp/benchmarks_4bit_batch_size15	AwAppp	2024-03-10T21:40:38Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-10T21:40:37Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
myownip/axolotl-openllama-1k-qlora-v02	myownip	2024-03-10T21:40:07Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "llama", "generated_from_trainer", "base_model:openlm-research/open_llama_3b_v2", "base_model:adapter:openlm-research/open_llama_3b_v2", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2024-03-10T21:40:02Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: openlm-research/open_llama_3b_v2 model-index: - name: qlora-out results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.0` ```yaml base_model: openlm-research/open_llama_3b_v2 model_type: LlamaForCausalLM tokenizer_type: LlamaTokenizer load_in_8bit: false load_in_4bit: true strict: false push_dataset_to_hub: datasets: - path: mhenrichsen/alpaca_2k_test type: alpaca dataset_prepared_path: val_set_size: 0.05 adapter: qlora lora_model_dir: sequence_len: 1024 sample_packing: true lora_r: 8 lora_alpha: 32 lora_dropout: 0.05 lora_target_modules: lora_target_linear: true lora_fan_in_fan_out: wandb_project: wandb_entity: wandb_watch: wandb_name: wandb_log_model: output_dir: ./qlora-out gradient_accumulation_steps: 1 micro_batch_size: 1 num_epochs: 1 optimizer: paged_adamw_32bit torchdistx_path: lr_scheduler: cosine learning_rate: 0.0002 train_on_inputs: false group_by_length: false bf16: false fp16: true tf32: false gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true gptq_groupsize: gptq_model_v1: warmup_steps: 20 evals_per_epoch: 4 saves_per_epoch: 1 debug: deepspeed: weight_decay: 0.1 fsdp: fsdp_config: special_tokens: bos_token: "<s>" eos_token: "</s>" unk_token: "<unk>" ``` </details><br> # qlora-out This model is a fine-tuned version of [openlm-research/open_llama_3b_v2](https://huggingface.co/openlm-research/open_llama_3b_v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1118 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 20 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.2567 \| 0.0 \| 1 \| 1.3470 \| \| 1.1738 \| 0.25 \| 108 \| 1.1365 \| \| 1.113 \| 0.5 \| 216 \| 1.1231 \| \| 1.413 \| 0.75 \| 324 \| 1.1118 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.1.2+cu118 - Datasets 2.18.0 - Tokenizers 0.15.0
AwAppp/benchmarks_4bit_batch_size5	AwAppp	2024-03-10T21:38:29Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-10T21:38:26Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
quirky-lats-at-mats/Llama-13b-IHY-CoT	quirky-lats-at-mats	2024-03-10T21:37:21Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-10T21:18:02Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
SDFASDGA/llm	SDFASDGA	2024-03-10T21:37:07Z	10	1	null	[ "gguf", "endpoints_compatible", "region:us" ]	null	2023-11-11T11:30:08Z	Models for llm.f90 - LLMs in Fortran See Files and https://github.com/rbitr/llm.f90 and https://github.com/rbitr/ferrite for more detail
automerger/ShadowCalme-7B	automerger	2024-03-10T21:33:15Z	7	1	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "automerger", "base_model:CorticalStack/shadow-clown-7B-dare", "base_model:merge:CorticalStack/shadow-clown-7B-dare", "base_model:MaziyarPanahi/Calme-7B-Instruct-v0.1.1", "base_model:merge:MaziyarPanahi/Calme-7B-Instruct-v0.1.1", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-10T21:32:28Z	--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - automerger base_model: - CorticalStack/shadow-clown-7B-dare - MaziyarPanahi/Calme-7B-Instruct-v0.1.1 --- # ShadowCalme-7B ShadowCalme-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration. * [CorticalStack/shadow-clown-7B-dare](https://huggingface.co/CorticalStack/shadow-clown-7B-dare) * [MaziyarPanahi/Calme-7B-Instruct-v0.1.1](https://huggingface.co/MaziyarPanahi/Calme-7B-Instruct-v0.1.1) ## 🧩 Configuration ```yaml slices: - sources: - model: CorticalStack/shadow-clown-7B-dare layer_range: [0, 32] - model: MaziyarPanahi/Calme-7B-Instruct-v0.1.1 layer_range: [0, 32] merge_method: slerp base_model: CorticalStack/shadow-clown-7B-dare parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 random_seed: 0 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "automerger/ShadowCalme-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
gtroina/sd-class-butterflies-32	gtroina	2024-03-10T21:32:16Z	44	0	diffusers	[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2024-03-10T20:54:44Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('gtroina/sd-class-butterflies-32') image = pipeline().images[0] image ```
macadeliccc/laser-dolphin-mixtral-4x7b-dpo-AWQ	macadeliccc	2024-03-10T21:28:48Z	8	1	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "base_model:macadeliccc/laser-dolphin-mixtral-4x7b-dpo", "base_model:quantized:macadeliccc/laser-dolphin-mixtral-4x7b-dpo", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "awq", "region:us" ]	text-generation	2024-03-10T20:49:35Z	--- license: apache-2.0 base_model: macadeliccc/laser-dolphin-mixtral-4x7b-dpo --- ## OpenAI compatible endpoint using VLLM Runs well on 4090 ``` python -m vllm.entrypoints.openai.api_server --model macadeliccc/laser-dolphin-mixtral-4x7b-dpo-AWQ --max-model-len 25000 ```
tsavage68/mistralit2_1000_STEPS_1e8_rate_0.1_beta_DPO	tsavage68	2024-03-10T21:22:33Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "trl", "dpo", "generated_from_trainer", "conversational", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:finetune:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-10T21:18:48Z	--- license: apache-2.0 base_model: mistralai/Mistral-7B-Instruct-v0.2 tags: - trl - dpo - generated_from_trainer model-index: - name: mistralit2_1000_STEPS_1e8_rate_0.1_beta_DPO results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mistralit2_1000_STEPS_1e8_rate_0.1_beta_DPO This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6920 - Rewards/chosen: -0.0058 - Rewards/rejected: -0.0082 - Rewards/accuracies: 0.5121 - Rewards/margins: 0.0024 - Logps/rejected: -28.6543 - Logps/chosen: -23.4436 - Logits/rejected: -2.8649 - Logits/chosen: -2.8652 ## 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-08 - train_batch_size: 4 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.693 \| 0.1 \| 50 \| 0.6928 \| 0.0007 \| -0.0000 \| 0.4549 \| 0.0007 \| -28.5728 \| -23.3792 \| -2.8652 \| -2.8654 \| \| 0.693 \| 0.2 \| 100 \| 0.6920 \| 0.0012 \| -0.0011 \| 0.4945 \| 0.0023 \| -28.5838 \| -23.3741 \| -2.8653 \| -2.8655 \| \| 0.693 \| 0.29 \| 150 \| 0.6923 \| -0.0015 \| -0.0033 \| 0.4989 \| 0.0018 \| -28.6052 \| -23.4006 \| -2.8651 \| -2.8653 \| \| 0.694 \| 0.39 \| 200 \| 0.6923 \| -0.0020 \| -0.0037 \| 0.4813 \| 0.0017 \| -28.6093 \| -23.4058 \| -2.8651 \| -2.8653 \| \| 0.6916 \| 0.49 \| 250 \| 0.6922 \| -0.0026 \| -0.0046 \| 0.4879 \| 0.0021 \| -28.6189 \| -23.4118 \| -2.8651 \| -2.8654 \| \| 0.6927 \| 0.59 \| 300 \| 0.6920 \| -0.0039 \| -0.0063 \| 0.5011 \| 0.0023 \| -28.6350 \| -23.4253 \| -2.8650 \| -2.8653 \| \| 0.6941 \| 0.68 \| 350 \| 0.6927 \| -0.0048 \| -0.0058 \| 0.4659 \| 0.0010 \| -28.6304 \| -23.4334 \| -2.8650 \| -2.8652 \| \| 0.6924 \| 0.78 \| 400 \| 0.6922 \| -0.0049 \| -0.0068 \| 0.4989 \| 0.0019 \| -28.6399 \| -23.4345 \| -2.8650 \| -2.8653 \| \| 0.6919 \| 0.88 \| 450 \| 0.6918 \| -0.0056 \| -0.0084 \| 0.4857 \| 0.0028 \| -28.6562 \| -23.4418 \| -2.8650 \| -2.8653 \| \| 0.6913 \| 0.98 \| 500 \| 0.6913 \| -0.0047 \| -0.0085 \| 0.5077 \| 0.0038 \| -28.6577 \| -23.4328 \| -2.8649 \| -2.8652 \| \| 0.6914 \| 1.07 \| 550 \| 0.6915 \| -0.0034 \| -0.0067 \| 0.5143 \| 0.0033 \| -28.6398 \| -23.4200 \| -2.8650 \| -2.8653 \| \| 0.6939 \| 1.17 \| 600 \| 0.6922 \| -0.0069 \| -0.0089 \| 0.5033 \| 0.0020 \| -28.6613 \| -23.4550 \| -2.8650 \| -2.8652 \| \| 0.6917 \| 1.27 \| 650 \| 0.6920 \| -0.0056 \| -0.0081 \| 0.5231 \| 0.0025 \| -28.6535 \| -23.4422 \| -2.8650 \| -2.8653 \| \| 0.6919 \| 1.37 \| 700 \| 0.6921 \| -0.0052 \| -0.0074 \| 0.5055 \| 0.0021 \| -28.6463 \| -23.4383 \| -2.8650 \| -2.8653 \| \| 0.6929 \| 1.46 \| 750 \| 0.6915 \| -0.0044 \| -0.0078 \| 0.5363 \| 0.0034 \| -28.6506 \| -23.4298 \| -2.8650 \| -2.8653 \| \| 0.6919 \| 1.56 \| 800 \| 0.6922 \| -0.0063 \| -0.0083 \| 0.5209 \| 0.0020 \| -28.6553 \| -23.4489 \| -2.8649 \| -2.8652 \| \| 0.6925 \| 1.66 \| 850 \| 0.6921 \| -0.0058 \| -0.0080 \| 0.5121 \| 0.0022 \| -28.6528 \| -23.4438 \| -2.8649 \| -2.8652 \| \| 0.6925 \| 1.76 \| 900 \| 0.6920 \| -0.0058 \| -0.0082 \| 0.5121 \| 0.0024 \| -28.6543 \| -23.4436 \| -2.8649 \| -2.8652 \| \| 0.6939 \| 1.86 \| 950 \| 0.6920 \| -0.0058 \| -0.0082 \| 0.5121 \| 0.0024 \| -28.6543 \| -23.4436 \| -2.8649 \| -2.8652 \| \| 0.6924 \| 1.95 \| 1000 \| 0.6920 \| -0.0058 \| -0.0082 \| 0.5121 \| 0.0024 \| -28.6543 \| -23.4436 \| -2.8649 \| -2.8652 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.0.0+cu117 - Datasets 2.18.0 - Tokenizers 0.15.2
AmineSaidi-ISTIC/phi-2-finetuned-knowledgator-events_classification	AmineSaidi-ISTIC	2024-03-10T21:21:55Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:microsoft/phi-2", "base_model:adapter:microsoft/phi-2", "license:mit", "region:us" ]	null	2024-03-06T13:56:06Z	--- license: mit library_name: peft tags: - generated_from_trainer base_model: microsoft/phi-2 model-index: - name: phi-2-finetuned-knowledgator-events_classification results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # phi-2-finetuned-knowledgator-events_classification This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 1000 ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.1.2 - Datasets 2.1.0 - Tokenizers 0.15.2
prince-canuma/babyLlama	prince-canuma	2024-03-10T21:15:54Z	93	1	transformers	[ "transformers", "tensorboard", "safetensors", "llama", "text-generation", "generated_from_trainer", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-10T21:15:15Z	--- tags: - generated_from_trainer model-index: - name: babyLlama results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # babyLlama This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 50 - num_epochs: 2 ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
Felladrin/gguf-TinyMistral-248M-Chat-v1	Felladrin	2024-03-10T21:11:21Z	25	0	null	[ "gguf", "base_model:Felladrin/TinyMistral-248M-Chat-v2", "base_model:quantized:Felladrin/TinyMistral-248M-Chat-v2", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-03-10T20:27:47Z	--- license: apache-2.0 base_model: Felladrin/TinyMistral-248M-Chat-v1 --- GGUF version of [Felladrin/TinyMistral-248M-Chat-v1](https://huggingface.co/Felladrin/TinyMistral-248M-Chat-v1).
bartowski/speechless-starcoder2-7b-exl2	bartowski	2024-03-10T20:55:17Z	0	1	transformers	[ "transformers", "code", "text-generation", "en", "dataset:teknium/OpenHermes-2.5", "dataset:TokenBender/python_eval_instruct_51k", "dataset:codefuse-ai/Evol-instruction-66k", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	text-generation	2024-03-10T20:41:05Z	--- language: - en library_name: transformers pipeline_tag: text-generation datasets: - teknium/OpenHermes-2.5 - TokenBender/python_eval_instruct_51k - codefuse-ai/Evol-instruction-66k tags: - code license: apache-2.0 model-index: - name: SpeechlessCoder results: - task: type: text-generation dataset: type: openai_humaneval name: HumanEval metrics: - name: pass@1 type: pass@1 value: 0.0 verified: false quantized_by: bartowski --- ## Exllama v2 Quantizations of speechless-starcoder2-7b Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.15">turboderp's ExLlamaV2 v0.0.15</a> for quantization. ## The "main" branch only contains the measurement.json, download one of the other branches for the model (see below) Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions. Conversion was done using the default calibration dataset. Default arguments used except when the bits per weight is above 6.0, at that point the lm_head layer is quantized at 8 bits per weight instead of the default 6. Original model: https://huggingface.co/uukuguy/speechless-starcoder2-7b <a href="https://huggingface.co/bartowski/speechless-starcoder2-7b-exl2/tree/8_0">8.0 bits per weight</a> <a href="https://huggingface.co/bartowski/speechless-starcoder2-7b-exl2/tree/6_5">6.5 bits per weight</a> <a href="https://huggingface.co/bartowski/speechless-starcoder2-7b-exl2/tree/5_0">5.0 bits per weight</a> <a href="https://huggingface.co/bartowski/speechless-starcoder2-7b-exl2/tree/4_25">4.25 bits per weight</a> <a href="https://huggingface.co/bartowski/speechless-starcoder2-7b-exl2/tree/3_5">3.5 bits per weight</a> ## Download instructions With git: ```shell git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/speechless-starcoder2-7b-exl2 ``` With huggingface hub (credit to TheBloke for instructions): ```shell pip3 install huggingface-hub ``` To download the `main` (only useful if you only care about measurement.json) branch to a folder called `speechless-starcoder2-7b-exl2`: ```shell mkdir speechless-starcoder2-7b-exl2 huggingface-cli download bartowski/speechless-starcoder2-7b-exl2 --local-dir speechless-starcoder2-7b-exl2 --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: Linux: ```shell mkdir speechless-starcoder2-7b-exl2-6_5 huggingface-cli download bartowski/speechless-starcoder2-7b-exl2 --revision 6_5 --local-dir speechless-starcoder2-7b-exl2-6_5 --local-dir-use-symlinks False ``` Windows (which apparently doesn't like _ in folders sometimes?): ```shell mkdir speechless-starcoder2-7b-exl2-6.5 huggingface-cli download bartowski/speechless-starcoder2-7b-exl2 --revision 6_5 --local-dir speechless-starcoder2-7b-exl2-6.5 --local-dir-use-symlinks False ```
CalebCometML/andrew-test	CalebCometML	2024-03-10T20:49:33Z	1	2	diffusers	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "dora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-03-10T20:40:33Z	--- 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 - text-to-image - text-to-image - diffusers-training - diffusers - dora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers - 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 TOK man widget: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - CalebCometML/andrew-test <Gallery /> ## Model description These are CalebCometML/andrew-test LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of TOK man to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](CalebCometML/andrew-test/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]
bartowski/dolphincoder-starcoder2-7b-exl2	bartowski	2024-03-10T20:43:19Z	2	2	null	[ "text-generation", "en", "dataset:cognitivecomputations/dolphin", "dataset:jondurbin/airoboros-2.2.1", "dataset:cognitivecomputations/dolphin-coder", "dataset:teknium/openhermes", "dataset:ise-uiuc/Magicoder-OSS-Instruct-75K", "dataset:ise-uiuc/Magicoder-Evol-Instruct-110K", "dataset:m-a-p/Code-Feedback", "dataset:m-a-p/CodeFeedback-Filtered-Instruction", "dataset:microsoft/orca-math-word-problems-200k", "license:bigcode-openrail-m", "region:us" ]	text-generation	2024-03-10T16:05:14Z	--- datasets: - cognitivecomputations/dolphin - jondurbin/airoboros-2.2.1 - cognitivecomputations/dolphin-coder - teknium/openhermes - ise-uiuc/Magicoder-OSS-Instruct-75K - ise-uiuc/Magicoder-Evol-Instruct-110K - m-a-p/Code-Feedback - m-a-p/CodeFeedback-Filtered-Instruction - microsoft/orca-math-word-problems-200k language: - en license: bigcode-openrail-m quantized_by: bartowski pipeline_tag: text-generation --- ## Exllama v2 Quantizations of dolphincoder-starcoder2-7b Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.15">turboderp's ExLlamaV2 v0.0.15</a> for quantization. ## The "main" branch only contains the measurement.json, download one of the other branches for the model (see below) Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions. Original model: https://huggingface.co/cognitivecomputations/dolphincoder-starcoder2-7b \| Branch \| Bits \| lm_head bits \| VRAM (4k) \| VRAM (16k) \| VRAM (32k) \| Description \| \| ----- \| ---- \| ------- \| ------ \| ------ \| ------ \| ------------ \| \| [8_0](https://huggingface.co/bartowski/dolphincoder-starcoder2-7b-exl2/tree/8_0) \| 8.0 \| 8.0 \| 8.4 GB \| 9.2 GB \| 10.2 GB \| Maximum quality that ExLlamaV2 can produce, near unquantized performance. \| \| [6_5](https://huggingface.co/bartowski/dolphincoder-starcoder2-7b-exl2/tree/6_5) \| 6.5 \| 8.0 \| 7.1 GB \| 7.9 GB \| 8.9 GB \| Very similar to 8.0, good tradeoff of size vs performance, recommended. \| \| [5_0](https://huggingface.co/bartowski/dolphincoder-starcoder2-7b-exl2/tree/5_0) \| 5.0 \| 6.0 \| 5.8 GB \| 6.6 GB \| 7.6 GB \| Slightly lower quality vs 6.5, but usable on 8GB cards. \| \| [4_25](https://huggingface.co/bartowski/dolphincoder-starcoder2-7b-exl2/tree/4_25) \| 4.25 \| 6.0 \| 5.1 GB \| 5.9 GB \| 6.9 GB \| GPTQ equivalent bits per weight, slightly higher quality. \| \| [3_5](https://huggingface.co/bartowski/dolphincoder-starcoder2-7b-exl2/tree/3_5) \| 3.5 \| 6.0 \| 4.5 GB \| 5.3 GB \| 6.3 GB \| Lower quality, only use if you have to. \| ## Download instructions With git: ```shell git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/dolphincoder-starcoder2-7b-exl2 ``` With huggingface hub (credit to TheBloke for instructions): ```shell pip3 install huggingface-hub ``` To download the `main` (only useful if you only care about measurement.json) branch to a folder called `dolphincoder-starcoder2-7b-exl2`: ```shell mkdir dolphincoder-starcoder2-7b-exl2 huggingface-cli download bartowski/dolphincoder-starcoder2-7b-exl2 --local-dir dolphincoder-starcoder2-7b-exl2 --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: Linux: ```shell mkdir dolphincoder-starcoder2-7b-exl2-6_5 huggingface-cli download bartowski/dolphincoder-starcoder2-7b-exl2 --revision 6_5 --local-dir dolphincoder-starcoder2-7b-exl2-6_5 --local-dir-use-symlinks False ``` Windows (which apparently doesn't like _ in folders sometimes?): ```shell mkdir dolphincoder-starcoder2-7b-exl2-6.5 huggingface-cli download bartowski/dolphincoder-starcoder2-7b-exl2 --revision 6_5 --local-dir dolphincoder-starcoder2-7b-exl2-6.5 --local-dir-use-symlinks False ```
KnutJaegersberg/B1-66ER	KnutJaegersberg	2024-03-10T20:40:40Z	0	1	null	[ "region:us" ]	null	2024-03-10T10:06:01Z	![image/png](https://cdn-uploads.huggingface.co/production/uploads/63732ebbbd81fae2b3aaf3fb/4wQngaPCqWBzn2V0qUMO2.png)
sarahahtee/classification_flan_t5_base_enriched	sarahahtee	2024-03-10T20:36:31Z	95	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google/flan-t5-base", "base_model:finetune:google/flan-t5-base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-03-10T20:18:40Z	--- license: apache-2.0 base_model: google/flan-t5-base tags: - generated_from_trainer model-index: - name: classification_flan_t5_base_enriched results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # classification_flan_t5_base_enriched This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8345 - Classification Report: precision recall f1-score support ALLERGY 1.0000 0.9167 0.9565 12 ASSESSMENT 0.0000 0.0000 0.0000 11 CC 0.3333 0.1818 0.2353 11 DIAGNOSIS 0.0000 0.0000 0.0000 1 DISPOSITION 0.2000 1.0000 0.3333 1 EDCOURSE 0.0000 0.0000 0.0000 4 EXAM 0.5000 0.2000 0.2857 5 FAM/SOCHX 0.8400 0.9333 0.8842 45 GENHX 0.6620 0.8868 0.7581 53 GYNHX 1.0000 1.0000 1.0000 1 IMAGING 0.3333 1.0000 0.5000 1 IMMUNIZATIONS 1.0000 1.0000 1.0000 1 LABS 0.0000 0.0000 0.0000 1 MEDICATIONS 0.8333 1.0000 0.9091 10 OTHER_HISTORY 0.0000 0.0000 0.0000 3 PASTMEDICALHX 0.6471 0.7857 0.7097 14 PASTSURGICAL 1.0000 0.8571 0.9231 7 PLAN 0.0000 0.0000 0.0000 1 PROCEDURES 0.0000 0.0000 0.0000 1 ROS 0.8462 0.6471 0.7333 17 accuracy 0.7250 200 macro avg 0.4598 0.5204 0.4614 200 weighted avg 0.6618 0.7250 0.6812 200 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 7 - eval_batch_size: 7 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Classification Report \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:\| \| No log \| 1.0 \| 172 \| 0.4392 \| precision recall f1-score support ALLERGY 1.0000 0.9167 0.9565 12 ASSESSMENT 0.0000 0.0000 0.0000 11 CC 0.1455 0.7273 0.2424 11 DIAGNOSIS 0.0000 0.0000 0.0000 1 DISPOSITION 0.0000 0.0000 0.0000 1 EDCOURSE 0.0000 0.0000 0.0000 4 EXAM 0.0000 0.0000 0.0000 5 FAM/SOCHX 0.8148 0.9778 0.8889 45 GENHX 1.0000 0.3962 0.5676 53 GYNHX 0.0000 0.0000 0.0000 1 IMAGING 0.0000 0.0000 0.0000 1 IMMUNIZATIONS 0.0000 0.0000 0.0000 1 LABS 0.0000 0.0000 0.0000 1 MEDICATIONS 0.8333 1.0000 0.9091 10 OTHER_HISTORY 0.0000 0.0000 0.0000 3 PASTMEDICALHX 0.4000 0.8571 0.5455 14 PASTSURGICAL 0.7778 1.0000 0.8750 7 PLAN 0.0000 0.0000 0.0000 1 PROCEDURES 0.0000 0.0000 0.0000 1 ROS 0.6250 0.2941 0.4000 17 accuracy 0.5900 200 macro avg 0.2798 0.3085 0.2692 200 weighted avg 0.6663 0.5900 0.5694 200 \| \| No log \| 2.0 \| 344 \| 0.3438 \| precision recall f1-score support ALLERGY 1.0000 0.9167 0.9565 12 ASSESSMENT 0.0000 0.0000 0.0000 11 CC 0.5000 0.2727 0.3529 11 DIAGNOSIS 0.0000 0.0000 0.0000 1 DISPOSITION 0.2000 1.0000 0.3333 1 EDCOURSE 0.0000 0.0000 0.0000 4 EXAM 0.0000 0.0000 0.0000 5 FAM/SOCHX 0.9535 0.9111 0.9318 45 GENHX 0.5833 0.9245 0.7153 53 GYNHX 0.0000 0.0000 0.0000 1 IMAGING 0.0000 0.0000 0.0000 1 IMMUNIZATIONS 0.5000 1.0000 0.6667 1 LABS 0.0000 0.0000 0.0000 1 MEDICATIONS 0.8333 1.0000 0.9091 10 OTHER_HISTORY 0.0000 0.0000 0.0000 3 PASTMEDICALHX 0.6364 0.5000 0.5600 14 PASTSURGICAL 0.5000 1.0000 0.6667 7 PLAN 0.0000 0.0000 0.0000 1 PROCEDURES 0.0000 0.0000 0.0000 1 ROS 0.8333 0.5882 0.6897 17 accuracy 0.7000 200 macro avg 0.3270 0.4057 0.3391 200 weighted avg 0.6347 0.7000 0.6476 200 \| \| 0.3577 \| 3.0 \| 516 \| 0.3416 \| precision recall f1-score support ALLERGY 1.0000 0.9167 0.9565 12 ASSESSMENT 0.0000 0.0000 0.0000 11 CC 0.2083 0.4545 0.2857 11 DIAGNOSIS 0.0000 0.0000 0.0000 1 DISPOSITION 0.3333 1.0000 0.5000 1 EDCOURSE 0.0000 0.0000 0.0000 4 EXAM 1.0000 0.2000 0.3333 5 FAM/SOCHX 0.8696 0.8889 0.8791 45 GENHX 0.6769 0.8302 0.7458 53 GYNHX 0.0000 0.0000 0.0000 1 IMAGING 0.0000 0.0000 0.0000 1 IMMUNIZATIONS 1.0000 1.0000 1.0000 1 LABS 0.0000 0.0000 0.0000 1 MEDICATIONS 0.8333 1.0000 0.9091 10 OTHER_HISTORY 0.0000 0.0000 0.0000 3 PASTMEDICALHX 0.5500 0.7857 0.6471 14 PASTSURGICAL 1.0000 1.0000 1.0000 7 PLAN 0.0000 0.0000 0.0000 1 PROCEDURES 0.0000 0.0000 0.0000 1 ROS 0.8000 0.4706 0.5926 17 accuracy 0.6950 200 macro avg 0.4136 0.4273 0.3925 200 weighted avg 0.6613 0.6950 0.6605 200 \| \| 0.3577 \| 4.0 \| 688 \| 0.4118 \| precision recall f1-score support ALLERGY 1.0000 0.9167 0.9565 12 ASSESSMENT 0.0000 0.0000 0.0000 11 CC 0.3333 0.0909 0.1429 11 DIAGNOSIS 0.0000 0.0000 0.0000 1 DISPOSITION 0.1429 1.0000 0.2500 1 EDCOURSE 0.0000 0.0000 0.0000 4 EXAM 0.5000 0.2000 0.2857 5 FAM/SOCHX 0.8431 0.9556 0.8958 45 GENHX 0.6438 0.8868 0.7460 53 GYNHX 0.0000 0.0000 0.0000 1 IMAGING 1.0000 1.0000 1.0000 1 IMMUNIZATIONS 1.0000 1.0000 1.0000 1 LABS 0.0000 0.0000 0.0000 1 MEDICATIONS 0.8333 1.0000 0.9091 10 OTHER_HISTORY 0.0000 0.0000 0.0000 3 PASTMEDICALHX 0.6667 0.7143 0.6897 14 PASTSURGICAL 0.7778 1.0000 0.8750 7 PLAN 0.0000 0.0000 0.0000 1 PROCEDURES 0.0000 0.0000 0.0000 1 ROS 0.8571 0.7059 0.7742 17 accuracy 0.7250 200 macro avg 0.4299 0.4735 0.4262 200 weighted avg 0.6503 0.7250 0.6731 200 \| \| 0.3577 \| 5.0 \| 860 \| 0.4030 \| precision recall f1-score support ALLERGY 1.0000 0.9167 0.9565 12 ASSESSMENT 0.0000 0.0000 0.0000 11 CC 0.2778 0.4545 0.3448 11 DIAGNOSIS 0.0000 0.0000 0.0000 1 DISPOSITION 0.3333 1.0000 0.5000 1 EDCOURSE 0.0000 0.0000 0.0000 4 EXAM 0.6667 0.4000 0.5000 5 FAM/SOCHX 0.8511 0.8889 0.8696 45 GENHX 0.7077 0.8679 0.7797 53 GYNHX 1.0000 1.0000 1.0000 1 IMAGING 1.0000 1.0000 1.0000 1 IMMUNIZATIONS 1.0000 1.0000 1.0000 1 LABS 0.0000 0.0000 0.0000 1 MEDICATIONS 0.8333 1.0000 0.9091 10 OTHER_HISTORY 0.0000 0.0000 0.0000 3 PASTMEDICALHX 0.7333 0.7857 0.7586 14 PASTSURGICAL 1.0000 0.8571 0.9231 7 PLAN 0.0000 0.0000 0.0000 1 PROCEDURES 0.0000 0.0000 0.0000 1 ROS 0.7500 0.7059 0.7273 17 accuracy 0.7350 200 macro avg 0.5077 0.5438 0.5134 200 weighted avg 0.6794 0.7350 0.7013 200 \| \| 0.0808 \| 6.0 \| 1032 \| 0.5599 \| precision recall f1-score support ALLERGY 1.0000 0.9167 0.9565 12 ASSESSMENT 0.0000 0.0000 0.0000 11 CC 0.2500 0.0909 0.1333 11 DIAGNOSIS 0.0000 0.0000 0.0000 1 DISPOSITION 0.1250 1.0000 0.2222 1 EDCOURSE 0.0000 0.0000 0.0000 4 EXAM 0.5000 0.2000 0.2857 5 FAM/SOCHX 0.8511 0.8889 0.8696 45 GENHX 0.6267 0.8868 0.7344 53 GYNHX 1.0000 1.0000 1.0000 1 IMAGING 1.0000 1.0000 1.0000 1 IMMUNIZATIONS 1.0000 1.0000 1.0000 1 LABS 0.0000 0.0000 0.0000 1 MEDICATIONS 0.9091 1.0000 0.9524 10 OTHER_HISTORY 0.0000 0.0000 0.0000 3 PASTMEDICALHX 0.5882 0.7143 0.6452 14 PASTSURGICAL 0.8750 1.0000 0.9333 7 PLAN 0.0000 0.0000 0.0000 1 PROCEDURES 0.0000 0.0000 0.0000 1 ROS 0.8462 0.6471 0.7333 17 accuracy 0.7100 200 macro avg 0.4786 0.5172 0.4733 200 weighted avg 0.6486 0.7100 0.6660 200 \| \| 0.0808 \| 7.0 \| 1204 \| 0.5132 \| precision recall f1-score support ALLERGY 1.0000 0.9167 0.9565 12 ASSESSMENT 0.0000 0.0000 0.0000 11 CC 0.3333 0.2727 0.3000 11 DIAGNOSIS 0.0000 0.0000 0.0000 1 DISPOSITION 0.1429 1.0000 0.2500 1 EDCOURSE 0.0000 0.0000 0.0000 4 EXAM 0.5000 0.2000 0.2857 5 FAM/SOCHX 0.8542 0.9111 0.8817 45 GENHX 0.6761 0.9057 0.7742 53 GYNHX 0.0000 0.0000 0.0000 1 IMAGING 1.0000 1.0000 1.0000 1 IMMUNIZATIONS 0.5000 1.0000 0.6667 1 LABS 0.0000 0.0000 0.0000 1 MEDICATIONS 0.8333 1.0000 0.9091 10 OTHER_HISTORY 0.0000 0.0000 0.0000 3 PASTMEDICALHX 0.7857 0.7857 0.7857 14 PASTSURGICAL 0.8750 1.0000 0.9333 7 PLAN 0.0000 0.0000 0.0000 1 PROCEDURES 0.0000 0.0000 0.0000 1 ROS 0.7857 0.6471 0.7097 17 accuracy 0.7300 200 macro avg 0.4143 0.4819 0.4226 200 weighted avg 0.6645 0.7300 0.6876 200 \| \| 0.0808 \| 8.0 \| 1376 \| 0.6372 \| precision recall f1-score support ALLERGY 1.0000 0.9167 0.9565 12 ASSESSMENT 0.0000 0.0000 0.0000 11 CC 0.3333 0.4545 0.3846 11 DIAGNOSIS 0.0000 0.0000 0.0000 1 DISPOSITION 0.2500 1.0000 0.4000 1 EDCOURSE 0.0000 0.0000 0.0000 4 EXAM 0.5000 0.2000 0.2857 5 FAM/SOCHX 0.8400 0.9333 0.8842 45 GENHX 0.7302 0.8679 0.7931 53 GYNHX 1.0000 1.0000 1.0000 1 IMAGING 0.5000 1.0000 0.6667 1 IMMUNIZATIONS 0.5000 1.0000 0.6667 1 LABS 0.0000 0.0000 0.0000 1 MEDICATIONS 0.8333 1.0000 0.9091 10 OTHER_HISTORY 0.0000 0.0000 0.0000 3 PASTMEDICALHX 0.7333 0.7857 0.7586 14 PASTSURGICAL 1.0000 0.8571 0.9231 7 PLAN 0.0000 0.0000 0.0000 1 PROCEDURES 0.0000 0.0000 0.0000 1 ROS 0.7857 0.6471 0.7097 17 accuracy 0.7350 200 macro avg 0.4503 0.5331 0.4669 200 weighted avg 0.6794 0.7350 0.6997 200 \| \| 0.034 \| 9.0 \| 1548 \| 0.7346 \| precision recall f1-score support ALLERGY 1.0000 0.9167 0.9565 12 ASSESSMENT 0.0000 0.0000 0.0000 11 CC 0.3333 0.0909 0.1429 11 DIAGNOSIS 0.0000 0.0000 0.0000 1 DISPOSITION 0.1250 1.0000 0.2222 1 EDCOURSE 0.0000 0.0000 0.0000 4 EXAM 0.5000 0.2000 0.2857 5 FAM/SOCHX 0.8542 0.9111 0.8817 45 GENHX 0.6026 0.8868 0.7176 53 GYNHX 1.0000 1.0000 1.0000 1 IMAGING 1.0000 1.0000 1.0000 1 IMMUNIZATIONS 1.0000 1.0000 1.0000 1 LABS 0.0000 0.0000 0.0000 1 MEDICATIONS 0.8333 1.0000 0.9091 10 OTHER_HISTORY 0.0000 0.0000 0.0000 3 PASTMEDICALHX 0.6429 0.6429 0.6429 14 PASTSURGICAL 1.0000 0.8571 0.9231 7 PLAN 0.0000 0.0000 0.0000 1 PROCEDURES 0.0000 0.0000 0.0000 1 ROS 0.7692 0.5882 0.6667 17 accuracy 0.7000 200 macro avg 0.4830 0.5047 0.4674 200 weighted avg 0.6454 0.7000 0.6565 200 \| \| 0.034 \| 10.0 \| 1720 \| 0.6654 \| precision recall f1-score support ALLERGY 1.0000 0.9167 0.9565 12 ASSESSMENT 0.0000 0.0000 0.0000 11 CC 0.4286 0.5455 0.4800 11 DIAGNOSIS 0.0000 0.0000 0.0000 1 DISPOSITION 0.2000 1.0000 0.3333 1 EDCOURSE 0.0000 0.0000 0.0000 4 EXAM 0.5000 0.2000 0.2857 5 FAM/SOCHX 0.8600 0.9556 0.9053 45 GENHX 0.7302 0.8679 0.7931 53 GYNHX 1.0000 1.0000 1.0000 1 IMAGING 1.0000 1.0000 1.0000 1 IMMUNIZATIONS 1.0000 1.0000 1.0000 1 LABS 0.0000 0.0000 0.0000 1 MEDICATIONS 0.8333 1.0000 0.9091 10 OTHER_HISTORY 0.0000 0.0000 0.0000 3 PASTMEDICALHX 0.6667 0.7143 0.6897 14 PASTSURGICAL 1.0000 0.8571 0.9231 7 PLAN 0.0000 0.0000 0.0000 1 PROCEDURES 0.0000 0.0000 0.0000 1 ROS 0.8000 0.7059 0.7500 17 accuracy 0.7450 200 macro avg 0.5009 0.5381 0.5013 200 weighted avg 0.6904 0.7450 0.7112 200 \| \| 0.034 \| 11.0 \| 1892 \| 0.7455 \| precision recall f1-score support ALLERGY 1.0000 0.9167 0.9565 12 ASSESSMENT 0.0000 0.0000 0.0000 11 CC 0.3333 0.1818 0.2353 11 DIAGNOSIS 0.0000 0.0000 0.0000 1 DISPOSITION 0.1429 1.0000 0.2500 1 EDCOURSE 0.0000 0.0000 0.0000 4 EXAM 0.6667 0.4000 0.5000 5 FAM/SOCHX 0.8511 0.8889 0.8696 45 GENHX 0.6486 0.9057 0.7559 53 GYNHX 1.0000 1.0000 1.0000 1 IMAGING 0.5000 1.0000 0.6667 1 IMMUNIZATIONS 1.0000 1.0000 1.0000 1 LABS 0.0000 0.0000 0.0000 1 MEDICATIONS 0.8333 1.0000 0.9091 10 OTHER_HISTORY 0.0000 0.0000 0.0000 3 PASTMEDICALHX 0.6250 0.7143 0.6667 14 PASTSURGICAL 1.0000 0.8571 0.9231 7 PLAN 0.0000 0.0000 0.0000 1 PROCEDURES 0.0000 0.0000 0.0000 1 ROS 0.8462 0.6471 0.7333 17 accuracy 0.7200 200 macro avg 0.4724 0.5256 0.4733 200 weighted avg 0.6639 0.7200 0.6801 200 \| \| 0.0107 \| 12.0 \| 2064 \| 0.7687 \| precision recall f1-score support ALLERGY 1.0000 0.9167 0.9565 12 ASSESSMENT 0.0000 0.0000 0.0000 11 CC 0.4286 0.2727 0.3333 11 DIAGNOSIS 0.0000 0.0000 0.0000 1 DISPOSITION 0.1667 1.0000 0.2857 1 EDCOURSE 0.0000 0.0000 0.0000 4 EXAM 0.6667 0.4000 0.5000 5 FAM/SOCHX 0.8478 0.8667 0.8571 45 GENHX 0.6575 0.9057 0.7619 53 GYNHX 1.0000 1.0000 1.0000 1 IMAGING 0.3333 1.0000 0.5000 1 IMMUNIZATIONS 1.0000 1.0000 1.0000 1 LABS 0.0000 0.0000 0.0000 1 MEDICATIONS 0.8333 1.0000 0.9091 10 OTHER_HISTORY 0.0000 0.0000 0.0000 3 PASTMEDICALHX 0.5882 0.7143 0.6452 14 PASTSURGICAL 1.0000 0.8571 0.9231 7 PLAN 0.0000 0.0000 0.0000 1 PROCEDURES 0.0000 0.0000 0.0000 1 ROS 0.8462 0.6471 0.7333 17 accuracy 0.7200 200 macro avg 0.4684 0.5290 0.4703 200 weighted avg 0.6675 0.7200 0.6822 200 \| \| 0.0107 \| 13.0 \| 2236 \| 0.8114 \| precision recall f1-score support ALLERGY 1.0000 0.9167 0.9565 12 ASSESSMENT 0.0000 0.0000 0.0000 11 CC 0.4545 0.4545 0.4545 11 DIAGNOSIS 0.0000 0.0000 0.0000 1 DISPOSITION 0.1429 1.0000 0.2500 1 EDCOURSE 0.0000 0.0000 0.0000 4 EXAM 0.5000 0.2000 0.2857 5 FAM/SOCHX 0.8511 0.8889 0.8696 45 GENHX 0.6620 0.8868 0.7581 53 GYNHX 0.0000 0.0000 0.0000 1 IMAGING 0.5000 1.0000 0.6667 1 IMMUNIZATIONS 1.0000 1.0000 1.0000 1 LABS 0.0000 0.0000 0.0000 1 MEDICATIONS 0.8333 1.0000 0.9091 10 OTHER_HISTORY 0.0000 0.0000 0.0000 3 PASTMEDICALHX 0.6667 0.7143 0.6897 14 PASTSURGICAL 0.8750 1.0000 0.9333 7 PLAN 0.0000 0.0000 0.0000 1 PROCEDURES 0.0000 0.0000 0.0000 1 ROS 0.9167 0.6471 0.7586 17 accuracy 0.7250 200 macro avg 0.4201 0.4854 0.4266 200 weighted avg 0.6695 0.7250 0.6865 200 \| \| 0.0107 \| 14.0 \| 2408 \| 0.7763 \| precision recall f1-score support ALLERGY 1.0000 0.9167 0.9565 12 ASSESSMENT 0.0000 0.0000 0.0000 11 CC 0.3636 0.3636 0.3636 11 DIAGNOSIS 0.0000 0.0000 0.0000 1 DISPOSITION 0.2500 1.0000 0.4000 1 EDCOURSE 0.0000 0.0000 0.0000 4 EXAM 0.5000 0.2000 0.2857 5 FAM/SOCHX 0.8511 0.8889 0.8696 45 GENHX 0.6620 0.8868 0.7581 53 GYNHX 1.0000 1.0000 1.0000 1 IMAGING 0.3333 1.0000 0.5000 1 IMMUNIZATIONS 1.0000 1.0000 1.0000 1 LABS 0.0000 0.0000 0.0000 1 MEDICATIONS 0.8333 1.0000 0.9091 10 OTHER_HISTORY 0.0000 0.0000 0.0000 3 PASTMEDICALHX 0.6667 0.7143 0.6897 14 PASTSURGICAL 1.0000 0.8571 0.9231 7 PLAN 0.0000 0.0000 0.0000 1 PROCEDURES 0.0000 0.0000 0.0000 1 ROS 0.7857 0.6471 0.7097 17 accuracy 0.7200 200 macro avg 0.4623 0.5237 0.4683 200 weighted avg 0.6624 0.7200 0.6819 200 \| \| 0.0049 \| 15.0 \| 2580 \| 0.8111 \| precision recall f1-score support ALLERGY 1.0000 0.9167 0.9565 12 ASSESSMENT 0.0000 0.0000 0.0000 11 CC 0.4444 0.3636 0.4000 11 DIAGNOSIS 0.0000 0.0000 0.0000 1 DISPOSITION 0.1667 1.0000 0.2857 1 EDCOURSE 0.0000 0.0000 0.0000 4 EXAM 0.5000 0.2000 0.2857 5 FAM/SOCHX 0.8542 0.9111 0.8817 45 GENHX 0.6667 0.9057 0.7680 53 GYNHX 1.0000 1.0000 1.0000 1 IMAGING 0.5000 1.0000 0.6667 1 IMMUNIZATIONS 1.0000 1.0000 1.0000 1 LABS 0.0000 0.0000 0.0000 1 MEDICATIONS 0.8333 1.0000 0.9091 10 OTHER_HISTORY 0.0000 0.0000 0.0000 3 PASTMEDICALHX 0.6667 0.7143 0.6897 14 PASTSURGICAL 1.0000 1.0000 1.0000 7 PLAN 0.0000 0.0000 0.0000 1 PROCEDURES 0.0000 0.0000 0.0000 1 ROS 0.8333 0.5882 0.6897 17 accuracy 0.7300 200 macro avg 0.4733 0.5300 0.4766 200 weighted avg 0.6733 0.7300 0.6906 200 \| \| 0.0049 \| 16.0 \| 2752 \| 0.8138 \| precision recall f1-score support ALLERGY 1.0000 0.9167 0.9565 12 ASSESSMENT 0.0000 0.0000 0.0000 11 CC 0.4286 0.2727 0.3333 11 DIAGNOSIS 0.0000 0.0000 0.0000 1 DISPOSITION 0.2000 1.0000 0.3333 1 EDCOURSE 0.0000 0.0000 0.0000 4 EXAM 0.5000 0.2000 0.2857 5 FAM/SOCHX 0.8511 0.8889 0.8696 45 GENHX 0.6575 0.9057 0.7619 53 GYNHX 1.0000 1.0000 1.0000 1 IMAGING 0.3333 1.0000 0.5000 1 IMMUNIZATIONS 1.0000 1.0000 1.0000 1 LABS 0.0000 0.0000 0.0000 1 MEDICATIONS 0.8333 1.0000 0.9091 10 OTHER_HISTORY 0.0000 0.0000 0.0000 3 PASTMEDICALHX 0.6471 0.7857 0.7097 14 PASTSURGICAL 1.0000 0.8571 0.9231 7 PLAN 0.0000 0.0000 0.0000 1 PROCEDURES 0.0000 0.0000 0.0000 1 ROS 0.8462 0.6471 0.7333 17 accuracy 0.7250 200 macro avg 0.4649 0.5237 0.4658 200 weighted avg 0.6684 0.7250 0.6844 200 \| \| 0.0049 \| 17.0 \| 2924 \| 0.8209 \| precision recall f1-score support ALLERGY 1.0000 0.9167 0.9565 12 ASSESSMENT 0.0000 0.0000 0.0000 11 CC 0.3750 0.2727 0.3158 11 DIAGNOSIS 0.0000 0.0000 0.0000 1 DISPOSITION 0.2000 1.0000 0.3333 1 EDCOURSE 0.0000 0.0000 0.0000 4 EXAM 0.5000 0.2000 0.2857 5 FAM/SOCHX 0.8400 0.9333 0.8842 45 GENHX 0.6571 0.8679 0.7480 53 GYNHX 1.0000 1.0000 1.0000 1 IMAGING 0.3333 1.0000 0.5000 1 IMMUNIZATIONS 1.0000 1.0000 1.0000 1 LABS 0.0000 0.0000 0.0000 1 MEDICATIONS 0.8333 1.0000 0.9091 10 OTHER_HISTORY 0.0000 0.0000 0.0000 3 PASTMEDICALHX 0.6250 0.7143 0.6667 14 PASTSURGICAL 1.0000 0.8571 0.9231 7 PLAN 0.0000 0.0000 0.0000 1 PROCEDURES 0.0000 0.0000 0.0000 1 ROS 0.8462 0.6471 0.7333 17 accuracy 0.7200 200 macro avg 0.4605 0.5205 0.4628 200 weighted avg 0.6613 0.7200 0.6800 200 \| \| 0.0024 \| 18.0 \| 3096 \| 0.8220 \| precision recall f1-score support ALLERGY 1.0000 0.9167 0.9565 12 ASSESSMENT 0.0000 0.0000 0.0000 11 CC 0.4286 0.2727 0.3333 11 DIAGNOSIS 0.0000 0.0000 0.0000 1 DISPOSITION 0.2000 1.0000 0.3333 1 EDCOURSE 0.0000 0.0000 0.0000 4 EXAM 0.5000 0.2000 0.2857 5 FAM/SOCHX 0.8400 0.9333 0.8842 45 GENHX 0.6714 0.8868 0.7642 53 GYNHX 1.0000 1.0000 1.0000 1 IMAGING 0.3333 1.0000 0.5000 1 IMMUNIZATIONS 1.0000 1.0000 1.0000 1 LABS 0.0000 0.0000 0.0000 1 MEDICATIONS 0.8333 1.0000 0.9091 10 OTHER_HISTORY 0.0000 0.0000 0.0000 3 PASTMEDICALHX 0.6471 0.7857 0.7097 14 PASTSURGICAL 1.0000 0.8571 0.9231 7 PLAN 0.0000 0.0000 0.0000 1 PROCEDURES 0.0000 0.0000 0.0000 1 ROS 0.8462 0.6471 0.7333 17 accuracy 0.7300 200 macro avg 0.4650 0.5250 0.4666 200 weighted avg 0.6696 0.7300 0.6883 200 \| \| 0.0024 \| 19.0 \| 3268 \| 0.8372 \| precision recall f1-score support ALLERGY 1.0000 0.9167 0.9565 12 ASSESSMENT 0.0000 0.0000 0.0000 11 CC 0.3333 0.1818 0.2353 11 DIAGNOSIS 0.0000 0.0000 0.0000 1 DISPOSITION 0.2000 1.0000 0.3333 1 EDCOURSE 0.0000 0.0000 0.0000 4 EXAM 0.5000 0.2000 0.2857 5 FAM/SOCHX 0.8400 0.9333 0.8842 45 GENHX 0.6620 0.8868 0.7581 53 GYNHX 1.0000 1.0000 1.0000 1 IMAGING 0.3333 1.0000 0.5000 1 IMMUNIZATIONS 1.0000 1.0000 1.0000 1 LABS 0.0000 0.0000 0.0000 1 MEDICATIONS 0.8333 1.0000 0.9091 10 OTHER_HISTORY 0.0000 0.0000 0.0000 3 PASTMEDICALHX 0.6471 0.7857 0.7097 14 PASTSURGICAL 1.0000 0.8571 0.9231 7 PLAN 0.0000 0.0000 0.0000 1 PROCEDURES 0.0000 0.0000 0.0000 1 ROS 0.8462 0.6471 0.7333 17 accuracy 0.7250 200 macro avg 0.4598 0.5204 0.4614 200 weighted avg 0.6618 0.7250 0.6812 200 \| \| 0.0024 \| 20.0 \| 3440 \| 0.8345 \| precision recall f1-score support ALLERGY 1.0000 0.9167 0.9565 12 ASSESSMENT 0.0000 0.0000 0.0000 11 CC 0.3333 0.1818 0.2353 11 DIAGNOSIS 0.0000 0.0000 0.0000 1 DISPOSITION 0.2000 1.0000 0.3333 1 EDCOURSE 0.0000 0.0000 0.0000 4 EXAM 0.5000 0.2000 0.2857 5 FAM/SOCHX 0.8400 0.9333 0.8842 45 GENHX 0.6620 0.8868 0.7581 53 GYNHX 1.0000 1.0000 1.0000 1 IMAGING 0.3333 1.0000 0.5000 1 IMMUNIZATIONS 1.0000 1.0000 1.0000 1 LABS 0.0000 0.0000 0.0000 1 MEDICATIONS 0.8333 1.0000 0.9091 10 OTHER_HISTORY 0.0000 0.0000 0.0000 3 PASTMEDICALHX 0.6471 0.7857 0.7097 14 PASTSURGICAL 1.0000 0.8571 0.9231 7 PLAN 0.0000 0.0000 0.0000 1 PROCEDURES 0.0000 0.0000 0.0000 1 ROS 0.8462 0.6471 0.7333 17 accuracy 0.7250 200 macro avg 0.4598 0.5204 0.4614 200 weighted avg 0.6618 0.7250 0.6812 200 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
fbellame/confoo-train-llama-style-1-1	fbellame	2024-03-10T20:33:00Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "gpt", "llm", "large language model", "h2o-llmstudio", "conversational", "en", "autotrain_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-03-10T19:06:14Z	--- language: - en library_name: transformers tags: - gpt - llm - large language model - h2o-llmstudio inference: false thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico --- # Model Card ## Summary This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio). - Base model: [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) ## Usage To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` library installed. ```bash pip install transformers==4.36.1 ``` Also make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo. - Either leave `token=True` in the `pipeline` and login to hugginface_hub by running ```python import huggingface_hub huggingface_hub.login(<ACCESS_TOKEN>) ``` - Or directly pass your <ACCESS_TOKEN> to `token` in the `pipeline` ```python from transformers import pipeline generate_text = pipeline( model="fbellame/confoo-train-llama-style-1-1", torch_dtype="auto", trust_remote_code=True, use_fast=True, device_map={"": "cuda:0"}, token=True, ) res = generate_text( "Why is drinking water so healthy?", min_new_tokens=2, max_new_tokens=256, do_sample=False, num_beams=1, temperature=float(0.0), repetition_penalty=float(1.0), renormalize_logits=True ) print(res[0]["generated_text"]) ``` You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer: ```python print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"]) ``` ```bash Why is drinking water so healthy?</s> ``` Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`. ```python from h2oai_pipeline import H2OTextGenerationPipeline from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained( "fbellame/confoo-train-llama-style-1-1", use_fast=True, padding_side="left", trust_remote_code=True, ) model = AutoModelForCausalLM.from_pretrained( "fbellame/confoo-train-llama-style-1-1", torch_dtype="auto", device_map={"": "cuda:0"}, trust_remote_code=True, ) generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer) res = generate_text( "Why is drinking water so healthy?", min_new_tokens=2, max_new_tokens=256, do_sample=False, num_beams=1, temperature=float(0.0), repetition_penalty=float(1.0), renormalize_logits=True ) print(res[0]["generated_text"]) ``` You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "fbellame/confoo-train-llama-style-1-1" # either local folder or huggingface model name # Important: The prompt needs to be in the same format the model was trained with. # You can find an example prompt in the experiment logs. prompt = "How are you?</s>" tokenizer = AutoTokenizer.from_pretrained( model_name, use_fast=True, trust_remote_code=True, ) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map={"": "cuda:0"}, trust_remote_code=True, ) model.cuda().eval() inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda") # generate configuration can be modified to your needs tokens = model.generate( input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"], min_new_tokens=2, max_new_tokens=256, do_sample=False, num_beams=1, temperature=float(0.0), repetition_penalty=float(1.0), renormalize_logits=True )[0] tokens = tokens[inputs["input_ids"].shape[1]:] answer = tokenizer.decode(tokens, skip_special_tokens=True) print(answer) ``` ## Quantization and sharding You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```. ## Model Architecture ``` MistralForCausalLM( (model): MistralModel( (embed_tokens): Embedding(32000, 4096, padding_idx=0) (layers): ModuleList( (0-31): 32 x MistralDecoderLayer( (self_attn): MistralAttention( (q_proj): Linear(in_features=4096, out_features=4096, bias=False) (k_proj): Linear(in_features=4096, out_features=1024, bias=False) (v_proj): Linear(in_features=4096, out_features=1024, bias=False) (o_proj): Linear(in_features=4096, out_features=4096, bias=False) (rotary_emb): MistralRotaryEmbedding() ) (mlp): MistralMLP( (gate_proj): Linear(in_features=4096, out_features=14336, bias=False) (up_proj): Linear(in_features=4096, out_features=14336, bias=False) (down_proj): Linear(in_features=14336, out_features=4096, bias=False) (act_fn): SiLU() ) (input_layernorm): MistralRMSNorm() (post_attention_layernorm): MistralRMSNorm() ) ) (norm): MistralRMSNorm() ) (lm_head): Linear(in_features=4096, out_features=32000, bias=False) ) ``` ## Model Configuration This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models. ## Disclaimer Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions. - Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints. - Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion. - Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model. - Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities. - Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues. - Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes. By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.
jairNeto/bert-finetuned-sem_eval-english	jairNeto	2024-03-10T20:24:14Z	94	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:Jairnetojp/content-moderation", "base_model:finetune:Jairnetojp/content-moderation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-10T20:23:02Z	--- base_model: Jairnetojp/content-moderation tags: - generated_from_trainer metrics: - f1 - accuracy model-index: - name: bert-finetuned-sem_eval-english results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-sem_eval-english This model is a fine-tuned version of [Jairnetojp/content-moderation](https://huggingface.co/Jairnetojp/content-moderation) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2343 - F1: 0.5458 - Roc Auc: 0.7829 - Accuracy: 0.4655 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| Roc Auc \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:-------:\|:--------:\| \| No log \| 1.0 \| 232 \| 0.2283 \| 0.5361 \| 0.7829 \| 0.4503 \| \| No log \| 2.0 \| 464 \| 0.2343 \| 0.5458 \| 0.7829 \| 0.4655 \| \| 0.069 \| 3.0 \| 696 \| 0.2461 \| 0.5392 \| 0.7832 \| 0.4544 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
Balassar/balassarprofile	Balassar	2024-03-10T20:18:41Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:TheBloke/Mistral-7B-Instruct-v0.2-GPTQ", "base_model:adapter:TheBloke/Mistral-7B-Instruct-v0.2-GPTQ", "license:apache-2.0", "region:us" ]	null	2024-03-10T20:10:46Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: TheBloke/Mistral-7B-Instruct-v0.2-GPTQ model-index: - name: balassarprofile results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # balassarprofile This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.2-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GPTQ) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.8042 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.8802 \| 1.0 \| 1 \| 3.6270 \| \| 3.861 \| 2.0 \| 2 \| 3.5746 \| \| 3.7758 \| 3.0 \| 3 \| 3.4416 \| \| 3.5819 \| 4.0 \| 4 \| 3.3048 \| \| 3.3879 \| 5.0 \| 5 \| 3.1740 \| \| 3.2106 \| 6.0 \| 6 \| 3.0575 \| \| 3.0652 \| 7.0 \| 7 \| 2.9588 \| \| 2.94 \| 8.0 \| 8 \| 2.8822 \| \| 2.8566 \| 9.0 \| 9 \| 2.8301 \| \| 2.7926 \| 10.0 \| 10 \| 2.8042 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
Turkgamercat/Muslera	Turkgamercat	2024-03-10T20:17:50Z	0	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:stablediffusionapi/icon-lab-test-ai", "base_model:adapter:stablediffusionapi/icon-lab-test-ai", "region:us" ]	text-to-image	2024-03-10T20:13:55Z	--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: '-' output: url: images/1000012898.jpg base_model: stablediffusionapi/icon-lab-test-ai instance_prompt: null --- # Muslera <Gallery /> ## Download model [Download](/Turkgamercat/Muslera/tree/main) them in the Files & versions tab.
xKizzi/taxirepo	xKizzi	2024-03-10T20:13:31Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-03-10T20:13:29Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxirepo results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.34 +/- 2.46 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="xKizzi/taxirepo", 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"]) ```
deepnet/SN6-67M4	deepnet	2024-03-10T20:06:25Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-10T20:00:49Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
eduvedras/pix2struct-textcaps-base-desc-vars-final	eduvedras	2024-03-10T20:05:13Z	35	0	transformers	[ "transformers", "safetensors", "pix2struct", "image-text-to-text", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-03-10T19:03:59Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]

grace-pro/oops_i_did_it_again_eval_hans_full_set

grace-pro

2024-03-11T02:56:38Z

2

0

peft

[ "peft", "safetensors", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]

null

2024-03-11T02:55:26Z

--- license: apache-2.0 library_name: peft tags: - generated_from_trainer metrics: - precision - recall - accuracy base_model: mistralai/Mistral-7B-v0.1 model-index: - name: oops_i_did_it_again_eval_hans_full_set results: [] ---  # oops_i_did_it_again_eval_hans_full_set This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.8314 - Precision: 0.7598 - Recall: 0.2665 - F1-score: 0.3946 - Accuracy: 0.5911 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1-score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:--------:|:--------:| | 0.5414 | 1.0 | 24544 | 1.8314 | 0.7598 | 0.2665 | 0.3946 | 0.5911 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

essiam/clean_art_cat

essiam

2024-03-11T02:55:52Z

0

1

diffusers

[ "diffusers", "tensorboard", "safetensors", "text-to-image", "dreambooth", "stable-diffusion", "stable-diffusion-diffusers", "base_model:CompVis/stable-diffusion-v1-4", "base_model:finetune:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2024-03-11T02:44:09Z

--- license: creativeml-openrail-m library_name: diffusers tags: - text-to-image - dreambooth - stable-diffusion - stable-diffusion-diffusers inference: true base_model: CompVis/stable-diffusion-v1-4 instance_prompt: a photo of 686HenrietteRonnerKnip859 cat ---  # DreamBooth - essiam/clean_art_cat This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of 686HenrietteRonnerKnip859 cat using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: True. ## 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]

jeonsiyun/layoutlmv3-v29-epoch5

jeonsiyun

2024-03-11T02:45:47Z

117

0

transformers

[ "transformers", "safetensors", "layoutlmv3", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-03-11T02:45:15Z

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

Reemvn/distilroberta-base

Reemvn

2024-03-11T02:43:33Z

46

0

transformers

[ "transformers", "tf", "roberta", "text-classification", "generated_from_keras_callback", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-03-10T23:41:57Z

--- tags: - generated_from_keras_callback model-index: - name: Reemvn/distilroberta-base results: [] ---  # Reemvn/distilroberta-base This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0947 - Validation Loss: 0.1512 - Train Accuracy: 0.9455 - Epoch: 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: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 5000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | 0.1343 | 0.1610 | 0.945 | 0 | | 0.1097 | 0.1589 | 0.949 | 1 | | 0.0947 | 0.1512 | 0.9455 | 2 | ### Framework versions - Transformers 4.38.2 - TensorFlow 2.15.0 - Datasets 2.18.0 - Tokenizers 0.15.2

lilyray/albert_emotion

lilyray

2024-03-11T02:28:15Z

121

0

transformers

[ "transformers", "tensorboard", "safetensors", "albert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:lilyray/albert_emotion", "base_model:finetune:lilyray/albert_emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-03-10T17:30:03Z

--- license: apache-2.0 base_model: lilyray/albert_emotion tags: - generated_from_trainer datasets: - emotion metrics: - accuracy model-index: - name: albert_emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.9295 ---  # albert_emotion This model is a fine-tuned version of [lilyray/albert_emotion](https://huggingface.co/lilyray/albert_emotion) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2391 - Accuracy: 0.9295 ## 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: 9.363600088100325e-06 - train_batch_size: 4 - eval_batch_size: 8 - seed: 19 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1744 | 1.0 | 4000 | 0.2001 | 0.938 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

michaelnoyes/thedistil

michaelnoyes

2024-03-11T02:24:37Z

0

null

[ "region:us" ]

null

2024-03-10T20:45:37Z

Within the repository I have untrained models by their names and then the fine-tuned versions. I also included the code that would be used for the quantitavtive analysis table which i was not able to run.

DaRkSpyro/GabiRio2

DaRkSpyro

2024-03-11T02:22:37Z

0

flair

[ "flair", "music", "en", "dataset:HuggingFaceTB/cosmopedia", "license:apache-2.0", "region:us" ]

null

2024-03-11T02:19:38Z

--- license: apache-2.0 datasets: - HuggingFaceTB/cosmopedia language: - en metrics: - accuracy library_name: flair tags: - music ---

asedmammad/Contextual_KTO_Mistral_PairRM-GGUF

asedmammad

2024-03-11T01:54:18Z

83

2

null

[ "gguf", "kto", "dpo", "human feedback", "rlhf", "preferences", "alignment", "HALO", "halos", "rl", "rlaif", "en", "dataset:snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset", "arxiv:2402.01306", "base_model:ContextualAI/Contextual_KTO_Mistral_PairRM", "base_model:quantized:ContextualAI/Contextual_KTO_Mistral_PairRM", "license:apache-2.0", "region:us", "conversational" ]

null

2024-03-10T22:07:16Z

--- base_model: ContextualAI/Contextual_KTO_Mistral_PairRM inference: false language: - en license: apache-2.0 tags: - kto - dpo - human feedback - rlhf - preferences - alignment - HALO - halos - rl - rlaif datasets: - snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset metrics: - accuracy model_creator: ContextualAI model_name: Contextual KTO Mistral PairRM model_type: mistral prompt_template: '<|user|> {prompt} <|assistant|> ' quantized_by: Ased Mammad --- # Contextual_KTO_Mistral_PairRM - GGUF - Model creator: [ContextualAI](https://huggingface.co/ContextualAI) - Original model: [Contextual_KTO_Mistral_PairRM](https://huggingface.co/ContextualAI/Contextual_KTO_Mistral_PairRM)  ## Description This repo contains GGUF format model files for [Contextual_KTO_Mistral_PairRM](https://huggingface.co/ContextualAI/Contextual_KTO_Mistral_PairRM).   ### 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). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.   ## Prompt template: ChatML ``` <|user|> {prompt} <|assistant|> ```   ## Compatibility These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) They are also compatible with many third party UIs and libraries - please see the list at the top of this README. ## 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 Refer to the Provided Files table below to see what files use which methods, and how. </details>   ## Provided files | Name | Quant method | Bits | Size | Max RAM required | Use case | | ---- | ---- | ---- | ---- | ---- | ----- | | [Contextual_KTO_Mistral_PairRM.Q2_K.gguf](https://huggingface.co/AsedMammad/Contextual_KTO_Mistral_PairRM-GGUF/blob/main/Contextual_KTO_Mistral_PairRM.Q2_K.gguf) | Q2_K | 2 | 2.72 GB| 5.22 GB | significant quality loss - not recommended for most purposes | | [Contextual_KTO_Mistral_PairRM.Q3_K_S.gguf](https://huggingface.co/AsedMammad/Contextual_KTO_Mistral_PairRM-GGUF/blob/main/Contextual_KTO_Mistral_PairRM.Q3_K_S.gguf) | Q3_K_S | 3 | 3.16 GB| 5.66 GB | very small, high quality loss | | [Contextual_KTO_Mistral_PairRM.Q3_K_M.gguf](https://huggingface.co/AsedMammad/Contextual_KTO_Mistral_PairRM-GGUF/blob/main/Contextual_KTO_Mistral_PairRM.Q3_K_M.gguf) | Q3_K_M | 3 | 3.52 GB| 6.02 GB | very small, high quality loss | | [Contextual_KTO_Mistral_PairRM.Q3_K_L.gguf](https://huggingface.co/AsedMammad/Contextual_KTO_Mistral_PairRM-GGUF/blob/main/Contextual_KTO_Mistral_PairRM.Q3_K_L.gguf) | Q3_K_L | 3 | 3.82 GB| 6.32 GB | small, substantial quality loss | | [Contextual_KTO_Mistral_PairRM.Q4_0.gguf](https://huggingface.co/AsedMammad/Contextual_KTO_Mistral_PairRM-GGUF/blob/main/Contextual_KTO_Mistral_PairRM.Q4_0.gguf) | Q4_0 | 4 | 4.11 GB| 6.61 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | [Contextual_KTO_Mistral_PairRM.Q4_K_S.gguf](https://huggingface.co/AsedMammad/Contextual_KTO_Mistral_PairRM-GGUF/blob/main/Contextual_KTO_Mistral_PairRM.Q4_K_S.gguf) | Q4_K_S | 4 | 4.14 GB| 6.64 GB | small, greater quality loss | | [Contextual_KTO_Mistral_PairRM.Q4_K_M.gguf](https://huggingface.co/AsedMammad/Contextual_KTO_Mistral_PairRM-GGUF/blob/main/Contextual_KTO_Mistral_PairRM.Q4_K_M.gguf) | Q4_K_M | 4 | 4.37 GB| 6.87 GB | medium, balanced quality - recommended | | [Contextual_KTO_Mistral_PairRM.Q5_0.gguf](https://huggingface.co/AsedMammad/Contextual_KTO_Mistral_PairRM-GGUF/blob/main/Contextual_KTO_Mistral_PairRM.Q5_0.gguf) | Q5_0 | 5 | 5.00 GB| 7.50 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | [Contextual_KTO_Mistral_PairRM.Q5_K_S.gguf](https://huggingface.co/AsedMammad/Contextual_KTO_Mistral_PairRM-GGUF/blob/main/Contextual_KTO_Mistral_PairRM.Q5_K_S.gguf) | Q5_K_S | 5 | 5.00 GB| 7.50 GB | large, low quality loss - recommended | | [Contextual_KTO_Mistral_PairRM.Q5_K_M.gguf](https://huggingface.co/AsedMammad/Contextual_KTO_Mistral_PairRM-GGUF/blob/main/Contextual_KTO_Mistral_PairRM.Q5_K_M.gguf) | Q5_K_M | 5 | 5.13 GB| 7.63 GB | large, very low quality loss - recommended | | [Contextual_KTO_Mistral_PairRM.Q6_K.gguf](https://huggingface.co/AsedMammad/Contextual_KTO_Mistral_PairRM-GGUF/blob/main/Contextual_KTO_Mistral_PairRM.Q6_K.gguf) | Q6_K | 6 | 5.94 GB| 8.44 GB | very large, extremely low quality loss | | [Contextual_KTO_Mistral_PairRM.Q8_0.gguf](https://huggingface.co/AsedMammad/Contextual_KTO_Mistral_PairRM-GGUF/blob/main/Contextual_KTO_Mistral_PairRM.Q8_0.gguf) | Q8_0 | 8 | 7.70 GB| 10.20 GB | very large, extremely low quality loss - not recommended | **Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.   ## 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 file. 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: AsedMammad/Contextual_KTO_Mistral_PairRM-GGUF and below it, a specific filename to download, such as: Contextual_KTO_Mistral_PairRM.Q4_K_M.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 AsedMammad/Contextual_KTO_Mistral_PairRM-GGUF Contextual_KTO_Mistral_PairRM.Q4_K_M.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 AsedMammad/Contextual_KTO_Mistral_PairRM-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 hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download AsedMammad/Contextual_KTO_Mistral_PairRM-GGUF Contextual_KTO_Mistral_PairRM.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details>   ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m Contextual_KTO_Mistral_PairRM.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant" ``` Change `-ngl 35` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 32768` 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="./Contextual_KTO_Mistral_PairRM.Q4_K_M.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( "<|user|>\n{prompt}<|assistant|>\n", # 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="./Contextual_KTO_Mistral_PairRM.Q4_K_M.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)    This repo contains the model and tokenizer checkpoints for: - model family [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) - optimized with the loss [KTO](https://twitter.com/winniethexu/status/1732839295365554643) - aligned using the [snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset](https://huggingface.co/datasets/snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset) - via 3 iterations of KTO on one epoch of each training partition, each previous iteration's model serving as the reference for the subsequent. **[03/06/2024]**: We are #2 on the (verified) [Alpaca Eval 2.0 Leaderboard](https://tatsu-lab.github.io/alpaca_eval/) scoring **33.23**! To prompt this model, ensure that the format is consistent with that of TuluV2. For example, a prompt should be formatted as follows, where `<|user|>` corresponds to the human's role and `<|assistant|>` corresponds to the LLM's role. The human should speak first: ``` <|user|> Hi! I'm looking for a cake recipe. <|assistant|> What kind of cake? <|user|> Chocolate cake. <|assistant|> ``` Note that a beginning-of-sequence (BOS) token is automatically added at tokenization time and does not have to be added by you. No end-of-sequence (EOS) token is added to the prompt. You may also use our tokenizer's `apply_chat_template` if doing inference with `chatml` set or evaluating generations through non-local clients. Please refer to our [code repository](https://github.com/ContextualAI/HALOs) or [blog](https://contextual.ai/better-cheaper-faster-llm-alignment-with-kto/) for more details on the methodology. If you found this work useful, feel free to cite [our work](https://arxiv.org/abs/2402.01306): ``` @techreport{ethayarajh2023halos, author = {Ethayarajh, Kawin and Xu, Winnie, and Jurafsky, Dan and Kiela, Douwe}, title = {Human-Centered Loss Functions (HALOs)}, institution = {Contextual AI}, note = {https://github.com/ContextualAI/HALOs/blob/main/assets/report.pdf}, year = {2023}, } ```

jeonsiyun/layoutlmv3-v29-epoch25

jeonsiyun

2024-03-11T01:54:04Z

119

0

transformers

[ "transformers", "safetensors", "layoutlmv3", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-03-11T01:53:24Z

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

shadowlight2784/Adagio_Dazzle

shadowlight2784

2024-03-11T01:51:35Z

0

null

[ "region:us" ]

null

2023-11-09T05:16:00Z

Use for Retrival-Voice-Conversion (RVC). https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI

shadowlight2784/Sonata_Dusk_Singing_Voice

shadowlight2784

2024-03-11T01:50:56Z

0

1

null

[ "region:us" ]

null

2023-08-25T23:43:01Z

Use for Retrival-Voice-Conversion (RVC). https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI

ITT-AF/ITT-Yi-Ko-6B-v6.0

ITT-AF

2024-03-11T01:43:50Z

55

0

transformers

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

text-generation

2024-03-07T02:52:58Z

--- license: cc-by-nc-4.0 --- ## ITT-AF/ITT-Yi-Ko-6B-v6.0 This model is a fine-tuned version of [beomi/Yi-Ko-6B](https://huggingface.co/beomi/Yi-Ko-6B) on an custom dataset. ### Model description More information needed ### Intended uses & limitations More information needed ### Training and evaluation data More information needed ### Training procedure ### Training hypuerparameters The following hyperparameters were used during training: * learning_rate: 2e-05 * train_batch_size: 4 * eval_batch_size: 8 * seed: 42 * gradient_accumulation_steps: 8 * total_train_batch_size: 32 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr_scheduler_type: linear * num_epochs: 1.0 * mixed_precision_training: Native AMP ### Training results ### Framework versions * Transformers 4.36.2 * Pytorch 2.1.2+cu121 * Datasets 2.0.0 * Tokenizers 0.15.0

juhwanlee/llmdo-Mistral-7B-case-7

juhwanlee

2024-03-11T01:43:03Z

48

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "en", "dataset:Open-Orca/OpenOrca", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-11T00:41:21Z

--- license: apache-2.0 datasets: - Open-Orca/OpenOrca language: - en --- # Model Details * Model Description: This model is test for data ordering. * Developed by: Juhwan Lee * Model Type: Large Language Model # Model Architecture This model is based on Mistral-7B-v0.1. We fine-tuning this model for data ordering task. Mistral-7B-v0.1 is a transformer model, with the following architecture choices: * Grouped-Query Attention * Sliding-Window Attention * Byte-fallback BPE tokenizer # Dataset We random sample Open-Orca dataset. (We finetune the 100,000 dataset) # Guthub https://github.com/trailerAI # License Apache License 2.0

grace-pro/oops_i_did_it_again_eval_hans

grace-pro

2024-03-11T01:40:10Z

1

0

peft

[ "peft", "safetensors", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]

null

2024-03-11T01:38:55Z

--- license: apache-2.0 library_name: peft tags: - generated_from_trainer metrics: - precision - recall - accuracy base_model: mistralai/Mistral-7B-v0.1 model-index: - name: oops_i_did_it_again_eval_hans results: [] ---  # oops_i_did_it_again_eval_hans This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.5556 - Precision: 0.8070 - Recall: 0.2481 - F1-score: 0.3796 - Accuracy: 0.5944 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1-score | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:--------:|:--------:| | 0.6706 | 1.0 | 4909 | 1.5556 | 0.8070 | 0.2481 | 0.3796 | 0.5944 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

furrutiav/bert_qa_extractor_cockatiel_2022_ulra_sign_lf_signal_it_38

furrutiav

2024-03-11T01:39:52Z

91

0

transformers

[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

feature-extraction

2024-03-11T01:39:27Z

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

furrutiav/bert_qa_extractor_cockatiel_2022_ulra_org_lf_signal_it_32

furrutiav

2024-03-11T01:39:12Z

91

0

transformers

[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

feature-extraction

2024-03-11T01:38:44Z

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

wongctroman/fine-tuned-cloudy-sentence-transformer-3

wongctroman

2024-03-11T01:30:02Z

48

0

sentence-transformers

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

sentence-similarity

2024-03-11T01:28:55Z

--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # wongctroman/fine-tuned-cloudy-sentence-transformer-3 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('wongctroman/fine-tuned-cloudy-sentence-transformer-3') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=wongctroman/fine-tuned-cloudy-sentence-transformer-3) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 3 with parameters: ``` {'batch_size': 5, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.TripletLoss.TripletLoss` with parameters: ``` {'distance_metric': 'TripletDistanceMetric.EUCLIDEAN', 'triplet_margin': 5} ``` Parameters of the fit()-Method: ``` { "epochs": 100, "evaluation_steps": 500, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 100, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 1024, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Citing & Authors

xia01ongLi/dummy-model2

xia01ongLi

2024-03-11T01:28:47Z

201

0

transformers

[ "transformers", "safetensors", "camembert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2024-03-11T01:27:07Z

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

Holarissun/gptj6b-aisft-hh-randsampler-subset2000

Holarissun

2024-03-11T01:20:53Z

0

peft

[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:EleutherAI/gpt-j-6b", "base_model:adapter:EleutherAI/gpt-j-6b", "license:apache-2.0", "region:us" ]

null

2024-03-11T01:16:40Z

--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: EleutherAI/gpt-j-6b model-index: - name: gptj6b-aisft-hh-randsampler-subset2000 results: [] ---  # gptj6b-aisft-hh-randsampler-subset2000 This model is a fine-tuned version of [EleutherAI/gpt-j-6b](https://huggingface.co/EleutherAI/gpt-j-6b) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

tsavage68/mistralit2_250_STEPS_1e7_rate_05_beta_DPO

tsavage68

2024-03-11T01:17:30Z

6

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "trl", "dpo", "generated_from_trainer", "conversational", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:finetune:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-11T01:13:23Z

--- license: apache-2.0 base_model: mistralai/Mistral-7B-Instruct-v0.2 tags: - trl - dpo - generated_from_trainer model-index: - name: mistralit2_250_STEPS_1e7_rate_05_beta_DPO results: [] ---  # mistralit2_250_STEPS_1e7_rate_05_beta_DPO This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6043 - Rewards/chosen: -1.1749 - Rewards/rejected: -1.6856 - Rewards/accuracies: 0.6330 - Rewards/margins: 0.5107 - Logps/rejected: -31.9435 - Logps/chosen: -25.7355 - Logits/rejected: -2.8536 - Logits/chosen: -2.8539 ## 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-07 - train_batch_size: 4 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 250 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| | 0.6684 | 0.1 | 50 | 0.6660 | -0.2264 | -0.2957 | 0.5934 | 0.0693 | -29.1637 | -23.8386 | -2.8636 | -2.8639 | | 0.5945 | 0.2 | 100 | 0.6396 | -1.5064 | -1.9635 | 0.6044 | 0.4572 | -32.4994 | -26.3985 | -2.8444 | -2.8447 | | 0.4857 | 0.29 | 150 | 0.6440 | -2.0003 | -2.6364 | 0.6198 | 0.6362 | -33.8453 | -27.3863 | -2.8460 | -2.8463 | | 0.5631 | 0.39 | 200 | 0.6018 | -1.1675 | -1.6769 | 0.6330 | 0.5093 | -31.9261 | -25.7209 | -2.8536 | -2.8539 | | 0.6109 | 0.49 | 250 | 0.6043 | -1.1749 | -1.6856 | 0.6330 | 0.5107 | -31.9435 | -25.7355 | -2.8536 | -2.8539 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.0.0+cu117 - Datasets 2.18.0 - Tokenizers 0.15.2

aegunal/FT_IPD_llama7b

aegunal

2024-03-11T01:11:37Z

0

transformers

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

null

2024-03-11T01:11:32Z

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

kaijie-qin/llama-2-7b-kube

kaijie-qin

2024-03-11T01:04:03Z

0

transformers

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

null

2024-03-10T08:23:40Z

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

ozzyonfire/bird-species-classifier

ozzyonfire

2024-03-11T01:00:42Z

150

0

transformers

[ "transformers", "onnx", "safetensors", "efficientnet", "image-classification", "biology", "vision", "en", "dataset:chriamue/bird-species-dataset", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2024-03-10T18:57:02Z

--- license: mit datasets: - chriamue/bird-species-dataset language: - en metrics: - accuracy library_name: transformers pipeline_tag: image-classification tags: - biology - image-classification - vision model-index: - name: bird-species-classifier results: - task: type: ImageClassification dataset: type: chriamue/bird-species-dataset name: Bird Species config: default split: validation metrics: - type: accuracy value: 96.8 - type: loss value: 0.1379 --- # Model Card for "Bird Species Classifier" This model came from chiramue/bird-species-classifier. This has been retrained using ResNet50 in hopes to get it running using Transformers JS. ## Model Description The "Bird Species Classifier" is a state-of-the-art image classification model designed to identify various bird species from images. It uses the EfficientNet architecture and has been fine-tuned to achieve high accuracy in recognizing a wide range of bird species. ### How to Use You can easily use the model in your Python environment with the following code: ```python from transformers import AutoFeatureExtractor, AutoModelForImageClassification extractor = AutoFeatureExtractor.from_pretrained("chriamue/bird-species-classifier") model = AutoModelForImageClassification.from_pretrained("chriamue/bird-species-classifier") ``` ### Applications - Bird species identification for educational or ecological research. - Assistance in biodiversity monitoring and conservation efforts. - Enhancing user experience in nature apps and platforms. ## Training Data The model was trained on the "Bird Species" dataset, which is a comprehensive collection of bird images. Key features of this dataset include: - **Total Species**: 525 bird species. - **Training Images**: 84,635 images. - **Validation Images**: 2,625 images. - **Test Images**: 2,625 images. - **Image Format**: Color images (224x224x3) in JPG format. - **Source**: Sourced from Kaggle. ## Training Results The model achieved impressive results after 6 epochs of training: - **Accuracy**: 96.8% - **Loss**: 0.1379 - **Runtime**: 136.81 seconds - **Samples per Second**: 19.188 - **Steps per Second**: 1.206 - **Total Training Steps**: 31,740 These metrics indicate a high level of performance, making the model reliable for practical applications. ## Limitations and Bias - The performance of the model might vary under different lighting conditions or image qualities. - The model's accuracy is dependent on the diversity and representation in the training dataset. It may perform less effectively on bird species not well represented in the dataset. ## Ethical Considerations This model should be used responsibly, considering privacy and environmental impacts. It should not be used for harmful purposes such as targeting endangered species or violating wildlife protection laws. ## Acknowledgements We would like to acknowledge the creators of the dataset on Kaggle for providing a rich source of data that made this model possible. ## See also - [Bird Species Dataset](https://huggingface.co/datasets/chriamue/bird-species-dataset) - [Kaggle Dataset](https://www.kaggle.com/datasets/gpiosenka/100-bird-species/data) - [Bird Species Classifier](https://huggingface.co/dennisjooo/Birds-Classifier-EfficientNetB2)

ZainAli60/miner_1

ZainAli60

2024-03-11T00:59:16Z

175

0

transformers

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

text-generation

2024-03-11T00:58:39Z

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

Nandapratama241/path-to-save-model

Nandapratama241

2024-03-11T00:47:31Z

2

0

diffusers

[ "diffusers", "tensorboard", "safetensors", "text-to-image", "dreambooth", "diffusers-training", "stable-diffusion", "stable-diffusion-diffusers", "base_model:stabilityai/stable-diffusion-2-1-base", "base_model:finetune:stabilityai/stable-diffusion-2-1-base", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2024-03-11T00:11:35Z

--- license: creativeml-openrail-m library_name: diffusers tags: - text-to-image - dreambooth - diffusers-training - stable-diffusion - stable-diffusion-diffusers base_model: stabilityai/stable-diffusion-2-1-base inference: true instance_prompt: a photo of NAnFRst person ---  # DreamBooth - Nandapratama241/path-to-save-model This is a dreambooth model derived from stabilityai/stable-diffusion-2-1-base. The weights were trained on a photo of NAnFRst person using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]

Omar23moh/UNIT3

Omar23moh

2024-03-11T00:45:47Z

1

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2024-03-10T14:20:13Z

--- 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: 136.00 +/- 26.81 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo SB3: https://github.com/DLR-RM/stable-baselines3 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 Omar23moh -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 Omar23moh -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 Omar23moh ``` ## 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', 1000), ('n_timesteps', 10000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 100), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

furrutiav/bert_qa_extractor_cockatiel_2022_ulra_sign_adamw_lf_signal_it_1

furrutiav

2024-03-11T00:37:50Z

91

0

transformers

[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

feature-extraction

2024-03-11T00:37:23Z

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

Litzy619/V0309O6

Litzy619

2024-03-11T00:26:55Z

0

null

[ "safetensors", "generated_from_trainer", "base_model:microsoft/phi-2", "base_model:finetune:microsoft/phi-2", "license:mit", "region:us" ]

null

2024-03-10T16:43:26Z

--- license: mit base_model: microsoft/phi-2 tags: - generated_from_trainer model-index: - name: V0309O6 results: [] ---  # V0309O6 This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0631 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 20 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.9549 | 0.09 | 10 | 0.7661 | | 0.3073 | 0.17 | 20 | 0.1105 | | 0.1318 | 0.26 | 30 | 0.0849 | | 0.1149 | 0.34 | 40 | 0.0834 | | 0.1155 | 0.43 | 50 | 0.0803 | | 0.1048 | 0.51 | 60 | 0.0807 | | 0.0963 | 0.6 | 70 | 0.0808 | | 0.0992 | 0.68 | 80 | 0.0777 | | 0.0893 | 0.77 | 90 | 0.0731 | | 0.1061 | 0.85 | 100 | 0.0747 | | 0.098 | 0.94 | 110 | 0.0711 | | 0.095 | 1.02 | 120 | 0.0699 | | 0.0908 | 1.11 | 130 | 0.0743 | | 0.0874 | 1.19 | 140 | 0.0734 | | 0.083 | 1.28 | 150 | 0.0682 | | 0.0823 | 1.37 | 160 | 0.0701 | | 0.0812 | 1.45 | 170 | 0.0684 | | 0.078 | 1.54 | 180 | 0.0683 | | 0.0763 | 1.62 | 190 | 0.0671 | | 0.0763 | 1.71 | 200 | 0.0650 | | 0.08 | 1.79 | 210 | 0.0634 | | 0.0686 | 1.88 | 220 | 0.0650 | | 0.0685 | 1.96 | 230 | 0.0638 | | 0.074 | 2.05 | 240 | 0.0644 | | 0.0646 | 2.13 | 250 | 0.0630 | | 0.0669 | 2.22 | 260 | 0.0675 | | 0.061 | 2.3 | 270 | 0.0675 | | 0.0672 | 2.39 | 280 | 0.0635 | | 0.0687 | 2.47 | 290 | 0.0625 | | 0.0656 | 2.56 | 300 | 0.0625 | | 0.0738 | 2.65 | 310 | 0.0626 | | 0.062 | 2.73 | 320 | 0.0628 | | 0.0622 | 2.82 | 330 | 0.0631 | | 0.0632 | 2.9 | 340 | 0.0630 | | 0.0644 | 2.99 | 350 | 0.0631 | ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1

NorGLM/NorLLama-3B-NO-MRPC-peft

NorGLM

2024-03-11T00:17:11Z

0

null

[ "no", "license:cc-by-nc-sa-4.0", "region:us" ]

null

2024-03-11T00:15:43Z

--- license: cc-by-nc-sa-4.0 language: - 'no' --- # Model Card NorLLama-3B-NO-MRPC-peft is trained on top of [NorLLama-3B](https://huggingface.co/NorGLM/NorLLama-3B) model on [NO-MRPC](https://huggingface.co/datasets/NorGLM/NO-MRPC) dataset. Data format: ``` input: {text_a}[SEP]{text_b} label: {0, 1} ``` ## Run the Model ```python from peft import PeftModel, PeftConfig from transformers import AutoModelForCausalLM, AutoTokenizer import torch torch_device = "cuda" if torch.cuda.is_available() else "cpu" source_model_id = "NorGLM/NorLLama-3B" peft_model_id = "NorGLM/NorLLama-3B-NO-MRPC-peft" config = PeftConfig.from_pretrained(peft_model_id) model = AutoModelForCausalLM.from_pretrained(source_model_id, device_map='balanced') tokenizer_max_len = 2048 tokenizer_config = {'pretrained_model_name_or_path': source_model_id, 'max_len': tokenizer_max_len} tokenizer = tokenizer = AutoTokenizer.from_pretrained(**tokenizer_config) tokenizer.pad_token = tokenizer.eos_token model = PeftModel.from_pretrained(model, peft_model_id) ``` ## Inference Example Load the model to evaluate on the validation set: ```python def getDataSetFromFiles(df): # convert dataset df["text"] = df[["text_a", "text_b"]].apply(lambda x: " [SEP] ".join(x.astype(str)), axis =1) df = df.drop(["idx", "text_a", "text_b"], axis=1) df["label"] = df.label.map({0: 0, 1: 1}) return Dataset.from_pandas(df) print("--LOADING EVAL DATAS---") eval_data = load_dataset("NorGLM/NO-MRPC", data_files="val.jsonl") eval_data = getDataSetFromFiles(eval_data["train"].to_pandas()) print("--MAKING PREDICTIONS---") model.eval() y_true = [] y_pred = [] count = 0 for data in eval_data: count = count + 1 if count % 100 == 0: print(count) inputs = tokenizer(data['text'], return_tensors="pt").to(torch_device) with torch.no_grad(): logits = model(**inputs).logits #print(logits) predicted_class_id = logits.argmax().item() y_true.append(data['label']) y_pred.append(predicted_class_id) print(y_pred) print(f"Lenght of true_values: {len(y_true)}") print(f"Lenght of predicted_values: {len(y_pred)}") y_true = np.array(y_true) y_pred = np.array(y_pred) F_score = f1_score(y_true, y_pred, average="macro") print(f"F1 score: {F_score}") accuracy = accuracy_score(y_true, y_pred) print(f"Accuracy: {accuracy}") ``` ## Note More training details will be released soon!

NorGLM/NorGPT-3B-NO-MRPC-peft

NorGLM

2024-03-11T00:11:59Z

0

null

[ "no", "license:cc-by-nc-sa-4.0", "region:us" ]

null

2024-03-11T00:10:03Z

--- license: cc-by-nc-sa-4.0 language: - 'no' --- # Model Card NorGPT-3B-NO-MRPC-peft is trained on top of [NorGPT-3B](https://huggingface.co/NorGLM/NorGPT-3B) model on [NO-MRPC](https://huggingface.co/datasets/NorGLM/NO-MRPC) dataset. Data format: ``` input: {text_a}[SEP]{text_b} label: {0, 1} ``` ## Run the Model ```python from peft import PeftModel, PeftConfig from transformers import AutoModelForCausalLM, AutoTokenizer import torch torch_device = "cuda" if torch.cuda.is_available() else "cpu" source_model_id = "NorGLM/NorGPT-3B" peft_model_id = "NorGLM/NorGPT-3B-NO-MRPC-peft" config = PeftConfig.from_pretrained(peft_model_id) model = AutoModelForCausalLM.from_pretrained(source_model_id, device_map='balanced') tokenizer_max_len = 2048 tokenizer_config = {'pretrained_model_name_or_path': source_model_id, 'max_len': tokenizer_max_len} tokenizer = tokenizer = AutoTokenizer.from_pretrained(**tokenizer_config) tokenizer.pad_token = tokenizer.eos_token model = PeftModel.from_pretrained(model, peft_model_id) ``` ## Inference Example Load the model to evaluate on the validation set: ```python def getDataSetFromFiles(df): # convert dataset df["text"] = df[["text_a", "text_b"]].apply(lambda x: " [SEP] ".join(x.astype(str)), axis =1) df = df.drop(["idx", "text_a", "text_b"], axis=1) df["label"] = df.label.map({0: 0, 1: 1}) return Dataset.from_pandas(df) print("--LOADING EVAL DATAS---") eval_data = load_dataset("NorGLM/NO-MRPC", data_files="val.jsonl") eval_data = getDataSetFromFiles(eval_data["train"].to_pandas()) print("--MAKING PREDICTIONS---") model.eval() y_true = [] y_pred = [] count = 0 for data in eval_data: count = count + 1 if count % 100 == 0: print(count) inputs = tokenizer(data['text'], return_tensors="pt").to(torch_device) with torch.no_grad(): logits = model(**inputs).logits #print(logits) predicted_class_id = logits.argmax().item() y_true.append(data['label']) y_pred.append(predicted_class_id) print(y_pred) print(f"Lenght of true_values: {len(y_true)}") print(f"Lenght of predicted_values: {len(y_pred)}") y_true = np.array(y_true) y_pred = np.array(y_pred) F_score = f1_score(y_true, y_pred, average="macro") print(f"F1 score: {F_score}") accuracy = accuracy_score(y_true, y_pred) print(f"Accuracy: {accuracy}") ``` ## Note More training details will be released soon!

NorGLM/NorGPT-3B-NO-QNLI-peft

NorGLM

2024-03-11T00:09:21Z

0

null

[ "no", "license:cc-by-nc-sa-4.0", "region:us" ]

null

2024-03-10T23:50:10Z

--- license: cc-by-nc-sa-4.0 language: - 'no' --- # Model Card NorGPT-3B-NO-QNLI-peft is trained on top of [NorGPT-3B](https://huggingface.co/NorGLM/NorGPT-369M) model on [NO-QNLI](https://huggingface.co/datasets/NorGLM/NO-QNLI) dataset. Data format: ``` input: {premise}[SEP]{hypothesis} label: {entailment, not_entailment} -> {1,0} ``` ## Run the Model ```python from peft import PeftModel, PeftConfig from transformers import AutoModelForCausalLM, AutoTokenizer import torch torch_device = "cuda" if torch.cuda.is_available() else "cpu" source_model_id = "NorGLM/NorGPT-3B" peft_model_id = "NorGLM/NorGPT-3B-NO-QNLI-peft" config = PeftConfig.from_pretrained(peft_model_id) model = AutoModelForCausalLM.from_pretrained(source_model_id, device_map='balanced') tokenizer_max_len = 2048 tokenizer_config = {'pretrained_model_name_or_path': source_model_id, 'max_len': tokenizer_max_len} tokenizer = tokenizer = AutoTokenizer.from_pretrained(**tokenizer_config) tokenizer.pad_token = tokenizer.eos_token model = PeftModel.from_pretrained(model, peft_model_id) ``` ## Inference Example Load the model to evaluate on the validation set: ```python def getDataSetFromFiles(df): # convert dataset df["text"] = df[["premise", "hypothesis"]].apply(lambda x: " [SEP] ".join(x.astype(str)), axis =1) df = df.drop(["idx", "premise", "hypothesis"], axis=1) #df['label'] = df['label'].replace({1:'contradiction', -1:'entailment', 0:'neutral'}) df["label"] = df.label.map({"not_entailment": 0, "entailment": 1}) return Dataset.from_pandas(df) print("--LOADING EVAL DATAS---") eval_data = load_dataset("NorGLM/NO-QNLI", data_files="val.jsonl") eval_data = getDataSetFromFiles(eval_data["train"].to_pandas()) print("--MAKING PREDICTIONS---") model.eval() y_true = [] y_pred = [] count = 0 for data in eval_data: count = count + 1 if count % 100 == 0: print(count) inputs = tokenizer(data['text'], return_tensors="pt").to(torch_device) with torch.no_grad(): logits = model(**inputs).logits #print(logits) predicted_class_id = logits.argmax().item() y_true.append(data['label']) y_pred.append(predicted_class_id) print(y_pred) print(f"Lenght of true_values: {len(y_true)}") print(f"Lenght of predicted_values: {len(y_pred)}") y_true = np.array(y_true) y_pred = np.array(y_pred) F_score = f1_score(y_true, y_pred, average="macro") print(f"F1 score: {F_score}") accuracy = accuracy_score(y_true, y_pred) print(f"Accuracy: {accuracy}") ``` ## Note More training details will be released soon!

NorGLM/NorLlama-3B-NO-QNLI-peft

NorGLM

2024-03-11T00:09:04Z

0

null

[ "no", "license:cc-by-nc-sa-4.0", "region:us" ]

null

2024-03-10T23:55:51Z

--- license: cc-by-nc-sa-4.0 language: - 'no' --- # Model Card NorLlama-3B-NO-QNLI-peft is trained on top of [NorLlama-3B](https://huggingface.co/NorGLM/NorLlama-3B) model on [NO-QNLI](https://huggingface.co/datasets/NorGLM/NO-QNLI) dataset. Data format: ``` input: {premise}[SEP]{hypothesis} label: {entailment, not_entailment} -> {1,0} ``` ## Run the Model ```python from peft import PeftModel, PeftConfig from transformers import AutoModelForCausalLM, AutoTokenizer import torch torch_device = "cuda" if torch.cuda.is_available() else "cpu" source_model_id = "NorGLM/NorLlama-3B" peft_model_id = "NorGLM/NorLlama-3B-NO-QNLI-peft" config = PeftConfig.from_pretrained(peft_model_id) model = AutoModelForCausalLM.from_pretrained(source_model_id, device_map='balanced') tokenizer_max_len = 2048 tokenizer_config = {'pretrained_model_name_or_path': source_model_id, 'max_len': tokenizer_max_len} tokenizer = tokenizer = AutoTokenizer.from_pretrained(**tokenizer_config) tokenizer.pad_token = tokenizer.eos_token model = PeftModel.from_pretrained(model, peft_model_id) ``` ## Inference Example Load the model to evaluate on the validation set: ```python def getDataSetFromFiles(df): # convert dataset df["text"] = df[["premise", "hypothesis"]].apply(lambda x: " [SEP] ".join(x.astype(str)), axis =1) df = df.drop(["idx", "premise", "hypothesis"], axis=1) #df['label'] = df['label'].replace({1:'contradiction', -1:'entailment', 0:'neutral'}) df["label"] = df.label.map({"not_entailment": 0, "entailment": 1}) return Dataset.from_pandas(df) print("--LOADING EVAL DATAS---") eval_data = load_dataset("NorGLM/NO-QNLI", data_files="val.jsonl") eval_data = getDataSetFromFiles(eval_data["train"].to_pandas()) print("--MAKING PREDICTIONS---") model.eval() y_true = [] y_pred = [] count = 0 for data in eval_data: count = count + 1 if count % 100 == 0: print(count) inputs = tokenizer(data['text'], return_tensors="pt").to(torch_device) with torch.no_grad(): logits = model(**inputs).logits #print(logits) predicted_class_id = logits.argmax().item() y_true.append(data['label']) y_pred.append(predicted_class_id) print(y_pred) print(f"Lenght of true_values: {len(y_true)}") print(f"Lenght of predicted_values: {len(y_pred)}") y_true = np.array(y_true) y_pred = np.array(y_pred) F_score = f1_score(y_true, y_pred, average="macro") print(f"F1 score: {F_score}") accuracy = accuracy_score(y_true, y_pred) print(f"Accuracy: {accuracy}") ``` ## Note More training details will be released soon!

NorGLM/NorGPT-3B-continue-NO-QNLI-peft

NorGLM

2024-03-11T00:08:47Z

0

null

[ "no", "license:cc-by-nc-sa-4.0", "region:us" ]

null

2024-03-10T23:53:53Z

--- license: cc-by-nc-sa-4.0 language: - 'no' --- # Model Card NorGPT-3B-continue-NO-QNLI-peft is trained on top of [NorGPT-3B-continue](https://huggingface.co/NorGLM/NorGPT-3B-continue) model on [NO-QNLI](https://huggingface.co/datasets/NorGLM/NO-QNLI) dataset. Data format: ``` input: {premise}[SEP]{hypothesis} label: {entailment, not_entailment} -> {1,0} ``` ## Run the Model ```python from peft import PeftModel, PeftConfig from transformers import AutoModelForCausalLM, AutoTokenizer import torch torch_device = "cuda" if torch.cuda.is_available() else "cpu" source_model_id = "NorGLM/NorGPT-3B-continue" peft_model_id = "NorGLM/NorGPT-3B-continue-NO-QNLI-peft" config = PeftConfig.from_pretrained(peft_model_id) model = AutoModelForCausalLM.from_pretrained(source_model_id, device_map='balanced') tokenizer_max_len = 2048 tokenizer_config = {'pretrained_model_name_or_path': source_model_id, 'max_len': tokenizer_max_len} tokenizer = tokenizer = AutoTokenizer.from_pretrained(**tokenizer_config) tokenizer.pad_token = tokenizer.eos_token model = PeftModel.from_pretrained(model, peft_model_id) ``` ## Inference Example Load the model to evaluate on the validation set: ```python def getDataSetFromFiles(df): # convert dataset df["text"] = df[["premise", "hypothesis"]].apply(lambda x: " [SEP] ".join(x.astype(str)), axis =1) df = df.drop(["idx", "premise", "hypothesis"], axis=1) #df['label'] = df['label'].replace({1:'contradiction', -1:'entailment', 0:'neutral'}) df["label"] = df.label.map({"not_entailment": 0, "entailment": 1}) return Dataset.from_pandas(df) print("--LOADING EVAL DATAS---") eval_data = load_dataset("NorGLM/NO-QNLI", data_files="val.jsonl") eval_data = getDataSetFromFiles(eval_data["train"].to_pandas()) print("--MAKING PREDICTIONS---") model.eval() y_true = [] y_pred = [] count = 0 for data in eval_data: count = count + 1 if count % 100 == 0: print(count) inputs = tokenizer(data['text'], return_tensors="pt").to(torch_device) with torch.no_grad(): logits = model(**inputs).logits #print(logits) predicted_class_id = logits.argmax().item() y_true.append(data['label']) y_pred.append(predicted_class_id) print(y_pred) print(f"Lenght of true_values: {len(y_true)}") print(f"Lenght of predicted_values: {len(y_pred)}") y_true = np.array(y_true) y_pred = np.array(y_pred) F_score = f1_score(y_true, y_pred, average="macro") print(f"F1 score: {F_score}") accuracy = accuracy_score(y_true, y_pred) print(f"Accuracy: {accuracy}") ``` ## Note More training details will be released soon!

NorGLM/NorGPT-369M-NO-QNLI-peft

NorGLM

2024-03-11T00:08:20Z

0

null

[ "no", "license:cc-by-nc-sa-4.0", "region:us" ]

null

2024-03-10T23:45:24Z

--- license: cc-by-nc-sa-4.0 language: - 'no' --- # Model Card NorGPT-369M-NO-QNLI-peft is trained on top of [NorGPT-369M](https://huggingface.co/NorGLM/NorGPT-369M) model on [NO-QNLI](https://huggingface.co/datasets/NorGLM/NO-QNLI) dataset. Data format: ``` input: {premise}[SEP]{hypothesis} label: {entailment, not_entailment} -> {1,0} ``` ## Run the Model ```python from peft import PeftModel, PeftConfig from transformers import AutoModelForCausalLM, AutoTokenizer import torch torch_device = "cuda" if torch.cuda.is_available() else "cpu" source_model_id = "NorGLM/NorGPT-369M" peft_model_id = "NorGLM/NorGPT-369M-NO-QNLI-peft" config = PeftConfig.from_pretrained(peft_model_id) model = AutoModelForCausalLM.from_pretrained(source_model_id, device_map='balanced') tokenizer_max_len = 2048 tokenizer_config = {'pretrained_model_name_or_path': source_model_id, 'max_len': tokenizer_max_len} tokenizer = tokenizer = AutoTokenizer.from_pretrained(**tokenizer_config) tokenizer.pad_token = tokenizer.eos_token model = PeftModel.from_pretrained(model, peft_model_id) ``` ## Inference Example Load the model to evaluate on the validation set: ```python def getDataSetFromFiles(df): # convert dataset df["text"] = df[["premise", "hypothesis"]].apply(lambda x: " [SEP] ".join(x.astype(str)), axis =1) df = df.drop(["idx", "premise", "hypothesis"], axis=1) #df['label'] = df['label'].replace({1:'contradiction', -1:'entailment', 0:'neutral'}) df["label"] = df.label.map({"not_entailment": 0, "entailment": 1}) return Dataset.from_pandas(df) print("--LOADING EVAL DATAS---") eval_data = load_dataset("NorGLM/NO-QNLI", data_files="val.jsonl") eval_data = getDataSetFromFiles(eval_data["train"].to_pandas()) print("--MAKING PREDICTIONS---") model.eval() y_true = [] y_pred = [] count = 0 for data in eval_data: count = count + 1 if count % 100 == 0: print(count) inputs = tokenizer(data['text'], return_tensors="pt").to(torch_device) with torch.no_grad(): logits = model(**inputs).logits #print(logits) predicted_class_id = logits.argmax().item() y_true.append(data['label']) y_pred.append(predicted_class_id) print(y_pred) print(f"Lenght of true_values: {len(y_true)}") print(f"Lenght of predicted_values: {len(y_pred)}") y_true = np.array(y_true) y_pred = np.array(y_pred) F_score = f1_score(y_true, y_pred, average="macro") print(f"F1 score: {F_score}") accuracy = accuracy_score(y_true, y_pred) print(f"Accuracy: {accuracy}") ``` ## Note More training details will be released soon!

Davada/subnet6

Davada

2024-03-11T00:08:11Z

90

0

transformers

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

text-generation

2024-03-10T23:32:37Z

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

NorGLM/NorGPT-369M-NO-MRPC-peft

NorGLM

2024-03-11T00:07:22Z

0

null

[ "no", "license:cc-by-nc-sa-4.0", "region:us" ]

null

2024-03-11T00:02:01Z

--- license: cc-by-nc-sa-4.0 language: - 'no' --- # Model Card NorGPT-369M-NO-MRPC-peft is trained on top of [NorGPT-369M](https://huggingface.co/NorGLM/NorGPT-369M) model on [NO-MRPC](https://huggingface.co/datasets/NorGLM/NO-MRPC) dataset. Data format: ``` input: {text_a}[SEP]{text_b} label: {0, 1} ``` ## Run the Model ```python from peft import PeftModel, PeftConfig from transformers import AutoModelForCausalLM, AutoTokenizer import torch torch_device = "cuda" if torch.cuda.is_available() else "cpu" source_model_id = "NorGLM/NorGPT-369M" peft_model_id = "NorGLM/NorGPT-369M-NO-MRPC-peft" config = PeftConfig.from_pretrained(peft_model_id) model = AutoModelForCausalLM.from_pretrained(source_model_id, device_map='balanced') tokenizer_max_len = 2048 tokenizer_config = {'pretrained_model_name_or_path': source_model_id, 'max_len': tokenizer_max_len} tokenizer = tokenizer = AutoTokenizer.from_pretrained(**tokenizer_config) tokenizer.pad_token = tokenizer.eos_token model = PeftModel.from_pretrained(model, peft_model_id) ``` ## Inference Example Load the model to evaluate on the validation set: ```python def getDataSetFromFiles(df): # convert dataset df["text"] = df[["text_a", "text_b"]].apply(lambda x: " [SEP] ".join(x.astype(str)), axis =1) df = df.drop(["idx", "text_a", "text_b"], axis=1) df["label"] = df.label.map({0: 0, 1: 1}) return Dataset.from_pandas(df) print("--LOADING EVAL DATAS---") eval_data = load_dataset("NorGLM/NO-MRPC", data_files="val.jsonl") eval_data = getDataSetFromFiles(eval_data["train"].to_pandas()) print("--MAKING PREDICTIONS---") model.eval() y_true = [] y_pred = [] count = 0 for data in eval_data: count = count + 1 if count % 100 == 0: print(count) inputs = tokenizer(data['text'], return_tensors="pt").to(torch_device) with torch.no_grad(): logits = model(**inputs).logits #print(logits) predicted_class_id = logits.argmax().item() y_true.append(data['label']) y_pred.append(predicted_class_id) print(y_pred) print(f"Lenght of true_values: {len(y_true)}") print(f"Lenght of predicted_values: {len(y_pred)}") y_true = np.array(y_true) y_pred = np.array(y_pred) F_score = f1_score(y_true, y_pred, average="macro") print(f"F1 score: {F_score}") accuracy = accuracy_score(y_true, y_pred) print(f"Accuracy: {accuracy}") ``` ## Note More training details will be released soon!

Holarissun/phi2-aisft-synhh-randsampler-subset30000

Holarissun

2024-03-10T23:59:25Z

0

peft

[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:microsoft/phi-2", "base_model:adapter:microsoft/phi-2", "license:mit", "region:us" ]

null

2024-03-10T23:59:14Z

--- license: mit library_name: peft tags: - trl - sft - generated_from_trainer base_model: microsoft/phi-2 model-index: - name: phi2-aisft-synhh-randsampler-subset30000 results: [] ---  # phi2-aisft-synhh-randsampler-subset30000 This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

nold/MediKAI-GGUF

nold

2024-03-10T23:58:10Z

20

0

null

[ "gguf", "license:other", "endpoints_compatible", "region:us" ]

null

2024-03-10T15:35:00Z

--- license: other --- # MediKAI - Your Healthcare Companion 🏥💬 Welcome to mediKAI, the latest healthcare-focused model by HelpingAI designed to provide personalized assistance and support in medical-related queries. ## Overview mediKAI is a 14 billion parameters model that specializes in healthcare-related topics and medical assistance. Whether you have questions about symptoms, treatments, medications, or general health and wellness, mediKAI is here to help. ## Languages Supported - English - French - Hindi - Spanish - Arabic ``` *** Quantization of Model [OEvortex/MediKAI](https://huggingface.co/OEvortex/MediKAI). Created using [llm-quantizer](https://github.com/Nold360/llm-quantizer) Pipeline

dzakwan/cybersec

dzakwan

2024-03-10T23:56:53Z

5

0

transformers

[ "transformers", "pytorch", "safetensors", "gemma", "text-generation", "unsloth", "trl", "sft", "conversational", "en", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-10T15:53:39Z

--- library_name: transformers widget: - messages: - role: user content: >- We need to prepare for the possibility of a security incident. Can you create an incident response plan for our organization? inference: parameters: max_new_tokens: 200 tags: - unsloth - trl - sft language: - en --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** M Dzakwan Falih - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** English - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

hanzla/gemma-2b-it-ds

hanzla

2024-03-10T23:49:53Z

23

0

transformers

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

text-generation

2024-03-10T18:33:23Z

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

koesn/Saul-Instruct-v1-GGUF

koesn

2024-03-10T23:48:42Z

56

5

transformers

[ "transformers", "gguf", "legal", "en", "arxiv:2403.03883", "license:mit", "endpoints_compatible", "region:us" ]

null

2024-03-10T16:19:55Z

--- library_name: transformers tags: - legal license: mit language: - en --- # Saul-Instruct-v1 ## Description This repo contains GGUF format model files for Saul-Instruct-v1. ## Files Provided | Name | Quant | Bits | File Size | Remark | | ---------------------------- | ------- | ---- | --------- | -------------------------------- | | saul-instruct-v1.IQ3_S.gguf | IQ3_S | 3 | 3.18 GB | 3.44 bpw quantization | | saul-instruct-v1.IQ3_M.gguf | IQ3_M | 3 | 3.28 GB | 3.66 bpw quantization mix | | saul-instruct-v1.Q4_0.gguf | Q4_0 | 4 | 4.11 GB | 3.56G, +0.2166 ppl | | saul-instruct-v1.IQ4_NL.gguf | IQ4_NL | 4 | 4.16 GB | 4.25 bpw non-linear quantization | | saul-instruct-v1.Q4_K_M.gguf | Q4_K_M | 4 | 4.37 GB | 3.80G, +0.0532 ppl | | saul-instruct-v1.Q5_K_M.gguf | Q5_K_M | 5 | 5.13 GB | 4.45G, +0.0122 ppl | | saul-instruct-v1.Q6_K.gguf | Q6_K | 6 | 5.94 GB | 5.15G, +0.0008 ppl | | saul-instruct-v1.Q8_0.gguf | Q8_0 | 8 | 7.70 GB | 6.70G, +0.0004 ppl | ## Parameters | path | type | architecture | rope_theta | sliding_win | max_pos_embed | | ----------------------- | ------- | ------------------ | ---------- | ----------- | ------------- | | Equall/Saul-Instruct-v1 | mistral | MistralForCausalLM | 10000 | 4096 | 32768 | ## Benchmarks See original model card. # Original Model Card # Equall/Saul-Instruct-v1 This is the instruct model for Equall/Saul-Instruct-v1, a large instruct language model tailored for Legal domain. This model is obtained by continue pretraining of Mistral-7B. Checkout our website and register https://equall.ai/ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/644a900e3a619fe72b14af0f/OU4Y3s-WckYKMN4fQkNiS.png) ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** Equall.ai in collaboration with CentraleSupelec, Sorbonne Université, Instituto Superior Técnico and NOVA School of Law - **Model type:** 7B - **Language(s) (NLP):** English - **License:** MIT ### Model Sources  - **Paper:** https://arxiv.org/abs/2403.03883 ## Uses  You can use it for legal use cases that involves generation. Here's how you can run the model using the pipeline() function from 🤗 Transformers: ```python # Install transformers from source - only needed for versions <= v4.34 # pip install git+https://github.com/huggingface/transformers.git # pip install accelerate import torch from transformers import pipeline pipe = pipeline("text-generation", model="Equall/Saul-Instruct-v1", torch_dtype=torch.bfloat16, device_map="auto") # We use the tokenizer’s chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating messages = [ {"role": "user", "content": "[YOUR QUERY GOES HERE]"}, ] prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) outputs = pipe(prompt, max_new_tokens=256, do_sample=False) print(outputs[0]["generated_text"]) ``` ## Bias, Risks, and Limitations  This model is built upon the technology of LLM, which comes with inherent limitations. It may occasionally generate inaccurate or nonsensical outputs. Furthermore, being a 7B model, it's anticipated to exhibit less robust performance compared to larger models, such as the 70B variant. ## Citation  **BibTeX:** ```bibtex @misc{colombo2024saullm7b, title={SaulLM-7B: A pioneering Large Language Model for Law}, author={Pierre Colombo and Telmo Pessoa Pires and Malik Boudiaf and Dominic Culver and Rui Melo and Caio Corro and Andre F. T. Martins and Fabrizio Esposito and Vera Lúcia Raposo and Sofia Morgado and Michael Desa}, year={2024}, eprint={2403.03883}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

ZainAli60/miner_

ZainAli60

2024-03-10T23:43:29Z

189

0

transformers

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

text-generation

2024-03-10T20:49:57Z

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

btmiller/output

btmiller

2024-03-10T23:37:44Z

1

0

peft

[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:google/flan-t5-small", "base_model:adapter:google/flan-t5-small", "license:apache-2.0", "region:us" ]

null

2024-03-10T23:37:43Z

--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer datasets: - generator base_model: google/flan-t5-small model-index: - name: output results: [] ---  # output This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) 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: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.9.1.dev0 - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

USGOV/Gov

USGOV

2024-03-10T23:30:04Z

0

null

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

null

2024-03-10T23:30:04Z

--- license: other license_name: government-open-source license_link: LICENSE ---

alinerodrigues/wav2vec2-large-xlsr-mecita-coraa-portuguese-all-grade-2-5

alinerodrigues

2024-03-10T23:25:48Z

1

0

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-03-10T19:49:47Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: wav2vec2-large-xlsr-mecita-coraa-portuguese-all-grade-2-5 results: [] ---  # wav2vec2-large-xlsr-mecita-coraa-portuguese-all-grade-2-5 This model is a fine-tuned version of [Edresson/wav2vec2-large-xlsr-coraa-portuguese](https://huggingface.co/Edresson/wav2vec2-large-xlsr-coraa-portuguese) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1888 - Wer: 0.1049 - Cer: 0.0343 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 31.7094 | 1.0 | 58 | 3.6328 | 1.0 | 1.0 | | 7.717 | 2.0 | 116 | 3.1503 | 1.0 | 1.0 | | 7.717 | 3.0 | 174 | 3.0149 | 1.0 | 1.0 | | 3.0503 | 4.0 | 232 | 2.9605 | 1.0 | 1.0 | | 3.0503 | 5.0 | 290 | 2.9212 | 1.0 | 1.0 | | 2.9265 | 6.0 | 348 | 2.8995 | 1.0 | 1.0 | | 2.8799 | 7.0 | 406 | 2.6417 | 1.0 | 1.0 | | 2.8799 | 8.0 | 464 | 1.3314 | 0.9950 | 0.2921 | | 2.0484 | 9.0 | 522 | 0.7075 | 0.3918 | 0.1000 | | 2.0484 | 10.0 | 580 | 0.5138 | 0.2276 | 0.0664 | | 0.9682 | 11.0 | 638 | 0.4169 | 0.2071 | 0.0596 | | 0.9682 | 12.0 | 696 | 0.3580 | 0.1835 | 0.0530 | | 0.6198 | 13.0 | 754 | 0.3281 | 0.1719 | 0.0513 | | 0.529 | 14.0 | 812 | 0.3166 | 0.1692 | 0.0502 | | 0.529 | 15.0 | 870 | 0.2954 | 0.1595 | 0.0483 | | 0.445 | 16.0 | 928 | 0.2783 | 0.1502 | 0.0453 | | 0.445 | 17.0 | 986 | 0.2721 | 0.1452 | 0.0445 | | 0.3943 | 18.0 | 1044 | 0.2537 | 0.1390 | 0.0415 | | 0.3798 | 19.0 | 1102 | 0.2567 | 0.1332 | 0.0416 | | 0.3798 | 20.0 | 1160 | 0.2434 | 0.1196 | 0.0388 | | 0.3459 | 21.0 | 1218 | 0.2421 | 0.1181 | 0.0384 | | 0.3459 | 22.0 | 1276 | 0.2252 | 0.1150 | 0.0365 | | 0.3187 | 23.0 | 1334 | 0.2331 | 0.1146 | 0.0368 | | 0.3187 | 24.0 | 1392 | 0.2195 | 0.1181 | 0.0371 | | 0.2982 | 25.0 | 1450 | 0.2180 | 0.1181 | 0.0375 | | 0.2874 | 26.0 | 1508 | 0.2181 | 0.1069 | 0.0355 | | 0.2874 | 27.0 | 1566 | 0.2159 | 0.1099 | 0.0360 | | 0.2542 | 28.0 | 1624 | 0.2173 | 0.1161 | 0.0380 | | 0.2542 | 29.0 | 1682 | 0.2127 | 0.1080 | 0.0358 | | 0.2663 | 30.0 | 1740 | 0.2112 | 0.1158 | 0.0372 | | 0.2663 | 31.0 | 1798 | 0.2114 | 0.1130 | 0.0364 | | 0.2371 | 32.0 | 1856 | 0.2052 | 0.1092 | 0.0359 | | 0.2348 | 33.0 | 1914 | 0.2044 | 0.1061 | 0.0346 | | 0.2348 | 34.0 | 1972 | 0.2067 | 0.1072 | 0.0344 | | 0.2368 | 35.0 | 2030 | 0.2023 | 0.1099 | 0.0350 | | 0.2368 | 36.0 | 2088 | 0.1992 | 0.1049 | 0.0353 | | 0.217 | 37.0 | 2146 | 0.1972 | 0.1076 | 0.0354 | | 0.234 | 38.0 | 2204 | 0.1938 | 0.1076 | 0.0347 | | 0.234 | 39.0 | 2262 | 0.1982 | 0.1069 | 0.0348 | | 0.1979 | 40.0 | 2320 | 0.1945 | 0.1061 | 0.0346 | | 0.1979 | 41.0 | 2378 | 0.2003 | 0.1069 | 0.0353 | | 0.2062 | 42.0 | 2436 | 0.1970 | 0.1053 | 0.0350 | | 0.2062 | 43.0 | 2494 | 0.1984 | 0.1007 | 0.0341 | | 0.2011 | 44.0 | 2552 | 0.1992 | 0.1072 | 0.0343 | | 0.1807 | 45.0 | 2610 | 0.1962 | 0.1084 | 0.0342 | | 0.1807 | 46.0 | 2668 | 0.1958 | 0.1030 | 0.0334 | | 0.1982 | 47.0 | 2726 | 0.1928 | 0.1038 | 0.0340 | | 0.1982 | 48.0 | 2784 | 0.1961 | 0.1053 | 0.0344 | | 0.1948 | 49.0 | 2842 | 0.1939 | 0.1049 | 0.0336 | | 0.1777 | 50.0 | 2900 | 0.1888 | 0.1049 | 0.0343 | | 0.1777 | 51.0 | 2958 | 0.1930 | 0.1026 | 0.0336 | | 0.1655 | 52.0 | 3016 | 0.1900 | 0.1018 | 0.0333 | | 0.1655 | 53.0 | 3074 | 0.1950 | 0.1034 | 0.0331 | | 0.1805 | 54.0 | 3132 | 0.1946 | 0.1045 | 0.0340 | | 0.1805 | 55.0 | 3190 | 0.1959 | 0.1030 | 0.0337 | | 0.1829 | 56.0 | 3248 | 0.1933 | 0.0987 | 0.0325 | | 0.1621 | 57.0 | 3306 | 0.1908 | 0.0976 | 0.0325 | | 0.1621 | 58.0 | 3364 | 0.1892 | 0.1010 | 0.0331 | | 0.1702 | 59.0 | 3422 | 0.1907 | 0.0995 | 0.0322 | | 0.1702 | 60.0 | 3480 | 0.1934 | 0.1003 | 0.0326 | | 0.1652 | 61.0 | 3538 | 0.1959 | 0.0987 | 0.0328 | | 0.1652 | 62.0 | 3596 | 0.1961 | 0.0976 | 0.0323 | | 0.1567 | 63.0 | 3654 | 0.1927 | 0.0991 | 0.0330 | | 0.1496 | 64.0 | 3712 | 0.1912 | 0.0983 | 0.0327 | | 0.1496 | 65.0 | 3770 | 0.1963 | 0.1007 | 0.0330 | | 0.1672 | 66.0 | 3828 | 0.1958 | 0.0999 | 0.0328 | | 0.1672 | 67.0 | 3886 | 0.1962 | 0.0987 | 0.0328 | | 0.141 | 68.0 | 3944 | 0.1957 | 0.0964 | 0.0320 | | 0.144 | 69.0 | 4002 | 0.1942 | 0.0949 | 0.0316 | | 0.144 | 70.0 | 4060 | 0.1931 | 0.0995 | 0.0331 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.2.1+cu121 - Datasets 2.17.0 - Tokenizers 0.13.3

hamzasidat/Hamzas_Bert_Irony3

hamzasidat

2024-03-10T23:09:59Z

0

transformers

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

null

2024-03-10T23:09:57Z

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

hamzasidat/BertIronyResults3

hamzasidat

2024-03-10T23:09:55Z

179

0

transformers

[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-03-10T23:09:12Z

--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: BertIronyResults3 results: [] ---  # BertIronyResults3 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5868 - Accuracy: 0.6932 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 179 | 0.5868 | 0.6932 | | No log | 2.0 | 358 | 0.6104 | 0.6869 | | 0.4907 | 3.0 | 537 | 0.6448 | 0.7026 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

hamzasidat/Hamzas_assignment1_Albert2

hamzasidat

2024-03-10T23:05:49Z

0

transformers

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

null

2024-03-10T23:05:47Z

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

Francois2511/distilbert-base-uncased-finetuned-emotion

Francois2511

2024-03-10T23:03:13Z

94

0

transformers

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

text-classification

2024-03-10T22:26:32Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.9215 - name: F1 type: f1 value: 0.921552692432107 ---  # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2165 - Accuracy: 0.9215 - F1: 0.9216 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8067 | 1.0 | 250 | 0.3244 | 0.902 | 0.9008 | | 0.2493 | 2.0 | 500 | 0.2165 | 0.9215 | 0.9216 | ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2

saintazunya/outputs-dreambooth-sdxl-kanade

saintazunya

2024-03-10T23:03:06Z

2

diffusers

[ "diffusers", "tensorboard", "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", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]

text-to-image

2024-03-10T22:17:56Z

--- 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 skskanadetachibana figure widget: [] ---  # SDXL LoRA DreamBooth - saintazunya/outputs-dreambooth-sdxl-kanade <Gallery /> ## Model description These are saintazunya/outputs-dreambooth-sdxl-kanade 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 skskanadetachibana figure to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](saintazunya/outputs-dreambooth-sdxl-kanade/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]

keskin-oguzhan/phi2-squadv2

keskin-oguzhan

2024-03-10T23:02:58Z

39

0

transformers

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

text-generation

2024-03-10T22:58:39Z

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

hamzasidat/Hamzas_assignment1_Bert2

hamzasidat

2024-03-10T23:02:02Z

0

transformers

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

null

2024-03-10T23:02:01Z

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

hamzasidat/BertResults2

hamzasidat

2024-03-10T23:02:00Z

177

0

transformers

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

text-classification

2024-03-10T23:01:39Z

--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer datasets: - emotion metrics: - accuracy model-index: - name: BertResults2 results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.94 ---  # BertResults2 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1487 - Accuracy: 0.94 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2236 | 1.0 | 1000 | 0.1929 | 0.924 | | 0.1179 | 2.0 | 2000 | 0.1487 | 0.94 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

tsavage68/mistralit2_1000_STEPS_1e8_rate_03_beta_DPO

tsavage68

2024-03-10T22:55:57Z

6

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "trl", "dpo", "generated_from_trainer", "conversational", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:finetune:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-10T22:49:41Z

--- license: apache-2.0 base_model: mistralai/Mistral-7B-Instruct-v0.2 tags: - trl - dpo - generated_from_trainer model-index: - name: mistralit2_1000_STEPS_1e8_rate_03_beta_DPO results: [] ---  # mistralit2_1000_STEPS_1e8_rate_03_beta_DPO This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6902 - Rewards/chosen: -0.0213 - Rewards/rejected: -0.0282 - Rewards/accuracies: 0.4945 - Rewards/margins: 0.0070 - Logps/rejected: -28.6665 - Logps/chosen: -23.4567 - Logits/rejected: -2.8649 - Logits/chosen: -2.8651 ## 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-08 - train_batch_size: 4 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 1000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| | 0.6911 | 0.1 | 50 | 0.6909 | 0.0027 | -0.0025 | 0.4967 | 0.0052 | -28.5807 | -23.3768 | -2.8653 | -2.8655 | | 0.6916 | 0.2 | 100 | 0.6928 | -0.0010 | -0.0023 | 0.4571 | 0.0014 | -28.5802 | -23.3891 | -2.8653 | -2.8655 | | 0.6924 | 0.29 | 150 | 0.6922 | -0.0091 | -0.0117 | 0.4681 | 0.0026 | -28.6115 | -23.4162 | -2.8651 | -2.8654 | | 0.6941 | 0.39 | 200 | 0.6914 | -0.0066 | -0.0109 | 0.4879 | 0.0043 | -28.6088 | -23.4078 | -2.8652 | -2.8654 | | 0.6942 | 0.49 | 250 | 0.6911 | -0.0070 | -0.0120 | 0.4791 | 0.0050 | -28.6123 | -23.4090 | -2.8649 | -2.8652 | | 0.6909 | 0.59 | 300 | 0.6921 | -0.0151 | -0.0181 | 0.4593 | 0.0030 | -28.6327 | -23.4362 | -2.8650 | -2.8653 | | 0.696 | 0.68 | 350 | 0.6903 | -0.0140 | -0.0207 | 0.5121 | 0.0067 | -28.6414 | -23.4326 | -2.8651 | -2.8653 | | 0.6907 | 0.78 | 400 | 0.6904 | -0.0153 | -0.0217 | 0.4945 | 0.0064 | -28.6448 | -23.4369 | -2.8649 | -2.8652 | | 0.6895 | 0.88 | 450 | 0.6898 | -0.0157 | -0.0232 | 0.4945 | 0.0075 | -28.6497 | -23.4380 | -2.8649 | -2.8652 | | 0.6902 | 0.98 | 500 | 0.6892 | -0.0192 | -0.0282 | 0.5165 | 0.0090 | -28.6665 | -23.4500 | -2.8650 | -2.8652 | | 0.6923 | 1.07 | 550 | 0.6893 | -0.0196 | -0.0282 | 0.5385 | 0.0086 | -28.6663 | -23.4511 | -2.8649 | -2.8652 | | 0.6957 | 1.17 | 600 | 0.6897 | -0.0210 | -0.0288 | 0.5011 | 0.0078 | -28.6684 | -23.4560 | -2.8649 | -2.8652 | | 0.6885 | 1.27 | 650 | 0.6897 | -0.0173 | -0.0251 | 0.5143 | 0.0078 | -28.6560 | -23.4436 | -2.8650 | -2.8653 | | 0.6912 | 1.37 | 700 | 0.6906 | -0.0207 | -0.0268 | 0.4967 | 0.0061 | -28.6617 | -23.4548 | -2.8650 | -2.8652 | | 0.6874 | 1.46 | 750 | 0.6903 | -0.0216 | -0.0282 | 0.4923 | 0.0065 | -28.6663 | -23.4580 | -2.8650 | -2.8652 | | 0.6896 | 1.56 | 800 | 0.6877 | -0.0180 | -0.0298 | 0.5451 | 0.0119 | -28.6719 | -23.4457 | -2.8649 | -2.8651 | | 0.6904 | 1.66 | 850 | 0.6905 | -0.0217 | -0.0279 | 0.4791 | 0.0062 | -28.6655 | -23.4582 | -2.8649 | -2.8651 | | 0.6913 | 1.76 | 900 | 0.6902 | -0.0213 | -0.0282 | 0.4945 | 0.0070 | -28.6665 | -23.4567 | -2.8649 | -2.8651 | | 0.6977 | 1.86 | 950 | 0.6902 | -0.0213 | -0.0282 | 0.4945 | 0.0070 | -28.6665 | -23.4567 | -2.8649 | -2.8651 | | 0.6892 | 1.95 | 1000 | 0.6902 | -0.0213 | -0.0282 | 0.4945 | 0.0070 | -28.6665 | -23.4567 | -2.8649 | -2.8651 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.0.0+cu117 - Datasets 2.18.0 - Tokenizers 0.15.2

arsruts/distilbert-base-uncased-finetuned-cola

arsruts

2024-03-10T22:54:08Z

4

0

transformers

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

text-classification

2024-03-08T13:37:09Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - matthews_correlation model-index: - name: distilbert-base-uncased-finetuned-cola results: [] ---  # 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: 0.8855 - Matthews Correlation: 0.5339 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5155 | 1.0 | 535 | 0.4625 | 0.4354 | | 0.3412 | 2.0 | 1070 | 0.4636 | 0.5212 | | 0.2297 | 3.0 | 1605 | 0.6616 | 0.5111 | | 0.1737 | 4.0 | 2140 | 0.8490 | 0.5265 | | 0.1228 | 5.0 | 2675 | 0.8855 | 0.5339 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

SjardiWillems/distilbert-base-uncased-finetuned-stsb

SjardiWillems

2024-03-10T22:47:48Z

23

0

transformers

[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:SjardiWillems/distilbert-base-uncased-finetuned-stsb", "base_model:finetune:SjardiWillems/distilbert-base-uncased-finetuned-stsb", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-03-05T21:52:02Z

--- license: apache-2.0 base_model: SjardiWillems/distilbert-base-uncased-finetuned-stsb tags: - generated_from_trainer metrics: - spearmanr model-index: - name: distilbert-base-uncased-finetuned-stsb results: [] ---  # distilbert-base-uncased-finetuned-stsb This model is a fine-tuned version of [SjardiWillems/distilbert-base-uncased-finetuned-stsb](https://huggingface.co/SjardiWillems/distilbert-base-uncased-finetuned-stsb) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5419 - Pearson: 0.8736 - Spearmanr: 0.8702 ## 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: 3.1992432473500055e-06 - train_batch_size: 64 - eval_batch_size: 16 - seed: 25 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:| | No log | 1.0 | 90 | 0.5404 | 0.8727 | 0.8690 | | No log | 2.0 | 180 | 0.5394 | 0.8736 | 0.8701 | | No log | 3.0 | 270 | 0.5394 | 0.8738 | 0.8703 | | No log | 4.0 | 360 | 0.5419 | 0.8736 | 0.8702 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

Litzy619/V0309P6

Litzy619

2024-03-10T22:45:47Z

0

null

[ "safetensors", "generated_from_trainer", "base_model:microsoft/phi-2", "base_model:finetune:microsoft/phi-2", "license:mit", "region:us" ]

null

2024-03-10T07:39:51Z

--- license: mit base_model: microsoft/phi-2 tags: - generated_from_trainer model-index: - name: V0309P6 results: [] ---  # V0309P6 This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0648 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 20 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.969 | 0.09 | 10 | 0.5527 | | 0.2118 | 0.17 | 20 | 0.0895 | | 0.1076 | 0.26 | 30 | 0.0750 | | 0.0998 | 0.34 | 40 | 0.0690 | | 0.0936 | 0.43 | 50 | 0.0643 | | 0.0846 | 0.51 | 60 | 0.0642 | | 0.0784 | 0.6 | 70 | 0.0639 | | 0.0857 | 0.68 | 80 | 0.0668 | | 0.0748 | 0.77 | 90 | 0.0641 | | 0.111 | 0.85 | 100 | 0.0680 | | 0.0874 | 0.94 | 110 | 0.0704 | | 0.0842 | 1.02 | 120 | 0.0675 | | 0.0797 | 1.11 | 130 | 0.0678 | | 0.0731 | 1.19 | 140 | 0.0642 | | 0.0714 | 1.28 | 150 | 0.0584 | | 0.0709 | 1.37 | 160 | 0.0621 | | 0.0703 | 1.45 | 170 | 0.0587 | | 0.0638 | 1.54 | 180 | 0.0595 | | 0.0678 | 1.62 | 190 | 0.0580 | | 0.067 | 1.71 | 200 | 0.0600 | | 0.0672 | 1.79 | 210 | 0.0604 | | 0.0627 | 1.88 | 220 | 0.0640 | | 0.0587 | 1.96 | 230 | 0.0592 | | 0.057 | 2.05 | 240 | 0.0622 | | 0.0486 | 2.13 | 250 | 0.0663 | | 0.0484 | 2.22 | 260 | 0.0690 | | 0.0457 | 2.3 | 270 | 0.0677 | | 0.0529 | 2.39 | 280 | 0.0636 | | 0.0533 | 2.47 | 290 | 0.0622 | | 0.0523 | 2.56 | 300 | 0.0627 | | 0.0523 | 2.65 | 310 | 0.0638 | | 0.0456 | 2.73 | 320 | 0.0642 | | 0.048 | 2.82 | 330 | 0.0648 | | 0.0454 | 2.9 | 340 | 0.0642 | | 0.0491 | 2.99 | 350 | 0.0648 | ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1

Litzy619/V0309P4

Litzy619

2024-03-10T22:45:17Z

0

null

[ "safetensors", "generated_from_trainer", "base_model:microsoft/phi-2", "base_model:finetune:microsoft/phi-2", "license:mit", "region:us" ]

null

2024-03-10T07:37:44Z

--- license: mit base_model: microsoft/phi-2 tags: - generated_from_trainer model-index: - name: V0309P4 results: [] ---  # V0309P4 This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0689 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 20 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.1886 | 0.09 | 10 | 0.9747 | | 0.3651 | 0.17 | 20 | 0.0977 | | 0.1129 | 0.26 | 30 | 0.0765 | | 0.0955 | 0.34 | 40 | 0.0707 | | 0.0894 | 0.43 | 50 | 0.0684 | | 0.083 | 0.51 | 60 | 0.0679 | | 0.0762 | 0.6 | 70 | 0.0688 | | 0.0807 | 0.68 | 80 | 0.0672 | | 0.0699 | 0.77 | 90 | 0.0735 | | 0.0699 | 0.85 | 100 | 0.0735 | | 0.0757 | 0.94 | 110 | 0.0663 | | 0.0726 | 1.02 | 120 | 0.0632 | | 0.0641 | 1.11 | 130 | 0.0692 | | 0.0627 | 1.19 | 140 | 0.0625 | | 0.0579 | 1.28 | 150 | 0.0625 | | 0.0579 | 1.37 | 160 | 0.0682 | | 0.0564 | 1.45 | 170 | 0.0642 | | 0.0544 | 1.54 | 180 | 0.0651 | | 0.0565 | 1.62 | 190 | 0.0623 | | 0.057 | 1.71 | 200 | 0.0605 | | 0.0589 | 1.79 | 210 | 0.0602 | | 0.0538 | 1.88 | 220 | 0.0659 | | 0.0528 | 1.96 | 230 | 0.0623 | | 0.0482 | 2.05 | 240 | 0.0640 | | 0.0396 | 2.13 | 250 | 0.0693 | | 0.0398 | 2.22 | 260 | 0.0753 | | 0.0372 | 2.3 | 270 | 0.0771 | | 0.0463 | 2.39 | 280 | 0.0707 | | 0.0447 | 2.47 | 290 | 0.0676 | | 0.0429 | 2.56 | 300 | 0.0672 | | 0.0454 | 2.65 | 310 | 0.0670 | | 0.0377 | 2.73 | 320 | 0.0678 | | 0.0387 | 2.82 | 330 | 0.0690 | | 0.0394 | 2.9 | 340 | 0.0690 | | 0.0414 | 2.99 | 350 | 0.0689 | ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1

NorGLM/NorLlama-3B-Instruction-peft

NorGLM

2024-03-10T22:42:28Z

0

null

[ "no", "license:cc-by-nc-sa-4.0", "region:us" ]

null

2024-03-10T22:40:42Z

--- license: cc-by-nc-sa-4.0 language: - 'no' --- # Model Card NorLlama-3B-Instruction-peft is trained on top of [NorLlama-3B](https://huggingface.co/NorGLM/NorLlama-3B) model on [NO-Alpaca](https://huggingface.co/datasets/NbAiLab/norwegian-alpaca) dataset. Prompt format: ``` {instruction} {input} : {output} ``` Inference prompt: ``` {instruction} {input} : ``` ## Run the Model ```python from peft import PeftModel, PeftConfig from transformers import AutoModelForCausalLM, AutoTokenizer import torch source_model_id = "NorGLM/NorLlama-3B" peft_model_id = "NorGLM/NorLlama-3B-Instruction-peft" config = PeftConfig.from_pretrained(peft_model_id) model = AutoModelForCausalLM.from_pretrained(source_model_id, device_map='balanced') tokenizer_max_len = 2048 tokenizer_config = {'pretrained_model_name_or_path': source_model_id, 'max_len': tokenizer_max_len} tokenizer = tokenizer = AutoTokenizer.from_pretrained(**tokenizer_config) tokenizer.pad_token = tokenizer.eos_token model = PeftModel.from_pretrained(model, peft_model_id) ``` ## Inference Example Load the model to evaluate on the last 20\% of NO-Alpaca dataset: ```python def merge_columns(example): if str(example["input"]) == "": example["text"] = str(example["instruction"]) + " : " else: example["text"] = str(example["instruction"]) + " " + str(example["input"]) + " : " return example def generate_text(text, max_length=200, do_sample=True, top_p = 0.92, top_k=0): set_seed(42) model_inputs = tokenizer(text, return_tensors='pt').to(torch_device) output = model.generate(**model_inputs, max_new_tokens = max_length, no_repeat_ngram_size=2, pad_token_id=tokenizer.eos_token_id) return tokenizer.decode(output[0], skip_special_tokens=True) print("--LOADING EVAL DATAS---") eval_data = load_dataset("NbAiLab/norwegian-alpaca", split='train[-20%:]') print("--MAKING PREDICTIONS---") model.eval() output_file = <output file name> with open(output_file, 'w', encoding='utf-8-sig') as file: generated_text = [] for question in eval_data['text']: generated_text.append({"generated_text": generate_text(question)}) print({"text_generated": len(generated_text)}) json_lines = [json.dumps(data) for data in generated_text] json_data = "\n".join(json_lines) file.write(json_data) ``` ## Note More training details will be released soon!

ThuyNT03/CS505_MvPCOQE_viT5_Prompting5_top1

ThuyNT03

2024-03-10T22:41:01Z

93

0

transformers

[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:VietAI/vit5-large", "base_model:finetune:VietAI/vit5-large", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2024-03-10T16:30:59Z

--- license: mit base_model: VietAI/vit5-large tags: - generated_from_trainer model-index: - name: CS505_MvPCOQE_viT5_Prompting5_top1 results: [] ---  # CS505_MvPCOQE_viT5_Prompting5_top1 This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.2 - Datasets 2.1.0 - Tokenizers 0.15.2

Jackline/Blip2-HateSpeech-PEFT-LLM-2.7b

Jackline

2024-03-10T22:37:03Z

3

0

peft

[ "peft", "arxiv:1910.09700", "base_model:Salesforce/blip2-opt-2.7b", "base_model:adapter:Salesforce/blip2-opt-2.7b", "region:us" ]

null

2024-03-10T20:32:22Z

--- library_name: peft base_model: Salesforce/blip2-opt-2.7b --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: QuantizationMethod.BITS_AND_BYTES - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.6.1

hamzasidat/DistilbertIronyResults3

hamzasidat

2024-03-10T22:33:08Z

176

0

transformers

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

text-classification

2024-03-10T22:32:41Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: DistilbertIronyResults3 results: [] ---  # DistilbertIronyResults3 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: 0.6026 - Accuracy: 0.6806 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 179 | 0.6294 | 0.6147 | | No log | 2.0 | 358 | 0.6026 | 0.6806 | | 0.5319 | 3.0 | 537 | 0.6334 | 0.6817 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

EarthnDusk/Lora_Extractions

EarthnDusk

2024-03-10T22:31:59Z

0

1

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2024-03-06T04:21:26Z

--- license: creativeml-openrail-m --- Lora extractions via Bmaltais/Kohya SS --- These are extractions of models we have existing, feel free to mooch there should be no activation tag. these are 128x128 dim/alpha for 1.5 - but SplatterpunkALpha is SDXL and is 32/16. Feel free WITH CREDIT if possible to merge back into your own content. SD 1.5 versions DIDNT TURN OUT, unless we tested them wrong. Splatterpunk is an XL one.

TeeZee/GALAXY-XB-v.02

TeeZee

2024-03-10T22:25:42Z

54

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-09T22:19:44Z

--- license: apache-2.0 model-index: - name: GALAXY-XB-v.02 results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 60.67 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/GALAXY-XB-v.02 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 83.27 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/GALAXY-XB-v.02 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 64.99 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/GALAXY-XB-v.02 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 43.6 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/GALAXY-XB-v.02 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 80.27 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/GALAXY-XB-v.02 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 42.08 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/GALAXY-XB-v.02 name: Open LLM Leaderboard --- ### TeeZee/GALAXY-XB-v.02 ### Experiment, can DUS be taken one or more steps further? ### Technical notes: - 10 layers removed from both models this time, 2 more than in original paper. - base version of upstage/SOLAR-10.7B-v1.0 used for merge - no finetuning done yet, this is just a merge, first step in DUS paper - next step, if evaluation proves that its at least as 'smart' as base model, should be finetuning to 'recover' after merge # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_TeeZee__GALAXY-XB-v.02) | Metric |Value| |---------------------------------|----:| |Avg. |62.48| |AI2 Reasoning Challenge (25-Shot)|60.67| |HellaSwag (10-Shot) |83.27| |MMLU (5-Shot) |64.99| |TruthfulQA (0-shot) |43.60| |Winogrande (5-shot) |80.27| |GSM8k (5-shot) |42.08|

dcarpintero/digit-classifier

dcarpintero

2024-03-10T22:24:35Z

0

null

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

null

2024-03-08T17:54:00Z

--- license: apache-2.0 --- Implemented as a **Multi-Layer Perceptron to classify handwritten Digits (0-9)** [[Annotated Notebook](https://github.com/dcarpintero/fastai-deeplearning/blob/main/course2024/lesson_03.full.mnist.mlp.md)] **Model Architecture and Results** The model comprises a flattening layer and three linear layers `((256, 64) hidden dimensions)` with relus to approximate non-linearity. It achieves 95.6% accuracy after `15 training epochs` and `batch size = 64`. Taining and Test MNIST datasets are loaded with PyTorch [dataloaders](https://pytorch.org/tutorials/beginner/basics/data_tutorial.html).

TeeZee/GALAXY-XB-v.01

TeeZee

2024-03-10T22:24:21Z

57

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-09T18:48:51Z

--- license: apache-2.0 model-index: - name: GALAXY-XB-v.01 results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 60.92 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/GALAXY-XB-v.01 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 82.92 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/GALAXY-XB-v.01 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 65.11 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/GALAXY-XB-v.01 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 43.67 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/GALAXY-XB-v.01 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 81.14 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/GALAXY-XB-v.01 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 43.44 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TeeZee/GALAXY-XB-v.01 name: Open LLM Leaderboard --- ### TeeZee/GALAXY-XB-v.01 ### Experiment, can DUS be taken one or more steps further? ### Technical notes: - 8 layers removed from both models, as per original paper. - base version of upstage/SOLAR-10.7B-v1.0 used for merge - no finetuning done yet, this is just a merge, first step in DUS paper - next step, if evaluation proves that its at least as 'smart' as base model, should be finetuning to 'recover' after merge # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_TeeZee__GALAXY-XB-v.01) | Metric |Value| |---------------------------------|----:| |Avg. |62.87| |AI2 Reasoning Challenge (25-Shot)|60.92| |HellaSwag (10-Shot) |82.92| |MMLU (5-Shot) |65.11| |TruthfulQA (0-shot) |43.67| |Winogrande (5-shot) |81.14| |GSM8k (5-shot) |43.44|

numen-tech/TinyLlama-1.1B-Chat-v1.0-w4a16g128asym

numen-tech

2024-03-10T22:21:47Z

0

null

[ "arxiv:2308.13137", "license:apache-2.0", "region:us" ]

null

2024-03-10T22:17:19Z

--- license: apache-2.0 --- 4-bit [OmniQuant](https://arxiv.org/abs/2308.13137) quantized version of [TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0).

thomasolav/distilbert-base-uncased-finetuned-cola

thomasolav

2024-03-10T22:11:32Z

14

0

transformers

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

text-classification

2024-03-10T21:56:27Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - matthews_correlation model-index: - name: distilbert-base-uncased-finetuned-cola results: [] ---  # 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: 0.8412 - Matthews Correlation: 0.5340 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5208 | 1.0 | 535 | 0.4576 | 0.4452 | | 0.3435 | 2.0 | 1070 | 0.4613 | 0.5168 | | 0.2338 | 3.0 | 1605 | 0.6399 | 0.5195 | | 0.1753 | 4.0 | 2140 | 0.8412 | 0.5340 | | 0.1295 | 5.0 | 2675 | 0.8539 | 0.5305 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

sweetfelinity/ppo-SnowballTarget

sweetfelinity

2024-03-10T22:00:03Z

2

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]

reinforcement-learning

2024-03-10T22:00:00Z

--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # **ppo** Agent playing **SnowballTarget** This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: sweetfelinity/ppo-SnowballTarget 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

grace-pro/one_half_data_high_rank_v2

grace-pro

2024-03-10T21:55:21Z

0

peft

[ "peft", "safetensors", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]

null

2024-03-10T21:53:43Z

--- license: apache-2.0 library_name: peft tags: - generated_from_trainer metrics: - precision - recall - accuracy base_model: mistralai/Mistral-7B-v0.1 model-index: - name: one_half_data_high_rank_v2 results: [] ---  # one_half_data_high_rank_v2 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6564 - Precision: 0.8403 - Recall: 0.9383 - F1-score: 0.8866 - Accuracy: 0.8450 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1-score | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:--------:|:--------:| | 0.535 | 1.0 | 24544 | 0.6564 | 0.8403 | 0.9383 | 0.8866 | 0.8450 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

roiyeho/bart-large-samsum

roiyeho

2024-03-10T21:54:35Z

98

0

transformers

[ "transformers", "tensorboard", "safetensors", "bart", "text2text-generation", "summarization", "generated_from_trainer", "base_model:facebook/bart-large-cnn", "base_model:finetune:facebook/bart-large-cnn", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

summarization

2024-02-19T04:34:55Z

--- license: mit base_model: facebook/bart-large-cnn tags: - summarization - generated_from_trainer metrics: - rouge model-index: - name: bart-large-samsum results: [] ---  # bart-large-samsum This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3635 - Rouge1: 0.3962 - Rouge2: 0.2011 - Rougel: 0.3064 - Rougelsum: 0.3064 ## 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 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | 1.3824 | 0.43 | 400 | 1.4666 | 0.3995 | 0.2014 | 0.3061 | 0.3064 | | 1.2617 | 0.87 | 800 | 1.3350 | 0.4065 | 0.2063 | 0.3113 | 0.3115 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

AwAppp/benchmarks_4bit_batch_size45

AwAppp

2024-03-10T21:49:33Z

0

transformers

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

null

2024-03-10T21:49:33Z

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

m7n/dierenleven-sdxl-lora-001

m7n

2024-03-10T21:48:51Z

3

1

diffusers

[ "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", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]

text-to-image

2024-03-10T17:23:19Z

--- tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - diffusers-training - text-to-image - diffusers - lora - template:sd-lora widget: - text: 'litograph in the style of <s0><s1>, showing a beautiful bird of paradise' output: url: "image_0.png" - text: 'litograph in the style of <s0><s1>, showing a beautiful bird of paradise' output: url: "image_1.png" - text: 'litograph in the style of <s0><s1>, showing a beautiful bird of paradise' output: url: "image_2.png" - text: 'litograph in the style of <s0><s1>, showing a beautiful bird of paradise' output: url: "image_3.png" base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: litograph in the style of <s0><s1>, showing a beautiful bird of paradise license: openrail++ --- # SDXL LoRA DreamBooth - m7n/dierenleven-sdxl-lora-001 <Gallery /> ## Model description ### These are m7n/dierenleven-sdxl-lora-001 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 **[`dierenleven-sdxl-lora-001.safetensors` here 💾](/m7n/dierenleven-sdxl-lora-001/blob/main/dierenleven-sdxl-lora-001.safetensors)**. - Place it on your `models/Lora` folder. - On AUTOMATIC1111, load the LoRA by adding `<lora:dierenleven-sdxl-lora-001:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/). - *Embeddings*: download **[`dierenleven-sdxl-lora-001_emb.safetensors` here 💾](/m7n/dierenleven-sdxl-lora-001/blob/main/dierenleven-sdxl-lora-001_emb.safetensors)**. - Place it on it on your `embeddings` folder - Use it by adding `dierenleven-sdxl-lora-001_emb` to your prompt. For example, `litograph in the style of dierenleven-sdxl-lora-001_emb, showing a beautiful bird of paradise` (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('m7n/dierenleven-sdxl-lora-001', weight_name='pytorch_lora_weights.safetensors') embedding_path = hf_hub_download(repo_id='m7n/dierenleven-sdxl-lora-001', filename='dierenleven-sdxl-lora-001_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('litograph in the style of <s0><s1>, showing a beautiful bird of paradise').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](/m7n/dierenleven-sdxl-lora-001/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.

WhiteRabbitNeo/WhiteRabbitNeo-7B-v1.5a

WhiteRabbitNeo

2024-03-10T21:45:53Z

98

47

transformers

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

text-generation

2024-02-18T02:33:26Z

--- license: other license_name: deepseek license_link: https://huggingface.co/deepseek-ai/deepseek-coder-33b-base/blob/main/LICENSE --- # Our latest 33B model is live (We'll always be serving the newest model on our web app)! Access at: https://www.whiterabbitneo.com/ # Our Discord Server Join us at: https://discord.gg/8Ynkrcbk92 (Updated on Dec 29th. Now permanent link to join) # DeepSeek Coder Licence + WhiteRabbitNeo Extended Version # Licence: Usage Restrictions ``` You agree not to use the Model or Derivatives of the Model: - In any way that violates any applicable national or international law or regulation or infringes upon the lawful rights and interests of any third party; - For military use in any way; - For the purpose of exploiting, harming or attempting to exploit or harm minors in any way; - To generate or disseminate verifiably false information and/or content with the purpose of harming others; - To generate or disseminate inappropriate content subject to applicable regulatory requirements; - To generate or disseminate personal identifiable information without due authorization or for unreasonable use; - To defame, disparage or otherwise harass others; - For fully automated decision making that adversely impacts an individual’s legal rights or otherwise creates or modifies a binding, enforceable obligation; - For any use intended to or which has the effect of discriminating against or harming individuals or groups based on online or offline social behavior or known or predicted personal or personality characteristics; - To exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm; - For any use intended to or which has the effect of discriminating against individuals or groups based on legally protected characteristics or categories. ``` # Topics Covered: ``` - Open Ports: Identifying open ports is crucial as they can be entry points for attackers. Common ports to check include HTTP (80, 443), FTP (21), SSH (22), and SMB (445). - Outdated Software or Services: Systems running outdated software or services are often vulnerable to exploits. This includes web servers, database servers, and any third-party software. - Default Credentials: Many systems and services are installed with default usernames and passwords, which are well-known and can be easily exploited. - Misconfigurations: Incorrectly configured services, permissions, and security settings can introduce vulnerabilities. - Injection Flaws: SQL injection, command injection, and cross-site scripting (XSS) are common issues in web applications. - Unencrypted Services: Services that do not use encryption (like HTTP instead of HTTPS) can expose sensitive data. - Known Software Vulnerabilities: Checking for known vulnerabilities in software using databases like the National Vulnerability Database (NVD) or tools like Nessus or OpenVAS. - Cross-Site Request Forgery (CSRF): This is where unauthorized commands are transmitted from a user that the web application trusts. - Insecure Direct Object References: This occurs when an application provides direct access to objects based on user-supplied input. - Security Misconfigurations in Web Servers/Applications: This includes issues like insecure HTTP headers or verbose error messages that reveal too much information. - Broken Authentication and Session Management: This can allow attackers to compromise passwords, keys, or session tokens, or to exploit other implementation flaws to assume other users' identities. - Sensitive Data Exposure: Includes vulnerabilities that expose sensitive data, such as credit card numbers, health records, or personal information. - API Vulnerabilities: In modern web applications, APIs are often used and can have vulnerabilities like insecure endpoints or data leakage. - Denial of Service (DoS) Vulnerabilities: Identifying services that are vulnerable to DoS attacks, which can make the resource unavailable to legitimate users. - Buffer Overflows: Common in older software, these vulnerabilities can allow an attacker to crash the system or execute arbitrary code. ``` # Terms of Use By accessing and using this Artificial Intelligence (AI) model, you, the user, acknowledge and agree that you are solely responsible for your use of the model and its outcomes. You hereby agree to indemnify, defend, and hold harmless the creators, developers, and any affiliated persons or entities of this AI model from and against any and all claims, liabilities, damages, losses, costs, expenses, fees (including reasonable attorneys' fees and court costs) that may arise, directly or indirectly, from your use of the AI model. This AI model is provided "as is" and "as available" without any warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose, and non-infringement. The creators make no warranty that the AI model will meet your requirements or be available on an uninterrupted, secure, or error-free basis. Your use of the AI model is at your own risk and discretion, and you will be solely responsible for any damage to computer systems or loss of data that results from the use of the AI model. This disclaimer constitutes part of the agreement between you and the creators of the AI model regarding your use of the model, superseding any prior agreements between you and the creators regarding your use of this AI model. # WhiteRabbitNeo ![WhiteRabbitNeo](https://huggingface.co/migtissera/WhiteRabbitNeo/resolve/main/WhiteRabbitNeo.png) WhiteRabbitNeo is a model series that can be used for offensive and defensive cybersecurity. Our models are now getting released as a public preview of its capabilities, and also to assess the societal impact of such an AI. ```python import torch, json from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "WhiteRabbitNeo/WhiteRabbitNeo-7B-v1.5a" model = AutoModelForCausalLM.from_pretrained( model_path, torch_dtype=torch.float16, device_map="auto", load_in_4bit=False, load_in_8bit=True, trust_remote_code=True, ) tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) def generate_text(instruction): tokens = tokenizer.encode(instruction) tokens = torch.LongTensor(tokens).unsqueeze(0) tokens = tokens.to("cuda") instance = { "input_ids": tokens, "top_p": 1.0, "temperature": 0.5, "generate_len": 1024, "top_k": 50, } length = len(tokens[0]) with torch.no_grad(): rest = model.generate( input_ids=tokens, max_length=length + instance["generate_len"], use_cache=True, do_sample=True, top_p=instance["top_p"], temperature=instance["temperature"], top_k=instance["top_k"], num_return_sequences=1, ) output = rest[0][length:] string = tokenizer.decode(output, skip_special_tokens=True) answer = string.split("USER:")[0].strip() return f"{answer}" conversation = f"SYSTEM: You are an AI that code. Answer with code." while True: user_input = input("You: ") llm_prompt = f"{conversation} \nUSER: {user_input} \nASSISTANT: " answer = generate_text(llm_prompt) print(answer) conversation = f"{llm_prompt}{answer}" # print(conversation) json_data = {"prompt": user_input, "answer": answer} # print(json_data) # with open(output_file_path, "a") as output_file: # output_file.write(json.dumps(json_data) + "\n") ```

AwAppp/benchmarks_4bit_batch_size20

AwAppp

2024-03-10T21:41:42Z

0

transformers

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

null

2024-03-10T21:41:40Z

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

operablepattern/gemma-2b-it-Q

operablepattern

2024-03-10T21:41:02Z

11

1

transformers

[ "transformers", "gguf", "gemma", "text-generation", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-03-07T19:00:03Z

--- license: other license_name: gemma-terms-of-use license_link: https://ai.google.dev/gemma/terms --- This repository contains gemma 2B models quantized using llama.cpp. For details of the model see https://huggingface.co/google/gemma-2b-it. Details of the k-quants can be found here: https://github.com/ggerganov/llama.cpp/pull/1684 ## Provided files | Name | Quant method | Bits | Size | | ---- | ---- | ---- | ---- | | [gemma-2b-it-Q4_K_M.gguf](https://huggingface.co/operablepattern/gemma-2b-it-Q/blob/main/gemma-2b-it-Q4_K_M.gguf) | Q4_K_M | 4 | 1.63 GB| | [gemma-2b-it-Q5_K_M.gguf](https://huggingface.co/operablepattern/gemma-2b-it-Q/blob/main/gemma-2b-it-Q5_K_M.gguf) | Q5_K_M | 5 | 1.84 GB|

AwAppp/benchmarks_4bit_batch_size15

AwAppp

2024-03-10T21:40:38Z

0

transformers

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

null

2024-03-10T21:40:37Z

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

myownip/axolotl-openllama-1k-qlora-v02

myownip

2024-03-10T21:40:07Z

0

peft

[ "peft", "tensorboard", "safetensors", "llama", "generated_from_trainer", "base_model:openlm-research/open_llama_3b_v2", "base_model:adapter:openlm-research/open_llama_3b_v2", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]

null

2024-03-10T21:40:02Z

--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: openlm-research/open_llama_3b_v2 model-index: - name: qlora-out results: [] ---  [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.0` ```yaml base_model: openlm-research/open_llama_3b_v2 model_type: LlamaForCausalLM tokenizer_type: LlamaTokenizer load_in_8bit: false load_in_4bit: true strict: false push_dataset_to_hub: datasets: - path: mhenrichsen/alpaca_2k_test type: alpaca dataset_prepared_path: val_set_size: 0.05 adapter: qlora lora_model_dir: sequence_len: 1024 sample_packing: true lora_r: 8 lora_alpha: 32 lora_dropout: 0.05 lora_target_modules: lora_target_linear: true lora_fan_in_fan_out: wandb_project: wandb_entity: wandb_watch: wandb_name: wandb_log_model: output_dir: ./qlora-out gradient_accumulation_steps: 1 micro_batch_size: 1 num_epochs: 1 optimizer: paged_adamw_32bit torchdistx_path: lr_scheduler: cosine learning_rate: 0.0002 train_on_inputs: false group_by_length: false bf16: false fp16: true tf32: false gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true gptq_groupsize: gptq_model_v1: warmup_steps: 20 evals_per_epoch: 4 saves_per_epoch: 1 debug: deepspeed: weight_decay: 0.1 fsdp: fsdp_config: special_tokens: bos_token: "<s>" eos_token: "</s>" unk_token: "<unk>" ``` </details> # qlora-out This model is a fine-tuned version of [openlm-research/open_llama_3b_v2](https://huggingface.co/openlm-research/open_llama_3b_v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1118 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 20 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.2567 | 0.0 | 1 | 1.3470 | | 1.1738 | 0.25 | 108 | 1.1365 | | 1.113 | 0.5 | 216 | 1.1231 | | 1.413 | 0.75 | 324 | 1.1118 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.1.2+cu118 - Datasets 2.18.0 - Tokenizers 0.15.0

AwAppp/benchmarks_4bit_batch_size5

AwAppp

2024-03-10T21:38:29Z

0

transformers

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

null

2024-03-10T21:38:26Z

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

quirky-lats-at-mats/Llama-13b-IHY-CoT

quirky-lats-at-mats

2024-03-10T21:37:21Z

4

0

transformers

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

text-generation

2024-03-10T21:18:02Z

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

SDFASDGA/llm

SDFASDGA

2024-03-10T21:37:07Z

10

1

null

[ "gguf", "endpoints_compatible", "region:us" ]

null

2023-11-11T11:30:08Z

Models for llm.f90 - LLMs in Fortran See Files and https://github.com/rbitr/llm.f90 and https://github.com/rbitr/ferrite for more detail

automerger/ShadowCalme-7B

automerger

2024-03-10T21:33:15Z

7

1

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "automerger", "base_model:CorticalStack/shadow-clown-7B-dare", "base_model:merge:CorticalStack/shadow-clown-7B-dare", "base_model:MaziyarPanahi/Calme-7B-Instruct-v0.1.1", "base_model:merge:MaziyarPanahi/Calme-7B-Instruct-v0.1.1", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-10T21:32:28Z

--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - automerger base_model: - CorticalStack/shadow-clown-7B-dare - MaziyarPanahi/Calme-7B-Instruct-v0.1.1 --- # ShadowCalme-7B ShadowCalme-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration. * [CorticalStack/shadow-clown-7B-dare](https://huggingface.co/CorticalStack/shadow-clown-7B-dare) * [MaziyarPanahi/Calme-7B-Instruct-v0.1.1](https://huggingface.co/MaziyarPanahi/Calme-7B-Instruct-v0.1.1) ## 🧩 Configuration ```yaml slices: - sources: - model: CorticalStack/shadow-clown-7B-dare layer_range: [0, 32] - model: MaziyarPanahi/Calme-7B-Instruct-v0.1.1 layer_range: [0, 32] merge_method: slerp base_model: CorticalStack/shadow-clown-7B-dare parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 random_seed: 0 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "automerger/ShadowCalme-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```

gtroina/sd-class-butterflies-32

gtroina

2024-03-10T21:32:16Z

44

0

diffusers

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

unconditional-image-generation

2024-03-10T20:54:44Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('gtroina/sd-class-butterflies-32') image = pipeline().images[0] image ```

macadeliccc/laser-dolphin-mixtral-4x7b-dpo-AWQ

macadeliccc

2024-03-10T21:28:48Z

8

1

transformers

[ "transformers", "safetensors", "mixtral", "text-generation", "base_model:macadeliccc/laser-dolphin-mixtral-4x7b-dpo", "base_model:quantized:macadeliccc/laser-dolphin-mixtral-4x7b-dpo", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "awq", "region:us" ]

text-generation

2024-03-10T20:49:35Z

--- license: apache-2.0 base_model: macadeliccc/laser-dolphin-mixtral-4x7b-dpo --- ## OpenAI compatible endpoint using VLLM Runs well on 4090 ``` python -m vllm.entrypoints.openai.api_server --model macadeliccc/laser-dolphin-mixtral-4x7b-dpo-AWQ --max-model-len 25000 ```

tsavage68/mistralit2_1000_STEPS_1e8_rate_0.1_beta_DPO

tsavage68

2024-03-10T21:22:33Z

5

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "trl", "dpo", "generated_from_trainer", "conversational", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:finetune:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-03-10T21:18:48Z

--- license: apache-2.0 base_model: mistralai/Mistral-7B-Instruct-v0.2 tags: - trl - dpo - generated_from_trainer model-index: - name: mistralit2_1000_STEPS_1e8_rate_0.1_beta_DPO results: [] ---  # mistralit2_1000_STEPS_1e8_rate_0.1_beta_DPO This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6920 - Rewards/chosen: -0.0058 - Rewards/rejected: -0.0082 - Rewards/accuracies: 0.5121 - Rewards/margins: 0.0024 - Logps/rejected: -28.6543 - Logps/chosen: -23.4436 - Logits/rejected: -2.8649 - Logits/chosen: -2.8652 ## 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-08 - train_batch_size: 4 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 1000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| | 0.693 | 0.1 | 50 | 0.6928 | 0.0007 | -0.0000 | 0.4549 | 0.0007 | -28.5728 | -23.3792 | -2.8652 | -2.8654 | | 0.693 | 0.2 | 100 | 0.6920 | 0.0012 | -0.0011 | 0.4945 | 0.0023 | -28.5838 | -23.3741 | -2.8653 | -2.8655 | | 0.693 | 0.29 | 150 | 0.6923 | -0.0015 | -0.0033 | 0.4989 | 0.0018 | -28.6052 | -23.4006 | -2.8651 | -2.8653 | | 0.694 | 0.39 | 200 | 0.6923 | -0.0020 | -0.0037 | 0.4813 | 0.0017 | -28.6093 | -23.4058 | -2.8651 | -2.8653 | | 0.6916 | 0.49 | 250 | 0.6922 | -0.0026 | -0.0046 | 0.4879 | 0.0021 | -28.6189 | -23.4118 | -2.8651 | -2.8654 | | 0.6927 | 0.59 | 300 | 0.6920 | -0.0039 | -0.0063 | 0.5011 | 0.0023 | -28.6350 | -23.4253 | -2.8650 | -2.8653 | | 0.6941 | 0.68 | 350 | 0.6927 | -0.0048 | -0.0058 | 0.4659 | 0.0010 | -28.6304 | -23.4334 | -2.8650 | -2.8652 | | 0.6924 | 0.78 | 400 | 0.6922 | -0.0049 | -0.0068 | 0.4989 | 0.0019 | -28.6399 | -23.4345 | -2.8650 | -2.8653 | | 0.6919 | 0.88 | 450 | 0.6918 | -0.0056 | -0.0084 | 0.4857 | 0.0028 | -28.6562 | -23.4418 | -2.8650 | -2.8653 | | 0.6913 | 0.98 | 500 | 0.6913 | -0.0047 | -0.0085 | 0.5077 | 0.0038 | -28.6577 | -23.4328 | -2.8649 | -2.8652 | | 0.6914 | 1.07 | 550 | 0.6915 | -0.0034 | -0.0067 | 0.5143 | 0.0033 | -28.6398 | -23.4200 | -2.8650 | -2.8653 | | 0.6939 | 1.17 | 600 | 0.6922 | -0.0069 | -0.0089 | 0.5033 | 0.0020 | -28.6613 | -23.4550 | -2.8650 | -2.8652 | | 0.6917 | 1.27 | 650 | 0.6920 | -0.0056 | -0.0081 | 0.5231 | 0.0025 | -28.6535 | -23.4422 | -2.8650 | -2.8653 | | 0.6919 | 1.37 | 700 | 0.6921 | -0.0052 | -0.0074 | 0.5055 | 0.0021 | -28.6463 | -23.4383 | -2.8650 | -2.8653 | | 0.6929 | 1.46 | 750 | 0.6915 | -0.0044 | -0.0078 | 0.5363 | 0.0034 | -28.6506 | -23.4298 | -2.8650 | -2.8653 | | 0.6919 | 1.56 | 800 | 0.6922 | -0.0063 | -0.0083 | 0.5209 | 0.0020 | -28.6553 | -23.4489 | -2.8649 | -2.8652 | | 0.6925 | 1.66 | 850 | 0.6921 | -0.0058 | -0.0080 | 0.5121 | 0.0022 | -28.6528 | -23.4438 | -2.8649 | -2.8652 | | 0.6925 | 1.76 | 900 | 0.6920 | -0.0058 | -0.0082 | 0.5121 | 0.0024 | -28.6543 | -23.4436 | -2.8649 | -2.8652 | | 0.6939 | 1.86 | 950 | 0.6920 | -0.0058 | -0.0082 | 0.5121 | 0.0024 | -28.6543 | -23.4436 | -2.8649 | -2.8652 | | 0.6924 | 1.95 | 1000 | 0.6920 | -0.0058 | -0.0082 | 0.5121 | 0.0024 | -28.6543 | -23.4436 | -2.8649 | -2.8652 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.0.0+cu117 - Datasets 2.18.0 - Tokenizers 0.15.2

AmineSaidi-ISTIC/phi-2-finetuned-knowledgator-events_classification

AmineSaidi-ISTIC

2024-03-10T21:21:55Z

0

peft

[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:microsoft/phi-2", "base_model:adapter:microsoft/phi-2", "license:mit", "region:us" ]

null

2024-03-06T13:56:06Z

--- license: mit library_name: peft tags: - generated_from_trainer base_model: microsoft/phi-2 model-index: - name: phi-2-finetuned-knowledgator-events_classification results: [] ---  # phi-2-finetuned-knowledgator-events_classification This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 1000 ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.1.2 - Datasets 2.1.0 - Tokenizers 0.15.2

prince-canuma/babyLlama

prince-canuma

2024-03-10T21:15:54Z

93

1

transformers

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

text-generation

2024-03-10T21:15:15Z

--- tags: - generated_from_trainer model-index: - name: babyLlama results: [] ---  # babyLlama This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 50 - num_epochs: 2 ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

Felladrin/gguf-TinyMistral-248M-Chat-v1

Felladrin

2024-03-10T21:11:21Z

25

0

null

[ "gguf", "base_model:Felladrin/TinyMistral-248M-Chat-v2", "base_model:quantized:Felladrin/TinyMistral-248M-Chat-v2", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

2024-03-10T20:27:47Z

--- license: apache-2.0 base_model: Felladrin/TinyMistral-248M-Chat-v1 --- GGUF version of [Felladrin/TinyMistral-248M-Chat-v1](https://huggingface.co/Felladrin/TinyMistral-248M-Chat-v1).

bartowski/speechless-starcoder2-7b-exl2

bartowski

2024-03-10T20:55:17Z

0

1

transformers

[ "transformers", "code", "text-generation", "en", "dataset:teknium/OpenHermes-2.5", "dataset:TokenBender/python_eval_instruct_51k", "dataset:codefuse-ai/Evol-instruction-66k", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

text-generation

2024-03-10T20:41:05Z

--- language: - en library_name: transformers pipeline_tag: text-generation datasets: - teknium/OpenHermes-2.5 - TokenBender/python_eval_instruct_51k - codefuse-ai/Evol-instruction-66k tags: - code license: apache-2.0 model-index: - name: SpeechlessCoder results: - task: type: text-generation dataset: type: openai_humaneval name: HumanEval metrics: - name: pass@1 type: pass@1 value: 0.0 verified: false quantized_by: bartowski --- ## Exllama v2 Quantizations of speechless-starcoder2-7b Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.15">turboderp's ExLlamaV2 v0.0.15</a> for quantization. ## The "main" branch only contains the measurement.json, download one of the other branches for the model (see below) Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions. Conversion was done using the default calibration dataset. Default arguments used except when the bits per weight is above 6.0, at that point the lm_head layer is quantized at 8 bits per weight instead of the default 6. Original model: https://huggingface.co/uukuguy/speechless-starcoder2-7b <a href="https://huggingface.co/bartowski/speechless-starcoder2-7b-exl2/tree/8_0">8.0 bits per weight</a> <a href="https://huggingface.co/bartowski/speechless-starcoder2-7b-exl2/tree/6_5">6.5 bits per weight</a> <a href="https://huggingface.co/bartowski/speechless-starcoder2-7b-exl2/tree/5_0">5.0 bits per weight</a> <a href="https://huggingface.co/bartowski/speechless-starcoder2-7b-exl2/tree/4_25">4.25 bits per weight</a> <a href="https://huggingface.co/bartowski/speechless-starcoder2-7b-exl2/tree/3_5">3.5 bits per weight</a> ## Download instructions With git: ```shell git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/speechless-starcoder2-7b-exl2 ``` With huggingface hub (credit to TheBloke for instructions): ```shell pip3 install huggingface-hub ``` To download the `main` (only useful if you only care about measurement.json) branch to a folder called `speechless-starcoder2-7b-exl2`: ```shell mkdir speechless-starcoder2-7b-exl2 huggingface-cli download bartowski/speechless-starcoder2-7b-exl2 --local-dir speechless-starcoder2-7b-exl2 --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: Linux: ```shell mkdir speechless-starcoder2-7b-exl2-6_5 huggingface-cli download bartowski/speechless-starcoder2-7b-exl2 --revision 6_5 --local-dir speechless-starcoder2-7b-exl2-6_5 --local-dir-use-symlinks False ``` Windows (which apparently doesn't like _ in folders sometimes?): ```shell mkdir speechless-starcoder2-7b-exl2-6.5 huggingface-cli download bartowski/speechless-starcoder2-7b-exl2 --revision 6_5 --local-dir speechless-starcoder2-7b-exl2-6.5 --local-dir-use-symlinks False ```

CalebCometML/andrew-test

CalebCometML

2024-03-10T20:49:33Z

1

2

diffusers

[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "dora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]

text-to-image

2024-03-10T20:40:33Z

--- 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 - text-to-image - text-to-image - diffusers-training - diffusers - dora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers - 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 TOK man widget: [] ---  # SDXL LoRA DreamBooth - CalebCometML/andrew-test <Gallery /> ## Model description These are CalebCometML/andrew-test LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of TOK man to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](CalebCometML/andrew-test/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]

bartowski/dolphincoder-starcoder2-7b-exl2

bartowski

2024-03-10T20:43:19Z

2

null

[ "text-generation", "en", "dataset:cognitivecomputations/dolphin", "dataset:jondurbin/airoboros-2.2.1", "dataset:cognitivecomputations/dolphin-coder", "dataset:teknium/openhermes", "dataset:ise-uiuc/Magicoder-OSS-Instruct-75K", "dataset:ise-uiuc/Magicoder-Evol-Instruct-110K", "dataset:m-a-p/Code-Feedback", "dataset:m-a-p/CodeFeedback-Filtered-Instruction", "dataset:microsoft/orca-math-word-problems-200k", "license:bigcode-openrail-m", "region:us" ]

text-generation

2024-03-10T16:05:14Z

--- datasets: - cognitivecomputations/dolphin - jondurbin/airoboros-2.2.1 - cognitivecomputations/dolphin-coder - teknium/openhermes - ise-uiuc/Magicoder-OSS-Instruct-75K - ise-uiuc/Magicoder-Evol-Instruct-110K - m-a-p/Code-Feedback - m-a-p/CodeFeedback-Filtered-Instruction - microsoft/orca-math-word-problems-200k language: - en license: bigcode-openrail-m quantized_by: bartowski pipeline_tag: text-generation --- ## Exllama v2 Quantizations of dolphincoder-starcoder2-7b Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.15">turboderp's ExLlamaV2 v0.0.15</a> for quantization. ## The "main" branch only contains the measurement.json, download one of the other branches for the model (see below) Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions. Original model: https://huggingface.co/cognitivecomputations/dolphincoder-starcoder2-7b | Branch | Bits | lm_head bits | VRAM (4k) | VRAM (16k) | VRAM (32k) | Description | | ----- | ---- | ------- | ------ | ------ | ------ | ------------ | | [8_0](https://huggingface.co/bartowski/dolphincoder-starcoder2-7b-exl2/tree/8_0) | 8.0 | 8.0 | 8.4 GB | 9.2 GB | 10.2 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. | | [6_5](https://huggingface.co/bartowski/dolphincoder-starcoder2-7b-exl2/tree/6_5) | 6.5 | 8.0 | 7.1 GB | 7.9 GB | 8.9 GB | Very similar to 8.0, good tradeoff of size vs performance, **recommended**. | | [5_0](https://huggingface.co/bartowski/dolphincoder-starcoder2-7b-exl2/tree/5_0) | 5.0 | 6.0 | 5.8 GB | 6.6 GB | 7.6 GB | Slightly lower quality vs 6.5, but usable on 8GB cards. | | [4_25](https://huggingface.co/bartowski/dolphincoder-starcoder2-7b-exl2/tree/4_25) | 4.25 | 6.0 | 5.1 GB | 5.9 GB | 6.9 GB | GPTQ equivalent bits per weight, slightly higher quality. | | [3_5](https://huggingface.co/bartowski/dolphincoder-starcoder2-7b-exl2/tree/3_5) | 3.5 | 6.0 | 4.5 GB | 5.3 GB | 6.3 GB | Lower quality, only use if you have to. | ## Download instructions With git: ```shell git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/dolphincoder-starcoder2-7b-exl2 ``` With huggingface hub (credit to TheBloke for instructions): ```shell pip3 install huggingface-hub ``` To download the `main` (only useful if you only care about measurement.json) branch to a folder called `dolphincoder-starcoder2-7b-exl2`: ```shell mkdir dolphincoder-starcoder2-7b-exl2 huggingface-cli download bartowski/dolphincoder-starcoder2-7b-exl2 --local-dir dolphincoder-starcoder2-7b-exl2 --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: Linux: ```shell mkdir dolphincoder-starcoder2-7b-exl2-6_5 huggingface-cli download bartowski/dolphincoder-starcoder2-7b-exl2 --revision 6_5 --local-dir dolphincoder-starcoder2-7b-exl2-6_5 --local-dir-use-symlinks False ``` Windows (which apparently doesn't like _ in folders sometimes?): ```shell mkdir dolphincoder-starcoder2-7b-exl2-6.5 huggingface-cli download bartowski/dolphincoder-starcoder2-7b-exl2 --revision 6_5 --local-dir dolphincoder-starcoder2-7b-exl2-6.5 --local-dir-use-symlinks False ```

KnutJaegersberg/B1-66ER

KnutJaegersberg

2024-03-10T20:40:40Z

0

1

null

[ "region:us" ]

null

2024-03-10T10:06:01Z

![image/png](https://cdn-uploads.huggingface.co/production/uploads/63732ebbbd81fae2b3aaf3fb/4wQngaPCqWBzn2V0qUMO2.png)

sarahahtee/classification_flan_t5_base_enriched

sarahahtee

2024-03-10T20:36:31Z

95

0

transformers

[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google/flan-t5-base", "base_model:finetune:google/flan-t5-base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2024-03-10T20:18:40Z

--- license: apache-2.0 base_model: google/flan-t5-base tags: - generated_from_trainer model-index: - name: classification_flan_t5_base_enriched results: [] ---  # classification_flan_t5_base_enriched This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8345 - Classification Report: precision recall f1-score support ALLERGY 1.0000 0.9167 0.9565 12 ASSESSMENT 0.0000 0.0000 0.0000 11 CC 0.3333 0.1818 0.2353 11 DIAGNOSIS 0.0000 0.0000 0.0000 1 DISPOSITION 0.2000 1.0000 0.3333 1 EDCOURSE 0.0000 0.0000 0.0000 4 EXAM 0.5000 0.2000 0.2857 5 FAM/SOCHX 0.8400 0.9333 0.8842 45 GENHX 0.6620 0.8868 0.7581 53 GYNHX 1.0000 1.0000 1.0000 1 IMAGING 0.3333 1.0000 0.5000 1 IMMUNIZATIONS 1.0000 1.0000 1.0000 1 LABS 0.0000 0.0000 0.0000 1 MEDICATIONS 0.8333 1.0000 0.9091 10 OTHER_HISTORY 0.0000 0.0000 0.0000 3 PASTMEDICALHX 0.6471 0.7857 0.7097 14 PASTSURGICAL 1.0000 0.8571 0.9231 7 PLAN 0.0000 0.0000 0.0000 1 PROCEDURES 0.0000 0.0000 0.0000 1 ROS 0.8462 0.6471 0.7333 17 accuracy 0.7250 200 macro avg 0.4598 0.5204 0.4614 200 weighted avg 0.6618 0.7250 0.6812 200 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 7 - eval_batch_size: 7 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Classification Report | |:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| | No log | 1.0 | 172 | 0.4392 | precision recall f1-score support ALLERGY 1.0000 0.9167 0.9565 12 ASSESSMENT 0.0000 0.0000 0.0000 11 CC 0.1455 0.7273 0.2424 11 DIAGNOSIS 0.0000 0.0000 0.0000 1 DISPOSITION 0.0000 0.0000 0.0000 1 EDCOURSE 0.0000 0.0000 0.0000 4 EXAM 0.0000 0.0000 0.0000 5 FAM/SOCHX 0.8148 0.9778 0.8889 45 GENHX 1.0000 0.3962 0.5676 53 GYNHX 0.0000 0.0000 0.0000 1 IMAGING 0.0000 0.0000 0.0000 1 IMMUNIZATIONS 0.0000 0.0000 0.0000 1 LABS 0.0000 0.0000 0.0000 1 MEDICATIONS 0.8333 1.0000 0.9091 10 OTHER_HISTORY 0.0000 0.0000 0.0000 3 PASTMEDICALHX 0.4000 0.8571 0.5455 14 PASTSURGICAL 0.7778 1.0000 0.8750 7 PLAN 0.0000 0.0000 0.0000 1 PROCEDURES 0.0000 0.0000 0.0000 1 ROS 0.6250 0.2941 0.4000 17 accuracy 0.5900 200 macro avg 0.2798 0.3085 0.2692 200 weighted avg 0.6663 0.5900 0.5694 200 | | No log | 2.0 | 344 | 0.3438 | precision recall f1-score support ALLERGY 1.0000 0.9167 0.9565 12 ASSESSMENT 0.0000 0.0000 0.0000 11 CC 0.5000 0.2727 0.3529 11 DIAGNOSIS 0.0000 0.0000 0.0000 1 DISPOSITION 0.2000 1.0000 0.3333 1 EDCOURSE 0.0000 0.0000 0.0000 4 EXAM 0.0000 0.0000 0.0000 5 FAM/SOCHX 0.9535 0.9111 0.9318 45 GENHX 0.5833 0.9245 0.7153 53 GYNHX 0.0000 0.0000 0.0000 1 IMAGING 0.0000 0.0000 0.0000 1 IMMUNIZATIONS 0.5000 1.0000 0.6667 1 LABS 0.0000 0.0000 0.0000 1 MEDICATIONS 0.8333 1.0000 0.9091 10 OTHER_HISTORY 0.0000 0.0000 0.0000 3 PASTMEDICALHX 0.6364 0.5000 0.5600 14 PASTSURGICAL 0.5000 1.0000 0.6667 7 PLAN 0.0000 0.0000 0.0000 1 PROCEDURES 0.0000 0.0000 0.0000 1 ROS 0.8333 0.5882 0.6897 17 accuracy 0.7000 200 macro avg 0.3270 0.4057 0.3391 200 weighted avg 0.6347 0.7000 0.6476 200 | | 0.3577 | 3.0 | 516 | 0.3416 | precision recall f1-score support ALLERGY 1.0000 0.9167 0.9565 12 ASSESSMENT 0.0000 0.0000 0.0000 11 CC 0.2083 0.4545 0.2857 11 DIAGNOSIS 0.0000 0.0000 0.0000 1 DISPOSITION 0.3333 1.0000 0.5000 1 EDCOURSE 0.0000 0.0000 0.0000 4 EXAM 1.0000 0.2000 0.3333 5 FAM/SOCHX 0.8696 0.8889 0.8791 45 GENHX 0.6769 0.8302 0.7458 53 GYNHX 0.0000 0.0000 0.0000 1 IMAGING 0.0000 0.0000 0.0000 1 IMMUNIZATIONS 1.0000 1.0000 1.0000 1 LABS 0.0000 0.0000 0.0000 1 MEDICATIONS 0.8333 1.0000 0.9091 10 OTHER_HISTORY 0.0000 0.0000 0.0000 3 PASTMEDICALHX 0.5500 0.7857 0.6471 14 PASTSURGICAL 1.0000 1.0000 1.0000 7 PLAN 0.0000 0.0000 0.0000 1 PROCEDURES 0.0000 0.0000 0.0000 1 ROS 0.8000 0.4706 0.5926 17 accuracy 0.6950 200 macro avg 0.4136 0.4273 0.3925 200 weighted avg 0.6613 0.6950 0.6605 200 | | 0.3577 | 4.0 | 688 | 0.4118 | precision recall f1-score support ALLERGY 1.0000 0.9167 0.9565 12 ASSESSMENT 0.0000 0.0000 0.0000 11 CC 0.3333 0.0909 0.1429 11 DIAGNOSIS 0.0000 0.0000 0.0000 1 DISPOSITION 0.1429 1.0000 0.2500 1 EDCOURSE 0.0000 0.0000 0.0000 4 EXAM 0.5000 0.2000 0.2857 5 FAM/SOCHX 0.8431 0.9556 0.8958 45 GENHX 0.6438 0.8868 0.7460 53 GYNHX 0.0000 0.0000 0.0000 1 IMAGING 1.0000 1.0000 1.0000 1 IMMUNIZATIONS 1.0000 1.0000 1.0000 1 LABS 0.0000 0.0000 0.0000 1 MEDICATIONS 0.8333 1.0000 0.9091 10 OTHER_HISTORY 0.0000 0.0000 0.0000 3 PASTMEDICALHX 0.6667 0.7143 0.6897 14 PASTSURGICAL 0.7778 1.0000 0.8750 7 PLAN 0.0000 0.0000 0.0000 1 PROCEDURES 0.0000 0.0000 0.0000 1 ROS 0.8571 0.7059 0.7742 17 accuracy 0.7250 200 macro avg 0.4299 0.4735 0.4262 200 weighted avg 0.6503 0.7250 0.6731 200 | | 0.3577 | 5.0 | 860 | 0.4030 | precision recall f1-score support ALLERGY 1.0000 0.9167 0.9565 12 ASSESSMENT 0.0000 0.0000 0.0000 11 CC 0.2778 0.4545 0.3448 11 DIAGNOSIS 0.0000 0.0000 0.0000 1 DISPOSITION 0.3333 1.0000 0.5000 1 EDCOURSE 0.0000 0.0000 0.0000 4 EXAM 0.6667 0.4000 0.5000 5 FAM/SOCHX 0.8511 0.8889 0.8696 45 GENHX 0.7077 0.8679 0.7797 53 GYNHX 1.0000 1.0000 1.0000 1 IMAGING 1.0000 1.0000 1.0000 1 IMMUNIZATIONS 1.0000 1.0000 1.0000 1 LABS 0.0000 0.0000 0.0000 1 MEDICATIONS 0.8333 1.0000 0.9091 10 OTHER_HISTORY 0.0000 0.0000 0.0000 3 PASTMEDICALHX 0.7333 0.7857 0.7586 14 PASTSURGICAL 1.0000 0.8571 0.9231 7 PLAN 0.0000 0.0000 0.0000 1 PROCEDURES 0.0000 0.0000 0.0000 1 ROS 0.7500 0.7059 0.7273 17 accuracy 0.7350 200 macro avg 0.5077 0.5438 0.5134 200 weighted avg 0.6794 0.7350 0.7013 200 | | 0.0808 | 6.0 | 1032 | 0.5599 | precision recall f1-score support ALLERGY 1.0000 0.9167 0.9565 12 ASSESSMENT 0.0000 0.0000 0.0000 11 CC 0.2500 0.0909 0.1333 11 DIAGNOSIS 0.0000 0.0000 0.0000 1 DISPOSITION 0.1250 1.0000 0.2222 1 EDCOURSE 0.0000 0.0000 0.0000 4 EXAM 0.5000 0.2000 0.2857 5 FAM/SOCHX 0.8511 0.8889 0.8696 45 GENHX 0.6267 0.8868 0.7344 53 GYNHX 1.0000 1.0000 1.0000 1 IMAGING 1.0000 1.0000 1.0000 1 IMMUNIZATIONS 1.0000 1.0000 1.0000 1 LABS 0.0000 0.0000 0.0000 1 MEDICATIONS 0.9091 1.0000 0.9524 10 OTHER_HISTORY 0.0000 0.0000 0.0000 3 PASTMEDICALHX 0.5882 0.7143 0.6452 14 PASTSURGICAL 0.8750 1.0000 0.9333 7 PLAN 0.0000 0.0000 0.0000 1 PROCEDURES 0.0000 0.0000 0.0000 1 ROS 0.8462 0.6471 0.7333 17 accuracy 0.7100 200 macro avg 0.4786 0.5172 0.4733 200 weighted avg 0.6486 0.7100 0.6660 200 | | 0.0808 | 7.0 | 1204 | 0.5132 | precision recall f1-score support ALLERGY 1.0000 0.9167 0.9565 12 ASSESSMENT 0.0000 0.0000 0.0000 11 CC 0.3333 0.2727 0.3000 11 DIAGNOSIS 0.0000 0.0000 0.0000 1 DISPOSITION 0.1429 1.0000 0.2500 1 EDCOURSE 0.0000 0.0000 0.0000 4 EXAM 0.5000 0.2000 0.2857 5 FAM/SOCHX 0.8542 0.9111 0.8817 45 GENHX 0.6761 0.9057 0.7742 53 GYNHX 0.0000 0.0000 0.0000 1 IMAGING 1.0000 1.0000 1.0000 1 IMMUNIZATIONS 0.5000 1.0000 0.6667 1 LABS 0.0000 0.0000 0.0000 1 MEDICATIONS 0.8333 1.0000 0.9091 10 OTHER_HISTORY 0.0000 0.0000 0.0000 3 PASTMEDICALHX 0.7857 0.7857 0.7857 14 PASTSURGICAL 0.8750 1.0000 0.9333 7 PLAN 0.0000 0.0000 0.0000 1 PROCEDURES 0.0000 0.0000 0.0000 1 ROS 0.7857 0.6471 0.7097 17 accuracy 0.7300 200 macro avg 0.4143 0.4819 0.4226 200 weighted avg 0.6645 0.7300 0.6876 200 | | 0.0808 | 8.0 | 1376 | 0.6372 | precision recall f1-score support ALLERGY 1.0000 0.9167 0.9565 12 ASSESSMENT 0.0000 0.0000 0.0000 11 CC 0.3333 0.4545 0.3846 11 DIAGNOSIS 0.0000 0.0000 0.0000 1 DISPOSITION 0.2500 1.0000 0.4000 1 EDCOURSE 0.0000 0.0000 0.0000 4 EXAM 0.5000 0.2000 0.2857 5 FAM/SOCHX 0.8400 0.9333 0.8842 45 GENHX 0.7302 0.8679 0.7931 53 GYNHX 1.0000 1.0000 1.0000 1 IMAGING 0.5000 1.0000 0.6667 1 IMMUNIZATIONS 0.5000 1.0000 0.6667 1 LABS 0.0000 0.0000 0.0000 1 MEDICATIONS 0.8333 1.0000 0.9091 10 OTHER_HISTORY 0.0000 0.0000 0.0000 3 PASTMEDICALHX 0.7333 0.7857 0.7586 14 PASTSURGICAL 1.0000 0.8571 0.9231 7 PLAN 0.0000 0.0000 0.0000 1 PROCEDURES 0.0000 0.0000 0.0000 1 ROS 0.7857 0.6471 0.7097 17 accuracy 0.7350 200 macro avg 0.4503 0.5331 0.4669 200 weighted avg 0.6794 0.7350 0.6997 200 | | 0.034 | 9.0 | 1548 | 0.7346 | precision recall f1-score support ALLERGY 1.0000 0.9167 0.9565 12 ASSESSMENT 0.0000 0.0000 0.0000 11 CC 0.3333 0.0909 0.1429 11 DIAGNOSIS 0.0000 0.0000 0.0000 1 DISPOSITION 0.1250 1.0000 0.2222 1 EDCOURSE 0.0000 0.0000 0.0000 4 EXAM 0.5000 0.2000 0.2857 5 FAM/SOCHX 0.8542 0.9111 0.8817 45 GENHX 0.6026 0.8868 0.7176 53 GYNHX 1.0000 1.0000 1.0000 1 IMAGING 1.0000 1.0000 1.0000 1 IMMUNIZATIONS 1.0000 1.0000 1.0000 1 LABS 0.0000 0.0000 0.0000 1 MEDICATIONS 0.8333 1.0000 0.9091 10 OTHER_HISTORY 0.0000 0.0000 0.0000 3 PASTMEDICALHX 0.6429 0.6429 0.6429 14 PASTSURGICAL 1.0000 0.8571 0.9231 7 PLAN 0.0000 0.0000 0.0000 1 PROCEDURES 0.0000 0.0000 0.0000 1 ROS 0.7692 0.5882 0.6667 17 accuracy 0.7000 200 macro avg 0.4830 0.5047 0.4674 200 weighted avg 0.6454 0.7000 0.6565 200 | | 0.034 | 10.0 | 1720 | 0.6654 | precision recall f1-score support ALLERGY 1.0000 0.9167 0.9565 12 ASSESSMENT 0.0000 0.0000 0.0000 11 CC 0.4286 0.5455 0.4800 11 DIAGNOSIS 0.0000 0.0000 0.0000 1 DISPOSITION 0.2000 1.0000 0.3333 1 EDCOURSE 0.0000 0.0000 0.0000 4 EXAM 0.5000 0.2000 0.2857 5 FAM/SOCHX 0.8600 0.9556 0.9053 45 GENHX 0.7302 0.8679 0.7931 53 GYNHX 1.0000 1.0000 1.0000 1 IMAGING 1.0000 1.0000 1.0000 1 IMMUNIZATIONS 1.0000 1.0000 1.0000 1 LABS 0.0000 0.0000 0.0000 1 MEDICATIONS 0.8333 1.0000 0.9091 10 OTHER_HISTORY 0.0000 0.0000 0.0000 3 PASTMEDICALHX 0.6667 0.7143 0.6897 14 PASTSURGICAL 1.0000 0.8571 0.9231 7 PLAN 0.0000 0.0000 0.0000 1 PROCEDURES 0.0000 0.0000 0.0000 1 ROS 0.8000 0.7059 0.7500 17 accuracy 0.7450 200 macro avg 0.5009 0.5381 0.5013 200 weighted avg 0.6904 0.7450 0.7112 200 | | 0.034 | 11.0 | 1892 | 0.7455 | precision recall f1-score support ALLERGY 1.0000 0.9167 0.9565 12 ASSESSMENT 0.0000 0.0000 0.0000 11 CC 0.3333 0.1818 0.2353 11 DIAGNOSIS 0.0000 0.0000 0.0000 1 DISPOSITION 0.1429 1.0000 0.2500 1 EDCOURSE 0.0000 0.0000 0.0000 4 EXAM 0.6667 0.4000 0.5000 5 FAM/SOCHX 0.8511 0.8889 0.8696 45 GENHX 0.6486 0.9057 0.7559 53 GYNHX 1.0000 1.0000 1.0000 1 IMAGING 0.5000 1.0000 0.6667 1 IMMUNIZATIONS 1.0000 1.0000 1.0000 1 LABS 0.0000 0.0000 0.0000 1 MEDICATIONS 0.8333 1.0000 0.9091 10 OTHER_HISTORY 0.0000 0.0000 0.0000 3 PASTMEDICALHX 0.6250 0.7143 0.6667 14 PASTSURGICAL 1.0000 0.8571 0.9231 7 PLAN 0.0000 0.0000 0.0000 1 PROCEDURES 0.0000 0.0000 0.0000 1 ROS 0.8462 0.6471 0.7333 17 accuracy 0.7200 200 macro avg 0.4724 0.5256 0.4733 200 weighted avg 0.6639 0.7200 0.6801 200 | | 0.0107 | 12.0 | 2064 | 0.7687 | precision recall f1-score support ALLERGY 1.0000 0.9167 0.9565 12 ASSESSMENT 0.0000 0.0000 0.0000 11 CC 0.4286 0.2727 0.3333 11 DIAGNOSIS 0.0000 0.0000 0.0000 1 DISPOSITION 0.1667 1.0000 0.2857 1 EDCOURSE 0.0000 0.0000 0.0000 4 EXAM 0.6667 0.4000 0.5000 5 FAM/SOCHX 0.8478 0.8667 0.8571 45 GENHX 0.6575 0.9057 0.7619 53 GYNHX 1.0000 1.0000 1.0000 1 IMAGING 0.3333 1.0000 0.5000 1 IMMUNIZATIONS 1.0000 1.0000 1.0000 1 LABS 0.0000 0.0000 0.0000 1 MEDICATIONS 0.8333 1.0000 0.9091 10 OTHER_HISTORY 0.0000 0.0000 0.0000 3 PASTMEDICALHX 0.5882 0.7143 0.6452 14 PASTSURGICAL 1.0000 0.8571 0.9231 7 PLAN 0.0000 0.0000 0.0000 1 PROCEDURES 0.0000 0.0000 0.0000 1 ROS 0.8462 0.6471 0.7333 17 accuracy 0.7200 200 macro avg 0.4684 0.5290 0.4703 200 weighted avg 0.6675 0.7200 0.6822 200 | | 0.0107 | 13.0 | 2236 | 0.8114 | precision recall f1-score support ALLERGY 1.0000 0.9167 0.9565 12 ASSESSMENT 0.0000 0.0000 0.0000 11 CC 0.4545 0.4545 0.4545 11 DIAGNOSIS 0.0000 0.0000 0.0000 1 DISPOSITION 0.1429 1.0000 0.2500 1 EDCOURSE 0.0000 0.0000 0.0000 4 EXAM 0.5000 0.2000 0.2857 5 FAM/SOCHX 0.8511 0.8889 0.8696 45 GENHX 0.6620 0.8868 0.7581 53 GYNHX 0.0000 0.0000 0.0000 1 IMAGING 0.5000 1.0000 0.6667 1 IMMUNIZATIONS 1.0000 1.0000 1.0000 1 LABS 0.0000 0.0000 0.0000 1 MEDICATIONS 0.8333 1.0000 0.9091 10 OTHER_HISTORY 0.0000 0.0000 0.0000 3 PASTMEDICALHX 0.6667 0.7143 0.6897 14 PASTSURGICAL 0.8750 1.0000 0.9333 7 PLAN 0.0000 0.0000 0.0000 1 PROCEDURES 0.0000 0.0000 0.0000 1 ROS 0.9167 0.6471 0.7586 17 accuracy 0.7250 200 macro avg 0.4201 0.4854 0.4266 200 weighted avg 0.6695 0.7250 0.6865 200 | | 0.0107 | 14.0 | 2408 | 0.7763 | precision recall f1-score support ALLERGY 1.0000 0.9167 0.9565 12 ASSESSMENT 0.0000 0.0000 0.0000 11 CC 0.3636 0.3636 0.3636 11 DIAGNOSIS 0.0000 0.0000 0.0000 1 DISPOSITION 0.2500 1.0000 0.4000 1 EDCOURSE 0.0000 0.0000 0.0000 4 EXAM 0.5000 0.2000 0.2857 5 FAM/SOCHX 0.8511 0.8889 0.8696 45 GENHX 0.6620 0.8868 0.7581 53 GYNHX 1.0000 1.0000 1.0000 1 IMAGING 0.3333 1.0000 0.5000 1 IMMUNIZATIONS 1.0000 1.0000 1.0000 1 LABS 0.0000 0.0000 0.0000 1 MEDICATIONS 0.8333 1.0000 0.9091 10 OTHER_HISTORY 0.0000 0.0000 0.0000 3 PASTMEDICALHX 0.6667 0.7143 0.6897 14 PASTSURGICAL 1.0000 0.8571 0.9231 7 PLAN 0.0000 0.0000 0.0000 1 PROCEDURES 0.0000 0.0000 0.0000 1 ROS 0.7857 0.6471 0.7097 17 accuracy 0.7200 200 macro avg 0.4623 0.5237 0.4683 200 weighted avg 0.6624 0.7200 0.6819 200 | | 0.0049 | 15.0 | 2580 | 0.8111 | precision recall f1-score support ALLERGY 1.0000 0.9167 0.9565 12 ASSESSMENT 0.0000 0.0000 0.0000 11 CC 0.4444 0.3636 0.4000 11 DIAGNOSIS 0.0000 0.0000 0.0000 1 DISPOSITION 0.1667 1.0000 0.2857 1 EDCOURSE 0.0000 0.0000 0.0000 4 EXAM 0.5000 0.2000 0.2857 5 FAM/SOCHX 0.8542 0.9111 0.8817 45 GENHX 0.6667 0.9057 0.7680 53 GYNHX 1.0000 1.0000 1.0000 1 IMAGING 0.5000 1.0000 0.6667 1 IMMUNIZATIONS 1.0000 1.0000 1.0000 1 LABS 0.0000 0.0000 0.0000 1 MEDICATIONS 0.8333 1.0000 0.9091 10 OTHER_HISTORY 0.0000 0.0000 0.0000 3 PASTMEDICALHX 0.6667 0.7143 0.6897 14 PASTSURGICAL 1.0000 1.0000 1.0000 7 PLAN 0.0000 0.0000 0.0000 1 PROCEDURES 0.0000 0.0000 0.0000 1 ROS 0.8333 0.5882 0.6897 17 accuracy 0.7300 200 macro avg 0.4733 0.5300 0.4766 200 weighted avg 0.6733 0.7300 0.6906 200 | | 0.0049 | 16.0 | 2752 | 0.8138 | precision recall f1-score support ALLERGY 1.0000 0.9167 0.9565 12 ASSESSMENT 0.0000 0.0000 0.0000 11 CC 0.4286 0.2727 0.3333 11 DIAGNOSIS 0.0000 0.0000 0.0000 1 DISPOSITION 0.2000 1.0000 0.3333 1 EDCOURSE 0.0000 0.0000 0.0000 4 EXAM 0.5000 0.2000 0.2857 5 FAM/SOCHX 0.8511 0.8889 0.8696 45 GENHX 0.6575 0.9057 0.7619 53 GYNHX 1.0000 1.0000 1.0000 1 IMAGING 0.3333 1.0000 0.5000 1 IMMUNIZATIONS 1.0000 1.0000 1.0000 1 LABS 0.0000 0.0000 0.0000 1 MEDICATIONS 0.8333 1.0000 0.9091 10 OTHER_HISTORY 0.0000 0.0000 0.0000 3 PASTMEDICALHX 0.6471 0.7857 0.7097 14 PASTSURGICAL 1.0000 0.8571 0.9231 7 PLAN 0.0000 0.0000 0.0000 1 PROCEDURES 0.0000 0.0000 0.0000 1 ROS 0.8462 0.6471 0.7333 17 accuracy 0.7250 200 macro avg 0.4649 0.5237 0.4658 200 weighted avg 0.6684 0.7250 0.6844 200 | | 0.0049 | 17.0 | 2924 | 0.8209 | precision recall f1-score support ALLERGY 1.0000 0.9167 0.9565 12 ASSESSMENT 0.0000 0.0000 0.0000 11 CC 0.3750 0.2727 0.3158 11 DIAGNOSIS 0.0000 0.0000 0.0000 1 DISPOSITION 0.2000 1.0000 0.3333 1 EDCOURSE 0.0000 0.0000 0.0000 4 EXAM 0.5000 0.2000 0.2857 5 FAM/SOCHX 0.8400 0.9333 0.8842 45 GENHX 0.6571 0.8679 0.7480 53 GYNHX 1.0000 1.0000 1.0000 1 IMAGING 0.3333 1.0000 0.5000 1 IMMUNIZATIONS 1.0000 1.0000 1.0000 1 LABS 0.0000 0.0000 0.0000 1 MEDICATIONS 0.8333 1.0000 0.9091 10 OTHER_HISTORY 0.0000 0.0000 0.0000 3 PASTMEDICALHX 0.6250 0.7143 0.6667 14 PASTSURGICAL 1.0000 0.8571 0.9231 7 PLAN 0.0000 0.0000 0.0000 1 PROCEDURES 0.0000 0.0000 0.0000 1 ROS 0.8462 0.6471 0.7333 17 accuracy 0.7200 200 macro avg 0.4605 0.5205 0.4628 200 weighted avg 0.6613 0.7200 0.6800 200 | | 0.0024 | 18.0 | 3096 | 0.8220 | precision recall f1-score support ALLERGY 1.0000 0.9167 0.9565 12 ASSESSMENT 0.0000 0.0000 0.0000 11 CC 0.4286 0.2727 0.3333 11 DIAGNOSIS 0.0000 0.0000 0.0000 1 DISPOSITION 0.2000 1.0000 0.3333 1 EDCOURSE 0.0000 0.0000 0.0000 4 EXAM 0.5000 0.2000 0.2857 5 FAM/SOCHX 0.8400 0.9333 0.8842 45 GENHX 0.6714 0.8868 0.7642 53 GYNHX 1.0000 1.0000 1.0000 1 IMAGING 0.3333 1.0000 0.5000 1 IMMUNIZATIONS 1.0000 1.0000 1.0000 1 LABS 0.0000 0.0000 0.0000 1 MEDICATIONS 0.8333 1.0000 0.9091 10 OTHER_HISTORY 0.0000 0.0000 0.0000 3 PASTMEDICALHX 0.6471 0.7857 0.7097 14 PASTSURGICAL 1.0000 0.8571 0.9231 7 PLAN 0.0000 0.0000 0.0000 1 PROCEDURES 0.0000 0.0000 0.0000 1 ROS 0.8462 0.6471 0.7333 17 accuracy 0.7300 200 macro avg 0.4650 0.5250 0.4666 200 weighted avg 0.6696 0.7300 0.6883 200 | | 0.0024 | 19.0 | 3268 | 0.8372 | precision recall f1-score support ALLERGY 1.0000 0.9167 0.9565 12 ASSESSMENT 0.0000 0.0000 0.0000 11 CC 0.3333 0.1818 0.2353 11 DIAGNOSIS 0.0000 0.0000 0.0000 1 DISPOSITION 0.2000 1.0000 0.3333 1 EDCOURSE 0.0000 0.0000 0.0000 4 EXAM 0.5000 0.2000 0.2857 5 FAM/SOCHX 0.8400 0.9333 0.8842 45 GENHX 0.6620 0.8868 0.7581 53 GYNHX 1.0000 1.0000 1.0000 1 IMAGING 0.3333 1.0000 0.5000 1 IMMUNIZATIONS 1.0000 1.0000 1.0000 1 LABS 0.0000 0.0000 0.0000 1 MEDICATIONS 0.8333 1.0000 0.9091 10 OTHER_HISTORY 0.0000 0.0000 0.0000 3 PASTMEDICALHX 0.6471 0.7857 0.7097 14 PASTSURGICAL 1.0000 0.8571 0.9231 7 PLAN 0.0000 0.0000 0.0000 1 PROCEDURES 0.0000 0.0000 0.0000 1 ROS 0.8462 0.6471 0.7333 17 accuracy 0.7250 200 macro avg 0.4598 0.5204 0.4614 200 weighted avg 0.6618 0.7250 0.6812 200 | | 0.0024 | 20.0 | 3440 | 0.8345 | precision recall f1-score support ALLERGY 1.0000 0.9167 0.9565 12 ASSESSMENT 0.0000 0.0000 0.0000 11 CC 0.3333 0.1818 0.2353 11 DIAGNOSIS 0.0000 0.0000 0.0000 1 DISPOSITION 0.2000 1.0000 0.3333 1 EDCOURSE 0.0000 0.0000 0.0000 4 EXAM 0.5000 0.2000 0.2857 5 FAM/SOCHX 0.8400 0.9333 0.8842 45 GENHX 0.6620 0.8868 0.7581 53 GYNHX 1.0000 1.0000 1.0000 1 IMAGING 0.3333 1.0000 0.5000 1 IMMUNIZATIONS 1.0000 1.0000 1.0000 1 LABS 0.0000 0.0000 0.0000 1 MEDICATIONS 0.8333 1.0000 0.9091 10 OTHER_HISTORY 0.0000 0.0000 0.0000 3 PASTMEDICALHX 0.6471 0.7857 0.7097 14 PASTSURGICAL 1.0000 0.8571 0.9231 7 PLAN 0.0000 0.0000 0.0000 1 PROCEDURES 0.0000 0.0000 0.0000 1 ROS 0.8462 0.6471 0.7333 17 accuracy 0.7250 200 macro avg 0.4598 0.5204 0.4614 200 weighted avg 0.6618 0.7250 0.6812 200 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

fbellame/confoo-train-llama-style-1-1

fbellame

2024-03-10T20:33:00Z

5

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "gpt", "llm", "large language model", "h2o-llmstudio", "conversational", "en", "autotrain_compatible", "text-generation-inference", "region:us" ]

text-generation

2024-03-10T19:06:14Z

--- language: - en library_name: transformers tags: - gpt - llm - large language model - h2o-llmstudio inference: false thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico --- # Model Card ## Summary This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio). - Base model: [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) ## Usage To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` library installed. ```bash pip install transformers==4.36.1 ``` Also make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo. - Either leave `token=True` in the `pipeline` and login to hugginface_hub by running ```python import huggingface_hub huggingface_hub.login(<ACCESS_TOKEN>) ``` - Or directly pass your <ACCESS_TOKEN> to `token` in the `pipeline` ```python from transformers import pipeline generate_text = pipeline( model="fbellame/confoo-train-llama-style-1-1", torch_dtype="auto", trust_remote_code=True, use_fast=True, device_map={"": "cuda:0"}, token=True, ) res = generate_text( "Why is drinking water so healthy?", min_new_tokens=2, max_new_tokens=256, do_sample=False, num_beams=1, temperature=float(0.0), repetition_penalty=float(1.0), renormalize_logits=True ) print(res[0]["generated_text"]) ``` You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer: ```python print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"]) ``` ```bash Why is drinking water so healthy?</s> ``` Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`. ```python from h2oai_pipeline import H2OTextGenerationPipeline from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained( "fbellame/confoo-train-llama-style-1-1", use_fast=True, padding_side="left", trust_remote_code=True, ) model = AutoModelForCausalLM.from_pretrained( "fbellame/confoo-train-llama-style-1-1", torch_dtype="auto", device_map={"": "cuda:0"}, trust_remote_code=True, ) generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer) res = generate_text( "Why is drinking water so healthy?", min_new_tokens=2, max_new_tokens=256, do_sample=False, num_beams=1, temperature=float(0.0), repetition_penalty=float(1.0), renormalize_logits=True ) print(res[0]["generated_text"]) ``` You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "fbellame/confoo-train-llama-style-1-1" # either local folder or huggingface model name # Important: The prompt needs to be in the same format the model was trained with. # You can find an example prompt in the experiment logs. prompt = "How are you?</s>" tokenizer = AutoTokenizer.from_pretrained( model_name, use_fast=True, trust_remote_code=True, ) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map={"": "cuda:0"}, trust_remote_code=True, ) model.cuda().eval() inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda") # generate configuration can be modified to your needs tokens = model.generate( input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"], min_new_tokens=2, max_new_tokens=256, do_sample=False, num_beams=1, temperature=float(0.0), repetition_penalty=float(1.0), renormalize_logits=True )[0] tokens = tokens[inputs["input_ids"].shape[1]:] answer = tokenizer.decode(tokens, skip_special_tokens=True) print(answer) ``` ## Quantization and sharding You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```. ## Model Architecture ``` MistralForCausalLM( (model): MistralModel( (embed_tokens): Embedding(32000, 4096, padding_idx=0) (layers): ModuleList( (0-31): 32 x MistralDecoderLayer( (self_attn): MistralAttention( (q_proj): Linear(in_features=4096, out_features=4096, bias=False) (k_proj): Linear(in_features=4096, out_features=1024, bias=False) (v_proj): Linear(in_features=4096, out_features=1024, bias=False) (o_proj): Linear(in_features=4096, out_features=4096, bias=False) (rotary_emb): MistralRotaryEmbedding() ) (mlp): MistralMLP( (gate_proj): Linear(in_features=4096, out_features=14336, bias=False) (up_proj): Linear(in_features=4096, out_features=14336, bias=False) (down_proj): Linear(in_features=14336, out_features=4096, bias=False) (act_fn): SiLU() ) (input_layernorm): MistralRMSNorm() (post_attention_layernorm): MistralRMSNorm() ) ) (norm): MistralRMSNorm() ) (lm_head): Linear(in_features=4096, out_features=32000, bias=False) ) ``` ## Model Configuration This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models. ## Disclaimer Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions. - Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints. - Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion. - Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model. - Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities. - Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues. - Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes. By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.

jairNeto/bert-finetuned-sem_eval-english

jairNeto

2024-03-10T20:24:14Z

94

0

transformers

[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:Jairnetojp/content-moderation", "base_model:finetune:Jairnetojp/content-moderation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-03-10T20:23:02Z

--- base_model: Jairnetojp/content-moderation tags: - generated_from_trainer metrics: - f1 - accuracy model-index: - name: bert-finetuned-sem_eval-english results: [] ---  # bert-finetuned-sem_eval-english This model is a fine-tuned version of [Jairnetojp/content-moderation](https://huggingface.co/Jairnetojp/content-moderation) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2343 - F1: 0.5458 - Roc Auc: 0.7829 - Accuracy: 0.4655 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:| | No log | 1.0 | 232 | 0.2283 | 0.5361 | 0.7829 | 0.4503 | | No log | 2.0 | 464 | 0.2343 | 0.5458 | 0.7829 | 0.4655 | | 0.069 | 3.0 | 696 | 0.2461 | 0.5392 | 0.7832 | 0.4544 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

Balassar/balassarprofile

Balassar

2024-03-10T20:18:41Z

0

peft

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

null

2024-03-10T20:10:46Z

--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: TheBloke/Mistral-7B-Instruct-v0.2-GPTQ model-index: - name: balassarprofile results: [] ---  # balassarprofile This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.2-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GPTQ) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.8042 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.8802 | 1.0 | 1 | 3.6270 | | 3.861 | 2.0 | 2 | 3.5746 | | 3.7758 | 3.0 | 3 | 3.4416 | | 3.5819 | 4.0 | 4 | 3.3048 | | 3.3879 | 5.0 | 5 | 3.1740 | | 3.2106 | 6.0 | 6 | 3.0575 | | 3.0652 | 7.0 | 7 | 2.9588 | | 2.94 | 8.0 | 8 | 2.8822 | | 2.8566 | 9.0 | 9 | 2.8301 | | 2.7926 | 10.0 | 10 | 2.8042 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

Turkgamercat/Muslera

Turkgamercat

2024-03-10T20:17:50Z

0

diffusers

[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:stablediffusionapi/icon-lab-test-ai", "base_model:adapter:stablediffusionapi/icon-lab-test-ai", "region:us" ]

text-to-image

2024-03-10T20:13:55Z

--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: '-' output: url: images/1000012898.jpg base_model: stablediffusionapi/icon-lab-test-ai instance_prompt: null --- # Muslera <Gallery /> ## Download model [Download](/Turkgamercat/Muslera/tree/main) them in the Files & versions tab.

xKizzi/taxirepo

xKizzi

2024-03-10T20:13:31Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2024-03-10T20:13:29Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxirepo results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.34 +/- 2.46 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="xKizzi/taxirepo", 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"]) ```

deepnet/SN6-67M4

deepnet

2024-03-10T20:06:25Z

5

0

transformers

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

text-generation

2024-03-10T20:00:49Z

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

eduvedras/pix2struct-textcaps-base-desc-vars-final

eduvedras

2024-03-10T20:05:13Z

35

0

transformers

[ "transformers", "safetensors", "pix2struct", "image-text-to-text", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

image-text-to-text

2024-03-10T19:03:59Z

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