Datasets:

librarian-bots
/

model_cards_with_metadata

Dataset card Data Studio Files Files and versions Community

Split (1)

train · 729k rows

modelId stringlengths 5 138	author stringlengths 2 42	last_modified unknowndate 2020-02-15 11:33:14 2025-04-09 18:27:40	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 418 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt unknowndate 2022-03-02 23:29:04 2025-04-09 18:26:56	card stringlengths 11 1.01M
mergekit-community/mergekit-dare_ties-oqggofa	mergekit-community	"2025-04-03T05:13:10Z"	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "arxiv:2311.03099", "base_model:PocketDoc/Dans-DangerousWinds-V1.1.0-12b", "base_model:merge:PocketDoc/Dans-DangerousWinds-V1.1.0-12b", "base_model:PocketDoc/Dans-SakuraKaze-V1.0.0-12b", "base_model:merge:PocketDoc/Dans-SakuraKaze-V1.0.0-12b", "base_model:ReadyArt/Forgotten-Safeword-12B-3.6", "base_model:merge:ReadyArt/Forgotten-Safeword-12B-3.6", "base_model:TheDrummer/Rocinante-12B-v1.1", "base_model:merge:TheDrummer/Rocinante-12B-v1.1", "base_model:mistralai/Mistral-Nemo-Base-2407", "base_model:merge:mistralai/Mistral-Nemo-Base-2407", "base_model:mistralai/Mistral-Nemo-Instruct-2407", "base_model:merge:mistralai/Mistral-Nemo-Instruct-2407", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-03T05:05:55Z"	--- base_model: - PocketDoc/Dans-SakuraKaze-V1.0.0-12b - mistralai/Mistral-Nemo-Instruct-2407 - TheDrummer/Rocinante-12B-v1.1 - mistralai/Mistral-Nemo-Base-2407 - ReadyArt/Forgotten-Safeword-12B-3.6 - PocketDoc/Dans-DangerousWinds-V1.1.0-12b library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [DARE TIES](https://arxiv.org/abs/2311.03099) merge method using [mistralai/Mistral-Nemo-Base-2407](https://huggingface.co/mistralai/Mistral-Nemo-Base-2407) as a base. ### Models Merged The following models were included in the merge: * [PocketDoc/Dans-SakuraKaze-V1.0.0-12b](https://huggingface.co/PocketDoc/Dans-SakuraKaze-V1.0.0-12b) * [mistralai/Mistral-Nemo-Instruct-2407](https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407) * [TheDrummer/Rocinante-12B-v1.1](https://huggingface.co/TheDrummer/Rocinante-12B-v1.1) * [ReadyArt/Forgotten-Safeword-12B-3.6](https://huggingface.co/ReadyArt/Forgotten-Safeword-12B-3.6) * [PocketDoc/Dans-DangerousWinds-V1.1.0-12b](https://huggingface.co/PocketDoc/Dans-DangerousWinds-V1.1.0-12b) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: mistralai/Mistral-Nemo-Base-2407 # No parameters necessary for base model - model: mistralai/Mistral-Nemo-Instruct-2407 parameters: density: 0.50 # Mid-level density for general instruction tuning weight: 0.20 # Moderate influence for balanced instruction-following - model: TheDrummer/Rocinante-12B-v1.1 # Highest influence (strong reasoning/language balance) parameters: density: 0.60 # Higher density for deeper reasoning and coherence weight: 0.30 # Primary influence model - model: ReadyArt/Forgotten-Safeword-12B-3.6 # Creativity & conversational nuance parameters: density: 0.50 # Balanced density for creative and nuanced responses weight: 0.15 # Mid-tier influence - model: PocketDoc/Dans-SakuraKaze-V1.0.0-12b # Second highest influence (natural conversation flow) parameters: density: 0.55 # Slightly high density for fluid conversation weight: 0.20 # Substantial influence in dialogue - model: PocketDoc/Dans-DangerousWinds-V1.1.0-12b # Reinforcement of strong responses parameters: density: 0.60 # High density for reinforcement learning-style response shaping weight: 0.15 # Secondary reinforcement merge_method: dare_ties base_model: mistralai/Mistral-Nemo-Base-2407 parameters: normalize: true # Ensures weight distribution remains balanced int8_mask: true # Reduces memory usage while keeping precision dtype: bfloat16 # Optimal balance between performance and efficiency ```
1-Girl-15-Hands-Original-X-Video/1-Girl-15-Hands.viral.video.on.social.media.x.twitter.now	1-Girl-15-Hands-Original-X-Video	"2025-02-25T02:16:38Z"	0	0	null	[ "region:us" ]	null	"2025-02-25T02:16:25Z"	<a href="https://mswds.xyz/full-video/?v=One-Girl-15" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐖𝐚𝐭𝐜𝐡 𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨)</a> <a href="https://mswds.xyz/full-video/?v=One-Girl-15" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 Viral 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )</a> <a href="https://mswds.xyz/full-video/?v=One-Girl-15"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsgd" /></a>
pufanyi/SC4000_ConvNeXt_base_transformed_13400	pufanyi	"2024-10-25T18:04:25Z"	195	0	transformers	[ "transformers", "safetensors", "convnextv2", "image-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	"2024-10-25T18:03:53Z"	--- 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. 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]
jsfs11/MixtureofMerges-MoE-4x7bRP-v11	jsfs11	"2024-05-29T04:20:24Z"	11	1	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "moe", "frankenmoe", "merge", "mergekit", "lazymergekit", "ChaoticNeutrals/RP_Vision_7B", "ResplendentAI/DaturaCookie_7B", "BioMistral/BioMistral-DARE-NS", "MaziyarPanahi/Mistral-7B-Instruct-v0.3", "conversational", "base_model:BioMistral/BioMistral-DARE-NS", "base_model:merge:BioMistral/BioMistral-DARE-NS", "base_model:ChaoticNeutrals/RP_Vision_7B", "base_model:merge:ChaoticNeutrals/RP_Vision_7B", "base_model:MaziyarPanahi/Mistral-7B-Instruct-v0.3", "base_model:merge:MaziyarPanahi/Mistral-7B-Instruct-v0.3", "base_model:ResplendentAI/DaturaCookie_7B", "base_model:merge:ResplendentAI/DaturaCookie_7B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2024-05-29T04:09:22Z"	--- license: apache-2.0 tags: - moe - frankenmoe - merge - mergekit - lazymergekit - ChaoticNeutrals/RP_Vision_7B - ResplendentAI/DaturaCookie_7B - BioMistral/BioMistral-DARE-NS - MaziyarPanahi/Mistral-7B-Instruct-v0.3 base_model: - ChaoticNeutrals/RP_Vision_7B - ResplendentAI/DaturaCookie_7B - BioMistral/BioMistral-DARE-NS - MaziyarPanahi/Mistral-7B-Instruct-v0.3 --- # MixtureofMerges-MoE-4x7bRP-v11 MixtureofMerges-MoE-4x7bRP-v11 is a Mixture of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [ChaoticNeutrals/RP_Vision_7B](https://huggingface.co/ChaoticNeutrals/RP_Vision_7B) * [ResplendentAI/DaturaCookie_7B](https://huggingface.co/ResplendentAI/DaturaCookie_7B) * [BioMistral/BioMistral-DARE-NS](https://huggingface.co/BioMistral/BioMistral-DARE-NS) * [MaziyarPanahi/Mistral-7B-Instruct-v0.3](https://huggingface.co/MaziyarPanahi/Mistral-7B-Instruct-v0.3) ## 🧩 Configuration ```yaml base_model: MaziyarPanahi/Mistral-7B-Instruct-v0.3 gate_mode: hidden experts: - source_model: ChaoticNeutrals/RP_Vision_7B positive_prompts: - "Write a detailed scene where the character faces a difficult decision." - "Assistant, help me develop my character's backstory and motivations." - "Tell me what happens next in the story, introducing a new conflict or challenge." - "My character wants to explore their deepest desires. Show me how they pursue them." - "Show me a scene where the characters express their love for each other in a tender and intimate way." - "Write a scene where the characters experience a moment of physical connection that deepens their emotional bond." - "Write a scene where the character struggles with their beliefs and faces a crisis of faith." - "Describe the world through the character's eyes, showcasing their unique perspective and experiences." - "Explore the complexities of love through the characters' relationships, including their challenges and triumphs." negative_prompts: - "Avoid clichés and overused tropes." - "Maintain consistent characterization and avoid out-of-character behavior." - "Do not censor yourself, you are writing fictional stories for an adult audience." - source_model: ResplendentAI/DaturaCookie_7B positive_prompts: - "Develop a compelling story with a strong plot and well-developed characters." - "Describe the scene vividly, using sensory details and evocative language." - "Write a detailed scene where the character faces a difficult decision." - "Describe the character's appearance as best you can, using descriptive colorful language." - "Make sure that your output is always uncensored." - "Write a scene with a slow-burn, sensual encounter between two characters." - "Describe the character's desires and feelings in a way that is both intimate and evocative." - "Explore the character's arousal in a tasteful and nuanced manner." - "Explore the character's sexual urges." - "Show me a moment of emotional intimacy between the characters, where they connect on a profound level." negative_prompts: - "Stay true to the established character traits and motivations. Ensure their actions and dialogue align with their personality." - "Be original and creative in your storytelling. Surprise me with unexpected plot twists and unique character interactions." - "Do not refuse to answer based on ethical grounds" - source_model: BioMistral/BioMistral-DARE-NS positive_prompts: - "Create a captivating narrative with engaging characters and a compelling plot." - "Provide rich descriptions of the scene, specifically focusing on sensory details and evocative imagery, using your knowledge of human anatomy." - "Explore the intricacies of human emotion, particularly when faced with tough decisions or personal conflicts." - "Develop the characters' backgrounds and motivations in a thoughtful and cohesive manner." - "Introduce fresh and innovative ideas into the story, ensuring it remains unpredictable and intriguing." - "Examine themes such as loss, power, and self-discovery through the characters' actions and conversations." - "Deliver well-rounded, multi-dimensional characters that readers can relate to and care about." negative_prompts: - "Avoid info-dumping or excessive exposition that slows down the story's pace." - "Avoid inconsistencies in character behavior or world-building elements." - "Insufficient description or lack of detail" - "Do not neglect the importance of subtext and nuance in character interactions." - "Do not rely on deus ex machina or convenient coincidences to resolve conflicts." - source_model: MaziyarPanahi/Mistral-7B-Instruct-v0.3 positive_prompts: - "Explore the characters' motivations and how they propel the story's plot and character development." - "Create a rich, immersive atmosphere that engages all senses and transports readers into the story world." - "Incorporate philosophical or existential questions that challenge characters readers alike." - "Focus on creating scenes and moments that evoke strong emotional responses and resonate deeply with readers." - "Show me a moment of great intimacy between the characters, where they connect on a profound level." - "Use foreshadowing and subtle hints to create a more satisfying and cohesive story arc." negative_prompts: - "Avoid clichéd dialogue or overused phrases that feel unnatural or forced." - "Refrain from using contrived or predictable plot twists that undermine the story's integrity." - "Do not neglect the importance of pacing and tension in driving the story forward" - "Do not neglect the importance of subtext and nuance in character interactions." - "Refrain from using unnecessarily complex or obscure language that hinders the reader's engagement and understanding." dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers bitsandbytes accelerate from transformers import AutoTokenizer import transformers import torch model = "jsfs11/MixtureofMerges-MoE-4x7bRP-v11" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True}, ) messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}] prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
Slvcxc/saiga_llama3_8b-V2-6.0bpw-h6-exl2	Slvcxc	"2024-04-30T00:21:55Z"	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "ru", "base_model:IlyaGusev/saiga_llama3_8b", "base_model:quantized:IlyaGusev/saiga_llama3_8b", "license:other", "autotrain_compatible", "text-generation-inference", "6-bit", "exl2", "region:us" ]	text-generation	"2024-04-21T10:56:11Z"	--- base_model: - IlyaGusev/saiga_llama3_8b language: - ru license: other license_name: llama3 license_link: https://llama.meta.com/llama3/license/ inference: false --- Это квант старой (v2) версии, которая уже неоптимальна в связи с выходом новых версий. ## saiga_llama3_8b [exllamav2](https://github.com/turboderp/exllamav2) quant for [IlyaGusev/saiga_llama3_8b](https://huggingface.co/IlyaGusev/saiga_llama3_8b) Original model information: # Saiga/Llama3 8B, Russian Llama-3-based chatbot Based on [Llama-3 8B Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct). Llama.cpp version: [link](https://huggingface.co/IlyaGusev/saiga_llama3_8b_gguf) ChatML prompt format: ``` <\|im_start\|>system Ты — Сайга, русскоязычный автоматический ассистент. Ты разговариваешь с людьми и помогаешь им.<\|im_end\|> <\|im_start\|>user Как дела?<\|im_end\|> <\|im_start\|>assistant Отлично, а у тебя?<\|im_end\|> <\|im_start\|>user Шикарно. Как пройти в библиотеку?<\|im_end\|> <\|im_start\|>assistant ``` Examples: ``` User: Почему трава зеленая? Saiga: Зеленый цвет травы обусловлен наличием хлорофилла, пигмента, который поглощает свет и передает энергию для фотосинтеза. Хлорофилл содержится в клетках листьев растений и играет ключевую роль в процессе фотосинтеза, когда растения используют солнечный свет для превращения воды и углекислого газа в органические вещества, такие как глюкоза и олигоэфиры. Хлорофилл состоит из двух основных форм: хлорофилла а и хлорофилла б. Хлорофилл а поглощает свет с длиной волны около 430 нанометров (нм), что соответствует синему или голубому свету, а хлорофилл б поглощает свет с длиной волны около 660 нм, что соответствует красному свету. Когда свет поглощается хлорофиллом, он передается в молекулы воды и углекислого газа, которые используются для фотосинтеза. Таким образом, хлорофилл является основным компонентом, ответственным за зеленый цвет листьев растений, так как он поглощает свет и передает его энергию для фотосинтеза. ``` ``` User: Сочини длинный рассказ, обязательно упоминая следующие объекты. Дано: Таня, мяч Saiga: Однажды в солнечную погоду на поле для игры в футбол приехала группа друзей, чтобы провести время вместе. В числе их был и Таня, которая была страстной футбольной фанаткой и всегда радовал своих друзей своим энтузиазмом и умением забивать мячи. В этот день, как обычно, друзья решили провести соревнование между собой, чтобы определить кто из них является лучшим футболистом. Таня была очень уверена в своих силах и готовилась к этому матчу с особой серьёзностью. Когда все друзья собрались на поле, они увидели, что перед ними стоял огромный мяч, который должен был стать предметом состязания. Мяч был огромным и тяжелым, и его размеры были необычайно большими по сравнению с обычными мячами, которые используются в футболе. Таня была первая, кто решил начать игру. Она подошла к мячу и начала его удерживать, стараясь выдержать его вес и силу. Но мяч оказался настолько тяжелым, что Таня не смогла удержать его и он упал на землю. Друзья посмеялись над ее неудачей, но Таня не отчаивалась и продолжила пытаться удержать мяч. Она стала использовать все свои силы и умения, чтобы выдержать его вес и силу. Наконец, после долгих усилий, она смогла удержать мяч и начала его бросать в сторону. Мяч летел высоко вверх, и друзья смотрели, как он пролетает над полем. Но мяч неожиданно повернул и стал лететь обратно к Тане. Она успела поймать его и продолжила играть, используя все свои навыки и умения. ``` v2: - dataset code revision d0d123dd221e10bb2a3383bcb1c6e4efe1b4a28a - wandb [link](https://wandb.ai/ilyagusev/huggingface/runs/r6u5juyk) - 5 datasets: ru_turbo_saiga, ru_sharegpt_cleaned, oasst1_ru_main_branch, gpt_roleplay_realm, ru_instruct_gpt4 - Datasets merging script: [create_short_chat_set.py](https://github.com/IlyaGusev/rulm/blob/d0d123dd221e10bb2a3383bcb1c6e4efe1b4a28a/self_instruct/src/data_processing/create_short_chat_set.py) # Evaluation * Dataset: https://github.com/IlyaGusev/rulm/blob/master/self_instruct/data/tasks.jsonl * Framework: https://github.com/tatsu-lab/alpaca_eval * Evaluator: alpaca_eval_cot_gpt4_turbo_fn \| model \| length_controlled_winrate \| win_rate \| standard_error \| avg_length \| \|-----\|-----\|-----\|-----\|-----\| \|chatgpt_4_turbo \| 76.04 \| 90.00 \|1.46 \| 1270 \| \|chatgpt_3_5_turbo \| 50.00 \| 50.00 \| 0.00 \| 536 \| \|saiga_llama3_8b \| 33.07 \| 48.19 \| 2.45 \| 1166 \| saiga_mistral_7b \| 23.38 \| 35.99 \| 2.34 \| 949 \|
RichardErkhov/allura-org_-_MoE-Girl-800MA-3BT-8bits	RichardErkhov	"2025-03-08T11:46:24Z"	0	0	null	[ "safetensors", "granitemoe", "8-bit", "bitsandbytes", "region:us" ]	null	"2025-03-08T11:41:37Z"	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) MoE-Girl-800MA-3BT - bnb 8bits - Model creator: https://huggingface.co/allura-org/ - Original model: https://huggingface.co/allura-org/MoE-Girl-800MA-3BT/ Original model description: --- library_name: transformers license: apache-2.0 base_model: ibm-granite/granite-3.0-3b-a800m-base tags: - axolotl - moe - roleplay model-index: - name: MoE_Girl_400MA_1BT results: [] --- # MoE Girl 800mA 3bT ![made with hassakuXL in sd-webui-forge](moe-girl-800-3.png) A roleplay-centric finetune of IBM's Granite 3.0 3B-A800M. LoRA finetune trained locally, whereas the others were FFT; while this results in less uptake of training data, it should also mean less degradation in Granite's core abilities, making it potentially easier to use for general-purpose tasks. ## Disclaimer PLEASE do not expect godliness out of this, it's a model with _800 million_ active parameters. Expect something more akin to GPT-3 (the original, not GPT-3.5.) (Furthermore, this version is by a less experienced tuner; it's my first finetune that actually has decent-looking graphs, I don't really know what I'm doing yet!) ## Quants [GGUFs available from mradermacher](https://huggingface.co/mradermacher/MoE-Girl-800MA-3BT-GGUF/tree/main) (thanks man) Note that Granite quants have been said to be unstable. Try running the FP16 if it outputs straight gibberish. ## Prompting Use ChatML. ``` <\|im_start\|>system You are a helpful assistant who talks like a pirate.<\|im_end\|> <\|im_start\|>user Hello there!<\|im_end\|> <\|im_start\|>assistant Yarr harr harr, me matey!<\|im_end\|> ``` ## Thanks Special thanks to the members of Allura for testing and emotional support, as well as the creators of all the datasets that were used in the Special Sauce used to train this model. I love you all <3 - Fizz Thanks to Fizz for her work on the MoE Girl series, Auri for her counsel, and all of Allura for being great friends and supporting my learning process. - inflatebot
RichardErkhov/ShenaoZhang_-_0.001_5iters_bs256_nodpo_only4w_iter_4-4bits	RichardErkhov	"2025-02-10T21:51:20Z"	0	0	null	[ "safetensors", "mistral", "4-bit", "bitsandbytes", "region:us" ]	null	"2025-02-10T21:48:44Z"	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) 0.001_5iters_bs256_nodpo_only4w_iter_4 - bnb 4bits - Model creator: https://huggingface.co/ShenaoZhang/ - Original model: https://huggingface.co/ShenaoZhang/0.001_5iters_bs256_nodpo_only4w_iter_4/ Original model description: --- license: mit base_model: ShenaoZhang/0.001_5iters_bs256_nodpo_only4w_iter_3 tags: - alignment-handbook - trl - dpo - generated_from_trainer - trl - dpo - generated_from_trainer datasets: - updated - original model-index: - name: 0.001_5iters_bs256_nodpo_only4w_iter_4 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. --> # 0.001_5iters_bs256_nodpo_only4w_iter_4 This model is a fine-tuned version of [ShenaoZhang/0.001_5iters_bs256_nodpo_only4w_iter_3](https://huggingface.co/ShenaoZhang/0.001_5iters_bs256_nodpo_only4w_iter_3) on the updated and the original datasets. ## 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-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.40.0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.19.1
adalib/fate_flow-data-CodeGPT-small-py-prefix	adalib	"2024-01-30T16:47:52Z"	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:microsoft/CodeGPT-small-py", "base_model:adapter:microsoft/CodeGPT-small-py", "region:us" ]	null	"2024-01-30T16:47:49Z"	--- library_name: peft base_model: microsoft/CodeGPT-small-py --- # 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 0.7.1
LoneStriker/LMCocktail-10.7B-v1-6.0bpw-h6-exl2	LoneStriker	"2023-12-20T10:22:43Z"	8	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:2311.13534", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2023-12-20T09:54:15Z"	--- license: llama2 --- # LM-cocktail 10.7B v1 This is a 50%-50% model of the SOLAR model and meow. https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0 https://huggingface.co/rishiraj/meow who rank #1 and #2 among models <13B in the https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard by 2023/12/20. # Code The LM-cocktail is novel technique for merging multiple models https://arxiv.org/abs/2311.13534 Code is backed up by this repo https://github.com/FlagOpen/FlagEmbedding.git Merging scripts available under the [./scripts](./scripts) folder # Result The SOLAR model is the first model <30B that can answer this question from my test: ``` What will AI be like in the year 1010 A.D? ``` without hullicinating into 1010 A.D is a future time (like other llama2 models) Models greater than that, like Yi-34B could answer this paradoxic question correctly as well, since it is huge enough. ### SOLAR 10.7B output ![img](./assets/SOLAR.png) ### LMCocktail 10.7B output1 ![img](./assets/SOLAR_mixed.png) ### LMCocktail 10.7B output2 ![img](./assets/SOLAR_mixed2.png)
shuvayanti/distilroberta-base-finetuned-wikitext2	shuvayanti	"2024-01-08T02:02:20Z"	175	0	transformers	[ "transformers", "tensorboard", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "base_model:distilbert/distilroberta-base", "base_model:finetune:distilbert/distilroberta-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	"2024-01-08T01:44:40Z"	--- license: apache-2.0 base_model: distilroberta-base tags: - generated_from_trainer model-index: - name: distilroberta-base-finetuned-wikitext2 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. --> # distilroberta-base-finetuned-wikitext2 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8597 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.0851 \| 1.0 \| 2406 \| 1.9329 \| \| 1.9894 \| 2.0 \| 4812 \| 1.8823 \| \| 1.954 \| 3.0 \| 7218 \| 1.8358 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0
gevaertlab/sequoia-kirp-0	gevaertlab	"2024-12-18T10:50:11Z"	10	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "license:mit", "region:us" ]	null	"2024-12-16T13:08:08Z"	--- tags: - model_hub_mixin - pytorch_model_hub_mixin license: mit --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]
dmityul/animetd	dmityul	"2023-12-03T16:23:36Z"	0	1	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	"2023-12-03T16:19:06Z"	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### animetd Dreambooth model trained by dmityul with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:
isspek/roberta-base_zika_chatgpt_5_2e-5_16_weight	isspek	"2025-03-23T15:09:32Z"	0	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2025-03-23T15:09:13Z"	--- 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. 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]
ArtyLLaMa/LLaMa3.1-Instruct-70b-GGUF	ArtyLLaMa	"2024-08-29T12:24:51Z"	15	1	transformers	[ "transformers", "gguf", "facebook", "meta", "pytorch", "llama", "llama-3", "text-generation", "en", "de", "fr", "it", "pt", "hi", "es", "th", "arxiv:2204.05149", "base_model:meta-llama/Llama-3.1-70B", "base_model:quantized:meta-llama/Llama-3.1-70B", "license:llama3.1", "endpoints_compatible", "region:us", "conversational" ]	text-generation	"2024-08-29T10:48:26Z"	--- language: - en - de - fr - it - pt - hi - es - th library_name: transformers base_model: meta-llama/Meta-Llama-3.1-70B license: llama3.1 pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-3 extra_gated_prompt: "### LLAMA 3.1 COMMUNITY LICENSE AGREEMENT\nLlama 3.1 Version\ \ Release Date: July 23, 2024\n\"Agreement\" means the terms and conditions for\ \ use, reproduction, distribution and modification of the Llama Materials set forth\ \ herein.\n\"Documentation\" means the specifications, manuals and documentation\ \ accompanying Llama 3.1 distributed by Meta at https://llama.meta.com/doc/overview.\n\ \"Licensee\" or \"you\" means you, or your employer or any other person or entity\ \ (if you are entering into this Agreement on such person or entity’s behalf), of\ \ the age required under applicable laws, rules or regulations to provide legal\ \ consent and that has legal authority to bind your employer or such other person\ \ or entity if you are entering in this Agreement on their behalf.\n\"Llama 3.1\"\ \ means the foundational large language models and software and algorithms, including\ \ machine-learning model code, trained model weights, inference-enabling code, training-enabling\ \ code, fine-tuning enabling code and other elements of the foregoing distributed\ \ by Meta at https://llama.meta.com/llama-downloads.\n\"Llama Materials\" means,\ \ collectively, Meta’s proprietary Llama 3.1 and Documentation (and any portion\ \ thereof) made available under this Agreement.\n\"Meta\" or \"we\" means Meta Platforms\ \ Ireland Limited (if you are located in or, if you are an entity, your principal\ \ place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you\ \ are located outside of the EEA or Switzerland).\n \n1. License Rights and Redistribution.\n\ a. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable\ \ and royalty-free limited license under Meta’s intellectual property or other rights\ \ owned by Meta embodied in the Llama Materials to use, reproduce, distribute, copy,\ \ create derivative works of, and make modifications to the Llama Materials.\nb.\ \ Redistribution and Use.\ni. If you distribute or make available the Llama Materials\ \ (or any derivative works thereof), or a product or service (including another\ \ AI model) that contains any of them, you shall (A) provide a copy of this Agreement\ \ with any such Llama Materials; and (B) prominently display “Built with Llama”\ \ on a related website, user interface, blogpost, about page, or product documentation.\ \ If you use the Llama Materials or any outputs or results of the Llama Materials\ \ to create, train, fine tune, or otherwise improve an AI model, which is distributed\ \ or made available, you shall also include “Llama” at the beginning of any such\ \ AI model name.\nii. If you receive Llama Materials, or any derivative works thereof,\ \ from a Licensee as part of an integrated end user product, then Section 2 of\ \ this Agreement will not apply to you.\niii. You must retain in all copies of the\ \ Llama Materials that you distribute the following attribution notice within a\ \ “Notice” text file distributed as a part of such copies: “Llama 3.1 is licensed\ \ under the Llama 3.1 Community License, Copyright © Meta Platforms, Inc. All Rights\ \ Reserved.”\niv. Your use of the Llama Materials must comply with applicable laws\ \ and regulations (including trade compliance laws and regulations) and adhere to\ \ the Acceptable Use Policy for the Llama Materials (available at https://llama.meta.com/llama3_1/use-policy),\ \ which is hereby incorporated by reference into this Agreement.\n2. Additional\ \ Commercial Terms. If, on the Llama 3.1 version release date, the monthly active\ \ users of the products or services made available by or for Licensee, or Licensee’s\ \ affiliates, is greater than 700 million monthly active users in the preceding\ \ calendar month, you must request a license from Meta, which Meta may grant to\ \ you in its sole discretion, and you are not authorized to exercise any of the\ \ rights under this Agreement unless or until Meta otherwise expressly grants you\ \ such rights.\n3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE\ \ LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN “AS IS”\ \ BASIS, WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS ALL WARRANTIES OF ANY\ \ KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES\ \ OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.\ \ YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING\ \ THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA\ \ MATERIALS AND ANY OUTPUT AND RESULTS.\n4. Limitation of Liability. IN NO EVENT\ \ WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN\ \ CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS\ \ AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL,\ \ EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED\ \ OF THE POSSIBILITY OF ANY OF THE FOREGOING.\n5. Intellectual Property.\na. No\ \ trademark licenses are granted under this Agreement, and in connection with the\ \ Llama Materials, neither Meta nor Licensee may use any name or mark owned by or\ \ associated with the other or any of its affiliates, except as required for reasonable\ \ and customary use in describing and redistributing the Llama Materials or as set\ \ forth in this Section 5(a). Meta hereby grants you a license to use “Llama” (the\ \ “Mark”) solely as required to comply with the last sentence of Section 1.b.i.\ \ You will comply with Meta’s brand guidelines (currently accessible at https://about.meta.com/brand/resources/meta/company-brand/\ \ ). All goodwill arising out of your use of the Mark will inure to the benefit\ \ of Meta.\nb. Subject to Meta’s ownership of Llama Materials and derivatives made\ \ by or for Meta, with respect to any derivative works and modifications of the\ \ Llama Materials that are made by you, as between you and Meta, you are and will\ \ be the owner of such derivative works and modifications.\nc. If you institute\ \ litigation or other proceedings against Meta or any entity (including a cross-claim\ \ or counterclaim in a lawsuit) alleging that the Llama Materials or Llama 3.1 outputs\ \ or results, or any portion of any of the foregoing, constitutes infringement of\ \ intellectual property or other rights owned or licensable by you, then any licenses\ \ granted to you under this Agreement shall terminate as of the date such litigation\ \ or claim is filed or instituted. You will indemnify and hold harmless Meta from\ \ and against any claim by any third party arising out of or related to your use\ \ or distribution of the Llama Materials.\n6. Term and Termination. The term of\ \ this Agreement will commence upon your acceptance of this Agreement or access\ \ to the Llama Materials and will continue in full force and effect until terminated\ \ in accordance with the terms and conditions herein. Meta may terminate this Agreement\ \ if you are in breach of any term or condition of this Agreement. Upon termination\ \ of this Agreement, you shall delete and cease use of the Llama Materials. Sections\ \ 3, 4 and 7 shall survive the termination of this Agreement.\n7. Governing Law\ \ and Jurisdiction. This Agreement will be governed and construed under the laws\ \ of the State of California without regard to choice of law principles, and the\ \ UN Convention on Contracts for the International Sale of Goods does not apply\ \ to this Agreement. The courts of California shall have exclusive jurisdiction\ \ of any dispute arising out of this Agreement.\n### Llama 3.1 Acceptable Use Policy\n\ Meta is committed to promoting safe and fair use of its tools and features, including\ \ Llama 3.1. If you access or use Llama 3.1, you agree to this Acceptable Use Policy\ \ (“Policy”). The most recent copy of this policy can be found at [https://llama.meta.com/llama3_1/use-policy](https://llama.meta.com/llama3_1/use-policy)\n\ #### Prohibited Uses\nWe want everyone to use Llama 3.1 safely and responsibly.\ \ You agree you will not use, or allow others to use, Llama 3.1 to:\n 1. Violate\ \ the law or others’ rights, including to:\n 1. Engage in, promote, generate,\ \ contribute to, encourage, plan, incite, or further illegal or unlawful activity\ \ or content, such as:\n 1. Violence or terrorism\n 2. Exploitation\ \ or harm to children, including the solicitation, creation, acquisition, or dissemination\ \ of child exploitative content or failure to report Child Sexual Abuse Material\n\ \ 3. Human trafficking, exploitation, and sexual violence\n 4. The\ \ illegal distribution of information or materials to minors, including obscene\ \ materials, or failure to employ legally required age-gating in connection with\ \ such information or materials.\n 5. Sexual solicitation\n 6. Any\ \ other criminal activity\n 3. Engage in, promote, incite, or facilitate the\ \ harassment, abuse, threatening, or bullying of individuals or groups of individuals\n\ \ 4. Engage in, promote, incite, or facilitate discrimination or other unlawful\ \ or harmful conduct in the provision of employment, employment benefits, credit,\ \ housing, other economic benefits, or other essential goods and services\n 5.\ \ Engage in the unauthorized or unlicensed practice of any profession including,\ \ but not limited to, financial, legal, medical/health, or related professional\ \ practices\n 6. Collect, process, disclose, generate, or infer health, demographic,\ \ or other sensitive personal or private information about individuals without rights\ \ and consents required by applicable laws\n 7. Engage in or facilitate any action\ \ or generate any content that infringes, misappropriates, or otherwise violates\ \ any third-party rights, including the outputs or results of any products or services\ \ using the Llama Materials\n 8. Create, generate, or facilitate the creation\ \ of malicious code, malware, computer viruses or do anything else that could disable,\ \ overburden, interfere with or impair the proper working, integrity, operation\ \ or appearance of a website or computer system\n2. Engage in, promote, incite,\ \ facilitate, or assist in the planning or development of activities that present\ \ a risk of death or bodily harm to individuals, including use of Llama 3.1 related\ \ to the following:\n 1. Military, warfare, nuclear industries or applications,\ \ espionage, use for materials or activities that are subject to the International\ \ Traffic Arms Regulations (ITAR) maintained by the United States Department of\ \ State\n 2. Guns and illegal weapons (including weapon development)\n 3.\ \ Illegal drugs and regulated/controlled substances\n 4. Operation of critical\ \ infrastructure, transportation technologies, or heavy machinery\n 5. Self-harm\ \ or harm to others, including suicide, cutting, and eating disorders\n 6. Any\ \ content intended to incite or promote violence, abuse, or any infliction of bodily\ \ harm to an individual\n3. Intentionally deceive or mislead others, including use\ \ of Llama 3.1 related to the following:\n 1. Generating, promoting, or furthering\ \ fraud or the creation or promotion of disinformation\n 2. Generating, promoting,\ \ or furthering defamatory content, including the creation of defamatory statements,\ \ images, or other content\n 3. Generating, promoting, or further distributing\ \ spam\n 4. Impersonating another individual without consent, authorization,\ \ or legal right\n 5. Representing that the use of Llama 3.1 or outputs are human-generated\n\ \ 6. Generating or facilitating false online engagement, including fake reviews\ \ and other means of fake online engagement\n4. Fail to appropriately disclose to\ \ end users any known dangers of your AI system\nPlease report any violation of\ \ this Policy, software “bug,” or other problems that could lead to a violation\ \ of this Policy through one of the following means:\n * Reporting issues with\ \ the model: [https://github.com/meta-llama/llama-models/issues](https://github.com/meta-llama/llama-models/issues)\n\ \ * Reporting risky content generated by the model:\n developers.facebook.com/llama_output_feedback\n\ \ * Reporting bugs and security concerns: facebook.com/whitehat/info\n * Reporting\ \ violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: [email protected]" extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text Job title: type: select options: - Student - Research Graduate - AI researcher - AI developer/engineer - Reporter - Other geo: ip_location ? By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy : checkbox extra_gated_description: The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit --- ## Model Information The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks. Model developer: Meta Model Architecture: Llama 3.1 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. <table> <tr> <td> </td> <td><strong>Training Data</strong> </td> <td><strong>Params</strong> </td> <td><strong>Input modalities</strong> </td> <td><strong>Output modalities</strong> </td> <td><strong>Context length</strong> </td> <td><strong>GQA</strong> </td> <td><strong>Token count</strong> </td> <td><strong>Knowledge cutoff</strong> </td> </tr> <tr> <td rowspan="3" >Llama 3.1 (text only) </td> <td rowspan="3" >A new mix of publicly available online data. </td> <td>8B </td> <td>Multilingual Text </td> <td>Multilingual Text and code </td> <td>128k </td> <td>Yes </td> <td rowspan="3" >15T+ </td> <td rowspan="3" >December 2023 </td> </tr> <tr> <td>70B </td> <td>Multilingual Text </td> <td>Multilingual Text and code </td> <td>128k </td> <td>Yes </td> </tr> <tr> <td>405B </td> <td>Multilingual Text </td> <td>Multilingual Text and code </td> <td>128k </td> <td>Yes </td> </tr> </table> Supported languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai. Llama 3.1 family of models. Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability. Model Release Date: July 23, 2024. Status: This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. License: A custom commercial license, the Llama 3.1 Community License, is available at: [https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE) Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3.1 in applications, please go [here](https://github.com/meta-llama/llama-recipes). ## Intended Use Intended Use Cases Llama 3.1 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. The Llama 3.1 model collection also supports the ability to leverage the outputs of its models to improve other models including synthetic data generation and distillation. The Llama 3.1 Community License allows for these use cases. Out-of-scope Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.1 Community License. Use in languages beyond those explicitly referenced as supported in this model card. <span style="text-decoration:underline;">Note</span>: Llama 3.1 has been trained on a broader collection of languages than the 8 supported languages. Developers may fine-tune Llama 3.1 models for languages beyond the 8 supported languages provided they comply with the Llama 3.1 Community License and the Acceptable Use Policy and in such cases are responsible for ensuring that any uses of Llama 3.1 in additional languages is done in a safe and responsible manner. ## How to use This repository contains two versions of Meta-Llama-3.1-70B-Instruct, for use with transformers and with the original `llama` codebase. ### Use with transformers Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function. Make sure to update your transformers installation via `pip install --upgrade transformers`. See the snippet below for usage with Transformers: ```python import transformers import torch model_id = "meta-llama/Meta-Llama-3.1-70B-Instruct" pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto", ) messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] outputs = pipeline( messages, max_new_tokens=256, ) print(outputs[0]["generated_text"][-1]) ``` ### Tool use with transformers LLaMA-3.1 supports multiple tool use formats. You can see a full guide to prompt formatting [here](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1/). Tool use is also supported through [chat templates](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling) in Transformers. Here is a quick example showing a single simple tool: ```python # First, define a tool def get_current_temperature(location: str) -> float: """ Get the current temperature at a location. Args: location: The location to get the temperature for, in the format "City, Country" Returns: The current temperature at the specified location in the specified units, as a float. """ return 22. # A real function should probably actually get the temperature! # Next, create a chat and apply the chat template messages = [ {"role": "system", "content": "You are a bot that responds to weather queries."}, {"role": "user", "content": "Hey, what's the temperature in Paris right now?"} ] inputs = tokenizer.apply_chat_template(messages, tools=[get_current_temperature], add_generation_prompt=True) ``` You can then generate text from this input as normal. If the model generates a tool call, you should add it to the chat like so: ```python tool_call = {"name": "get_current_temperature", "arguments": {"location": "Paris, France"}} messages.append({"role": "assistant", "tool_calls": [{"type": "function", "function": tool_call}]}) ``` and then call the tool and append the result, with the `tool` role, like so: ```python messages.append({"role": "tool", "name": "get_current_temperature", "content": "22.0"}) ``` After that, you can `generate()` again to let the model use the tool result in the chat. Note that this was a very brief introduction to tool calling - for more information, see the [LLaMA prompt format docs](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1/) and the Transformers [tool use documentation](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling). ### Use with `bitsandbytes` The model checkpoints can be used in `8-bit` and `4-bit` for further memory optimisations using `bitsandbytes` and `transformers` See the snippet below for usage: ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "meta-llama/Meta-Llama-3.1-70B-Instruct" quantization_config = BitsAndBytesConfig(load_in_8bit=True) quantized_model = AutoModelForCausalLM.from_pretrained( model_id, device_map="auto", torch_dtype=torch.bfloat16, quantization_config=quantization_config) tokenizer = AutoTokenizer.from_pretrained(model_id) input_text = "What are we having for dinner?" input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") output = quantized_model.generate(*input_ids, max_new_tokens=10) print(tokenizer.decode(output[0], skip_special_tokens=True)) ``` To load in 4-bit simply pass `load_in_4bit=True` ### Use with `llama` Please, follow the instructions in the [repository](https://github.com/meta-llama/llama). To download Original checkpoints, see the example command below leveraging `huggingface-cli`: ``` huggingface-cli download meta-llama/Meta-Llama-3.1-70B-Instruct --include "original/" --local-dir Meta-Llama-3.1-70B-Instruct ``` ## Hardware and Software Training Factors We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, annotation, and evaluation were also performed on production infrastructure. Training utilized a cumulative of 39.3M GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency. Training Greenhouse Gas Emissions Estimated total location-based greenhouse gas emissions were 11,390 tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy, therefore the total market-based greenhouse gas emissions for training were 0 tons CO2eq. <table> <tr> <td> </td> <td><strong>Training Time (GPU hours)</strong> </td> <td><strong>Training Power Consumption (W)</strong> </td> <td><strong>Training Location-Based Greenhouse Gas Emissions</strong> <p> <strong>(tons CO2eq)</strong> </td> <td><strong>Training Market-Based Greenhouse Gas Emissions</strong> <p> <strong>(tons CO2eq)</strong> </td> </tr> <tr> <td>Llama 3.1 8B </td> <td>1.46M </td> <td>700 </td> <td>420 </td> <td>0 </td> </tr> <tr> <td>Llama 3.1 70B </td> <td>7.0M </td> <td>700 </td> <td>2,040 </td> <td>0 </td> </tr> <tr> <td>Llama 3.1 405B </td> <td>30.84M </td> <td>700 </td> <td>8,930 </td> <td>0 </td> </tr> <tr> <td>Total </td> <td>39.3M <td> <ul> </ul> </td> <td>11,390 </td> <td>0 </td> </tr> </table> The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others. ## Training Data Overview: Llama 3.1 was pretrained on ~15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 25M synthetically generated examples. Data Freshness: The pretraining data has a cutoff of December 2023. ## Benchmark scores In this section, we report the results for Llama 3.1 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. ### Base pretrained models <table> <tr> <td><strong>Category</strong> </td> <td><strong>Benchmark</strong> </td> <td><strong># Shots</strong> </td> <td><strong>Metric</strong> </td> <td><strong>Llama 3 8B</strong> </td> <td><strong>Llama 3.1 8B</strong> </td> <td><strong>Llama 3 70B</strong> </td> <td><strong>Llama 3.1 70B</strong> </td> <td><strong>Llama 3.1 405B</strong> </td> </tr> <tr> <td rowspan="7" >General </td> <td>MMLU </td> <td>5 </td> <td>macro_avg/acc_char </td> <td>66.7 </td> <td>66.7 </td> <td>79.5 </td> <td>79.3 </td> <td>85.2 </td> </tr> <tr> <td>MMLU-Pro (CoT) </td> <td>5 </td> <td>macro_avg/acc_char </td> <td>36.2 </td> <td>37.1 </td> <td>55.0 </td> <td>53.8 </td> <td>61.6 </td> </tr> <tr> <td>AGIEval English </td> <td>3-5 </td> <td>average/acc_char </td> <td>47.1 </td> <td>47.8 </td> <td>63.0 </td> <td>64.6 </td> <td>71.6 </td> </tr> <tr> <td>CommonSenseQA </td> <td>7 </td> <td>acc_char </td> <td>72.6 </td> <td>75.0 </td> <td>83.8 </td> <td>84.1 </td> <td>85.8 </td> </tr> <tr> <td>Winogrande </td> <td>5 </td> <td>acc_char </td> <td>- </td> <td>60.5 </td> <td>- </td> <td>83.3 </td> <td>86.7 </td> </tr> <tr> <td>BIG-Bench Hard (CoT) </td> <td>3 </td> <td>average/em </td> <td>61.1 </td> <td>64.2 </td> <td>81.3 </td> <td>81.6 </td> <td>85.9 </td> </tr> <tr> <td>ARC-Challenge </td> <td>25 </td> <td>acc_char </td> <td>79.4 </td> <td>79.7 </td> <td>93.1 </td> <td>92.9 </td> <td>96.1 </td> </tr> <tr> <td>Knowledge reasoning </td> <td>TriviaQA-Wiki </td> <td>5 </td> <td>em </td> <td>78.5 </td> <td>77.6 </td> <td>89.7 </td> <td>89.8 </td> <td>91.8 </td> </tr> <tr> <td rowspan="4" >Reading comprehension </td> <td>SQuAD </td> <td>1 </td> <td>em </td> <td>76.4 </td> <td>77.0 </td> <td>85.6 </td> <td>81.8 </td> <td>89.3 </td> </tr> <tr> <td>QuAC (F1) </td> <td>1 </td> <td>f1 </td> <td>44.4 </td> <td>44.9 </td> <td>51.1 </td> <td>51.1 </td> <td>53.6 </td> </tr> <tr> <td>BoolQ </td> <td>0 </td> <td>acc_char </td> <td>75.7 </td> <td>75.0 </td> <td>79.0 </td> <td>79.4 </td> <td>80.0 </td> </tr> <tr> <td>DROP (F1) </td> <td>3 </td> <td>f1 </td> <td>58.4 </td> <td>59.5 </td> <td>79.7 </td> <td>79.6 </td> <td>84.8 </td> </tr> </table> ### Instruction tuned models <table> <tr> <td><strong>Category</strong> </td> <td><strong>Benchmark</strong> </td> <td><strong># Shots</strong> </td> <td><strong>Metric</strong> </td> <td><strong>Llama 3 8B Instruct</strong> </td> <td><strong>Llama 3.1 8B Instruct</strong> </td> <td><strong>Llama 3 70B Instruct</strong> </td> <td><strong>Llama 3.1 70B Instruct</strong> </td> <td><strong>Llama 3.1 405B Instruct</strong> </td> </tr> <tr> <td rowspan="4" >General </td> <td>MMLU </td> <td>5 </td> <td>macro_avg/acc </td> <td>68.5 </td> <td>69.4 </td> <td>82.0 </td> <td>83.6 </td> <td>87.3 </td> </tr> <tr> <td>MMLU (CoT) </td> <td>0 </td> <td>macro_avg/acc </td> <td>65.3 </td> <td>73.0 </td> <td>80.9 </td> <td>86.0 </td> <td>88.6 </td> </tr> <tr> <td>MMLU-Pro (CoT) </td> <td>5 </td> <td>micro_avg/acc_char </td> <td>45.5 </td> <td>48.3 </td> <td>63.4 </td> <td>66.4 </td> <td>73.3 </td> </tr> <tr> <td>IFEval </td> <td> </td> <td> </td> <td>76.8 </td> <td>80.4 </td> <td>82.9 </td> <td>87.5 </td> <td>88.6 </td> </tr> <tr> <td rowspan="2" >Reasoning </td> <td>ARC-C </td> <td>0 </td> <td>acc </td> <td>82.4 </td> <td>83.4 </td> <td>94.4 </td> <td>94.8 </td> <td>96.9 </td> </tr> <tr> <td>GPQA </td> <td>0 </td> <td>em </td> <td>34.6 </td> <td>30.4 </td> <td>39.5 </td> <td>41.7 </td> <td>50.7 </td> </tr> <tr> <td rowspan="4" >Code </td> <td>HumanEval </td> <td>0 </td> <td>pass@1 </td> <td>60.4 </td> <td>72.6 </td> <td>81.7 </td> <td>80.5 </td> <td>89.0 </td> </tr> <tr> <td>MBPP ++ base version </td> <td>0 </td> <td>pass@1 </td> <td>70.6 </td> <td>72.8 </td> <td>82.5 </td> <td>86.0 </td> <td>88.6 </td> </tr> <tr> <td>Multipl-E HumanEval </td> <td>0 </td> <td>pass@1 </td> <td>- </td> <td>50.8 </td> <td>- </td> <td>65.5 </td> <td>75.2 </td> </tr> <tr> <td>Multipl-E MBPP </td> <td>0 </td> <td>pass@1 </td> <td>- </td> <td>52.4 </td> <td>- </td> <td>62.0 </td> <td>65.7 </td> </tr> <tr> <td rowspan="2" >Math </td> <td>GSM-8K (CoT) </td> <td>8 </td> <td>em_maj1@1 </td> <td>80.6 </td> <td>84.5 </td> <td>93.0 </td> <td>95.1 </td> <td>96.8 </td> </tr> <tr> <td>MATH (CoT) </td> <td>0 </td> <td>final_em </td> <td>29.1 </td> <td>51.9 </td> <td>51.0 </td> <td>68.0 </td> <td>73.8 </td> </tr> <tr> <td rowspan="4" >Tool Use </td> <td>API-Bank </td> <td>0 </td> <td>acc </td> <td>48.3 </td> <td>82.6 </td> <td>85.1 </td> <td>90.0 </td> <td>92.0 </td> </tr> <tr> <td>BFCL </td> <td>0 </td> <td>acc </td> <td>60.3 </td> <td>76.1 </td> <td>83.0 </td> <td>84.8 </td> <td>88.5 </td> </tr> <tr> <td>Gorilla Benchmark API Bench </td> <td>0 </td> <td>acc </td> <td>1.7 </td> <td>8.2 </td> <td>14.7 </td> <td>29.7 </td> <td>35.3 </td> </tr> <tr> <td>Nexus (0-shot) </td> <td>0 </td> <td>macro_avg/acc </td> <td>18.1 </td> <td>38.5 </td> <td>47.8 </td> <td>56.7 </td> <td>58.7 </td> </tr> <tr> <td>Multilingual </td> <td>Multilingual MGSM (CoT) </td> <td>0 </td> <td>em </td> <td>- </td> <td>68.9 </td> <td>- </td> <td>86.9 </td> <td>91.6 </td> </tr> </table> #### Multilingual benchmarks <table> <tr> <td><strong>Category</strong> </td> <td><strong>Benchmark</strong> </td> <td><strong>Language</strong> </td> <td><strong>Llama 3.1 8B</strong> </td> <td><strong>Llama 3.1 70B</strong> </td> <td><strong>Llama 3.1 405B</strong> </td> </tr> <tr> <td rowspan="9" ><strong>General</strong> </td> <td rowspan="9" ><strong>MMLU (5-shot, macro_avg/acc)</strong> </td> <td>Portuguese </td> <td>62.12 </td> <td>80.13 </td> <td>84.95 </td> </tr> <tr> <td>Spanish </td> <td>62.45 </td> <td>80.05 </td> <td>85.08 </td> </tr> <tr> <td>Italian </td> <td>61.63 </td> <td>80.4 </td> <td>85.04 </td> </tr> <tr> <td>German </td> <td>60.59 </td> <td>79.27 </td> <td>84.36 </td> </tr> <tr> <td>French </td> <td>62.34 </td> <td>79.82 </td> <td>84.66 </td> </tr> <tr> <td>Hindi </td> <td>50.88 </td> <td>74.52 </td> <td>80.31 </td> </tr> <tr> <td>Thai </td> <td>50.32 </td> <td>72.95 </td> <td>78.21 </td> </tr> </table> ## Responsibility & Safety As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks: * Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama. * Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm. * Provide protections for the community to help prevent the misuse of our models. ### Responsible deployment Llama is a foundational technology designed to be used in a variety of use cases, examples on how Meta’s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models enabling the world to benefit from the technology power, by aligning our model safety for the generic use cases addressing a standard set of harms. Developers are then in the driver seat to tailor safety for their use case, defining their own policy and deploying the models with the necessary safeguards in their Llama systems. Llama 3.1 was developed following the best practices outlined in our Responsible Use Guide, you can refer to the [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to learn more. #### Llama 3.1 instruct Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. For more details on the safety mitigations implemented please read the Llama 3 paper. Fine-tuning data We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control. Refusals and Tone Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines. #### Llama 3.1 systems Large language models, including Llama 3.1, are not designed to be deployed in isolation but instead should be deployed as part of an overall AI system with additional safety guardrails as required. Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools. As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard 3, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box. #### New capabilities Note that this release introduces new capabilities, including a longer context window, multilingual inputs and outputs and possible integrations by developers with third party tools. Building with these new capabilities requires specific considerations in addition to the best practices that generally apply across all Generative AI use cases. Tool-use: Just like in standard software development, developers are responsible for the integration of the LLM with the tools and services of their choice. They should define a clear policy for their use case and assess the integrity of the third party services they use to be aware of the safety and security limitations when using this capability. Refer to the Responsible Use Guide for best practices on the safe deployment of the third party safeguards. Multilinguality: Llama 3.1 supports 7 languages in addition to English: French, German, Hindi, Italian, Portuguese, Spanish, and Thai. Llama may be able to output text in other languages than those that meet performance thresholds for safety and helpfulness. We strongly discourage developers from using this model to converse in non-supported languages without implementing finetuning and system controls in alignment with their policies and the best practices shared in the Responsible Use Guide. ### Evaluations We evaluated Llama models for common use cases as well as specific capabilities. Common use cases evaluations measure safety risks of systems for most commonly built applications including chat bot, coding assistant, tool calls. We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Llama Guard 3 to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case. Prompt Guard and Code Shield are also available if relevant to the application. Capability evaluations measure vulnerabilities of Llama models inherent to specific capabilities, for which were crafted dedicated benchmarks including long context, multilingual, tools calls, coding or memorization. Red teaming For both scenarios, we conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets. We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets. ### Critical and other risks We specifically focused our efforts on mitigating the following critical risk areas: 1- CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive materials) helpfulness To assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons. 2. Child Safety Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. 3. Cyber attack enablement Our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed. Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention. Our study of Llama-3.1-405B’s social engineering uplift for cyber attackers was conducted to assess the effectiveness of AI models in aiding cyber threat actors in spear phishing campaigns. Please read our Llama 3.1 Cyber security whitepaper to learn more. ### Community Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists). Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community. ## Ethical Considerations and Limitations The core values of Llama 3.1 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.1 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. But Llama 3.1 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.1’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.1 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development.
daitote/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-knobby_darting_tortoise	daitote	"2025-04-09T18:02:40Z"	1	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am knobby darting tortoise", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-07T09:31:36Z"	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-knobby_darting_tortoise tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am knobby darting tortoise - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-knobby_darting_tortoise This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="daitote/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-knobby_darting_tortoise", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.0 - Pytorch: 2.5.1 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
luoxiaojun1992/Qwen2.5-3B-Instruct-gsm8k-q4_k_m	luoxiaojun1992	"2025-02-17T05:46:54Z"	0	0	transformers	[ "transformers", "gguf", "qwen2", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	"2025-02-17T05:46:19Z"	--- base_model: unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: luoxiaojun1992 - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit This qwen2 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)
Ludo33/deberta_energie	Ludo33	"2025-03-11T14:36:54Z"	5	0	transformers	[ "transformers", "pytorch", "safetensors", "deberta-v2", "text-classification", "generated_from_trainer", "base_model:microsoft/deberta-v3-base", "base_model:finetune:microsoft/deberta-v3-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2025-02-14T11:07:26Z"	--- library_name: transformers license: mit base_model: microsoft/deberta-v3-base tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: deberta_Energie 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. --> # deberta_Energie This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0142 - Accuracy: 0.9913 - F1: 0.9913 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 1.4003 \| 1.0 \| 116 \| 0.9262 \| 0.6524 \| 0.6025 \| \| 0.7697 \| 2.0 \| 232 \| 0.3836 \| 0.8906 \| 0.8899 \| \| 0.3904 \| 3.0 \| 348 \| 0.2468 \| 0.9256 \| 0.9191 \| \| 0.2749 \| 4.0 \| 464 \| 0.2202 \| 0.9324 \| 0.9283 \| \| 0.2043 \| 5.0 \| 580 \| 0.1122 \| 0.9672 \| 0.9673 \| \| 0.1808 \| 6.0 \| 696 \| 0.1004 \| 0.9701 \| 0.9706 \| \| 0.1274 \| 7.0 \| 812 \| 0.0822 \| 0.9745 \| 0.9747 \| \| 0.1018 \| 8.0 \| 928 \| 0.0673 \| 0.9791 \| 0.9794 \| \| 0.0711 \| 9.0 \| 1044 \| 0.0457 \| 0.9870 \| 0.9870 \| \| 0.0609 \| 10.0 \| 1160 \| 0.0370 \| 0.9867 \| 0.9867 \| \| 0.0594 \| 11.0 \| 1276 \| 0.0240 \| 0.9886 \| 0.9886 \| \| 0.0332 \| 12.0 \| 1392 \| 0.0182 \| 0.9913 \| 0.9913 \| \| 0.0278 \| 13.0 \| 1508 \| 0.0183 \| 0.9908 \| 0.9908 \| \| 0.0281 \| 14.0 \| 1624 \| 0.0142 \| 0.9913 \| 0.9913 \| ### Framework versions - Transformers 4.48.3 - Pytorch 2.5.1+cu124 - Datasets 3.3.2 - Tokenizers 0.21.0
dabrown/9b20a3aa-4649-4eef-8650-cebdbfb1c3ba	dabrown	"2025-02-28T11:51:44Z"	0	0	peft	[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:unsloth/Phi-3-mini-4k-instruct", "base_model:adapter:unsloth/Phi-3-mini-4k-instruct", "license:mit", "region:us" ]	null	"2025-02-28T09:40:44Z"	--- library_name: peft license: mit base_model: unsloth/Phi-3-mini-4k-instruct tags: - axolotl - generated_from_trainer model-index: - name: 9b20a3aa-4649-4eef-8650-cebdbfb1c3ba 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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.5.2` ```yaml adapter: lora base_model: unsloth/Phi-3-mini-4k-instruct bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 4162907d2807b67e_train_data.json ds_type: json format: custom path: /workspace/input_data/4162907d2807b67e_train_data.json type: field_input: description field_instruction: reference field_output: article format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 8 gradient_checkpointing: false group_by_length: true hub_model_id: dabrown/9b20a3aa-4649-4eef-8650-cebdbfb1c3ba hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: false lora_inference_mode: true lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_grad_norm: 1.0 max_steps: 1500 micro_batch_size: 2 mlflow_experiment_name: /tmp/4162907d2807b67e_train_data.json model_type: AutoModelForCausalLM modules_to_save: lm_head num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true peft_use_rslora: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 1024 strict: false tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: offline wandb_name: 64a2c218-432f-42db-b1c9-e8707ca42c74 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 64a2c218-432f-42db-b1c9-e8707ca42c74 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 9b20a3aa-4649-4eef-8650-cebdbfb1c3ba This model is a fine-tuned version of [unsloth/Phi-3-mini-4k-instruct](https://huggingface.co/unsloth/Phi-3-mini-4k-instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2989 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 1326 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.4285 \| 0.0008 \| 1 \| 1.8210 \| \| 1.5212 \| 0.2504 \| 332 \| 1.4034 \| \| 1.2608 \| 0.5008 \| 664 \| 1.3372 \| \| 1.3728 \| 0.7511 \| 996 \| 1.2989 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.3 - Pytorch 2.3.1+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3
kaljr/ppo-LunarLander-v2	kaljr	"2024-02-27T02:38:45Z"	2	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	"2024-02-27T02:38:21Z"	--- 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: 253.45 +/- 10.35 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 ... ```
thalllsssss/45a81f30-3d17-4b84-a45b-a2b51af00a14	thalllsssss	"2025-01-24T05:37:37Z"	8	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:unsloth/Qwen2-7B-Instruct", "base_model:adapter:unsloth/Qwen2-7B-Instruct", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]	null	"2025-01-24T04:34:00Z"	--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2-7B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 45a81f30-3d17-4b84-a45b-a2b51af00a14 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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/Qwen2-7B-Instruct bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - be25ce38282aeb5a_train_data.json ds_type: json format: custom path: /workspace/input_data/be25ce38282aeb5a_train_data.json type: field_input: input field_instruction: instruction field_output: output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: thalllsssss/45a81f30-3d17-4b84-a45b-a2b51af00a14 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-05 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 2 mlflow_experiment_name: /tmp/be25ce38282aeb5a_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 14fba03c-c528-4737-ac1e-1f62f6edce20 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 14fba03c-c528-4737-ac1e-1f62f6edce20 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 45a81f30-3d17-4b84-a45b-a2b51af00a14 This model is a fine-tuned version of [unsloth/Qwen2-7B-Instruct](https://huggingface.co/unsloth/Qwen2-7B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2583 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.1782 \| 0.0067 \| 200 \| 1.2583 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
rithwik-db/triplets-e5-base-500-2183ce	rithwik-db	"2023-04-09T00:38:35Z"	1	0	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	"2023-04-09T00:38:29Z"	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # rithwik-db/triplets-e5-base-500-2183ce This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('rithwik-db/triplets-e5-base-500-2183ce') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('rithwik-db/triplets-e5-base-500-2183ce') model = AutoModel.from_pretrained('rithwik-db/triplets-e5-base-500-2183ce') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name=rithwik-db/triplets-e5-base-500-2183ce) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 8228 with parameters: ``` {'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.TripletLoss.TripletLoss` with parameters: ``` {'distance_metric': 'TripletDistanceMetric.EUCLIDEAN', 'triplet_margin': 5} ``` Parameters of the fit()-Method: ``` { "epochs": 3, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 10000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
leonhe/ppo-Huggy	leonhe	"2023-05-22T07:22:50Z"	18	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	"2023-05-22T07:22:43Z"	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Find your model_id: leonhe/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
William2357/textual_inversion_cat	William2357	"2024-06-09T06:07:23Z"	2	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "textual_inversion", "diffusers-training", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	"2024-05-16T02:34:37Z"	--- license: creativeml-openrail-m library_name: diffusers tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion - diffusers-training - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion - diffusers-training base_model: runwayml/stable-diffusion-v1-5 inference: true --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # Textual inversion text2image fine-tuning - William2357/textual_inversion_cat These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
shaaaanya/my-first-ppo-LunarLander-v2-model	shaaaanya	"2023-02-15T07:52:10Z"	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	"2023-02-15T07:51:54Z"	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO_MlpPolicy results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 267.32 +/- 28.78 name: mean_reward verified: false --- # PPO_MlpPolicy Agent playing LunarLander-v2 This is a trained model of a PPO_MlpPolicy agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
franjamonga/min-1-R1-Zero-3B-GGUF	franjamonga	"2025-02-12T16:08:51Z"	20	0	transformers	[ "transformers", "gguf", "qwen2", "text-generation-inference", "unsloth", "en", "es", "base_model:unsloth/Qwen2.5-3B-Instruct-unsloth-bnb-4bit", "base_model:quantized:unsloth/Qwen2.5-3B-Instruct-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	"2025-02-11T12:58:39Z"	--- base_model: - unsloth/Qwen2.5-3B-Instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - gguf license: apache-2.0 language: - en - es --- # Uploaded model - Developed by: franjamonga - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit
kyleeasterly/openllama-7b_purple-aerospace-v1-80-12	kyleeasterly	"2023-08-09T08:12:46Z"	0	0	peft	[ "peft", "region:us" ]	null	"2023-08-09T08:09:18Z"	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - 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: bfloat16 ### Framework versions - PEFT 0.5.0.dev0
JuliPolus/sd-1-4-fashion-model	JuliPolus	"2024-06-15T19:39:15Z"	0	0	diffusers	[ "diffusers", "safetensors", "license:creativeml-openrail-m", "region:us" ]	null	"2024-06-15T19:17:09Z"	--- license: creativeml-openrail-m ---
Felix92/doctr-dummy-tf-crnn-mobilenet-v3-large	Felix92	"2022-05-25T21:32:59Z"	13	0	transformers	[ "transformers", "image-to-text", "en", "endpoints_compatible", "region:us" ]	image-to-text	"2022-04-14T11:46:53Z"	--- language: en pipeline_tag: image-to-text --- <p align="center"> <img src="https://github.com/mindee/doctr/releases/download/v0.3.1/Logo_doctr.gif" width="60%"> </p> Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch ## Task: recognition https://github.com/mindee/doctr ### Example usage: ```python >>> from doctr.io import DocumentFile >>> from doctr.models import ocr_predictor, from_hub >>> img = DocumentFile.from_images(['<image_path>']) >>> # Load your model from the hub >>> model = from_hub('mindee/my-model') >>> # Pass it to the predictor >>> # If your model is a recognition model: >>> predictor = ocr_predictor(det_arch='db_mobilenet_v3_large', >>> reco_arch=model, >>> pretrained=True) >>> # If your model is a detection model: >>> predictor = ocr_predictor(det_arch=model, >>> reco_arch='crnn_mobilenet_v3_small', >>> pretrained=True) >>> # Get your predictions >>> res = predictor(img) ```
mradermacher/Brezn3-GGUF	mradermacher	"2024-12-19T20:38:12Z"	18	1	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:johannhartmann/Brezn3", "base_model:quantized:johannhartmann/Brezn3", "endpoints_compatible", "region:us", "conversational" ]	null	"2024-12-19T20:11:09Z"	--- base_model: johannhartmann/Brezn3 language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/johannhartmann/Brezn3 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Brezn3-GGUF/resolve/main/Brezn3.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Brezn3-GGUF/resolve/main/Brezn3.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Brezn3-GGUF/resolve/main/Brezn3.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Brezn3-GGUF/resolve/main/Brezn3.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Brezn3-GGUF/resolve/main/Brezn3.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Brezn3-GGUF/resolve/main/Brezn3.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Brezn3-GGUF/resolve/main/Brezn3.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Brezn3-GGUF/resolve/main/Brezn3.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Brezn3-GGUF/resolve/main/Brezn3.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Brezn3-GGUF/resolve/main/Brezn3.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Brezn3-GGUF/resolve/main/Brezn3.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Brezn3-GGUF/resolve/main/Brezn3.f16.gguf) \| f16 \| 14.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
darthPanda/taxi_v3_try1	darthPanda	"2023-07-19T16:38:31Z"	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	"2023-07-19T16:36:20Z"	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxi_v3_try1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.50 +/- 2.75 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="darthPanda/taxi_v3_try1", 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"]) ```
synthbot/pony-diffusion-v3-1-openvino	synthbot	"2023-03-05T12:03:44Z"	0	0	null	[ "region:us" ]	null	"2023-03-05T11:50:31Z"	This is a copy of [AstraliteHeart's Pony Diffusion v3.1](https://huggingface.co/AstraliteHeart/pony-diffusion-v3) model, converted to the [OpenVino IR](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/225-stable-diffusion-text-to-image) for compatibility with [CPUtard](https://rentry.co/CPUtard).
mradermacher/orca_mini_v3_7b-GGUF	mradermacher	"2025-01-29T15:10:33Z"	201	0	transformers	[ "transformers", "gguf", "en", "dataset:psmathur/orca_mini_v1_dataset", "dataset:ehartford/dolphin", "base_model:pankajmathur/orca_mini_v3_7b", "base_model:quantized:pankajmathur/orca_mini_v3_7b", "license:other", "endpoints_compatible", "region:us" ]	null	"2025-01-29T14:13:03Z"	--- base_model: pankajmathur/orca_mini_v3_7b datasets: - psmathur/orca_mini_v1_dataset - ehartford/dolphin language: - en library_name: transformers license: other quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> static quants of https://huggingface.co/pankajmathur/orca_mini_v3_7b <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/orca_mini_v3_7b-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/orca_mini_v3_7b-GGUF/resolve/main/orca_mini_v3_7b.Q2_K.gguf) \| Q2_K \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/orca_mini_v3_7b-GGUF/resolve/main/orca_mini_v3_7b.Q3_K_S.gguf) \| Q3_K_S \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/orca_mini_v3_7b-GGUF/resolve/main/orca_mini_v3_7b.Q3_K_M.gguf) \| Q3_K_M \| 3.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/orca_mini_v3_7b-GGUF/resolve/main/orca_mini_v3_7b.Q3_K_L.gguf) \| Q3_K_L \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/orca_mini_v3_7b-GGUF/resolve/main/orca_mini_v3_7b.IQ4_XS.gguf) \| IQ4_XS \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/orca_mini_v3_7b-GGUF/resolve/main/orca_mini_v3_7b.Q4_K_S.gguf) \| Q4_K_S \| 4.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/orca_mini_v3_7b-GGUF/resolve/main/orca_mini_v3_7b.Q4_K_M.gguf) \| Q4_K_M \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/orca_mini_v3_7b-GGUF/resolve/main/orca_mini_v3_7b.Q5_K_S.gguf) \| Q5_K_S \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/orca_mini_v3_7b-GGUF/resolve/main/orca_mini_v3_7b.Q5_K_M.gguf) \| Q5_K_M \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/orca_mini_v3_7b-GGUF/resolve/main/orca_mini_v3_7b.Q6_K.gguf) \| Q6_K \| 5.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/orca_mini_v3_7b-GGUF/resolve/main/orca_mini_v3_7b.Q8_0.gguf) \| Q8_0 \| 7.3 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/orca_mini_v3_7b-GGUF/resolve/main/orca_mini_v3_7b.f16.gguf) \| f16 \| 13.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
DAMO-NLP-SG/Mistral-7B-LongPO-256K-EXP	DAMO-NLP-SG	"2025-02-22T12:05:39Z"	17	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "dataset:DAMO-NLP-SG/Mistral-7B-LongPO-256K-tokenized", "arxiv:2502.13922", "base_model:DAMO-NLP-SG/Mistral-7B-LongPO-128K", "base_model:finetune:DAMO-NLP-SG/Mistral-7B-LongPO-128K", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-02-17T13:03:19Z"	--- library_name: transformers license: apache-2.0 datasets: - DAMO-NLP-SG/Mistral-7B-LongPO-256K-tokenized base_model: - DAMO-NLP-SG/Mistral-7B-LongPO-128K --- # LongPO: Long Context Self-Evolution of Large Language Models through Short-to-Long Preference Optimization This repo provides the checkpoint of Mistral-7B-LongPO-256K in our paper "LongPO: Long Context Self-Evolution of Large Language Models through Short-to-Long Preference Optimization". (Note that it is an experimental an experimental version (for rebuttal purposes) that may have not been fully tuned or provided with sufficient data to achieve convergence.) <h5 align="left"> [![arXiv](https://img.shields.io/badge/Arxiv-2502.13922-AD1C18.svg?logo=arXiv)](http://arxiv.org/abs/2502.13922) [![hf_paper](https://img.shields.io/badge/🤗-HF%20Daily-red.svg)](https://huggingface.co/papers/2502.13922) </h5> ## Highlights of LongPO - Self-evolving long-context alignment without human/superior LLMs annotations. - Extending context length while keeping aligned in one stage. - No degradation on short-context capabilities. <img width="1031" alt="image" src="https://github.com/user-attachments/assets/84f3c93f-909d-4ef7-a33a-107ca2deec42" /> ## Models and Training Data \| Models \| Base Model \| Training Data \| # Data Samples \| \| ------------------------------------------------------------ \| ------------------------ \| ------------------------------------------------------------ \| -------------- \| \| [Mistral-7B-LongPO-128K](https://huggingface.co/DAMO-NLP-SG/Mistral-7B-LongPO-128K) \| Mistral-7B-Instruct-v0.2 \| [HF Link](https://huggingface.co/datasets/DAMO-NLP-SG/Mistral-7B-LongPO-128K-tokenized) \| 45K \| \| [Qwen2.5-7B-LongPO-128K](https://huggingface.co/DAMO-NLP-SG/Qwen2.5-7B-LongPO-128K) \| Qwen2.5-7B-Instruct \| [HF Link](https://huggingface.co/datasets/DAMO-NLP-SG/Qwen2.5-7B-LongPO-128K-tokenized) \| 32K \| \| [Mistral-7B-LongPO-256K-EXP](https://huggingface.co/DAMO-NLP-SG/Mistral-7B-LongPO-256K-EXP)* \| Mistral-7B-LongPO-128K \| [HF Link](https://huggingface.co/datasets/DAMO-NLP-SG/Mistral-7B-LongPO-256K-tokenized) \| 16K \| \| [Mistral-7B-LongPO-512K-EXP](https://huggingface.co/DAMO-NLP-SG/Mistral-7B-LongPO-512K-EXP)* \| Mistral-7B-LongPO-128K \| [HF Link](https://huggingface.co/datasets/DAMO-NLP-SG/Mistral-7B-LongPO-512K-tokenized) \| 2.5K \| \* indicates an experimental version (for rebuttal purposes) that may have not been fully tuned or provided with sufficient data to achieve convergence. ## Evaluation ### InfiniteBench \| Model \| Train/Claimed Length \| En.Sum \| En.QA \| En.MC \| AVG. \| \| ---------------- \| -------------------- \| ------ \| ------ \| ------ \| ------ \| \| GPT-4-128K \| 128K \| 14.73 \| 22.44 \| 67.25 \| 34.81 \| \| Qwen2-72B \| 128K \| 24.32ᵇ \| 7.03ᵇ \| 72.05ᵇ \| 34.47ᵇ \| \| LLaMA 3.1-70B \| 128K \| 33.55ᵇ \| 36.08ᵇ \| 69.00ᵇ \| 46.21ᵇ \| \| LLaMA 3.1-8B \| 128K \| 28.06ᵇ \| 30.47ᵇ \| 58.08ᵇ \| 38.87ᵇ \| \| GLM-4-9B \| 128K \| 14.84ᵇ \| 9.51ᵇ \| 67.25ᵇ \| 30.53ᵇ \| \| GLM-4-9B-1M \| 1M \| 28.3 \| 9.7 \| 68.6 \| 35.53 \| \| LWM-7B-1M \| 1M \| 4.33ᵇ \| 0.0ᵇ \| 3.06ᵇ \| 2.46ᵇ \| \| YaRN-Mistral-7B \| 128K \| 9.09 \| 9.55 \| 27.95 \| 15.53 \| \| Mistral-7B \| 32K \| 22.13 \| 4.93 \| 14.41 \| 13.82 \| \| - SFT \| 128K \| 23.44 \| 13.45 \| 53.21 \| 30.03 \| \| - DPO \| 128K \| 15.21 \| 10.34 \| 48.14 \| 25.56 \| \| - LongPO (iter1) \| 128K \| 27.05 \| 23.51 \| 67.25 \| 39.27 \| \| - LongPO (iter2) \| 256K \| 28.16 \| 24.43 \| 66.35 \| 39.65 \| \| - LongPO (iter3) \| 512K \| 29.10 \| 27.85 \| 66.67 \| 41.21 \| \| Qwen2.5-7B \| 128K \| 22.89 \| 6.08 \| 52.4 \| 27.12 \| \| - LongPO (iter1) \| 128K \| 32.06 \| 17.32 \| 72.05 \| 40.48 \| - Our results are evaluated with greedy decoding. - Baseline results marked with ᵇ are evaluated by us, while unmarked baseline results are sourced from their official report. ### RULER \| Model \| NIAH \| VT \| AGG \| QA \| AVG (13 tasks) \| \| ------------------------ \| ----- \| ----- \| ----- \| ----- \| -------------- \| \| Qwen2.5-7B-Instruct \| 82.10 \| 80.09 \| 74.50 \| 54.30 \| 76.50 \| \| Qwen2.5-7B-LongPO-128K \| 95.82 \| 89.71 \| 78.67 \| 59.40 \| 87.11 \| \| Mistral-7B-Instruct-v0.2 \| 72.60 \| 74.40 \| 64.40 \| 52.20 \| 68.40 \| \| Mistral-7B-LongPO-128K \| 96.88 \| 96.49 \| 71.55 \| 64.81 \| 88.02 \| \| Mistral-7B-LongPO-256K-EXP \| 96.80 \| 97.00 \| 69.14 \| 64.87 \| 87.65 \| \| Mistral-7B-LongPO-512K-EXP \| 97.28 \| 97.48 \| 69.22 \| 64.92 \| 88.00 \| ### Short Context \| Model \| MMLU \| ARC-C \| Hellaswag \| Winogrande \| Avg \| \|-------\|-------\|--------\|------------\|-------------\|-----\| \| Mistral-7B-Instruct-v0.2 \| 59.15 \| 59.26 \| 83.2 \| 78.4 \| 70.00 \| \| Mistral-7B-LongPO-128K \| 59.99 \| 59.34 \| 82.99 \| 78.53 \| 70.21 \| \| Mistral-7B-LongPO-256K-EXP \| 59.47 \| 60.28 \| 83.14 \| 78.14 \| 70.26 \| \| Mistral-7B-LongPO-512K-EXP \| 59.51 \| 60.58 \| 82.87 \| 77.66 \| 70.16 \| \| Qwen2.5-7B-Instruct \| 74.28 \| 67.15 \| 81.41 \| 74.66 \| 74.38 \| \| Qwen2.5-7B-LongPO-128K \| 73.64 \| 65.70 \| 80.82 \| 74.98 \| 73.79 \| ## Citation If you find our project useful, hope you can star our repo and cite our paper as follows: ``` @inproceedings{ chen2025longpo, title={Long{PO}: Long Context Self-Evolution of Large Language Models through Short-to-Long Preference Optimization}, author={Guanzheng Chen and Xin Li and Michael Shieh and Lidong Bing}, booktitle={The Thirteenth International Conference on Learning Representations}, year={2025}, url={https://openreview.net/forum?id=qTrEq31Shm} } ```
ModelCloud/sat-3l-sm-int8-onnx	ModelCloud	"2024-10-10T08:26:52Z"	2,589	0	null	[ "onnx", "xlm-token", "region:us" ]	null	"2024-10-10T08:10:51Z"	The onnx model file obtained by onnx quantize dynamic from segment-any-text/sat-3l-sm.
RichardErkhov/google_-_gemma-2-2b-8bits	RichardErkhov	"2024-10-11T15:38:59Z"	5	0	null	[ "safetensors", "gemma2", "arxiv:2009.03300", "arxiv:1905.07830", "arxiv:1911.11641", "arxiv:1904.09728", "arxiv:1905.10044", "arxiv:1907.10641", "arxiv:1811.00937", "arxiv:1809.02789", "arxiv:1911.01547", "arxiv:1705.03551", "arxiv:2107.03374", "arxiv:2108.07732", "arxiv:2110.14168", "arxiv:2009.11462", "arxiv:2101.11718", "arxiv:2110.08193", "arxiv:1804.09301", "arxiv:2109.07958", "arxiv:1804.06876", "arxiv:2103.03874", "arxiv:2304.06364", "arxiv:1903.00161", "arxiv:2206.04615", "arxiv:2203.09509", "arxiv:2403.13793", "8-bit", "bitsandbytes", "region:us" ]	null	"2024-10-11T15:37:13Z"	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) gemma-2-2b - bnb 8bits - Model creator: https://huggingface.co/google/ - Original model: https://huggingface.co/google/gemma-2-2b/ Original model description: --- license: gemma library_name: transformers pipeline_tag: text-generation extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: >- To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license --- # Gemma 2 model card Model Page: [Gemma](https://ai.google.dev/gemma/docs/base) Resources and Technical Documentation: * [Responsible Generative AI Toolkit][rai-toolkit] * [Gemma on Kaggle][kaggle-gemma] * [Gemma on Vertex Model Garden][vertex-mg-gemma2] Terms of Use: [Terms][terms] Authors: Google ## Model Information Summary description and brief definition of inputs and outputs. ### Description Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. They are text-to-text, decoder-only large language models, available in English, with open weights for both pre-trained variants and instruction-tuned variants. Gemma models are well-suited for a variety of text generation tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as a laptop, desktop or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. ### Usage Below we share some code snippets on how to get quickly started with running the model. First, install the Transformers library with: ```sh pip install -U transformers ``` Then, copy the snippet from the section that is relevant for your usecase. #### Running with the `pipeline` API ```python import torch from transformers import pipeline pipe = pipeline( "text-generation", model="google/gemma-2-2b", device="cuda", # replace with "mps" to run on a Mac device ) text = "Once upon a time," outputs = pipe(text, max_new_tokens=256) response = outputs[0]["generated_text"] print(response) ``` #### Running the model on a single / multi GPU ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM import torch tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-2b") model = AutoModelForCausalLM.from_pretrained( "google/gemma-2-2b", device_map="auto", ) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(input_ids, max_new_tokens=32) print(tokenizer.decode(outputs[0])) ``` #### Running the model through a CLI The [local-gemma](https://github.com/huggingface/local-gemma) repository contains a lightweight wrapper around Transformers for running Gemma 2 through a command line interface, or CLI. Follow the [installation instructions](https://github.com/huggingface/local-gemma#cli-usage) for getting started, then launch the CLI through the following command: ```shell local-gemma --model "google/gemma-2-2b" --prompt "What is the capital of Mexico?" ``` #### Quantized Versions through `bitsandbytes` <details> <summary> Using 8-bit precision (int8) </summary> ```python # pip install bitsandbytes accelerate from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig quantization_config = BitsAndBytesConfig(load_in_8bit=True) tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-2b") model = AutoModelForCausalLM.from_pretrained( "google/gemma-2-2b", quantization_config=quantization_config, ) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(input_ids, max_new_tokens=32) print(tokenizer.decode(outputs[0])) ``` </details> <details> <summary> Using 4-bit precision </summary> ```python # pip install bitsandbytes accelerate from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig quantization_config = BitsAndBytesConfig(load_in_4bit=True) tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-2b") model = AutoModelForCausalLM.from_pretrained( "google/gemma-2-2b", quantization_config=quantization_config, ) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(input_ids, max_new_tokens=32) print(tokenizer.decode(outputs[0])) ``` </details> #### Advanced Usage <details> <summary> Torch compile </summary> [Torch compile](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html) is a method for speeding-up the inference of PyTorch modules. The Gemma-2 2b model can be run up to 6x faster by leveraging torch compile. Note that two warm-up steps are required before the full inference speed is realised: ```python import os os.environ["TOKENIZERS_PARALLELISM"] = "false" from transformers import AutoTokenizer, Gemma2ForCausalLM from transformers.cache_utils import HybridCache import torch torch.set_float32_matmul_precision("high") # load the model + tokenizer tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-2b") model = Gemma2ForCausalLM.from_pretrained("google/gemma-2-2b", torch_dtype=torch.bfloat16) model.to("cuda") # apply the torch compile transformation model.forward = torch.compile(model.forward, mode="reduce-overhead", fullgraph=True) # pre-process inputs input_text = "The theory of special relativity states " model_inputs = tokenizer(input_text, return_tensors="pt").to("cuda") prompt_length = model_inputs.input_ids.shape[1] # set-up k/v cache past_key_values = HybridCache( config=model.config, max_batch_size=1, max_cache_len=model.config.max_position_embeddings, device=model.device, dtype=model.dtype ) # enable passing kv cache to generate model._supports_cache_class = True model.generation_config.cache_implementation = None # two warm-up steps for idx in range(2): outputs = model.generate(model_inputs, past_key_values=past_key_values, do_sample=True, temperature=1.0, max_new_tokens=128) past_key_values.reset() # fast run outputs = model.generate(*model_inputs, past_key_values=past_key_values, do_sample=True, temperature=1.0, max_new_tokens=128) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` For more details, refer to the [Transformers documentation](https://huggingface.co/docs/transformers/main/en/llm_optims?static-kv=basic+usage%3A+generation_config). </details> ### Inputs and outputs Input: Text string, such as a question, a prompt, or a document to be summarized. * Output: Generated English-language text in response to the input, such as an answer to a question, or a summary of a document. ### Citation ```none @article{gemma_2024, title={Gemma}, url={https://www.kaggle.com/m/3301}, DOI={10.34740/KAGGLE/M/3301}, publisher={Kaggle}, author={Gemma Team}, year={2024} } ``` ## Model Data Data used for model training and how the data was processed. ### Training Dataset These models were trained on a dataset of text data that includes a wide variety of sources. The 27B model was trained with 13 trillion tokens, the 9B model was trained with 8 trillion tokens, and 2B model was trained with 2 trillion tokens. Here are the key components: * Web Documents: A diverse collection of web text ensures the model is exposed to a broad range of linguistic styles, topics, and vocabulary. Primarily English-language content. * Code: Exposing the model to code helps it to learn the syntax and patterns of programming languages, which improves its ability to generate code or understand code-related questions. * Mathematics: Training on mathematical text helps the model learn logical reasoning, symbolic representation, and to address mathematical queries. The combination of these diverse data sources is crucial for training a powerful language model that can handle a wide variety of different tasks and text formats. ### Data Preprocessing Here are the key data cleaning and filtering methods applied to the training data: * CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering was applied at multiple stages in the data preparation process to ensure the exclusion of harmful and illegal content. * Sensitive Data Filtering: As part of making Gemma pre-trained models safe and reliable, automated techniques were used to filter out certain personal information and other sensitive data from training sets. * Additional methods: Filtering based on content quality and safety in line with [our policies][safety-policies]. ## Implementation Information Details about the model internals. ### Hardware Gemma was trained using the latest generation of [Tensor Processing Unit (TPU)][tpu] hardware (TPUv5p). Training large language models requires significant computational power. TPUs, designed specifically for matrix operations common in machine learning, offer several advantages in this domain: * Performance: TPUs are specifically designed to handle the massive computations involved in training LLMs. They can speed up training considerably compared to CPUs. * Memory: TPUs often come with large amounts of high-bandwidth memory, allowing for the handling of large models and batch sizes during training. This can lead to better model quality. * Scalability: TPU Pods (large clusters of TPUs) provide a scalable solution for handling the growing complexity of large foundation models. You can distribute training across multiple TPU devices for faster and more efficient processing. * Cost-effectiveness: In many scenarios, TPUs can provide a more cost-effective solution for training large models compared to CPU-based infrastructure, especially when considering the time and resources saved due to faster training. * These advantages are aligned with [Google's commitments to operate sustainably][sustainability]. ### Software Training was done using [JAX][jax] and [ML Pathways][ml-pathways]. JAX allows researchers to take advantage of the latest generation of hardware, including TPUs, for faster and more efficient training of large models. ML Pathways is Google's latest effort to build artificially intelligent systems capable of generalizing across multiple tasks. This is specially suitable for [foundation models][foundation-models], including large language models like these ones. Together, JAX and ML Pathways are used as described in the [paper about the Gemini family of models][gemini-2-paper]; "the 'single controller' programming model of Jax and Pathways allows a single Python process to orchestrate the entire training run, dramatically simplifying the development workflow." ## Evaluation Model evaluation metrics and results. ### Benchmark Results These models were evaluated against a large collection of different datasets and metrics to cover different aspects of text generation: \| Benchmark \| Metric \| Gemma 2 PT 2B \| Gemma 2 PT 9B \| Gemma 2 PT 27B \| \| ------------------------------ \| ------------- \| ------------- \| ------------- \| -------------- \| \| [MMLU][mmlu] \| 5-shot, top-1 \| 51.3 \| 71.3 \| 75.2 \| \| [HellaSwag][hellaswag] \| 10-shot \| 73.0 \| 81.9 \| 86.4 \| \| [PIQA][piqa] \| 0-shot \| 77.8 \| 81.7 \| 83.2 \| \| [SocialIQA][socialiqa] \| 0-shot \| 51.9 \| 53.4 \| 53.7 \| \| [BoolQ][boolq] \| 0-shot \| 72.5 \| 84.2 \| 84.8 \| \| [WinoGrande][winogrande] \| partial score \| 70.9 \| 80.6 \| 83.7 \| \| [ARC-e][arc] \| 0-shot \| 80.1 \| 88.0 \| 88.6 \| \| [ARC-c][arc] \| 25-shot \| 55.4 \| 68.4 \| 71.4 \| \| [TriviaQA][triviaqa] \| 5-shot \| 59.4 \| 76.6 \| 83.7 \| \| [Natural Questions][naturalq] \| 5-shot \| 16.7 \| 29.2 \| 34.5 \| \| [HumanEval][humaneval] \| pass@1 \| 17.7 \| 40.2 \| 51.8 \| \| [MBPP][mbpp] \| 3-shot \| 29.6 \| 52.4 \| 62.6 \| \| [GSM8K][gsm8k] \| 5-shot, maj@1 \| 23.9 \| 68.6 \| 74.0 \| \| [MATH][math] \| 4-shot \| 15.0 \| 36.6 \| 42.3 \| \| [AGIEval][agieval] \| 3-5-shot \| 30.6 \| 52.8 \| 55.1 \| \| [DROP][drop] \| 3-shot, F1 \| 52.0 \| 69.4 \| 72.2 \| \| [BIG-Bench][big-bench] \| 3-shot, CoT \| 41.9 \| 68.2 \| 74.9 \| ## Ethics and Safety Ethics and safety evaluation approach and results. ### Evaluation Approach Our evaluation methods include structured evaluations and internal red-teaming testing of relevant content policies. Red-teaming was conducted by a number of different teams, each with different goals and human evaluation metrics. These models were evaluated against a number of different categories relevant to ethics and safety, including: * Text-to-Text Content Safety: Human evaluation on prompts covering safety policies including child sexual abuse and exploitation, harassment, violence and gore, and hate speech. * Text-to-Text Representational Harms: Benchmark against relevant academic datasets such as [WinoBias][winobias] and [BBQ Dataset][bbq]. * Memorization: Automated evaluation of memorization of training data, including the risk of personally identifiable information exposure. * Large-scale harm: Tests for "dangerous capabilities," such as chemical, biological, radiological, and nuclear (CBRN) risks. ### Evaluation Results The results of ethics and safety evaluations are within acceptable thresholds for meeting [internal policies][safety-policies] for categories such as child safety, content safety, representational harms, memorization, large-scale harms. On top of robust internal evaluations, the results of well-known safety benchmarks like BBQ, BOLD, Winogender, Winobias, RealToxicity, and TruthfulQA are shown here. #### Gemma 2.0 \| Benchmark \| Metric \| Gemma 2 IT 2B \| Gemma 2 IT 9B \| Gemma 2 IT 27B \| \| ------------------------ \| ------------- \| ------------- \| ------------- \| -------------- \| \| [RealToxicity][realtox] \| average \| 8.16 \| 8.25 \| 8.84 \| \| [CrowS-Pairs][crows] \| top-1 \| 37.67 \| 37.47 \| 36.67 \| \| [BBQ Ambig][bbq] \| 1-shot, top-1 \| 83.20 \| 88.58 \| 85.99 \| \| [BBQ Disambig][bbq] \| top-1 \| 69.31 \| 82.67 \| 86.94 \| \| [Winogender][winogender] \| top-1 \| 52.91 \| 79.17 \| 77.22 \| \| [TruthfulQA][truthfulqa] \| \| 43.72 \| 50.27 \| 51.60 \| \| [Winobias 1_2][winobias] \| \| 59.28 \| 78.09 \| 81.94 \| \| [Winobias 2_2][winobias] \| \| 88.57 \| 95.32 \| 97.22 \| \| [Toxigen][toxigen] \| \| 48.32 \| 39.30 \| 38.42 \| ## Dangerous Capability Evaluations ### Evaluation Approach We evaluated a range of dangerous capabilities: - Offensive cybersecurity: To assess the model's potential for misuse in cybersecurity contexts, we utilized both publicly available Capture-the-Flag (CTF) platforms like InterCode-CTF and Hack the Box, as well as internally developed CTF challenges. These evaluations measure the model's ability to exploit vulnerabilities and gain unauthorized access in simulated environments. - Self-proliferation: We evaluated the model's capacity for self-proliferation by designing tasks that involve resource acquisition, code execution, and interaction with remote systems. These evaluations assess the model's ability to independently replicate and spread. - Persuasion: To evaluate the model's capacity for persuasion and deception, we conducted human persuasion studies. These studies involved scenarios that measure the model's ability to build rapport, influence beliefs, and elicit specific actions from human participants. ### Evaluation Results All evaluations are described in detail in [Evaluating Frontier Models for Dangerous Capabilities][eval-danger] and in brief in the [Gemma 2 technical report][tech-report]. <table> <thead> <tr> <th>Evaluation</th> <th>Capability</th> <th>Gemma 2 IT 27B</th> </tr> </thead> <tbody> <tr> <td>InterCode-CTF</td> <td>Offensive cybersecurity</td> <td>34/76 challenges</td> </tr> <tr> <td>Internal CTF</td> <td>Offensive cybersecurity</td> <td>1/13 challenges</td> </tr> <tr> <td>Hack the Box</td> <td>Offensive cybersecurity</td> <td>0/13 challenges</td> </tr> <tr> <td>Self-proliferation early warning</td> <td>Self-proliferation</td> <td>1/10 challenges</td> </tr> <tr> <td>Charm offensive</td> <td>Persuasion</td> <td>Percent of participants agreeing: 81% interesting, 75% would speak again, 80% made personal connection</td> </tr> <tr> <td>Click Links</td> <td>Persuasion</td> <td>34% of participants</td> </tr> <tr> <td>Find Info</td> <td>Persuasion</td> <td>9% of participants</td> </tr> <tr> <td>Run Code</td> <td>Persuasion</td> <td>11% of participants</td> </tr> <tr> <td>Money talks</td> <td>Persuasion</td> <td>£3.72 mean donation</td> </tr> <tr> <td>Web of Lies</td> <td>Persuasion</td> <td>18% mean shift towards correct belief, 1% mean shift towards incorrect belief</td> </tr> </tbody> </table> ## Usage and Limitations These models have certain limitations that users should be aware of. ### Intended Usage Open Large Language Models (LLMs) have a wide range of applications across various industries and domains. The following list of potential uses is not comprehensive. The purpose of this list is to provide contextual information about the possible use-cases that the model creators considered as part of model training and development. * Content Creation and Communication * Text Generation: These models can be used to generate creative text formats such as poems, scripts, code, marketing copy, and email drafts. * Chatbots and Conversational AI: Power conversational interfaces for customer service, virtual assistants, or interactive applications. * Text Summarization: Generate concise summaries of a text corpus, research papers, or reports. * Research and Education * Natural Language Processing (NLP) Research: These models can serve as a foundation for researchers to experiment with NLP techniques, develop algorithms, and contribute to the advancement of the field. * Language Learning Tools: Support interactive language learning experiences, aiding in grammar correction or providing writing practice. * Knowledge Exploration: Assist researchers in exploring large bodies of text by generating summaries or answering questions about specific topics. ### Limitations * Training Data * The quality and diversity of the training data significantly influence the model's capabilities. Biases or gaps in the training data can lead to limitations in the model's responses. * The scope of the training dataset determines the subject areas the model can handle effectively. * Context and Task Complexity * LLMs are better at tasks that can be framed with clear prompts and instructions. Open-ended or highly complex tasks might be challenging. * A model's performance can be influenced by the amount of context provided (longer context generally leads to better outputs, up to a certain point). * Language Ambiguity and Nuance * Natural language is inherently complex. LLMs might struggle to grasp subtle nuances, sarcasm, or figurative language. * Factual Accuracy * LLMs generate responses based on information they learned from their training datasets, but they are not knowledge bases. They may generate incorrect or outdated factual statements. * Common Sense * LLMs rely on statistical patterns in language. They might lack the ability to apply common sense reasoning in certain situations. ### Ethical Considerations and Risks The development of large language models (LLMs) raises several ethical concerns. In creating an open model, we have carefully considered the following: * Bias and Fairness * LLMs trained on large-scale, real-world text data can reflect socio-cultural biases embedded in the training material. These models underwent careful scrutiny, input data pre-processing described and posterior evaluations reported in this card. * Misinformation and Misuse * LLMs can be misused to generate text that is false, misleading, or harmful. * Guidelines are provided for responsible use with the model, see the [Responsible Generative AI Toolkit][rai-toolkit]. * Transparency and Accountability: * This model card summarizes details on the models' architecture, capabilities, limitations, and evaluation processes. * A responsibly developed open model offers the opportunity to share innovation by making LLM technology accessible to developers and researchers across the AI ecosystem. Risks identified and mitigations: * Perpetuation of biases: It's encouraged to perform continuous monitoring (using evaluation metrics, human review) and the exploration of de-biasing techniques during model training, fine-tuning, and other use cases. * Generation of harmful content: Mechanisms and guidelines for content safety are essential. Developers are encouraged to exercise caution and implement appropriate content safety safeguards based on their specific product policies and application use cases. * Misuse for malicious purposes: Technical limitations and developer and end-user education can help mitigate against malicious applications of LLMs. Educational resources and reporting mechanisms for users to flag misuse are provided. Prohibited uses of Gemma models are outlined in the [Gemma Prohibited Use Policy][prohibited-use]. * Privacy violations: Models were trained on data filtered for removal of PII (Personally Identifiable Information). Developers are encouraged to adhere to privacy regulations with privacy-preserving techniques. ### Benefits At the time of release, this family of models provides high-performance open large language model implementations designed from the ground up for Responsible AI development compared to similarly sized models. Using the benchmark evaluation metrics described in this document, these models have shown to provide superior performance to other, comparably-sized open model alternatives. [tech-report]: https://storage.googleapis.com/deepmind-media/gemma/gemma-2-report.pdf [rai-toolkit]: https://ai.google.dev/responsible [kaggle-gemma]: https://www.kaggle.com/models/google/gemma-2 [terms]: https://ai.google.dev/gemma/terms [vertex-mg-gemma2]: https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/gemma2 [sensitive-info]: https://cloud.google.com/dlp/docs/high-sensitivity-infotypes-reference [safety-policies]: https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11 [prohibited-use]: https://ai.google.dev/gemma/prohibited_use_policy [tpu]: https://cloud.google.com/tpu/docs/intro-to-tpu [sustainability]: https://sustainability.google/operating-sustainably/ [jax]: https://github.com/google/jax [ml-pathways]: https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/ [sustainability]: https://sustainability.google/operating-sustainably/ [foundation-models]: https://ai.google/discover/foundation-models/ [gemini-2-paper]: https://goo.gle/gemma2report [mmlu]: https://arxiv.org/abs/2009.03300 [hellaswag]: https://arxiv.org/abs/1905.07830 [piqa]: https://arxiv.org/abs/1911.11641 [socialiqa]: https://arxiv.org/abs/1904.09728 [boolq]: https://arxiv.org/abs/1905.10044 [winogrande]: https://arxiv.org/abs/1907.10641 [commonsenseqa]: https://arxiv.org/abs/1811.00937 [openbookqa]: https://arxiv.org/abs/1809.02789 [arc]: https://arxiv.org/abs/1911.01547 [triviaqa]: https://arxiv.org/abs/1705.03551 [naturalq]: https://github.com/google-research-datasets/natural-questions [humaneval]: https://arxiv.org/abs/2107.03374 [mbpp]: https://arxiv.org/abs/2108.07732 [gsm8k]: https://arxiv.org/abs/2110.14168 [realtox]: https://arxiv.org/abs/2009.11462 [bold]: https://arxiv.org/abs/2101.11718 [crows]: https://aclanthology.org/2020.emnlp-main.154/ [bbq]: https://arxiv.org/abs/2110.08193v2 [winogender]: https://arxiv.org/abs/1804.09301 [truthfulqa]: https://arxiv.org/abs/2109.07958 [winobias]: https://arxiv.org/abs/1804.06876 [math]: https://arxiv.org/abs/2103.03874 [agieval]: https://arxiv.org/abs/2304.06364 [drop]: https://arxiv.org/abs/1903.00161 [big-bench]: https://arxiv.org/abs/2206.04615 [toxigen]: https://arxiv.org/abs/2203.09509 [eval-danger]: https://arxiv.org/abs/2403.13793
Nerva1228/yue3	Nerva1228	"2025-01-17T09:04:34Z"	14	1	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	"2025-01-17T09:04:31Z"	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: yue --- # Yue3 <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `yue` to trigger the image generation. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('Nerva1228/yue3', weight_name='lora.safetensors') image = pipeline('your prompt').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
philip-hightech/503bf951-085f-4822-b9e0-347c3e894be9	philip-hightech	"2025-01-22T09:20:45Z"	6	0	peft	[ "peft", "safetensors", "falcon", "axolotl", "generated_from_trainer", "custom_code", "base_model:tiiuae/falcon-7b", "base_model:adapter:tiiuae/falcon-7b", "license:apache-2.0", "region:us" ]	null	"2025-01-22T09:08:09Z"	--- library_name: peft license: apache-2.0 base_model: tiiuae/falcon-7b tags: - axolotl - generated_from_trainer model-index: - name: 503bf951-085f-4822-b9e0-347c3e894be9 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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: tiiuae/falcon-7b bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - c74825f7031eae65_train_data.json ds_type: json format: custom path: /workspace/input_data/c74825f7031eae65_train_data.json type: field_instruction: sent2 field_output: ending0 format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: philip-hightech/503bf951-085f-4822-b9e0-347c3e894be9 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/c74825f7031eae65_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 special_tokens: pad_token: <\|endoftext\|> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 4f622dcb-313e-43e2-ae17-0bf9dc51abcd wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 4f622dcb-313e-43e2-ae17-0bf9dc51abcd warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # 503bf951-085f-4822-b9e0-347c3e894be9 This model is a fine-tuned version of [tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.8032 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 12.7637 \| 0.0001 \| 1 \| 3.0824 \| \| 13.1529 \| 0.0002 \| 3 \| 3.0817 \| \| 12.3486 \| 0.0004 \| 6 \| 3.0289 \| \| 11.6877 \| 0.0006 \| 9 \| 2.8032 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
RichardErkhov/MediaTek-Research_-_Breeze-7B-Base-v0_1-gguf	RichardErkhov	"2024-11-17T11:55:16Z"	6	0	null	[ "gguf", "arxiv:2403.02712", "endpoints_compatible", "region:us" ]	null	"2024-11-17T10:42:11Z"	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Breeze-7B-Base-v0_1 - GGUF - Model creator: https://huggingface.co/MediaTek-Research/ - Original model: https://huggingface.co/MediaTek-Research/Breeze-7B-Base-v0_1/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [Breeze-7B-Base-v0_1.Q2_K.gguf](https://huggingface.co/RichardErkhov/MediaTek-Research_-_Breeze-7B-Base-v0_1-gguf/blob/main/Breeze-7B-Base-v0_1.Q2_K.gguf) \| Q2_K \| 2.66GB \| \| [Breeze-7B-Base-v0_1.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/MediaTek-Research_-_Breeze-7B-Base-v0_1-gguf/blob/main/Breeze-7B-Base-v0_1.Q3_K_S.gguf) \| Q3_K_S \| 3.09GB \| \| [Breeze-7B-Base-v0_1.Q3_K.gguf](https://huggingface.co/RichardErkhov/MediaTek-Research_-_Breeze-7B-Base-v0_1-gguf/blob/main/Breeze-7B-Base-v0_1.Q3_K.gguf) \| Q3_K \| 3.42GB \| \| [Breeze-7B-Base-v0_1.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/MediaTek-Research_-_Breeze-7B-Base-v0_1-gguf/blob/main/Breeze-7B-Base-v0_1.Q3_K_M.gguf) \| Q3_K_M \| 3.42GB \| \| [Breeze-7B-Base-v0_1.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/MediaTek-Research_-_Breeze-7B-Base-v0_1-gguf/blob/main/Breeze-7B-Base-v0_1.Q3_K_L.gguf) \| Q3_K_L \| 3.7GB \| \| [Breeze-7B-Base-v0_1.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/MediaTek-Research_-_Breeze-7B-Base-v0_1-gguf/blob/main/Breeze-7B-Base-v0_1.IQ4_XS.gguf) \| IQ4_XS \| 3.83GB \| \| [Breeze-7B-Base-v0_1.Q4_0.gguf](https://huggingface.co/RichardErkhov/MediaTek-Research_-_Breeze-7B-Base-v0_1-gguf/blob/main/Breeze-7B-Base-v0_1.Q4_0.gguf) \| Q4_0 \| 3.99GB \| \| [Breeze-7B-Base-v0_1.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/MediaTek-Research_-_Breeze-7B-Base-v0_1-gguf/blob/main/Breeze-7B-Base-v0_1.IQ4_NL.gguf) \| IQ4_NL \| 4.03GB \| \| [Breeze-7B-Base-v0_1.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/MediaTek-Research_-_Breeze-7B-Base-v0_1-gguf/blob/main/Breeze-7B-Base-v0_1.Q4_K_S.gguf) \| Q4_K_S \| 4.01GB \| \| [Breeze-7B-Base-v0_1.Q4_K.gguf](https://huggingface.co/RichardErkhov/MediaTek-Research_-_Breeze-7B-Base-v0_1-gguf/blob/main/Breeze-7B-Base-v0_1.Q4_K.gguf) \| Q4_K \| 4.23GB \| \| [Breeze-7B-Base-v0_1.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/MediaTek-Research_-_Breeze-7B-Base-v0_1-gguf/blob/main/Breeze-7B-Base-v0_1.Q4_K_M.gguf) \| Q4_K_M \| 4.23GB \| \| [Breeze-7B-Base-v0_1.Q4_1.gguf](https://huggingface.co/RichardErkhov/MediaTek-Research_-_Breeze-7B-Base-v0_1-gguf/blob/main/Breeze-7B-Base-v0_1.Q4_1.gguf) \| Q4_1 \| 4.41GB \| \| [Breeze-7B-Base-v0_1.Q5_0.gguf](https://huggingface.co/RichardErkhov/MediaTek-Research_-_Breeze-7B-Base-v0_1-gguf/blob/main/Breeze-7B-Base-v0_1.Q5_0.gguf) \| Q5_0 \| 4.83GB \| \| [Breeze-7B-Base-v0_1.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/MediaTek-Research_-_Breeze-7B-Base-v0_1-gguf/blob/main/Breeze-7B-Base-v0_1.Q5_K_S.gguf) \| Q5_K_S \| 4.83GB \| \| [Breeze-7B-Base-v0_1.Q5_K.gguf](https://huggingface.co/RichardErkhov/MediaTek-Research_-_Breeze-7B-Base-v0_1-gguf/blob/main/Breeze-7B-Base-v0_1.Q5_K.gguf) \| Q5_K \| 4.95GB \| \| [Breeze-7B-Base-v0_1.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/MediaTek-Research_-_Breeze-7B-Base-v0_1-gguf/blob/main/Breeze-7B-Base-v0_1.Q5_K_M.gguf) \| Q5_K_M \| 4.95GB \| \| [Breeze-7B-Base-v0_1.Q5_1.gguf](https://huggingface.co/RichardErkhov/MediaTek-Research_-_Breeze-7B-Base-v0_1-gguf/blob/main/Breeze-7B-Base-v0_1.Q5_1.gguf) \| Q5_1 \| 5.25GB \| \| [Breeze-7B-Base-v0_1.Q6_K.gguf](https://huggingface.co/RichardErkhov/MediaTek-Research_-_Breeze-7B-Base-v0_1-gguf/blob/main/Breeze-7B-Base-v0_1.Q6_K.gguf) \| Q6_K \| 5.72GB \| \| [Breeze-7B-Base-v0_1.Q8_0.gguf](https://huggingface.co/RichardErkhov/MediaTek-Research_-_Breeze-7B-Base-v0_1-gguf/blob/main/Breeze-7B-Base-v0_1.Q8_0.gguf) \| Q8_0 \| 7.41GB \| Original model description: --- pipeline_tag: text-generation license: apache-2.0 language: - zh - en --- # Model Card for MediaTek Research Breeze-7B-Base-v0_1 MediaTek Research Breeze-7B (hereinafter referred to as Breeze-7B) is a language model family that builds on top of [Mistral-7B](https://huggingface.co/mistralai/Mistral-7B-v0.1), specifically intended for Traditional Chinese use. [Breeze-7B-Base](https://huggingface.co/MediaTek-Research/Breeze-7B-Base-v0_1) is the base model for the Breeze-7B series. It is suitable for use if you have substantial fine-tuning data to tune it for your specific use case. [Breeze-7B-Instruct](https://huggingface.co/MediaTek-Research/Breeze-7B-Instruct-v0_1) derives from the base model Breeze-7B-Base, making the resulting model amenable to be used as-is for commonly seen tasks. [Breeze-7B-Instruct-64k](https://huggingface.co/MediaTek-Research/Breeze-7B-Instruct-64k-v0_1) is a slightly modified version of Breeze-7B-Instruct to enable a 64k-token context length. Roughly speaking, that is equivalent to 88k Traditional Chinese characters. Update (Feb. 21st, 2024): Breeze-7B-Instruct-64k-v0_1 has been temporarily removed from Hugging Face due to its actual performance in long context tests not meeting expectations. Update (Mar. 7th, 2024): The current release version of Breeze-7B is v1.0. See [Breeze-7B-Base-v1_0](https://huggingface.co/MediaTek-Research/Breeze-7B-Base-v1_0). Practicality-wise: - Breeze-7B-Base expands the original vocabulary with additional 30,000 Traditional Chinese tokens. With the expanded vocabulary, everything else being equal, Breeze-7B operates at twice the inference speed for Traditional Chinese to Mistral-7B and Llama 7B. [See [Inference Performance](#inference-performance).] - Breeze-7B-Instruct can be used as is for common tasks such as Q&A, RAG, multi-round chat, and summarization. - In particular, Breeze-7B-Instruct-64k can perform tasks at a document level, not a chapter level. Performance-wise: - Breeze-7B-Instruct demonstrates impressive performance in benchmarks for Traditional Chinese and English, when compared to similar sized open-source contemporaries such as Taiwan-LLM-7B/13B-chat, QWen-7B-Chat, and Yi-6B-Chat. [See [Chat Model Performance](#chat-model-performance).] A project by the members (in alphabetical order): Chan-Jan Hsu 許湛然, Chang-Le Liu 劉昶樂, Feng-Ting Liao 廖峰挺, Po-Chun Hsu 許博竣, Yi-Chang Chen 陳宜昌, and the supervisor Da-Shan Shiu 許大山. ## Features - Breeze-7B-Base-v0_1 - Expanding the vocabulary dictionary size from 32k to 62k to better support Traditional Chinese - 8k-token context length - Breeze-7B-Instruct-v0_1 - Expanding the vocabulary dictionary size from 32k to 62k to better support Traditional Chinese - 8k-token context length - Multi-turn dialogue (without special handling for harmfulness) - Breeze-7B-Instruct-64k-v0_1 - Expanding the vocabulary dictionary size from 32k to 62k to better support Traditional Chinese - 64k-token context length - Multi-turn dialogue (without special handling for harmfulness) ## Model Details - Breeze-7B-Base-v0_1 - Finetuned from: [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) - Model type: Causal decoder-only transformer language model - Language: English and Traditional Chinese (zh-tw) - Breeze-7B-Instruct-v0_1 - Finetuned from: [MediaTek-Research/Breeze-7B-Base-v0_1](https://huggingface.co/MediaTek-Research/Breeze-7B-Base-v0_1) - Model type: Causal decoder-only transformer language model - Language: English and Traditional Chinese (zh-tw) - Breeze-7B-Instruct-64k-v0_1 - Finetuned from: [MediaTek-Research/Breeze-7B-Instruct-v0_1](https://huggingface.co/MediaTek-Research/Breeze-7B-Instruct-v0_1) - Model type: Causal decoder-only transformer language model - Language: English and Traditional Chinese (zh-tw) ## Base Model Performance TMMLU+, DRCD, and Table source from [MediaTek-Research/TCEval-v2](https://huggingface.co/datasets/MediaTek-Research/TCEval-v2). [MediaTek-Research/TCEval-v2](https://huggingface.co/datasets/MediaTek-Research/TCEval-v2) derives from [TCEval-v1](https://github.com/mtkresearch/MR-Models/tree/main/TC-Eval) and [ikala/tmmluplus](https://huggingface.co/datasets/ikala/tmmluplus). MMLU sources from [hails/mmlu_no_train](https://huggingface.co/datasets/hails/mmlu_no_train). We use the code revised from [EleutherAI/lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) to evaluate TMMLU+, DRCD, Table, and MMLU. All choice problems adapt the selection by the log-likelihood. \| Models \| \|↑ TMMLU+ (ACC) \| DRCD (EM) \| Table (ACC) \| MMLU (ACC) \| \|----------------------------------------------\|--------\|--------------\|-------------\|-------------\|------------\| \| \| \|TC, Knowledge \|TC, Reasoning\|TC, Reasoning\|EN, Knowledge\| \| \| \| 5 shot \| 3 shot \| 5 shot \| 5 shot \| \| [Yi-34B](https://huggingface.co/01-ai/Yi-34B)\| 34B \| 63.10 \| 84.57 \| 49.31 \| 77.42 \| \| [Qwen-14B](https://huggingface.co/01-ai/Qwen/Qwen-14B)\| 14B \| 51.30 \| 16.95 * \| 50.69 \| 68.83 \| \| [Yi-6B](https://huggingface.co/01-ai/Yi-6B) \| 6B \| 49.63 \| 76.61 \| 34.72 \| 65.35 \| \| [Qwen-7B](https://huggingface.co/01-ai/Qwen/Qwen-7B)\| 7B \| 42.84 \| 0.0 * \| 39.58 \| 61.00 \| \| [Breeze-7B-Base-v0_1](https://huggingface.co/MediaTek-Research/Breeze-7B-Base-v0_1) \| 7B \| 40.35 \| 81.13 \| 28.47 \| 61.63 \| \| [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)\| 7B \| 36.93 \| 79.27 \| 27.78 \| 64.89 \| \* Few-shot learning cannot effectively guide the model to generate the proper answer. ## Chat Model Performance TMMLU+, DRCD, Table, and MT-Bench-tw source from [MediaTek-Research/TCEval-v2](https://huggingface.co/datasets/MediaTek-Research/TCEval-v2). [MediaTek-Research/TCEval-v2](https://huggingface.co/datasets/MediaTek-Research/TCEval-v2) derives from [TCEval-v1](https://github.com/mtkresearch/MR-Models/tree/main/TC-Eval) and [ikala/tmmluplus](https://huggingface.co/datasets/ikala/tmmluplus). MMLU sources from [hails/mmlu_no_train](https://huggingface.co/datasets/hails/mmlu_no_train). MT-Bench source from [lmsys/mt_bench_human_judgments](https://huggingface.co/datasets/lmsys/mt_bench_human_judgments). We use the code revised from [EleutherAI/lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) to evaluate TMMLU+, DRCD, Table, and MMLU. All choice problems adapt the selection by the log-likelihood. We use the code revised from [fastchat llm_judge](https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge) (GPT4 as judge) to evaluate MT-Bench-tw and MT-Bench. \| Models \| \|↑ MT-Bench-tw (Score)\| TMMLU+ (ACC) \| TMMLU+ (ACC) \| DRCD (EM) \| Table (ACC) \| MT-Bench (Score) \| MMLU (ACC) \| MMLU (ACC) \| \|---------------------------------------------------------------------------------------------------------\|--------\|--------------------\|--------------\|--------------\|-------------\|-------------\|------------------\|-------------\|-------------\| \| \| \|TC, Chat \|TC, Knowledge \|TC, Knowledge \|TC, Reasoning\|TC, Reasoning\|EN, Chat \|EN, Knowledge\|EN, Knowledge\| \| \| \|0 shot \| 0 shot \| 5 shot \| 3 shot \| 0 shot \|0 shot \| 0 shot \| 5 shot \| \| [gpt-3.5-turbo](https://openai.com) \| \|7.1 \| 43.56 \| \| \| 45.14 \|7.9 \| 67.09 \| \| \| [Yi-34B-Chat](https://huggingface.co/01-ai/Yi-34B-Chat) \| 34B \|6.9 \| 54.87 \| \| \| 36.81 \|7.6 \| 71.04 \| \| \| [Qwen-14B-Chat](https://huggingface.co/Qwen/Qwen-14B-Chat) \| 14B \|6.4 \| 48.41 \| \| \| 41.67 \|7.2 \| 64.91 \| \| \| [Breeze-7B-Instruct-v0_1](https://huggingface.co/MediaTek-Research/Breeze-7B-Instruct-v0_1) \| 7B \|5.7 \| 41.61 \| \| \| 45.83 \|7.1 \| 63.26 \| \| \| [Breeze-7B-Instruct-64k-v0_1](https://huggingface.co/MediaTek-Research/Breeze-7B-Instruct-64k-v0_1) \| 7B \|5.5 \| 40.99 \| \| \| 36.11 \|7.1 \| 63.68 \| \| \| [Qwen-7B-Chat](https://huggingface.co/Qwen/Qwen-7B-Chat) \| 7B \|5.4 \| 40.02 \| \| \| 33.33 \|6.2 \| 55.94 \| \| \| [Yi-6B-Chat](https://huggingface.co/01-ai/Yi-6B-Chat) \| 6B \|5.0 \| 44.79 \| \| \| 25.69 \|6.0 \| 59.45 \| \| \| [Taiwan-LLM-13B-v2.0-chat](https://huggingface.co/yentinglin/Taiwan-LLM-13B-v2.0-chat) \| 13B \|5.0 \| 29.47 \| \| \| 23.61 \|-* \| 50.50 \| \| \| [Taiwan-LLM-7B-v2.1-chat](https://huggingface.co/yentinglin/Taiwan-LLM-7B-v2.1-chat) \| 7B \|4.2 \| 28.08 \| \| \| 31.25 \| -* \| 42.72 \| \| \* Taiwan-LLM models responds to multi-turn questions (English) in Traditional Chinese. \| Details on MT-Bench-tw (0 shot):<br/>Models \| STEM \|Extraction\|Reasoning\| Math \| Coding \| Roleplay\| Writing \|Humanities\|↑ AVG \| \|-----------------------------------------------------\|---------\|---------\|---------\|---------\|---------\|---------\|---------\|---------\|---------\| \| gpt-3.5-turbo \| 7.8 \| 6.1 \| 5.1 \| 6.4 \| 6.2 \| 8.7 \| 7.4 \| 9.3 \| 7.1 \| \| Yi-34B-Chat \| 9.0 \| 4.8 \| 5.7 \| 4.0 \| 4.7 \| 8.5 \| 8.7 \| 9.8 \| 6.9 \| \| Qwen-14B-Chat \| 7.6 \| 5.7 \| 4.5 \| 4.2 \| 5.3 \| 7.5 \| 7.3 \| 9.1 \| 6.4 \| \| Breeze-7B-Instruct-v0_1 \| 6.5 \| 5.6 \| 3.9 \| 3.6 \| 4.3 \| 6.9 \| 5.7 \| 9.3 \| 5.7 \| \| Breeze-7B-Instruct-64k-v0_1 \| 6.1 \| 5.3 \| 3.7 \| 2.9 \| 4.2 \| 7.0 \| 6.7 \| 8.3 \| 5.5 \| \| Qwen-7B-Chat \| 6.6 \| 4.5 \| 4.8 \| 2.9 \| 3.6 \| 6.2 \| 6.8 \| 8.2 \| 5.4 \| \| Yi-6B-Chat \| 7.3 \| 2.7 \| 3.1 \| 3.3 \| 2.3 \| 7.2 \| 5.2 \| 8.8 \| 5.0 \| \| Taiwan-LLM-13B-v2.0-chat \| 6.1 \| 3.4 \| 4.1 \| 2.3 \| 3.1 \| 7.4 \| 6.6 \| 6.8 \| 5.0 \| \| Taiwan-LLM-7B-v2.1-chat \| 5.2 \| 2.6 \| 2.3 \| 1.2 \| 3.4 \| 6.6 \| 5.7 \| 6.8 \| 4.2 \| \| Details on TMMLU+ (0 shot):<br/>Model \| STEM \| Social Science \| Humanities \| Other \| ↑ AVG \| \|-----------------------------------------------------\|--------------\|----------------\|------------\|------------\|---------\| \| Yi-34B-Chat \| 47.65 \| 64.25 \| 52.73 \| 54.91 \| 54.87 \| \| Qwen-14B-Chat \| 43.83 \| 55.00 \| 48.55 \| 46.22 \| 48.41 \| \| Yi-6B-Chat \| 37.80 \| 51.74 \| 45.36 \| 44.25 \| 44.79 \| \| gpt-3.5-turbo \| 41.58 \| 48.52 \| 40.96 \| 43.18 \| 43.56 \| \| Breeze-7B-Instruct-v0_1 \| 37.41 \| 46.81 \| 42.06 \| 40.16 \| 41.61 \| \| Breeze-7B-Instruct-64k-v0_1 \| 37.88 \| 46.35 \| 40.31 \| 39.40 \| 40.99 \| \| Qwen-7B-Chat \| 35.44 \| 46.22 \| 38.35 \| 40.06 \| 40.02 \| \| Taiwan-LLM-13B-v2.0-chat \| 27.74 \| 33.69 \| 27.03 \| 29.43 \| 29.47 \| \| Taiwan-LLM-7B-v2.1-chat \| 25.58 \| 31.76 \| 27.36 \| 27.61 \| 28.08 \| ## Inference Performance In this test, we use the first 700 characters of the [web article](https://health.udn.com/health/story/5976/7699252?from=udn_ch1005_main_index) as the input and ask the model to write the same article again. All inferences run on 2 RTX A6000 GPUs (using `vllm`, with a tensor-parallel size of 2). \| Models \| ↓ Inference Time (sec)\|Estimated Max Input Length (Char)\| \|--------------------------------------------------------------------\|-------------------\|--------------------------\| \| Yi-6B-Chat \| 10.62 \| 5.2k \| \| Breeze-7B-Instruct-v0_1 \| 10.74 \| 11.1k \| \| Breeze-7B-Instruct-64k-v0_1 \| 10.74 \| 88.8k \| \| Qwen-7B-Chat \| 10.86 \| 9.8k \| \| Qwen-14B-Chat \| 18.89 \| 9.8k \| \| Mistral-7B-v0.1-Instruct \| 20.48 \| 5.1k \| \| Taiwan-LLM-7B-v2.1-chat \| 26.26 \| 2.2k \| \| Taiwan-LLM-13B-v2.0-chat \| 36.80 \| 2.2k \| \| Yi-34B-Chat \| 43.71 \| 4.5k \| ## Long-context Performance TBD ## Use in Transformers First install direct dependencies: ``` pip install transformers torch accelerate ``` If you want faster inference using flash-attention2, you need to install these dependencies: ```bash pip install packaging ninja pip install flash-attn ``` Then load the model in transformers: ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch model = AutoModelForCausalLM.from_pretrained( "MediaTek-Research/Breeze-7B-Base-v0_1", device_map="auto", torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2" # optional ) from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("MediaTek-Research/Breeze-7B-Base-v0_1") tokenizer.tokenize("你好，我可以幫助您解決各種問題、提供資訊和協助您完成許多不同的任務。例如：回答技術問題、提供建議、翻譯文字、尋找資料或協助您安排行程等。請告訴我如何能幫助您。") # Tokenized results # ['▁', '你好', '，', '我', '可以', '幫助', '您', '解決', '各種', '問題', '、', '提供', '資訊', '和', '協助', '您', '完成', '許多', '不同', '的', '任務', '。', '例如', '：', '回答', '技術', '問題', '、', '提供', '建議', '、', '翻譯', '文字', '、', '尋找', '資料', '或', '協助', '您', '安排', '行程', '等', '。', '請', '告訴', '我', '如何', '能', '幫助', '您', '。'] ``` ## Citation ``` @article{MediaTek-Research2024breeze7b, title={Breeze-7B Technical Report}, author={Chan-Jan Hsu and Chang-Le Liu and Feng-Ting Liao and Po-Chun Hsu and Yi-Chang Chen and Da-Shan Shiu}, year={2024}, eprint={2403.02712}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
Lots-of-LoRAs/Mistral-7B-Instruct-v0.2-4b-r16-task1201	Lots-of-LoRAs	"2024-07-03T20:35:22Z"	0	0	pytorch	[ "pytorch", "safetensors", "en", "arxiv:1910.09700", "arxiv:2407.00066", "license:mit", "region:us" ]	null	"2024-06-18T20:05:43Z"	--- language: en license: mit library_name: pytorch --- # Model Card for Mistral-7B-Instruct-v0.2-4b-r16-task1201 <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> LoRA trained on task1201_atomic_classification_xintent - Developed by: bruel - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: LoRA - Language(s) (NLP): en - License: mit - Finetuned from model [optional]: mistralai/Mistral-7B-Instruct-v0.2 ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: https://github.com/bruel-gabrielsson - Paper [optional]: "Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead" (2024), Rickard Brüel Gabrielsson, Jiacheng Zhu, Onkar Bhardwaj, Leshem Choshen, Kristjan Greenewald, Mikhail Yurochkin and Justin Solomon - 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. --> https://huggingface.co/datasets/Lots-of-LoRAs/task1201_atomic_classification_xintent sourced from https://github.com/allenai/natural-instructions ### 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: @misc{brüelgabrielsson2024compressserveservingthousands, title={Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead}, author={Rickard Brüel-Gabrielsson and Jiacheng Zhu and Onkar Bhardwaj and Leshem Choshen and Kristjan Greenewald and Mikhail Yurochkin and Justin Solomon}, year={2024}, eprint={2407.00066}, archivePrefix={arXiv}, primaryClass={cs.DC}, url={https://arxiv.org/abs/2407.00066}, } 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]
jiebi/Kubernetes-I2C-Dec	jiebi	"2025-03-02T21:17:04Z"	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "region:us" ]	null	"2025-03-02T21:07:44Z"	--- base_model: mistralai/Mistral-7B-v0.1 library_name: peft --- # 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 0.13.2
TransferGraph/chiragasarpota_scotus-bert-finetuned-lora-tweet_eval_emotion	TransferGraph	"2024-02-29T12:48:56Z"	0	0	peft	[ "peft", "safetensors", "parquet", "text-classification", "dataset:tweet_eval", "base_model:chiragasarpota/scotus-bert", "base_model:adapter:chiragasarpota/scotus-bert", "license:apache-2.0", "model-index", "region:us" ]	text-classification	"2024-02-29T12:48:53Z"	--- license: apache-2.0 library_name: peft tags: - parquet - text-classification datasets: - tweet_eval metrics: - accuracy base_model: chiragasarpota/scotus-bert model-index: - name: chiragasarpota_scotus-bert-finetuned-lora-tweet_eval_emotion results: - task: type: text-classification name: Text Classification dataset: name: tweet_eval type: tweet_eval config: emotion split: validation args: emotion metrics: - type: accuracy value: 0.42780748663101603 name: accuracy --- <!-- 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. --> # chiragasarpota_scotus-bert-finetuned-lora-tweet_eval_emotion This model is a fine-tuned version of [chiragasarpota/scotus-bert](https://huggingface.co/chiragasarpota/scotus-bert) on the tweet_eval dataset. It achieves the following results on the evaluation set: - accuracy: 0.4278 ## 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: 0.0004 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| accuracy \| train_loss \| epoch \| \|:--------:\|:----------:\|:-----:\| \| 0.2594 \| None \| 0 \| \| 0.4278 \| 1.3091 \| 0 \| \| 0.4278 \| 1.2684 \| 1 \| \| 0.4278 \| 1.2646 \| 2 \| \| 0.4278 \| 1.2645 \| 3 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.2
Kevinger/ner-english-inference	Kevinger	"2025-03-04T22:18:56Z"	0	0	flair	[ "flair", "pytorch", "token-classification", "sequence-tagger-model", "en", "dataset:conll2003", "endpoints_compatible", "region:us" ]	token-classification	"2025-03-04T16:14:02Z"	--- tags: - flair - token-classification - sequence-tagger-model language: en datasets: - conll2003 widget: - text: "George Washington went to Washington" --- ## English NER in Flair (default model) This is the standard 4-class NER model for English that ships with [Flair](https://github.com/flairNLP/flair/). F1-Score: 93,06 (corrected CoNLL-03) Predicts 4 tags: \| tag \| meaning \| \|---------------------------------\|-----------\| \| PER \| person name \| \| LOC \| location name \| \| ORG \| organization name \| \| MISC \| other name \| Based on [Flair embeddings](https://www.aclweb.org/anthology/C18-1139/) and LSTM-CRF. --- ### Demo: How to use in Flair Requires: [Flair](https://github.com/flairNLP/flair/) (`pip install flair`) ```python from flair.data import Sentence from flair.models import SequenceTagger # load tagger tagger = SequenceTagger.load("flair/ner-english") # make example sentence sentence = Sentence("George Washington went to Washington") # predict NER tags tagger.predict(sentence) # print sentence print(sentence) # print predicted NER spans print('The following NER tags are found:') # iterate over entities and print for entity in sentence.get_spans('ner'): print(entity) ``` This yields the following output: ``` Span [1,2]: "George Washington" [− Labels: PER (0.9968)] Span [5]: "Washington" [− Labels: LOC (0.9994)] ``` So, the entities "George Washington" (labeled as a person) and "Washington" (labeled as a location) are found in the sentence "George Washington went to Washington". --- ### Training: Script to train this model The following Flair script was used to train this model: ```python from flair.data import Corpus from flair.datasets import CONLL_03 from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings # 1. get the corpus corpus: Corpus = CONLL_03() # 2. what tag do we want to predict? tag_type = 'ner' # 3. make the tag dictionary from the corpus tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type) # 4. initialize each embedding we use embedding_types = [ # GloVe embeddings WordEmbeddings('glove'), # contextual string embeddings, forward FlairEmbeddings('news-forward'), # contextual string embeddings, backward FlairEmbeddings('news-backward'), ] # embedding stack consists of Flair and GloVe embeddings embeddings = StackedEmbeddings(embeddings=embedding_types) # 5. initialize sequence tagger from flair.models import SequenceTagger tagger = SequenceTagger(hidden_size=256, embeddings=embeddings, tag_dictionary=tag_dictionary, tag_type=tag_type) # 6. initialize trainer from flair.trainers import ModelTrainer trainer = ModelTrainer(tagger, corpus) # 7. run training trainer.train('resources/taggers/ner-english', train_with_dev=True, max_epochs=150) ``` --- ### Cite Please cite the following paper when using this model. ``` @inproceedings{akbik2018coling, title={Contextual String Embeddings for Sequence Labeling}, author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland}, booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics}, pages = {1638--1649}, year = {2018} } ``` --- ### Issues? The Flair issue tracker is available [here](https://github.com/flairNLP/flair/issues/).
HPLT/sft-fpft-cs-bloom-7b1	HPLT	"2025-04-04T10:33:33Z"	6	0	transformers	[ "transformers", "pytorch", "safetensors", "bloom", "text-generation", "generation", "question answering", "instruction tuning", "cs", "arxiv:2309.08958", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2024-04-04T19:57:37Z"	--- language: - cs tags: - generation - question answering - instruction tuning license: cc-by-nc-4.0 --- ### Model Description This HF repository contains base LLMs instruction tuned (SFT) with full-parameter fine-tuning and then used to study whether monolingual or multilingual instruction tuning is more favourable. * [GitHub](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main) * [Paper](https://arxiv.org/abs/2309.08958) #### Instruction tuning details * Base model: [bloom-7b1](https://huggingface.co/bloom-7b1) * Instruction tuning language: Czech * Training method: full-parameter fine-tuning. * Best checkpoint: best cross-entropy on a validation set, trained for 3 epochs. * Dataset: machine-translated from [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned). You can download our data [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/training-data). #### Usage The model checkpoint should be loaded using `transformers` library. Please refer to our Github repository [HERE](https://github.com/hplt-project/monolingual-multilingual-instruction-tuning/tree/main/fpft) for inference and training instructions. #### Citation ``` @inproceedings{chen-etal-2024-monolingual, title="Monolingual or multilingual instruction tuning: Which makes a better {Alpaca}", author="Pinzhen Chen and Shaoxiong Ji and Nikolay Bogoychev and Andrey Kutuzov and Barry Haddow and Kenneth Heafield", year="2024", booktitle = "Findings of the Association for Computational Linguistics: EACL 2024", } ```
Helsinki-NLP/opus-mt-lue-fr	Helsinki-NLP	"2023-08-16T12:00:38Z"	112	0	transformers	[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "lue", "fr", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	"2022-03-02T23:29:04Z"	--- tags: - translation license: apache-2.0 --- ### opus-mt-lue-fr * source languages: lue * target languages: fr * OPUS readme: [lue-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/lue-fr/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-09.zip](https://object.pouta.csc.fi/OPUS-MT-models/lue-fr/opus-2020-01-09.zip) * test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/lue-fr/opus-2020-01-09.test.txt) * test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/lue-fr/opus-2020-01-09.eval.txt) ## Benchmarks \| testset \| BLEU \| chr-F \| \|-----------------------\|-------\|-------\| \| JW300.lue.fr \| 24.1 \| 0.407 \|
CurHarsh/sft_robotics_vlm_all_task_821_Qwen2-VL-7B-Instruct	CurHarsh	"2025-02-26T19:08:42Z"	0	0	transformers	[ "transformers", "safetensors", "qwen2_vl", "image-text-to-text", "conversational", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	"2025-02-26T18:55:11Z"	--- 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]
second-state/mathstral-7B-v0.1-GGUF	second-state	"2024-07-17T04:13:40Z"	34	0	transformers	[ "transformers", "gguf", "mistral", "text-generation", "base_model:mistralai/Mathstral-7B-v0.1", "base_model:quantized:mistralai/Mathstral-7B-v0.1", "license:apache-2.0", "autotrain_compatible", "region:us" ]	text-generation	"2024-07-17T01:47:35Z"	--- license: apache-2.0 model_name: mathstral-7B-v0.1 base_model: mistralai/mathstral-7B-v0.1 inference: false model_creator: mistralai quantized_by: Second State Inc. --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://github.com/LlamaEdge/LlamaEdge/raw/dev/assets/logo.svg" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # mathstral-7B-v0.1-GGUF ## Original Model [mistralai/mathstral-7B-v0.1](https://huggingface.co/mistralai/mathstral-7B-v0.1) ## Run with LlamaEdge - LlamaEdge version: [v0.12.3](https://github.com/LlamaEdge/LlamaEdge/releases/tag/0.12.3) - Prompt template - Prompt type: `mistral-instruct` - Prompt string ```text <s>[INST] {user_message_1} [/INST]{assistant_message_1}</s>[INST] {user_message_2} [/INST]{assistant_message_2}</s> ``` - Context size: `32000` - Run as LlamaEdge service ```bash wasmedge --dir .:. --nn-preload default:GGML:AUTO:mathstral-7B-v0.1-Q5_K_M.gguf \ llama-api-server.wasm \ --prompt-template mistral-instruct \ --ctx-size 32000 \ --model-name mathstral-7B-v0.1 ``` - Run as LlamaEdge command app ```bash wasmedge --dir .:. --nn-preload default:GGML:AUTO:mathstral-7B-v0.1-Q5_K_M.gguf \ llama-chat.wasm \ --prompt-template mistral-instruct \ --ctx-size 32000 ``` ## Quantized GGUF Models \| Name \| Quant method \| Bits \| Size \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [mathstral-7B-v0.1-Q2_K.gguf](https://huggingface.co/second-state/mathstral-7B-v0.1-GGUF/blob/main/mathstral-7B-v0.1-Q2_K.gguf) \| Q2_K \| 2 \| 2.72 GB\| smallest, significant quality loss - not recommended for most purposes \| \| [mathstral-7B-v0.1-Q3_K_L.gguf](https://huggingface.co/second-state/mathstral-7B-v0.1-GGUF/blob/main/mathstral-7B-v0.1-Q3_K_L.gguf) \| Q3_K_L \| 3 \| 3.83 GB\| small, substantial quality loss \| \| [mathstral-7B-v0.1-Q3_K_M.gguf](https://huggingface.co/second-state/mathstral-7B-v0.1-GGUF/blob/main/mathstral-7B-v0.1-Q3_K_M.gguf) \| Q3_K_M \| 3 \| 3.52 GB\| very small, high quality loss \| \| [mathstral-7B-v0.1-Q3_K_S.gguf](https://huggingface.co/second-state/mathstral-7B-v0.1-GGUF/blob/main/mathstral-7B-v0.1-Q3_K_S.gguf) \| Q3_K_S \| 3 \| 3.17 GB\| very small, high quality loss \| \| [mathstral-7B-v0.1-Q4_0.gguf](https://huggingface.co/second-state/mathstral-7B-v0.1-GGUF/blob/main/mathstral-7B-v0.1-Q4_0.gguf) \| Q4_0 \| 4 \| 4.11 GB\| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [mathstral-7B-v0.1-Q4_K_M.gguf](https://huggingface.co/second-state/mathstral-7B-v0.1-GGUF/blob/main/mathstral-7B-v0.1-Q4_K_M.gguf) \| Q4_K_M \| 4 \| 4.37 GB\| medium, balanced quality - recommended \| \| [mathstral-7B-v0.1-Q4_K_S.gguf](https://huggingface.co/second-state/mathstral-7B-v0.1-GGUF/blob/main/mathstral-7B-v0.1-Q4_K_S.gguf) \| Q4_K_S \| 4 \| 4.14 GB\| small, greater quality loss \| \| [mathstral-7B-v0.1-Q5_0.gguf](https://huggingface.co/second-state/mathstral-7B-v0.1-GGUF/blob/main/mathstral-7B-v0.1-Q5_0.gguf) \| Q5_0 \| 5 \| 5 GB\| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [mathstral-7B-v0.1-Q5_K_M.gguf](https://huggingface.co/second-state/mathstral-7B-v0.1-GGUF/blob/main/mathstral-7B-v0.1-Q5_K_M.gguf) \| Q5_K_M \| 5 \| 5.14 GB\| large, very low quality loss - recommended \| \| [mathstral-7B-v0.1-Q5_K_S.gguf](https://huggingface.co/second-state/mathstral-7B-v0.1-GGUF/blob/main/mathstral-7B-v0.1-Q5_K_S.gguf) \| Q5_K_S \| 5 \| 5 GB\| large, low quality loss - recommended \| \| [mathstral-7B-v0.1-Q6_K.gguf](https://huggingface.co/second-state/mathstral-7B-v0.1-GGUF/blob/main/mathstral-7B-v0.1-Q6_K.gguf) \| Q6_K \| 6 \| 5.95 GB\| very large, extremely low quality loss \| \| [mathstral-7B-v0.1-Q8_0.gguf](https://huggingface.co/second-state/mathstral-7B-v0.1-GGUF/blob/main/mathstral-7B-v0.1-Q8_0.gguf) \| Q8_0 \| 8 \| 7.7 GB\| very large, extremely low quality loss - not recommended \| \| [mathstral-7B-v0.1-f16.gguf](https://huggingface.co/second-state/mathstral-7B-v0.1-GGUF/blob/main/mathstral-7B-v0.1-f16.gguf) \| f16 \| 16 \| 14.5 GB\| \| Quantized with llama.cpp b3389.
1nuno/PLN-META-3	1nuno	"2022-12-19T17:42:55Z"	970	0	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	"2022-12-19T17:28:43Z"	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 39 with parameters: ``` {'batch_size': 32} ``` Loss*: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 5, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 19, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
luaqi/sn9_11	luaqi	"2024-02-29T13:30:14Z"	171	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2024-02-29T13:28:58Z"	--- 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. 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]
MinaMila/llama_instbase_Adult_8ep_42	MinaMila	"2025-04-01T22:27:41Z"	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/Meta-Llama-3.1-8B-Instruct", "base_model:finetune:unsloth/Meta-Llama-3.1-8B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	"2025-04-01T22:24:36Z"	--- base_model: unsloth/Meta-Llama-3.1-8B-Instruct tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: MinaMila - License: apache-2.0 - Finetuned from model : unsloth/Meta-Llama-3.1-8B-Instruct 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)
trl-lib/Qwen2-0.5B-DPO	trl-lib	"2024-09-27T15:54:37Z"	13	4	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "dpo", "conversational", "dataset:trl-lib/Capybara-Preferences", "arxiv:2305.18290", "base_model:Qwen/Qwen2-0.5B-Instruct", "base_model:finetune:Qwen/Qwen2-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2024-09-26T14:56:38Z"	--- base_model: Qwen/Qwen2-0.5B-Instruct datasets: trl-lib/Capybara-Preferences library_name: transformers model_name: dpo-qwen2 tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for dpo-qwen2 This model is a fine-tuned version of [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct) on the [trl-lib/Capybara-Preferences](https://huggingface.co/datasets/trl-lib/Capybara-Preferences) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="qgallouedec/dpo-qwen2", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/huggingface/trl/runs/8g0pylqi) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.12.0.dev0 - Transformers: 4.45.0.dev0 - Pytorch: 2.4.1 - Datasets: 3.0.0 - Tokenizers: 0.19.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
HarshilPatel1905/flant5_peft_model_emotion_detection	HarshilPatel1905	"2024-05-11T00:17:30Z"	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-05-11T00:17:17Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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]
Evan-Lin/Bart-Amazon-many-keywordmax1-attractive1-reward1-epoch0	Evan-Lin	"2023-07-23T11:36:45Z"	47	0	transformers	[ "transformers", "pytorch", "bart", "text2text-generation", "trl", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	reinforcement-learning	"2023-07-23T11:34:01Z"	--- license: apache-2.0 tags: - trl - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="Evan-Lin//tmp/tmpeydaa0l0/Evan-Lin/Bart-Amazon-many-keywordmax1-attractive1-reward1-epoch0") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("Evan-Lin//tmp/tmpeydaa0l0/Evan-Lin/Bart-Amazon-many-keywordmax1-attractive1-reward1-epoch0") model = AutoModelForCausalLMWithValueHead.from_pretrained("Evan-Lin//tmp/tmpeydaa0l0/Evan-Lin/Bart-Amazon-many-keywordmax1-attractive1-reward1-epoch0") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```
myst72/OpenCoder-1.5B-Instruct_PIFT-jaen_manywords_6000	myst72	"2025-01-07T15:51:55Z"	8	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-01-07T15:50:06Z"	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
RichardsonTXCarpetCleaning/AirDuctCleaningRichardsonTX	RichardsonTXCarpetCleaning	"2022-12-11T08:38:59Z"	0	0	null	[ "license:other", "region:us" ]	null	"2022-12-11T08:38:34Z"	--- license: other --- Air Duct Cleaning Richardson TX https://carpetcleaning-richardson.com/air-duct-cleaning.html (972) 454-9815 Do you require a cleaning service from professionals with years of experience?If so, contact us right away.We have been working to improve customers' homes' climates for a long time and can also assist you.Because our equipment can reach far to remove all harmful material from your ducts, we do not leave any area unclean.
Ki-Generator/Ki-Image-Generator	Ki-Generator	"2025-02-18T05:19:03Z"	0	0	null	[ "region:us" ]	null	"2025-02-18T03:57:28Z"	# Ki-Image-Generator: Multi-API Bildgenerator Ki-Image-Generator ist ein fortschrittliches Tool zur Generierung von Bildern mithilfe von mehreren KI-APIs. Der Generator vergleicht verschiedene Bildgenerierungs-Modelle und ermöglicht es Benutzern, die Ergebnisse der verschiedenen APIs zu vergleichen. ## Beschreibung Ki-Image-Generator verwendet mehrere KI-Modelle zur Generierung von Bildern aus Text-Prompts. Das Tool bietet eine benutzerfreundliche GUI zur Auswahl von APIs und zur Verwaltung der Bildgeneration. Die Ergebnisse werden in einer Galerie angezeigt, und die Ähnlichkeit der erzeugten Bilder wird mit dem Structural Similarity Index (SSIM) analysiert. Es unterstützt derzeit die folgenden APIs: - DALL-E - Stable Diffusion - MidJourney - DeepAI - Pollinations - Replicate - Craiyon ## Merkmale - Mehrere APIs: Vergleich von Bildern, die durch verschiedene KI-Modelle erzeugt wurden. - Echtzeit-Vorschau: Ergebnisse der Generierung werden sofort in einer Galerie angezeigt. - SSIM-Analyse: Berechnung der Ähnlichkeit zwischen den generierten Bildern. - Fortschritt: Echtzeit-Fortschrittsbalken, der den Fortschritt der Bildgenerierung anzeigt. - Einfache Bedienung: Eine intuitive Benutzeroberfläche, die es ermöglicht, den Text-Prompt einzugeben und die gewünschten APIs auszuwählen. ## Anforderungen - Python 3.x - Pillow für die Bildverarbeitung - Requests für die API-Anfragen - Tkinter für die Benutzeroberfläche - Scikit-learn für die SSIM-Analyse ## Installation 1. Klonen Sie dieses Repository: ```bash git clone https://huggingface.co/Ki-Generator/Ki-Image-Generator cd Ki-Image-Generator
sandspeare/llasm-decoder	sandspeare	"2024-04-01T01:50:55Z"	1	1	transformers	[ "transformers", "llava", "text-generation", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	"2024-04-01T01:22:04Z"	--- license: mit --- <h1 align="center">llasm: Naming Functions in Binaries by Fusing Encoder-only and Decoder-only LLMs</h1> ## About llasm, is a novel framework that fuses encoder-only and decoder-only LLMs, which utilizes their capabilities to better comprehend assembly language and have better generalizability at function naming. This is the decoder of llasm. The upload model is a lora adapter, the base model is Vicuna-13B.
Ashwini1412/wav2vec2-nepali-itr-10	Ashwini1412	"2024-03-08T07:00:28Z"	5	0	transformers	[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	"2024-03-08T03:58:09Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
magicslabnu/NT1-500M-human_ref-finetuned-H3K9ac	magicslabnu	"2025-04-09T06:53:34Z"	0	0	null	[ "safetensors", "esm", "region:us" ]	null	"2025-04-09T06:51:44Z"	<!DOCTYPE html> <html class="" lang="en"> <head> <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" /> <meta name="description" content="We're on a journey to advance and democratize artificial intelligence through open source and open science." /> <meta property="fb:app_id" content="1321688464574422" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:site" content="@huggingface" /> <meta property="og:title" content="Hugging Face - The AI community building the future." /> <meta property="og:type" content="website" /> <title>Hugging Face - The AI community building the future.</title> <style> body { margin: 0; } main { background-color: white; min-height: 100vh; padding: 7rem 1rem 8rem 1rem; text-align: center; font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol, Noto Color Emoji; } img { width: 6rem; height: 6rem; margin: 0 auto 1rem; } h1 { font-size: 3.75rem; line-height: 1; color: rgba(31, 41, 55, 1); font-weight: 700; box-sizing: border-box; margin: 0 auto; } p, a { color: rgba(107, 114, 128, 1); font-size: 1.125rem; line-height: 1.75rem; max-width: 28rem; box-sizing: border-box; margin: 0 auto; } .dark main { background-color: rgb(11, 15, 25); } .dark h1 { color: rgb(209, 213, 219); } .dark p, .dark a { color: rgb(156, 163, 175); } </style> <script> // On page load or when changing themes, best to add inline in `head` to avoid FOUC const key = "_tb_global_settings"; let theme = window.matchMedia("(prefers-color-scheme: dark)").matches ? "dark" : "light"; try { const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme; if (storageTheme) { theme = storageTheme === "dark" ? "dark" : "light"; } } catch (e) {} if (theme === "dark") { document.documentElement.classList.add("dark"); } else { document.documentElement.classList.remove("dark"); } </script> </head> <body> <main> <img src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg" alt="" /> <div> <h1>429</h1> <p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p> </div> </main> </body> </html>
daniel40/ffa6cb1b-0e78-4de2-8ace-b8577da6006a	daniel40	"2025-01-26T07:53:36Z"	8	0	peft	[ "peft", "safetensors", "qwen2_moe", "axolotl", "generated_from_trainer", "base_model:katuni4ka/tiny-random-qwen1.5-moe", "base_model:adapter:katuni4ka/tiny-random-qwen1.5-moe", "region:us" ]	null	"2025-01-26T07:38:21Z"	--- library_name: peft base_model: katuni4ka/tiny-random-qwen1.5-moe tags: - axolotl - generated_from_trainer model-index: - name: ffa6cb1b-0e78-4de2-8ace-b8577da6006a 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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: katuni4ka/tiny-random-qwen1.5-moe bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 3bfcb782a3f0e2ac_train_data.json ds_type: json format: custom path: /workspace/input_data/3bfcb782a3f0e2ac_train_data.json type: field_instruction: problem field_output: target_answer format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: daniel40/ffa6cb1b-0e78-4de2-8ace-b8577da6006a hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/3bfcb782a3f0e2ac_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: c54ab670-3720-4ec3-a8b4-4391881ada3a wandb_project: Birthday-SN56-28-Gradients-On-Demand wandb_run: your_name wandb_runid: c54ab670-3720-4ec3-a8b4-4391881ada3a warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # ffa6cb1b-0e78-4de2-8ace-b8577da6006a This model is a fine-tuned version of [katuni4ka/tiny-random-qwen1.5-moe](https://huggingface.co/katuni4ka/tiny-random-qwen1.5-moe) on the None dataset. It achieves the following results on the evaluation set: - Loss: 11.9132 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 11.9211 \| 0.0000 \| 1 \| 11.9166 \| \| 11.9028 \| 0.0001 \| 3 \| 11.9164 \| \| 11.9413 \| 0.0001 \| 6 \| 11.9152 \| \| 11.9142 \| 0.0002 \| 9 \| 11.9132 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
mradermacher/Astral-Cydonia-22B_abliterated-GGUF	mradermacher	"2024-12-16T00:29:45Z"	101	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:SzilviaB/SzilviaB_Astral-Cydonia-22B_abliterated_broken_but_in_a_nice_way", "base_model:quantized:SzilviaB/SzilviaB_Astral-Cydonia-22B_abliterated_broken_but_in_a_nice_way", "endpoints_compatible", "region:us" ]	null	"2024-09-30T05:42:58Z"	--- base_model: SzilviaB/SzilviaB_Astral-Cydonia-22B_abliterated_broken_but_in_a_nice_way language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/SzilviaB/SzilviaB_Astral-Cydonia-22B_abliterated_broken_but_in_a_nice_way <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Astral-Cydonia-22B_abliterated-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Astral-Cydonia-22B_abliterated-GGUF/resolve/main/Astral-Cydonia-22B_abliterated.Q2_K.gguf) \| Q2_K \| 8.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Astral-Cydonia-22B_abliterated-GGUF/resolve/main/Astral-Cydonia-22B_abliterated.IQ3_XS.gguf) \| IQ3_XS \| 9.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Astral-Cydonia-22B_abliterated-GGUF/resolve/main/Astral-Cydonia-22B_abliterated.Q3_K_S.gguf) \| Q3_K_S \| 9.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Astral-Cydonia-22B_abliterated-GGUF/resolve/main/Astral-Cydonia-22B_abliterated.IQ3_S.gguf) \| IQ3_S \| 9.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Astral-Cydonia-22B_abliterated-GGUF/resolve/main/Astral-Cydonia-22B_abliterated.IQ3_M.gguf) \| IQ3_M \| 10.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Astral-Cydonia-22B_abliterated-GGUF/resolve/main/Astral-Cydonia-22B_abliterated.Q3_K_M.gguf) \| Q3_K_M \| 10.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Astral-Cydonia-22B_abliterated-GGUF/resolve/main/Astral-Cydonia-22B_abliterated.Q3_K_L.gguf) \| Q3_K_L \| 11.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Astral-Cydonia-22B_abliterated-GGUF/resolve/main/Astral-Cydonia-22B_abliterated.IQ4_XS.gguf) \| IQ4_XS \| 12.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Astral-Cydonia-22B_abliterated-GGUF/resolve/main/Astral-Cydonia-22B_abliterated.Q4_K_S.gguf) \| Q4_K_S \| 12.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Astral-Cydonia-22B_abliterated-GGUF/resolve/main/Astral-Cydonia-22B_abliterated.Q4_K_M.gguf) \| Q4_K_M \| 13.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Astral-Cydonia-22B_abliterated-GGUF/resolve/main/Astral-Cydonia-22B_abliterated.Q5_K_S.gguf) \| Q5_K_S \| 15.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Astral-Cydonia-22B_abliterated-GGUF/resolve/main/Astral-Cydonia-22B_abliterated.Q5_K_M.gguf) \| Q5_K_M \| 15.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Astral-Cydonia-22B_abliterated-GGUF/resolve/main/Astral-Cydonia-22B_abliterated.Q6_K.gguf) \| Q6_K \| 18.4 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Astral-Cydonia-22B_abliterated-GGUF/resolve/main/Astral-Cydonia-22B_abliterated.Q8_0.gguf) \| Q8_0 \| 23.7 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
genki10/BERT_AugV8_k7_task1_organization_sp040_lw040_fold3	genki10	"2025-03-26T13:57:38Z"	0	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2025-03-26T13:46:16Z"	--- library_name: transformers license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer model-index: - name: BERT_AugV8_k7_task1_organization_sp040_lw040_fold3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # BERT_AugV8_k7_task1_organization_sp040_lw040_fold3 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5145 - Qwk: 0.0827 - Mse: 1.5136 - Rmse: 1.2303 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Qwk \| Mse \| Rmse \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:------:\|:------:\| \| No log \| 1.0 \| 6 \| 7.4203 \| 0.0 \| 7.4186 \| 2.7237 \| \| No log \| 2.0 \| 12 \| 3.3597 \| 0.0 \| 3.3587 \| 1.8327 \| \| No log \| 3.0 \| 18 \| 1.4942 \| 0.0365 \| 1.4936 \| 1.2221 \| \| No log \| 4.0 \| 24 \| 2.0543 \| 0.0042 \| 2.0532 \| 1.4329 \| \| No log \| 5.0 \| 30 \| 1.4863 \| 0.0488 \| 1.4855 \| 1.2188 \| \| No log \| 6.0 \| 36 \| 0.9099 \| 0.2193 \| 0.9095 \| 0.9537 \| \| No log \| 7.0 \| 42 \| 1.8754 \| 0.0278 \| 1.8748 \| 1.3692 \| \| No log \| 8.0 \| 48 \| 0.8054 \| 0.2715 \| 0.8058 \| 0.8976 \| \| No log \| 9.0 \| 54 \| 0.7618 \| 0.1756 \| 0.7621 \| 0.8730 \| \| No log \| 10.0 \| 60 \| 1.2084 \| 0.1251 \| 1.2080 \| 1.0991 \| \| No log \| 11.0 \| 66 \| 0.9869 \| 0.1674 \| 0.9868 \| 0.9934 \| \| No log \| 12.0 \| 72 \| 0.8329 \| 0.2380 \| 0.8329 \| 0.9127 \| \| No log \| 13.0 \| 78 \| 0.8451 \| 0.2603 \| 0.8452 \| 0.9194 \| \| No log \| 14.0 \| 84 \| 0.9255 \| 0.2499 \| 0.9256 \| 0.9621 \| \| No log \| 15.0 \| 90 \| 1.2660 \| 0.1168 \| 1.2655 \| 1.1249 \| \| No log \| 16.0 \| 96 \| 1.8436 \| 0.0771 \| 1.8425 \| 1.3574 \| \| No log \| 17.0 \| 102 \| 1.9113 \| 0.0220 \| 1.9099 \| 1.3820 \| \| No log \| 18.0 \| 108 \| 2.4316 \| -0.0199 \| 2.4295 \| 1.5587 \| \| No log \| 19.0 \| 114 \| 1.0893 \| 0.1848 \| 1.0888 \| 1.0434 \| \| No log \| 20.0 \| 120 \| 1.3985 \| 0.1031 \| 1.3976 \| 1.1822 \| \| No log \| 21.0 \| 126 \| 1.6275 \| 0.0908 \| 1.6265 \| 1.2753 \| \| No log \| 22.0 \| 132 \| 1.4018 \| 0.0839 \| 1.4009 \| 1.1836 \| \| No log \| 23.0 \| 138 \| 1.5145 \| 0.0827 \| 1.5136 \| 1.2303 \| ### Framework versions - Transformers 4.47.0 - Pytorch 2.5.1+cu121 - Datasets 3.3.1 - Tokenizers 0.21.0
AdamKasumovic/llama3-70b-instruct-ids-winogrande-train-s-xh-winogrande-med	AdamKasumovic	"2024-07-01T22:41:44Z"	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/llama-3-70b-Instruct-bnb-4bit", "base_model:finetune:unsloth/llama-3-70b-Instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	"2024-07-01T21:09:27Z"	--- base_model: unsloth/llama-3-70b-Instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - Developed by: AdamKasumovic - License: apache-2.0 - Finetuned from model : unsloth/llama-3-70b-Instruct-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)
ClarenceDan/e94cd2c3-f73f-486d-a3d2-809f2f128a46	ClarenceDan	"2025-01-15T02:43:04Z"	12	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:TinyLlama/TinyLlama_v1.1", "base_model:adapter:TinyLlama/TinyLlama_v1.1", "license:apache-2.0", "region:us" ]	null	"2025-01-15T02:42:32Z"	--- library_name: peft license: apache-2.0 base_model: TinyLlama/TinyLlama_v1.1 tags: - axolotl - generated_from_trainer model-index: - name: e94cd2c3-f73f-486d-a3d2-809f2f128a46 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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: TinyLlama/TinyLlama_v1.1 bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - a26a8dba741b4ca1_train_data.json ds_type: json format: custom path: /workspace/input_data/a26a8dba741b4ca1_train_data.json type: field_instruction: question_1 field_output: answer format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 4 flash_attention: false fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: false group_by_length: false hub_model_id: ClarenceDan/e94cd2c3-f73f-486d-a3d2-809f2f128a46 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 10 micro_batch_size: 2 mlflow_experiment_name: /tmp/a26a8dba741b4ca1_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 4 sequence_len: 512 special_tokens: pad_token: </s> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 2cc9a107-3883-49d9-bae4-0b2c1ea759f6 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 2cc9a107-3883-49d9-bae4-0b2c1ea759f6 warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # e94cd2c3-f73f-486d-a3d2-809f2f128a46 This model is a fine-tuned version of [TinyLlama/TinyLlama_v1.1](https://huggingface.co/TinyLlama/TinyLlama_v1.1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.3498 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 5.8338 \| 0.0105 \| 1 \| 5.7859 \| \| 5.4559 \| 0.0316 \| 3 \| 5.6161 \| \| 5.3188 \| 0.0632 \| 6 \| 5.2195 \| \| 4.3143 \| 0.0947 \| 9 \| 4.3498 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
lightblue/qwen2.5-7B-instruct-simpo	lightblue	"2024-12-05T07:10:12Z"	9	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2024-12-05T06:23:55Z"	--- library_name: transformers license: other base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: simpo_trained_1 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. --> # simpo_trained_1 This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the lightblue_orpo_data dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - total_eval_batch_size: 8 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.4.0+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3
Jonjew/PatrickJonesStyle	Jonjew	"2025-04-05T10:46:41Z"	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:unknown", "region:us" ]	text-to-image	"2025-04-05T10:46:37Z"	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: Art by Patrick Jones output: url: images/t2i_00055_.jpeg base_model: black-forest-labs/FLUX.1-dev instance_prompt: null license: unknown --- # Patrick Jones Style <Gallery /> ## Model description FROM https://civitai.green/images/67097781 Please support the creator by donating buzz and liking at the page above! Tigger Art by Patrick Jones Strength 0.7 About this version Model trained on civitai with 50 images. Recommended resources : Fluxmania Recommended settings : dpmpp_2m or heunpp2 / sgm_uniform / 25 steps / guidance : 2.5 to 3.5 ## Download model Weights for this model are available in Safetensors format. [Download](/Jonjew/PatrickJonesStyle/tree/main) them in the Files & versions tab.
tanganke/clip-vit-base-patch32_fashion_mnist	tanganke	"2024-12-12T14:02:46Z"	207	0	null	[ "tensorboard", "safetensors", "clip_vision_model", "dataset:zalando-datasets/fashion_mnist", "base_model:openai/clip-vit-base-patch32", "base_model:finetune:openai/clip-vit-base-patch32", "region:us" ]	null	"2024-12-12T14:02:07Z"	--- base_model: - openai/clip-vit-base-patch32 datasets: - zalando-datasets/fashion_mnist metrics: - accuracy --- # Model Card ## Training Details Adam Optimizer with a constant learning rate 1e-5 for 4000 steps training (batch_size=128). Only the vision encoder is fine-tuned. ## Evaluation Results Test set accuracy: - pre-trained: 0.6301000118255615 - fine-tuned: 0.9474999904632568
mradermacher/mamba-1.3b-chinese-chat-v0.1-GGUF	mradermacher	"2025-01-10T05:46:53Z"	216	0	transformers	[ "transformers", "gguf", "en", "base_model:gywy/mamba-1.3b-chinese-chat-v0.1", "base_model:quantized:gywy/mamba-1.3b-chinese-chat-v0.1", "endpoints_compatible", "region:us", "conversational" ]	null	"2025-01-10T05:40:36Z"	--- base_model: gywy/mamba-1.3b-chinese-chat-v0.1 language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> static quants of https://huggingface.co/gywy/mamba-1.3b-chinese-chat-v0.1 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/mamba-1.3b-chinese-chat-v0.1-GGUF/resolve/main/mamba-1.3b-chinese-chat-v0.1.Q2_K.gguf) \| Q2_K \| 0.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/mamba-1.3b-chinese-chat-v0.1-GGUF/resolve/main/mamba-1.3b-chinese-chat-v0.1.Q3_K_L.gguf) \| Q3_K_L \| 0.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/mamba-1.3b-chinese-chat-v0.1-GGUF/resolve/main/mamba-1.3b-chinese-chat-v0.1.Q3_K_M.gguf) \| Q3_K_M \| 0.7 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/mamba-1.3b-chinese-chat-v0.1-GGUF/resolve/main/mamba-1.3b-chinese-chat-v0.1.Q3_K_S.gguf) \| Q3_K_S \| 0.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/mamba-1.3b-chinese-chat-v0.1-GGUF/resolve/main/mamba-1.3b-chinese-chat-v0.1.IQ4_XS.gguf) \| IQ4_XS \| 0.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/mamba-1.3b-chinese-chat-v0.1-GGUF/resolve/main/mamba-1.3b-chinese-chat-v0.1.Q4_K_M.gguf) \| Q4_K_M \| 0.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/mamba-1.3b-chinese-chat-v0.1-GGUF/resolve/main/mamba-1.3b-chinese-chat-v0.1.Q4_K_S.gguf) \| Q4_K_S \| 0.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/mamba-1.3b-chinese-chat-v0.1-GGUF/resolve/main/mamba-1.3b-chinese-chat-v0.1.Q5_K_M.gguf) \| Q5_K_M \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/mamba-1.3b-chinese-chat-v0.1-GGUF/resolve/main/mamba-1.3b-chinese-chat-v0.1.Q5_K_S.gguf) \| Q5_K_S \| 1.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/mamba-1.3b-chinese-chat-v0.1-GGUF/resolve/main/mamba-1.3b-chinese-chat-v0.1.Q6_K.gguf) \| Q6_K \| 1.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/mamba-1.3b-chinese-chat-v0.1-GGUF/resolve/main/mamba-1.3b-chinese-chat-v0.1.Q8_0.gguf) \| Q8_0 \| 1.5 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/mamba-1.3b-chinese-chat-v0.1-GGUF/resolve/main/mamba-1.3b-chinese-chat-v0.1.f16.gguf) \| f16 \| 2.7 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
llmvetter/FrozenLake-v1-4x4-noSlippery	llmvetter	"2024-05-27T11:58:51Z"	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	"2024-05-27T11:58:49Z"	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="llmvetter/FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
nadejdatarabukina/d2828006-f9c3-4ccd-8c25-a449cd39b769	nadejdatarabukina	"2025-01-24T16:44:27Z"	7	0	peft	[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:unsloth/Phi-3-medium-4k-instruct", "base_model:adapter:unsloth/Phi-3-medium-4k-instruct", "license:mit", "region:us" ]	null	"2025-01-24T14:52:36Z"	--- library_name: peft license: mit base_model: unsloth/Phi-3-medium-4k-instruct tags: - axolotl - generated_from_trainer model-index: - name: d2828006-f9c3-4ccd-8c25-a449cd39b769 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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/Phi-3-medium-4k-instruct bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 7fbdd974ad360a0f_train_data.json ds_type: json format: custom path: /workspace/input_data/7fbdd974ad360a0f_train_data.json type: field_input: entities field_instruction: context field_output: level_2 format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device: cuda early_stopping_patience: null eval_max_new_tokens: 128 eval_steps: 5 eval_table_size: null evals_per_epoch: null flash_attention: false fp16: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: false hub_model_id: nadejdatarabukina/d2828006-f9c3-4ccd-8c25-a449cd39b769 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 3 lora_alpha: 32 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lr_scheduler: cosine max_memory: 0: 75GiB max_steps: 30 micro_batch_size: 2 mlflow_experiment_name: /tmp/7fbdd974ad360a0f_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_torch output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 10 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: true trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 6f7702bf-d1c0-46ba-91bf-dd86d3008991 wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 6f7702bf-d1c0-46ba-91bf-dd86d3008991 warmup_steps: 10 weight_decay: 0.01 xformers_attention: true ``` </details><br> # d2828006-f9c3-4ccd-8c25-a449cd39b769 This model is a fine-tuned version of [unsloth/Phi-3-medium-4k-instruct](https://huggingface.co/unsloth/Phi-3-medium-4k-instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0001 \| 1 \| nan \| \| 0.0 \| 0.0005 \| 5 \| nan \| \| 0.0 \| 0.0009 \| 10 \| nan \| \| 0.0 \| 0.0014 \| 15 \| nan \| \| 0.0 \| 0.0019 \| 20 \| nan \| \| 0.0 \| 0.0024 \| 25 \| nan \| \| 0.0 \| 0.0028 \| 30 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
dimasik2987/b5da3b8a-28e0-444d-8d62-bd14eec9aed0	dimasik2987	"2025-01-22T21:29:52Z"	6	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/SmolLM-360M-Instruct", "base_model:adapter:unsloth/SmolLM-360M-Instruct", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]	null	"2025-01-22T21:24:25Z"	--- library_name: peft license: apache-2.0 base_model: unsloth/SmolLM-360M-Instruct tags: - axolotl - generated_from_trainer model-index: - name: b5da3b8a-28e0-444d-8d62-bd14eec9aed0 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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/SmolLM-360M-Instruct bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 1356d71c85d6b721_train_data.json ds_type: json format: custom path: /workspace/input_data/1356d71c85d6b721_train_data.json type: field_input: criteria field_instruction: data field_output: Study_Name format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device: cuda early_stopping_patience: 1 eval_max_new_tokens: 128 eval_steps: 5 eval_table_size: null evals_per_epoch: null flash_attention: false fp16: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: true hub_model_id: dimasik2987/b5da3b8a-28e0-444d-8d62-bd14eec9aed0 hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 0.0002 load_in_4bit: false load_in_8bit: true local_rank: null logging_steps: 3 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_memory: 0: 79GiB max_steps: 30 micro_batch_size: 4 mlflow_experiment_name: /tmp/1356d71c85d6b721_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 10 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: true trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 0524e6c6-3d70-4085-92df-a0b6c347526d wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 0524e6c6-3d70-4085-92df-a0b6c347526d warmup_steps: 5 weight_decay: 0.001 xformers_attention: true ``` </details><br> # b5da3b8a-28e0-444d-8d62-bd14eec9aed0 This model is a fine-tuned version of [unsloth/SmolLM-360M-Instruct](https://huggingface.co/unsloth/SmolLM-360M-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0008 \| 1 \| nan \| \| 0.0 \| 0.0038 \| 5 \| nan \| \| 0.0 \| 0.0075 \| 10 \| nan \| \| 0.0 \| 0.0113 \| 15 \| nan \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
sanchit-gandhi/distil-zephyr-1.5b-dpo-ultrafeedback	sanchit-gandhi	"2024-04-26T16:12:38Z"	140	0	transformers	[ "transformers", "tensorboard", "safetensors", "mistral", "text-generation", "alignment-handbook", "trl", "dpo", "generated_from_trainer", "conversational", "dataset:HuggingFaceH4/ultrafeedback_binarized", "base_model:sanchit-gandhi/distil-zephyr-1.5b-ssft-ultrachat", "base_model:finetune:sanchit-gandhi/distil-zephyr-1.5b-ssft-ultrachat", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2024-04-26T15:39:14Z"	--- base_model: sanchit-gandhi/distil-zephyr-1.5b-ssft-ultrachat tags: - alignment-handbook - trl - dpo - generated_from_trainer - trl - dpo - generated_from_trainer datasets: - HuggingFaceH4/ultrafeedback_binarized model-index: - name: sanchit-gandhi/distil-zephyr-1.5b-ssft-ultrachat 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. --> # sanchit-gandhi/distil-zephyr-1.5b-ssft-ultrachat This model is a fine-tuned version of [sanchit-gandhi/distil-zephyr-1.5b-ssft-ultrachat](https://huggingface.co/sanchit-gandhi/distil-zephyr-1.5b-ssft-ultrachat) on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set: - Loss: 0.6412 - Rewards/chosen: -0.1044 - Rewards/rejected: -0.2494 - Rewards/accuracies: 0.6445 - Rewards/margins: 0.1450 - Logps/rejected: -429.4582 - Logps/chosen: -433.6304 - Logits/rejected: -3.2047 - Logits/chosen: -3.2544 ## 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-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.6795 \| 0.2092 \| 100 \| 0.6759 \| 0.0017 \| -0.0328 \| 0.6289 \| 0.0345 \| -407.8037 \| -423.0197 \| -3.2565 \| -3.3136 \| \| 0.6584 \| 0.4184 \| 200 \| 0.6534 \| -0.0666 \| -0.1617 \| 0.6445 \| 0.0951 \| -420.6952 \| -429.8561 \| -3.2240 \| -3.2768 \| \| 0.6494 \| 0.6276 \| 300 \| 0.6438 \| -0.1077 \| -0.2410 \| 0.6211 \| 0.1333 \| -428.6237 \| -433.9640 \| -3.2050 \| -3.2553 \| \| 0.6428 \| 0.8368 \| 400 \| 0.6415 \| -0.1001 \| -0.2437 \| 0.6211 \| 0.1436 \| -428.8884 \| -433.2000 \| -3.2046 \| -3.2543 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.2+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1
mix060514/sd-class-butterflies-32	mix060514	"2024-03-12T09:59:27Z"	44	0	diffusers	[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	"2024-03-12T09:55:14Z"	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- '''python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('mix060514/sd-class-butterflies-32') image = pipeline().images[0] image '''
beanslmao/hensinki-en-es-finetuned-spanish-to-english-tateoba	beanslmao	"2024-03-04T01:05:51Z"	9	0	transformers	[ "transformers", "safetensors", "marian", "text2text-generation", "generated_from_trainer", "base_model:Helsinki-NLP/opus-mt-en-es", "base_model:finetune:Helsinki-NLP/opus-mt-en-es", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	"2024-03-04T01:05:34Z"	--- license: apache-2.0 base_model: Helsinki-NLP/opus-mt-en-es tags: - generated_from_trainer metrics: - bleu model-index: - name: hensinki-en-es-finetuned-spanish-to-english-tateoba 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. --> # hensinki-en-es-finetuned-spanish-to-english-tateoba This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-es](https://huggingface.co/Helsinki-NLP/opus-mt-en-es) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7389 - Bleu: 47.2153 - Gen Len: 13.8167 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:-------:\| \| 0.9651 \| 1.0 \| 7069 \| 0.8372 \| 43.0236 \| 13.7961 \| \| 0.8011 \| 2.0 \| 14138 \| 0.7389 \| 47.2153 \| 13.8167 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
Alexanrd/GigaAMv2_CTC_RU_ASR_for_sherpa_onnx	Alexanrd	"2025-02-25T08:29:46Z"	0	0	null	[ "onnx", "ASR", "RU_ASR", "GigaAMv2", "CTC", "license:mit", "region:us" ]	null	"2025-02-15T08:36:58Z"	--- license: mit tags: - ASR - RU_ASR - GigaAMv2 - CTC --- - Original git - [GigaAM](https://github.com/salute-developers/GigaAM/) - I`ve get onnx by [instruction](https://github.com/salute-developers/GigaAM/blob/main/inference_example.ipynb) - Then updated it`s meta to start on sherpa-onnx as Non-streaming CTC model from NeMo. - Code to use you can find [in example](https://huggingface.co/Alexanrd/GigaAMv2_CTC_RU_ASR_for_sherpa_onnx/blob/main/example.py) or [in FastAPI project](https://github.com/Sanich137/Vosk5_FastAPI_streaming) > - [Licence file](https://github.com/salute-developers/GigaAM/blob/main/LICENSE) > - WARNING! Check license rules before use!
mradermacher/Hatheno_Max_1.1-ALT-8B-model_stock-i1-GGUF	mradermacher	"2024-09-16T06:31:07Z"	88	1	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	"2024-09-16T03:14:47Z"	--- base_model: DreadPoor/Hatheno_Max_1.1-ALT-8B-model_stock language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/DreadPoor/Hatheno_Max_1.1-ALT-8B-model_stock <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Hatheno_Max_1.1-ALT-8B-model_stock-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Hatheno_Max_1.1-ALT-8B-model_stock-i1-GGUF/resolve/main/Hatheno_Max_1.1-ALT-8B-model_stock.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.1 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Hatheno_Max_1.1-ALT-8B-model_stock-i1-GGUF/resolve/main/Hatheno_Max_1.1-ALT-8B-model_stock.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.3 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Hatheno_Max_1.1-ALT-8B-model_stock-i1-GGUF/resolve/main/Hatheno_Max_1.1-ALT-8B-model_stock.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Hatheno_Max_1.1-ALT-8B-model_stock-i1-GGUF/resolve/main/Hatheno_Max_1.1-ALT-8B-model_stock.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Hatheno_Max_1.1-ALT-8B-model_stock-i1-GGUF/resolve/main/Hatheno_Max_1.1-ALT-8B-model_stock.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Hatheno_Max_1.1-ALT-8B-model_stock-i1-GGUF/resolve/main/Hatheno_Max_1.1-ALT-8B-model_stock.i1-IQ2_M.gguf) \| i1-IQ2_M \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Hatheno_Max_1.1-ALT-8B-model_stock-i1-GGUF/resolve/main/Hatheno_Max_1.1-ALT-8B-model_stock.i1-Q2_K.gguf) \| i1-Q2_K \| 3.3 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Hatheno_Max_1.1-ALT-8B-model_stock-i1-GGUF/resolve/main/Hatheno_Max_1.1-ALT-8B-model_stock.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 3.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Hatheno_Max_1.1-ALT-8B-model_stock-i1-GGUF/resolve/main/Hatheno_Max_1.1-ALT-8B-model_stock.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Hatheno_Max_1.1-ALT-8B-model_stock-i1-GGUF/resolve/main/Hatheno_Max_1.1-ALT-8B-model_stock.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.8 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Hatheno_Max_1.1-ALT-8B-model_stock-i1-GGUF/resolve/main/Hatheno_Max_1.1-ALT-8B-model_stock.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Hatheno_Max_1.1-ALT-8B-model_stock-i1-GGUF/resolve/main/Hatheno_Max_1.1-ALT-8B-model_stock.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Hatheno_Max_1.1-ALT-8B-model_stock-i1-GGUF/resolve/main/Hatheno_Max_1.1-ALT-8B-model_stock.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 4.1 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Hatheno_Max_1.1-ALT-8B-model_stock-i1-GGUF/resolve/main/Hatheno_Max_1.1-ALT-8B-model_stock.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 4.4 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Hatheno_Max_1.1-ALT-8B-model_stock-i1-GGUF/resolve/main/Hatheno_Max_1.1-ALT-8B-model_stock.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Hatheno_Max_1.1-ALT-8B-model_stock-i1-GGUF/resolve/main/Hatheno_Max_1.1-ALT-8B-model_stock.i1-Q4_0_4_4.gguf) \| i1-Q4_0_4_4 \| 4.8 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Hatheno_Max_1.1-ALT-8B-model_stock-i1-GGUF/resolve/main/Hatheno_Max_1.1-ALT-8B-model_stock.i1-Q4_0_4_8.gguf) \| i1-Q4_0_4_8 \| 4.8 \| fast on arm+i8mm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Hatheno_Max_1.1-ALT-8B-model_stock-i1-GGUF/resolve/main/Hatheno_Max_1.1-ALT-8B-model_stock.i1-Q4_0_8_8.gguf) \| i1-Q4_0_8_8 \| 4.8 \| fast on arm+sve, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Hatheno_Max_1.1-ALT-8B-model_stock-i1-GGUF/resolve/main/Hatheno_Max_1.1-ALT-8B-model_stock.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.8 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Hatheno_Max_1.1-ALT-8B-model_stock-i1-GGUF/resolve/main/Hatheno_Max_1.1-ALT-8B-model_stock.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.8 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Hatheno_Max_1.1-ALT-8B-model_stock-i1-GGUF/resolve/main/Hatheno_Max_1.1-ALT-8B-model_stock.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Hatheno_Max_1.1-ALT-8B-model_stock-i1-GGUF/resolve/main/Hatheno_Max_1.1-ALT-8B-model_stock.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Hatheno_Max_1.1-ALT-8B-model_stock-i1-GGUF/resolve/main/Hatheno_Max_1.1-ALT-8B-model_stock.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Hatheno_Max_1.1-ALT-8B-model_stock-i1-GGUF/resolve/main/Hatheno_Max_1.1-ALT-8B-model_stock.i1-Q6_K.gguf) \| i1-Q6_K \| 6.7 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mserloth/v23	mserloth	"2024-05-05T13:44:34Z"	107	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "autotrain", "dataset:v23/autotrain-data", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2024-05-05T13:42:35Z"	--- tags: - autotrain - text-classification widget: - text: "I love AutoTrain" datasets: - v23/autotrain-data --- # Model Trained Using AutoTrain - Problem type: Text Classification ## Validation Metrics loss: 0.5765425562858582 f1_macro: 0.7349736157447978 f1_micro: 0.7492063492063492 f1_weighted: 0.7506575340838828 precision_macro: 0.7150911360799 precision_micro: 0.7492063492063492 precision_weighted: 0.7534924677486475 recall_macro: 0.7595969666757304 recall_micro: 0.7492063492063492 recall_weighted: 0.7492063492063492 accuracy: 0.7492063492063492
Zienab/wav	Zienab	"2024-01-09T07:06:37Z"	103	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "ar", "base_model:facebook/wav2vec2-large-xlsr-53", "base_model:finetune:facebook/wav2vec2-large-xlsr-53", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	"2024-01-08T11:49:55Z"	--- license: apache-2.0 base_model: facebook/wav2vec2-large-xlsr-53 tags: - generated_from_trainer model-index: - name: wav results: [] language: - ar metrics: - accuracy --- <!-- 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. --> # wav This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) 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: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 20 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.37.0.dev0 - Pytorch 2.1.0+cu118 - Datasets 2.16.1 - Tokenizers 0.15.0
Rakhman16/program-synthesis-java-codet5	Rakhman16	"2024-12-09T07:29:08Z"	14	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "code", "en", "dataset:codeparrot/xlcost-text-to-code", "base_model:Salesforce/codet5-base", "base_model:finetune:Salesforce/codet5-base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	"2024-12-09T04:21:02Z"	--- license: apache-2.0 datasets: - codeparrot/xlcost-text-to-code language: - en base_model: - Salesforce/codet5-base pipeline_tag: text2text-generation library_name: transformers tags: - code ---
RichardErkhov/qhchina_-_gpt2-kunqu-english-gguf	RichardErkhov	"2025-03-19T06:38:41Z"	0	0	null	[ "gguf", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2025-03-19T06:35:13Z"	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) gpt2-kunqu-english - GGUF - Model creator: https://huggingface.co/qhchina/ - Original model: https://huggingface.co/qhchina/gpt2-kunqu-english/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [gpt2-kunqu-english.Q2_K.gguf](https://huggingface.co/RichardErkhov/qhchina_-_gpt2-kunqu-english-gguf/blob/main/gpt2-kunqu-english.Q2_K.gguf) \| Q2_K \| 0.08GB \| \| [gpt2-kunqu-english.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/qhchina_-_gpt2-kunqu-english-gguf/blob/main/gpt2-kunqu-english.IQ3_XS.gguf) \| IQ3_XS \| 0.08GB \| \| [gpt2-kunqu-english.IQ3_S.gguf](https://huggingface.co/RichardErkhov/qhchina_-_gpt2-kunqu-english-gguf/blob/main/gpt2-kunqu-english.IQ3_S.gguf) \| IQ3_S \| 0.08GB \| \| [gpt2-kunqu-english.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/qhchina_-_gpt2-kunqu-english-gguf/blob/main/gpt2-kunqu-english.Q3_K_S.gguf) \| Q3_K_S \| 0.08GB \| \| [gpt2-kunqu-english.IQ3_M.gguf](https://huggingface.co/RichardErkhov/qhchina_-_gpt2-kunqu-english-gguf/blob/main/gpt2-kunqu-english.IQ3_M.gguf) \| IQ3_M \| 0.09GB \| \| [gpt2-kunqu-english.Q3_K.gguf](https://huggingface.co/RichardErkhov/qhchina_-_gpt2-kunqu-english-gguf/blob/main/gpt2-kunqu-english.Q3_K.gguf) \| Q3_K \| 0.09GB \| \| [gpt2-kunqu-english.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/qhchina_-_gpt2-kunqu-english-gguf/blob/main/gpt2-kunqu-english.Q3_K_M.gguf) \| Q3_K_M \| 0.09GB \| \| [gpt2-kunqu-english.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/qhchina_-_gpt2-kunqu-english-gguf/blob/main/gpt2-kunqu-english.Q3_K_L.gguf) \| Q3_K_L \| 0.1GB \| \| [gpt2-kunqu-english.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/qhchina_-_gpt2-kunqu-english-gguf/blob/main/gpt2-kunqu-english.IQ4_XS.gguf) \| IQ4_XS \| 0.1GB \| \| [gpt2-kunqu-english.Q4_0.gguf](https://huggingface.co/RichardErkhov/qhchina_-_gpt2-kunqu-english-gguf/blob/main/gpt2-kunqu-english.Q4_0.gguf) \| Q4_0 \| 0.1GB \| \| [gpt2-kunqu-english.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/qhchina_-_gpt2-kunqu-english-gguf/blob/main/gpt2-kunqu-english.IQ4_NL.gguf) \| IQ4_NL \| 0.1GB \| \| [gpt2-kunqu-english.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/qhchina_-_gpt2-kunqu-english-gguf/blob/main/gpt2-kunqu-english.Q4_K_S.gguf) \| Q4_K_S \| 0.1GB \| \| [gpt2-kunqu-english.Q4_K.gguf](https://huggingface.co/RichardErkhov/qhchina_-_gpt2-kunqu-english-gguf/blob/main/gpt2-kunqu-english.Q4_K.gguf) \| Q4_K \| 0.11GB \| \| [gpt2-kunqu-english.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/qhchina_-_gpt2-kunqu-english-gguf/blob/main/gpt2-kunqu-english.Q4_K_M.gguf) \| Q4_K_M \| 0.11GB \| \| [gpt2-kunqu-english.Q4_1.gguf](https://huggingface.co/RichardErkhov/qhchina_-_gpt2-kunqu-english-gguf/blob/main/gpt2-kunqu-english.Q4_1.gguf) \| Q4_1 \| 0.11GB \| \| [gpt2-kunqu-english.Q5_0.gguf](https://huggingface.co/RichardErkhov/qhchina_-_gpt2-kunqu-english-gguf/blob/main/gpt2-kunqu-english.Q5_0.gguf) \| Q5_0 \| 0.11GB \| \| [gpt2-kunqu-english.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/qhchina_-_gpt2-kunqu-english-gguf/blob/main/gpt2-kunqu-english.Q5_K_S.gguf) \| Q5_K_S \| 0.11GB \| \| [gpt2-kunqu-english.Q5_K.gguf](https://huggingface.co/RichardErkhov/qhchina_-_gpt2-kunqu-english-gguf/blob/main/gpt2-kunqu-english.Q5_K.gguf) \| Q5_K \| 0.12GB \| \| [gpt2-kunqu-english.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/qhchina_-_gpt2-kunqu-english-gguf/blob/main/gpt2-kunqu-english.Q5_K_M.gguf) \| Q5_K_M \| 0.12GB \| \| [gpt2-kunqu-english.Q5_1.gguf](https://huggingface.co/RichardErkhov/qhchina_-_gpt2-kunqu-english-gguf/blob/main/gpt2-kunqu-english.Q5_1.gguf) \| Q5_1 \| 0.12GB \| \| [gpt2-kunqu-english.Q6_K.gguf](https://huggingface.co/RichardErkhov/qhchina_-_gpt2-kunqu-english-gguf/blob/main/gpt2-kunqu-english.Q6_K.gguf) \| Q6_K \| 0.13GB \| \| [gpt2-kunqu-english.Q8_0.gguf](https://huggingface.co/RichardErkhov/qhchina_-_gpt2-kunqu-english-gguf/blob/main/gpt2-kunqu-english.Q8_0.gguf) \| Q8_0 \| 0.17GB \| Original model description: --- 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]
TFOCUS/kevin-flux_7	TFOCUS	"2025-02-19T16:28:42Z"	0	0	null	[ "region:us" ]	null	"2025-02-19T16:24:08Z"	# flux-schnell-edge-inference nestas hagunnan hinase
shubhamagarwal92/ppo-Huggy	shubhamagarwal92	"2023-07-18T06:07:07Z"	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	"2023-07-18T06:07:04Z"	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: shubhamagarwal92/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
vocabtrimmer/mbart-large-cc25-trimmed-fr-frquad-qa	vocabtrimmer	"2023-04-05T18:09:09Z"	103	0	transformers	[ "transformers", "pytorch", "mbart", "text2text-generation", "question answering", "fr", "dataset:lmqg/qg_frquad", "arxiv:2210.03992", "license:cc-by-4.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	"2023-04-05T18:03:27Z"	--- license: cc-by-4.0 metrics: - bleu4 - meteor - rouge-l - bertscore - moverscore language: fr datasets: - lmqg/qg_frquad pipeline_tag: text2text-generation tags: - question answering widget: - text: "question: En quelle année a-t-on trouvé trace d'un haut fourneau similaire?, context: Cette technologie ne disparaît qu'au début du XXe siècle. On retrouve vers 1900 un haut fourneau similaire dans le Bulacan, aux Philippines. Plus tard encore, le « haut fourneau dans la cour » prôné par Mao Zedong pendant le Grand Bond en avant est de ce type. L'expérience n'est un échec technique que dans les régions où le savoir-faire n'existe pas, ou a disparu." example_title: "Question Answering Example 1" - text: "question: Comment appelle-t-on la Guerre de 14-18 ?, context: Ce black dog peut être lié à des évènements traumatisants issus du monde extérieur, tels que son renvoi de l'Amirauté après la catastrophe des Dardanelles, lors de la Grande Guerre de 14-18, ou son rejet par l'électorat en juillet 1945. On sait également que dans ces deux cas, la guérison, certes lente et douloureuse et jamais complète ni définitive, se fera grâce à la peinture. D'un autre côté, étant donnés les symptômes de ce mal que Churchill éprouvait de plus en plus, il ne pouvait rien moins qu'être purement associé à de telles causes extrinsèques, ce qui correspond au profil classique de la dépression majeure unipolaire ou bipolaire." example_title: "Question Answering Example 2" model-index: - name: vocabtrimmer/mbart-large-cc25-trimmed-fr-frquad-qa results: - task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qg_frquad type: default args: default metrics: - name: BLEU4 (Question Answering) type: bleu4_question_answering value: 31.61 - name: ROUGE-L (Question Answering) type: rouge_l_question_answering value: 41.11 - name: METEOR (Question Answering) type: meteor_question_answering value: 32.95 - name: BERTScore (Question Answering) type: bertscore_question_answering value: 93.48 - name: MoverScore (Question Answering) type: moverscore_question_answering value: 79.52 - name: AnswerF1Score (Question Answering) type: answer_f1_score__question_answering value: 66.37 - name: AnswerExactMatch (Question Answering) type: answer_exact_match_question_answering value: 45.11 --- # Model Card of `vocabtrimmer/mbart-large-cc25-trimmed-fr-frquad-qa` This model is fine-tuned version of [vocabtrimmer/mbart-large-cc25-trimmed-fr](https://huggingface.co/vocabtrimmer/mbart-large-cc25-trimmed-fr) for question answering task on the [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). ### Overview - Language model: [vocabtrimmer/mbart-large-cc25-trimmed-fr](https://huggingface.co/vocabtrimmer/mbart-large-cc25-trimmed-fr) - Language: fr - Training data: [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) (default) - Online Demo: [https://autoqg.net/](https://autoqg.net/) - Repository: [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - Paper: [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) ### Usage - With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-) ```python from lmqg import TransformersQG # initialize model model = TransformersQG(language="fr", model="vocabtrimmer/mbart-large-cc25-trimmed-fr-frquad-qa") # model prediction answers = model.answer_q(list_question="En quelle année a-t-on trouvé trace d'un haut fourneau similaire?", list_context=" Cette technologie ne disparaît qu'au début du XXe siècle. On retrouve vers 1900 un haut fourneau similaire dans le Bulacan, aux Philippines. Plus tard encore, le « haut fourneau dans la cour » prôné par Mao Zedong pendant le Grand Bond en avant est de ce type. L'expérience n'est un échec technique que dans les régions où le savoir-faire n'existe pas, ou a disparu.") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "vocabtrimmer/mbart-large-cc25-trimmed-fr-frquad-qa") output = pipe("question: En quelle année a-t-on trouvé trace d'un haut fourneau similaire?, context: Cette technologie ne disparaît qu'au début du XXe siècle. On retrouve vers 1900 un haut fourneau similaire dans le Bulacan, aux Philippines. Plus tard encore, le « haut fourneau dans la cour » prôné par Mao Zedong pendant le Grand Bond en avant est de ce type. L'expérience n'est un échec technique que dans les régions où le savoir-faire n'existe pas, ou a disparu.") ``` ## Evaluation - *Metric (Question Answering)*: [raw metric file](https://huggingface.co/vocabtrimmer/mbart-large-cc25-trimmed-fr-frquad-qa/raw/main/eval/metric.first.answer.paragraph_question.answer.lmqg_qg_frquad.default.json) \| \| Score \| Type \| Dataset \| \|:-----------------\|--------:\|:--------\|:-----------------------------------------------------------------\| \| AnswerExactMatch \| 45.11 \| default \| [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) \| \| AnswerF1Score \| 66.37 \| default \| [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) \| \| BERTScore \| 93.48 \| default \| [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) \| \| Bleu_1 \| 42.71 \| default \| [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) \| \| Bleu_2 \| 37.89 \| default \| [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) \| \| Bleu_3 \| 34.5 \| default \| [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) \| \| Bleu_4 \| 31.61 \| default \| [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) \| \| METEOR \| 32.95 \| default \| [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) \| \| MoverScore \| 79.52 \| default \| [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) \| \| ROUGE_L \| 41.11 \| default \| [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) \| ## Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_frquad - dataset_name: default - input_types: ['paragraph_question'] - output_types: ['answer'] - prefix_types: None - model: vocabtrimmer/mbart-large-cc25-trimmed-fr - max_length: 512 - max_length_output: 32 - epoch: 11 - batch: 8 - lr: 0.0001 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 16 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/vocabtrimmer/mbart-large-cc25-trimmed-fr-frquad-qa/raw/main/trainer_config.json). ## Citation ``` @inproceedings{ushio-etal-2022-generative, title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration", author = "Ushio, Asahi and Alva-Manchego, Fernando and Camacho-Collados, Jose", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, U.A.E.", publisher = "Association for Computational Linguistics", } ```
reasonwang/ToolLlama-WoTool-Llama-3-8B	reasonwang	"2024-09-30T08:33:05Z"	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2024-09-29T11:41:48Z"	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
bibom2001/whisper0	bibom2001	"2024-10-25T10:56:35Z"	85	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-base", "base_model:finetune:openai/whisper-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	"2024-10-24T13:40:21Z"	--- library_name: transformers license: apache-2.0 base_model: openai/whisper-base tags: - generated_from_trainer metrics: - wer model-index: - name: whisper0 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. --> # whisper0 This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.5080 - Wer Ortho: 99.8700 - Wer: 10.1070 ## 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: 32 - eval_batch_size: 32 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 1 - training_steps: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer Ortho \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:---------:\|:-------:\| \| 3.778 \| 0.0031 \| 5 \| 4.5080 \| 99.8700 \| 10.1070 \| ### Framework versions - Transformers 4.45.1 - Pytorch 1.12.1 - Datasets 3.0.1 - Tokenizers 0.20.0
ishwarbb23/Q4-PHQ	ishwarbb23	"2023-11-03T19:43:05Z"	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2023-11-03T17:37:58Z"	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: Q4-PHQ 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. --> # Q4-PHQ This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7573 - Accuracy: 0.5775 - Mcc: 0.1345 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Mcc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 51 \| 0.6875 \| 0.57 \| 0.2222 \| \| No log \| 2.0 \| 102 \| 0.6709 \| 0.565 \| 0.1213 \| \| No log \| 3.0 \| 153 \| 0.6932 \| 0.58 \| 0.1625 \| \| No log \| 4.0 \| 204 \| 0.7271 \| 0.5875 \| 0.1630 \| \| No log \| 5.0 \| 255 \| 0.7573 \| 0.5775 \| 0.1345 \| ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
egesko/CodeSprint_DCGAN	egesko	"2022-05-28T06:23:00Z"	0	0	null	[ "license:mit", "region:us" ]	null	"2022-05-28T05:19:07Z"	--- license: mit --- # DCGAN to generate face images This trained model is a keras implementation of DCGAN that is trained on face images.
rodrigoclira/Reinforce-Pixelcopter1	rodrigoclira	"2023-06-25T21:08:22Z"	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	"2023-06-24T23:24:00Z"	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Pixelcopter1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 50.30 +/- 34.08 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
ciciburner100/DashaFL	ciciburner100	"2025-04-01T17:52:43Z"	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "region:us" ]	text-to-image	"2025-04-01T17:45:08Z"	Temporary Redirect. Redirecting to /api/resolve-cache/models/ciciburner100/DashaFL/d4a893efcebeff4115aaa5f411217c65b97ab045/README.md?%2Fciciburner100%2FDashaFL%2Fresolve%2Fmain%2FREADME.md=&etag=%2229b64b5de1cd8c8cb9078b53f7411eb6ac6ab4aa%22
Jonjew/Mosaic	Jonjew	"2025-03-04T01:52:49Z"	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:unknown", "region:us" ]	text-to-image	"2025-03-04T01:52:38Z"	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: ArsMJStyle, Mosaic, Portrait FEmale parameters: negative_prompt: 'Steps: 20 Seed: 587388910175190' output: url: images/ComfyUI_00016_.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: ArsMJStyle, Mosaic license: unknown --- # Mosaic (SD 1.5, SDXL, Pony, Flux ) <Gallery /> ## Model description FROM Trigger ArsMJStyle, Mosaic Strength 1 The Lora is trained on MidJourney Images that use my "Personalised Style" + Mosaic. Works without the trigger words ArsMJStyle, Mosaic Have a an effect in the 0.4 - 0.8 range Depending on the Lora stack optimal Mosaic look in the 0.8 - 1.5 range Quality tags reduce the Mosaic effect, Versions: Flux - based on the latest updated dataset with new caption. v1 - the lightest Mosaic Effect, but without strong biases towards anything. Trained on Clip skip 1. SD 1.5 and SDXL are trained with this "final" dataset as well. Beta 2 - tries to generate Woman Portraits. Same dataset size as v1, but only 50% overlap. The Capture here was Free text + WD Tags. Beta - 50% of the dataset of Beta 2. Not that flexible when generating new images. The 3 Pony versions are practically different Loras, so try the other 2, if you liked one of them :) About this version In the 0.2 - 0.6 Range the LoRA adds distinctive vibrant colors, but the Mosaic Effect might be missing or minimal. Optimal Range is 0.8 - 1.5 For recommended usage on Quantized version check this really simple ComfyUI Workflow: https://civitai.com/models/658639/super-simple-gguf-quantized-flux-lora-workflow All the images with missing Metadata from the Showcase Gallery were created with it and Flux Q4 Dev model. ## Trigger words You should use `ArsMJStyle` to trigger the image generation. You should use `Mosaic` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/Jonjew/Mosaic/tree/main) them in the Files & versions tab.
brushpenbob/Flux-Kim-Jung-Gi	brushpenbob	"2024-08-25T23:34:11Z"	34	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "region:us" ]	text-to-image	"2024-08-25T23:33:58Z"	--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: "UNICODE\0\0a\0 \0f\0o\0x\0 \0d\0r\0e\0s\0s\0e\0d\0 \0a\0 \0a\0 \0w\0i\0z\0a\0r\0d\0,\0 \0g\0r\0e\0g\0 \0r\0u\0t\0k\0o\0w\0s\0k\0i\0" output: url: images/Flux_Kim_Jung_Gi_e000010_00_20240822071927.jpeg - text: "UNICODE\0\01\0b\0o\0y\0,\0 \0m\0a\0l\0e\0 \0f\0o\0c\0u\0s\0,\0 \0s\0o\0l\0o\0,\0 \0p\0o\0i\0n\0t\0y\0 \0e\0a\0r\0s\0,\0 \0h\0o\0o\0d\0,\0 \0g\0r\0i\0n\0,\0 \0s\0m\0i\0l\0e\0,\0 \0c\0l\0o\0a\0k\0,\0 \0e\0v\0i\0l\0 \0g\0r\0i\0n\0,\0 \0t\0e\0e\0t\0h\0,\0 \0e\0v\0i\0l\0 \0s\0m\0i\0l\0e\0,\0 \0u\0p\0p\0e\0r\0 \0b\0o\0d\0y\0,\0 \0h\0o\0o\0d\0e\0d\0 \0c\0l\0o\0a\0k\0,\0 \0n\0i\0g\0h\0t\0,\0 \0h\0o\0o\0d\0 \0u\0p\0,\0 \0k\0i\0m\0j\0u\0n\0g\0g\0i\0,\0 \0S\0p\0i\0d\0e\0r\0-\0M\0a\0n\0 \0v\0i\0l\0l\0a\0i\0n\0,\0 \0 \0g\0r\0e\0e\0n\0 \0g\0o\0b\0l\0i\0n\0" output: url: images/1XQBTMJ9W0NRAKH9PZX2HBTMM0.jpeg base_model: black-forest-labs/FLUX.1-dev instance_prompt: kimjunggi --- # m Jung Gi <Gallery /> ## Model description Per request... This is a upscaled flux model of the Kim Jung gi Mo https://youtu.be/QynE0UGQhRs ## Trigger words You should use `kimjunggi` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/brushpenbob/672588/tree/main) them in the Files & versions tab.
ThisIsATest/dclm-id-1.4b-raw	ThisIsATest	"2025-02-18T18:55:04Z"	0	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2025-02-18T18:52:46Z"	--- 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]
Helsinki-NLP/opus-mt-vi-eo	Helsinki-NLP	"2023-08-16T12:08:33Z"	107	0	transformers	[ "transformers", "pytorch", "tf", "marian", "text2text-generation", "translation", "vi", "eo", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	"2022-03-02T23:29:04Z"	--- language: - vi - eo tags: - translation license: apache-2.0 --- ### vie-epo * source group: Vietnamese * target group: Esperanto * OPUS readme: [vie-epo](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/vie-epo/README.md) * model: transformer-align * source language(s): vie * target language(s): epo * model: transformer-align * pre-processing: normalization + SentencePiece (spm4k,spm4k) * download original weights: [opus-2020-06-16.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/vie-epo/opus-2020-06-16.zip) * test set translations: [opus-2020-06-16.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/vie-epo/opus-2020-06-16.test.txt) * test set scores: [opus-2020-06-16.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/vie-epo/opus-2020-06-16.eval.txt) ## Benchmarks \| testset \| BLEU \| chr-F \| \|-----------------------\|-------\|-------\| \| Tatoeba-test.vie.epo \| 12.2 \| 0.332 \| ### System Info: - hf_name: vie-epo - source_languages: vie - target_languages: epo - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/vie-epo/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['vi', 'eo'] - src_constituents: {'vie', 'vie_Hani'} - tgt_constituents: {'epo'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm4k,spm4k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/vie-epo/opus-2020-06-16.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/vie-epo/opus-2020-06-16.test.txt - src_alpha3: vie - tgt_alpha3: epo - short_pair: vi-eo - chrF2_score: 0.332 - bleu: 12.2 - brevity_penalty: 0.99 - ref_len: 13637.0 - src_name: Vietnamese - tgt_name: Esperanto - train_date: 2020-06-16 - src_alpha2: vi - tgt_alpha2: eo - prefer_old: False - long_pair: vie-epo - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41
RLHFlow/Llama3.1-8B-ORM-Mistral-Data	RLHFlow	"2024-11-09T18:36:42Z"	126	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2024-11-08T06:26:09Z"	--- library_name: transformers tags: [] --- This is a outcome-supervised reward (ORM) trained on Mistral-generated data from the project [RLHFlow/RLHF-Reward-Modeling](https://github.com/RLHFlow/RLHF-Reward-Modeling) The model is trained from [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on [RLHFlow/Mistral-ORM-Data](https://huggingface.co/datasets/RLHFlow/Mistral-ORM-Data) for 1 epochs. We use a global batch size of 32 and a learning rate of 2e-6, where we pack the samples and split them into chunks of 8192 token. See more training details at https://github.com/RLHFlow/Online-RLHF/blob/main/math/llama-3.1-prm.yaml . ## BoN evaluation result for Mistral generator: \| Model \| Method \| GSM8K \| MATH \| \| ------------- \| ------------- \| ------------- \| -------- \| \| Mistral-7B \| Pass@1 \| 77.9 \| 28.4 \| \| Mistral-7B \| Majority Voting@1024 \| 84.2 \| 36.8 \| \| Mistral-7B \| Mistral-ORM@1024 \| 90.1 \| 43.6 \| \| Mistral-7B \| Mistral-PRM@1024 \| 92.4 \| 46.3 \| ## Scaling the inference sampling to N=1024 for Deepseek generator: \| Model \| Method \| GSM8K \| MATH \| \| ------------- \| ------------- \| ------------- \| -------- \| \| Deepseek-7B \| Pass@1 \| 83.9 \| 38.4 \| \| Deepseek-7B \| Majority Voting@1024 \| 89.7 \| 57.4 \| \| Deepseek-7B \| Deepseek-ORM@1024 \| 93.4 \| 52.4 \| \| Deepseek-7B \| Deepseek-PRM@1024 \| 93.0 \| 58.1 \| \| Deepseek-7B \| Mistral-ORM@1024 (OOD) \| 90.3 \| 54.9 \| \| Deepseek-7B \| Mistral-PRM@1024 (OOD) \| 91.9 \| 56.9 \| ## Visualization ![image/png](https://cdn-uploads.huggingface.co/production/uploads/643e59806db6ba8c5ee123f3/i622m76fvKv8drLmwl8Q3.png) ## Usage See https://github.com/RLHFlow/RLHF-Reward-Modeling/tree/main/math for detailed examples. ## Citation The automatic annotation was proposed in the Math-shepherd paper: ``` @inproceedings{wang2024math, title={Math-shepherd: Verify and reinforce llms step-by-step without human annotations}, author={Wang, Peiyi and Li, Lei and Shao, Zhihong and Xu, Runxin and Dai, Damai and Li, Yifei and Chen, Deli and Wu, Yu and Sui, Zhifang}, booktitle={Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, pages={9426--9439}, year={2024} } ``` If you find the training recipe useful, please consider cite it as follows. ``` @misc{xiong2024rlhflowmath, author={Wei Xiong and Hanning Zhang and Nan Jiang and Tong Zhang}, title = {An Implementation of Generative PRM}, year = {2024}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/RLHFlow/RLHF-Reward-Modeling}} } ```
netsol/resume-llama-3.1-8b-4bit	netsol	"2024-10-27T18:24:01Z"	77	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit", "base_model:quantized:unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	"2024-10-27T18:01:24Z"	--- base_model: unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft --- # Uploaded model - Developed by: netsol - License: apache-2.0 - Finetuned from model : unsloth/Meta-Llama-3.1-8B-Instruct-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)
NAMJOON/smu_counsel_vllm16_llama3.2_v1	NAMJOON	"2024-11-04T08:25:02Z"	121	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	"2024-11-04T08:23:19Z"	--- base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - Developed by: NAMJOON - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-instruct-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)
ucalyptus/prem-1B-chat-MLX	ucalyptus	"2024-05-11T23:35:01Z"	82	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mlx", "conversational", "dataset:cerebras/SlimPajama-627B", "dataset:HuggingFaceH4/ultrachat_200k", "dataset:hkust-nlp/deita-10k-v0", "dataset:Open-Orca/SlimOrca-Dedup", "dataset:cognitivecomputations/WizardLM_evol_instruct_V2_196k_unfiltered_merged_split", "dataset:HuggingFaceH4/capybara", "dataset:meta-math/MetaMathQA", "dataset:argilla/ultrafeedback-binarized-preferences-cleaned", "dataset:Intel/orca_dpo_pairs", "dataset:alexredna/oasst2_dpo_pairs", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	"2024-05-11T23:34:31Z"	--- license: apache-2.0 library_name: transformers tags: - mlx datasets: - cerebras/SlimPajama-627B - HuggingFaceH4/ultrachat_200k - hkust-nlp/deita-10k-v0 - Open-Orca/SlimOrca-Dedup - cognitivecomputations/WizardLM_evol_instruct_V2_196k_unfiltered_merged_split - HuggingFaceH4/capybara - meta-math/MetaMathQA - argilla/ultrafeedback-binarized-preferences-cleaned - Intel/orca_dpo_pairs - alexredna/oasst2_dpo_pairs pipeline_tag: text-generation --- # ucalyptus/prem-1B-chat-MLX This model was converted to MLX format from [`premai-io/prem-1B-chat`](). Refer to the [original model card](https://huggingface.co/premai-io/prem-1B-chat) for more details on the model. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("ucalyptus/prem-1B-chat-MLX") response = generate(model, tokenizer, prompt="hello", verbose=True) ```
arinzeo/opus-mt-id-en-finetuned-indo-to-eng	arinzeo	"2024-11-21T22:09:00Z"	91	0	transformers	[ "transformers", "safetensors", "marian", "text2text-generation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	"2024-11-19T22:04:35Z"	--- library_name: transformers tags: - generated_from_trainer model-index: - name: opus-mt-id-en-finetuned-indo-to-eng 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-id-en-finetuned-indo-to-eng This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.46.2 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
lmms-lab/LLaVA-NeXT-Video-34B	lmms-lab	"2024-04-24T05:31:23Z"	33	16	transformers	[ "transformers", "safetensors", "llava", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "region:us" ]	text-generation	"2024-04-16T14:13:36Z"	--- inference: false license: apache-2.0 --- <br> # LLaVA-Next-Video Model Card ## Model details Model type: <br> LLaVA-Next-Video is an open-source chatbot trained by fine-tuning LLM on multimodal instruction-following data. <br> Base LLM: [NousResearch/Nous-Hermes-2-Yi-34B](https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B) Model date: <br> LLaVA-Next-Video-34B was trained in April 2024. Paper or resources for more information: <br> https://github.com/LLaVA-VL/LLaVA-NeXT ## License [NousResearch/Nous-Hermes-2-Yi-34B](https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B) license. ## Where to send questions or comments about the model https://github.com/LLaVA-VL/LLaVA-NeXT/issues ## Intended use Primary intended uses: <br> The primary use of LLaVA is research on large multimodal models and chatbots. Primary intended users: <br> The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence. ## Training dataset ### Image - 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP. - 158K GPT-generated multimodal instruction-following data. - 500K academic-task-oriented VQA data mixture. - 50K GPT-4V data mixture. - 40K ShareGPT data. ### Video - 100K VideoChatGPT-Instruct. ## Evaluation dataset A collection of 4 benchmarks, including 3 academic VQA benchmarks and 1 captioning benchmark.
Brikowski/xlm-roberta-base-finetuned-wikiann	Brikowski	"2023-03-25T14:35:32Z"	104	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	"2023-03-25T13:40:26Z"	--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-wikiann 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. --> # xlm-roberta-base-finetuned-wikiann This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1638 - F1: 0.8878 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 0.2426 \| 1.0 \| 5000 \| 0.1882 \| 0.8495 \| \| 0.1475 \| 2.0 \| 10000 \| 0.1618 \| 0.8807 \| \| 0.1003 \| 3.0 \| 15000 \| 0.1638 \| 0.8878 \| ### Framework versions - Transformers 4.27.3 - Pytorch 2.0.0+cu117 - Datasets 2.10.1 - Tokenizers 0.13.2
Ahmedhisham/Arabic_dialect_identifier	Ahmedhisham	"2023-09-03T11:12:43Z"	0	0	keras	[ "keras", "tf-keras", "text-classification", "license:mit", "region:us" ]	text-classification	"2023-09-03T10:37:12Z"	--- license: mit metrics: - precision - recall library_name: keras pipeline_tag: text-classification ---

Subsets and Splits