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train · 841k rows

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-06-25 06:27:54	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 495 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-06-25 06:24:22	card stringlengths 11 1.01M
fzzhang/mistralv1_dora_r8_25e5_e05	fzzhang	2024-05-18T13:46:00Z	0	0	peft	[ "peft", "tensorboard", "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	2024-05-18T13:45:58Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 model-index: - name: mistralv1_dora_r8_25e5_e05 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mistralv1_dora_r8_25e5_e05 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset. ## 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: 2.5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.38.2 - Pytorch 2.2.1 - Datasets 2.18.0 - Tokenizers 0.15.2
ATTIABATOOL/XRAY	ATTIABATOOL	2024-05-18T13:42:38Z	0	0	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2024-05-18T13:38:48Z	--- license: bigscience-openrail-m ---
stablediffusionapi/cetus-mix-whalefall2	stablediffusionapi	2024-05-18T13:41:56Z	30	0	diffusers	[ "diffusers", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-05-18T13:39:41Z	--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # Cetus-Mix WhaleFall2 API Inference ![generated from modelslab.com](https://pub-3626123a908346a7a8be8d9295f44e26.r2.dev/generations/21278661231716039480.png) ## Get API Key Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. Replace Key in below code, change model_id to "cetus-mix-whalefall2" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://docs.modelslab.com) Try model for free: [Generate Images](https://modelslab.com/models/cetus-mix-whalefall2) Model link: [View model](https://modelslab.com/models/cetus-mix-whalefall2) View all models: [View Models](https://modelslab.com/models) import requests import json url = "https://modelslab.com/api/v6/images/text2img" payload = json.dumps({ "key": "your_api_key", "model_id": "cetus-mix-whalefall2", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off DMGG0RBN
Ayeesha/Yeesha	Ayeesha	2024-05-18T13:37:47Z	0	1	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-18T13:37:47Z	--- license: apache-2.0 ---
bartowski/kunoichi-lemon-royale-v2-32K-7B-exl2	bartowski	2024-05-18T13:37:26Z	0	0	transformers	[ "transformers", "mergekit", "merge", "text-generation", "base_model:grimjim/Mistral-7B-Instruct-demi-merge-v0.2-7B", "base_model:merge:grimjim/Mistral-7B-Instruct-demi-merge-v0.2-7B", "base_model:grimjim/kunoichi-lemon-royale-7B", "base_model:merge:grimjim/kunoichi-lemon-royale-7B", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T13:37:25Z	--- base_model: - grimjim/Mistral-7B-Instruct-demi-merge-v0.2-7B - grimjim/kunoichi-lemon-royale-7B library_name: transformers tags: - mergekit - merge license: cc-by-nc-4.0 pipeline_tag: text-generation quantized_by: bartowski --- ## Exllama v2 Quantizations of kunoichi-lemon-royale-v2-32K-7B Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.21">turboderp's ExLlamaV2 v0.0.21</a> for quantization. <b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b> 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/grimjim/kunoichi-lemon-royale-v2-32K-7B ## Prompt format ``` <s> [INST] {prompt} [/INST]</s> ``` Note that this model does not support a System prompt. ## Available sizes \| Branch \| Bits \| lm_head bits \| VRAM (4k) \| VRAM (16k) \| VRAM (32k) \| Description \| \| ----- \| ---- \| ------- \| ------ \| ------ \| ------ \| ------------ \| \| [8_0](https://huggingface.co/bartowski/kunoichi-lemon-royale-v2-32K-7B-exl2/tree/8_0) \| 8.0 \| 8.0 \| 8.4 GB \| 9.8 GB \| 11.8 GB \| Maximum quality that ExLlamaV2 can produce, near unquantized performance. \| \| [6_5](https://huggingface.co/bartowski/kunoichi-lemon-royale-v2-32K-7B-exl2/tree/6_5) \| 6.5 \| 8.0 \| 7.2 GB \| 8.6 GB \| 10.6 GB \| Very similar to 8.0, good tradeoff of size vs performance, recommended. \| \| [5_0](https://huggingface.co/bartowski/kunoichi-lemon-royale-v2-32K-7B-exl2/tree/5_0) \| 5.0 \| 6.0 \| 6.0 GB \| 7.4 GB \| 9.4 GB \| Slightly lower quality vs 6.5, but usable on 8GB cards. \| \| [4_25](https://huggingface.co/bartowski/kunoichi-lemon-royale-v2-32K-7B-exl2/tree/4_25) \| 4.25 \| 6.0 \| 5.3 GB \| 6.7 GB \| 8.7 GB \| GPTQ equivalent bits per weight, slightly higher quality. \| \| [3_5](https://huggingface.co/bartowski/kunoichi-lemon-royale-v2-32K-7B-exl2/tree/3_5) \| 3.5 \| 6.0 \| 4.7 GB \| 6.1 GB \| 8.1 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/kunoichi-lemon-royale-v2-32K-7B-exl2 kunoichi-lemon-royale-v2-32K-7B-exl2-6_5 ``` With huggingface hub (credit to TheBloke for instructions): ```shell pip3 install huggingface-hub ``` To download a specific branch, use the `--revision` parameter. For example, to download the 6.5 bpw branch: Linux: ```shell huggingface-cli download bartowski/kunoichi-lemon-royale-v2-32K-7B-exl2 --revision 6_5 --local-dir kunoichi-lemon-royale-v2-32K-7B-exl2-6_5 --local-dir-use-symlinks False ``` Windows (which apparently doesn't like _ in folders sometimes?): ```shell huggingface-cli download bartowski/kunoichi-lemon-royale-v2-32K-7B-exl2 --revision 6_5 --local-dir kunoichi-lemon-royale-v2-32K-7B-exl2-6.5 --local-dir-use-symlinks False ``` Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
Dandan0K/Pilot_vox_french	Dandan0K	2024-05-18T13:36:48Z	79	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "fr", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-18T13:12:53Z	--- language: - fr license: apache-2.0 tags: - automatic-speech-recognition - fr datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2t_fr_vp-100k_s973 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
Dandan0K/Pilot_vox_italian	Dandan0K	2024-05-18T13:36:31Z	79	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "it", "dataset:mozilla-foundation/common_voice_7_0", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-18T13:35:39Z	--- language: - it license: apache-2.0 tags: - automatic-speech-recognition - it datasets: - mozilla-foundation/common_voice_7_0 --- # exp_w2v2t_it_vp-100k_s449 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition using the train split of [Common Voice 7.0 (it)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
AliSaadatV/virus_pythia_14_1024_2d_representation	AliSaadatV	2024-05-18T13:33:27Z	129	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "generated_from_trainer", "base_model:EleutherAI/pythia-14m", "base_model:finetune:EleutherAI/pythia-14m", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T12:50:03Z	--- base_model: EleutherAI/pythia-14m tags: - generated_from_trainer model-index: - name: virus_pythia_14_1024_2d_representation 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. --> # virus_pythia_14_1024_2d_representation This model is a fine-tuned version of [EleutherAI/pythia-14m](https://huggingface.co/EleutherAI/pythia-14m) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
Tobi-Bueck/atc-queue-dbert-1	Tobi-Bueck	2024-05-18T13:33:16Z	108	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-18T13:21:20Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
chocopiee/vietnamese-news-summarization-vistral-7b	chocopiee	2024-05-18T13:31:58Z	1	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:Viet-Mistral/Vistral-7B-Chat", "base_model:adapter:Viet-Mistral/Vistral-7B-Chat", "license:afl-3.0", "region:us" ]	null	2024-05-18T06:46:51Z	--- license: afl-3.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: Viet-Mistral/Vistral-7B-Chat datasets: - generator model-index: - name: vietnamese-news-summarization-vistral-7b 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/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/tritran/vietnamese-news-summarization/runs/as3mgbsl) # vietnamese-news-summarization-vistral-7b This model is a fine-tuned version of [Viet-Mistral/Vistral-7B-Chat](https://huggingface.co/Viet-Mistral/Vistral-7B-Chat) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 1.2452 ## 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: constant - lr_scheduler_warmup_steps: 0.03 - training_steps: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.3699 \| 0.0060 \| 20 \| 1.3159 \| \| 1.4501 \| 0.0119 \| 40 \| 1.2761 \| \| 1.2554 \| 0.0179 \| 60 \| 1.2583 \| \| 1.1901 \| 0.0239 \| 80 \| 1.2474 \| \| 1.4126 \| 0.0298 \| 100 \| 1.2452 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.41.0 - Pytorch 2.1.2 - Datasets 2.16.0 - Tokenizers 0.19.1
maneln/1.1b-tinyllama	maneln	2024-05-18T13:30:19Z	128	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T13:11:00Z	--- license: apache-2.0 ---
thienann/results-news-dataset	thienann	2024-05-18T13:27:58Z	106	0	transformers	[ "transformers", "tensorboard", "safetensors", "pegasus", "text2text-generation", "generated_from_trainer", "base_model:PoseyATX/GPTxLege_FoxHunter", "base_model:finetune:PoseyATX/GPTxLege_FoxHunter", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-18T12:29:59Z	--- base_model: PoseyATX/GPTxLege_FoxHunter tags: - generated_from_trainer model-index: - name: results-news-dataset results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # results-news-dataset This model is a fine-tuned version of [PoseyATX/GPTxLege_FoxHunter](https://huggingface.co/PoseyATX/GPTxLege_FoxHunter) on the None dataset. It achieves the following results on the evaluation set: - Loss: 9.4768 ## 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-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 10.4458 \| 1.0 \| 791 \| 9.6071 \| \| 9.805 \| 2.0 \| 1582 \| 9.4768 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
carlesoctav/coba-pth	carlesoctav	2024-05-18T13:27:43Z	40	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-18T13:27:30Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
selmamalak/organsmnist-deit-base-finetuned	selmamalak	2024-05-18T13:24:31Z	1	0	peft	[ "peft", "safetensors", "generated_from_trainer", "dataset:medmnist-v2", "base_model:facebook/deit-base-patch16-224", "base_model:adapter:facebook/deit-base-patch16-224", "license:apache-2.0", "region:us" ]	null	2024-05-18T12:31:56Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: facebook/deit-base-patch16-224 datasets: - medmnist-v2 metrics: - accuracy - precision - recall - f1 model-index: - name: organsmnist-deit-base-finetuned 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. --> # organsmnist-deit-base-finetuned This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the medmnist-v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.4815 - Accuracy: 0.8080 - Precision: 0.7703 - Recall: 0.7686 - F1: 0.7650 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.005 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.9804 \| 1.0 \| 218 \| 0.6885 \| 0.7243 \| 0.7883 \| 0.6661 \| 0.6426 \| \| 0.9277 \| 2.0 \| 436 \| 0.3513 \| 0.8503 \| 0.7635 \| 0.7943 \| 0.7680 \| \| 0.8144 \| 3.0 \| 654 \| 0.3614 \| 0.8544 \| 0.8331 \| 0.7961 \| 0.7909 \| \| 0.7344 \| 4.0 \| 872 \| 0.3371 \| 0.8609 \| 0.8327 \| 0.8018 \| 0.7886 \| \| 0.7181 \| 5.0 \| 1090 \| 0.2934 \| 0.8923 \| 0.8060 \| 0.8389 \| 0.8096 \| \| 0.5857 \| 6.0 \| 1308 \| 0.2927 \| 0.8858 \| 0.8493 \| 0.8358 \| 0.8315 \| \| 0.5607 \| 7.0 \| 1526 \| 0.2209 \| 0.9062 \| 0.8658 \| 0.8547 \| 0.8416 \| \| 0.5423 \| 8.0 \| 1744 \| 0.2513 \| 0.9025 \| 0.8545 \| 0.8470 \| 0.8487 \| \| 0.4053 \| 9.0 \| 1962 \| 0.2561 \| 0.9038 \| 0.8543 \| 0.8457 \| 0.8373 \| \| 0.4417 \| 10.0 \| 2180 \| 0.2558 \| 0.8997 \| 0.8463 \| 0.8395 \| 0.8416 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
HariprasathSB/whispeerr	HariprasathSB	2024-05-18T13:22:33Z	94	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:HariprasathSB/whispeer", "base_model:finetune:HariprasathSB/whispeer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-18T13:06:48Z	--- license: apache-2.0 base_model: HariprasathSB/whispeer tags: - generated_from_trainer model-index: - name: whispeerr 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. --> # whispeerr This model is a fine-tuned version of [HariprasathSB/whispeer](https://huggingface.co/HariprasathSB/whispeer) 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: 0.003 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - training_steps: 100 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.41.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
basakdemirok/bert-base-turkish-cased-off_detect_v03_seed42	basakdemirok	2024-05-18T13:09:27Z	62	0	transformers	[ "transformers", "tf", "tensorboard", "bert", "text-classification", "generated_from_keras_callback", "base_model:dbmdz/bert-base-turkish-cased", "base_model:finetune:dbmdz/bert-base-turkish-cased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-18T12:37:49Z	--- license: mit base_model: dbmdz/bert-base-turkish-cased tags: - generated_from_keras_callback model-index: - name: basakdemirok/bert-base-turkish-cased-off_detect_v03_seed42 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. --> # basakdemirok/bert-base-turkish-cased-off_detect_v03_seed42 This model is a fine-tuned version of [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0094 - Validation Loss: 0.6556 - Train F1: 0.7023 - Epoch: 3 ## 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': 14988, '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 F1 \| Epoch \| \|:----------:\|:---------------:\|:--------:\|:-----:\| \| 0.2628 \| 0.2933 \| 0.6989 \| 0 \| \| 0.0985 \| 0.4294 \| 0.6954 \| 1 \| \| 0.0247 \| 0.5613 \| 0.6909 \| 2 \| \| 0.0094 \| 0.6556 \| 0.7023 \| 3 \| ### Framework versions - Transformers 4.31.0 - TensorFlow 2.13.1 - Datasets 2.4.0 - Tokenizers 0.13.3
Dhaniahmad/whisper-tiny-id	Dhaniahmad	2024-05-18T13:00:25Z	94	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "id", "dataset:mozilla-foundation/common_voice_11_0", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-17T08:46:34Z	--- language: - id license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper tiny Id - Dhani results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: id split: None args: 'config: id, split: test' metrics: - name: Wer type: wer value: 36.16447276617517 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper tiny Id - Dhani This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.5239 - Wer: 36.1645 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\| \| 0.5362 \| 1.9305 \| 1000 \| 0.5488 \| 38.4111 \| \| 0.2848 \| 3.8610 \| 2000 \| 0.5154 \| 36.7087 \| \| 0.1704 \| 5.7915 \| 3000 \| 0.5182 \| 36.2622 \| \| 0.1217 \| 7.7220 \| 4000 \| 0.5239 \| 36.1645 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
Toshifumi/Llama3-Toshi-Ja-claim-classifier_20240518v1	Toshifumi	2024-05-18T12:57:27Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T12:50:34Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: Toshifumi - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
piika919/phi_bnb	piika919	2024-05-18T12:54:49Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-18T12:51:32Z	--- 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. 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presencesw/mt5-base-snli_contradiction-triplet	presencesw	2024-05-18T12:52:38Z	50	0	transformers	[ "transformers", "safetensors", "mt5", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-18T12:52:01Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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OwOpeepeepoopoo/NoSoup4U_1	OwOpeepeepoopoo	2024-05-18T12:52:38Z	93	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T23:21:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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nvdenisov2002/llama-longLoRA-v5-8k-all-samples-3-epochs	nvdenisov2002	2024-05-18T12:50:41Z	0	0	peft	[ "peft", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]	null	2024-05-18T12:50:17Z	--- library_name: peft base_model: meta-llama/Llama-2-7b-hf --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.10.0
emilykang/Phi_medmcqa_question_generation-social_n_preventive_medicine_lora	emilykang	2024-05-18T12:46:58Z	0	0	peft	[ "peft", "safetensors", "phi", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:microsoft/phi-2", "base_model:adapter:microsoft/phi-2", "license:mit", "region:us" ]	null	2024-05-17T15:31:33Z	--- license: mit library_name: peft tags: - trl - sft - generated_from_trainer base_model: microsoft/phi-2 datasets: - generator model-index: - name: Phi_medmcqa_question_generation-social_n_preventive_medicine_lora 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_medmcqa_question_generation-social_n_preventive_medicine_lora This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) 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: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 10 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu118 - Datasets 2.19.0 - Tokenizers 0.19.1
OsherElhadad/ppo-PandaReachJointsDense-v3-1000000	OsherElhadad	2024-05-18T12:45:20Z	0	0	stable-baselines3	[ "stable-baselines3", "PandaReachJointsDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-18T12:42:12Z	--- library_name: stable-baselines3 tags: - PandaReachJointsDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachJointsDense-v3 type: PandaReachJointsDense-v3 metrics: - type: mean_reward value: -0.21 +/- 0.11 name: mean_reward verified: false --- # PPO Agent playing PandaReachJointsDense-v3 This is a trained model of a PPO agent playing PandaReachJointsDense-v3 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
akbargherbal/gemma_7b_en_to_ar_ft_01	akbargherbal	2024-05-18T12:43:25Z	7	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/gemma-7b-it-bnb-4bit", "base_model:finetune:unsloth/gemma-7b-it-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T12:05:03Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma - trl base_model: unsloth/gemma-7b-it-bnb-4bit --- # Uploaded model - Developed by: akbargherbal - License: apache-2.0 - Finetuned from model : unsloth/gemma-7b-it-bnb-4bit This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
theosun/gemma-2b-it-sharegpt	theosun	2024-05-18T12:38:32Z	7	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T09:38:43Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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]
selmamalak/blood-beit-base-finetuned	selmamalak	2024-05-18T12:38:22Z	2	0	peft	[ "peft", "safetensors", "generated_from_trainer", "dataset:medmnist-v2", "base_model:microsoft/beit-base-patch16-224-pt22k-ft22k", "base_model:adapter:microsoft/beit-base-patch16-224-pt22k-ft22k", "license:apache-2.0", "region:us" ]	null	2024-05-18T12:11:10Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: microsoft/beit-base-patch16-224-pt22k-ft22k datasets: - medmnist-v2 metrics: - accuracy - precision - recall - f1 model-index: - name: blood-beit-base-finetuned 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. --> # blood-beit-base-finetuned This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the medmnist-v2 dataset. It achieves the following results on the evaluation set: - Loss: 0.0847 - Accuracy: 0.9737 - Precision: 0.9726 - Recall: 0.9724 - F1: 0.9724 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.005 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.4657 \| 1.0 \| 187 \| 0.2452 \| 0.9095 \| 0.8964 \| 0.9083 \| 0.8973 \| \| 0.4327 \| 2.0 \| 374 \| 0.2111 \| 0.9182 \| 0.9299 \| 0.8921 \| 0.9007 \| \| 0.3977 \| 3.0 \| 561 \| 0.1743 \| 0.9340 \| 0.9229 \| 0.9282 \| 0.9244 \| \| 0.3318 \| 4.0 \| 748 \| 0.1776 \| 0.9352 \| 0.9248 \| 0.9353 \| 0.9285 \| \| 0.3461 \| 5.0 \| 935 \| 0.1703 \| 0.9381 \| 0.9311 \| 0.9344 \| 0.9305 \| \| 0.3309 \| 6.0 \| 1122 \| 0.1956 \| 0.9369 \| 0.9336 \| 0.9397 \| 0.9335 \| \| 0.3088 \| 7.0 \| 1309 \| 0.1179 \| 0.9533 \| 0.9427 \| 0.9525 \| 0.9461 \| \| 0.2129 \| 8.0 \| 1496 \| 0.0992 \| 0.9638 \| 0.9569 \| 0.9674 \| 0.9611 \| \| 0.2049 \| 9.0 \| 1683 \| 0.0847 \| 0.9679 \| 0.9627 \| 0.9683 \| 0.9651 \| \| 0.2007 \| 10.0 \| 1870 \| 0.0785 \| 0.9708 \| 0.9668 \| 0.9737 \| 0.9698 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
akbargherbal/BACKUP_gemma_7b_en_to_ar_ft_01	akbargherbal	2024-05-18T12:33:12Z	9	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T12:28:59Z	--- license: apache-2.0 ---
RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf	RichardErkhov	2024-05-18T12:33:06Z	32	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-18T01:49:29Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Mixtral-8x7B-MoE-RP-Story - GGUF - Model creator: https://huggingface.co/Undi95/ - Original model: https://huggingface.co/Undi95/Mixtral-8x7B-MoE-RP-Story/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [Mixtral-8x7B-MoE-RP-Story.Q2_K.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.Q2_K.gguf) \| Q2_K \| 16.12GB \| \| [Mixtral-8x7B-MoE-RP-Story.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.IQ3_XS.gguf) \| IQ3_XS \| 18.02GB \| \| [Mixtral-8x7B-MoE-RP-Story.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.IQ3_S.gguf) \| IQ3_S \| 19.03GB \| \| [Mixtral-8x7B-MoE-RP-Story.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.Q3_K_S.gguf) \| Q3_K_S \| 19.03GB \| \| [Mixtral-8x7B-MoE-RP-Story.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.IQ3_M.gguf) \| IQ3_M \| 19.96GB \| \| [Mixtral-8x7B-MoE-RP-Story.Q3_K.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.Q3_K.gguf) \| Q3_K \| 21.0GB \| \| [Mixtral-8x7B-MoE-RP-Story.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.Q3_K_M.gguf) \| Q3_K_M \| 21.0GB \| \| [Mixtral-8x7B-MoE-RP-Story.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.Q3_K_L.gguf) \| Q3_K_L \| 22.51GB \| \| [Mixtral-8x7B-MoE-RP-Story.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.IQ4_XS.gguf) \| IQ4_XS \| 23.63GB \| \| [Mixtral-8x7B-MoE-RP-Story.Q4_0.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.Q4_0.gguf) \| Q4_0 \| 24.63GB \| \| [Mixtral-8x7B-MoE-RP-Story.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.IQ4_NL.gguf) \| IQ4_NL \| 24.91GB \| \| [Mixtral-8x7B-MoE-RP-Story.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.Q4_K_S.gguf) \| Q4_K_S \| 24.91GB \| \| [Mixtral-8x7B-MoE-RP-Story.Q4_K.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.Q4_K.gguf) \| Q4_K \| 26.49GB \| \| [Mixtral-8x7B-MoE-RP-Story.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.Q4_K_M.gguf) \| Q4_K_M \| 26.49GB \| \| [Mixtral-8x7B-MoE-RP-Story.Q4_1.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.Q4_1.gguf) \| Q4_1 \| 27.32GB \| \| [Mixtral-8x7B-MoE-RP-Story.Q5_0.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.Q5_0.gguf) \| Q5_0 \| 30.02GB \| \| [Mixtral-8x7B-MoE-RP-Story.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.Q5_K_S.gguf) \| Q5_K_S \| 30.02GB \| \| [Mixtral-8x7B-MoE-RP-Story.Q5_K.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.Q5_K.gguf) \| Q5_K \| 30.95GB \| \| [Mixtral-8x7B-MoE-RP-Story.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.Q5_K_M.gguf) \| Q5_K_M \| 30.95GB \| \| [Mixtral-8x7B-MoE-RP-Story.Q5_1.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.Q5_1.gguf) \| Q5_1 \| 32.71GB \| \| [Mixtral-8x7B-MoE-RP-Story.Q6_K.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/blob/main/Mixtral-8x7B-MoE-RP-Story.Q6_K.gguf) \| Q6_K \| 35.74GB \| \| [Mixtral-8x7B-MoE-RP-Story.Q8_0.gguf](https://huggingface.co/RichardErkhov/Undi95_-_Mixtral-8x7B-MoE-RP-Story-gguf/tree/main/) \| Q8_0 \| 46.22GB \| Original model description: --- license: cc-by-nc-4.0 tags: - not-for-all-audiences - nsfw --- Mixtral-8x7B-MoE-RP-Story is a model made primarely for chatting, RP (Roleplay) and storywriting. 2 RP model, 2 chat model, 1 occult model, 1 storywritting model, 1 mathematic model and 1 DPO model was used for a MoE. Bagel was the base. The DPO chat model is here to help get more human reply. This is my first try at doing this, so don't hesitate to give feedback! WARNING: ALL THE "K" GGUF QUANT OF MIXTRAL MODELS SEEMS TO BE [BROKEN](https://cdn-uploads.huggingface.co/production/uploads/63ab1241ad514ca8d1430003/TvjEP14ps7ZUgJ-0-mhIX.png), PREFER Q4_0, Q5_0 or Q8_0! <!-- description start --> ## Description This repo contains fp16 files of Mixtral-8x7B-MoE-RP-Story. <!-- description end --> <!-- description start --> ## Models used The list of model used and their activator/theme can be found [here](https://huggingface.co/Undi95/Mixtral-8x7B-MoE-RP-Story/blob/main/config.yaml) <!-- description end --> <!-- prompt-template start --> ## Prompt template: Custom Using Bagel as a base let us a lot of different prompting system theorically, you can see all the prompting available [here](https://huggingface.co/jondurbin/bagel-7b-v0.1#prompt-formatting). If you want to support me, you can [here](https://ko-fi.com/undiai).
alexandro767/stable-diffusion-v1-5-finetuned_5e_r2_v1	alexandro767	2024-05-18T12:29:08Z	29	0	diffusers	[ "diffusers", "safetensors", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-05-18T12:26:20Z	--- library_name: diffusers --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🧨 diffusers model that has been pushed on the Hub. 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maywell/Yi-Ko-34B-Instruct	maywell	2024-05-18T12:28:27Z	13	3	transformers	[ "transformers", "safetensors", "llama", "text-generation", "pytorch", "Yi-Ko", "01-ai", "Yi", "en", "ko", "base_model:beomi/Yi-Ko-34B", "base_model:finetune:beomi/Yi-Ko-34B", "license:other", "autotrain_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-05-18T11:44:33Z	--- license: other license_name: yi-license license_link: LICENSE language: - en - ko pipeline_tag: text-generation inference: false base_model: beomi/Yi-Ko-34B tags: - pytorch - Yi-Ko - 01-ai - Yi library_name: transformers --- # Yi Ko 34B Instruct ## Training Process 1. Further trained with Korean corpus. 2. SFT 3. DPO [(Dataset URL)](https://huggingface.co/datasets/argilla/distilabel-capybara-dpo-7k-binarized) ## Model Info \| Context Length \| Parameter \| Prompt Template \| KMMLU(5-shot) \| \| --- \| --- \| --- \| --- \| \| 4k(4096) \| 34B \| ChatML \| 49.03 \| ## Acknowledgement The training is supported by [Sionic AI](https://sionic.ai). # Original Model Card by [beomi](https://huggingface.co/beomi) Yi-Ko series models serve as advanced iterations of 01-ai/Yi models, benefiting from an expanded vocabulary and the inclusion of Korean/English corpus in its further pretraining. Just like its predecessor, Yi-Ko series models operate within the broad range of generative text models that stretch from 6 billion to 34 billion parameters. This repository focuses on the 34B pretrained version, which is tailored to fit the Hugging Face Transformers format. For access to the other models, feel free to consult the index provided below. ## Model Details Model Developers Junbum Lee (Beomi) Variations Yi-Ko-34B will come in a range of parameter sizes — 6B and 34B — with Ko(Korean+English). Input Models input text only. Output Models generate text only. Model Architecture Yi-Ko series models are an auto-regressive language model that uses an optimized transformer architecture based on Llama-2. <small>Yi model architecture is based on Llama2, so it can be loaded via `LlamaForCausalLM` class on HF.</small> \|Model Name\|Training Data\|Params\|Context Length\|GQA\|Trained Tokens\|LR\|Train tokens (per batch)\| \|---\|---\|---\|---\|---\|---\|---\|---\| \|Yi-Ko-34B\|A mix of Korean + English online data\|34B\|4k\|O\|40B+\|5e<sup>-5</sup>\|4M\| Vocab Expansion \| Model Name \| Vocabulary Size \| Description \| \| --- \| --- \| --- \| \| Original Yi-Series \| 64000 \| Sentencepiece BPE \| \| Expanded Yi-Ko Series \| 78464 \| Sentencepiece BPE. Added Korean vocab and merges \| Tokenizing "안녕하세요, 오늘은 날씨가 좋네요.ㅎㅎ" \| Model \| # of tokens \| Tokens \| \| --- \| --- \| --- \| \| Original Yi-Series \| 47 \| `['<0xEC>', '<0x95>', '<0x88>', '<0xEB>', '<0x85>', '<0x95>', '하', '<0xEC>', '<0x84>', '<0xB8>', '<0xEC>', '<0x9A>', '<0x94>', ',', '▁', '<0xEC>', '<0x98>', '<0xA4>', '<0xEB>', '<0x8A>', '<0x98>', '은', '▁', '<0xEB>', '<0x82>', '<0xA0>', '<0xEC>', '<0x94>', '<0xA8>', '가', '▁', '<0xEC>', '<0xA2>', '<0x8B>', '<0xEB>', '<0x84>', '<0xA4>', '<0xEC>', '<0x9A>', '<0x94>', '.', '<0xE3>', '<0x85>', '<0x8E>', '<0xE3>', '<0x85>', '<0x8E>']` \| \| Expanded Yi-Ko Series \| 10 \| `['▁안녕', '하세요', ',', '▁오늘은', '▁날', '씨가', '▁좋네요', '.', 'ㅎ', 'ㅎ']` \| \|<small>Equal Korean vocab with Llama-2-Ko Series</small>\|\| Tokenizing "Llama 2: Open Foundation and Fine-Tuned Chat Models"* \| Model \| # of tokens \| Tokens \| \| --- \| --- \| --- \| \| Original Yi-Series \| 21 \| `['The', '▁Y', 'i', '▁series', '▁models', '▁are', '▁large', '▁language', '▁models', '▁trained', '▁from', '▁scratch', '▁by', '▁developers', '▁at', '▁', '0', '1', '.', 'AI', '.']` \| \| Expanded Yi-Ko Series \| 21 \| `['▁The', '▁Y', 'i', '▁series', '▁models', '▁are', '▁large', '▁language', '▁models', '▁trained', '▁from', '▁scratch', '▁by', '▁developers', '▁at', '▁', '0', '1', '.', 'AI', '.']` \| \|<small>Equal Korean vocab with Llama-2-Ko Series</small>\| \| <small>Since Expanded Yi-Ko Series prepends `_` at the beginning of the text(to ensure same tokenization for Korean sentences), it shows negilible difference for the first token on English tokenization. </small>\| # Model Benchmark ## LM Eval Harness - Korean Benchmarks \| Tasks \|Version\|Filter\|n-shot\| Metric \|Value \| \|Stderr\| \|----------------\|------:\|------\|-----:\|--------\|-----:\|---\|------\| \|kmmlu_direct\|N/A \|none \| 5\|exact_match\|0.5027\|± \|0.1019\| \|kobest_boolq \| 1\|none \| 5\|acc \|0.9202\|± \|0.0072\| \| \| \|none \| 5\|f1 \|0.9202\|± \|N/A \| \|kobest_copa \| 1\|none \| 5\|acc \|0.8480\|± \|0.0114\| \| \| \|none \| 5\|f1 \|0.8479\|± \|N/A \| \|kobest_hellaswag\| 1\|none \| 5\|acc \|0.5320\|± \|0.0223\| \| \| \|none \| 5\|f1 \|0.5281\|± \|N/A \| \| \| \|none \| 5\|acc_norm\|0.6340\|± \|0.0216\| \|kobest_sentineg \| 1\|none \| 5\|acc \|0.9874\|± \|0.0056\| \| \| \|none \| 5\|f1 \|0.9874\|± \|N/A \| \|haerae \|N/A \|none \| 5\|acc \|0.7965\|± \|0.0116\| \| \| \|none \| 5\|acc_norm\|0.7965\|± \|0.0116\| \| - haerae_general_knowledge \| 1\|none \| 5\|acc \|0.5114\|± \|0.0378\| \| \| \|none \| 5\|acc_norm\|0.5114\|± \|0.0378\| \| - haerae_history \| 1\|none \| 5\|acc \|0.8511\|± \|0.0260\| \| \| \|none \| 5\|acc_norm\|0.8511\|± \|0.0260\| \| - haerae_loan_word \| 1\|none \| 5\|acc \|0.8402\|± \|0.0283\| \| \| \|none \| 5\|acc_norm\|0.8402\|± \|0.0283\| \| - haerae_rare_word \| 1\|none \| 5\|acc \|0.8642\|± \|0.0170\| \| \| \|none \| 5\|acc_norm\|0.8642\|± \|0.0170\| \| - haerae_standard_nomenclature\| 1\|none \| 5\|acc \|0.8301\|± \|0.0305\| \| \| \|none \| 5\|acc_norm\|0.8301\|± \|0.0305\| ## LICENSE Follows Yi License ## Citation ## Acknowledgement The training is supported by [TPU Research Cloud](https://sites.research.google/trc/) program.
basakdemirok/bert-base-turkish-cased-off_detect_v02_seed42	basakdemirok	2024-05-18T12:20:29Z	61	0	transformers	[ "transformers", "tf", "tensorboard", "bert", "text-classification", "generated_from_keras_callback", "base_model:dbmdz/bert-base-turkish-cased", "base_model:finetune:dbmdz/bert-base-turkish-cased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-18T11:48:28Z	--- license: mit base_model: dbmdz/bert-base-turkish-cased tags: - generated_from_keras_callback model-index: - name: basakdemirok/bert-base-turkish-cased-off_detect_v02_seed42 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. --> # basakdemirok/bert-base-turkish-cased-off_detect_v02_seed42 This model is a fine-tuned version of [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0105 - Validation Loss: 0.6091 - Train F1: 0.7065 - Epoch: 3 ## 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': 14944, '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 F1 \| Epoch \| \|:----------:\|:---------------:\|:--------:\|:-----:\| \| 0.2631 \| 0.2907 \| 0.6690 \| 0 \| \| 0.0934 \| 0.4221 \| 0.6997 \| 1 \| \| 0.0274 \| 0.5827 \| 0.6968 \| 2 \| \| 0.0105 \| 0.6091 \| 0.7065 \| 3 \| ### Framework versions - Transformers 4.31.0 - TensorFlow 2.13.1 - Datasets 2.4.0 - Tokenizers 0.13.3
ruslandev/llama-3-70b-tagengo-GGUF	ruslandev	2024-05-18T12:20:03Z	33	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "llama", "trl", "en", "dataset:lightblue/tagengo-gpt4", "base_model:unsloth/llama-3-70b-bnb-4bit", "base_model:quantized:unsloth/llama-3-70b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-17T06:42:40Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-70b-bnb-4bit datasets: - lightblue/tagengo-gpt4 --- # Uploaded model - Developed by: ruslandev - License: apache-2.0 - Finetuned from model : unsloth/llama-3-70b-bnb-4bit This model is finetuned on the Tagengo dataset. Please note - this model has been created for educational purposes and it needs further training/fine tuning. # How to use The easiest way to use this model on your own computer is to use the GGUF version of this model ([ruslandev/llama-3-70b-tagengo-GGUF](https://huggingface.co/ruslandev/llama-3-70b-tagengo-GGUF)) using a program such as [llama.cpp](https://github.com/ggerganov/llama.cpp). If you want to use this model directly with the Huggingface Transformers stack, I recommend using my framework [gptchain](https://github.com/RuslanPeresy/gptchain). ``` git clone https://github.com/RuslanPeresy/gptchain.git cd gptchain pip install -r requirements-train.txt python gptchain.py chat -m ruslandev/llama-3-70b-tagengo \ --chatml true \ -q '[{"from": "human", "value": "Из чего состоит нейронная сеть?"}]' ``` # Training [gptchain](https://github.com/RuslanPeresy/gptchain) framework has been used for training. ``` python gptchain.py train -m unsloth/llama-3-70b-bnb-4bit \ -dn tagengo_gpt4 \ -sp checkpoints/llama-3-70b-tagengo \ -hf llama-3-70b-tagengo \ --max-steps 2400 ``` # Training hyperparameters - learning_rate: 2e-4 - seed: 3407 - gradient_accumulation_steps: 4 - per_device_train_batch_size: 2 - optimizer: adamw_8bit - lr_scheduler_type: linear - warmup_steps: 5 - max_steps: 2400 - weight_decay: 0.01 # Training results [wandb report](https://api.wandb.ai/links/ruslandev/rilj60ra) 2400 steps took 7 hours on a single H100 [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
ahmedgongi/Llama_dev3model_finale3	ahmedgongi	2024-05-18T12:17:51Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-18T12:17:48Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ahmedgongi/Llama_dev3tokenizer_finale3	ahmedgongi	2024-05-18T12:17:43Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-18T12:17:42Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ruslandev/llama-3-70b-tagengo	ruslandev	2024-05-18T12:17:10Z	18	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "en", "dataset:lightblue/tagengo-gpt4", "base_model:unsloth/llama-3-70b-bnb-4bit", "base_model:finetune:unsloth/llama-3-70b-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T03:53:13Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-70b-bnb-4bit datasets: - lightblue/tagengo-gpt4 --- # Uploaded model - Developed by: ruslandev - License: apache-2.0 - Finetuned from model : unsloth/llama-3-70b-bnb-4bit This model is finetuned on the Tagengo dataset. Please note - this model has been created for educational purposes and it needs further training/fine tuning. # How to use The easiest way to use this model on your own computer is to use the GGUF version of this model ([ruslandev/llama-3-70b-tagengo-GGUF](https://huggingface.co/ruslandev/llama-3-70b-tagengo-GGUF)) using a program such as [llama.cpp](https://github.com/ggerganov/llama.cpp). If you want to use this model directly with the Huggingface Transformers stack, I recommend using my framework [gptchain](https://github.com/RuslanPeresy/gptchain). ``` git clone https://github.com/RuslanPeresy/gptchain.git cd gptchain pip install -r requirements-train.txt python gptchain.py chat -m ruslandev/llama-3-70b-tagengo \ --chatml true \ -q '[{"from": "human", "value": "Из чего состоит нейронная сеть?"}]' ``` # Training [gptchain](https://github.com/RuslanPeresy/gptchain) framework has been used for training. ``` python gptchain.py train -m unsloth/llama-3-70b-bnb-4bit \ -dn tagengo_gpt4 \ -sp checkpoints/llama-3-70b-tagengo \ -hf llama-3-70b-tagengo \ --max-steps 2400 ``` # Training hyperparameters - learning_rate: 2e-4 - seed: 3407 - gradient_accumulation_steps: 4 - per_device_train_batch_size: 2 - optimizer: adamw_8bit - lr_scheduler_type: linear - warmup_steps: 5 - max_steps: 2400 - weight_decay: 0.01 # Training results [wandb report](https://api.wandb.ai/links/ruslandev/rilj60ra) 2400 steps took 7 hours on a single H100 [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
fzzhang/mistralv1_lora_r8_25e5_e05	fzzhang	2024-05-18T12:12:30Z	0	0	peft	[ "peft", "tensorboard", "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	2024-05-18T12:12:28Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 model-index: - name: mistralv1_lora_r8_25e5_e05 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mistralv1_lora_r8_25e5_e05 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset. ## 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: 2.5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.38.2 - Pytorch 2.2.1 - Datasets 2.18.0 - Tokenizers 0.15.2
uisikdag/vit-base-oxford-iiit-pets	uisikdag	2024-05-18T12:11:40Z	222	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224", "base_model:finetune:google/vit-base-patch16-224", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-17T05:49:58Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224 tags: - image-classification - generated_from_trainer metrics: - accuracy model-index: - name: vit-base-oxford-iiit-pets 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. --> # vit-base-oxford-iiit-pets This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the pcuenq/oxford-pets dataset. It achieves the following results on the evaluation set: - Loss: 0.1992 - Accuracy: 0.9350 ## 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: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.3808 \| 1.0 \| 370 \| 0.2939 \| 0.9229 \| \| 0.2337 \| 2.0 \| 740 \| 0.2166 \| 0.9432 \| \| 0.1762 \| 3.0 \| 1110 \| 0.2010 \| 0.9459 \| \| 0.1414 \| 4.0 \| 1480 \| 0.1922 \| 0.9513 \| \| 0.136 \| 5.0 \| 1850 \| 0.1895 \| 0.9499 \| ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
Skhaled/acegpt-sa-2-model	Skhaled	2024-05-18T12:09:43Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T11:28:56Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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presencesw/mt5-base-snli_entailment-triplet	presencesw	2024-05-18T12:05:05Z	50	0	transformers	[ "transformers", "safetensors", "mt5", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-18T12:04:16Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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akbargherbal/gemma_7b_en_to_ar_ft_01_LORA	akbargherbal	2024-05-18T12:04:50Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma", "trl", "en", "base_model:unsloth/gemma-7b-it-bnb-4bit", "base_model:finetune:unsloth/gemma-7b-it-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-18T12:04:37Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma - trl base_model: unsloth/gemma-7b-it-bnb-4bit --- # Uploaded model - Developed by: akbargherbal - License: apache-2.0 - Finetuned from model : unsloth/gemma-7b-it-bnb-4bit This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
svjack/DPO_Bactrian_X_ZH_RJ_EN_ORPO_Mistral7B_v2_inst_lora_small	svjack	2024-05-18T12:04:32Z	2	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2", "region:us" ]	null	2024-05-18T11:45:25Z	--- library_name: peft base_model: mistralai/Mistral-7B-Instruct-v0.2 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> # Install ```bash pip install peft transformers bitsandbytes ``` # Run by transformers ```python from transformers import TextStreamer, AutoTokenizer, AutoModelForCausalLM from peft import PeftModel tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2",) mis_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2", load_in_4bit = True) mis_model = PeftModel.from_pretrained(mis_model, "svjack/DPO_Bactrian_X_ZH_RJ_EN_ORPO_Mistral7B_v2_inst_lora_small") mis_model = mis_model.eval() streamer = TextStreamer(tokenizer) def mistral_hf_predict(messages, mis_model = mis_model, tokenizer = tokenizer, streamer = streamer, do_sample = True, top_p = 0.95, top_k = 40, max_new_tokens = 512, max_input_length = 3500, temperature = 0.9, repetition_penalty = 1.0, device = "cuda"): encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt") model_inputs = encodeds.to(device) generated_ids = mis_model.generate(model_inputs, max_new_tokens=max_new_tokens, do_sample=do_sample, streamer = streamer, top_p = top_p, top_k = top_k, temperature = temperature, repetition_penalty = repetition_penalty, ) out = tokenizer.batch_decode(generated_ids)[0].split("[/INST]")[-1].replace("</s>", "").strip() return out out = mistral_hf_predict([ { "role": "user", "content": "不使用sorted，写一份简单的Python排序程序，使用markdown进行输出。" } ], repetition_penalty = 1.0, temperature = 0.01, max_new_tokens=1024 ) print(out) ``` # Output ```python def sort_list(lst): for i in range(len(lst)): for j in range(i+1, len(lst)): if lst[i] > lst[j]: lst[i], lst[j] = lst[j], lst[i] # 输入一个列表 lst = [5, 3, 8, 1, 6, 2, 7, 4] # 调用sort_list函数进行排序 sort_list(lst) # 输出排序后的列表 print("排序后的列表：") print(lst) ``` 输出： ``` 排序后的列表： [1, 2, 3, 4, 5, 6, 7, 8] ``` ```python out = mistral_hf_predict([ { "role": "user", "content": "不使用sorted，写一份简单的Python排序程序，使用markdown进行输出。" }, { "role": "assistant", "content": ''' ```python def sort_list(lst): for i in range(len(lst)): for j in range(i+1, len(lst)): if lst[i] > lst[j]: lst[i], lst[j] = lst[j], lst[i] # 输入一个列表 lst = [5, 3, 8, 1, 6, 2, 7, 4] # 调用sort_list函数进行排序 sort_list(lst) # 输出排序后的列表 print("排序后的列表：") print(lst) ``` 输出： ``` 排序后的列表： [1, 2, 3, 4, 5, 6, 7, 8] ``` ''' }, { "role": "user", "content": "将上面的语言翻译成C++。" } ], repetition_penalty = 1.0, temperature = 0.01, max_new_tokens=1024 ) print(out) ``` # Output ```cpp #include <iostream> #include <algorithm> using namespace std; void sortList(int arr[], int n) { for (int i = 0; i < n; i++) { for (int j = i + 1; j < n; j++) { if (arr[i] > arr[j]) { swap(arr[i], arr[j]); } } } } int main() { int arr[] = {5, 3, 8, 1, 6, 2, 7, 4}; int n = sizeof(arr) / sizeof(arr[0]); sortList(arr, n); cout << "Sorted array: "; for (int i = 0; i < n; i++) { cout << arr[i] << " "; } cout << endl; return 0; } ``` 输出： ``` Sorted array: 1 2 3 4 5 6 7 8 ``` - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Cran-May/openbuddy-mistral-22b-v21.1-32k-Q4_K_S-GGUF	Cran-May	2024-05-18T12:01:21Z	1	0	transformers	[ "transformers", "gguf", "mixtral", "llama-cpp", "gguf-my-repo", "text-generation", "zh", "en", "fr", "de", "ja", "ko", "it", "ru", "fi", "license:apache-2.0", "region:us", "conversational" ]	text-generation	2024-05-18T12:00:52Z	--- language: - zh - en - fr - de - ja - ko - it - ru - fi license: apache-2.0 library_name: transformers tags: - mixtral - llama-cpp - gguf-my-repo pipeline_tag: text-generation inference: false --- # Cran-May/openbuddy-mistral-22b-v21.1-32k-Q4_K_S-GGUF This model was converted to GGUF format from [`OpenBuddy/openbuddy-mistral-22b-v21.1-32k`](https://huggingface.co/OpenBuddy/openbuddy-mistral-22b-v21.1-32k) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/OpenBuddy/openbuddy-mistral-22b-v21.1-32k) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo Cran-May/openbuddy-mistral-22b-v21.1-32k-Q4_K_S-GGUF --model openbuddy-mistral-22b-v21.1-32k.Q4_K_S.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo Cran-May/openbuddy-mistral-22b-v21.1-32k-Q4_K_S-GGUF --model openbuddy-mistral-22b-v21.1-32k.Q4_K_S.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m openbuddy-mistral-22b-v21.1-32k.Q4_K_S.gguf -n 128 ```
ddnahm/ddn_qa_model	ddnahm	2024-05-18T11:59:29Z	69	0	transformers	[ "transformers", "tf", "distilbert", "question-answering", "generated_from_keras_callback", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2024-05-18T09:06:45Z	--- license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_keras_callback model-index: - name: ddnahm/ddn_qa_model 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. --> # ddnahm/ddn_qa_model This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.5135 - Validation Loss: 2.3658 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - 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': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 500, '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 \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 3.5135 \| 2.3658 \| 0 \| ### Framework versions - Transformers 4.40.2 - TensorFlow 2.15.0 - Datasets 2.19.1 - Tokenizers 0.19.1
duyntnet/neural-chat-7b-v3-3-imatrix-GGUF	duyntnet	2024-05-18T11:58:15Z	12	0	transformers	[ "transformers", "gguf", "imatrix", "neural-chat-7b-v3-3", "text-generation", "en", "license:other", "region:us" ]	text-generation	2024-05-18T09:58:39Z	--- license: other language: - en pipeline_tag: text-generation inference: false tags: - transformers - gguf - imatrix - neural-chat-7b-v3-3 --- Quantizations of https://huggingface.co/Intel/neural-chat-7b-v3-3 # From original readme ## How To Use Context length for this model: 8192 tokens (same as https://huggingface.co/mistralai/Mistral-7B-v0.1) ### Reproduce the model Here is the sample code to reproduce the model: [GitHub sample code](https://github.com/intel/intel-extension-for-transformers/blob/main/intel_extension_for_transformers/neural_chat/examples/finetuning/finetune_neuralchat_v3). Here is the documentation to reproduce building the model: ```bash git clone https://github.com/intel/intel-extension-for-transformers.git cd intel-extension-for-transformers docker build --no-cache ./ --target hpu --build-arg REPO=https://github.com/intel/intel-extension-for-transformers.git --build-arg ITREX_VER=main -f ./intel_extension_for_transformers/neural_chat/docker/Dockerfile -t chatbot_finetuning:latest docker run -it --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --net=host --ipc=host chatbot_finetuning:latest # after entering docker container cd examples/finetuning/finetune_neuralchat_v3 ``` We select the latest pretrained mistralai/Mistral-7B-v0.1 and the open source dataset Open-Orca/SlimOrca to conduct the experiment. The below script use deepspeed zero2 to lanuch the training with 8 cards Gaudi2. In the `finetune_neuralchat_v3.py`, the default `use_habana=True, use_lazy_mode=True, device="hpu"` for Gaudi2. And if you want to run it on NVIDIA GPU, you can set them `use_habana=False, use_lazy_mode=False, device="auto"`. ```python deepspeed --include localhost:0,1,2,3,4,5,6,7 \ --master_port 29501 \ finetune_neuralchat_v3.py ``` Merge the LoRA weights: ```python python apply_lora.py \ --base-model-path mistralai/Mistral-7B-v0.1 \ --lora-model-path finetuned_model/ \ --output-path finetuned_model_lora ``` ### Use the model ### FP32 Inference with Transformers ```python import transformers model_name = 'Intel/neural-chat-7b-v3-3' model = transformers.AutoModelForCausalLM.from_pretrained(model_name) tokenizer = transformers.AutoTokenizer.from_pretrained(model_name) def generate_response(system_input, user_input): # Format the input using the provided template prompt = f"### System:\n{system_input}\n### User:\n{user_input}\n### Assistant:\n" # Tokenize and encode the prompt inputs = tokenizer.encode(prompt, return_tensors="pt", add_special_tokens=False) # Generate a response outputs = model.generate(inputs, max_length=1000, num_return_sequences=1) response = tokenizer.decode(outputs[0], skip_special_tokens=True) # Extract only the assistant's response return response.split("### Assistant:\n")[-1] # Example usage system_input = "You are a math expert assistant. Your mission is to help users understand and solve various math problems. You should provide step-by-step solutions, explain reasonings and give the correct answer." user_input = "calculate 100 + 520 + 60" response = generate_response(system_input, user_input) print(response) # expected response """ To calculate the sum of 100, 520, and 60, we will follow these steps: 1. Add the first two numbers: 100 + 520 2. Add the result from step 1 to the third number: (100 + 520) + 60 Step 1: Add 100 and 520 100 + 520 = 620 Step 2: Add the result from step 1 to the third number (60) (620) + 60 = 680 So, the sum of 100, 520, and 60 is 680. """ ``` ### BF16 Inference with Intel Extension for Transformers and Intel Extension for Pytorch ```python from transformers import AutoTokenizer, TextStreamer import torch from intel_extension_for_transformers.transformers import AutoModelForCausalLM import intel_extension_for_pytorch as ipex model_name = "Intel/neural-chat-7b-v3-3" prompt = "Once upon a time, there existed a little girl," tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) inputs = tokenizer(prompt, return_tensors="pt").input_ids streamer = TextStreamer(tokenizer) model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16) model = ipex.optimize(model.eval(), dtype=torch.bfloat16, inplace=True, level="O1", auto_kernel_selection=True) outputs = model.generate(inputs, streamer=streamer, max_new_tokens=300) ``` ### INT4 Inference with Transformers and Intel Extension for Transformers ```python from transformers import AutoTokenizer, TextStreamer from intel_extension_for_transformers.transformers import AutoModelForCausalLM, WeightOnlyQuantConfig model_name = "Intel/neural-chat-7b-v3-3" # for int8, should set weight_dtype="int8" config = WeightOnlyQuantConfig(compute_dtype="bf16", weight_dtype="int4") prompt = "Once upon a time, there existed a little girl," tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) inputs = tokenizer(prompt, return_tensors="pt").input_ids streamer = TextStreamer(tokenizer) model = AutoModelForCausalLM.from_pretrained(model_name, quantization_config=config) outputs = model.generate(inputs, streamer=streamer, max_new_tokens=300) ```
uw-vta/bloominzer-0.1	uw-vta	2024-05-18T11:50:49Z	113	3	transformers	[ "transformers", "pytorch", "bert", "text-classification", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-18T11:24:39Z	--- license: apache-2.0 language: - en widget: - text: "What is a goat?" --- # What is the Bloominizer The bloominer is a fine-tuned version of BERT that classifies questions by the Bloom's Taxonomy level: Knowledge, Comprehension, Application, Analysis, Synthesis, Evaluation. Tests during training indicate that the Bloominizer is approximately 93% accurate in its classifications, with most misclassifications being for either one level below or above (for instance, it may misclassify a Comprehension question as a Knowledge question, but rately as an Evaluation question). The Bloominizer has been used for large-scale classification of questions from a corpus. For example, a useful usecase is to run all questions in a long multiple choice exam through the Bloominizer and compute the relative percentages of questions from the six Bloom's levels. This can give you an idea of the approximate cognitive level of the overall exam. # Using in transformers The Bloominizer is easiest to use through a pipeline. Sample code is below: ``` import transformers import torch from transformers import pipeline pipe = pipeline("text-classification", model="uw-vta/bloominzer-0.1") print(pipe("What is a goat?")) ``` If you run this code, the output should be something like: ``` [{'label': 'Knowledge', 'score': 0.9993932247161865}] ```
ankushkr2898/Taxi-v3	ankushkr2898	2024-05-18T11:38:47Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-05-18T11:38:45Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="ankushkr2898/Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
geunukj/ppo-LunarLander-v2	geunukj	2024-05-18T11:33:51Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-18T11:33:32Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 255.02 +/- 18.64 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
hardikch05/Pytest_gen	hardikch05	2024-05-18T11:33:50Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-18T11:31:50Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ar08/TINYLLAMA-LAPTOP	ar08	2024-05-18T11:32:26Z	107	0	transformers	[ "transformers", "tensorboard", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-18T11:21:24Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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PaulR79/llama_finetuned_synthetic	PaulR79	2024-05-18T11:32:20Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-18T11:32:17Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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]
RichardErkhov/yanolja_-_KoSOLAR-10.7B-v0.2-gguf	RichardErkhov	2024-05-18T11:29:56Z	52	0	null	[ "gguf", "endpoints_compatible", "region:us" ]	null	2024-05-18T09:29:25Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) KoSOLAR-10.7B-v0.2 - GGUF - Model creator: https://huggingface.co/yanolja/ - Original model: https://huggingface.co/yanolja/KoSOLAR-10.7B-v0.2/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [KoSOLAR-10.7B-v0.2.Q2_K.gguf](https://huggingface.co/RichardErkhov/yanolja_-_KoSOLAR-10.7B-v0.2-gguf/blob/main/KoSOLAR-10.7B-v0.2.Q2_K.gguf) \| Q2_K \| 3.77GB \| \| [KoSOLAR-10.7B-v0.2.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/yanolja_-_KoSOLAR-10.7B-v0.2-gguf/blob/main/KoSOLAR-10.7B-v0.2.IQ3_XS.gguf) \| IQ3_XS \| 4.18GB \| \| [KoSOLAR-10.7B-v0.2.IQ3_S.gguf](https://huggingface.co/RichardErkhov/yanolja_-_KoSOLAR-10.7B-v0.2-gguf/blob/main/KoSOLAR-10.7B-v0.2.IQ3_S.gguf) \| IQ3_S \| 4.41GB \| \| [KoSOLAR-10.7B-v0.2.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/yanolja_-_KoSOLAR-10.7B-v0.2-gguf/blob/main/KoSOLAR-10.7B-v0.2.Q3_K_S.gguf) \| Q3_K_S \| 4.39GB \| \| [KoSOLAR-10.7B-v0.2.IQ3_M.gguf](https://huggingface.co/RichardErkhov/yanolja_-_KoSOLAR-10.7B-v0.2-gguf/blob/main/KoSOLAR-10.7B-v0.2.IQ3_M.gguf) \| IQ3_M \| 4.56GB \| \| [KoSOLAR-10.7B-v0.2.Q3_K.gguf](https://huggingface.co/RichardErkhov/yanolja_-_KoSOLAR-10.7B-v0.2-gguf/blob/main/KoSOLAR-10.7B-v0.2.Q3_K.gguf) \| Q3_K \| 4.88GB \| \| [KoSOLAR-10.7B-v0.2.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/yanolja_-_KoSOLAR-10.7B-v0.2-gguf/blob/main/KoSOLAR-10.7B-v0.2.Q3_K_M.gguf) \| Q3_K_M \| 4.88GB \| \| [KoSOLAR-10.7B-v0.2.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/yanolja_-_KoSOLAR-10.7B-v0.2-gguf/blob/main/KoSOLAR-10.7B-v0.2.Q3_K_L.gguf) \| Q3_K_L \| 5.31GB \| \| [KoSOLAR-10.7B-v0.2.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/yanolja_-_KoSOLAR-10.7B-v0.2-gguf/blob/main/KoSOLAR-10.7B-v0.2.IQ4_XS.gguf) \| IQ4_XS \| 5.47GB \| \| [KoSOLAR-10.7B-v0.2.Q4_0.gguf](https://huggingface.co/RichardErkhov/yanolja_-_KoSOLAR-10.7B-v0.2-gguf/blob/main/KoSOLAR-10.7B-v0.2.Q4_0.gguf) \| Q4_0 \| 5.7GB \| \| [KoSOLAR-10.7B-v0.2.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/yanolja_-_KoSOLAR-10.7B-v0.2-gguf/blob/main/KoSOLAR-10.7B-v0.2.IQ4_NL.gguf) \| IQ4_NL \| 5.77GB \| \| [KoSOLAR-10.7B-v0.2.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/yanolja_-_KoSOLAR-10.7B-v0.2-gguf/blob/main/KoSOLAR-10.7B-v0.2.Q4_K_S.gguf) \| Q4_K_S \| 5.75GB \| \| [KoSOLAR-10.7B-v0.2.Q4_K.gguf](https://huggingface.co/RichardErkhov/yanolja_-_KoSOLAR-10.7B-v0.2-gguf/blob/main/KoSOLAR-10.7B-v0.2.Q4_K.gguf) \| Q4_K \| 6.07GB \| \| [KoSOLAR-10.7B-v0.2.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/yanolja_-_KoSOLAR-10.7B-v0.2-gguf/blob/main/KoSOLAR-10.7B-v0.2.Q4_K_M.gguf) \| Q4_K_M \| 6.07GB \| \| [KoSOLAR-10.7B-v0.2.Q4_1.gguf](https://huggingface.co/RichardErkhov/yanolja_-_KoSOLAR-10.7B-v0.2-gguf/blob/main/KoSOLAR-10.7B-v0.2.Q4_1.gguf) \| Q4_1 \| 6.32GB \| \| [KoSOLAR-10.7B-v0.2.Q5_0.gguf](https://huggingface.co/RichardErkhov/yanolja_-_KoSOLAR-10.7B-v0.2-gguf/blob/main/KoSOLAR-10.7B-v0.2.Q5_0.gguf) \| Q5_0 \| 6.94GB \| \| [KoSOLAR-10.7B-v0.2.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/yanolja_-_KoSOLAR-10.7B-v0.2-gguf/blob/main/KoSOLAR-10.7B-v0.2.Q5_K_S.gguf) \| Q5_K_S \| 6.94GB \| \| [KoSOLAR-10.7B-v0.2.Q5_K.gguf](https://huggingface.co/RichardErkhov/yanolja_-_KoSOLAR-10.7B-v0.2-gguf/blob/main/KoSOLAR-10.7B-v0.2.Q5_K.gguf) \| Q5_K \| 7.13GB \| \| [KoSOLAR-10.7B-v0.2.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/yanolja_-_KoSOLAR-10.7B-v0.2-gguf/blob/main/KoSOLAR-10.7B-v0.2.Q5_K_M.gguf) \| Q5_K_M \| 7.13GB \| \| [KoSOLAR-10.7B-v0.2.Q5_1.gguf](https://huggingface.co/RichardErkhov/yanolja_-_KoSOLAR-10.7B-v0.2-gguf/blob/main/KoSOLAR-10.7B-v0.2.Q5_1.gguf) \| Q5_1 \| 7.56GB \| \| [KoSOLAR-10.7B-v0.2.Q6_K.gguf](https://huggingface.co/RichardErkhov/yanolja_-_KoSOLAR-10.7B-v0.2-gguf/blob/main/KoSOLAR-10.7B-v0.2.Q6_K.gguf) \| Q6_K \| 8.26GB \| \| [KoSOLAR-10.7B-v0.2.Q8_0.gguf](https://huggingface.co/RichardErkhov/yanolja_-_KoSOLAR-10.7B-v0.2-gguf/blob/main/KoSOLAR-10.7B-v0.2.Q8_0.gguf) \| Q8_0 \| 10.69GB \| Original model description: --- license: apache-2.0 base_model: upstage/SOLAR-10.7B-v1.0 tags: - generated_from_trainer model-index: - name: yanolja/KoSOLAR-10.7B-v0.2 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) # KoSOLAR-10.7B-v0.2 ## Join Our Community on Discord! If you're passionate about the field of Large Language Models and wish to exchange knowledge and insights, we warmly invite you to join our Discord server. It's worth noting that Korean is the primary language used in this server. The landscape of LLM is evolving rapidly, and without active sharing, our collective knowledge risks becoming outdated swiftly. Let's collaborate and drive greater impact together! Join us here: [Discord Link](https://discord.gg/b27bAHg95m). ## Our Dedicated Team (Alphabetical Order) \| Research \| Engineering \| Product Management \| UX Design \| \|-----------------\|-----------------\|--------------------\|-------------- \| Myeongho Jeong \| Geon Kim \| Bokyung Huh \| Eunsue Choi \| \| Seungduk Kim \| Rifqi Alfi \| \| \| \| Seungtaek Choi \| Sanghoon Han \| \| \| \| \| Suhyun Kang \| \| \| ## About the Model This model is a Korean vocabulary-extended version of [upstage/SOLAR-10.7B-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-v1.0), specifically fine-tuned on various Korean web-crawled datasets available on HuggingFace. Our approach was to expand the model's understanding of Korean by pre-training the embeddings for new tokens and partially fine-tuning the `lm_head` embeddings for the already existing tokens while preserving the original parameters of the base model. ### Technical Deep Dive Here’s a glimpse into our technical approach: ```python def freeze_partial_embedding_hook(grad): grad[:32000] = 0 return grad for name, param in model.named_parameters(): if ("lm_head" in name or "embed_tokens" in name) and "original" not in name: param.requires_grad = True if "embed_tokens" in name: param.register_hook(freeze_partial_embedding_hook) else: param.requires_grad = False ``` Our strategy involved a selective freeze of model parameters. Specifically, we kept most parameters of the base model unchanged while focusing on enhancing the Korean language capabilities. Through our experiments, we discovered: 1. Freezing the `embed_tokens` layer for existing tokens is crucial to maintain overall performance. 2. Unfreezing the `lm_head` layer for existing tokens actually boosts performance. As a result, we froze the internal layers and the first 32,000 `embed_tokens`, directing our training efforts on a rich mix of Korean and multi-lingual corpora. This balanced approach has notably improved the model’s proficiency in Korean, without compromising its original language capabilities. ### Usage and Limitations Keep in mind that this model hasn't been fine-tuned with instruction-based training. While it excels in Korean language tasks, we advise careful consideration and further training for specific applications. ### Training Details Our model’s training was comprehensive and diverse: - Data Sources: - English to Korean paragraph pairs: 5.86% - Multi-lingual corpus (primarily English): 10.69% - Korean web content: 83.46% - Vocabulary Expansion: We meticulously selected 8,960 Korean tokens based on their frequency in our Korean web corpus. This process involved multiple rounds of tokenizer training, manual curation, and token frequency analysis, ensuring a rich and relevant vocabulary for our model. 1. Initial Tokenizer Training: We trained an intermediate tokenizer on a Korean web corpus, with a vocabulary of 40,000 tokens. 2. Extraction of New Korean Tokens: From the intermediate tokenizer, we identified all Korean tokens not present in the original SOLAR's tokenizer. 3. Manual Tokenizer Construction: We then built the target tokenizer, focusing on these new Korean tokens. 4. Frequency Analysis: Using the target tokenizer, we processed a 100GB Korean corpus to count each token's frequency. 5. Refinement of Token List: We removed tokens appearing less than 6,000 times, ensuring to secure enough tokens to train models later. 6. Inclusion of Single-Letter Characters: Counted missing Korean single-letter characters and added them to the target tokenizer that appeared more than 6,000 times. 7. Iterative Refinement: We repeated steps 2 to 6 until there were no tokens to drop or add. 8. Training Bias Towards New Tokens: Our training data was biased to include more texts with new tokens, for effective learning. This rigorous approach ensured a comprehensive and contextually rich Korean vocabulary for the model.
PQlet/lora-narutoblip-v1-ablation-r16-a16-module_to_q	PQlet	2024-05-18T11:23:37Z	1	0	diffusers	[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers-training", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-05-18T11:23:32Z	--- license: creativeml-openrail-m library_name: diffusers tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - diffusers-training - lora base_model: runwayml/stable-diffusion-v1-5 inference: true --- <!-- 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. --> # LoRA text2image fine-tuning - PQlet/lora-narutoblip-v1-ablation-r16-a16-module_to_q These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the Naruto-BLIP dataset. You can find some example images in the following. ![img_0](./image_a_man_with_glasses_and_a_shirt_on.png) ![img_1](./image_a_group_of_people_sitting_on_the_ground.png) ![img_2](./image_a_man_in_a_green_hoodie_standing_in_front_of_a_mountain.png) ![img_3](./image_a_man_with_a_gun_in_his_hand.png) ![img_4](./image_a_woman_with_red_hair_and_a_cat_on_her_head.png) ![img_5](./image_two_pokemons_sitting_on_top_of_a_cloud.png) ![img_6](./image_a_man_standing_in_front_of_a_bridge.png) ## 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]
Koreander/task4-2	Koreander	2024-05-18T11:19:22Z	111	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-17T23:22:47Z	--- 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]
NikolayKozloff/RoLlama2-7b-Chat-Q8_0-GGUF	NikolayKozloff	2024-05-18T11:18:29Z	0	1	null	[ "gguf", "llama-cpp", "gguf-my-repo", "ro", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	null	2024-05-18T11:18:07Z	--- language: - ro license: cc-by-nc-4.0 tags: - llama-cpp - gguf-my-repo --- # NikolayKozloff/RoLlama2-7b-Chat-Q8_0-GGUF This model was converted to GGUF format from [`OpenLLM-Ro/RoLlama2-7b-Chat`](https://huggingface.co/OpenLLM-Ro/RoLlama2-7b-Chat) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/OpenLLM-Ro/RoLlama2-7b-Chat) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo NikolayKozloff/RoLlama2-7b-Chat-Q8_0-GGUF --model rollama2-7b-chat.Q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo NikolayKozloff/RoLlama2-7b-Chat-Q8_0-GGUF --model rollama2-7b-chat.Q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m rollama2-7b-chat.Q8_0.gguf -n 128 ```
ucla-nb-project/electra-finetuned	ucla-nb-project	2024-05-18T11:11:49Z	114	0	transformers	[ "transformers", "safetensors", "electra", "fill-mask", "generated_from_trainer", "dataset:datasets/all_binary_and_xe_ey_fae_counterfactual", "base_model:google/electra-base-generator", "base_model:finetune:google/electra-base-generator", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-05-18T10:12:34Z	--- license: apache-2.0 base_model: google/electra-base-generator tags: - generated_from_trainer datasets: - datasets/all_binary_and_xe_ey_fae_counterfactual metrics: - accuracy model-index: - name: electra-base-finetuned-xe_ey_fae results: - task: name: Masked Language Modeling type: fill-mask dataset: name: datasets/all_binary_and_xe_ey_fae_counterfactual type: datasets/all_binary_and_xe_ey_fae_counterfactual metrics: - name: Accuracy type: accuracy value: 0.667333329363415 --- <!-- 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. --> # electra-base-finetuned-xe_ey_fae This model is a fine-tuned version of [google/electra-base-generator](https://huggingface.co/google/electra-base-generator) on the datasets/all_binary_and_xe_ey_fae_counterfactual dataset. It achieves the following results on the evaluation set: - Loss: 1.7211 - Accuracy: 0.6673 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 100 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 2.5359 \| 0.06 \| 500 \| 2.0696 \| 0.6228 \| \| 2.1807 \| 0.13 \| 1000 \| 1.9677 \| 0.6352 \| \| 2.1028 \| 0.19 \| 1500 \| 1.9192 \| 0.6415 \| \| 2.0658 \| 0.26 \| 2000 \| 1.8923 \| 0.6451 \| \| 2.0426 \| 0.32 \| 2500 \| 1.8699 \| 0.6478 \| \| 2.0133 \| 0.39 \| 3000 \| 1.8580 \| 0.6490 \| \| 1.9978 \| 0.45 \| 3500 \| 1.8411 \| 0.6507 \| \| 1.9862 \| 0.52 \| 4000 \| 1.8297 \| 0.6524 \| \| 1.9745 \| 0.58 \| 4500 \| 1.8154 \| 0.6545 \| \| 1.9606 \| 0.64 \| 5000 \| 1.8056 \| 0.6557 \| \| 1.9486 \| 0.71 \| 5500 \| 1.8033 \| 0.6560 \| \| 1.9416 \| 0.77 \| 6000 \| 1.7894 \| 0.6581 \| \| 1.9279 \| 0.84 \| 6500 \| 1.7848 \| 0.6582 \| \| 1.9196 \| 0.9 \| 7000 \| 1.7786 \| 0.6593 \| \| 1.9168 \| 0.97 \| 7500 \| 1.7762 \| 0.6592 \| \| 1.9123 \| 1.03 \| 8000 \| 1.7744 \| 0.6597 \| \| 1.8942 \| 1.1 \| 8500 \| 1.7625 \| 0.6611 \| \| 1.9053 \| 1.16 \| 9000 \| 1.7576 \| 0.6623 \| \| 1.898 \| 1.22 \| 9500 \| 1.7588 \| 0.6620 \| \| 1.8896 \| 1.29 \| 10000 \| 1.7518 \| 0.6625 \| \| 1.8796 \| 1.35 \| 10500 \| 1.7557 \| 0.6619 \| \| 1.8838 \| 1.42 \| 11000 \| 1.7511 \| 0.6628 \| \| 1.8869 \| 1.48 \| 11500 \| 1.7437 \| 0.6640 \| \| 1.8756 \| 1.55 \| 12000 \| 1.7425 \| 0.6641 \| \| 1.8775 \| 1.61 \| 12500 \| 1.7409 \| 0.6641 \| \| 1.8757 \| 1.68 \| 13000 \| 1.7372 \| 0.6649 \| \| 1.8616 \| 1.74 \| 13500 \| 1.7387 \| 0.6646 \| \| 1.8675 \| 1.8 \| 14000 \| 1.7335 \| 0.6648 \| \| 1.8725 \| 1.87 \| 14500 \| 1.7288 \| 0.6660 \| \| 1.8678 \| 1.93 \| 15000 \| 1.7305 \| 0.6659 \| \| 1.8611 \| 2.0 \| 15500 \| 1.7256 \| 0.6666 \| \| 1.853 \| 2.06 \| 16000 \| 1.7286 \| 0.6661 \| \| 1.8487 \| 2.13 \| 16500 \| 1.7285 \| 0.6659 \| \| 1.8543 \| 2.19 \| 17000 \| 1.7229 \| 0.6668 \| \| 1.8519 \| 2.26 \| 17500 \| 1.7240 \| 0.6670 \| \| 1.851 \| 2.32 \| 18000 \| 1.7275 \| 0.6662 \| \| 1.8547 \| 2.38 \| 18500 \| 1.7197 \| 0.6673 \| \| 1.8476 \| 2.45 \| 19000 \| 1.7164 \| 0.6675 \| \| 1.8444 \| 2.51 \| 19500 \| 1.7214 \| 0.6676 \| \| 1.8544 \| 2.58 \| 20000 \| 1.7217 \| 0.6668 \| \| 1.8491 \| 2.64 \| 20500 \| 1.7175 \| 0.6678 \| ### Framework versions - Transformers 4.36.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2
stablediffusionapi/aiponyanime	stablediffusionapi	2024-05-18T11:10:15Z	30	1	diffusers	[ "diffusers", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2024-05-18T11:07:31Z	--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # AiPonyAnime API Inference ![generated from modelslab.com](https://pub-3626123a908346a7a8be8d9295f44e26.r2.dev/generations/2052428931716030210.png) ## Get API Key Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. Replace Key in below code, change model_id to "aiponyanime" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://docs.modelslab.com) Try model for free: [Generate Images](https://modelslab.com/models/aiponyanime) Model link: [View model](https://modelslab.com/models/aiponyanime) View all models: [View Models](https://modelslab.com/models) import requests import json url = "https://modelslab.com/api/v6/images/text2img" payload = json.dumps({ "key": "your_api_key", "model_id": "aiponyanime", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off DMGG0RBN
LoneStriker/dolphin-2.9.1-yi-1.5-34b-6.0bpw-h6-exl2	LoneStriker	2024-05-18T11:10:09Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "axolotl", "conversational", "dataset:cognitivecomputations/Dolphin-2.9", "dataset:teknium/OpenHermes-2.5", "dataset:m-a-p/CodeFeedback-Filtered-Instruction", "dataset:cognitivecomputations/dolphin-coder", "dataset:cognitivecomputations/samantha-data", "dataset:microsoft/orca-math-word-problems-200k", "dataset:Locutusque/function-calling-chatml", "dataset:internlm/Agent-FLAN", "base_model:01-ai/Yi-1.5-34B", "base_model:quantized:01-ai/Yi-1.5-34B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "6-bit", "exl2", "region:us" ]	text-generation	2024-05-18T10:59:24Z	--- license: apache-2.0 base_model: 01-ai/Yi-1.5-34B tags: - generated_from_trainer - axolotl datasets: - cognitivecomputations/Dolphin-2.9 - teknium/OpenHermes-2.5 - m-a-p/CodeFeedback-Filtered-Instruction - cognitivecomputations/dolphin-coder - cognitivecomputations/samantha-data - microsoft/orca-math-word-problems-200k - Locutusque/function-calling-chatml - internlm/Agent-FLAN --- # Dolphin 2.9.1 Yi 1.5 34b 🐬 Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations This is our most spectacular outcome ever. FFT, all parameters, 16bit. 77.4 MMLU on 34b. And it talks like a dream. Although the max positional embeddings is 4k, we used rope theta of 1000000.0 and we trained with sequence length 8k. We plan to train on the upcoming 32k version as well. Discord: https://discord.gg/8fbBeC7ZGx <img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/ldkN1J0WIDQwU4vutGYiD.png" width="600" /> Our appreciation for the sponsors of Dolphin 2.9.1: - [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 8xH100 node - [OnDemand](https://on-demand.io/) - provided inference sponsorship This model is based on Yi-1.5-34b, and is governed by apache 2.0 license. The base model has 4k context, but we used rope theta of 1000000.0 and the full-weight fine-tuning was with 8k sequence length. Dolphin 2.9.1 uses ChatML prompt template format. example: ``` <\|im_start\|>system You are Dolphin, a helpful AI assistant.<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` Dolphin-2.9.1 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling. Dolphin is uncensored. We have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models You are responsible for any content you create using this model. Enjoy responsibly. Dolphin is licensed according to apache 2.0 license. We grant permission for any use, including commercial. Dolphin was trained on data generated from GPT4, among other models. ## Evals ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/coI4WEJEJD4lhSWgMOjIr.png) ## Training [<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: 01-ai/Yi-1.5-34B model_type: LlamaForCausalLM tokenizer_type: LlamaTokenizer trust_remote_code: true # load_in_8bit: false # load_in_4bit: true # strict: false # adapter: qlora # lora_modules_to_save: [embed_tokens, lm_head] # lora_r: 32 # lora_alpha: 16 # lora_dropout: 0.05 # lora_target_linear: True # lora_fan_in_fan_out: datasets: - path: /workspace/datasets/dolphin-2.9/dolphin201-sharegpt2.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/dolphin-coder-translate-sharegpt2.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/dolphin-coder-codegen-sharegpt2.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/m-a-p_Code-Feedback-sharegpt-unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/m-a-p_CodeFeedback-Filtered-Instruction-sharegpt-unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/not_samantha_norefusals.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/Orca-Math-resort-unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/agent_instruct_react_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_instruct_j1s1_3k_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_negative_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_react_10p_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_tflan_cot_30p_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/openhermes200k_unfiltered.jsonl type: sharegpt conversation: chatml chat_template: chatml dataset_prepared_path: yi34b val_set_size: 0.01 output_dir: ./out-yi sequence_len: 8192 sample_packing: true pad_to_sequence_len: true wandb_project: dolphin-2.9-yi-34b wandb_watch: wandb_run_id: wandb_log_model: gradient_accumulation_steps: 8 micro_batch_size: 1 num_epochs: 3 optimizer: adamw_8bit lr_scheduler: cosine learning_rate: 1e-5 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: true gradient_checkpointing: true gradient_checkpointing_kwargs: use_reentrant: false early_stopping_patience: # resume_from_checkpoint: /workspace/axolotl/dbrx-checkpoint logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 10 evals_per_epoch: 4 eval_table_size: saves_per_epoch: 4 save_total_limit: 2 save_steps: debug: deepspeed: /workspace/axolotl/deepspeed_configs/zero3_bf16.json weight_decay: 0.05 fsdp: fsdp_config: special_tokens: bos_token: "<\|startoftext\|>" eos_token: "<\|im_end\|>" pad_token: "<unk>" unk_token: "<unk>" tokens: - "<\|im_start\|>" ``` </details><br> # out-yi This model is a fine-tuned version of [01-ai/Yi-1.5-34B](https://huggingface.co/01-ai/Yi-1.5-34B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4425 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.6265 \| 0.0 \| 1 \| 0.6035 \| \| 0.4674 \| 0.25 \| 327 \| 0.4344 \| \| 0.4337 \| 0.5 \| 654 \| 0.4250 \| \| 0.4346 \| 0.75 \| 981 \| 0.4179 \| \| 0.3985 \| 1.0 \| 1308 \| 0.4118 \| \| 0.3128 \| 1.23 \| 1635 \| 0.4201 \| \| 0.3261 \| 1.48 \| 1962 \| 0.4157 \| \| 0.3259 \| 1.73 \| 2289 \| 0.4122 \| \| 0.3126 \| 1.98 \| 2616 \| 0.4079 \| \| 0.2265 \| 2.21 \| 2943 \| 0.4441 \| \| 0.2297 \| 2.46 \| 3270 \| 0.4427 \| \| 0.2424 \| 2.71 \| 3597 \| 0.4425 \| ### Framework versions - Transformers 4.40.0.dev0 - Pytorch 2.2.2+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
NikolayKozloff/RoLlama2-7b-Instruct-Q8_0-GGUF	NikolayKozloff	2024-05-18T11:02:07Z	3	1	null	[ "gguf", "llama-cpp", "gguf-my-repo", "ro", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	null	2024-05-18T11:01:49Z	--- language: - ro license: cc-by-nc-4.0 tags: - llama-cpp - gguf-my-repo --- # NikolayKozloff/RoLlama2-7b-Instruct-Q8_0-GGUF This model was converted to GGUF format from [`OpenLLM-Ro/RoLlama2-7b-Instruct`](https://huggingface.co/OpenLLM-Ro/RoLlama2-7b-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/OpenLLM-Ro/RoLlama2-7b-Instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo NikolayKozloff/RoLlama2-7b-Instruct-Q8_0-GGUF --model rollama2-7b-instruct.Q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo NikolayKozloff/RoLlama2-7b-Instruct-Q8_0-GGUF --model rollama2-7b-instruct.Q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m rollama2-7b-instruct.Q8_0.gguf -n 128 ```
tuquyennnn/Bart-base-v2	tuquyennnn	2024-05-18T10:56:31Z	122	0	transformers	[ "transformers", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "base_model:facebook/bart-base", "base_model:finetune:facebook/bart-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-18T10:56:20Z	--- license: apache-2.0 base_model: facebook/bart-base tags: - generated_from_trainer model-index: - name: Bart-base-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. --> # Bart-base-v2 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: nan ## 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: 3 - eval_batch_size: 3 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 12 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 985982.912 \| 0.24 \| 250 \| nan \| \| 0.0 \| 0.48 \| 500 \| nan \| \| 0.0 \| 0.72 \| 750 \| nan \| \| 0.0 \| 0.96 \| 1000 \| nan \| \| 0.0 \| 1.2 \| 1250 \| nan \| \| 0.0 \| 1.44 \| 1500 \| nan \| \| 0.0 \| 1.69 \| 1750 \| nan \| \| 0.0 \| 1.93 \| 2000 \| nan \| \| 0.0 \| 2.17 \| 2250 \| nan \| \| 0.0 \| 2.41 \| 2500 \| nan \| \| 0.0 \| 2.65 \| 2750 \| nan \| \| 0.0 \| 2.89 \| 3000 \| nan \| \| 0.0 \| 3.13 \| 3250 \| nan \| \| 0.0 \| 3.37 \| 3500 \| nan \| \| 0.0 \| 3.61 \| 3750 \| nan \| \| 0.0 \| 3.85 \| 4000 \| nan \| \| 0.0 \| 4.09 \| 4250 \| nan \| \| 0.0 \| 4.33 \| 4500 \| nan \| \| 0.0 \| 4.57 \| 4750 \| nan \| \| 0.0 \| 4.81 \| 5000 \| nan \| \| 0.0 \| 5.06 \| 5250 \| nan \| \| 0.0 \| 5.3 \| 5500 \| nan \| \| 0.0 \| 5.54 \| 5750 \| nan \| \| 0.0 \| 5.78 \| 6000 \| nan \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.19.1 - Tokenizers 0.15.2
SurtMcGert/NLP-group-CW-test	SurtMcGert	2024-05-18T10:54:58Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-18T10:54:57Z	--- 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]
amithm3/whisper-small	amithm3	2024-05-18T10:54:24Z	94	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "kn", "dataset:amithm3/shrutilipi", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-18T02:52:28Z	--- language: - kn license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - amithm3/shrutilipi model-index: - name: Whisper Small Kn - Amith Mundur results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Kn - Amith Mundur This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the AI4Bharat Shrutilipi dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 10000 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.40.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
AneeqMalik/llama3_gearchain_model	AneeqMalik	2024-05-18T10:49:12Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-18T10:48:57Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: AneeqMalik - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
quangantang/Mistral-7B-Instruct-v0.2-GPTQ-Discharge-Instructions	quangantang	2024-05-18T10:48:03Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "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	2024-05-18T10:45:16Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: TheBloke/Mistral-7B-Instruct-v0.2-GPTQ model-index: - name: Mistral-7B-Instruct-v0.2-GPTQ-Brief-Hospital-Course results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Mistral-7B-Instruct-v0.2-GPTQ-Brief-Hospital-Course 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 the None dataset. The model is part of the work submitted to the Discharge Me! Shared Task instruction-fintuned for generating the 'Discharge Instructions' section in the discharge summary. ## 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 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 1.0 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.38.0 - Pytorch 2.2.1 - Datasets 2.18.0 - Tokenizers 0.15.2
quangantang/Mistral-7B-Instruct-v0.2-GPTQ-Brief-Hospital-Course-Old	quangantang	2024-05-18T10:34:31Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "en", "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	2024-05-18T10:28:20Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: TheBloke/Mistral-7B-Instruct-v0.2-GPTQ model-index: - name: Mistral-7B-Instruct-v0.2-GPTQ-Brief-Hospital-Course results: [] language: - en --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Mistral-7B-Instruct-v0.2-GPTQ-Brief-Hospital-Course 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 the None dataset. The model is part of the work submitted to the Discharge Me! Shared Task instruction-fintuned for generating the 'Brief Hospital Course' section in the discharge summary. ## 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 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 1.0 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.38.0 - Pytorch 2.2.1 - Datasets 2.18.0 - Tokenizers 0.15.2
LoneStriker/dolphin-2.9.1-yi-1.5-34b-3.0bpw-h6-exl2	LoneStriker	2024-05-18T10:34:12Z	10	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "axolotl", "conversational", "dataset:cognitivecomputations/Dolphin-2.9", "dataset:teknium/OpenHermes-2.5", "dataset:m-a-p/CodeFeedback-Filtered-Instruction", "dataset:cognitivecomputations/dolphin-coder", "dataset:cognitivecomputations/samantha-data", "dataset:microsoft/orca-math-word-problems-200k", "dataset:Locutusque/function-calling-chatml", "dataset:internlm/Agent-FLAN", "base_model:01-ai/Yi-1.5-34B", "base_model:quantized:01-ai/Yi-1.5-34B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "3-bit", "exl2", "region:us" ]	text-generation	2024-05-18T10:28:30Z	--- license: apache-2.0 base_model: 01-ai/Yi-1.5-34B tags: - generated_from_trainer - axolotl datasets: - cognitivecomputations/Dolphin-2.9 - teknium/OpenHermes-2.5 - m-a-p/CodeFeedback-Filtered-Instruction - cognitivecomputations/dolphin-coder - cognitivecomputations/samantha-data - microsoft/orca-math-word-problems-200k - Locutusque/function-calling-chatml - internlm/Agent-FLAN --- # Dolphin 2.9.1 Yi 1.5 34b 🐬 Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations This is our most spectacular outcome ever. FFT, all parameters, 16bit. 77.4 MMLU on 34b. And it talks like a dream. Although the max positional embeddings is 4k, we used rope theta of 1000000.0 and we trained with sequence length 8k. We plan to train on the upcoming 32k version as well. Discord: https://discord.gg/8fbBeC7ZGx <img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/ldkN1J0WIDQwU4vutGYiD.png" width="600" /> Our appreciation for the sponsors of Dolphin 2.9.1: - [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 8xH100 node - [OnDemand](https://on-demand.io/) - provided inference sponsorship This model is based on Yi-1.5-34b, and is governed by apache 2.0 license. The base model has 4k context, but we used rope theta of 1000000.0 and the full-weight fine-tuning was with 8k sequence length. Dolphin 2.9.1 uses ChatML prompt template format. example: ``` <\|im_start\|>system You are Dolphin, a helpful AI assistant.<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` Dolphin-2.9.1 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling. Dolphin is uncensored. We have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models You are responsible for any content you create using this model. Enjoy responsibly. Dolphin is licensed according to apache 2.0 license. We grant permission for any use, including commercial. Dolphin was trained on data generated from GPT4, among other models. ## Evals ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/coI4WEJEJD4lhSWgMOjIr.png) ## Training [<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: 01-ai/Yi-1.5-34B model_type: LlamaForCausalLM tokenizer_type: LlamaTokenizer trust_remote_code: true # load_in_8bit: false # load_in_4bit: true # strict: false # adapter: qlora # lora_modules_to_save: [embed_tokens, lm_head] # lora_r: 32 # lora_alpha: 16 # lora_dropout: 0.05 # lora_target_linear: True # lora_fan_in_fan_out: datasets: - path: /workspace/datasets/dolphin-2.9/dolphin201-sharegpt2.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/dolphin-coder-translate-sharegpt2.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/dolphin-coder-codegen-sharegpt2.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/m-a-p_Code-Feedback-sharegpt-unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/m-a-p_CodeFeedback-Filtered-Instruction-sharegpt-unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/not_samantha_norefusals.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/Orca-Math-resort-unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/agent_instruct_react_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_instruct_j1s1_3k_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_negative_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_react_10p_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/toolbench_tflan_cot_30p_unfiltered.jsonl type: sharegpt conversation: chatml - path: /workspace/datasets/dolphin-2.9/openhermes200k_unfiltered.jsonl type: sharegpt conversation: chatml chat_template: chatml dataset_prepared_path: yi34b val_set_size: 0.01 output_dir: ./out-yi sequence_len: 8192 sample_packing: true pad_to_sequence_len: true wandb_project: dolphin-2.9-yi-34b wandb_watch: wandb_run_id: wandb_log_model: gradient_accumulation_steps: 8 micro_batch_size: 1 num_epochs: 3 optimizer: adamw_8bit lr_scheduler: cosine learning_rate: 1e-5 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: true gradient_checkpointing: true gradient_checkpointing_kwargs: use_reentrant: false early_stopping_patience: # resume_from_checkpoint: /workspace/axolotl/dbrx-checkpoint logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 10 evals_per_epoch: 4 eval_table_size: saves_per_epoch: 4 save_total_limit: 2 save_steps: debug: deepspeed: /workspace/axolotl/deepspeed_configs/zero3_bf16.json weight_decay: 0.05 fsdp: fsdp_config: special_tokens: bos_token: "<\|startoftext\|>" eos_token: "<\|im_end\|>" pad_token: "<unk>" unk_token: "<unk>" tokens: - "<\|im_start\|>" ``` </details><br> # out-yi This model is a fine-tuned version of [01-ai/Yi-1.5-34B](https://huggingface.co/01-ai/Yi-1.5-34B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4425 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.6265 \| 0.0 \| 1 \| 0.6035 \| \| 0.4674 \| 0.25 \| 327 \| 0.4344 \| \| 0.4337 \| 0.5 \| 654 \| 0.4250 \| \| 0.4346 \| 0.75 \| 981 \| 0.4179 \| \| 0.3985 \| 1.0 \| 1308 \| 0.4118 \| \| 0.3128 \| 1.23 \| 1635 \| 0.4201 \| \| 0.3261 \| 1.48 \| 1962 \| 0.4157 \| \| 0.3259 \| 1.73 \| 2289 \| 0.4122 \| \| 0.3126 \| 1.98 \| 2616 \| 0.4079 \| \| 0.2265 \| 2.21 \| 2943 \| 0.4441 \| \| 0.2297 \| 2.46 \| 3270 \| 0.4427 \| \| 0.2424 \| 2.71 \| 3597 \| 0.4425 \| ### Framework versions - Transformers 4.40.0.dev0 - Pytorch 2.2.2+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
marczenko/timit-ft	marczenko	2024-05-18T10:33:46Z	78	0	transformers	[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dataset:timit_asr", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-18T10:21:30Z	--- license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - timit_asr model-index: - name: timit-ft 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. --> # timit-ft This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the timit_asr dataset. It achieves the following results on the evaluation set: - eval_loss: 0.7722 - eval_wer: 7.1566 - eval_runtime: 335.4678 - eval_samples_per_second: 5.008 - eval_steps_per_second: 0.158 - step: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 64 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.39.3 - Pytorch 2.0.1+cu117 - Datasets 2.18.0 - Tokenizers 0.15.2
LarryAIDraw/echidna-12	LarryAIDraw	2024-05-18T10:28:14Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-05-18T10:27:01Z	--- license: creativeml-openrail-m --- https://civitai.com/models/105117/rezero-or-echidna
LarryAIDraw/echidna2-000009	LarryAIDraw	2024-05-18T10:27:29Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-05-18T10:25:34Z	--- license: creativeml-openrail-m --- https://civitai.com/models/6492/echidna-rezero-or-character-lora-252
Talha185/speecht5_finetuned_voxpopuli_nl2	Talha185	2024-05-18T10:19:29Z	75	0	transformers	[ "transformers", "tensorboard", "safetensors", "speecht5", "text-to-audio", "generated_from_trainer", "dataset:common_voice_13_0", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]	text-to-audio	2024-05-15T10:48:59Z	--- license: mit base_model: microsoft/speecht5_tts tags: - generated_from_trainer datasets: - common_voice_13_0 model-index: - name: speecht5_finetuned_voxpopuli_nl2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # speecht5_finetuned_voxpopuli_nl2 This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the common_voice_13_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.4723 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 2000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-------:\|:----:\|:---------------:\| \| 0.5424 \| 4.3057 \| 500 \| 0.4988 \| \| 0.5093 \| 8.6114 \| 1000 \| 0.4795 \| \| 0.4886 \| 12.9171 \| 1500 \| 0.4686 \| \| 0.4656 \| 17.2228 \| 2000 \| 0.4723 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
emilykang/Gemma_medner-soap_chart_progressnotes	emilykang	2024-05-18T10:17:14Z	154	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T22:08:21Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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emilykang/Gemma_medner-gastroenterology	emilykang	2024-05-18T10:16:46Z	154	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T21:44:06Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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emilykang/medner-urology	emilykang	2024-05-18T10:16:23Z	153	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T19:00:09Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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emilykang/Gemma_medner-obstetrics_gynecology	emilykang	2024-05-18T10:16:19Z	154	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T21:16:40Z	--- 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. 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emilykang/medner-soap_chart_progressnotes	emilykang	2024-05-18T10:16:10Z	153	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T18:49:44Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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emilykang/medner-gastroenterology	emilykang	2024-05-18T10:15:59Z	154	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T18:40:07Z	--- 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. 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emilykang/medner-neurology	emilykang	2024-05-18T10:15:36Z	153	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T18:14:48Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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emilykang/medner-generalmedicine	emilykang	2024-05-18T10:15:25Z	153	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T18:01:18Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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emilykang/medner-orthopedic	emilykang	2024-05-18T10:15:13Z	153	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T17:43:53Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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emilykang/medner-surgery	emilykang	2024-05-18T10:15:00Z	153	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T17:20:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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emilykang/medner-consult-historyandphy	emilykang	2024-05-18T10:14:46Z	154	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T16:30:32Z	--- 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. 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emilykang/Gemma_medner-surgery	emilykang	2024-05-18T10:14:22Z	153	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T19:11:19Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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emilykang/Gemma_medner-consult-historyandphy	emilykang	2024-05-18T10:13:52Z	153	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T17:44:12Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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(2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
emilykang/Gemma_medner-cardiovascular_pulmonary	emilykang	2024-05-18T10:13:22Z	3	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T14:36:43Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
antitheft159/intcomboson	antitheft159	2024-05-18T10:11:46Z	0	0	null	[ "license:cc-by-nc-sa-4.0", "region:us" ]	null	2024-05-18T10:11:04Z	--- license: cc-by-nc-sa-4.0 ---
KnutJaegersberg/Deita-34b-exl-8.0bpw	KnutJaegersberg	2024-05-18T10:10:14Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T15:52:46Z	--- license: apache-2.0 ---
egepaksoy/elalem	egepaksoy	2024-05-18T10:08:27Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-18T10:08:27Z	--- license: apache-2.0 ---
YorkieOH10/dolphin-2.9.1-yi-1.5-34b-Q8_0-GGUF	YorkieOH10	2024-05-18T09:57:44Z	1	0	null	[ "gguf", "generated_from_trainer", "axolotl", "llama-cpp", "gguf-my-repo", "dataset:cognitivecomputations/Dolphin-2.9", "dataset:teknium/OpenHermes-2.5", "dataset:m-a-p/CodeFeedback-Filtered-Instruction", "dataset:cognitivecomputations/dolphin-coder", "dataset:cognitivecomputations/samantha-data", "dataset:microsoft/orca-math-word-problems-200k", "dataset:Locutusque/function-calling-chatml", "dataset:internlm/Agent-FLAN", "base_model:01-ai/Yi-1.5-34B", "base_model:quantized:01-ai/Yi-1.5-34B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-18T09:56:22Z	--- license: apache-2.0 tags: - generated_from_trainer - axolotl - llama-cpp - gguf-my-repo base_model: 01-ai/Yi-1.5-34B datasets: - cognitivecomputations/Dolphin-2.9 - teknium/OpenHermes-2.5 - m-a-p/CodeFeedback-Filtered-Instruction - cognitivecomputations/dolphin-coder - cognitivecomputations/samantha-data - microsoft/orca-math-word-problems-200k - Locutusque/function-calling-chatml - internlm/Agent-FLAN --- # YorkieOH10/dolphin-2.9.1-yi-1.5-34b-Q8_0-GGUF This model was converted to GGUF format from [`cognitivecomputations/dolphin-2.9.1-yi-1.5-34b`](https://huggingface.co/cognitivecomputations/dolphin-2.9.1-yi-1.5-34b) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/cognitivecomputations/dolphin-2.9.1-yi-1.5-34b) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo YorkieOH10/dolphin-2.9.1-yi-1.5-34b-Q8_0-GGUF --model dolphin-2.9.1-yi-1.5-34b.Q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo YorkieOH10/dolphin-2.9.1-yi-1.5-34b-Q8_0-GGUF --model dolphin-2.9.1-yi-1.5-34b.Q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m dolphin-2.9.1-yi-1.5-34b.Q8_0.gguf -n 128 ```
euiyulsong/ORPO-synth1k-20kdomaintask-semi	euiyulsong	2024-05-18T09:47:27Z	79	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "trl", "orpo", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-18T09:43:10Z	--- library_name: transformers tags: - trl - orpo --- # 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]
theegnas/llama3-8b-4bit-swedish	theegnas	2024-05-18T09:42:06Z	0	0	null	[ "safetensors", "region:us" ]	null	2024-05-18T09:23:36Z	### LLAMA 3 8B from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("undefined") model.load_adapter("theegnas/llama3-8b-4bit-swedish", set_active=True) --- license: apache-2.0 datasets: - yahma/alpaca-cleaned - jeremyc/Alpaca-Lora-GPT4-Swedish language: - en - sv library_name: adapter-transformers pipeline_tag: text-generation ---
tjasad/prompt_fine_tuned_boolq_googlemt_sloberta	tjasad	2024-05-18T09:30:54Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:EMBEDDIA/sloberta", "base_model:adapter:EMBEDDIA/sloberta", "license:cc-by-sa-4.0", "region:us" ]	null	2024-05-18T09:30:48Z	--- license: cc-by-sa-4.0 library_name: peft tags: - generated_from_trainer base_model: EMBEDDIA/sloberta metrics: - accuracy - f1 model-index: - name: prompt_fine_tuned_boolq_googlemt_sloberta 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. --> # prompt_fine_tuned_boolq_googlemt_sloberta This model is a fine-tuned version of [EMBEDDIA/sloberta](https://huggingface.co/EMBEDDIA/sloberta) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6648 - Accuracy: 0.6187 - F1: 0.4828 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 400 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.702 \| 0.0424 \| 50 \| 0.6852 \| 0.5856 \| 0.5231 \| \| 0.6764 \| 0.0848 \| 100 \| 0.6712 \| 0.6061 \| 0.5086 \| \| 0.6879 \| 0.1272 \| 150 \| 0.6696 \| 0.6052 \| 0.5037 \| \| 0.6585 \| 0.1696 \| 200 \| 0.6670 \| 0.6116 \| 0.4966 \| \| 0.6559 \| 0.2120 \| 250 \| 0.6655 \| 0.6107 \| 0.5001 \| \| 0.6648 \| 0.2545 \| 300 \| 0.6649 \| 0.6138 \| 0.4849 \| \| 0.6715 \| 0.2969 \| 350 \| 0.6648 \| 0.6190 \| 0.4834 \| \| 0.6773 \| 0.3393 \| 400 \| 0.6648 \| 0.6187 \| 0.4828 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
lgk03/NDD-petclinic_test-tags	lgk03	2024-05-18T09:29:29Z	121	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	2024-05-18T08:13:13Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: NDD-petclinic_test-tags 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. --> # NDD-petclinic_test-tags 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: 0.2183 - Accuracy: 0.8535 - F1: 0.7861 - Precision: 0.7285 - Recall: 0.8535 ## 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: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - 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 \| F1 \| Precision \| Recall \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| 0.1985 \| 0.9993 \| 674 \| 0.2183 \| 0.8535 \| 0.7861 \| 0.7285 \| 0.8535 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
gordonweng/llama3_chinese_med_lora	gordonweng	2024-05-18T09:26:34Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:shenzhi-wang/Llama3-8B-Chinese-Chat", "base_model:finetune:shenzhi-wang/Llama3-8B-Chinese-Chat", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-18T07:39:05Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: shenzhi-wang/Llama3-8B-Chinese-Chat --- # Uploaded model - Developed by: gordonweng - License: apache-2.0 - Finetuned from model : shenzhi-wang/Llama3-8B-Chinese-Chat This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
RichardErkhov/yanolja_-_KoSOLAR-10.7B-v0.2-8bits	RichardErkhov	2024-05-18T09:25:57Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-18T09:18:21Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) KoSOLAR-10.7B-v0.2 - bnb 8bits - Model creator: https://huggingface.co/yanolja/ - Original model: https://huggingface.co/yanolja/KoSOLAR-10.7B-v0.2/ Original model description: --- license: apache-2.0 base_model: upstage/SOLAR-10.7B-v1.0 tags: - generated_from_trainer model-index: - name: yanolja/KoSOLAR-10.7B-v0.2 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) # KoSOLAR-10.7B-v0.2 ## Join Our Community on Discord! If you're passionate about the field of Large Language Models and wish to exchange knowledge and insights, we warmly invite you to join our Discord server. It's worth noting that Korean is the primary language used in this server. The landscape of LLM is evolving rapidly, and without active sharing, our collective knowledge risks becoming outdated swiftly. Let's collaborate and drive greater impact together! Join us here: [Discord Link](https://discord.gg/b27bAHg95m). ## Our Dedicated Team (Alphabetical Order) \| Research \| Engineering \| Product Management \| UX Design \| \|-----------------\|-----------------\|--------------------\|-------------- \| Myeongho Jeong \| Geon Kim \| Bokyung Huh \| Eunsue Choi \| \| Seungduk Kim \| Rifqi Alfi \| \| \| \| Seungtaek Choi \| Sanghoon Han \| \| \| \| \| Suhyun Kang \| \| \| ## About the Model This model is a Korean vocabulary-extended version of [upstage/SOLAR-10.7B-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-v1.0), specifically fine-tuned on various Korean web-crawled datasets available on HuggingFace. Our approach was to expand the model's understanding of Korean by pre-training the embeddings for new tokens and partially fine-tuning the `lm_head` embeddings for the already existing tokens while preserving the original parameters of the base model. ### Technical Deep Dive Here’s a glimpse into our technical approach: ```python def freeze_partial_embedding_hook(grad): grad[:32000] = 0 return grad for name, param in model.named_parameters(): if ("lm_head" in name or "embed_tokens" in name) and "original" not in name: param.requires_grad = True if "embed_tokens" in name: param.register_hook(freeze_partial_embedding_hook) else: param.requires_grad = False ``` Our strategy involved a selective freeze of model parameters. Specifically, we kept most parameters of the base model unchanged while focusing on enhancing the Korean language capabilities. Through our experiments, we discovered: 1. Freezing the `embed_tokens` layer for existing tokens is crucial to maintain overall performance. 2. Unfreezing the `lm_head` layer for existing tokens actually boosts performance. As a result, we froze the internal layers and the first 32,000 `embed_tokens`, directing our training efforts on a rich mix of Korean and multi-lingual corpora. This balanced approach has notably improved the model’s proficiency in Korean, without compromising its original language capabilities. ### Usage and Limitations Keep in mind that this model hasn't been fine-tuned with instruction-based training. While it excels in Korean language tasks, we advise careful consideration and further training for specific applications. ### Training Details Our model’s training was comprehensive and diverse: - Data Sources: - English to Korean paragraph pairs: 5.86% - Multi-lingual corpus (primarily English): 10.69% - Korean web content: 83.46% - Vocabulary Expansion: We meticulously selected 8,960 Korean tokens based on their frequency in our Korean web corpus. This process involved multiple rounds of tokenizer training, manual curation, and token frequency analysis, ensuring a rich and relevant vocabulary for our model. 1. Initial Tokenizer Training: We trained an intermediate tokenizer on a Korean web corpus, with a vocabulary of 40,000 tokens. 2. Extraction of New Korean Tokens: From the intermediate tokenizer, we identified all Korean tokens not present in the original SOLAR's tokenizer. 3. Manual Tokenizer Construction: We then built the target tokenizer, focusing on these new Korean tokens. 4. Frequency Analysis: Using the target tokenizer, we processed a 100GB Korean corpus to count each token's frequency. 5. Refinement of Token List: We removed tokens appearing less than 6,000 times, ensuring to secure enough tokens to train models later. 6. Inclusion of Single-Letter Characters: Counted missing Korean single-letter characters and added them to the target tokenizer that appeared more than 6,000 times. 7. Iterative Refinement: We repeated steps 2 to 6 until there were no tokens to drop or add. 8. Training Bias Towards New Tokens: Our training data was biased to include more texts with new tokens, for effective learning. This rigorous approach ensured a comprehensive and contextually rich Korean vocabulary for the model.
tistak/sn6_2	tistak	2024-05-18T09:21:51Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-13T08:20:58Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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RichardErkhov/yanolja_-_KoSOLAR-10.7B-v0.2-4bits	RichardErkhov	2024-05-18T09:17:24Z	79	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-18T09:12:47Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) KoSOLAR-10.7B-v0.2 - bnb 4bits - Model creator: https://huggingface.co/yanolja/ - Original model: https://huggingface.co/yanolja/KoSOLAR-10.7B-v0.2/ Original model description: --- license: apache-2.0 base_model: upstage/SOLAR-10.7B-v1.0 tags: - generated_from_trainer model-index: - name: yanolja/KoSOLAR-10.7B-v0.2 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) # KoSOLAR-10.7B-v0.2 ## Join Our Community on Discord! If you're passionate about the field of Large Language Models and wish to exchange knowledge and insights, we warmly invite you to join our Discord server. It's worth noting that Korean is the primary language used in this server. The landscape of LLM is evolving rapidly, and without active sharing, our collective knowledge risks becoming outdated swiftly. Let's collaborate and drive greater impact together! Join us here: [Discord Link](https://discord.gg/b27bAHg95m). ## Our Dedicated Team (Alphabetical Order) \| Research \| Engineering \| Product Management \| UX Design \| \|-----------------\|-----------------\|--------------------\|-------------- \| Myeongho Jeong \| Geon Kim \| Bokyung Huh \| Eunsue Choi \| \| Seungduk Kim \| Rifqi Alfi \| \| \| \| Seungtaek Choi \| Sanghoon Han \| \| \| \| \| Suhyun Kang \| \| \| ## About the Model This model is a Korean vocabulary-extended version of [upstage/SOLAR-10.7B-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-v1.0), specifically fine-tuned on various Korean web-crawled datasets available on HuggingFace. Our approach was to expand the model's understanding of Korean by pre-training the embeddings for new tokens and partially fine-tuning the `lm_head` embeddings for the already existing tokens while preserving the original parameters of the base model. ### Technical Deep Dive Here’s a glimpse into our technical approach: ```python def freeze_partial_embedding_hook(grad): grad[:32000] = 0 return grad for name, param in model.named_parameters(): if ("lm_head" in name or "embed_tokens" in name) and "original" not in name: param.requires_grad = True if "embed_tokens" in name: param.register_hook(freeze_partial_embedding_hook) else: param.requires_grad = False ``` Our strategy involved a selective freeze of model parameters. Specifically, we kept most parameters of the base model unchanged while focusing on enhancing the Korean language capabilities. Through our experiments, we discovered: 1. Freezing the `embed_tokens` layer for existing tokens is crucial to maintain overall performance. 2. Unfreezing the `lm_head` layer for existing tokens actually boosts performance. As a result, we froze the internal layers and the first 32,000 `embed_tokens`, directing our training efforts on a rich mix of Korean and multi-lingual corpora. This balanced approach has notably improved the model’s proficiency in Korean, without compromising its original language capabilities. ### Usage and Limitations Keep in mind that this model hasn't been fine-tuned with instruction-based training. While it excels in Korean language tasks, we advise careful consideration and further training for specific applications. ### Training Details Our model’s training was comprehensive and diverse: - Data Sources: - English to Korean paragraph pairs: 5.86% - Multi-lingual corpus (primarily English): 10.69% - Korean web content: 83.46% - Vocabulary Expansion: We meticulously selected 8,960 Korean tokens based on their frequency in our Korean web corpus. This process involved multiple rounds of tokenizer training, manual curation, and token frequency analysis, ensuring a rich and relevant vocabulary for our model. 1. Initial Tokenizer Training: We trained an intermediate tokenizer on a Korean web corpus, with a vocabulary of 40,000 tokens. 2. Extraction of New Korean Tokens: From the intermediate tokenizer, we identified all Korean tokens not present in the original SOLAR's tokenizer. 3. Manual Tokenizer Construction: We then built the target tokenizer, focusing on these new Korean tokens. 4. Frequency Analysis: Using the target tokenizer, we processed a 100GB Korean corpus to count each token's frequency. 5. Refinement of Token List: We removed tokens appearing less than 6,000 times, ensuring to secure enough tokens to train models later. 6. Inclusion of Single-Letter Characters: Counted missing Korean single-letter characters and added them to the target tokenizer that appeared more than 6,000 times. 7. Iterative Refinement: We repeated steps 2 to 6 until there were no tokens to drop or add. 8. Training Bias Towards New Tokens: Our training data was biased to include more texts with new tokens, for effective learning. This rigorous approach ensured a comprehensive and contextually rich Korean vocabulary for the model.
tistak/sn6_0	tistak	2024-05-18T09:16:31Z	34	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-13T08:10:56Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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avery0/pipeline1model1	avery0	2024-05-18T09:14:53Z	88	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-18T09:05:17Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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avery0/p1model1	avery0	2024-05-18T09:14:43Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-18T09:14:42Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Chinjuj/protebert-protfam-peft-lora	Chinjuj	2024-05-18T09:12:56Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-17T06:55:01Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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