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train · 793k rows

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-06-02 06:28:08	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 462 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-06-02 06:27:40	card stringlengths 11 1.01M
damgomz/ft_bs16_lr7_base_x2	damgomz	2024-05-17T18:11:56Z	113	0	transformers	[ "transformers", "safetensors", "albert", "text-classification", "fill-mask", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-05-16T15:20:38Z	--- language: en tags: - fill-mask kwargs: timestamp: '2024-05-17T20:11:35' project_name: ft_bs16_lr7_base_x2_emissions_tracker run_id: 6bae0093-717f-46c3-bcdd-8f19fcccb3a8 duration: 36881.51029586792 emissions: 0.0226794872442836 emissions_rate: 6.149283763692448e-07 cpu_power: 42.5 gpu_power: 0.0 ram_power: 4.500000000000001 cpu_energy: 0.4354060074516468 gpu_energy: 0 ram_energy: 0.0461015453451873 energy_consumed: 0.4815075527968334 country_name: Switzerland country_iso_code: CHE region: .nan cloud_provider: .nan cloud_region: .nan os: Linux-5.14.0-70.30.1.el9_0.x86_64-x86_64-with-glibc2.34 python_version: 3.10.4 codecarbon_version: 2.3.4 cpu_count: 2 cpu_model: Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz gpu_count: .nan gpu_model: .nan longitude: .nan latitude: .nan ram_total_size: 12 tracking_mode: machine on_cloud: N pue: 1.0 --- ## Environmental Impact (CODE CARBON DEFAULT) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| Duration (in seconds) \| 36881.51029586792 \| \| Emissions (Co2eq in kg) \| 0.0226794872442836 \| \| CPU power (W) \| 42.5 \| \| GPU power (W) \| [No GPU] \| \| RAM power (W) \| 4.500000000000001 \| \| CPU energy (kWh) \| 0.4354060074516468 \| \| GPU energy (kWh) \| [No GPU] \| \| RAM energy (kWh) \| 0.0461015453451873 \| \| Consumed energy (kWh) \| 0.4815075527968334 \| \| Country name \| Switzerland \| \| Cloud provider \| nan \| \| Cloud region \| nan \| \| CPU count \| 2 \| \| CPU model \| Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz \| \| GPU count \| nan \| \| GPU model \| nan \| ## Environmental Impact (for one core) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| CPU energy (kWh) \| 0.07099690731954574 \| \| Emissions (Co2eq in kg) \| 0.014445258199214933 \| ## Note 17 May 2024 ## My Config \| Config \| Value \| \|--------------------------\|-----------------\| \| checkpoint \| albert-base-v2 \| \| model_name \| ft_bs16_lr7_base_x2 \| \| sequence_length \| 400 \| \| num_epoch \| 15 \| \| learning_rate \| 5e-07 \| \| batch_size \| 16 \| \| weight_decay \| 0.0 \| \| warm_up_prop \| 0.0 \| \| drop_out_prob \| 0.1 \| \| packing_length \| 100 \| \| train_test_split \| 0.2 \| \| num_steps \| 81450 \| ## Training and Testing steps Epoch \| Train Loss \| Test Loss \| Accuracy \| Recall ---\|---\|---\|---\|--- \| 0 \| 0.588079 \| 0.517896 \| 0.740059 \| 0.863497 \| \| 1 \| 0.477829 \| 0.460693 \| 0.781296 \| 0.848160 \| \| 2 \| 0.429019 \| 0.428960 \| 0.805596 \| 0.881902 \| \| 3 \| 0.391332 \| 0.404802 \| 0.807806 \| 0.832822 \| \| 4 \| 0.368315 \| 0.398500 \| 0.819588 \| 0.863497 \| \| 5 \| 0.350588 \| 0.389129 \| 0.821060 \| 0.863497 \| \| 6 \| 0.335994 \| 0.382235 \| 0.822533 \| 0.874233 \| \| 7 \| 0.324425 \| 0.373543 \| 0.834315 \| 0.838957 \| \| 8 \| 0.310990 \| 0.373090 \| 0.831370 \| 0.854294 \| \| 9 \| 0.300017 \| 0.368493 \| 0.834315 \| 0.849693 \| \| 10 \| 0.286613 \| 0.377919 \| 0.832842 \| 0.872699 \| \| 11 \| 0.275215 \| 0.370514 \| 0.836524 \| 0.831288 \| \| 12 \| 0.260308 \| 0.383199 \| 0.834315 \| 0.872699 \| \| 13 \| 0.249657 \| 0.378506 \| 0.837997 \| 0.842025 \| \| 14 \| 0.234344 \| 0.385054 \| 0.834315 \| 0.835890 \|
apwic/sentiment-lora-r4a0d0.05-0	apwic	2024-05-17T18:10:15Z	0	0	null	[ "tensorboard", "generated_from_trainer", "id", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "region:us" ]	null	2024-05-17T17:36:35Z	--- language: - id license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: sentiment-lora-r4a0d0.05-0 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. --> # sentiment-lora-r4a0d0.05-0 This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3486 - Accuracy: 0.8396 - Precision: 0.8055 - Recall: 0.8115 - F1: 0.8084 ## 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: 30 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.5619 \| 1.0 \| 122 \| 0.5127 \| 0.7168 \| 0.6536 \| 0.6446 \| 0.6484 \| \| 0.5059 \| 2.0 \| 244 \| 0.4967 \| 0.7343 \| 0.6956 \| 0.7220 \| 0.7022 \| \| 0.4822 \| 3.0 \| 366 \| 0.4506 \| 0.7469 \| 0.7006 \| 0.7159 \| 0.7065 \| \| 0.4402 \| 4.0 \| 488 \| 0.3984 \| 0.8195 \| 0.7876 \| 0.7623 \| 0.7728 \| \| 0.4068 \| 5.0 \| 610 \| 0.4136 \| 0.7870 \| 0.7473 \| 0.7718 \| 0.7561 \| \| 0.3791 \| 6.0 \| 732 \| 0.3771 \| 0.8321 \| 0.7972 \| 0.7987 \| 0.7979 \| \| 0.3635 \| 7.0 \| 854 \| 0.3916 \| 0.8195 \| 0.7822 \| 0.8048 \| 0.7912 \| \| 0.3433 \| 8.0 \| 976 \| 0.3799 \| 0.8296 \| 0.7934 \| 0.8019 \| 0.7974 \| \| 0.3379 \| 9.0 \| 1098 \| 0.3714 \| 0.8271 \| 0.7903 \| 0.8026 \| 0.7959 \| \| 0.3296 \| 10.0 \| 1220 \| 0.3635 \| 0.8371 \| 0.8032 \| 0.8047 \| 0.8040 \| \| 0.3105 \| 11.0 \| 1342 \| 0.3652 \| 0.8296 \| 0.7933 \| 0.8044 \| 0.7984 \| \| 0.3024 \| 12.0 \| 1464 \| 0.3702 \| 0.8346 \| 0.7988 \| 0.8180 \| 0.8069 \| \| 0.309 \| 13.0 \| 1586 \| 0.3512 \| 0.8371 \| 0.8032 \| 0.8047 \| 0.8040 \| \| 0.3021 \| 14.0 \| 1708 \| 0.3505 \| 0.8396 \| 0.8060 \| 0.8090 \| 0.8075 \| \| 0.2903 \| 15.0 \| 1830 \| 0.3553 \| 0.8421 \| 0.8077 \| 0.8208 \| 0.8136 \| \| 0.2834 \| 16.0 \| 1952 \| 0.3530 \| 0.8396 \| 0.8046 \| 0.8215 \| 0.8119 \| \| 0.2811 \| 17.0 \| 2074 \| 0.3471 \| 0.8446 \| 0.8120 \| 0.8151 \| 0.8135 \| \| 0.288 \| 18.0 \| 2196 \| 0.3505 \| 0.8446 \| 0.8107 \| 0.8226 \| 0.8161 \| \| 0.277 \| 19.0 \| 2318 \| 0.3479 \| 0.8396 \| 0.8055 \| 0.8115 \| 0.8084 \| \| 0.2775 \| 20.0 \| 2440 \| 0.3486 \| 0.8396 \| 0.8055 \| 0.8115 \| 0.8084 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.15.2
hideax/llama2_stage2_iter40000_chatbot_arena_orpo_3	hideax	2024-05-17T18:03:09Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T17:54:40Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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RichardErkhov/stanford-oval_-_Llama-2-7b-WikiChat-fused-8bits	RichardErkhov	2024-05-17T18:02:29Z	77	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:2305.14292", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-17T17:55:38Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Llama-2-7b-WikiChat-fused - bnb 8bits - Model creator: https://huggingface.co/stanford-oval/ - Original model: https://huggingface.co/stanford-oval/Llama-2-7b-WikiChat-fused/ Original model description: --- license: llama2 language: - en --- This model is a fine-tuned LLaMA-2 (7B) model. Please accept the [LLaMA-2 license agreement](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) before downloading this model. Refer to the following for more information: GitHub repository: https://github.com/stanford-oval/WikiChat Paper: https://aclanthology.org/2023.findings-emnlp.157/ <p align="center"> <img src="./images/wikipedia.png" width="100px" alt="Wikipedia" /> <h1 align="center"> <b>WikiChat</b> <br> <a href="https://arxiv.org/abs/2305.14292"> <img src="https://img.shields.io/badge/cs.CL-2305.14292-b31b1b" alt="arXiv"> </a> <a href="https://github.com/stanford-oval/WikiChat/stargazers"> <img src="https://img.shields.io/github/stars/stanford-oval/WikiChat?style=social" alt="Github Stars"> </a> </h1> </p> <p align="center"> Stopping the Hallucination of Large Language Model Chatbots by Few-Shot Grounding on Wikipedia </p> <p align="center"> Online demo: <a href="https://wikichat.genie.stanford.edu" target="_blank"> https://wikichat.genie.stanford.edu </a> <br> </p> <p align="center"> <img src="./images/pipeline.svg" width="700px" alt="WikiChat Pipeline" /> </p>
RichardErkhov/lcw99_-_zephykor-ko-beta-7b-chang-4bits	RichardErkhov	2024-05-17T18:01:39Z	77	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-17T17:56:50Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) zephykor-ko-beta-7b-chang - bnb 4bits - Model creator: https://huggingface.co/lcw99/ - Original model: https://huggingface.co/lcw99/zephykor-ko-beta-7b-chang/ Original model description: --- language: - ko - en --- * Under construction, be carefull.
minindu-liya99/Reinforce-PixelCopter	minindu-liya99	2024-05-17T18:00:23Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2024-04-16T18:22:37Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-PixelCopter results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 33.60 +/- 20.80 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
PaulR79/phi2_finetuned_synthetic	PaulR79	2024-05-17T17:59:56Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-17T17:59:51Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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theglassofwater/mistral_pretraining_1	theglassofwater	2024-05-17T17:57:45Z	209	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T17:57:34Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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emilykang/medmcqa_question_generation-physiology_lora	emilykang	2024-05-17T17:47:44Z	6	0	peft	[ "peft", "safetensors", "llama", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "region:us" ]	null	2024-05-17T17:15:38Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 datasets: - generator model-index: - name: medmcqa_question_generation-physiology_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. --> # medmcqa_question_generation-physiology_lora This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) 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
MAli-Farooq/ChildDiffusion	MAli-Farooq	2024-05-17T17:43:36Z	0	0	null	[ "license:mit", "region:us" ]	null	2024-05-17T17:25:24Z	--- license: mit --- ChildDiffusion Model for rendering high quality child facial data with smart transformations. Model tuned and uploaded by Muhammad Ali Farooq, PhD
giannisan/dolphin-einstein-llama3-dare-ties	giannisan	2024-05-17T17:42:20Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2311.03099", "arxiv:2306.01708", "base_model:Weyaxi/Einstein-v6.1-Llama3-8B", "base_model:merge:Weyaxi/Einstein-v6.1-Llama3-8B", "base_model:cognitivecomputations/dolphin-2.9-llama3-8b", "base_model:merge:cognitivecomputations/dolphin-2.9-llama3-8b", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T17:23:17Z	--- base_model: - cognitivecomputations/dolphin-2.9-llama3-8b - Weyaxi/Einstein-v6.1-Llama3-8B library_name: transformers tags: - mergekit - merge --- # dolphin-einstein-llama3 This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [cognitivecomputations/dolphin-2.9-llama3-8b](https://huggingface.co/cognitivecomputations/dolphin-2.9-llama3-8b) as a base. ### Models Merged The following models were included in the merge: * [Weyaxi/Einstein-v6.1-Llama3-8B](https://huggingface.co/Weyaxi/Einstein-v6.1-Llama3-8B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: cognitivecomputations/dolphin-2.9-llama3-8b - model: Weyaxi/Einstein-v6.1-Llama3-8B parameters: weight: 0.5 density: 0.8 merge_method: dare_ties base_model: cognitivecomputations/dolphin-2.9-llama3-8b parameters: int8_mask: true dtype: bfloat16 ```
Gajebald/my-autotrain-llm	Gajebald	2024-05-17T17:38:51Z	3	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "autotrain", "text-generation-inference", "peft", "conversational", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T09:25:11Z	--- tags: - autotrain - text-generation-inference - text-generation - peft library_name: transformers widget: - messages: - role: user content: What is your favorite condiment? license: other pipeline_tag: text-generation --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "Gajebald/my-autotrain-llm" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
apwic/sentiment-lora-r2a2d0.15-0	apwic	2024-05-17T17:36:18Z	0	0	null	[ "tensorboard", "generated_from_trainer", "id", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "region:us" ]	null	2024-05-17T17:03:05Z	--- language: - id license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: sentiment-lora-r2a2d0.15-0 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. --> # sentiment-lora-r2a2d0.15-0 This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3672 - Accuracy: 0.8321 - Precision: 0.7961 - Recall: 0.8087 - F1: 0.8018 ## 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: 30 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.563 \| 1.0 \| 122 \| 0.5138 \| 0.7243 \| 0.6636 \| 0.6549 \| 0.6586 \| \| 0.509 \| 2.0 \| 244 \| 0.5057 \| 0.7168 \| 0.6763 \| 0.6996 \| 0.6820 \| \| 0.4924 \| 3.0 \| 366 \| 0.4708 \| 0.7393 \| 0.6877 \| 0.6931 \| 0.6901 \| \| 0.468 \| 4.0 \| 488 \| 0.4379 \| 0.7845 \| 0.7412 \| 0.7200 \| 0.7286 \| \| 0.4495 \| 5.0 \| 610 \| 0.4466 \| 0.7594 \| 0.7233 \| 0.7548 \| 0.7313 \| \| 0.4334 \| 6.0 \| 732 \| 0.4041 \| 0.8271 \| 0.7927 \| 0.7851 \| 0.7887 \| \| 0.415 \| 7.0 \| 854 \| 0.4057 \| 0.7995 \| 0.7590 \| 0.7756 \| 0.7660 \| \| 0.3974 \| 8.0 \| 976 \| 0.3852 \| 0.8321 \| 0.7982 \| 0.7937 \| 0.7959 \| \| 0.3849 \| 9.0 \| 1098 \| 0.3829 \| 0.8246 \| 0.7880 \| 0.7909 \| 0.7894 \| \| 0.3771 \| 10.0 \| 1220 \| 0.3786 \| 0.8396 \| 0.8065 \| 0.8065 \| 0.8065 \| \| 0.3633 \| 11.0 \| 1342 \| 0.3843 \| 0.8296 \| 0.7931 \| 0.8069 \| 0.7993 \| \| 0.3591 \| 12.0 \| 1464 \| 0.3833 \| 0.8296 \| 0.7931 \| 0.8069 \| 0.7993 \| \| 0.354 \| 13.0 \| 1586 \| 0.3705 \| 0.8396 \| 0.8065 \| 0.8065 \| 0.8065 \| \| 0.3451 \| 14.0 \| 1708 \| 0.3709 \| 0.8371 \| 0.8028 \| 0.8072 \| 0.8049 \| \| 0.3403 \| 15.0 \| 1830 \| 0.3733 \| 0.8321 \| 0.7960 \| 0.8112 \| 0.8027 \| \| 0.3282 \| 16.0 \| 1952 \| 0.3715 \| 0.8346 \| 0.7988 \| 0.8155 \| 0.8061 \| \| 0.3286 \| 17.0 \| 2074 \| 0.3664 \| 0.8321 \| 0.7965 \| 0.8037 \| 0.7999 \| \| 0.3348 \| 18.0 \| 2196 \| 0.3670 \| 0.8271 \| 0.7904 \| 0.8001 \| 0.7949 \| \| 0.325 \| 19.0 \| 2318 \| 0.3669 \| 0.8321 \| 0.7961 \| 0.8087 \| 0.8018 \| \| 0.3266 \| 20.0 \| 2440 \| 0.3672 \| 0.8321 \| 0.7961 \| 0.8087 \| 0.8018 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.15.2
cgus/MiniChat-2-3B-iMat-GGUF	cgus	2024-05-17T17:35:49Z	26	0	transformers	[ "transformers", "gguf", "en", "zh", "arxiv:2311.07052", "arxiv:2310.05914", "arxiv:2305.18290", "base_model:GeneZC/MiniChat-2-3B", "base_model:quantized:GeneZC/MiniChat-2-3B", "license:apache-2.0", "region:us", "imatrix" ]	null	2024-05-15T17:59:54Z	--- license: apache-2.0 language: - en - zh inference: false library_name: transformers base_model: GeneZC/MiniChat-2-3B widget: - text: "<s> [\|User\|] Hi 👋 </s>[\|Assistant\|]" --- ## MiniChat-2-3B-iMat-GGUF Original model: [MiniChat-2-3B](https://huggingface.co/GeneZC/MiniChat-2-3B) Model creator: [GeneZC](https://huggingface.co/GeneZC) ## Quantization notes Quantized with llama.cpp b2885. All quants are made with iMatrix file based on the default Exllamav2 dataset. ## How to run GGUF quants are supported by wide variety of software such as llama.cpp, ollama, Text Generation WebUI, LM Studio, Jan AI and many others. # Original model card: ## MiniChat-2-3B 📑 [arXiv](https://arxiv.org/abs/2311.07052) \| 👻 [GitHub](https://github.com/GeneZC/MiniMA) \| 🤗 [HuggingFace-MiniMA](https://huggingface.co/GeneZC/MiniMA-3B) \| 🤗 [HuggingFace-MiniChat](https://huggingface.co/GeneZC/MiniChat-3B) \| 🤖 [ModelScope-MiniMA](https://modelscope.cn/models/GeneZC/MiniMA-3B) \| 🤖 [ModelScope-MiniChat](https://modelscope.cn/models/GeneZC/MiniChat-3B) \| 🤗 [HuggingFace-MiniChat-1.5](https://huggingface.co/GeneZC/MiniChat-1.5-3B) \| 🤗 [HuggingFace-MiniMA-2](https://huggingface.co/GeneZC/MiniMA-2-3B) \| 🤗 [HuggingFace-MiniChat-2](https://huggingface.co/GeneZC/MiniChat-2-3B) 🆕 Updates from MiniChat-3B: - better base model MiniMA-2-3B; - better data mixture; - use of [NEFTune](https://arxiv.org/abs/2310.05914); - use of [DPO](https://arxiv.org/abs/2305.18290). ❗ Must comply with LICENSE of LLaMA2 since it is derived from LLaMA2. A language model continued from MiniMA-3B and finetuned on both instruction and preference data. Surpassing Vicuna-7B and approximating LLaMA-2-Chat-7B on MT-Bench. <img src="https://huggingface.co/GeneZC/MiniChat-2-3B/resolve/main/teaser_b.jpg" alt="teaser_b" width="687" /> Standard Benchmarks \|Method\|TFLOPs\|MMLU (5-shot)\|CEval (5-shot)\|DROP (3-shot)\|HumanEval (0-shot)\|BBH (3-shot)\|GSM8K (8-shot)\| \|--\|--\|--\|--\|--\|--\|--\|--\| \|Mamba-2.8B\|4.6E9\|25.58\|24.74\|15.72\|7.32\|29.37\|3.49\| \|ShearedLLaMA-2.7B\|0.8E9\|26.97\|22.88\|19.98\|4.88\|30.48\|3.56\| \|BTLM-3B\|11.3E9\|27.20\|26.00\|17.84\|10.98\|30.87\|4.55\| \|StableLM-3B\|72.0E9\|44.75\|31.05\|22.35\|15.85\|32.59\|10.99\| \|Qwen-1.8B\|23.8E9\|44.05\|54.75\|12.97\|14.02\|30.80\|22.97\| \|Phi-2-2.8B\|159.9E9\|56.74\|34.03\|30.74\|46.95\|44.13\|55.42\| \|LLaMA-2-7B\|84.0E9\|46.00\|34.40\|31.57\|12.80\|32.02\|14.10\| \|\| \|MiniMA-3B\|4.0E9\|28.51\|28.23\|22.50\|10.98\|31.61\|8.11\| \|MiniChat-3B\|4.0E9\|38.40\|36.48\|22.58\|18.29\|31.36\|29.72\| \|MiniMA-2-3B\|13.4E9\|40.14\|44.65\|23.10\|14.63\|31.43\|8.87\| \|MiniChat-2-3B\|13.4E9\|46.17\|43.91\|30.26\|22.56\|34.95\|38.13\| Instruction-following Benchmarks \|Method\|AlpacaEval\|MT-Bench\|MT-Bench-ZH\| \|--\|--\|--\|--\| \|GPT-4\|95.28\|9.18\|8.96\| \|Zephyr-7B-Beta\|90.60\|7.34\|6.27<sup>#</sup>\| \|Vicuna-7B\|76.84\|6.17\|5.22<sup>#</sup>\| \|LLaMA-2-Chat-7B\|71.37\|6.27\|5.43<sup>#</sup>\| \|Qwen-Chat-7B\|-\|-\|6.24\| \|Phi-2-DPO\|81.37\|-\|1.59<sup>#</sup><sup>$</sup>\| \|StableLM-Zephyr-3B\|76.00\|6.64\|4.31<sup>#</sup>\| \|Rocket-3B\|79.75\|6.56\|4.07<sup>#</sup>\| \|Qwen-Chat-1.8B\|-\|-\|5.65\| \|\| \|MiniChat-3B\|48.82\|-\|-\| \|MiniChat-2-3B\|77.30\|6.23\|6.04\| <sup>#</sup> specialized mainly for English. <sup>$</sup> finetuned without multi-turn instruction data. The following is an example code snippet to use MiniChat-2-3B: ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer from conversation import get_default_conv_template # MiniChat tokenizer = AutoTokenizer.from_pretrained("GeneZC/MiniChat-2-3B", use_fast=False) # GPU. model = AutoModelForCausalLM.from_pretrained("GeneZC/MiniChat-2-3B", use_cache=True, device_map="auto", torch_dtype=torch.float16).eval() # CPU. # model = AutoModelForCausalLM.from_pretrained("GeneZC/MiniChat-2-3B", use_cache=True, device_map="cpu", torch_dtype=torch.float16).eval() conv = get_default_conv_template("minichat") question = "Implement a program to find the common elements in two arrays without using any extra data structures." conv.append_message(conv.roles[0], question) conv.append_message(conv.roles[1], None) prompt = conv.get_prompt() input_ids = tokenizer([prompt]).input_ids output_ids = model.generate( torch.as_tensor(input_ids).cuda(), do_sample=True, temperature=0.7, max_new_tokens=1024, ) output_ids = output_ids[0][len(input_ids[0]):] output = tokenizer.decode(output_ids, skip_special_tokens=True).strip() # output: "def common_elements(arr1, arr2):\n if len(arr1) == 0:\n return []\n if len(arr2) == 0:\n return arr1\n\n common_elements = []\n for element in arr1:\n if element in arr2:\n common_elements.append(element)\n\n return common_elements" # Multiturn conversation could be realized by continuously appending questions to `conv`. ``` ## Bibtex ```bibtex @article{zhang2023law, title={Towards the Law of Capacity Gap in Distilling Language Models}, author={Zhang, Chen and Song, Dawei and Ye, Zheyu and Gao, Yan}, year={2023}, url={https://arxiv.org/abs/2311.07052} } ```
nc33/llama3-8b-4bit_orpo_law	nc33	2024-05-17T17:32:28Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-16T09:29:40Z	--- 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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wovik253/0001softrealistic_v187xxx	wovik253	2024-05-17T17:31:02Z	0	0	null	[ "realistic, nsfw, girl, portreit", "text-to-image", "arxiv:1910.09700", "region:us" ]	text-to-image	2024-05-17T16:36:28Z	--- pipeline_tag: text-to-image tags: - realistic, nsfw, girl, portreit --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. 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presencesw/phobert-large-snli_entailment-triplet	presencesw	2024-05-17T17:25:54Z	181	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-13T09:53:55Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Kha37lid/autotrain-5pwz2-t4v28	Kha37lid	2024-05-17T17:21:55Z	13	0	diffusers	[ "diffusers", "autotrain", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-05-17T17:21:52Z	--- tags: - autotrain - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: <khalid> license: openrail++ --- # AutoTrain SDXL LoRA DreamBooth - Kha37lid/autotrain-5pwz2-t4v28 <Gallery /> ## Model description These are Kha37lid/autotrain-5pwz2-t4v28 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: None. ## Trigger words You should use <khalid> to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](Kha37lid/autotrain-5pwz2-t4v28/tree/main) them in the Files & versions tab.
DokHee/JSLLMV4	DokHee	2024-05-17T17:20:31Z	77	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-17T17:04:23Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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tomasonjo/text2cypher-demo-16bit	tomasonjo	2024-05-17T17:18:08Z	301	23	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "dataset:tomasonjo/text2cypher-gpt4o-clean", "base_model:unsloth/llama-3-8b-Instruct", "base_model:finetune:unsloth/llama-3-8b-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T13:36:05Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft base_model: unsloth/llama-3-8b-Instruct datasets: - tomasonjo/text2cypher-gpt4o-clean --- # Uploaded model - Developed by: tomasonjo - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. For more information visit [this link](https://github.com/neo4j-labs/text2cypher/tree/main/finetuning/unsloth-llama3#using-chat-prompt-template) ## Example usage: Install dependencies. Check [Unsloth documentation](https://github.com/unslothai/unsloth) for specific installation for other environments. ````python %%capture # Installs Unsloth, Xformers (Flash Attention) and all other packages! !pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git" !pip install --no-deps "xformers<0.0.26" trl peft accelerate bitsandbytes ```` Then you can load the model and use it as inference ```python from unsloth.chat_templates import get_chat_template tokenizer = get_chat_template( tokenizer, chat_template = "llama-3", map_eos_token = True, ) FastLanguageModel.for_inference(model) # Enable native 2x faster inference schema = """Node properties: - Question - `favorites`: INTEGER Example: "0" - `answered`: BOOLEAN - `text`: STRING Example: "### This is: Bug ### Specifications OS: Win10" - `link`: STRING Example: "https://stackoverflow.com/questions/62224586/playg" - `createdAt`: DATE_TIME Min: 2020-06-05T16:57:19Z, Max: 2020-06-05T21:49:16Z - `title`: STRING Example: "Playground is not loading with apollo-server-lambd" - `id`: INTEGER Min: 62220505, Max: 62224586 - `upVotes`: INTEGER Example: "0" - `score`: INTEGER Example: "-1" - `downVotes`: INTEGER Example: "1" - Tag - `name`: STRING Example: "aws-lambda" - User - `image`: STRING Example: "https://lh3.googleusercontent.com/-NcFYSuXU0nk/AAA" - `link`: STRING Example: "https://stackoverflow.com/users/10251021/alexandre" - `id`: INTEGER Min: 751, Max: 13681006 - `reputation`: INTEGER Min: 1, Max: 420137 - `display_name`: STRING Example: "Alexandre Le" Relationship properties: The relationships: (:Question)-[:TAGGED]->(:Tag) (:User)-[:ASKED]->(:Question)""" question = "Identify the top 5 questions with the most downVotes." messages = [ {"role": "system", "content": "Given an input question, convert it to a Cypher query. No pre-amble."}, {"role": "user", "content": f"""Based on the Neo4j graph schema below, write a Cypher query that would answer the user's question: {schema} Question: {question} Cypher query:"""} ] inputs = tokenizer.apply_chat_template( messages, tokenize = True, add_generation_prompt = True, # Must add for generation return_tensors = "pt", ).to("cuda") outputs = model.generate(input_ids = inputs, max_new_tokens = 128, use_cache = True) tokenizer.batch_decode(outputs) ```
emilykang/medmcqa_question_generation-pediatrics_lora	emilykang	2024-05-17T17:15:33Z	1	0	peft	[ "peft", "safetensors", "llama", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "region:us" ]	null	2024-05-17T16:42:11Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 datasets: - generator model-index: - name: medmcqa_question_generation-pediatrics_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. --> # medmcqa_question_generation-pediatrics_lora This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) 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
worldboss/meta-llama3-8b-alpaca-qlora-peft-axolotl-merged	worldboss	2024-05-17T17:12:40Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T17:02:36Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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]
taufeeq28/vehicles	taufeeq28	2024-05-17T17:12:34Z	222	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "pytorch", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-17T17:12:28Z	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: vehicles results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.8358209133148193 --- # vehicles Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for anything by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### bikes ![bikes](images/bikes.jpg) #### cars ![cars](images/cars.jpg) #### cycles ![cycles](images/cycles.jpg)
Ellight/whisper-tiny-en	Ellight	2024-05-17T17:10:49Z	90	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dataset:PolyAI/minds14", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-17T13:13:07Z	--- license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_trainer datasets: - PolyAI/minds14 metrics: - wer model-index: - name: whisper-tiny-en results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: PolyAI/minds14 type: PolyAI/minds14 config: en-US split: train[450:] args: en-US metrics: - name: Wer type: wer value: 0.3140495867768595 --- <!-- 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-en This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset. It achieves the following results on the evaluation set: - Loss: 0.5100 - Wer Ortho: 0.3233 - Wer: 0.3140 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 5 - training_steps: 100 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer Ortho \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:---------:\|:------:\| \| 0.101 \| 3.5714 \| 100 \| 0.5100 \| 0.3233 \| 0.3140 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
mdosama39/mt5-base-headline-base	mdosama39	2024-05-17T17:06:32Z	8	0	transformers	[ "transformers", "safetensors", "mt5", "text2text-generation", "generated_from_trainer", "base_model:google/mt5-base", "base_model:finetune:google/mt5-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-17T16:57:15Z	--- license: apache-2.0 base_model: google/mt5-base tags: - generated_from_trainer metrics: - rouge model-index: - name: mt5-base-headline-base 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. --> # mt5-base-headline-base This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6856 - Rouge1: 0.0 - Rouge2: 0.0 - Rougel: 0.0 - Rougelsum: 0.0 - Gen Len: 16.0174 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| 2.5318 \| 1.0 \| 202 \| 1.8787 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 16.4268 \| \| 2.2047 \| 2.0 \| 404 \| 1.7674 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 15.5285 \| \| 2.1322 \| 3.0 \| 606 \| 1.7092 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 15.866 \| \| 1.7199 \| 4.0 \| 808 \| 1.6856 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 16.0174 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
pt4c/opus-mt-fr-yat	pt4c	2024-05-17T17:06:16Z	111	0	transformers	[ "transformers", "tensorboard", "safetensors", "marian", "text2text-generation", "generated_from_trainer", "base_model:Helsinki-NLP/opus-mt-fr-en", "base_model:finetune:Helsinki-NLP/opus-mt-fr-en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-17T16:34:05Z	--- license: apache-2.0 tags: - generated_from_trainer base_model: Helsinki-NLP/opus-mt-fr-en model-index: - name: opus-mt-fr-yat 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. --> # opus-mt-fr-yat This model is a fine-tuned version of [Helsinki-NLP/opus-mt-fr-en](https://huggingface.co/Helsinki-NLP/opus-mt-fr-en) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 6.7630 - Bert score: 0.6005 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 20 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bert score \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:----------:\| \| No log \| 1.0 \| 62 \| 7.7730 \| 0.5980 \| \| No log \| 2.0 \| 124 \| 6.9707 \| 0.5976 \| \| No log \| 3.0 \| 186 \| 6.7630 \| 0.6005 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
bartowski/Llama-3-8B-Synthia-v3.5-GGUF	bartowski	2024-05-17T17:04:28Z	127	1	null	[ "gguf", "text-generation", "license:llama3", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-05-17T16:47:37Z	--- license: llama3 quantized_by: bartowski pipeline_tag: text-generation --- ## Llamacpp imatrix Quantizations of Llama-3-8B-Synthia-v3.5 Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b2901">b2901</a> for quantization. Original model: https://huggingface.co/migtissera/Llama-3-8B-Synthia-v3.5 All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/b6ac44691e994344625687afe3263b3a) ## Prompt format ``` <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> {system_prompt}<\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|> {prompt}<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> ``` ## Download a file (not the whole branch) from below: \| Filename \| Quant type \| File Size \| Description \| \| -------- \| ---------- \| --------- \| ----------- \| \| [Llama-3-8B-Synthia-v3.5-Q8_0.gguf](https://huggingface.co/bartowski/Llama-3-8B-Synthia-v3.5-GGUF/blob/main/Llama-3-8B-Synthia-v3.5-Q8_0.gguf) \| Q8_0 \| 8.54GB \| Extremely high quality, generally unneeded but max available quant. \| \| [Llama-3-8B-Synthia-v3.5-Q6_K.gguf](https://huggingface.co/bartowski/Llama-3-8B-Synthia-v3.5-GGUF/blob/main/Llama-3-8B-Synthia-v3.5-Q6_K.gguf) \| Q6_K \| 6.59GB \| Very high quality, near perfect, recommended. \| \| [Llama-3-8B-Synthia-v3.5-Q5_K_M.gguf](https://huggingface.co/bartowski/Llama-3-8B-Synthia-v3.5-GGUF/blob/main/Llama-3-8B-Synthia-v3.5-Q5_K_M.gguf) \| Q5_K_M \| 5.73GB \| High quality, recommended. \| \| [Llama-3-8B-Synthia-v3.5-Q5_K_S.gguf](https://huggingface.co/bartowski/Llama-3-8B-Synthia-v3.5-GGUF/blob/main/Llama-3-8B-Synthia-v3.5-Q5_K_S.gguf) \| Q5_K_S \| 5.59GB \| High quality, recommended. \| \| [Llama-3-8B-Synthia-v3.5-Q4_K_M.gguf](https://huggingface.co/bartowski/Llama-3-8B-Synthia-v3.5-GGUF/blob/main/Llama-3-8B-Synthia-v3.5-Q4_K_M.gguf) \| Q4_K_M \| 4.92GB \| Good quality, uses about 4.83 bits per weight, recommended. \| \| [Llama-3-8B-Synthia-v3.5-Q4_K_S.gguf](https://huggingface.co/bartowski/Llama-3-8B-Synthia-v3.5-GGUF/blob/main/Llama-3-8B-Synthia-v3.5-Q4_K_S.gguf) \| Q4_K_S \| 4.69GB \| Slightly lower quality with more space savings, recommended. \| \| [Llama-3-8B-Synthia-v3.5-IQ4_NL.gguf](https://huggingface.co/bartowski/Llama-3-8B-Synthia-v3.5-GGUF/blob/main/Llama-3-8B-Synthia-v3.5-IQ4_NL.gguf) \| IQ4_NL \| 4.67GB \| Decent quality, slightly smaller than Q4_K_S with similar performance recommended. \| \| [Llama-3-8B-Synthia-v3.5-IQ4_XS.gguf](https://huggingface.co/bartowski/Llama-3-8B-Synthia-v3.5-GGUF/blob/main/Llama-3-8B-Synthia-v3.5-IQ4_XS.gguf) \| IQ4_XS \| 4.44GB \| Decent quality, smaller than Q4_K_S with similar performance, recommended. \| \| [Llama-3-8B-Synthia-v3.5-Q3_K_L.gguf](https://huggingface.co/bartowski/Llama-3-8B-Synthia-v3.5-GGUF/blob/main/Llama-3-8B-Synthia-v3.5-Q3_K_L.gguf) \| Q3_K_L \| 4.32GB \| Lower quality but usable, good for low RAM availability. \| \| [Llama-3-8B-Synthia-v3.5-Q3_K_M.gguf](https://huggingface.co/bartowski/Llama-3-8B-Synthia-v3.5-GGUF/blob/main/Llama-3-8B-Synthia-v3.5-Q3_K_M.gguf) \| Q3_K_M \| 4.01GB \| Even lower quality. \| \| [Llama-3-8B-Synthia-v3.5-IQ3_M.gguf](https://huggingface.co/bartowski/Llama-3-8B-Synthia-v3.5-GGUF/blob/main/Llama-3-8B-Synthia-v3.5-IQ3_M.gguf) \| IQ3_M \| 3.78GB \| Medium-low quality, new method with decent performance comparable to Q3_K_M. \| \| [Llama-3-8B-Synthia-v3.5-IQ3_S.gguf](https://huggingface.co/bartowski/Llama-3-8B-Synthia-v3.5-GGUF/blob/main/Llama-3-8B-Synthia-v3.5-IQ3_S.gguf) \| IQ3_S \| 3.68GB \| Lower quality, new method with decent performance, recommended over Q3_K_S quant, same size with better performance. \| \| [Llama-3-8B-Synthia-v3.5-Q3_K_S.gguf](https://huggingface.co/bartowski/Llama-3-8B-Synthia-v3.5-GGUF/blob/main/Llama-3-8B-Synthia-v3.5-Q3_K_S.gguf) \| Q3_K_S \| 3.66GB \| Low quality, not recommended. \| \| [Llama-3-8B-Synthia-v3.5-IQ3_XS.gguf](https://huggingface.co/bartowski/Llama-3-8B-Synthia-v3.5-GGUF/blob/main/Llama-3-8B-Synthia-v3.5-IQ3_XS.gguf) \| IQ3_XS \| 3.51GB \| Lower quality, new method with decent performance, slightly better than Q3_K_S. \| \| [Llama-3-8B-Synthia-v3.5-IQ3_XXS.gguf](https://huggingface.co/bartowski/Llama-3-8B-Synthia-v3.5-GGUF/blob/main/Llama-3-8B-Synthia-v3.5-IQ3_XXS.gguf) \| IQ3_XXS \| 3.27GB \| Lower quality, new method with decent performance, comparable to Q3 quants. \| \| [Llama-3-8B-Synthia-v3.5-Q2_K.gguf](https://huggingface.co/bartowski/Llama-3-8B-Synthia-v3.5-GGUF/blob/main/Llama-3-8B-Synthia-v3.5-Q2_K.gguf) \| Q2_K \| 3.17GB \| Very low quality but surprisingly usable. \| \| [Llama-3-8B-Synthia-v3.5-IQ2_M.gguf](https://huggingface.co/bartowski/Llama-3-8B-Synthia-v3.5-GGUF/blob/main/Llama-3-8B-Synthia-v3.5-IQ2_M.gguf) \| IQ2_M \| 2.94GB \| Very low quality, uses SOTA techniques to also be surprisingly usable. \| \| [Llama-3-8B-Synthia-v3.5-IQ2_S.gguf](https://huggingface.co/bartowski/Llama-3-8B-Synthia-v3.5-GGUF/blob/main/Llama-3-8B-Synthia-v3.5-IQ2_S.gguf) \| IQ2_S \| 2.75GB \| Very low quality, uses SOTA techniques to be usable. \| \| [Llama-3-8B-Synthia-v3.5-IQ2_XS.gguf](https://huggingface.co/bartowski/Llama-3-8B-Synthia-v3.5-GGUF/blob/main/Llama-3-8B-Synthia-v3.5-IQ2_XS.gguf) \| IQ2_XS \| 2.60GB \| Very low quality, uses SOTA techniques to be usable. \| \| [Llama-3-8B-Synthia-v3.5-IQ2_XXS.gguf](https://huggingface.co/bartowski/Llama-3-8B-Synthia-v3.5-GGUF/blob/main/Llama-3-8B-Synthia-v3.5-IQ2_XXS.gguf) \| IQ2_XXS \| 2.39GB \| Lower quality, uses SOTA techniques to be usable. \| \| [Llama-3-8B-Synthia-v3.5-IQ1_M.gguf](https://huggingface.co/bartowski/Llama-3-8B-Synthia-v3.5-GGUF/blob/main/Llama-3-8B-Synthia-v3.5-IQ1_M.gguf) \| IQ1_M \| 2.16GB \| Extremely low quality, not recommended. \| \| [Llama-3-8B-Synthia-v3.5-IQ1_S.gguf](https://huggingface.co/bartowski/Llama-3-8B-Synthia-v3.5-GGUF/blob/main/Llama-3-8B-Synthia-v3.5-IQ1_S.gguf) \| IQ1_S \| 2.01GB \| Extremely low quality, not recommended. \| ## Downloading using huggingface-cli First, make sure you have hugginface-cli installed: ``` pip install -U "huggingface_hub[cli]" ``` Then, you can target the specific file you want: ``` huggingface-cli download bartowski/Llama-3-8B-Synthia-v3.5-GGUF --include "Llama-3-8B-Synthia-v3.5-Q4_K_M.gguf" --local-dir ./ --local-dir-use-symlinks False ``` If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: ``` huggingface-cli download bartowski/Llama-3-8B-Synthia-v3.5-GGUF --include "Llama-3-8B-Synthia-v3.5-Q8_0.gguf/" --local-dir Llama-3-8B-Synthia-v3.5-Q8_0 --local-dir-use-symlinks False ``` You can either specify a new local-dir (Llama-3-8B-Synthia-v3.5-Q8_0) or download them all in place (./) ## Which file should I choose? A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have. If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM. If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total. Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'. If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M. If you want to get more into the weeds, you can check out this extremely useful feature chart: [llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix) But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size. These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide. The I-quants are not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm. Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
Fernando1305/llama-3-7b-chat-guanacoPrueba	Fernando1305	2024-05-17T16:59:32Z	1	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Meta-Llama-3-8B", "base_model:adapter:meta-llama/Meta-Llama-3-8B", "region:us" ]	null	2024-05-17T16:17:10Z	--- library_name: peft base_model: meta-llama/Meta-Llama-3-8B --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.1
guillaumephd/t5-french-base	guillaumephd	2024-05-17T16:54:32Z	131	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "fr", "dataset:togethercomputer/RedPajama-Data-V2", "license:cc-by-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-17T16:01:37Z	--- license: cc-by-4.0 datasets: - togethercomputer/RedPajama-Data-V2 language: - fr library_name: transformers --- # T5-french-base Model ## Model Overview The T5-French-Base model is a ~250M params only T5 model trained (entirely from scratch) solely on French data from the RedPajama 2 dataset. This model was trained for 85,000 steps and was only pre-trained from scratch without any supervised training. Therefore, this model has to be fine-tuned before it is useable on a downstream task. It is intended to serve as a foundation for further fine-tuning and as a starting point for downstream tasks in the French language. Since the training compute buget was very limited, the model is mainly useful for research only. ## Model Details - Model Architecture: T5 Base, version 1.1 (GEGLU activation in feed-forward hidden layer, rather than ReLU) - Training Dataset: RedPajama 2 dataset (French-only) - Training Steps: 85,000 (from scratch) - Tokenizer: T5 Tokenizer ## Intended Use The T5-French-Base model is intended to be used for research only, in order to serve as a pre-trained model for further fine-tuning on specific French language tasks. It may be used as a starting point for fine-tuning on tasks such as: - French text generation - French question answering - French language understanding - French text summarization ## Limitations The T5-French-Base model may not be suitable for user-facing, or production applications. It is mainly meant for researchers only. It was trained entirely from scratch. The training budget was really limited (85k steps only, ~250M params only, for a final loss of ~1.1). The model is a base model that hasn't been fine-tuned yet. As such, it does NOT follow instructions. Additionally, the model was trained solely on French data and won't work for tasks that require cross-lingual understanding or multilingual capabilities. ## Ethical Considerations The T5-French-Base model was trained from scratch on publicly available data and does not contain any known biases or ethical concerns. However, researchers should be aware of potential biases in the RedPajama 2 training data and should carefully evaluate the model's outputs for any unintended consequences. ## Citation If you use the RedPajama-T5-Base-French model in your work, please cite the original Google T5 model, as well as the following: ``` @article{guillaumeT5french, title={T5-French-Base model: A T5 model trained on french data only}, author={guillaumephd}, url={https://huggingface.co/guillaumephd/t5-french-base}, year={2024} } ```
abbenedek/abbenedekwhisper-tiny.en-finetuning3-D3K	abbenedek	2024-05-17T16:53:26Z	124	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-tiny.en", "base_model:finetune:openai/whisper-tiny.en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-17T15:08:45Z	--- license: apache-2.0 base_model: openai/whisper-tiny.en tags: - generated_from_trainer metrics: - wer model-index: - name: abbenedekwhisper-tiny.en-finetuning3-D3K 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. --> # abbenedekwhisper-tiny.en-finetuning3-D3K This model is a fine-tuned version of [openai/whisper-tiny.en](https://huggingface.co/openai/whisper-tiny.en) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.2102 - Cer: 48.9705 - Wer: 91.3907 - Ser: 100.0 - Cer Clean: 6.0657 - Wer Clean: 12.9139 - Ser Clean: 13.1579 ## 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-08 - train_batch_size: 16 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - training_steps: 2000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Cer \| Wer \| Ser \| Cer Clean \| Wer Clean \| Ser Clean \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:--------:\|:-----:\|:---------:\|:---------:\|:---------:\| \| 6.2196 \| 1.06 \| 200 \| 5.5899 \| 52.5320 \| 112.9139 \| 100.0 \| 7.3456 \| 14.2384 \| 14.9123 \| \| 5.2943 \| 2.13 \| 400 \| 4.9201 \| 52.4763 \| 110.2649 \| 100.0 \| 7.6238 \| 14.9007 \| 15.7895 \| \| 4.5662 \| 3.19 \| 600 \| 4.4164 \| 51.1964 \| 105.6291 \| 100.0 \| 7.6238 \| 14.9007 \| 15.7895 \| \| 4.0943 \| 4.26 \| 800 \| 4.0825 \| 50.5843 \| 103.3113 \| 100.0 \| 7.1786 \| 14.5695 \| 14.9123 \| \| 3.6948 \| 5.32 \| 1000 \| 3.7923 \| 51.5303 \| 101.9868 \| 100.0 \| 6.3439 \| 12.9139 \| 13.1579 \| \| 3.3742 \| 6.38 \| 1200 \| 3.5565 \| 50.3617 \| 98.3444 \| 100.0 \| 6.3439 \| 13.5762 \| 14.0351 \| \| 3.1519 \| 7.45 \| 1400 \| 3.3895 \| 49.0262 \| 93.7086 \| 100.0 \| 6.3439 \| 13.5762 \| 14.0351 \| \| 2.9995 \| 8.51 \| 1600 \| 3.2845 \| 48.6366 \| 92.7152 \| 100.0 \| 6.3439 \| 13.5762 \| 14.0351 \| \| 2.9152 \| 9.57 \| 1800 \| 3.2282 \| 47.9688 \| 91.7219 \| 100.0 \| 6.0657 \| 12.9139 \| 13.1579 \| \| 2.884 \| 10.64 \| 2000 \| 3.2102 \| 48.9705 \| 91.3907 \| 100.0 \| 6.0657 \| 12.9139 \| 13.1579 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2+cu121 - Datasets 2.14.5 - Tokenizers 0.15.2
santoshtyss/lex-32k-1300	santoshtyss	2024-05-17T16:52:11Z	8	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T16:35:07Z	--- 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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Xenova/tiny-random-GemmaForCausalLM	Xenova	2024-05-17T16:49:41Z	417	3	transformers	[ "transformers", "onnx", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-22T00:35:17Z	--- library_name: transformers license: apache-2.0 --- # 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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damgomz/ft_bs32_lr6_base_x4	damgomz	2024-05-17T16:48:10Z	108	0	transformers	[ "transformers", "safetensors", "albert", "text-classification", "fill-mask", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-05-17T09:32:40Z	--- language: en tags: - fill-mask kwargs: timestamp: '2024-05-17T18:48:05' project_name: ft_bs32_lr6_base_x4_emissions_tracker run_id: 9280d1e4-ef4e-4526-bbd2-72f003482752 duration: 31476.51491165161 emissions: 0.0193557967525063 emissions_rate: 6.149282030375424e-07 cpu_power: 42.5 gpu_power: 0.0 ram_power: 4.500000000000001 cpu_energy: 0.3715970706832078 gpu_energy: 0 ram_energy: 0.0393453032049536 energy_consumed: 0.4109423738881623 country_name: Switzerland country_iso_code: CHE region: .nan cloud_provider: .nan cloud_region: .nan os: Linux-5.14.0-70.30.1.el9_0.x86_64-x86_64-with-glibc2.34 python_version: 3.10.4 codecarbon_version: 2.3.4 cpu_count: 2 cpu_model: Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz gpu_count: .nan gpu_model: .nan longitude: .nan latitude: .nan ram_total_size: 12 tracking_mode: machine on_cloud: N pue: 1.0 --- ## Environmental Impact (CODE CARBON DEFAULT) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| Duration (in seconds) \| 31476.51491165161 \| \| Emissions (Co2eq in kg) \| 0.0193557967525063 \| \| CPU power (W) \| 42.5 \| \| GPU power (W) \| [No GPU] \| \| RAM power (W) \| 4.500000000000001 \| \| CPU energy (kWh) \| 0.3715970706832078 \| \| GPU energy (kWh) \| [No GPU] \| \| RAM energy (kWh) \| 0.0393453032049536 \| \| Consumed energy (kWh) \| 0.4109423738881623 \| \| Country name \| Switzerland \| \| Cloud provider \| nan \| \| Cloud region \| nan \| \| CPU count \| 2 \| \| CPU model \| Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz \| \| GPU count \| nan \| \| GPU model \| nan \| ## Environmental Impact (for one core) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| CPU energy (kWh) \| 0.060592291204929344 \| \| Emissions (Co2eq in kg) \| 0.012328301673730212 \| ## Note 17 May 2024 ## My Config \| Config \| Value \| \|--------------------------\|-----------------\| \| checkpoint \| albert-base-v2 \| \| model_name \| ft_bs32_lr6_base_x4 \| \| sequence_length \| 400 \| \| num_epoch \| 12 \| \| learning_rate \| 5e-06 \| \| batch_size \| 32 \| \| weight_decay \| 0.0 \| \| warm_up_prop \| 0.0 \| \| drop_out_prob \| 0.1 \| \| packing_length \| 100 \| \| train_test_split \| 0.2 \| \| num_steps \| 65160 \| ## Training and Testing steps Epoch \| Train Loss \| Test Loss \| Accuracy \| Recall ---\|---\|---\|---\|--- \| 0 \| 0.464978 \| 0.393267 \| 0.820324 \| 0.863497 \| \| 1 \| 0.357935 \| 0.372957 \| 0.837261 \| 0.881902 \| \| 2 \| 0.307998 \| 0.368084 \| 0.840206 \| 0.819018 \| \| 3 \| 0.270172 \| 0.422406 \| 0.818851 \| 0.742331 \| \| 4 \| 0.219624 \| 0.438727 \| 0.824742 \| 0.814417 \| \| 5 \| 0.160742 \| 0.480765 \| 0.811487 \| 0.891104 \| \| 6 \| 0.098260 \| 0.613929 \| 0.811487 \| 0.866564 \| \| 7 \| 0.068914 \| 0.678225 \| 0.814433 \| 0.771472 \| \| 8 \| 0.034506 \| 0.775600 \| 0.809278 \| 0.826687 \| \| 9 \| 0.030098 \| 0.782375 \| 0.811487 \| 0.842025 \| \| 10 \| 0.035147 \| 0.840999 \| 0.804860 \| 0.849693 \| \| 11 \| 0.019798 \| 0.855098 \| 0.821060 \| 0.860429 \|
damgomz/ft_bs16_lr6_base_x4	damgomz	2024-05-17T16:47:33Z	108	0	transformers	[ "transformers", "safetensors", "albert", "text-classification", "fill-mask", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-05-17T09:29:03Z	--- language: en tags: - fill-mask kwargs: timestamp: '2024-05-17T18:47:30' project_name: ft_bs16_lr6_base_x4_emissions_tracker run_id: 80ff29c1-d4ea-4272-b363-794bf58f2de3 duration: 31525.332528352737 emissions: 0.0193858122759137 emissions_rate: 6.149280823121802e-07 cpu_power: 42.5 gpu_power: 0.0 ram_power: 4.500000000000001 cpu_energy: 0.3721733759154877 gpu_energy: 0 ram_energy: 0.0394062567019462 energy_consumed: 0.4115796326174337 country_name: Switzerland country_iso_code: CHE region: .nan cloud_provider: .nan cloud_region: .nan os: Linux-5.14.0-70.30.1.el9_0.x86_64-x86_64-with-glibc2.34 python_version: 3.10.4 codecarbon_version: 2.3.4 cpu_count: 2 cpu_model: Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz gpu_count: .nan gpu_model: .nan longitude: .nan latitude: .nan ram_total_size: 12 tracking_mode: machine on_cloud: N pue: 1.0 --- ## Environmental Impact (CODE CARBON DEFAULT) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| Duration (in seconds) \| 31525.332528352737 \| \| Emissions (Co2eq in kg) \| 0.0193858122759137 \| \| CPU power (W) \| 42.5 \| \| GPU power (W) \| [No GPU] \| \| RAM power (W) \| 4.500000000000001 \| \| CPU energy (kWh) \| 0.3721733759154877 \| \| GPU energy (kWh) \| [No GPU] \| \| RAM energy (kWh) \| 0.0394062567019462 \| \| Consumed energy (kWh) \| 0.4115796326174337 \| \| Country name \| Switzerland \| \| Cloud provider \| nan \| \| Cloud region \| nan \| \| CPU count \| 2 \| \| CPU model \| Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz \| \| GPU count \| nan \| \| GPU model \| nan \| ## Environmental Impact (for one core) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| CPU energy (kWh) \| 0.060686265117079016 \| \| Emissions (Co2eq in kg) \| 0.012347421906938156 \| ## Note 17 May 2024 ## My Config \| Config \| Value \| \|--------------------------\|-----------------\| \| checkpoint \| albert-base-v2 \| \| model_name \| ft_bs16_lr6_base_x4 \| \| sequence_length \| 400 \| \| num_epoch \| 12 \| \| learning_rate \| 5e-06 \| \| batch_size \| 16 \| \| weight_decay \| 0.0 \| \| warm_up_prop \| 0.0 \| \| drop_out_prob \| 0.1 \| \| packing_length \| 100 \| \| train_test_split \| 0.2 \| \| num_steps \| 65160 \| ## Training and Testing steps Epoch \| Train Loss \| Test Loss \| Accuracy \| Recall ---\|---\|---\|---\|--- \| 0 \| 0.448223 \| 0.404151 \| 0.823270 \| 0.918712 \| \| 1 \| 0.347356 \| 0.380528 \| 0.839470 \| 0.904908 \| \| 2 \| 0.298852 \| 0.393898 \| 0.837261 \| 0.829755 \| \| 3 \| 0.248956 \| 0.408337 \| 0.831370 \| 0.800613 \| \| 4 \| 0.188713 \| 0.523134 \| 0.826951 \| 0.811350 \| \| 5 \| 0.127459 \| 0.518127 \| 0.814433 \| 0.855828 \| \| 6 \| 0.073867 \| 0.667144 \| 0.815169 \| 0.874233 \| \| 7 \| 0.046921 \| 0.809258 \| 0.814433 \| 0.812883 \| \| 8 \| 0.036878 \| 0.876000 \| 0.803387 \| 0.838957 \| \| 9 \| 0.036106 \| 0.637194 \| 0.809278 \| 0.762270 \| \| 10 \| 0.027892 \| 0.864272 \| 0.817378 \| 0.785276 \| \| 11 \| 0.011581 \| 0.962748 \| 0.812960 \| 0.832822 \|
justin-shopcapsule/BLIP-dress	justin-shopcapsule	2024-05-17T16:45:11Z	64	0	transformers	[ "transformers", "safetensors", "blip", "image-text-to-text", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-05-17T16:41:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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]
Bzbr/a2c-PandaReachDense-v3	Bzbr	2024-05-17T16:42:13Z	0	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-17T16:37:55Z	--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -0.22 +/- 0.13 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v3 This is a trained model of a A2C agent playing PandaReachDense-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 ... ```
emilykang/medmcqa_question_generation-pathology_lora	emilykang	2024-05-17T16:42:06Z	0	0	peft	[ "peft", "safetensors", "llama", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "region:us" ]	null	2024-05-17T15:51:41Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 datasets: - generator model-index: - name: medmcqa_question_generation-pathology_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. --> # medmcqa_question_generation-pathology_lora This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) 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
XueyingJia/llama3_4_bit_mnli_0_shot_transformed_data_score_use_full_row_dataset	XueyingJia	2024-05-17T16:39:58Z	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	2024-05-17T16:39:54Z	--- 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: XueyingJia - 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)
arianhosseini/patricia-walters-darkmagenta	arianhosseini	2024-05-17T16:36:51Z	48	0	transformers	[ "transformers", "safetensors", "gpt_neox", "generated_from_trainer", "base_model:EleutherAI/pythia-2.8b", "base_model:finetune:EleutherAI/pythia-2.8b", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2024-05-17T13:46:33Z	--- license: apache-2.0 base_model: EleutherAI/pythia-2.8b tags: - generated_from_trainer metrics: - accuracy model-index: - name: patricia-walters-darkmagenta 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. --> # patricia-walters-darkmagenta This model is a fine-tuned version of [EleutherAI/pythia-2.8b](https://huggingface.co/EleutherAI/pythia-2.8b) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5059 - Accuracy: 0.7 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 16 - seed: 24 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_ratio: 0.1 - training_steps: 2500 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| 0.4856 \| 1.2490 \| 400 \| 0.3020 \| 1.0 \| \| 0.0737 \| 2.4980 \| 800 \| 0.4773 \| 0.7 \| \| 0.0886 \| 3.7471 \| 1200 \| 1.2119 \| 0.9 \| \| 0.1489 \| 4.9961 \| 1600 \| 0.5459 \| 0.8 \| \| 0.0285 \| 6.2451 \| 2000 \| 2.4004 \| 0.7 \| \| 0.0338 \| 7.4941 \| 2400 \| 0.5059 \| 0.7 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
OwOpeepeepoopoo/DancingElaineL	OwOpeepeepoopoo	2024-05-17T16:35:07Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T16:32:27Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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]
XueyingJia/llama3_mnli_0_shot_transformed_data_score_use_full_row_dataset	XueyingJia	2024-05-17T16:32:50Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:meta-llama/Meta-Llama-3-8B", "base_model:finetune:meta-llama/Meta-Llama-3-8B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-17T16:32:46Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: meta-llama/Meta-Llama-3-8B --- # Uploaded model - Developed by: XueyingJia - License: apache-2.0 - Finetuned from model : meta-llama/Meta-Llama-3-8B This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Onlysmokehuazi/Huazi_Sentiment_Analysis_latest	Onlysmokehuazi	2024-05-17T16:29:35Z	109	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-17T16:28:58Z	--- 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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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]
apwic/sentiment-lora-r2a2d0.05-0	apwic	2024-05-17T16:29:19Z	0	0	null	[ "tensorboard", "generated_from_trainer", "id", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "region:us" ]	null	2024-05-17T15:56:11Z	--- language: - id license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: sentiment-lora-r2a2d0.05-0 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. --> # sentiment-lora-r2a2d0.05-0 This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3642 - Accuracy: 0.8346 - Precision: 0.7993 - Recall: 0.8080 - F1: 0.8034 ## 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: 30 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.5633 \| 1.0 \| 122 \| 0.5100 \| 0.7168 \| 0.6536 \| 0.6446 \| 0.6484 \| \| 0.5083 \| 2.0 \| 244 \| 0.4999 \| 0.7243 \| 0.6825 \| 0.7049 \| 0.6887 \| \| 0.4904 \| 3.0 \| 366 \| 0.4595 \| 0.7619 \| 0.7120 \| 0.7065 \| 0.7091 \| \| 0.4644 \| 4.0 \| 488 \| 0.4287 \| 0.7920 \| 0.7520 \| 0.7253 \| 0.7358 \| \| 0.4439 \| 5.0 \| 610 \| 0.4399 \| 0.7519 \| 0.7127 \| 0.7395 \| 0.7203 \| \| 0.4241 \| 6.0 \| 732 \| 0.4027 \| 0.8221 \| 0.7860 \| 0.7816 \| 0.7837 \| \| 0.4092 \| 7.0 \| 854 \| 0.4019 \| 0.8070 \| 0.7674 \| 0.7835 \| 0.7743 \| \| 0.3891 \| 8.0 \| 976 \| 0.3805 \| 0.8271 \| 0.7912 \| 0.7926 \| 0.7919 \| \| 0.3777 \| 9.0 \| 1098 \| 0.3789 \| 0.8271 \| 0.7912 \| 0.7926 \| 0.7919 \| \| 0.369 \| 10.0 \| 1220 \| 0.3758 \| 0.8396 \| 0.8071 \| 0.8040 \| 0.8055 \| \| 0.3531 \| 11.0 \| 1342 \| 0.3805 \| 0.8296 \| 0.7933 \| 0.8044 \| 0.7984 \| \| 0.3486 \| 12.0 \| 1464 \| 0.3801 \| 0.8321 \| 0.7960 \| 0.8112 \| 0.8027 \| \| 0.3472 \| 13.0 \| 1586 \| 0.3675 \| 0.8421 \| 0.8098 \| 0.8083 \| 0.8091 \| \| 0.3379 \| 14.0 \| 1708 \| 0.3654 \| 0.8371 \| 0.8032 \| 0.8047 \| 0.8040 \| \| 0.3353 \| 15.0 \| 1830 \| 0.3703 \| 0.8421 \| 0.8080 \| 0.8183 \| 0.8127 \| \| 0.3213 \| 16.0 \| 1952 \| 0.3709 \| 0.8371 \| 0.8019 \| 0.8147 \| 0.8077 \| \| 0.3214 \| 17.0 \| 2074 \| 0.3641 \| 0.8371 \| 0.8024 \| 0.8097 \| 0.8059 \| \| 0.3225 \| 18.0 \| 2196 \| 0.3640 \| 0.8371 \| 0.8024 \| 0.8097 \| 0.8059 \| \| 0.3159 \| 19.0 \| 2318 \| 0.3649 \| 0.8346 \| 0.7993 \| 0.8080 \| 0.8034 \| \| 0.3195 \| 20.0 \| 2440 \| 0.3642 \| 0.8346 \| 0.7993 \| 0.8080 \| 0.8034 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.15.2
omersezer/TE_Instruct_L3	omersezer	2024-05-17T16:25:53Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Meta-Llama-3-8B", "base_model:adapter:meta-llama/Meta-Llama-3-8B", "region:us" ]	null	2024-05-17T16:24:54Z	--- library_name: peft base_model: meta-llama/Meta-Llama-3-8B --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.1
buming/ppo-LunarLander-v2	buming	2024-05-17T16:25:45Z	1	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-17T16:25:19Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 254.83 +/- 21.41 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 ... ```
Wellyowo/hubert-esc50-finetuned-v2	Wellyowo	2024-05-17T16:24:23Z	4	0	transformers	[ "transformers", "safetensors", "hubert", "audio-classification", "esc50", "generated_from_trainer", "base_model:facebook/hubert-base-ls960", "base_model:finetune:facebook/hubert-base-ls960", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2024-05-17T13:22:26Z	--- license: apache-2.0 base_model: facebook/hubert-base-ls960 tags: - audio-classification - hubert - esc50 - generated_from_trainer metrics: - accuracy model-index: - name: hubert-esc50-finetuned-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. --> # hubert-esc50-finetuned-v2 This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the ESC-50 dataset. It achieves the following results on the evaluation set: - Loss: 1.9551 - Accuracy: 0.85 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 3.5337 \| 1.0 \| 200 \| 3.4929 \| 0.0775 \| \| 3.1679 \| 2.0 \| 400 \| 3.1355 \| 0.1675 \| \| 2.8042 \| 3.0 \| 600 \| 2.8673 \| 0.2075 \| \| 2.5055 \| 4.0 \| 800 \| 2.6202 \| 0.2125 \| \| 2.0268 \| 5.0 \| 1000 \| 2.3768 \| 0.3375 \| \| 2.1337 \| 6.0 \| 1200 \| 2.0171 \| 0.4225 \| \| 1.6061 \| 7.0 \| 1400 \| 1.7294 \| 0.5075 \| \| 1.5169 \| 8.0 \| 1600 \| 1.8017 \| 0.5025 \| \| 1.0634 \| 9.0 \| 1800 \| 1.5051 \| 0.5475 \| \| 0.9651 \| 10.0 \| 2000 \| 1.3431 \| 0.635 \| \| 0.8616 \| 11.0 \| 2200 \| 1.3417 \| 0.6375 \| \| 0.6799 \| 12.0 \| 2400 \| 1.2891 \| 0.63 \| \| 0.445 \| 13.0 \| 2600 \| 1.2285 \| 0.6575 \| \| 0.2984 \| 14.0 \| 2800 \| 1.2008 \| 0.7125 \| \| 0.5947 \| 15.0 \| 3000 \| 1.3225 \| 0.71 \| \| 0.4194 \| 16.0 \| 3200 \| 1.1032 \| 0.775 \| \| 0.3128 \| 17.0 \| 3400 \| 1.8309 \| 0.6625 \| \| 0.237 \| 18.0 \| 3600 \| 1.3349 \| 0.7325 \| \| 0.1701 \| 19.0 \| 3800 \| 1.4491 \| 0.7275 \| \| 0.2618 \| 20.0 \| 4000 \| 1.4919 \| 0.7525 \| \| 0.1336 \| 21.0 \| 4200 \| 1.6088 \| 0.7325 \| \| 0.113 \| 22.0 \| 4400 \| 1.3687 \| 0.7725 \| \| 0.0757 \| 23.0 \| 4600 \| 1.4691 \| 0.7875 \| \| 0.0558 \| 24.0 \| 4800 \| 1.8059 \| 0.7525 \| \| 0.1442 \| 25.0 \| 5000 \| 1.7809 \| 0.7475 \| \| 0.1023 \| 26.0 \| 5200 \| 1.8423 \| 0.7875 \| \| 0.0075 \| 27.0 \| 5400 \| 1.7945 \| 0.79 \| \| 0.0054 \| 28.0 \| 5600 \| 1.8221 \| 0.7825 \| \| 0.0584 \| 29.0 \| 5800 \| 1.7593 \| 0.785 \| \| 0.07 \| 30.0 \| 6000 \| 1.8601 \| 0.7925 \| \| 0.0827 \| 31.0 \| 6200 \| 1.8467 \| 0.7875 \| \| 0.1128 \| 32.0 \| 6400 \| 2.1020 \| 0.765 \| \| 0.2679 \| 33.0 \| 6600 \| 2.0718 \| 0.775 \| \| 0.0647 \| 34.0 \| 6800 \| 1.9542 \| 0.7875 \| \| 0.0376 \| 35.0 \| 7000 \| 2.1877 \| 0.7675 \| \| 0.0019 \| 36.0 \| 7200 \| 2.4088 \| 0.745 \| \| 0.1009 \| 37.0 \| 7400 \| 2.2295 \| 0.765 \| \| 0.0039 \| 38.0 \| 7600 \| 2.0022 \| 0.7825 \| \| 0.0006 \| 39.0 \| 7800 \| 2.0640 \| 0.795 \| \| 0.0512 \| 40.0 \| 8000 \| 2.3373 \| 0.78 \| \| 0.0282 \| 41.0 \| 8200 \| 1.9908 \| 0.795 \| \| 0.0113 \| 42.0 \| 8400 \| 2.3893 \| 0.775 \| \| 0.035 \| 43.0 \| 8600 \| 2.3017 \| 0.7775 \| \| 0.006 \| 44.0 \| 8800 \| 2.1261 \| 0.7825 \| \| 0.0556 \| 45.0 \| 9000 \| 2.3122 \| 0.775 \| \| 0.0003 \| 46.0 \| 9200 \| 2.1505 \| 0.79 \| \| 0.0115 \| 47.0 \| 9400 \| 2.0387 \| 0.805 \| \| 0.0001 \| 48.0 \| 9600 \| 2.1915 \| 0.8 \| \| 0.2299 \| 49.0 \| 9800 \| 2.6715 \| 0.76 \| \| 0.0017 \| 50.0 \| 10000 \| 2.7250 \| 0.755 \| \| 0.2944 \| 51.0 \| 10200 \| 2.5766 \| 0.79 \| \| 0.1269 \| 52.0 \| 10400 \| 2.3590 \| 0.785 \| \| 0.0941 \| 53.0 \| 10600 \| 2.9789 \| 0.755 \| \| 0.0477 \| 54.0 \| 10800 \| 2.7512 \| 0.75 \| \| 0.2068 \| 55.0 \| 11000 \| 2.5162 \| 0.7725 \| \| 0.0004 \| 56.0 \| 11200 \| 2.4355 \| 0.7525 \| \| 0.0657 \| 57.0 \| 11400 \| 2.5043 \| 0.7775 \| \| 0.0002 \| 58.0 \| 11600 \| 2.4236 \| 0.785 \| \| 0.0133 \| 59.0 \| 11800 \| 2.4225 \| 0.78 \| \| 0.0 \| 60.0 \| 12000 \| 2.3476 \| 0.79 \| \| 0.0159 \| 61.0 \| 12200 \| 2.3234 \| 0.7975 \| \| 0.0002 \| 62.0 \| 12400 \| 2.3763 \| 0.78 \| \| 0.0626 \| 63.0 \| 12600 \| 2.0386 \| 0.835 \| \| 0.0112 \| 64.0 \| 12800 \| 2.3345 \| 0.81 \| \| 0.0004 \| 65.0 \| 13000 \| 2.3710 \| 0.8075 \| \| 0.0714 \| 66.0 \| 13200 \| 2.0527 \| 0.82 \| \| 0.0008 \| 67.0 \| 13400 \| 2.2063 \| 0.8175 \| \| 0.0001 \| 68.0 \| 13600 \| 2.5772 \| 0.795 \| \| 0.0001 \| 69.0 \| 13800 \| 2.4176 \| 0.7975 \| \| 0.0001 \| 70.0 \| 14000 \| 2.1132 \| 0.8125 \| \| 0.0017 \| 71.0 \| 14200 \| 2.2163 \| 0.8125 \| \| 0.2347 \| 72.0 \| 14400 \| 2.0444 \| 0.8275 \| \| 0.0 \| 73.0 \| 14600 \| 2.3745 \| 0.8275 \| \| 0.0001 \| 74.0 \| 14800 \| 2.0128 \| 0.8325 \| \| 0.0037 \| 75.0 \| 15000 \| 2.0867 \| 0.8375 \| \| 0.0 \| 76.0 \| 15200 \| 2.2285 \| 0.825 \| \| 0.0001 \| 77.0 \| 15400 \| 2.0214 \| 0.8425 \| \| 0.0001 \| 78.0 \| 15600 \| 2.4193 \| 0.82 \| \| 0.0002 \| 79.0 \| 15800 \| 2.4296 \| 0.815 \| \| 0.1198 \| 80.0 \| 16000 \| 2.3698 \| 0.8175 \| \| 0.0001 \| 81.0 \| 16200 \| 2.3521 \| 0.82 \| \| 0.0 \| 82.0 \| 16400 \| 2.1241 \| 0.8325 \| \| 0.0001 \| 83.0 \| 16600 \| 2.1642 \| 0.8275 \| \| 0.0005 \| 84.0 \| 16800 \| 2.0545 \| 0.835 \| \| 0.0 \| 85.0 \| 17000 \| 2.0386 \| 0.8475 \| \| 0.0003 \| 86.0 \| 17200 \| 2.1348 \| 0.83 \| \| 0.0004 \| 87.0 \| 17400 \| 2.2024 \| 0.83 \| \| 0.0 \| 88.0 \| 17600 \| 2.1521 \| 0.835 \| \| 0.0001 \| 89.0 \| 17800 \| 2.2244 \| 0.83 \| \| 0.0 \| 90.0 \| 18000 \| 2.1535 \| 0.8325 \| \| 0.0 \| 91.0 \| 18200 \| 2.2048 \| 0.835 \| \| 0.1711 \| 92.0 \| 18400 \| 2.1023 \| 0.83 \| \| 0.0 \| 93.0 \| 18600 \| 2.0534 \| 0.845 \| \| 0.0 \| 94.0 \| 18800 \| 2.0220 \| 0.845 \| \| 0.0 \| 95.0 \| 19000 \| 2.0061 \| 0.845 \| \| 0.0001 \| 96.0 \| 19200 \| 1.9270 \| 0.8475 \| \| 0.0001 \| 97.0 \| 19400 \| 1.9710 \| 0.84 \| \| 0.0001 \| 98.0 \| 19600 \| 1.9561 \| 0.845 \| \| 0.0 \| 99.0 \| 19800 \| 1.9567 \| 0.845 \| \| 0.0 \| 100.0 \| 20000 \| 1.9551 \| 0.85 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.0.1+cu117 - Datasets 2.16.1 - Tokenizers 0.15.1
mohit15/med-llava-v1.5-13b-lora	mohit15	2024-05-17T16:22:14Z	4	0	transformers	[ "transformers", "safetensors", "llava_llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T16:14:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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anonymous1266/MS_Models	anonymous1266	2024-05-17T16:19:54Z	0	0	null	[ "region:us" ]	null	2024-04-05T20:40:36Z	These models are used as supplementary material for a paper in review. See the code base for more information.
hjskhan/gemma-2b-fine-tuned-math	hjskhan	2024-05-17T16:19:21Z	155	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T16:14:23Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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pigas/Phi-2-GPTQ-2bits-g128	pigas	2024-05-17T16:18:09Z	76	0	transformers	[ "transformers", "safetensors", "phi", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "2-bit", "gptq", "region:us" ]	text-generation	2024-05-17T16:13:50Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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lock-rr/Bahasa-4b-chat-Q4_K_M-GGUF	lock-rr	2024-05-17T16:18:00Z	0	0	null	[ "gguf", "llama-cpp", "gguf-my-repo", "id", "license:other", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-17T16:17:50Z	--- language: - id license: other tags: - llama-cpp - gguf-my-repo license_name: tongyi-qianwen --- # lock-rr/Bahasa-4b-chat-Q4_K_M-GGUF This model was converted to GGUF format from [`Bahasalab/Bahasa-4b-chat`](https://huggingface.co/Bahasalab/Bahasa-4b-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/Bahasalab/Bahasa-4b-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 lock-rr/Bahasa-4b-chat-Q4_K_M-GGUF --model bahasa-4b-chat.Q4_K_M.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo lock-rr/Bahasa-4b-chat-Q4_K_M-GGUF --model bahasa-4b-chat.Q4_K_M.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 bahasa-4b-chat.Q4_K_M.gguf -n 128 ```
mohit15/med-llava-recall-v1.5-13b-lora	mohit15	2024-05-17T16:17:38Z	4	0	transformers	[ "transformers", "safetensors", "llava_llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T16:09:38Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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mika5883/finetune_rugec	mika5883	2024-05-17T16:15:35Z	106	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:mika5883/pretrain_rugec", "base_model:finetune:mika5883/pretrain_rugec", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-17T16:09:20Z	--- base_model: mika5883/pretrain_rugec tags: - generated_from_trainer metrics: - bleu model-index: - name: finetune_rugec 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. --> # finetune_rugec This model is a fine-tuned version of [mika5883/pretrain_rugec](https://huggingface.co/mika5883/pretrain_rugec) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2114 - Bleu: 60.3251 - Gen Len: 16.2364 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3.83229e-05 - train_batch_size: 128 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\| \| No log \| 1.0 \| 20 \| 0.2264 \| 59.675 \| 16.2312 \| \| No log \| 2.0 \| 40 \| 0.2114 \| 60.3251 \| 16.2364 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
Cidewalk/autotrain-trained-slady-m38s3	Cidewalk	2024-05-17T16:15:15Z	2	0	transformers	[ "transformers", "tensorboard", "safetensors", "llama", "text-generation", "autotrain", "text-generation-inference", "peft", "conversational", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T15:04:18Z	--- tags: - autotrain - text-generation-inference - text-generation - peft library_name: transformers widget: - messages: - role: user content: What is your favorite condiment? license: other --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
XueyingJia/llama3_4_bit_mnli_openai_3_shots_generated_data_openai	XueyingJia	2024-05-17T16:13:59Z	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	2024-05-17T01:53:43Z	--- 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: XueyingJia - 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)
FusionQuill/Llama-3-8B-Instruct-Onnx	FusionQuill	2024-05-17T16:13:57Z	0	0	null	[ "onnx", "license:llama3", "region:us" ]	null	2024-05-16T15:18:59Z	--- license: llama3 --- Onnx 4bit version of meta-llama/Meta-Llama-3-8B used by FusionQuill.AI
Snoopy47/CustomModel_yelp	Snoopy47	2024-05-17T16:11:28Z	119	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-17T16:10:46Z	--- 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]
leoben/lora_model	leoben	2024-05-17T16:05:32Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-bnb-4bit", "base_model:finetune:unsloth/mistral-7b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-17T16:05:08Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/mistral-7b-bnb-4bit --- # Uploaded model - Developed by: leoben - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Ai-Marshal/Mistral_Sentiment_Classification_2024-05-17	Ai-Marshal	2024-05-17T16:02:33Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "region:us" ]	null	2024-05-17T15:30:21Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: mistralai/Mistral-7B-Instruct-v0.2 datasets: - generator model-index: - name: Mistral_Sentiment_Classification_2024-05-17 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_Sentiment_Classification_2024-05-17 This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 0.3003 ## 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: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 0.03 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.4299 \| 0.4587 \| 100 \| 0.3342 \| \| 0.3351 \| 0.9174 \| 200 \| 0.3189 \| \| 0.3218 \| 1.3761 \| 300 \| 0.3112 \| \| 0.3164 \| 1.8349 \| 400 \| 0.3067 \| \| 0.3021 \| 2.2936 \| 500 \| 0.3035 \| \| 0.2892 \| 2.7523 \| 600 \| 0.3006 \| \| 0.2825 \| 3.2110 \| 700 \| 0.3010 \| \| 0.2719 \| 3.6697 \| 800 \| 0.2994 \| \| 0.2807 \| 4.1284 \| 900 \| 0.3002 \| \| 0.2622 \| 4.5872 \| 1000 \| 0.3003 \| ### Framework versions - PEFT 0.11.0 - Transformers 4.40.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
bhoopendrakumar/passport_10_images	bhoopendrakumar	2024-05-17T15:57:12Z	48	0	transformers	[ "transformers", "safetensors", "vision-encoder-decoder", "image-text-to-text", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-05-17T15:52:13Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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. 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apwic/sentiment-lora-r2a1d0.15-0	apwic	2024-05-17T15:55:53Z	0	0	null	[ "tensorboard", "generated_from_trainer", "id", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "region:us" ]	null	2024-05-17T15:22:42Z	--- language: - id license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: sentiment-lora-r2a1d0.15-0 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. --> # sentiment-lora-r2a1d0.15-0 This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3672 - Accuracy: 0.8321 - Precision: 0.7961 - Recall: 0.8087 - F1: 0.8018 ## 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: 30 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.563 \| 1.0 \| 122 \| 0.5138 \| 0.7243 \| 0.6636 \| 0.6549 \| 0.6586 \| \| 0.509 \| 2.0 \| 244 \| 0.5057 \| 0.7168 \| 0.6763 \| 0.6996 \| 0.6820 \| \| 0.4924 \| 3.0 \| 366 \| 0.4708 \| 0.7393 \| 0.6877 \| 0.6931 \| 0.6901 \| \| 0.468 \| 4.0 \| 488 \| 0.4379 \| 0.7845 \| 0.7412 \| 0.7200 \| 0.7286 \| \| 0.4495 \| 5.0 \| 610 \| 0.4466 \| 0.7594 \| 0.7233 \| 0.7548 \| 0.7313 \| \| 0.4334 \| 6.0 \| 732 \| 0.4041 \| 0.8271 \| 0.7927 \| 0.7851 \| 0.7887 \| \| 0.415 \| 7.0 \| 854 \| 0.4057 \| 0.7995 \| 0.7590 \| 0.7756 \| 0.7660 \| \| 0.3974 \| 8.0 \| 976 \| 0.3852 \| 0.8321 \| 0.7982 \| 0.7937 \| 0.7959 \| \| 0.3849 \| 9.0 \| 1098 \| 0.3829 \| 0.8246 \| 0.7880 \| 0.7909 \| 0.7894 \| \| 0.3771 \| 10.0 \| 1220 \| 0.3786 \| 0.8396 \| 0.8065 \| 0.8065 \| 0.8065 \| \| 0.3633 \| 11.0 \| 1342 \| 0.3843 \| 0.8296 \| 0.7931 \| 0.8069 \| 0.7993 \| \| 0.3591 \| 12.0 \| 1464 \| 0.3833 \| 0.8296 \| 0.7931 \| 0.8069 \| 0.7993 \| \| 0.354 \| 13.0 \| 1586 \| 0.3705 \| 0.8396 \| 0.8065 \| 0.8065 \| 0.8065 \| \| 0.3451 \| 14.0 \| 1708 \| 0.3709 \| 0.8371 \| 0.8028 \| 0.8072 \| 0.8049 \| \| 0.3403 \| 15.0 \| 1830 \| 0.3733 \| 0.8321 \| 0.7960 \| 0.8112 \| 0.8027 \| \| 0.3282 \| 16.0 \| 1952 \| 0.3715 \| 0.8346 \| 0.7988 \| 0.8155 \| 0.8061 \| \| 0.3286 \| 17.0 \| 2074 \| 0.3664 \| 0.8321 \| 0.7965 \| 0.8037 \| 0.7999 \| \| 0.3348 \| 18.0 \| 2196 \| 0.3670 \| 0.8271 \| 0.7904 \| 0.8001 \| 0.7949 \| \| 0.325 \| 19.0 \| 2318 \| 0.3669 \| 0.8321 \| 0.7961 \| 0.8087 \| 0.8018 \| \| 0.3266 \| 20.0 \| 2440 \| 0.3672 \| 0.8321 \| 0.7961 \| 0.8087 \| 0.8018 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.15.2
bartowski/kunoichi-lemon-royale-v2-32K-7B-GGUF	bartowski	2024-05-17T15:53:39Z	136	2	transformers	[ "transformers", "gguf", "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", "conversational" ]	text-generation	2024-05-17T15:35:50Z	--- 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 --- ## Llamacpp imatrix Quantizations of kunoichi-lemon-royale-v2-32K-7B Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b2901">b2901</a> for quantization. Original model: https://huggingface.co/grimjim/kunoichi-lemon-royale-v2-32K-7B All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/b6ac44691e994344625687afe3263b3a) ## Prompt format ``` <s> [INST] {prompt} [/INST]</s> ``` Note that this model does not support a System prompt. ## Download a file (not the whole branch) from below: \| Filename \| Quant type \| File Size \| Description \| \| -------- \| ---------- \| --------- \| ----------- \| \| [kunoichi-lemon-royale-v2-32K-7B-Q8_0.gguf](https://huggingface.co/bartowski/kunoichi-lemon-royale-v2-32K-7B-GGUF/blob/main/kunoichi-lemon-royale-v2-32K-7B-Q8_0.gguf) \| Q8_0 \| 7.69GB \| Extremely high quality, generally unneeded but max available quant. \| \| [kunoichi-lemon-royale-v2-32K-7B-Q6_K.gguf](https://huggingface.co/bartowski/kunoichi-lemon-royale-v2-32K-7B-GGUF/blob/main/kunoichi-lemon-royale-v2-32K-7B-Q6_K.gguf) \| Q6_K \| 5.94GB \| Very high quality, near perfect, recommended. \| \| [kunoichi-lemon-royale-v2-32K-7B-Q5_K_M.gguf](https://huggingface.co/bartowski/kunoichi-lemon-royale-v2-32K-7B-GGUF/blob/main/kunoichi-lemon-royale-v2-32K-7B-Q5_K_M.gguf) \| Q5_K_M \| 5.13GB \| High quality, recommended. \| \| [kunoichi-lemon-royale-v2-32K-7B-Q5_K_S.gguf](https://huggingface.co/bartowski/kunoichi-lemon-royale-v2-32K-7B-GGUF/blob/main/kunoichi-lemon-royale-v2-32K-7B-Q5_K_S.gguf) \| Q5_K_S \| 4.99GB \| High quality, recommended. \| \| [kunoichi-lemon-royale-v2-32K-7B-Q4_K_M.gguf](https://huggingface.co/bartowski/kunoichi-lemon-royale-v2-32K-7B-GGUF/blob/main/kunoichi-lemon-royale-v2-32K-7B-Q4_K_M.gguf) \| Q4_K_M \| 4.36GB \| Good quality, uses about 4.83 bits per weight, recommended. \| \| [kunoichi-lemon-royale-v2-32K-7B-Q4_K_S.gguf](https://huggingface.co/bartowski/kunoichi-lemon-royale-v2-32K-7B-GGUF/blob/main/kunoichi-lemon-royale-v2-32K-7B-Q4_K_S.gguf) \| Q4_K_S \| 4.14GB \| Slightly lower quality with more space savings, recommended. \| \| [kunoichi-lemon-royale-v2-32K-7B-IQ4_NL.gguf](https://huggingface.co/bartowski/kunoichi-lemon-royale-v2-32K-7B-GGUF/blob/main/kunoichi-lemon-royale-v2-32K-7B-IQ4_NL.gguf) \| IQ4_NL \| 4.12GB \| Decent quality, slightly smaller than Q4_K_S with similar performance recommended. \| \| [kunoichi-lemon-royale-v2-32K-7B-IQ4_XS.gguf](https://huggingface.co/bartowski/kunoichi-lemon-royale-v2-32K-7B-GGUF/blob/main/kunoichi-lemon-royale-v2-32K-7B-IQ4_XS.gguf) \| IQ4_XS \| 3.90GB \| Decent quality, smaller than Q4_K_S with similar performance, recommended. \| \| [kunoichi-lemon-royale-v2-32K-7B-Q3_K_L.gguf](https://huggingface.co/bartowski/kunoichi-lemon-royale-v2-32K-7B-GGUF/blob/main/kunoichi-lemon-royale-v2-32K-7B-Q3_K_L.gguf) \| Q3_K_L \| 3.82GB \| Lower quality but usable, good for low RAM availability. \| \| [kunoichi-lemon-royale-v2-32K-7B-Q3_K_M.gguf](https://huggingface.co/bartowski/kunoichi-lemon-royale-v2-32K-7B-GGUF/blob/main/kunoichi-lemon-royale-v2-32K-7B-Q3_K_M.gguf) \| Q3_K_M \| 3.51GB \| Even lower quality. \| \| [kunoichi-lemon-royale-v2-32K-7B-IQ3_M.gguf](https://huggingface.co/bartowski/kunoichi-lemon-royale-v2-32K-7B-GGUF/blob/main/kunoichi-lemon-royale-v2-32K-7B-IQ3_M.gguf) \| IQ3_M \| 3.28GB \| Medium-low quality, new method with decent performance comparable to Q3_K_M. \| \| [kunoichi-lemon-royale-v2-32K-7B-IQ3_S.gguf](https://huggingface.co/bartowski/kunoichi-lemon-royale-v2-32K-7B-GGUF/blob/main/kunoichi-lemon-royale-v2-32K-7B-IQ3_S.gguf) \| IQ3_S \| 3.18GB \| Lower quality, new method with decent performance, recommended over Q3_K_S quant, same size with better performance. \| \| [kunoichi-lemon-royale-v2-32K-7B-Q3_K_S.gguf](https://huggingface.co/bartowski/kunoichi-lemon-royale-v2-32K-7B-GGUF/blob/main/kunoichi-lemon-royale-v2-32K-7B-Q3_K_S.gguf) \| Q3_K_S \| 3.16GB \| Low quality, not recommended. \| \| [kunoichi-lemon-royale-v2-32K-7B-IQ3_XS.gguf](https://huggingface.co/bartowski/kunoichi-lemon-royale-v2-32K-7B-GGUF/blob/main/kunoichi-lemon-royale-v2-32K-7B-IQ3_XS.gguf) \| IQ3_XS \| 3.01GB \| Lower quality, new method with decent performance, slightly better than Q3_K_S. \| \| [kunoichi-lemon-royale-v2-32K-7B-IQ3_XXS.gguf](https://huggingface.co/bartowski/kunoichi-lemon-royale-v2-32K-7B-GGUF/blob/main/kunoichi-lemon-royale-v2-32K-7B-IQ3_XXS.gguf) \| IQ3_XXS \| 2.82GB \| Lower quality, new method with decent performance, comparable to Q3 quants. \| \| [kunoichi-lemon-royale-v2-32K-7B-Q2_K.gguf](https://huggingface.co/bartowski/kunoichi-lemon-royale-v2-32K-7B-GGUF/blob/main/kunoichi-lemon-royale-v2-32K-7B-Q2_K.gguf) \| Q2_K \| 2.71GB \| Very low quality but surprisingly usable. \| \| [kunoichi-lemon-royale-v2-32K-7B-IQ2_M.gguf](https://huggingface.co/bartowski/kunoichi-lemon-royale-v2-32K-7B-GGUF/blob/main/kunoichi-lemon-royale-v2-32K-7B-IQ2_M.gguf) \| IQ2_M \| 2.50GB \| Very low quality, uses SOTA techniques to also be surprisingly usable. \| \| [kunoichi-lemon-royale-v2-32K-7B-IQ2_S.gguf](https://huggingface.co/bartowski/kunoichi-lemon-royale-v2-32K-7B-GGUF/blob/main/kunoichi-lemon-royale-v2-32K-7B-IQ2_S.gguf) \| IQ2_S \| 2.31GB \| Very low quality, uses SOTA techniques to be usable. \| \| [kunoichi-lemon-royale-v2-32K-7B-IQ2_XS.gguf](https://huggingface.co/bartowski/kunoichi-lemon-royale-v2-32K-7B-GGUF/blob/main/kunoichi-lemon-royale-v2-32K-7B-IQ2_XS.gguf) \| IQ2_XS \| 2.19GB \| Very low quality, uses SOTA techniques to be usable. \| \| [kunoichi-lemon-royale-v2-32K-7B-IQ2_XXS.gguf](https://huggingface.co/bartowski/kunoichi-lemon-royale-v2-32K-7B-GGUF/blob/main/kunoichi-lemon-royale-v2-32K-7B-IQ2_XXS.gguf) \| IQ2_XXS \| 1.99GB \| Lower quality, uses SOTA techniques to be usable. \| \| [kunoichi-lemon-royale-v2-32K-7B-IQ1_M.gguf](https://huggingface.co/bartowski/kunoichi-lemon-royale-v2-32K-7B-GGUF/blob/main/kunoichi-lemon-royale-v2-32K-7B-IQ1_M.gguf) \| IQ1_M \| 1.75GB \| Extremely low quality, not recommended. \| \| [kunoichi-lemon-royale-v2-32K-7B-IQ1_S.gguf](https://huggingface.co/bartowski/kunoichi-lemon-royale-v2-32K-7B-GGUF/blob/main/kunoichi-lemon-royale-v2-32K-7B-IQ1_S.gguf) \| IQ1_S \| 1.61GB \| Extremely low quality, not recommended. \| ## Downloading using huggingface-cli First, make sure you have hugginface-cli installed: ``` pip install -U "huggingface_hub[cli]" ``` Then, you can target the specific file you want: ``` huggingface-cli download bartowski/kunoichi-lemon-royale-v2-32K-7B-GGUF --include "kunoichi-lemon-royale-v2-32K-7B-Q4_K_M.gguf" --local-dir ./ --local-dir-use-symlinks False ``` If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: ``` huggingface-cli download bartowski/kunoichi-lemon-royale-v2-32K-7B-GGUF --include "kunoichi-lemon-royale-v2-32K-7B-Q8_0.gguf/" --local-dir kunoichi-lemon-royale-v2-32K-7B-Q8_0 --local-dir-use-symlinks False ``` You can either specify a new local-dir (kunoichi-lemon-royale-v2-32K-7B-Q8_0) or download them all in place (./) ## Which file should I choose? A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have. If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM. If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total. Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'. If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M. If you want to get more into the weeds, you can check out this extremely useful feature chart: [llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix) But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size. These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide. The I-quants are not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm. Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
emilykang/medmcqa_question_generation-pharmacology_lora	emilykang	2024-05-17T15:51:35Z	7	0	peft	[ "peft", "safetensors", "llama", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "region:us" ]	null	2024-05-17T15:11:20Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 datasets: - generator model-index: - name: medmcqa_question_generation-pharmacology_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. --> # medmcqa_question_generation-pharmacology_lora This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) 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
Aadithyak/WHISPERtestmodel	Aadithyak	2024-05-17T15:49:43Z	116	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-14T13:06:39Z	--- license: apache-2.0 ---
RichardErkhov/walebadr_-_Mistral-7B-v0.1-DPO-gguf	RichardErkhov	2024-05-17T15:48:13Z	14	0	null	[ "gguf", "endpoints_compatible", "region:us" ]	null	2024-05-17T14:27:34Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Mistral-7B-v0.1-DPO - GGUF - Model creator: https://huggingface.co/walebadr/ - Original model: https://huggingface.co/walebadr/Mistral-7B-v0.1-DPO/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [Mistral-7B-v0.1-DPO.Q2_K.gguf](https://huggingface.co/RichardErkhov/walebadr_-_Mistral-7B-v0.1-DPO-gguf/blob/main/Mistral-7B-v0.1-DPO.Q2_K.gguf) \| Q2_K \| 2.53GB \| \| [Mistral-7B-v0.1-DPO.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/walebadr_-_Mistral-7B-v0.1-DPO-gguf/blob/main/Mistral-7B-v0.1-DPO.IQ3_XS.gguf) \| IQ3_XS \| 2.81GB \| \| [Mistral-7B-v0.1-DPO.IQ3_S.gguf](https://huggingface.co/RichardErkhov/walebadr_-_Mistral-7B-v0.1-DPO-gguf/blob/main/Mistral-7B-v0.1-DPO.IQ3_S.gguf) \| IQ3_S \| 2.96GB \| \| [Mistral-7B-v0.1-DPO.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/walebadr_-_Mistral-7B-v0.1-DPO-gguf/blob/main/Mistral-7B-v0.1-DPO.Q3_K_S.gguf) \| Q3_K_S \| 2.95GB \| \| [Mistral-7B-v0.1-DPO.IQ3_M.gguf](https://huggingface.co/RichardErkhov/walebadr_-_Mistral-7B-v0.1-DPO-gguf/blob/main/Mistral-7B-v0.1-DPO.IQ3_M.gguf) \| IQ3_M \| 3.06GB \| \| [Mistral-7B-v0.1-DPO.Q3_K.gguf](https://huggingface.co/RichardErkhov/walebadr_-_Mistral-7B-v0.1-DPO-gguf/blob/main/Mistral-7B-v0.1-DPO.Q3_K.gguf) \| Q3_K \| 3.28GB \| \| [Mistral-7B-v0.1-DPO.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/walebadr_-_Mistral-7B-v0.1-DPO-gguf/blob/main/Mistral-7B-v0.1-DPO.Q3_K_M.gguf) \| Q3_K_M \| 3.28GB \| \| [Mistral-7B-v0.1-DPO.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/walebadr_-_Mistral-7B-v0.1-DPO-gguf/blob/main/Mistral-7B-v0.1-DPO.Q3_K_L.gguf) \| Q3_K_L \| 3.56GB \| \| [Mistral-7B-v0.1-DPO.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/walebadr_-_Mistral-7B-v0.1-DPO-gguf/blob/main/Mistral-7B-v0.1-DPO.IQ4_XS.gguf) \| IQ4_XS \| 3.67GB \| \| [Mistral-7B-v0.1-DPO.Q4_0.gguf](https://huggingface.co/RichardErkhov/walebadr_-_Mistral-7B-v0.1-DPO-gguf/blob/main/Mistral-7B-v0.1-DPO.Q4_0.gguf) \| Q4_0 \| 3.83GB \| \| [Mistral-7B-v0.1-DPO.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/walebadr_-_Mistral-7B-v0.1-DPO-gguf/blob/main/Mistral-7B-v0.1-DPO.IQ4_NL.gguf) \| IQ4_NL \| 3.87GB \| \| [Mistral-7B-v0.1-DPO.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/walebadr_-_Mistral-7B-v0.1-DPO-gguf/blob/main/Mistral-7B-v0.1-DPO.Q4_K_S.gguf) \| Q4_K_S \| 3.86GB \| \| [Mistral-7B-v0.1-DPO.Q4_K.gguf](https://huggingface.co/RichardErkhov/walebadr_-_Mistral-7B-v0.1-DPO-gguf/blob/main/Mistral-7B-v0.1-DPO.Q4_K.gguf) \| Q4_K \| 4.07GB \| \| [Mistral-7B-v0.1-DPO.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/walebadr_-_Mistral-7B-v0.1-DPO-gguf/blob/main/Mistral-7B-v0.1-DPO.Q4_K_M.gguf) \| Q4_K_M \| 4.07GB \| \| [Mistral-7B-v0.1-DPO.Q4_1.gguf](https://huggingface.co/RichardErkhov/walebadr_-_Mistral-7B-v0.1-DPO-gguf/blob/main/Mistral-7B-v0.1-DPO.Q4_1.gguf) \| Q4_1 \| 4.24GB \| \| [Mistral-7B-v0.1-DPO.Q5_0.gguf](https://huggingface.co/RichardErkhov/walebadr_-_Mistral-7B-v0.1-DPO-gguf/blob/main/Mistral-7B-v0.1-DPO.Q5_0.gguf) \| Q5_0 \| 4.65GB \| \| [Mistral-7B-v0.1-DPO.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/walebadr_-_Mistral-7B-v0.1-DPO-gguf/blob/main/Mistral-7B-v0.1-DPO.Q5_K_S.gguf) \| Q5_K_S \| 4.65GB \| \| [Mistral-7B-v0.1-DPO.Q5_K.gguf](https://huggingface.co/RichardErkhov/walebadr_-_Mistral-7B-v0.1-DPO-gguf/blob/main/Mistral-7B-v0.1-DPO.Q5_K.gguf) \| Q5_K \| 4.78GB \| \| [Mistral-7B-v0.1-DPO.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/walebadr_-_Mistral-7B-v0.1-DPO-gguf/blob/main/Mistral-7B-v0.1-DPO.Q5_K_M.gguf) \| Q5_K_M \| 4.78GB \| \| [Mistral-7B-v0.1-DPO.Q5_1.gguf](https://huggingface.co/RichardErkhov/walebadr_-_Mistral-7B-v0.1-DPO-gguf/blob/main/Mistral-7B-v0.1-DPO.Q5_1.gguf) \| Q5_1 \| 5.07GB \| \| [Mistral-7B-v0.1-DPO.Q6_K.gguf](https://huggingface.co/RichardErkhov/walebadr_-_Mistral-7B-v0.1-DPO-gguf/blob/main/Mistral-7B-v0.1-DPO.Q6_K.gguf) \| Q6_K \| 5.53GB \| \| [Mistral-7B-v0.1-DPO.Q8_0.gguf](https://huggingface.co/RichardErkhov/walebadr_-_Mistral-7B-v0.1-DPO-gguf/blob/main/Mistral-7B-v0.1-DPO.Q8_0.gguf) \| Q8_0 \| 7.17GB \| Original model description: --- license: apache-2.0 --- Mistral-7b-v0.1-DPO is a finetuned adapter from the original Mistral-7b model. In this adaptor, I am finetuning the LM head in addition to the regular modules that are normally finetuned. Below is the list of the finetuned modules: 'k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj', 'lm_head'
jinwoo1126/llama-3-8b-open-korean-it	jinwoo1126	2024-05-17T15:47:20Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T15:43:01Z	--- 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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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]
damgomz/ThunBERT_bs8_lr5	damgomz	2024-05-17T15:46:48Z	108	0	transformers	[ "transformers", "safetensors", "albert", "pretraining", "fill-mask", "en", "endpoints_compatible", "region:us" ]	fill-mask	2024-05-15T09:46:09Z	--- language: en tags: - fill-mask kwargs: timestamp: '2024-05-17T17:46:44' project_name: ThunBERT_bs8_lr5_emissions_tracker run_id: 525cc3ea-c30d-41f0-83c7-26fb501d8395 duration: 199505.3919699192 emissions: 0.2088190820973 emissions_rate: 1.0466839018004345e-06 cpu_power: 42.5 gpu_power: 0.0 ram_power: 37.5 cpu_energy: 2.3552678664051694 gpu_energy: 0 ram_energy: 2.078164164704575 energy_consumed: 4.433432031109744 country_name: Switzerland country_iso_code: CHE region: .nan cloud_provider: .nan cloud_region: .nan os: Linux-5.14.0-70.30.1.el9_0.x86_64-x86_64-with-glibc2.34 python_version: 3.10.4 codecarbon_version: 2.3.4 cpu_count: 4 cpu_model: Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz gpu_count: .nan gpu_model: .nan longitude: .nan latitude: .nan ram_total_size: 100 tracking_mode: machine on_cloud: N pue: 1.0 --- ## Environmental Impact (CODE CARBON DEFAULT) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| Duration (in seconds) \| 199505.3919699192 \| \| Emissions (Co2eq in kg) \| 0.2088190820973 \| \| CPU power (W) \| 42.5 \| \| GPU power (W) \| [No GPU] \| \| RAM power (W) \| 37.5 \| \| CPU energy (kWh) \| 2.3552678664051694 \| \| GPU energy (kWh) \| [No GPU] \| \| RAM energy (kWh) \| 2.078164164704575 \| \| Consumed energy (kWh) \| 4.433432031109744 \| \| Country name \| Switzerland \| \| Cloud provider \| nan \| \| Cloud region \| nan \| \| CPU count \| 4 \| \| CPU model \| Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz \| \| GPU count \| nan \| \| GPU model \| nan \| ## Environmental Impact (for one core) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| CPU energy (kWh) \| 0.38404787954209446 \| \| Emissions (Co2eq in kg) \| 0.07813961185488502 \| ## Note 15 May 2024 ## My Config \| Config \| Value \| \|--------------------------\|-----------------\| \| checkpoint \| albert-base-v2 \| \| model_name \| ThunBERT_bs8_lr5 \| \| sequence_length \| 400 \| \| num_epoch \| 6 \| \| learning_rate \| 5e-05 \| \| batch_size \| 8 \| \| weight_decay \| 0.0 \| \| warm_up_prop \| 0.0 \| \| drop_out_prob \| 0.1 \| \| packing_length \| 100 \| \| train_test_split \| 0.2 \| \| num_steps \| 82627 \| ## Training and Testing steps Epoch \| Train Loss \| Test Loss ---\|---\|--- \| 0.0 \| 7.067743 \| 3.953574 \| \| 0.5 \| 3.522399 \| 3.376064 \| \| 1.0 \| 3.303520 \| 3.226876 \| \| 1.5 \| 3.154914 \| 3.167486 \| \| 2.0 \| 3.058335 \| 3.051049 \| \| 2.5 \| 2.983440 \| 2.994546 \| \| 3.0 \| 2.966602 \| 2.926526 \| \| 3.5 \| 2.846851 \| 2.879127 \| \| 4.0 \| 2.785210 \| 2.832286 \| \| 4.5 \| 2.718725 \| 2.795912 \| \| 5.0 \| 2.670722 \| 2.733300 \| \| 5.5 \| 2.628934 \| 2.693741 \| \| 6.0 \| 2.589258 \| 2.672380 \|
Resi/finetune-donut-doctype-v2	Resi	2024-05-17T15:38:27Z	49	0	transformers	[ "transformers", "safetensors", "vision-encoder-decoder", "image-text-to-text", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-05-17T15:37:48Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Rhma/LlamaDialo10	Rhma	2024-05-17T15:34:07Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T15:30:40Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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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]
CourtneyCC/Haxiro	CourtneyCC	2024-05-17T15:33:43Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-17T15:33:43Z	--- license: apache-2.0 ---
vuminhtue/Bert_NER_CoNLL2003	vuminhtue	2024-05-17T15:29:22Z	107	0	transformers	[ "transformers", "safetensors", "bert", "token-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-05-17T15:29:04Z	--- license: apache-2.0 base_model: google-bert/bert-base-uncased tags: - generated_from_trainer model-index: - name: Bert_NER_CoNLL2003 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/tuevu_smu/huggingface/runs/6j8wt2rd) # Bert_NER_CoNLL2003 This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 1.13.1 - Datasets 2.18.0 - Tokenizers 0.19.1
damgomz/ft_bs64_lr7_base_x4	damgomz	2024-05-17T15:28:44Z	113	0	transformers	[ "transformers", "safetensors", "albert", "text-classification", "fill-mask", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-05-17T09:22:30Z	--- language: en tags: - fill-mask kwargs: timestamp: '2024-05-17T17:28:41' project_name: ft_bs64_lr7_base_x4_emissions_tracker run_id: e2122246-4fcb-4eba-8efa-d204cb43712a duration: 26475.748703956604 emissions: 0.0173198704923115 emissions_rate: 6.541786857843705e-07 cpu_power: 42.5 gpu_power: 0.0 ram_power: 7.5 cpu_energy: 0.3125603431133757 gpu_energy: 0 ram_energy: 0.0551573378766576 energy_consumed: 0.3677176809900338 country_name: Switzerland country_iso_code: CHE region: .nan cloud_provider: .nan cloud_region: .nan os: Linux-5.14.0-70.30.1.el9_0.x86_64-x86_64-with-glibc2.34 python_version: 3.10.4 codecarbon_version: 2.3.4 cpu_count: 3 cpu_model: Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz gpu_count: .nan gpu_model: .nan longitude: .nan latitude: .nan ram_total_size: 20 tracking_mode: machine on_cloud: N pue: 1.0 --- ## Environmental Impact (CODE CARBON DEFAULT) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| Duration (in seconds) \| 26475.748703956604 \| \| Emissions (Co2eq in kg) \| 0.0173198704923115 \| \| CPU power (W) \| 42.5 \| \| GPU power (W) \| [No GPU] \| \| RAM power (W) \| 7.5 \| \| CPU energy (kWh) \| 0.3125603431133757 \| \| GPU energy (kWh) \| [No GPU] \| \| RAM energy (kWh) \| 0.0551573378766576 \| \| Consumed energy (kWh) \| 0.3677176809900338 \| \| Country name \| Switzerland \| \| Cloud provider \| nan \| \| Cloud region \| nan \| \| CPU count \| 3 \| \| CPU model \| Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz \| \| GPU count \| nan \| \| GPU model \| nan \| ## Environmental Impact (for one core) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| CPU energy (kWh) \| 0.05096581625511646 \| \| Emissions (Co2eq in kg) \| 0.010369668242383001 \| ## Note 17 May 2024 ## My Config \| Config \| Value \| \|--------------------------\|-----------------\| \| checkpoint \| albert-base-v2 \| \| model_name \| ft_bs64_lr7_base_x4 \| \| sequence_length \| 400 \| \| num_epoch \| 15 \| \| learning_rate \| 5e-07 \| \| batch_size \| 64 \| \| weight_decay \| 0.0 \| \| warm_up_prop \| 0.0 \| \| drop_out_prob \| 0.1 \| \| packing_length \| 100 \| \| train_test_split \| 0.2 \| \| num_steps \| 81450 \| ## Training and Testing steps Epoch \| Train Loss \| Test Loss \| Accuracy \| Recall ---\|---\|---\|---\|--- \| 0 \| 0.640842 \| 0.587673 \| 0.723122 \| 0.808282 \| \| 1 \| 0.545254 \| 0.528299 \| 0.735641 \| 0.806748 \| \| 2 \| 0.498372 \| 0.501702 \| 0.748895 \| 0.838957 \| \| 3 \| 0.471714 \| 0.477527 \| 0.768778 \| 0.829755 \| \| 4 \| 0.449965 \| 0.455789 \| 0.776878 \| 0.846626 \| \| 5 \| 0.423347 \| 0.442834 \| 0.784978 \| 0.884969 \| \| 6 \| 0.402362 \| 0.414682 \| 0.806333 \| 0.832822 \| \| 7 \| 0.380589 \| 0.400692 \| 0.811487 \| 0.855828 \| \| 8 \| 0.367983 \| 0.393791 \| 0.814433 \| 0.852761 \| \| 9 \| 0.355760 \| 0.386102 \| 0.822533 \| 0.825153 \| \| 10 \| 0.348000 \| 0.381949 \| 0.824006 \| 0.865031 \| \| 11 \| 0.343452 \| 0.382962 \| 0.824006 \| 0.875767 \| \| 12 \| 0.334328 \| 0.381772 \| 0.824006 \| 0.878834 \| \| 13 \| 0.328141 \| 0.387798 \| 0.823270 \| 0.892638 \| \| 14 \| 0.323139 \| 0.377890 \| 0.824742 \| 0.878834 \|
lakshankarunathilake/biomegatron-ner_model	lakshankarunathilake	2024-05-17T15:27:46Z	121	0	transformers	[ "transformers", "safetensors", "megatron-bert", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-05-17T15:19:01Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Yntec/ModernDisney	Yntec	2024-05-17T15:22:55Z	218	0	diffusers	[ "diffusers", "safetensors", "3D Animation", "Anime", "Art", "XpucT", "nitrosocke", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "en", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-05-17T12:16:42Z	--- license: creativeml-openrail-m language: - en library_name: diffusers pipeline_tag: text-to-image tags: - 3D Animation - Anime - Art - XpucT - nitrosocke - stable-diffusion - stable-diffusion-diffusers - text-to-image inference: true --- Use "Modern Disney" in your prompts if you want the effect. # Modern Disney Mo-Di-Diffusion mixed with Deliberate to create a model that falls to Deliberate when you don't use this token. The vae version has the kl-f8-anime2 one baked in. Since I released another model that mixes Mo-Di-Diffusion I feel I need to justify this one, well, check this comparison: ![iModern Disney Comparison](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/IZr2X-0VipneRCl9Kzoec.png) (Click for larger) Neither produced a pikachu but the point is you don't need to have "person human" as a negative prompts anymore! Samples and prompts: ![Free AI image generarator Modern Disney](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/j-M5P12pJ44UqJ6AgYK30.png) (Click for larger) Top left: cute modern disney pikachu sitting Top right: Cartoon Pretty CUTE Girl, sitting on Overwatch, DETAILED CHIBI EYES, soaking in the rain, gorgeous detailed hair, Ponytail, Magazine ad, iconic, 1940, sharp focus, aerial photography, trending on artstation, peter lloyd. Illustration By ROSSDRAWS and Dave Rapoza and artgerm and leyendecker and Clay Bottom left: modern disney loli girl Bottom right: disney movie modern man and little daughter ponytail, Santa claus. cute faces Original pages: https://huggingface.co/nitrosocke/mo-di-diffusion https://huggingface.co/XpucT/Deliberate # Recipe - SuperMerger Weight sum Use MBW 1,0,0,0,0,0,0,1,1,1,1,1,1,0,1,1,1,1,1,1,0,0,0,0,0,0 Model A: Deliberate Model B: Mo-Di-Diffusion Output Model: Modern Disney Bake kl-f8-anime2.ckpt VAE: Modern Disney VAE
apwic/sentiment-lora-r2a1d0.1-0	apwic	2024-05-17T15:22:24Z	0	0	null	[ "tensorboard", "generated_from_trainer", "id", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "region:us" ]	null	2024-05-17T14:49:10Z	--- language: - id license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: sentiment-lora-r2a1d0.1-0 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. --> # sentiment-lora-r2a1d0.1-0 This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3608 - Accuracy: 0.8471 - Precision: 0.8138 - Recall: 0.8243 - F1: 0.8187 ## 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: 30 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.5634 \| 1.0 \| 122 \| 0.5108 \| 0.7193 \| 0.6572 \| 0.6489 \| 0.6524 \| \| 0.5081 \| 2.0 \| 244 \| 0.5049 \| 0.7218 \| 0.6829 \| 0.7082 \| 0.6888 \| \| 0.4924 \| 3.0 \| 366 \| 0.4667 \| 0.7494 \| 0.6977 \| 0.6977 \| 0.6977 \| \| 0.4698 \| 4.0 \| 488 \| 0.4392 \| 0.7794 \| 0.7349 \| 0.7114 \| 0.7207 \| \| 0.4519 \| 5.0 \| 610 \| 0.4548 \| 0.7469 \| 0.7169 \| 0.7534 \| 0.7226 \| \| 0.4356 \| 6.0 \| 732 \| 0.4111 \| 0.8145 \| 0.7770 \| 0.7713 \| 0.7740 \| \| 0.421 \| 7.0 \| 854 \| 0.4101 \| 0.7945 \| 0.7538 \| 0.7721 \| 0.7612 \| \| 0.4039 \| 8.0 \| 976 \| 0.3829 \| 0.8296 \| 0.7949 \| 0.7919 \| 0.7934 \| \| 0.3887 \| 9.0 \| 1098 \| 0.3800 \| 0.8321 \| 0.7972 \| 0.7987 \| 0.7979 \| \| 0.3797 \| 10.0 \| 1220 \| 0.3768 \| 0.8371 \| 0.8044 \| 0.7997 \| 0.8020 \| \| 0.368 \| 11.0 \| 1342 \| 0.3842 \| 0.8221 \| 0.7846 \| 0.8016 \| 0.7918 \| \| 0.3598 \| 12.0 \| 1464 \| 0.3778 \| 0.8271 \| 0.7902 \| 0.8051 \| 0.7968 \| \| 0.3548 \| 13.0 \| 1586 \| 0.3624 \| 0.8471 \| 0.8167 \| 0.8118 \| 0.8142 \| \| 0.3469 \| 14.0 \| 1708 \| 0.3637 \| 0.8446 \| 0.8120 \| 0.8151 \| 0.8135 \| \| 0.3431 \| 15.0 \| 1830 \| 0.3685 \| 0.8396 \| 0.8049 \| 0.8165 \| 0.8102 \| \| 0.3275 \| 16.0 \| 1952 \| 0.3664 \| 0.8371 \| 0.8017 \| 0.8172 \| 0.8086 \| \| 0.3288 \| 17.0 \| 2074 \| 0.3590 \| 0.8396 \| 0.8055 \| 0.8115 \| 0.8084 \| \| 0.3335 \| 18.0 \| 2196 \| 0.3607 \| 0.8471 \| 0.8138 \| 0.8243 \| 0.8187 \| \| 0.3239 \| 19.0 \| 2318 \| 0.3613 \| 0.8446 \| 0.8107 \| 0.8226 \| 0.8161 \| \| 0.327 \| 20.0 \| 2440 \| 0.3608 \| 0.8471 \| 0.8138 \| 0.8243 \| 0.8187 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.15.2
mharb/q-Taxi-v3	mharb	2024-05-17T15:20:02Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-05-17T15:20:00Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 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="mharb/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
damgomz/ft_bs64_lr7_base_x2	damgomz	2024-05-17T15:18:51Z	108	0	transformers	[ "transformers", "safetensors", "albert", "text-classification", "fill-mask", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-05-16T15:09:06Z	--- language: en tags: - fill-mask kwargs: timestamp: '2024-05-17T17:18:48' project_name: ft_bs64_lr7_base_x2_emissions_tracker run_id: 0f492e82-6d6f-4e1a-913d-bcd043cc24c3 duration: 26423.72340154648 emissions: 0.0172858379899915 emissions_rate: 6.541787365583701e-07 cpu_power: 42.5 gpu_power: 0.0 ram_power: 7.5 cpu_energy: 0.3119461647588344 gpu_energy: 0 ram_energy: 0.0550489731361468 energy_consumed: 0.3669951378949813 country_name: Switzerland country_iso_code: CHE region: .nan cloud_provider: .nan cloud_region: .nan os: Linux-5.14.0-70.30.1.el9_0.x86_64-x86_64-with-glibc2.34 python_version: 3.10.4 codecarbon_version: 2.3.4 cpu_count: 3 cpu_model: Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz gpu_count: .nan gpu_model: .nan longitude: .nan latitude: .nan ram_total_size: 20 tracking_mode: machine on_cloud: N pue: 1.0 --- ## Environmental Impact (CODE CARBON DEFAULT) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| Duration (in seconds) \| 26423.72340154648 \| \| Emissions (Co2eq in kg) \| 0.0172858379899915 \| \| CPU power (W) \| 42.5 \| \| GPU power (W) \| [No GPU] \| \| RAM power (W) \| 7.5 \| \| CPU energy (kWh) \| 0.3119461647588344 \| \| GPU energy (kWh) \| [No GPU] \| \| RAM energy (kWh) \| 0.0550489731361468 \| \| Consumed energy (kWh) \| 0.3669951378949813 \| \| Country name \| Switzerland \| \| Cloud provider \| nan \| \| Cloud region \| nan \| \| CPU count \| 3 \| \| CPU model \| Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz \| \| GPU count \| nan \| \| GPU model \| nan \| ## Environmental Impact (for one core) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| CPU energy (kWh) \| 0.050865667547976966 \| \| Emissions (Co2eq in kg) \| 0.010349291665605703 \| ## Note 17 May 2024 ## My Config \| Config \| Value \| \|--------------------------\|-----------------\| \| checkpoint \| albert-base-v2 \| \| model_name \| ft_bs64_lr7_base_x2 \| \| sequence_length \| 400 \| \| num_epoch \| 15 \| \| learning_rate \| 5e-07 \| \| batch_size \| 64 \| \| weight_decay \| 0.0 \| \| warm_up_prop \| 0.0 \| \| drop_out_prob \| 0.1 \| \| packing_length \| 100 \| \| train_test_split \| 0.2 \| \| num_steps \| 81450 \| ## Training and Testing steps Epoch \| Train Loss \| Test Loss \| Accuracy \| Recall ---\|---\|---\|---\|--- \| 0 \| 0.664386 \| 0.632545 \| 0.740795 \| 0.837423 \| \| 1 \| 0.589979 \| 0.557067 \| 0.743741 \| 0.838957 \| \| 2 \| 0.512979 \| 0.506961 \| 0.767305 \| 0.808282 \| \| 3 \| 0.471688 \| 0.479002 \| 0.779823 \| 0.869632 \| \| 4 \| 0.439011 \| 0.454375 \| 0.788660 \| 0.803681 \| \| 5 \| 0.413775 \| 0.434841 \| 0.802651 \| 0.848160 \| \| 6 \| 0.392609 \| 0.420262 \| 0.807806 \| 0.842025 \| \| 7 \| 0.380271 \| 0.409428 \| 0.809278 \| 0.803681 \| \| 8 \| 0.365458 \| 0.399789 \| 0.825479 \| 0.861963 \| \| 9 \| 0.353928 \| 0.391207 \| 0.829161 \| 0.858896 \| \| 10 \| 0.342954 \| 0.388762 \| 0.827688 \| 0.863497 \| \| 11 \| 0.335871 \| 0.389029 \| 0.827688 \| 0.880368 \| \| 12 \| 0.328735 \| 0.381536 \| 0.827688 \| 0.863497 \| \| 13 \| 0.320389 \| 0.374983 \| 0.827688 \| 0.837423 \| \| 14 \| 0.314211 \| 0.374905 \| 0.826951 \| 0.826687 \|
emilykang/medmcqa_question_generation-medicine_lora	emilykang	2024-05-17T15:11:15Z	0	0	peft	[ "peft", "safetensors", "llama", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "region:us" ]	null	2024-05-17T14:09:00Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 datasets: - generator model-index: - name: medmcqa_question_generation-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. --> # medmcqa_question_generation-medicine_lora This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) 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
common-canvas/CommonCanvas-XL-C	common-canvas	2024-05-17T15:11:00Z	32	33	diffusers	[ "diffusers", "onnx", "safetensors", "common-canvas", "stable-diffusion", "sdxl", "en", "dataset:common-canvas/commoncatalog-cc-by-sa", "dataset:common-canvas/commoncatalog-cc-by", "arxiv:2310.16825", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2024-04-19T09:59:48Z	--- license: cc-by-sa-4.0 tags: - common-canvas - stable-diffusion - sdxl datasets: - common-canvas/commoncatalog-cc-by-sa - common-canvas/commoncatalog-cc-by language: - en --- # CommonCanvas-XL-C ## Summary CommonCanvas is a family of latent diffusion models capable of generating images from a given text prompt. The architecture is based off of Stable Diffusion XL. Different CommonCanvas models are trained exclusively on subsets of the CommonCatalog Dataset (See Data Card), a large dataset of Creative Commons licensed images with synthetic captions produced using a pre-trained BLIP-2 captioning model. Input: CommonCatalog Text Captions Output: CommonCatalog Images Architecture: Stable Diffusion XL Version Number: 0.1 The goal of this purpose is to produce a model that is competitive with Stable Diffusion XL, but to do so using an easily accessible dataset of known provenance. Doing so makes replicating the model significantly easier and provides proper attribution to all the creative commons work used to train the model. The exact training recipe of the model can be found in the paper hosted at this link. https://arxiv.org/abs/2310.16825 ## Performance Limitations CommonCanvas under-performs in several categories, including faces, general photography, and paintings (see paper, Figure 8). These datasets all originated from the Conceptual Captions dataset, which relies on web-scraped data. These web-sourced captions, while abundant, may not always align with human-generated language nuances. Transitioning to synthetic captions introduces certain performance challenges, however, the drop in performance is not as dramatic as one might assume. ## Training Dataset Limitations The model is trained on 10 year old YFCC data and may not have modern concepts or recent events in its training corpus. Performance on this model will be worse on certain proper nouns or specific celebrities, but this is a feature not a bug. The model may not generate known artwork, individual celebrities, or specific locations due to the autogenerated nature of the caption data. Note: The non-commercial variants of this model are explicitly not intended to be use * It is trained on data derived from the Flickr100M dataset. The information is dated and known to have a bias towards internet connected Western countries. Some areas such as the global south lack representation. ## Associated Risks * Text in images produced by the model will likely be difficult to read. * The model struggles with more complex tasks that require compositional understanding * It may not accurately generate faces or representations of specific people. * The model primarily learned from English descriptions and may not perform as effectively in other languages. * The autoencoder aspect of the model introduces some information loss. * It may be possible to guide the model to generate objectionable content, i.e. nudity or other NSFW material. ## Intended Uses * Using the model for generative AI research * Safe deployment of models which have the potential to generate harmful content. * Probing and understanding the limitations and biases of generative models. * Generation of artworks and use in design and other artistic processes. * Applications in educational or creative tools. * Research on generative models. ## Usage We recommend using the MosaicML Diffusion Repo to finetune / train the model: https://github.com/mosaicml/diffusion. Example finetuning code coming soon. ### Spaces demo Try the model demo on [Hugging Face Spaces](https://huggingface.co/spaces/common-canvas/CommonCanvas) ### Inference with 🧨 diffusers ```py from diffusers import StableDiffusionXLPipeline pipe = StableDiffusionXLPipeline.from_pretrained( "common-canvas/CommonCanvas-XL-C", custom_pipeline="multimodalart/sdxl_perturbed_attention_guidance", #read more at https://huggingface.co/multimodalart/sdxl_perturbed_attention_guidance torch_dtype=torch.float16 ).to(device) prompt = "a cat sitting in a car seat" image = pipe(prompt, num_inference_steps=25).images[0] ``` ### Inference with ComfyUI / AUTOMATIC1111 [Download safetensors ⬇️](https://huggingface.co/common-canvas/CommonCanvas-XLC/resolve/main/commoncanvas_xl_c.safetensors?download=true) ## Evaluation/Validation We validated the model against Stability AI’s SD2 model and compared human user study ## Acknowledgements We thank @multimodalart, @Wauplin, and @lhoestq at Hugging Face for helping us host the dataset, and model weights. ## Citation ``` @article{gokaslan2023commoncanvas, title={CommonCanvas: An Open Diffusion Model Trained with Creative-Commons Images}, author={Gokaslan, Aaron and Cooper, A Feder and Collins, Jasmine and Seguin, Landan and Jacobson, Austin and Patel, Mihir and Frankle, Jonathan and Stephenson, Cory and Kuleshov, Volodymyr}, journal={arXiv preprint arXiv:2310.16825}, year={2023} } ```
Plmanwaring/ADR_Detector_Toxigen	Plmanwaring	2024-05-17T15:09:29Z	108	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-17T15:08:53Z	--- 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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matthieuzone/TETE_DE_MOINES2	matthieuzone	2024-05-17T15:06:20Z	2	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-05-17T15:02:42Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of TÊTE DE MOINES cheese widget: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - matthieuzone/TETE_DE_MOINES2 <Gallery /> ## Model description These are matthieuzone/TETE_DE_MOINES2 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of TÊTE DE MOINES cheese to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](matthieuzone/TETE_DE_MOINES2/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
hjskhan/gemma-2b-fine-tuned-docbot	hjskhan	2024-05-17T15:04:26Z	154	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T15:01:14Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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minindu-liya99/Reinforce-CartPole-v1	minindu-liya99	2024-05-17T15:02:22Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2024-04-16T14:55:50Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
akbargherbal/test_proof_of_concept_01	akbargherbal	2024-05-17T15:02:02Z	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	2024-05-17T15:01:22Z	--- 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)
Shadow09/myphi2-customdata-tiny-chatbot	Shadow09	2024-05-17T14:58:19Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:microsoft/Phi-3-mini-4k-instruct", "base_model:adapter:microsoft/Phi-3-mini-4k-instruct", "license:mit", "region:us" ]	null	2024-05-17T14:57:41Z	--- license: mit library_name: peft tags: - trl - sft - generated_from_trainer base_model: microsoft/Phi-3-mini-4k-instruct model-index: - name: myphi2-customdata-tiny-chatbot 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. --> # myphi2-customdata-tiny-chatbot This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.37.2 - Pytorch 2.3.0+cu118 - Datasets 2.18.0 - Tokenizers 0.15.0
shushan-li/GLM6B	shushan-li	2024-05-17T14:55:25Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-17T14:55:25Z	--- license: apache-2.0 ---
sam-2577/zephyr-support-chatbot	sam-2577	2024-05-17T14:53:08Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:TheBloke/zephyr-7B-alpha-GPTQ", "base_model:adapter:TheBloke/zephyr-7B-alpha-GPTQ", "license:mit", "region:us" ]	null	2024-05-17T14:17:42Z	--- license: mit library_name: peft tags: - trl - sft - generated_from_trainer base_model: TheBloke/zephyr-7B-alpha-GPTQ model-index: - name: zephyr-support-chatbot 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. --> # zephyr-support-chatbot This model is a fine-tuned version of [TheBloke/zephyr-7B-alpha-GPTQ](https://huggingface.co/TheBloke/zephyr-7B-alpha-GPTQ) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 250 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.11.1 - Transformers 4.41.0.dev0 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
caesium94/models_colorist-v1-3e-5	caesium94	2024-05-17T14:52:22Z	152	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T14:50:52Z	--- 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]
matthieuzone/SAINT-NECTAIRE2	matthieuzone	2024-05-17T14:50:42Z	2	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-05-17T14:47:02Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of SAINT-NECTAIRE cheese widget: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - matthieuzone/SAINT-NECTAIRE2 <Gallery /> ## Model description These are matthieuzone/SAINT-NECTAIRE2 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of SAINT-NECTAIRE cheese to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](matthieuzone/SAINT-NECTAIRE2/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
FARIQ22/Lunar-landerr-v2	FARIQ22	2024-05-17T14:50:29Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-17T13:58:53Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 274.80 +/- 17.87 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 ... ```
caesium94/models-colorist-3e-5	caesium94	2024-05-17T14:49:39Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "region:us" ]	null	2024-05-17T13:42:53Z	--- license: apache-2.0 base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 tags: - trl - sft - generated_from_trainer model-index: - name: models-colorist-3e-5 results: [] library_name: peft --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # models-colorist-3e-5 This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - _load_in_8bit: False - _load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: float16 - bnb_4bit_quant_storage: uint8 - load_in_4bit: True - load_in_8bit: False ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.4.0 - Transformers 4.40.2 - Pytorch 2.1.0 - Datasets 2.19.1 - Tokenizers 0.19.1
Shadow09/myphi2-tiny-chatbot	Shadow09	2024-05-17T14:49:13Z	1	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:microsoft/Phi-3-mini-4k-instruct", "base_model:adapter:microsoft/Phi-3-mini-4k-instruct", "license:mit", "region:us" ]	null	2024-05-17T14:44:13Z	--- license: mit library_name: peft tags: - trl - sft - generated_from_trainer base_model: microsoft/Phi-3-mini-4k-instruct model-index: - name: myphi2-tiny-chatbot 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. --> # myphi2-tiny-chatbot This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.37.2 - Pytorch 2.3.0+cu118 - Datasets 2.18.0 - Tokenizers 0.15.0
brugmark/all-MiniLM-L6-v2-personal-project-default-2024-05-17	brugmark	2024-05-17T14:47:50Z	127	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "fill-mask", "generated_from_trainer", "base_model:sentence-transformers/all-MiniLM-L6-v2", "base_model:finetune:sentence-transformers/all-MiniLM-L6-v2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-05-17T12:00:02Z	--- license: apache-2.0 base_model: sentence-transformers/all-MiniLM-L6-v2 tags: - generated_from_trainer model-index: - name: all-MiniLM-L6-v2-personal-project-default-2024-05-17 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. --> # all-MiniLM-L6-v2-personal-project-default-2024-05-17 This model is a fine-tuned version of [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 10.8319 - eval_runtime: 1.8704 - eval_samples_per_second: 6.416 - eval_steps_per_second: 1.604 - 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: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
matthieuzone/SAINT-_FELICIEN2	matthieuzone	2024-05-17T14:46:46Z	5	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-05-17T14:43:03Z	--- license: openrail++ library_name: diffusers tags: - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of SAINT- FÉLICIEN cheese widget: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - matthieuzone/SAINT-_FELICIEN2 <Gallery /> ## Model description These are matthieuzone/SAINT-_FELICIEN2 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of SAINT- FÉLICIEN cheese to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](matthieuzone/SAINT-_FELICIEN2/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
52100176-NguyenTrongDat/nlp-vietnamese	52100176-NguyenTrongDat	2024-05-17T14:45:21Z	7	0	transformers	[ "transformers", "safetensors", "mbart", "text2text-generation", "vietnamese-model", "generated_from_trainer", "base_model:vinai/bartpho-syllable", "base_model:finetune:vinai/bartpho-syllable", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-16T14:49:52Z	--- base_model: vinai/bartpho-syllable tags: - vietnamese-model - generated_from_trainer metrics: - sacrebleu model-index: - name: nlp-vietnamese 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. --> # nlp-vietnamese This model is a fine-tuned version of [vinai/bartpho-syllable](https://huggingface.co/vinai/bartpho-syllable) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0590 - Sacrebleu: 21.1408 ## 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: 12 - eval_batch_size: 12 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 48 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Sacrebleu \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\| \| No log \| 1.0 \| 166 \| 0.2705 \| 11.9103 \| \| No log \| 2.0 \| 332 \| 0.0998 \| 18.3922 \| \| No log \| 3.0 \| 498 \| 0.0668 \| 20.3883 \| \| No log \| 4.0 \| 664 \| 0.0611 \| 20.8298 \| \| No log \| 5.0 \| 830 \| 0.0590 \| 21.1408 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
M4-ai/Orca-2.0-Tau-1.8B	M4-ai	2024-05-17T14:41:42Z	526	9	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "en", "dataset:Open-Orca/SlimOrca", "dataset:m-a-p/Code-Feedback", "dataset:MaziyarPanahi/WizardLM_evol_instruct_V2_196k", "dataset:camel-ai/math", "dataset:camel-ai/physics", "dataset:camel-ai/biology", "dataset:camel-ai/chemistry", "dataset:LDJnr/Capybara", "dataset:jondurbin/airoboros-3.2", "dataset:microsoft/orca-math-word-problems-200k", "license:other", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-11T08:54:34Z	--- language: - en license: other library_name: transformers datasets: - Open-Orca/SlimOrca - m-a-p/Code-Feedback - MaziyarPanahi/WizardLM_evol_instruct_V2_196k - camel-ai/math - camel-ai/physics - camel-ai/biology - camel-ai/chemistry - LDJnr/Capybara - jondurbin/airoboros-3.2 - microsoft/orca-math-word-problems-200k inference: parameters: do_sample: true temperature: 0.8 top_p: 0.95 top_k: 40 max_new_tokens: 250 repetition_penalty: 1.1 model-index: - name: Orca-2.0-Tau-1.8B results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 37.12 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=M4-ai/Orca-2.0-Tau-1.8B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 61.13 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=M4-ai/Orca-2.0-Tau-1.8B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 45.27 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=M4-ai/Orca-2.0-Tau-1.8B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 39.1 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=M4-ai/Orca-2.0-Tau-1.8B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 59.59 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=M4-ai/Orca-2.0-Tau-1.8B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 28.96 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=M4-ai/Orca-2.0-Tau-1.8B name: Open LLM Leaderboard --- # Orca-2.0-Tau-1.8B <!-- Provide a quick summary of what the model is/does. --> We fine-tuned tau-1.8B on a high quality mix for general-purpose assistants. A DPO version of this will be released soon. We use the ChatML prompt format. ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This model has capabilities in math, coding, writing, and more. We fine-tuned it using a high quality mix for general-purpose assistants. - Developed by: M4-ai - Language(s) (NLP): English and maybe Chinese - License: tongyi-qianwen license - Finetuned from model: [tau-1.8B](https://huggingface.co/M4-ai/tau-1.8B) ## 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. --> General purpose assistant, question answering, chain-of-thought, etc.. ### 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. ## Evaluation Coming soon ## Training Details ### Training Data - Open-Orca/SlimOrca - m-a-p/Code-Feedback - MaziyarPanahi/WizardLM_evol_instruct_V2_196k - camel-ai/math - camel-ai/physics - camel-ai/biology - camel-ai/chemistry - LDJnr/Capybara - jondurbin/airoboros-3.2 - microsoft/orca-math-word-problems-200k ## Evaluations \| Tasks \|Version\|Filter\|n-shot\| Metric \|Value \| \|Stderr\| \|---------------------------------\|-------\|------\|-----:\|--------\|-----:\|---\|-----:\| \|agieval_nous \|N/A \|none \| 0\|acc \|0.2537\|± \|0.0086\| \| \| \|none \| 0\|acc_norm\|0.2474\|± \|0.0085\| \| - agieval_aqua_rat \| 1\|none \| 0\|acc \|0.2283\|± \|0.0264\| \| \| \|none \| 0\|acc_norm\|0.2441\|± \|0.0270\| \| - agieval_logiqa_en \| 1\|none \| 0\|acc \|0.2750\|± \|0.0175\| \| \| \|none \| 0\|acc_norm\|0.3164\|± \|0.0182\| \| - agieval_lsat_ar \| 1\|none \| 0\|acc \|0.2087\|± \|0.0269\| \| \| \|none \| 0\|acc_norm\|0.1739\|± \|0.0250\| \| - agieval_lsat_lr \| 1\|none \| 0\|acc \|0.1843\|± \|0.0172\| \| \| \|none \| 0\|acc_norm\|0.2353\|± \|0.0188\| \| - agieval_lsat_rc \| 1\|none \| 0\|acc \|0.2602\|± \|0.0268\| \| \| \|none \| 0\|acc_norm\|0.1784\|± \|0.0234\| \| - agieval_sat_en \| 1\|none \| 0\|acc \|0.3544\|± \|0.0334\| \| \| \|none \| 0\|acc_norm\|0.2961\|± \|0.0319\| \| - agieval_sat_en_without_passage\| 1\|none \| 0\|acc \|0.3107\|± \|0.0323\| \| \| \|none \| 0\|acc_norm\|0.2282\|± \|0.0293\| \| - agieval_sat_math \| 1\|none \| 0\|acc \|0.2727\|± \|0.0301\| \| \| \|none \| 0\|acc_norm\|0.2091\|± \|0.0275\| \|truthfulqa_mc2 \| 2\|none \| 0\|acc \|0.3923\|± \|0.0139\| #### Training Hyperparameters - Training regime: bf16 non-mixed precision ## Technical Specifications #### Hardware We used 8 Kaggle TPUs, and we trained at a global batch size of 128 and sequence length of 2048. # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_M4-ai__Orca-2.0-Tau-1.8B) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|45.20\| \|AI2 Reasoning Challenge (25-Shot)\|37.12\| \|HellaSwag (10-Shot) \|61.13\| \|MMLU (5-Shot) \|45.27\| \|TruthfulQA (0-shot) \|39.10\| \|Winogrande (5-shot) \|59.59\| \|GSM8k (5-shot) \|28.96\|
teasan/Aurorique	teasan	2024-05-17T14:40:09Z	0	1	diffusers	[ "diffusers", "anime", "art", "stable-diffusion", "ja", "license:creativeml-openrail-m", "region:us" ]	null	2024-05-17T12:35:52Z	--- license: creativeml-openrail-m language: - ja tags: - anime - art - stable-diffusion library_name: diffusers --- ![](img/kv.png) # Auroriqueについて ## 概要 Agelesnateの特徴を受け継ぐ「線」が粗いタッチが特徴の弟分みたいなモデルです。元々AgelesnateのV4予定だったモデルなのもあるので、高コントラスト＋アニメ調～イラストの間ぐらいの出力になります。人物は切れ長の美人系が多いかと思います。 ## CHANGE LOG - AuroriqueV1の追加 ## 使い方モデルをcloneもしくはDLした後、以下に格納してください。 ``` webui\models\Stable-diffusion\ ``` ## 推奨設定（作者の設定） <details> <summary>AuroriqueV1</summary> <div> - Steps: 50 - Sampler: DPM++ 2M Karras - CFG scale: 10 - Denoising strength: 0.55 - Clip skip: 2 - Hires upscale: 2 - Hires steps: 10 - Hires upscaler: R-ESRGAN 4x+ or R-ESRGAN 4x+ Anime6B - VAE：mse840000_klf8anime_klf8anime2 </div> </details> ## 推奨NP <details> <summary>AuroriqueV1</summary> <div> ``` aid210, [(FastNegativeV2:1.35)::0.6], (negative_hand-neg:1.5), [:(badhandv4:1.25):0.55], [:(bad-hands-5:1):0.6], (worst quality, bad quality:1.4), (extra fingers, deformed hands, polydactyl:1.5), (bad hands, bad fingers, bad arm, missing finger, Incomplete hand:1.5), monochrome, text, nsfw, (blush:1.2), (embarrassed:1.2) ``` </div> </details> ## 作例 <summary>AuroriqueV1</summary> ![](img/v1/img01.png) ``` beautiful person, long hair, blond hair, saintly woman, sacred garment, seraph, seraph six wing, cathedral, kaleidoscope, light effects, divine effects, feather effects, ``` ![](img/v1/img02.png) ``` (close view:0.8), beautiful person, solo, long braided hair, rose gold hair, gold eye, shining sky, vast world, gazing, awe-inspiring expression, distant horizon, clouds, high hill, natural beauty, inspiration, night sky, Shining Stars, ``` ![](img/v1/img03.png) ``` beautiful person, solo, long hair with curls at the ends, mint green hair, red eye, (smile:0.6), (all over flower garden:1.4), (Flower Effects:1.2), (Floral Background:1.2), (Background filled with flowers:1.4), (Flashy background:1.1), ``` # 免責事項 - 本モデルを使用して作成された画像に関しては、個々の利用者に委ねておりますので、生成された画像に関する如何なる問題や係争について、モデル製作者は一切の責任を負いません。 - 本モデルはアダルトコンテンツを目的とした用途を想定しておりません。成人向けコンテンツを生成し、発生した問題についてはモデル製作者は一切の責任を負いません。 - ライセンスに関して問題が発生した場合は、本モデルを予告なく削除させて頂く可能性があります。ご了承ください。 - 犯罪への利用や医療用などの専門的な用途への使用は禁止されております。ライセンス不履行による過失については、モデル製作者は一切の責任を負いません。 --- # Stable Diffusionのライセンスについて - このモデルはオープンアクセスで誰でも利用可能であり、CreativeML OpenRAIL-Mライセンスでさらに権利と使用方法が規定されています。 - CreativeML OpenRAILライセンスでは、次のように規定されています。 1. このモデルを使用して、違法または有害な出力やコンテンツを意図的に作成したり、共有したりすることはできません。 2. 作者はあなたが生成した出力に対していかなる権利も主張しません。あなたはそれらを自由に使用することができますが、ライセンスで定められた規定を守ってください。利用は自己責任でお願いします。 3. あなたはウェイトを再配布し、モデルを商業的またはサービスとして使用することができます。その場合、ライセンスにあるものと同じ使用制限を含め、CreativeML OpenRAIL-Mのコピーをあなたのすべてのユーザーに共有しなければならないことに注意してください（ライセンスを完全にかつ注意深く読んでください）。 - (ライセンスの全文: [https://huggingface.co/spaces/CompVis/stable-diffusion-license](https://huggingface.co/spaces/CompVis/stable-diffusion-license)) --- # 作者について x(Twitter)<a href="https://x.com/wims_Tea" target="_blank"> https://x.com/wims_Tea</a> ---
Khallef/my_awesome_mind_model	Khallef	2024-05-17T14:31:23Z	162	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "audio-classification", "generated_from_trainer", "dataset:minds14", "base_model:facebook/wav2vec2-base", "base_model:finetune:facebook/wav2vec2-base", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	audio-classification	2024-05-17T07:40:22Z	--- license: apache-2.0 base_model: facebook/wav2vec2-base tags: - generated_from_trainer datasets: - minds14 metrics: - accuracy model-index: - name: my_awesome_mind_model results: - task: name: Audio Classification type: audio-classification dataset: name: minds14 type: minds14 config: en-US split: train args: en-US metrics: - name: Accuracy type: accuracy value: 0.061946902654867256 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_mind_model This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the minds14 dataset. It achieves the following results on the evaluation set: - Loss: 2.6459 - Accuracy: 0.0619 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 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 - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| No log \| 0.8 \| 3 \| 2.6352 \| 0.0708 \| \| No log \| 1.8667 \| 7 \| 2.6428 \| 0.0885 \| \| 2.6331 \| 2.9333 \| 11 \| 2.6425 \| 0.0796 \| \| 2.6331 \| 4.0 \| 15 \| 2.6437 \| 0.0531 \| \| 2.6331 \| 4.8 \| 18 \| 2.6432 \| 0.0619 \| \| 2.6238 \| 5.8667 \| 22 \| 2.6453 \| 0.0619 \| \| 2.6238 \| 6.9333 \| 26 \| 2.6460 \| 0.0619 \| \| 2.6214 \| 8.0 \| 30 \| 2.6459 \| 0.0619 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.3.0 - Datasets 2.19.1 - Tokenizers 0.19.1
tomaszki/llama-23-a	tomaszki	2024-05-17T14:29:34Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T14:25:47Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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richie-ghost/unsloth-Yi-1-5-9B-Chat-quantized_merge_4Bit	richie-ghost	2024-05-17T14:28:44Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:01-ai/Yi-1.5-9B-Chat", "base_model:finetune:01-ai/Yi-1.5-9B-Chat", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-17T14:16:46Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft base_model: 01-ai/Yi-1.5-9B-Chat --- # Uploaded model - Developed by: richie-ghost - License: apache-2.0 - Finetuned from model : 01-ai/Yi-1.5-9B-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)

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