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open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_SimNPO_lr5e-05_b4.5_a1_d1_g0.125_ep5	open-unlearning	2025-05-24T18:15:15Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-24T17:56:03Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_AltPO_lr5e-05_beta0.5_alpha1_epoch5	open-unlearning	2025-05-24T18:12:17Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-15T22:13:41Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Proximile/LLaDA-8B-Tools	Proximile	2025-05-24T18:09:24Z	102	7	transformers	[ "transformers", "safetensors", "llada", "feature-extraction", "tool-calling", "lora", "peft", "function-calling", "tools", "chatbot", "assistant", "sft", "text-generation", "conversational", "custom_code", "en", "base_model:GSAI-ML/LLaDA-8B-Instruct", "base_model:adapter:GSAI-ML/LLaDA-8B-Instruct", "license:mit", "region:us" ]	text-generation	2025-05-14T11:06:15Z	--- license: mit library_name: transformers pipeline_tag: text-generation base_model: GSAI-ML/LLaDA-8B-Instruct language: - en tags: - llada - tool-calling - lora - peft - function-calling - tools - chatbot - assistant - sft --- # LLaDA-8B-Tools This repository contains a variant of [GSAI-ML/LLaDA-8B-Instruct](https://huggingface.co/GSAI-ML/LLaDA-8B-Instruct), fine-tuned by [Proximile LLC](https://proximile.llc) to enhance model tool calling capabilities. Proximile specializes in secure, on-premise AI solutions for small and medium-sized businesses. ## Update Timeline - May 14 2025 – Initial public release. Training examples were missing the pad tokens filling out the rest of the generation window. - May 17 2025 – Patched training script to include correct padding; updated model weights pushed to this repository. - May 20 2025 – Google announces [Gemini Diffusion](https://blog.google/technology/google-deepmind/gemini-diffusion/). ![Demo](demo.gif) ## About LLaDA LLaDA (Large Language Diffusion with mAsking) is a novel language model architecture that uses discrete diffusion for text generation. Unlike traditional autoregressive models, LLaDA generates text through an iterative denoising process, progressively replacing mask tokens with predicted tokens based on confidence scores. ## Model Description This merged LoRA model was trained to improve LLaDA's ability to handle tool calling tasks, including: - Generating proper JSON for tool invocation - Processing tool response data - Providing helpful answers based on tool outputs ### Training Details - Base Model: GSAI-ML/LLaDA-8B-Instruct - Training Method: Supervised Fine-Tuning (SFT) with LoRA - LoRA Configuration: - Rank (r): 128 - Alpha: 256 - Target Modules: `q_proj`, `k_proj`, `v_proj`, `gate_proj` - Training Data: A modified subset of the [ToolACE](https://huggingface.co/datasets/Team-ACE/ToolACE) dataset. ## Installation ```bash pip install transformers peft torch bitsandbytes ``` ## Usage To use this model: ```python from transformers import AutoTokenizer, AutoModel from peft import PeftModel # Load the base model and tokenizer model_name = "Proximile/LLaDA-8B-Tools" tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) model = AutoModel.from_pretrained(model_name, trust_remote_code=True, device_map="auto") ``` ## Example Chat Completion Script Here's a complete example of using the model for chat completion with tool calling: ```python import torch import json from transformers import AutoTokenizer, AutoModel # Constants MASK_TOKEN_ID = 126336 def add_gumbel_noise(logits, temperature): ''' The Gumbel max is a method for sampling categorical distributions. For diffusion models, low-precision Gumbel Max affects generation quality. ''' if temperature <= 0: return logits logits = logits.to(torch.float64) noise = torch.rand_like(logits, dtype=torch.float64) gumbel_noise = (- torch.log(noise)) ** temperature return logits.exp() / gumbel_noise def get_num_transfer_tokens(mask_index, steps): ''' In the reverse process, we precompute the number of tokens to transition at each step. ''' mask_num = mask_index.sum(dim=1, keepdim=True) # Ensure we have at least one step if steps == 0: steps = 1 base = mask_num // steps remainder = mask_num % steps num_transfer_tokens = torch.zeros(mask_num.size(0), steps, device=mask_index.device, dtype=torch.int64) + base for i in range(mask_num.size(0)): if remainder[i] > 0: num_transfer_tokens[i, :remainder[i]] += 1 return num_transfer_tokens def generate(model, prompt, steps=128, gen_length=128, block_length=32, temperature=0., remasking='low_confidence', mask_id=MASK_TOKEN_ID): ''' Generate text using LLaDA's diffusion-based generation process. ''' device = next(model.parameters()).device prompt = prompt.to(device) x = torch.full((1, prompt.shape[1] + gen_length), mask_id, dtype=torch.long).to(device) x[:, :prompt.shape[1]] = prompt.clone() prompt_index = (x != mask_id) assert gen_length % block_length == 0 num_blocks = gen_length // block_length assert steps % num_blocks == 0 steps_per_block = steps // num_blocks for num_block in range(num_blocks): block_mask_index = (x[:, prompt.shape[1] + num_block * block_length: prompt.shape[1] + (num_block + 1) * block_length:] == mask_id) num_transfer_tokens = get_num_transfer_tokens(block_mask_index, steps_per_block) for i in range(steps_per_block): mask_index = (x == mask_id) if not mask_index.any(): break outputs = model(x) logits = outputs.logits logits_with_noise = add_gumbel_noise(logits, temperature=temperature) x0 = torch.argmax(logits_with_noise, dim=-1) # b, l if remasking == 'low_confidence': p = torch.nn.functional.softmax(logits.to(torch.float64), dim=-1) x0_p = torch.squeeze( torch.gather(p, dim=-1, index=torch.unsqueeze(x0, -1)), -1) # b, l elif remasking == 'random': x0_p = torch.rand((x0.shape[0], x0.shape[1]), device=x0.device) else: raise NotImplementedError(remasking) x0_p[:, prompt.shape[1] + (num_block + 1) * block_length:] = -float('inf') x0 = torch.where(mask_index, x0, x) confidence = torch.where(mask_index, x0_p, -float('inf')) transfer_index = torch.zeros_like(x0, dtype=torch.bool, device=x0.device) for j in range(confidence.shape[0]): _, select_index = torch.topk(confidence[j], k=num_transfer_tokens[j, i]) transfer_index[j, select_index] = True x[transfer_index] = x0[transfer_index] return x def chat_completion(model, tokenizer, messages, temperature=0.1, gen_length=128, steps=128): """ Generate a chat completion. Args: model: The LLaDA tool calling model tokenizer: The tokenizer messages: List of message dictionaries with 'role' and 'content' keys temperature: Temperature for generation (0 for greedy) gen_length: Maximum length of generated text steps: Number of denoising steps Returns: The generated response text """ # Format input for the model formatted_input = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) # Tokenize input input_ids = tokenizer(formatted_input, return_tensors="pt")["input_ids"] # Generate response with torch.no_grad(): output_ids = generate( model, input_ids, steps=steps, gen_length=gen_length, block_length=32, temperature=temperature, remasking='low_confidence' ) # Decode the generated output generated_text = tokenizer.decode(output_ids[0, input_ids.shape[1]:], skip_special_tokens=False).split("<\|")[0] return generated_text # Example usage if __name__ == "__main__": # Load the base model and tokenizer model_name = "Proximile/LLaDA-8B-Tools" tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) model = AutoModel.from_pretrained(model_name, trust_remote_code=True, device_map="auto") # Define tool calling function schema tool_schema = [ { "type": "function", "function": { "name": "get_weather", "description": "Get the current weather in a given location", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA" }, "unit": { "type": "string", "enum": ["celsius", "fahrenheit"], "description": "The unit of temperature" } }, "required": ["location", "unit"] } } } ] # Create conversation with system prompt including tool description system_prompt = """You are a helpful assistant with tool calling capabilities. When you receive a tool call response, use the output to format an answer to the orginal user question. If you choose to use one or more of the following tool functions, respond with a list of JSON function calls, each with the proper arguments that best answers the given prompt. Each tool request within the list should be in the exact format {"name": function name, "parameters": {dictionary of argument names and values}}. Do not use variables. Just a list of two-key dictionaries, each starting with the function name, followed by a dictionary of parameters. Here are the tool functions available to you: """ + json.dumps(tool_schema, indent=4) + """ After receiving the results back from a function call, you have to formulate your response to the user. If the information needed is not found in the returned data, either attempt a new function call, or inform the user that you cannot answer based on your available knowledge. The user cannot see the function results. You have to interpret the data and provide a response based on it. If the user request does not necessitate a function call, simply respond to the user's query directly.""" messages = [ {"role": "system", "content": system_prompt}, {"role": "user", "content": "What's the weather like in New York?"} ] # Generate assistant response (expecting tool call) assistant_response = chat_completion(model, tokenizer, messages) print(f"Assistant: {assistant_response}") # Mock tool response tool_response = json.dumps({ "location": "New York, NY", "temperature": 72, "unit": "fahrenheit", "condition": "Partly Cloudy", "humidity": 65, "wind_speed": 8, "wind_direction": "NE" }) # Add assistant and tool responses to the conversation messages.append({"role": "assistant", "content": assistant_response}) messages.append({"role": "ipython", "content": tool_response}) # Generate final assistant response final_response = chat_completion(model, tokenizer, messages) print(f"Assistant (with tool data): {final_response}") # Assistant: [{"name": "get_weather", "parameters": {"location": "New York", "unit": "fahrenheit"}}] # Assistant (with tool data): The current weather in New York is as follows: # - Temperature: 72°F # - Weather Condition: Partly Cloudy # - Humidity: 65% # - Wind Speed: 8 miles per hour # - Wind Direction: Northeast ``` ## Limitations - LLaDA's diffusion-based generation is different from standard LLMs and may behave differently in certain contexts - The model may still hallucinate or generate incorrect tool call formats - The format of the tool call must precisely match what is shown in the example (which is a modified version of [the official llama 3.1 format](https://www.llama.com/docs/model-cards-and-prompt-formats/llama3_1/)) ## Citation If you use this model in your research, please cite the original LLaDA paper as well as this adapter: ``` @misc{llada-8b-tools, author = {Proximile LLC}, title = {LLaDA-8B-Tools}, year = {2025}, publisher = {Hugging Face}, howpublished = {\url{https://huggingface.co/Proximile/LLaDA-8B-Tools}} } ``` ## About Proximile LLC Proximile LLC provides secure, cost-effective, and private AI solutions tailored to small and medium-sized businesses. We specialize in: - On-premise AI inference solutions that ensure unparalleled privacy - Cost-effective hardware configurations including the Jetson Orin Nano Super - Secure Local AI applications including chatbots, RAG systems, and custom AI tools - Specialized services for compliance & governance, knowledge management, and IT automation Visit [proximile.llc](https://proximile.llc) to learn more about our secure, local AI solutions for your business. ## License This adapter is released under the same license as the base LLaDA model.
open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_SimNPO_lr1e-05_b4.5_a1_d0_g0.125_ep5	open-unlearning	2025-05-24T18:09:01Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-24T18:07:44Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
MohamedAliFarhat/ppo-Huggy	MohamedAliFarhat	2025-05-24T18:07:04Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2025-05-24T18:06:41Z	--- 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: MohamedAliFarhat/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
ayush7/sarvam-m_fp4	ayush7	2025-05-24T18:05:37Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-24T11:51:33Z	--- library_name: transformers license: apache-2.0 --- # Model Card for Model ID FP4 quantization of Sarvam-m model for educational purpose. Any and all copyright belongs to the original publishers. Please visit the original developers of the model at sarvam.ai No copyright infringement intended. ## 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: sarvam.ai[https://www.sarvam.ai/blogs/sarvam-m] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: FP4 model. 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mohamed2811/Muffakir_Embedding_V2	mohamed2811	2025-05-24T18:00:27Z	19	0	sentence-transformers	[ "sentence-transformers", "safetensors", "xlm-roberta", "sentence-similarity", "ar", "dataset:castorini/mr-tydi", "dataset:hsseinmz/arcd", "dataset:Omartificial-Intelligence-Space/Arabic-finanical-rag-embedding-dataset", "dataset:arbml/Arabic_RC", "base_model:sayed0am/arabic-english-bge-m3", "base_model:finetune:sayed0am/arabic-english-bge-m3", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-05-23T12:24:56Z	--- language: - ar base_model: - sayed0am/arabic-english-bge-m3 tags: - sentence-similarity - sentence-transformers datasets: - castorini/mr-tydi - hsseinmz/arcd - Omartificial-Intelligence-Space/Arabic-finanical-rag-embedding-dataset - arbml/Arabic_RC --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/662294730e805d4fcb06a892/n3whDLHDmEAhbFgYCbhRj.png) # 🧠 Muffakir: Fine-tuned Arabic Model for RAG & Dense Retrieval [Muffakir](https://huggingface.co/mohamed2811/Muffakir_Embedding_V2) This is the second version of the [Muffakir_Embedding model](https://huggingface.co/mohamed2811/Muffakir_Embedding). It shows strong performance in Arabic retrieval-augmented generation (RAG) and dense retrieval tasks. We plan to release a series of models focused on different topics and domains to further enhance Arabic information retrieval. 🚀 --- ## 🔍 Model Overview * 🧬 Base model: [`sayed0am/arabic-english-bge-m3`](https://huggingface.co/sayed0am/arabic-english-bge-m3) * 📚 Fine-tuning dataset: \~70,000 Arabic sentence pairs from various topics * 🏫 20K curated from Egyptian legal books * 🌐 50K collected from Hugging Face datasets (multi-domain) * 🏋️ Training epochs: 3 * 📏 Embedding dimension: 1024 * 🔗 Loss functions: * [`MultipleNegativesRankingLoss`](https://www.sbert.net/docs/package_reference/losses.html#multiplenegativesrankingloss) * [`MatryoshkaLoss`](https://huggingface.co/blog/matryoshka-representations) for multi-resolution embeddings --- ## 🌟 Key Features * 🥇 Strong performance in Arabic RAG and dense retrieval tasks * 🎯 Multi-resolution embeddings via Matryoshka (dims: `1024 → 64`) * 🌍 Supports (Arabic) encoding * 📦 Ready for use in real-world search, Q\&A, and AI agent systems --- ## ⚙️ Training Details * 🧾 Dataset size: 70K examples * 🗂️ Topics: Multi-domain (educational, legal, general knowledge, etc.) * 🔁 Epochs: 3 * 🧪 Batch size: 8 (gradient accumulation enabled) * 🚀 Learning rate: 2e-5 * 🧰 Framework: [sentence-transformers](https://www.sbert.net) --- ## 📀 Model Specs * 🔢 Embedding size: `1024` * 🔄 Supports Matryoshka-style dimension truncation * 🧠 Bi-encoder setup, ideal for fast and scalable retrieval tasks --- --- ## 🏆 Leaderboard Performance * The Muffakir\_Embedding\_V2 model has achieved notable rankings on the [Arabic RAG Leaderboard](https://huggingface.co/spaces/Navid-AI/The-Arabic-Rag-Leaderboard), securing: * 5th place in the Retrieval category * These results underscore the model's effectiveness in both retrieving relevant information and accurately ranking it within Arabic Retrieval-Augmented Generation (RAG) systems. --- ## 🧪 Example Usage ```python from sentence_transformers import SentenceTransformer import torch # Load the fine-tuned Muffakir model model = SentenceTransformer("mohamed2811/Muffakir_Embedding_V2") # Example query and candidate passages query = "ما هي شروط صحة العقد؟" passages = [ "يشترط التراضي لصحة العقد.", "ينقسم القانون إلى عام وخاص.", "العقد شريعة المتعاقدين.", "تنتهي الولاية القانونية ببلوغ سن الرشد." ] # Encode query and passages embedding_query = model.encode([query], convert_to_tensor=True, normalize_embeddings=True) embedding_passages = model.encode(passages, convert_to_tensor=True, normalize_embeddings=True) # Compute cosine similarities cosine_scores = torch.matmul(embedding_query, embedding_passages.T) # Get best matching passage best_idx = cosine_scores.argmax().item() best_passage = passages[best_idx] print(f"🔍 Best matching passage: {best_passage}") ``` ```python @misc{muffakir2025, author = {Mohamed Khaled}, title = {Muffakir: State-of-the-art Arabic-English Bi-Encoder for Dense Retrieval}, year = {2025}, howpublished = {\url{https://huggingface.co/your-username/Muffakir-embeddings-v2}}, } ``` ---
OmarIDK/MNLP_M2_document_encoder	OmarIDK	2025-05-24T17:52:41Z	0	0	sentence-transformers	[ "sentence-transformers", "pytorch", "tf", "rust", "onnx", "safetensors", "openvino", "bert", "feature-extraction", "sentence-similarity", "transformers", "en", "dataset:s2orc", "dataset:flax-sentence-embeddings/stackexchange_xml", "dataset:ms_marco", "dataset:gooaq", "dataset:yahoo_answers_topics", "dataset:code_search_net", "dataset:search_qa", "dataset:eli5", "dataset:snli", "dataset:multi_nli", "dataset:wikihow", "dataset:natural_questions", "dataset:trivia_qa", "dataset:embedding-data/sentence-compression", "dataset:embedding-data/flickr30k-captions", "dataset:embedding-data/altlex", "dataset:embedding-data/simple-wiki", "dataset:embedding-data/QQP", "dataset:embedding-data/SPECTER", "dataset:embedding-data/PAQ_pairs", "dataset:embedding-data/WikiAnswers", "arxiv:1904.06472", "arxiv:2102.07033", "arxiv:2104.08727", "arxiv:1704.05179", "arxiv:1810.09305", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-05-24T17:42:16Z	--- language: en license: apache-2.0 library_name: sentence-transformers tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers datasets: - s2orc - flax-sentence-embeddings/stackexchange_xml - ms_marco - gooaq - yahoo_answers_topics - code_search_net - search_qa - eli5 - snli - multi_nli - wikihow - natural_questions - trivia_qa - embedding-data/sentence-compression - embedding-data/flickr30k-captions - embedding-data/altlex - embedding-data/simple-wiki - embedding-data/QQP - embedding-data/SPECTER - embedding-data/PAQ_pairs - embedding-data/WikiAnswers pipeline_tag: sentence-similarity --- # all-MiniLM-L6-v2 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2') 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 import torch.nn.functional as F #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('sentence-transformers/all-MiniLM-L6-v2') model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2') # 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 sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) # Normalize embeddings sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1) print("Sentence embeddings:") print(sentence_embeddings) ``` ------ ## Background The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised contrastive learning objective. We used the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model and fine-tuned in on a 1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset. We developed this model during the [Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organized by Hugging Face. We developed this model as part of the project: [Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks. ## Intended uses Our model is intended to be used as a sentence and short paragraph encoder. Given an input text, it outputs a vector which captures the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks. By default, input text longer than 256 word pieces is truncated. ## Training procedure ### Pre-training We use the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model. Please refer to the model card for more detailed information about the pre-training procedure. ### Fine-tuning We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch. We then apply the cross entropy loss by comparing with true pairs. #### Hyper parameters We trained our model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core). We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with a 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`. #### Training data We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences. We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file. \| Dataset \| Paper \| Number of training tuples \| \|--------------------------------------------------------\|:----------------------------------------:\|:--------------------------:\| \| [Reddit comments (2015-2018)](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) \| [paper](https://arxiv.org/abs/1904.06472) \| 726,484,430 \| \| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Abstracts) \| [paper](https://aclanthology.org/2020.acl-main.447/) \| 116,288,806 \| \| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs \| [paper](https://doi.org/10.1145/2623330.2623677) \| 77,427,422 \| \| [PAQ](https://github.com/facebookresearch/PAQ) (Question, Answer) pairs \| [paper](https://arxiv.org/abs/2102.07033) \| 64,371,441 \| \| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Titles) \| [paper](https://aclanthology.org/2020.acl-main.447/) \| 52,603,982 \| \| [S2ORC](https://github.com/allenai/s2orc) (Title, Abstract) \| [paper](https://aclanthology.org/2020.acl-main.447/) \| 41,769,185 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs \| - \| 25,316,456 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title+Body, Answer) pairs \| - \| 21,396,559 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs \| - \| 21,396,559 \| \| [MS MARCO](https://microsoft.github.io/msmarco/) triplets \| [paper](https://doi.org/10.1145/3404835.3462804) \| 9,144,553 \| \| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) \| [paper](https://arxiv.org/pdf/2104.08727.pdf) \| 3,012,496 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) \| [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) \| 1,198,260 \| \| [Code Search](https://huggingface.co/datasets/code_search_net) \| - \| 1,151,414 \| \| [COCO](https://cocodataset.org/#home) Image captions \| [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) \| 828,395\| \| [SPECTER](https://github.com/allenai/specter) citation triplets \| [paper](https://doi.org/10.18653/v1/2020.acl-main.207) \| 684,100 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) \| [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) \| 681,164 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) \| [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) \| 659,896 \| \| [SearchQA](https://huggingface.co/datasets/search_qa) \| [paper](https://arxiv.org/abs/1704.05179) \| 582,261 \| \| [Eli5](https://huggingface.co/datasets/eli5) \| [paper](https://doi.org/10.18653/v1/p19-1346) \| 325,475 \| \| [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) \| [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) \| 317,695 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles) \| \| 304,525 \| \| AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) \| [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) \| 277,230 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (bodies) \| \| 250,519 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles+bodies) \| \| 250,460 \| \| [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) \| [paper](https://www.aclweb.org/anthology/D13-1155/) \| 180,000 \| \| [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) \| [paper](https://arxiv.org/abs/1810.09305) \| 128,542 \| \| [Altlex](https://github.com/chridey/altlex/) \| [paper](https://aclanthology.org/P16-1135.pdf) \| 112,696 \| \| [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) \| - \| 103,663 \| \| [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) \| [paper](https://www.aclweb.org/anthology/P11-2117/) \| 102,225 \| \| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) \| [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) \| 100,231 \| \| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) \| [paper](https://aclanthology.org/P18-2124.pdf) \| 87,599 \| \| [TriviaQA](https://huggingface.co/datasets/trivia_qa) \| - \| 73,346 \| \| Total \| \| 1,170,060,424** \|
talphaidze/qwen3-mcqa	talphaidze	2025-05-24T17:51:23Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-24T17:46:12Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. 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vermoney/43a09428-1daa-4584-8587-dbd8067c9a33	vermoney	2025-05-24T17:48:57Z	0	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "llama", "text-generation", "generated_from_trainer", "axolotl", "dpo", "trl", "conversational", "arxiv:2305.18290", "base_model:NousResearch/Hermes-2-Pro-Llama-3-8B", "base_model:quantized:NousResearch/Hermes-2-Pro-Llama-3-8B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-24T17:31:05Z	--- base_model: NousResearch/Hermes-2-Pro-Llama-3-8B library_name: transformers model_name: 43a09428-1daa-4584-8587-dbd8067c9a33 tags: - generated_from_trainer - axolotl - dpo - trl licence: license --- # Model Card for 43a09428-1daa-4584-8587-dbd8067c9a33 This model is a fine-tuned version of [NousResearch/Hermes-2-Pro-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B). 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="vermoney/43a09428-1daa-4584-8587-dbd8067c9a33", 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/dedok-yo/s56-9/runs/l3uxvp6c) 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: 4.46.0 - Pytorch: 2.5.0+cu124 - Datasets: 3.0.1 - Tokenizers: 0.20.1 ## 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}} } ```
lucyknada/nvidia_Llama-3.1-Nemotron-8B-UltraLong-1M-Instruct-exl3	lucyknada	2025-05-24T17:44:17Z	0	0	transformers	[ "transformers", "en", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	null	2025-05-24T17:43:28Z	--- library_name: transformers language: - en license: cc-by-nc-4.0 --- ### exl3 quant --- ### check revisions for quants --- # Model Information We introduce Nemotron-UltraLong-8B, a series of ultra-long context language models designed to process extensive sequences of text (up to 1M, 2M, and 4M tokens) while maintaining competitive performance on standard benchmarks. Built on the Llama-3.1, UltraLong-8B leverages a systematic training recipe that combines efficient continued pretraining with instruction tuning to enhance long-context understanding and instruction-following capabilities. This approach enables our models to efficiently scale their context windows without sacrificing general performance. ## The UltraLong Models - [nvidia/Llama-3.1-Nemotron-8B-UltraLong-1M-Instruct](https://huggingface.co/nvidia/Llama-3.1-Nemotron-8B-UltraLong-1M-Instruct) - [nvidia/Llama-3.1-Nemotron-8B-UltraLong-2M-Instruct](https://huggingface.co/nvidia/Llama-3.1-Nemotron-8B-UltraLong-2M-Instruct) - [nvidia/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct](https://huggingface.co/nvidia/Llama-3.1-Nemotron-8B-UltraLong-4M-Instruct) ## Uses 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`. ```python import transformers import torch model_id = "nvidia/Llama-3.1-Nemotron-8B-UltraLong-1M-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]) ``` ## Model Card * Base model: [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) * Continued Pretraining: The training data consists of 1B tokens sourced from a pretraining corpus using per-domain upsampling based on sample length. The model was trained for 125 iterations with a sequence length of 1M and a global batch size of 8. * Supervised fine-tuning (SFT): 1B tokens on open-source instruction datasets across general, mathematics, and code domains. We subsample the data from the ‘general_sft_stage2’ from [AceMath-Instruct](https://huggingface.co/datasets/nvidia/AceMath-Instruct-Training-Data). * Maximum context window: 1M tokens ## Evaluation Results We evaluate Nemotron-UltraLong-8B on a diverse set of benchmarks, including long-context tasks (e.g., RULER, LV-Eval, and InfiniteBench) and standard tasks (e.g., MMLU, MATH, GSM-8K, and HumanEval). UltraLong-8B achieves superior performance on ultra-long context tasks while maintaining competitive results on standard benchmarks. ### Needle in a Haystack <img width="80%" alt="image" src="Llama-3.1-8B-UltraLong-1M-Instruct.png"> ### Long context evaluation <img width="80%" alt="image" src="long_benchmark.png"> ### Standard capability evaluation <img width="80%" alt="image" src="standard_benchmark.png"> ## Correspondence to Chejian Xu ([email protected]), Wei Ping ([email protected]) ## Citation <pre> @article{ulralong2025, title={From 128K to 4M: Efficient Training of Ultra-Long Context Large Language Models}, author={Xu, Chejian and Ping, Wei and Xu, Peng and Liu, Zihan and Wang, Boxin and Shoeybi, Mohammad and Catanzaro, Bryan}, journal={arXiv preprint}, year={2025} } </pre>
Delta-Vector/Archaeo-12B-V2	Delta-Vector	2025-05-24T17:43:26Z	70	4	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "roleplay", "creative-writing", "merge", "mergekit", "conversational", "base_model:Delta-Vector/Francois-PE-V2-Huali-12B", "base_model:merge:Delta-Vector/Francois-PE-V2-Huali-12B", "base_model:Delta-Vector/Rei-V3-KTO-12B", "base_model:merge:Delta-Vector/Rei-V3-KTO-12B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-19T23:35:24Z	--- tags: - roleplay - creative-writing - merge - mergekit base_model: - Delta-Vector/Francois-PE-V2-Huali-12B - Delta-Vector/Rei-V3-KTO-12B pipeline_tag: text-generation library_name: transformers --- ``` __~a~_ ~~; ~_ _ ~ ~_ _ '_\;__._._._._._._] ~_._._._._._.__;/_` '(/'/'/'/'\|'\|'\|'\| ( )\|'\|'\|'\|'\'\'\'\)' (/ / / /, \| \| \| \|(/ \) \| \| \| ,\ \ \ \) (/ / / / / \| \| \| ~(/ \) ~ \| \| \ \ \ \ \) (/ / / / / ~ ~ ~ (/ \) ~ ~ \ \ \ \ \) (/ / / / ~ / (\|\|)\| ~ \ \ \ \) ~ / / ~ M /\|\|\M ~ \ \ ~ ~ ~ /\|\|\ ~ ~ //\|\|\\ //\|\|\\ //\|\|\\ '/\|\|\' "Archaeopteryx" ``` <html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <style> @import url('https://fonts.googleapis.com/css2?family=VT323&display=swap'); body { background: #0a0017; margin: 0; padding: 20px; font-family: 'VT323', monospace; color: #ff00aa; text-shadow: 0 0 8px #ff00aa; animation: glitch-flicker 0.2s infinite alternate; } @keyframes glitch-flicker { 0% { text-shadow: 0 0 5px #ff00aa, 0 0 15px #ff00aa; } 100% { text-shadow: 0 0 8px #ff0066, 0 0 18px #ff0066; } } .crt-container { padding: 10px; max-width: 900px; margin: auto; } .crt-case { background: linear-gradient(135deg, #130021, #20002c); border-radius: 10px; padding: 15px; box-shadow: inset 2px 2px 10px rgba(255,0,170,0.5), 2px 2px 5px rgba(255,0,170,0.3), 0 0 25px rgba(255,0,170,0.2); } .crt-screen { background: #0c011a; padding: 20px; border-radius: 10px; box-shadow: inset 0 0 25px rgba(255,0,170,0.3), 0 0 15px rgba(255,0,170,0.7); filter: contrast(1.2) brightness(1.2); text-shadow: 0px 0px 5px #ff00aa; animation: glow-pulse 3s infinite alternate; } @keyframes glow-pulse { 0% { box-shadow: inset 0 0 20px rgba(255,0,170,0.3), 0 0 15px rgba(255,0,170,0.3); } 100% { box-shadow: inset 0 0 30px rgba(255,0,170,0.5), 0 0 25px rgba(255,0,170,0.5); } } h2 { color: #ff33cc; text-align: center; font-size: 28px; text-shadow: 0 0 8px #ff33cc, 0 0 18px #ff0044; } pre { background: rgba(255,0,170,0.1); padding: 10px; border-radius: 10px; color: #ff66cc; font-size: 14px; box-shadow: inset 0 0 10px rgba(255,0,170,0.5); } .glitch { animation: text-glitch 0.5s infinite alternate; } @keyframes text-glitch { 0% { transform: translateX(-2px); text-shadow: 0 0 5px #ff0066, 0 0 10px #ff33cc; } 100% { transform: translateX(2px); text-shadow: 0 0 8px #ff00aa, 0 0 20px #ff0099; } } .neon-link { color: #ff66cc; text-decoration: none; transition: text-shadow 0.3s ease; } .neon-link:hover { text-shadow: 0px 0px 15px #ff66cc, 0 0 25px rgba(255,0,170,0.5); } .ascii-art { text-align: center; font-size: 12px; color: #ff33cc; text-shadow: 0px 0px 5px #ff00ff; margin-bottom: 20px; } .quantso-container { display: flex; justify-content: center; gap: 20px; margin-top: 20px; } .quantso-box { background: rgba(255,0,170,0.1); padding: 15px; border-radius: 10px; text-align: center; box-shadow: inset 0 0 10px rgba(255,0,170,0.5); flex: 1; max-width: 150px; } </style> </head> <body> <div class="crt-container"> <div class="crt-case"> <div class="crt-screen"> <p>A series of Merges made for Roleplaying & Creative Writing, This model uses Rei-V3-KTO-12B and Francois-PE-V2-Huali-12B and Slerp to merge the 2 models - as a sequel to the OG Archaeo.</p> <h3>ChatML formatting</h3> <pre> """<\|im_start\|>system system prompt<\|im_end\|> <\|im_start\|>user Hi there!<\|im_end\|> <\|im_start\|>assistant Nice to meet you!<\|im_end\|> <\|im_start\|>user Can I ask a question?<\|im_end\|> <\|im_start\|>assistant """ </pre> <h3>MergeKit Configuration</h3> <pre> models: - model: Delta-Vector/Rei-V3-KTO-12B - model: Delta-Vector/Francois-PE-V2-Huali-12B merge_method: slerp base_model: Delta-Vector/Rei-V3-KTO-12B parameters: t: - value: 0.2 dtype: bfloat16 tokenizer_source: base </pre> <h3>Quants:</h3> <div class="quantso-container"> <div class="quantso-box"> <strong>GGUF</strong><br> <a class="neon-link" href="#">https://huggingface.co/bartowski/Delta-Vector_Archaeo-12B-V2-GGUF/</a> </div> <div class="quantso-box"> <strong>EXL2</strong><br> <a class="neon-link" href="#">https://huggingface.co/collections/ReadyArt/delta-vector-archaeo-12b-v2-exl2-682ca1508f01103d9554e553</a> </div> </div> <h3>Credits</h3> <p>Thank you to: Kubernetes-bad, LucyKnada, Intervitens, Samantha Twinkman, Tav, Alicat, Auri, Trappu & The rest of Anthracite</p> </div> </div> </div> </body> </html>
kimxxxx/mistral_r64_a128_b8_gas8_Ler5e-5_hackcehctfmansub_1epoch	kimxxxx	2025-05-24T17:41:15Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T17:39:59Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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mradermacher/InForage-3B-PPO-GGUF	mradermacher	2025-05-24T17:40:54Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:TommyChien/InForage-3B-PPO", "base_model:quantized:TommyChien/InForage-3B-PPO", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-24T17:17:25Z	--- base_model: TommyChien/InForage-3B-PPO language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/TommyChien/InForage-3B-PPO <!-- 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/InForage-3B-PPO-GGUF/resolve/main/InForage-3B-PPO.Q2_K.gguf) \| Q2_K \| 1.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/InForage-3B-PPO-GGUF/resolve/main/InForage-3B-PPO.Q3_K_S.gguf) \| Q3_K_S \| 1.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/InForage-3B-PPO-GGUF/resolve/main/InForage-3B-PPO.Q3_K_M.gguf) \| Q3_K_M \| 1.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/InForage-3B-PPO-GGUF/resolve/main/InForage-3B-PPO.Q3_K_L.gguf) \| Q3_K_L \| 1.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/InForage-3B-PPO-GGUF/resolve/main/InForage-3B-PPO.IQ4_XS.gguf) \| IQ4_XS \| 2.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/InForage-3B-PPO-GGUF/resolve/main/InForage-3B-PPO.Q4_K_S.gguf) \| Q4_K_S \| 2.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/InForage-3B-PPO-GGUF/resolve/main/InForage-3B-PPO.Q4_K_M.gguf) \| Q4_K_M \| 2.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/InForage-3B-PPO-GGUF/resolve/main/InForage-3B-PPO.Q5_K_S.gguf) \| Q5_K_S \| 2.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/InForage-3B-PPO-GGUF/resolve/main/InForage-3B-PPO.Q5_K_M.gguf) \| Q5_K_M \| 2.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/InForage-3B-PPO-GGUF/resolve/main/InForage-3B-PPO.Q6_K.gguf) \| Q6_K \| 2.9 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/InForage-3B-PPO-GGUF/resolve/main/InForage-3B-PPO.Q8_0.gguf) \| Q8_0 \| 3.7 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/InForage-3B-PPO-GGUF/resolve/main/InForage-3B-PPO.f16.gguf) \| f16 \| 6.9 \| 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 -->
polyglots/SinLlama-Instruct-si-News-Category-Transliterated-2661	polyglots	2025-05-24T17:34:19Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b", "base_model:finetune:unsloth/llama-3-8b", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-24T17:33:15Z	--- base_model: unsloth/llama-3-8b tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: polyglots - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
LiliaBakh/gorelik_lora_1_may_2025	LiliaBakh	2025-05-24T17:32:33Z	0	0	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	2025-05-24T17:01:36Z	--- 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: gorelik --- # Gorelik_Lora_1_May_2025 <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `gorelik` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "gorelik", "lora_weights": "https://huggingface.co/LiliaBakh/gorelik_lora_1_may_2025/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## 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('LiliaBakh/gorelik_lora_1_may_2025', weight_name='lora.safetensors') image = pipeline('gorelik').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) ## Training details - Steps: 1000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/LiliaBakh/gorelik_lora_1_may_2025/discussions) to add images that show off what you’ve made with this LoRA.
dimasik2987/90478020-ec3a-4093-a5c4-2013dd600750	dimasik2987	2025-05-24T17:29:48Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:heegyu/WizardVicuna-open-llama-3b-v2", "base_model:adapter:heegyu/WizardVicuna-open-llama-3b-v2", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-24T16:55:59Z	--- library_name: peft license: apache-2.0 base_model: heegyu/WizardVicuna-open-llama-3b-v2 tags: - axolotl - generated_from_trainer model-index: - name: 90478020-ec3a-4093-a5c4-2013dd600750 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 absolute_data_files: false adapter: lora base_model: heegyu/WizardVicuna-open-llama-3b-v2 bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - cc1f5b1959c57013_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_input: input field_instruction: instruct field_output: output format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null dpo: beta: 0.1 enabled: true group_by_length: false rank_loss: true reference_model: 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: 2 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: dimasik2987/90478020-ec3a-4093-a5c4-2013dd600750 hub_repo: null hub_strategy: end hub_token: null learning_rate: 2.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 500 micro_batch_size: 6 mixed_precision: bf16 mlflow_experiment_name: /tmp/cc1f5b1959c57013_train_data.json model_type: AutoModelForCausalLM num_epochs: 2 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 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: 718ac179-f573-4920-8e2e-046d87265652 wandb_project: s56-7 wandb_run: your_name wandb_runid: 718ac179-f573-4920-8e2e-046d87265652 warmup_steps: 50 weight_decay: 0.02 xformers_attention: true ``` </details><br> # 90478020-ec3a-4093-a5c4-2013dd600750 This model is a fine-tuned version of [heegyu/WizardVicuna-open-llama-3b-v2](https://huggingface.co/heegyu/WizardVicuna-open-llama-3b-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9409 ## 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-06 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 12 - 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: 50 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 2.126 \| 0.0001 \| 1 \| 1.9922 \| \| 1.3232 \| 0.0155 \| 250 \| 1.0599 \| \| 1.4041 \| 0.0311 \| 500 \| 0.9409 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
davgauch/MNLP_M2_mcqa_model_sft_rationale	davgauch	2025-05-24T17:19:34Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "conversational", "base_model:Qwen/Qwen3-0.6B-Base", "base_model:finetune:Qwen/Qwen3-0.6B-Base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-24T14:53:35Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen3-0.6B-Base tags: - generated_from_trainer model-index: - name: MNLP_M2_mcqa_model_sft_rationale 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. --> # MNLP_M2_mcqa_model_sft_rationale This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2689 ## 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: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 8 - 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: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.5437 \| 1.0 \| 1526 \| 1.2729 \| \| 1.4521 \| 2.0 \| 3052 \| 1.2641 \| \| 1.434 \| 2.9984 \| 4575 \| 1.2689 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.5.1+cu124 - Datasets 3.2.0 - Tokenizers 0.21.0
cragtmp/task1o	cragtmp	2025-05-24T17:13:18Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-3.2-11B-Vision-Instruct", "base_model:adapter:meta-llama/Llama-3.2-11B-Vision-Instruct", "region:us" ]	null	2025-05-24T15:49:09Z	--- base_model: meta-llama/Llama-3.2-11B-Vision-Instruct 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 0.15.2
FormlessAI/080bcf2f-eac4-47f9-9439-b106b1902f95	FormlessAI	2025-05-24T17:11:09Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "dpo", "arxiv:2305.18290", "base_model:Qwen/Qwen2.5-1.5B", "base_model:finetune:Qwen/Qwen2.5-1.5B", "endpoints_compatible", "region:us" ]	null	2025-05-24T16:10:42Z	--- base_model: Qwen/Qwen2.5-1.5B library_name: transformers model_name: 080bcf2f-eac4-47f9-9439-b106b1902f95 tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for 080bcf2f-eac4-47f9-9439-b106b1902f95 This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B](https://huggingface.co/Qwen/Qwen2.5-1.5B). 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="FormlessAI/080bcf2f-eac4-47f9-9439-b106b1902f95", 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/phoenix-formless/Gradients/runs/5e4wjr9l) 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: 0.17.0 - Transformers: 4.52.3 - Pytorch: 2.7.0+cu128 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## 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 Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
pangjin001/lora_model-llama-shige	pangjin001	2025-05-24T17:10:14Z	0	0	transformers	[ "transformers", "safetensors", "gguf", "llama", "text-generation-inference", "unsloth", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-24T15:43:45Z	--- base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: pangjin001 - License: apache-2.0 - Finetuned from model : unsloth/meta-llama-3.1-8b-unsloth-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)
Okroshich/t5_hw3	Okroshich	2025-05-24T17:07:27Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-24T17:06:28Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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WhoRitikrana/news-classification	WhoRitikrana	2025-05-24T17:03:02Z	10	0	null	[ "text-classification", "en", "license:apache-2.0", "region:us" ]	text-classification	2025-05-20T05:46:35Z	--- license: apache-2.0 language: - en metrics: - accuracy pipeline_tag: text-classification --- # Model Card for Model ID This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description 📰 Model Card: scratch-news-classifier 📌 Model Description scratch-news-classifier is a custom-built neural network model trained from scratch for multi-class news article classification. It takes raw news text as input and classifies it into categories such as World, Sports, Business, Science/Technology, and Entertainment This model was built without using pre-trained transformer architectures, and it was trained end-to-end using a custom dataset. - Developed by: [Mr.Ritik Rana] - Model type: [Text Classification ] ## Uses You can use this model to extract the category of a news article from raw text input. It helps in automating the categorization of news content for applications like news aggregators, content filters, and information retrieval systems. ### Direct Use This model can be used directly by passing a news article or headline as input and obtaining the category as output. Example categories: • World • Sports • Business • Science/Technology ### Downstream Use This model can be integrated into: • News recommendation engines • Content moderation systems • News trend analysis tools • Automated tagging/classification for CMS ### Out-of-Scope Use • Not suitable for non-English news content • Not trained for detecting fake news or misinformation • Does not handle sarcasm, bias detection, or satire • Not ideal for mixed-topic or very short informal texts (e.g., tweets) ## Bias, Risks, and Limitations • Data Bias: The model may inherit biases present in the training data, such as overrepresentation of certain news categories or topics. • Language Limitation: Only trained on English-language articles. May misclassify non-English or multilingual content. • Temporal Limitation: The model’s training data may not reflect the most current topics or slang used in news. • Contextual Limitation: It may misclassify articles where context is subtle or relies heavily on real-world knowledge. ### Recommendations Users (both direct and downstream) should be aware that: • The model may show degraded performance on out-of-distribution or biased datasets. • A human-in-the-loop approach is recommended for high-stakes or sensitive applications. • Retraining on updated or domain-specific datasets can improve performance. ⸻ ## Training Details ### Training Data Training Data • Dataset: Ag_news dataset with labeled articles across 4 categories. • Source:Tensorflow datasets • Preprocessing: • Lowercasing • Stopword removal • Tokenization (Word-level or Subword depending on model) • Padding and truncation to max length 100 ### Training Procedure Preprocessing • Text tokenization using Keras Tokenizer (or custom tokenizer) • Label encoding into integer categories • Train-test split: 80/20 #### Training Hyperparameters • Epochs: 5 • Batch Size: 32 • Optimizer: Adam • Loss: sparse_categorical_crossentropy #### Speeds, Sizes, Times [optional] Speeds, Sizes, Times • Model size: ~5 MB • Training time: ~20 minutes on GogleColab with T4 GPU support • Training framework: TensorFlow 2.x ## Evaluation • Accuracy: 92.2% • F1 Score: 90.6% • Precision: 91.0% • Recall: 90.2% ### Results The model performs well across all categories, with minor confusion between Business and Technology due to overlapping vocabulary. #### Summary Summary This model is lightweight, fast, and suitable for real-time classification tasks. It provides strong performance across major news categories and can be adapted for other domains with additional training.
rinabuoy/mms-tts-khm-finetuned	rinabuoy	2025-05-24T17:00:02Z	23	0	transformers	[ "transformers", "safetensors", "vits", "text-to-audio", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	text-to-audio	2025-05-03T08:41:12Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Mohibrehman31/custom-head-unsw-llama-3.2-1b	Mohibrehman31	2025-05-24T16:56:24Z	0	0	peft	[ "peft", "safetensors", "llama", "arxiv:1910.09700", "base_model:meta-llama/Llama-3.2-1B", "base_model:adapter:meta-llama/Llama-3.2-1B", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-24T16:56:00Z	--- base_model: meta-llama/Llama-3.2-1B 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. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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allura-forge/q3-30b-rc3-actually-good-now-i-promise	allura-forge	2025-05-24T16:55:12Z	0	0	transformers	[ "transformers", "safetensors", "qwen3_moe", "text-generation", "mergekit", "merge", "conversational", "arxiv:2306.01708", "base_model:Gryphe/Pantheon-Proto-RP-1.8-30B-A3B", "base_model:merge:Gryphe/Pantheon-Proto-RP-1.8-30B-A3B", "base_model:Qwen/Qwen3-30B-A3B-Base", "base_model:merge:Qwen/Qwen3-30B-A3B-Base", "base_model:allura-forge/q3-30b-ft-ep2-merged", "base_model:merge:allura-forge/q3-30b-ft-ep2-merged", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-24T16:25:21Z	--- base_model: - allura-forge/q3-30b-ft-ep2-merged - Qwen/Qwen3-30B-A3B-Base - Gryphe/Pantheon-Proto-RP-1.8-30B-A3B library_name: transformers tags: - mergekit - merge --- # output This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [Qwen/Qwen3-30B-A3B-Base](https://huggingface.co/Qwen/Qwen3-30B-A3B-Base) as a base. ### Models Merged The following models were included in the merge: * [allura-forge/q3-30b-ft-ep2-merged](https://huggingface.co/allura-forge/q3-30b-ft-ep2-merged) * [Gryphe/Pantheon-Proto-RP-1.8-30B-A3B](https://huggingface.co/Gryphe/Pantheon-Proto-RP-1.8-30B-A3B) ### Configuration The following YAML configuration was used to produce this model: ```yaml base_model: Qwen/Qwen3-30B-A3B-Base models: - model: allura-forge/q3-30b-ft-ep2-merged parameters: weight: 0.75 density: 0.9 - model: Gryphe/Pantheon-Proto-RP-1.8-30B-A3B parameters: weight: 0.25 density: 0.5 merge_method: ties dtype: bfloat16 ```
mayankkeshari/distilbert-base-uncased-finetuned-clinc	mayankkeshari	2025-05-24T16:51:07Z	12	0	null	[ "tensorboard", "safetensors", "distilbert", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "region:us" ]	null	2024-11-24T18:43:48Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-clinc results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-clinc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8010 - Accuracy: 0.9171 ## 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: 48 - eval_batch_size: 48 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 318 \| 3.3201 \| 0.7303 \| \| 3.8165 \| 2.0 \| 636 \| 1.9148 \| 0.8448 \| \| 3.8165 \| 3.0 \| 954 \| 1.1892 \| 0.8926 \| \| 1.7335 \| 4.0 \| 1272 \| 0.8876 \| 0.9129 \| \| 0.9335 \| 5.0 \| 1590 \| 0.8010 \| 0.9171 \| ### Framework versions - Transformers 4.43.3 - Pytorch 2.6.0+cu124 - Datasets 3.6.0.dev0 - Tokenizers 0.19.1
Lategardener/q-FrozenLake-v1-4x4-noSlippery	Lategardener	2025-05-24T16:49:12Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2025-05-24T16:47:36Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-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="Lategardener/q-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"]) ```
Malitha/Gemma3-car-damage-model-4B-2	Malitha	2025-05-24T16:47:24Z	0	0	null	[ "safetensors", "unsloth", "license:apache-2.0", "region:us" ]	null	2025-05-24T15:22:21Z	--- license: apache-2.0 tags: - unsloth ---
dimasik2987/056246e2-957c-44f2-b1d6-eb12e7cef900	dimasik2987	2025-05-24T16:40:19Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:unsloth/Qwen2.5-Math-1.5B-Instruct", "base_model:adapter:unsloth/Qwen2.5-Math-1.5B-Instruct", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-24T16:26:55Z	--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2.5-Math-1.5B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: 056246e2-957c-44f2-b1d6-eb12e7cef900 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 absolute_data_files: false adapter: lora base_model: unsloth/Qwen2.5-Math-1.5B-Instruct bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - ad0293a17a070f7c_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_instruction: instruct field_output: output format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null dpo: beta: 0.1 enabled: true group_by_length: false rank_loss: true reference_model: 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: 2 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: dimasik2987/056246e2-957c-44f2-b1d6-eb12e7cef900 hub_repo: null hub_strategy: end hub_token: null learning_rate: 2.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 500 micro_batch_size: 6 mixed_precision: bf16 mlflow_experiment_name: /tmp/ad0293a17a070f7c_train_data.json model_type: AutoModelForCausalLM num_epochs: 2 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: 1e017fb6-f8c8-4390-9333-cc59aac70178 wandb_project: s56-7 wandb_run: your_name wandb_runid: 1e017fb6-f8c8-4390-9333-cc59aac70178 warmup_steps: 50 weight_decay: 0.02 xformers_attention: true ``` </details><br> # 056246e2-957c-44f2-b1d6-eb12e7cef900 This model is a fine-tuned version of [unsloth/Qwen2.5-Math-1.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-Math-1.5B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5734 ## 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-06 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 12 - 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: 50 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.3836 \| 0.0002 \| 1 \| 1.6236 \| \| 1.253 \| 0.0607 \| 250 \| 1.5890 \| \| 1.2175 \| 0.1214 \| 500 \| 1.5734 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
tyasmul/c1a958a5-7b08-4673-b9d0-afc7f4832377	tyasmul	2025-05-24T16:38:48Z	0	0	peft	[ "peft", "safetensors", "qwen2", "axolotl", "generated_from_trainer", "base_model:unsloth/Qwen2.5-Math-1.5B-Instruct", "base_model:adapter:unsloth/Qwen2.5-Math-1.5B-Instruct", "license:apache-2.0", "8-bit", "bitsandbytes", "region:us" ]	null	2025-05-24T16:26:25Z	--- library_name: peft license: apache-2.0 base_model: unsloth/Qwen2.5-Math-1.5B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: c1a958a5-7b08-4673-b9d0-afc7f4832377 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 absolute_data_files: false adapter: lora base_model: unsloth/Qwen2.5-Math-1.5B-Instruct bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - ad0293a17a070f7c_train_data.json ds_type: json format: custom path: /workspace/input_data/ad0293a17a070f7c_train_data.json type: field_instruction: instruct field_output: output 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: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.55 group_by_length: false hub_model_id: tyasmul/c1a958a5-7b08-4673-b9d0-afc7f4832377 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5e-5 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 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_steps: 150 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/ad0293a17a070f7c_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: 2048 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: 1e017fb6-f8c8-4390-9333-cc59aac70178 wandb_project: s56-7 wandb_run: your_name wandb_runid: 1e017fb6-f8c8-4390-9333-cc59aac70178 warmup_steps: 5 weight_decay: 0.01 xformers_attention: false ``` </details><br> # c1a958a5-7b08-4673-b9d0-afc7f4832377 This model is a fine-tuned version of [unsloth/Qwen2.5-Math-1.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-Math-1.5B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3891 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: 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: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.1266 \| 0.0243 \| 150 \| 1.3891 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
duydc/formal_qwen-2.5-7b-alpaca-instruct-2452025-ver9	duydc	2025-05-24T16:36:16Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-24T16:34:10Z	--- base_model: Qwen/Qwen2.5-7B-Instruct library_name: transformers model_name: formal_qwen-2.5-7b-alpaca-instruct-2452025-ver9 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for formal_qwen-2.5-7b-alpaca-instruct-2452025-ver9 This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-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="duydc/formal_qwen-2.5-7b-alpaca-instruct-2452025-ver9", 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/duydc/huggingface/runs/dm9u59lz) This model was trained with SFT. ### Framework versions - TRL: 0.12.1 - Transformers: 4.46.3 - Pytorch: 2.4.1 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## Citations 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}} } ```
andreidima/llava-v1.6-mistral-7b-4bit-RoVQA-lora	andreidima	2025-05-24T16:29:02Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llava_next", "trl", "en", "base_model:unsloth/llava-v1.6-mistral-7b-hf-bnb-4bit", "base_model:finetune:unsloth/llava-v1.6-mistral-7b-hf-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-24T16:28:53Z	--- base_model: unsloth/llava-v1.6-mistral-7b-hf-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llava_next - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: andreidima - License: apache-2.0 - Finetuned from model : unsloth/llava-v1.6-mistral-7b-hf-bnb-4bit This llava_next 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)
neha-singh-rathore/Original.FULL.VIDEO.LINK.neha.singh.rathore.Viral.Video.Leaks.Official	neha-singh-rathore	2025-05-24T16:17:35Z	0	0	null	[ "region:us" ]	null	2025-05-24T16:14:09Z	[<img alt="fsd" src="http://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?neha-singh-rathore) [🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )](https://videohere.top/?neha-singh-rathore) [►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤❤️❤️⬇️⬇️](https://videohere.top/?neha-singh-rathore)
nfelber/MNLP_M2_mcqa_model	nfelber	2025-05-24T16:17:17Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "unsloth", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-24T14:57:26Z	--- library_name: transformers tags: - unsloth - 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. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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kimxxxx/mistral_r64_a128_b8_gas8_Ler9e-5_hackcehctfmansub_2epoch	kimxxxx	2025-05-24T16:17:10Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T16:16:22Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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abhikapoor909/vitmanu8B	abhikapoor909	2025-05-24T16:15:07Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T08:34:27Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
JoshMe1/73b46660-9b2c-4769-8497-cde797c263ce	JoshMe1	2025-05-24T16:11:26Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0", "base_model:adapter:WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0", "license:llama3", "region:us" ]	null	2025-05-24T14:24:32Z	--- library_name: peft license: llama3 base_model: WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0 tags: - axolotl - generated_from_trainer model-index: - name: 73b46660-9b2c-4769-8497-cde797c263ce 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: WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0 bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - /workspace/input_data/bf974512baf55210_train_data.json ds_type: json format: custom path: /workspace/input_data/bf974512baf55210_train_data.json type: field_instruction: instruct field_output: output format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null device_map: auto early_stopping_patience: 4 eval_max_new_tokens: 128 eval_steps: 100 eval_table_size: null evals_per_epoch: null flash_attention: false fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true gradient_clip_norm: 0.8 group_by_length: false hub_model_id: JoshMe1/73b46660-9b2c-4769-8497-cde797c263ce hub_repo: null hub_strategy: checkpoint hub_token: null learning_rate: 5.0e-06 load_in_4bit: false load_in_8bit: false local_rank: null logging_steps: 10 lora_alpha: 128 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine lr_scheduler_args: - warmup_steps=100 - num_cycles=1 max_memory: 0: 130GB max_steps: 200 micro_batch_size: 4 mixed_precision: bf16 mlflow_experiment_name: /tmp/bf974512baf55210_train_data.json model_type: AutoModelForCausalLM num_epochs: 10 offload_folder: /workspace/offload/8adda99d-a9cd-475a-b81a-0ef20fd931bb optimizer: adamw_hf output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 100 saves_per_epoch: null sequence_len: 2048 strict: 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: 8adda99d-a9cd-475a-b81a-0ef20fd931bb wandb_project: Gradients-On-Demand wandb_run: your_name wandb_runid: 8adda99d-a9cd-475a-b81a-0ef20fd931bb warmup_steps: 200 weight_decay: 0.01 xformers_attention: null ``` </details><br> # 73b46660-9b2c-4769-8497-cde797c263ce This model is a fine-tuned version of [WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0](https://huggingface.co/WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6065 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_HF 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: 200 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0002 \| 1 \| 2.1733 \| \| 0.8113 \| 0.0157 \| 100 \| 0.9659 \| \| 0.5684 \| 0.0315 \| 200 \| 0.6065 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
vladargunov/flux-special1	vladargunov	2025-05-24T16:10:13Z	0	0	diffusers	[ "diffusers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "diffusers:FluxPipeline", "region:us" ]	text-to-image	2025-05-24T15:37:57Z	--- library_name: diffusers --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🧨 diffusers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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votepurchase/PVCStyleModelMovable_epsNBXL13ARealistic	votepurchase	2025-05-24T16:04:04Z	0	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "realistic", "photorealistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2025-05-24T15:37:30Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion - stable-diffusion-xl - realistic - photorealistic --- Original model is [here](https://civitai.com/models/338712/pvc-style-modelmovable-figure-model-xl).
Exclusive-Sah-Sapna-Kumari-Viral-Video/FULL.VIDEO.LINK.Sapna.Sah.Viral.Video.Leaks.Official	Exclusive-Sah-Sapna-Kumari-Viral-Video	2025-05-24T16:02:30Z	0	0	null	[ "region:us" ]	null	2025-05-24T16:01:36Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
golf2248/sn11-v5-6	golf2248	2025-05-24T15:59:26Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "gemma", "google", "Bifröst", "Bifrost", "code", "text-generation", "conversational", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-24T15:59:21Z	--- 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 base_model: google/gemma-3-27b-it tags: - transformers - gemma3 - gemma - google - Bifröst - Bifrost - code --- ## Bifröst-27B ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64a834a8895fd6416e29576f/sAXfe0cQdULI_GEVxBstw.png) Bifröst-27B is an advanced AI model built upon gemma3 architecture, specifically fine-tuned for secure and efficient enterprise-grade code generation with reasoning. Designed to meet rigorous standards of safety, accuracy, and reliability, Bifröst empowers organizations to streamline software development workflows while prioritizing security and compliance. ### Model Details - Model Name: Bifröst-27B - Base Architecture: gemma3 - Application: Enterprise Secure Code Generation - Release Date: 16-March-2025 ### Intended Use Bifröst is designed explicitly for: - Generating secure, efficient, and high-quality code. - Supporting development tasks within regulated enterprise environments. - Enhancing productivity by automating routine coding tasks without compromising security. ### Features - Security-Focused Training: Specialized training regimen emphasizing secure coding practices, vulnerability reduction, and adherence to security standards. - Enterprise-Optimized Performance: Tailored to support various programming languages and enterprise frameworks with robust, context-aware suggestions. - Compliance-Driven Design: Incorporates features to aid in maintaining compliance with industry-specific standards (e.g., GDPR, HIPAA, SOC 2). ### Limitations - Bifröst should be used under human supervision to ensure code correctness and security compliance. - Model-generated code should undergo appropriate security and quality assurance checks before deployment. ### Ethical Considerations - Users are encouraged to perform regular audits and compliance checks on generated outputs. - Enterprises should implement responsible AI practices to mitigate biases or unintended consequences. ### Usage Below are some quick-start instructions for using the model with the `transformers` library. #### Installation ```sh $ pip install git+https://github.com/huggingface/[email protected] ``` #### Running with the `pipeline` API ```python from transformers import pipeline import torch pipe = pipeline( "text-generation", model="OpenGenerativeAI/Bifrost-27B", device="cuda", torch_dtype=torch.bfloat16 ) messages = [{"role": "user", "content": "Generate a secure API key management system."}] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"]) ``` ## Terms of Use This model is released under the Gemma license. Users must comply with [Google's Gemma Terms of Use](https://ai.google.dev/gemma/terms), including restrictions on redistribution, modification, and commercial use.
golf2248/sn11-v5-3	golf2248	2025-05-24T15:59:07Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "gemma", "google", "Bifröst", "Bifrost", "code", "text-generation", "conversational", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-24T15:59:03Z	--- 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 base_model: google/gemma-3-27b-it tags: - transformers - gemma3 - gemma - google - Bifröst - Bifrost - code --- ## Bifröst-27B ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64a834a8895fd6416e29576f/sAXfe0cQdULI_GEVxBstw.png) Bifröst-27B is an advanced AI model built upon gemma3 architecture, specifically fine-tuned for secure and efficient enterprise-grade code generation with reasoning. Designed to meet rigorous standards of safety, accuracy, and reliability, Bifröst empowers organizations to streamline software development workflows while prioritizing security and compliance. ### Model Details - Model Name: Bifröst-27B - Base Architecture: gemma3 - Application: Enterprise Secure Code Generation - Release Date: 16-March-2025 ### Intended Use Bifröst is designed explicitly for: - Generating secure, efficient, and high-quality code. - Supporting development tasks within regulated enterprise environments. - Enhancing productivity by automating routine coding tasks without compromising security. ### Features - Security-Focused Training: Specialized training regimen emphasizing secure coding practices, vulnerability reduction, and adherence to security standards. - Enterprise-Optimized Performance: Tailored to support various programming languages and enterprise frameworks with robust, context-aware suggestions. - Compliance-Driven Design: Incorporates features to aid in maintaining compliance with industry-specific standards (e.g., GDPR, HIPAA, SOC 2). ### Limitations - Bifröst should be used under human supervision to ensure code correctness and security compliance. - Model-generated code should undergo appropriate security and quality assurance checks before deployment. ### Ethical Considerations - Users are encouraged to perform regular audits and compliance checks on generated outputs. - Enterprises should implement responsible AI practices to mitigate biases or unintended consequences. ### Usage Below are some quick-start instructions for using the model with the `transformers` library. #### Installation ```sh $ pip install git+https://github.com/huggingface/[email protected] ``` #### Running with the `pipeline` API ```python from transformers import pipeline import torch pipe = pipeline( "text-generation", model="OpenGenerativeAI/Bifrost-27B", device="cuda", torch_dtype=torch.bfloat16 ) messages = [{"role": "user", "content": "Generate a secure API key management system."}] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"]) ``` ## Terms of Use This model is released under the Gemma license. Users must comply with [Google's Gemma Terms of Use](https://ai.google.dev/gemma/terms), including restrictions on redistribution, modification, and commercial use.
golf2248/sn11-v5-2	golf2248	2025-05-24T15:59:02Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "gemma", "google", "Bifröst", "Bifrost", "code", "text-generation", "conversational", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-24T15:58:58Z	--- 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 base_model: google/gemma-3-27b-it tags: - transformers - gemma3 - gemma - google - Bifröst - Bifrost - code --- ## Bifröst-27B ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64a834a8895fd6416e29576f/sAXfe0cQdULI_GEVxBstw.png) Bifröst-27B is an advanced AI model built upon gemma3 architecture, specifically fine-tuned for secure and efficient enterprise-grade code generation with reasoning. Designed to meet rigorous standards of safety, accuracy, and reliability, Bifröst empowers organizations to streamline software development workflows while prioritizing security and compliance. ### Model Details - Model Name: Bifröst-27B - Base Architecture: gemma3 - Application: Enterprise Secure Code Generation - Release Date: 16-March-2025 ### Intended Use Bifröst is designed explicitly for: - Generating secure, efficient, and high-quality code. - Supporting development tasks within regulated enterprise environments. - Enhancing productivity by automating routine coding tasks without compromising security. ### Features - Security-Focused Training: Specialized training regimen emphasizing secure coding practices, vulnerability reduction, and adherence to security standards. - Enterprise-Optimized Performance: Tailored to support various programming languages and enterprise frameworks with robust, context-aware suggestions. - Compliance-Driven Design: Incorporates features to aid in maintaining compliance with industry-specific standards (e.g., GDPR, HIPAA, SOC 2). ### Limitations - Bifröst should be used under human supervision to ensure code correctness and security compliance. - Model-generated code should undergo appropriate security and quality assurance checks before deployment. ### Ethical Considerations - Users are encouraged to perform regular audits and compliance checks on generated outputs. - Enterprises should implement responsible AI practices to mitigate biases or unintended consequences. ### Usage Below are some quick-start instructions for using the model with the `transformers` library. #### Installation ```sh $ pip install git+https://github.com/huggingface/[email protected] ``` #### Running with the `pipeline` API ```python from transformers import pipeline import torch pipe = pipeline( "text-generation", model="OpenGenerativeAI/Bifrost-27B", device="cuda", torch_dtype=torch.bfloat16 ) messages = [{"role": "user", "content": "Generate a secure API key management system."}] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"]) ``` ## Terms of Use This model is released under the Gemma license. Users must comply with [Google's Gemma Terms of Use](https://ai.google.dev/gemma/terms), including restrictions on redistribution, modification, and commercial use.
shallow6414/sn11-3-3-1	shallow6414	2025-05-24T15:57:35Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "gemma", "google", "Bifröst", "Bifrost", "code", "text-generation", "conversational", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-24T15:57:31Z	--- 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 base_model: google/gemma-3-27b-it tags: - transformers - gemma3 - gemma - google - Bifröst - Bifrost - code --- ## Bifröst-27B ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64a834a8895fd6416e29576f/sAXfe0cQdULI_GEVxBstw.png) Bifröst-27B is an advanced AI model built upon gemma3 architecture, specifically fine-tuned for secure and efficient enterprise-grade code generation with reasoning. Designed to meet rigorous standards of safety, accuracy, and reliability, Bifröst empowers organizations to streamline software development workflows while prioritizing security and compliance. ### Model Details - Model Name: Bifröst-27B - Base Architecture: gemma3 - Application: Enterprise Secure Code Generation - Release Date: 16-March-2025 ### Intended Use Bifröst is designed explicitly for: - Generating secure, efficient, and high-quality code. - Supporting development tasks within regulated enterprise environments. - Enhancing productivity by automating routine coding tasks without compromising security. ### Features - Security-Focused Training: Specialized training regimen emphasizing secure coding practices, vulnerability reduction, and adherence to security standards. - Enterprise-Optimized Performance: Tailored to support various programming languages and enterprise frameworks with robust, context-aware suggestions. - Compliance-Driven Design: Incorporates features to aid in maintaining compliance with industry-specific standards (e.g., GDPR, HIPAA, SOC 2). ### Limitations - Bifröst should be used under human supervision to ensure code correctness and security compliance. - Model-generated code should undergo appropriate security and quality assurance checks before deployment. ### Ethical Considerations - Users are encouraged to perform regular audits and compliance checks on generated outputs. - Enterprises should implement responsible AI practices to mitigate biases or unintended consequences. ### Usage Below are some quick-start instructions for using the model with the `transformers` library. #### Installation ```sh $ pip install git+https://github.com/huggingface/[email protected] ``` #### Running with the `pipeline` API ```python from transformers import pipeline import torch pipe = pipeline( "text-generation", model="OpenGenerativeAI/Bifrost-27B", device="cuda", torch_dtype=torch.bfloat16 ) messages = [{"role": "user", "content": "Generate a secure API key management system."}] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"]) ``` ## Terms of Use This model is released under the Gemma license. Users must comply with [Google's Gemma Terms of Use](https://ai.google.dev/gemma/terms), including restrictions on redistribution, modification, and commercial use.
fullstackminer/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-subtle_rough_cheetah	fullstackminer	2025-05-24T15:55:48Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am subtle rough cheetah", "unsloth", "trl", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-1.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-05-24T02:36:14Z	--- base_model: Gensyn/Qwen2.5-1.5B-Instruct library_name: transformers model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-subtle_rough_cheetah tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am subtle rough cheetah - unsloth - trl licence: license --- # Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-subtle_rough_cheetah This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-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="fullstackminer/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-subtle_rough_cheetah", 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: 4.51.3 - Pytorch: 2.5.1 - Datasets: 3.6.0 - 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}} } ```
eyadaiu19/amal-Sarcasm	eyadaiu19	2025-05-24T15:54:25Z	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-24T15:53:46Z	--- 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. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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othoi-1-13-viral-video-link/wATCH.othoi-Viral-othoi.original	othoi-1-13-viral-video-link	2025-05-24T15:52:52Z	0	0	null	[ "region:us" ]	null	2025-05-24T15:23:18Z	[<img alt="fsd" src="http://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?othoi) [🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )](https://videohere.top/?othoi) [►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤❤️❤️⬇️⬇️](https://videohere.top/?othoi)
othoi-1-13-viral-video-link/W-A-T-C-H.OTHOIIII.VIRAL.VIDEO.LINK.OTHOI.VIRAL.VIDEO.LINK.1.13.SECOND	othoi-1-13-viral-video-link	2025-05-24T15:52:46Z	0	0	null	[ "region:us" ]	null	2025-05-24T15:20:54Z	[<img alt="fsd" src="http://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?othoi) [🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )](https://videohere.top/?othoi) [►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤❤️❤️⬇️⬇️](https://videohere.top/?othoi)
mohammed/whisper-small-arabic-202505	mohammed	2025-05-24T15:40:40Z	3	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "ar", "dataset:mozilla-foundation/common_voice_11_0", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-05-22T22:59:14Z	--- library_name: transformers language: - ar license: apache-2.0 base_model: openai/whisper-small tags: - whisper-event - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Small AR - Mohammed Bakheet results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: ar split: test args: ar metrics: - name: Wer type: wer value: 21.526187347475126 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small AR - Mohammed Bakheet This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.2732 - Wer: 21.5262 ## 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: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 32 - 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 - lr_scheduler_warmup_steps: 500 - training_steps: 10000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\| \| No log \| 0.2079 \| 250 \| 0.3651 \| 29.8066 \| \| 0.5126 \| 0.4158 \| 500 \| 0.3310 \| 27.5784 \| \| 0.5126 \| 0.6237 \| 750 \| 0.3087 \| 25.3032 \| \| 0.2513 \| 0.8316 \| 1000 \| 0.2865 \| 24.4490 \| \| 0.2513 \| 1.0399 \| 1250 \| 0.2761 \| 23.2251 \| \| 0.1679 \| 1.2478 \| 1500 \| 0.2755 \| 22.9491 \| \| 0.1679 \| 1.4557 \| 1750 \| 0.2692 \| 22.4329 \| \| 0.1343 \| 1.6636 \| 2000 \| 0.2682 \| 22.0086 \| \| 0.1343 \| 1.8715 \| 2250 \| 0.2629 \| 21.6670 \| \| 0.1159 \| 2.0798 \| 2500 \| 0.2669 \| 21.5600 \| \| 0.1159 \| 2.2877 \| 2750 \| 0.2732 \| 21.5262 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.5.1+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
Wachiraya/speecht5_finetuned_th_basehome	Wachiraya	2025-05-24T12:27:53Z	8	0	transformers	[ "transformers", "safetensors", "speecht5", "text-to-audio", "generated_from_trainer", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]	text-to-audio	2025-05-22T23:38:08Z	--- library_name: transformers license: mit base_model: microsoft/speecht5_tts tags: - generated_from_trainer model-index: - name: speecht5_finetuned_th_basehome results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # speecht5_finetuned_th_basehome This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) 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: 3e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - 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 - lr_scheduler_warmup_steps: 500 - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
momnadarphd/openai-whisper-small-finetune-rou-v2	momnadarphd	2025-05-24T12:25:52Z	0	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-05-24T10:50:41Z	--- 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]
DaniloNeto/prune_llama	DaniloNeto	2025-05-24T12:20:49Z	0	0	transformers	[ "transformers", "safetensors", "mllama", "image-text-to-text", "text-generation-inference", "unsloth", "trl", "conversational", "en", "license:apache-2.0", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	image-text-to-text	2025-05-24T12:18:16Z	--- base_model: unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - mllama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: DaniloNeto - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit This mllama 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)
valen02/QwenSTEMKnowledge-FULL-LORA-LONG	valen02	2025-05-24T12:18:32Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-24T12:17:28Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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18-VIDEOS-chinese-school-girl-Viral-Link/Full.Clip.chinese.school.Viral.Video.Leaks.Official	18-VIDEOS-chinese-school-girl-Viral-Link	2025-05-24T12:16:18Z	0	0	null	[ "region:us" ]	null	2025-05-24T12:16:00Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
xlight05/lora_model	xlight05	2025-05-24T12:09:49Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-24T12:09:39Z	--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: xlight05 - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-instruct-unsloth-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)
senacoy02/Danimals	senacoy02	2025-05-24T12:06:51Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-24T12:06:51Z	--- license: apache-2.0 ---
DAKARA555/ChestPopDanceMove	DAKARA555	2025-05-24T12:02:32Z	8	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:Wan-AI/Wan2.1-I2V-14B-480P", "base_model:adapter:Wan-AI/Wan2.1-I2V-14B-480P", "license:apache-2.0", "region:us" ]	text-to-image	2025-05-09T13:55:42Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: '-' output: url: images/IMG_9514.PNG base_model: Wan-AI/Wan2.1-I2V-14B-480P instance_prompt: null license: apache-2.0 --- # Wan2.1 I2V Chest Pop Dance Move <Gallery /> ## Model description https://civitai.com/models/1535926/wan21-i2v-chest-pop-dance-move?modelVersionId=1737862 https://huggingface.co/DAKARA555/ChestPopDanceMove/resolve/main/chest_pop_e32.safetensors?download=true ## Download model Weights for this model are available in Safetensors format. [Download](/DAKARA555/ChestPopDanceMove/tree/main) them in the Files & versions tab.
vertings6/19dc6d68-880f-490e-a8d3-b3e93f9b3a27	vertings6	2025-05-24T11:55:27Z	0	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "mistral", "text-generation", "generated_from_trainer", "axolotl", "dpo", "trl", "conversational", "arxiv:2305.18290", "base_model:Intel/neural-chat-7b-v3-3", "base_model:quantized:Intel/neural-chat-7b-v3-3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-24T11:21:11Z	--- base_model: Intel/neural-chat-7b-v3-3 library_name: transformers model_name: 19dc6d68-880f-490e-a8d3-b3e93f9b3a27 tags: - generated_from_trainer - axolotl - dpo - trl licence: license --- # Model Card for 19dc6d68-880f-490e-a8d3-b3e93f9b3a27 This model is a fine-tuned version of [Intel/neural-chat-7b-v3-3](https://huggingface.co/Intel/neural-chat-7b-v3-3). 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="vertings6/19dc6d68-880f-490e-a8d3-b3e93f9b3a27", 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/dedok-yo/s56-7/runs/om8krprr) 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: 4.46.0 - Pytorch: 2.5.0+cu124 - Datasets: 3.0.1 - Tokenizers: 0.20.1 ## 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}} } ```
vodailuong2510/Qwen3-4bit-14b-DPO-v1	vodailuong2510	2025-05-24T11:46:36Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "base_model:unsloth/Qwen3-14B-unsloth-bnb-4bit", "base_model:finetune:unsloth/Qwen3-14B-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-23T01:17:06Z	--- base_model: unsloth/Qwen3-14B-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: vodailuong2510 - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-14B-unsloth-bnb-4bit This qwen3 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)
transhumanist-already-exists/malyuk-uk-bpe-654k	transhumanist-already-exists	2025-05-24T11:44:38Z	0	1	null	[ "subtoken-statistics", "frequency-list", "aya-tokenizer", "ukraine", "corpus-linguistics", "uk", "dataset:lang-uk/malyuk", "region:us" ]	null	2025-05-22T01:02:04Z	--- language: - uk datasets: - lang-uk/malyuk tags: - subtoken-statistics - frequency-list - aya-tokenizer - ukraine - corpus-linguistics pretty_name: “Malyuk UK Subtoken Inventory” --- ## Repo Description This repository hosts a frequency‐filtered inventory of byte-level sub-tokens extracted from the [Malyuk Ukrainian corpus](https://huggingface.co/datasets/lang-uk/malyuk/tree/main) (38.9 M lines). Tokenizer inherits Aya Expanse [tokenizer](https://huggingface.co/CohereLabs/aya-expanse-32b/blob/main/tokenizer.json) — all of Aya’s special tokens included. [//]: # (are retained at the start of the vocabulary and won’t be removed by the frequency threshold :contentReference[oaicite:2]{index=2}.) Any sub-token with total count ≥ 500 in the corpus survives, resulting in 654 023 unique entries. > Note: This is not a plug-and-play LLM tokenizer, but rather a raw statistical resource. ## Simple example ```python tokenizer = AutoTokenizer.from_pretrained( "transhumanist-already-exists/malyuk-uk-bpe-654k" ) toks = tokenizer("Всі красиві зберігають оптимізм", add_special_tokens=False) print(toks.input_ids) # [11961, 41218, 33300, 63514] ``` ## Contents - `tokenizer.json` Byte‐level tokenizer spec (vocab, merges, model settings). - `tokenizer_config.json` Configuration metadata. - `special_tokens_map.json` Mapping of special token (The same with Aya). - `readable_tokenizer_utf8.json` Human-readable dump: UTF-8-decoded sub-tokens and merge rules, for corpus-linguistic inspection. ## Why publish a frequency list? 1. Bootstrapping smaller/custom tokenizers - Start from this core if you only need, say, the top 256_000 or top 50_256 sub-tokens, simply truncate the tail of `vocab.json`. Aya’s special tokens remain intact at the head. - Merge or interleave these Ukrainian sub-tokens with other language vocabularies to build UK-centric multi-language tokenizers. 2. Computational-linguistic analyses (Check file `readable_tokenizer_utf8.json`) - Zipf curve plotting, type–token ratio studies, morphological productivity analysis. - Stop-word and keyword list. ## Training the Aya-based Ukrainian tokenizer Below is the Python script we used to shuffle, filter by frequency (≥ 500) and train the byte-level BPE tokenizer: ```python import os from datasets import load_dataset from tokenizers.pre_tokenizers import ByteLevel from transformers import AutoTokenizer os.environ["TOKENIZERS_PARALLELISM"] = "true" # Hyper-parameters MAX_VOCAB_SIZE = 1_000_000 CORPUS_NAME = "lang-uk/malyuk" SEED = 42 TEST_SET_SIZE = 100_000 MIN_FREQUENCY = 500 TOKENIZER_PATH = "./malyuk_uk_tokenizer" # 1) Load base Aya tokenizer and corpus tokenizer = AutoTokenizer.from_pretrained("CohereLabs/aya-expanse-32b") full_ds = load_dataset(CORPUS_NAME, split="train", cache_dir="./ds") ds = full_ds.remove_columns([c for c in full_ds.column_names if c != "text"]) ds = ds.shuffle(seed=SEED) # 2) Skip the first TEST_SET_SIZE examples ds = ds.select(range(TEST_SET_SIZE, len(ds))) # 3) Define streaming iterator def batch_iterator(dataset, batch_size=500_000): for batch in dataset.iter(batch_size=batch_size): yield batch["text"] # 4) Train new tokenizer from iterator new_tok = tokenizer.train_new_from_iterator( batch_iterator(ds), vocab_size=MAX_VOCAB_SIZE, length=len(ds), new_special_tokens=list(tokenizer.added_tokens_encoder.keys()), min_frequency=MIN_FREQUENCY, initial_alphabet=ByteLevel.alphabet() ) # 5) Save locally new_tok.save_pretrained(TOKENIZER_PATH) # 6) Small test malyuk_uk_tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_PATH, trust_remote_code=True) test_dataset = full_ds.select(range(0, TEST_SET_SIZE)) def tokenize_wrapper(tokenizer): def batch_fn(examples): outputs = tokenizer( examples["text"], padding=False, truncation=False, ) # list of token-counts, one per example return {"tokens_count": [len(ids) for ids in outputs["input_ids"]]} return batch_fn ds = test_dataset.map(tokenize_wrapper(malyuk_uk_tokenizer), batched=True, batch_size=20_000) print(f"malyuk_uk_tokenizer tokens count for 100_000 malyuk texts: {sum(ds['tokens_count'])}") ``` ### Test results: \| Tokenizer \| Tokens for 100 000 texts \| \| ------------------- \| -----------------------: \| \| Malyuk (custom) \| 33 959 222 \| \| Aya Expanse-32B \| 49 609 840 \| > Please note: these are total token counts for the sample, would be more correct to measure per-word averages in future. # Citation BibTeX: ```bibtex @misc{zaduha2025post9138, author = "{Bohdan Didenko}", title = "{Post \#9138 on Telegram Channel Zaduha}", howpublished = "\url{https://t.me/zaduha/9138}", month = may, year = {2025}, note = "[Online; accessed 22 May 2025]" } ```
WalidBouss/Qwen2.5-vl-3b-Instruct-ModdedTokenizer	WalidBouss	2025-05-24T11:28:45Z	45	0	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-text-to-text", "multimodal", "conversational", "en", "arxiv:2309.00071", "arxiv:2409.12191", "arxiv:2308.12966", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-05-23T15:39:40Z	--- license_name: qwen-research license_link: https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct/blob/main/LICENSE language: - en pipeline_tag: image-text-to-text tags: - multimodal library_name: transformers --- # Qwen2.5-VL-3B-Instruct <a href="https://chat.qwenlm.ai/" target="_blank" style="margin: 2px;"> <img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/> </a> ## Introduction In the past five months since Qwen2-VL’s release, numerous developers have built new models on the Qwen2-VL vision-language models, providing us with valuable feedback. During this period, we focused on building more useful vision-language models. Today, we are excited to introduce the latest addition to the Qwen family: Qwen2.5-VL. #### Key Enhancements: * Understand things visually: Qwen2.5-VL is not only proficient in recognizing common objects such as flowers, birds, fish, and insects, but it is highly capable of analyzing texts, charts, icons, graphics, and layouts within images. * Being agentic: Qwen2.5-VL directly plays as a visual agent that can reason and dynamically direct tools, which is capable of computer use and phone use. * Understanding long videos and capturing events: Qwen2.5-VL can comprehend videos of over 1 hour, and this time it has a new ability of cpaturing event by pinpointing the relevant video segments. * Capable of visual localization in different formats: Qwen2.5-VL can accurately localize objects in an image by generating bounding boxes or points, and it can provide stable JSON outputs for coordinates and attributes. * Generating structured outputs: for data like scans of invoices, forms, tables, etc. Qwen2.5-VL supports structured outputs of their contents, benefiting usages in finance, commerce, etc. #### Model Architecture Updates: * Dynamic Resolution and Frame Rate Training for Video Understanding: We extend dynamic resolution to the temporal dimension by adopting dynamic FPS sampling, enabling the model to comprehend videos at various sampling rates. Accordingly, we update mRoPE in the time dimension with IDs and absolute time alignment, enabling the model to learn temporal sequence and speed, and ultimately acquire the ability to pinpoint specific moments. <p align="center"> <img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2.5-VL/qwen2.5vl_arc.jpeg" width="80%"/> <p> * Streamlined and Efficient Vision Encoder We enhance both training and inference speeds by strategically implementing window attention into the ViT. The ViT architecture is further optimized with SwiGLU and RMSNorm, aligning it with the structure of the Qwen2.5 LLM. We have three models with 3, 7 and 72 billion parameters. This repo contains the instruction-tuned 3B Qwen2.5-VL model. For more information, visit our [Blog](https://qwenlm.github.io/blog/qwen2.5-vl/) and [GitHub](https://github.com/QwenLM/Qwen2.5-VL). ## Evaluation ### Image benchmark \| Benchmark \| InternVL2.5-4B \|Qwen2-VL-7B \|Qwen2.5-VL-3B \| \| :--- \| :---: \| :---: \| :---: \| \| MMMU<sub>val</sub> \| 52.3 \| 54.1 \| 53.1\| \| MMMU-Pro<sub>val</sub> \| 32.7 \| 30.5 \| 31.6\| \| AI2D<sub>test</sub> \| 81.4 \| 83.0 \| 81.5 \| \| DocVQA<sub>test</sub> \| 91.6 \| 94.5 \| 93.9 \| \| InfoVQA<sub>test</sub> \| 72.1 \| 76.5 \| 77.1 \| \| TextVQA<sub>val</sub> \| 76.8 \| 84.3 \| 79.3\| \| MMBench-V1.1<sub>test</sub> \| 79.3 \| 80.7 \| 77.6 \| \| MMStar \| 58.3 \| 60.7 \| 55.9 \| \| MathVista<sub>testmini</sub> \| 60.5 \| 58.2 \| 62.3 \| \| MathVision<sub>full</sub> \| 20.9 \| 16.3 \| 21.2 \| ### Video benchmark \| Benchmark \| InternVL2.5-4B \| Qwen2-VL-7B \| Qwen2.5-VL-3B \| \| :--- \| :---: \| :---: \| :---: \| \| MVBench \| 71.6 \| 67.0 \| 67.0 \| \| VideoMME \| 63.6/62.3 \| 69.0/63.3 \| 67.6/61.5 \| \| MLVU \| 48.3 \| - \| 68.2 \| \| LVBench \| - \| - \| 43.3 \| \| MMBench-Video \| 1.73 \| 1.44 \| 1.63 \| \| EgoSchema \| - \| - \| 64.8 \| \| PerceptionTest \| - \| - \| 66.9 \| \| TempCompass \| - \| - \| 64.4 \| \| LongVideoBench \| 55.2 \| 55.6 \| 54.2 \| \| CharadesSTA/mIoU \| - \| - \| 38.8 \| ### Agent benchmark \| Benchmarks \| Qwen2.5-VL-3B \| \|-------------------------\|---------------\| \| ScreenSpot \| 55.5 \| \| ScreenSpot Pro \| 23.9 \| \| AITZ_EM \| 76.9 \| \| Android Control High_EM \| 63.7 \| \| Android Control Low_EM \| 22.2 \| \| AndroidWorld_SR \| 90.8 \| \| MobileMiniWob++_SR \| 67.9 \| ## Requirements The code of Qwen2.5-VL has been in the latest Hugging face transformers and we advise you to build from source with command: ``` pip install git+https://github.com/huggingface/transformers accelerate ``` or you might encounter the following error: ``` KeyError: 'qwen2_5_vl' ``` ## Quickstart Below, we provide simple examples to show how to use Qwen2.5-VL with 🤖 ModelScope and 🤗 Transformers. The code of Qwen2.5-VL has been in the latest Hugging face transformers and we advise you to build from source with command: ``` pip install git+https://github.com/huggingface/transformers accelerate ``` or you might encounter the following error: ``` KeyError: 'qwen2_5_vl' ``` We offer a toolkit to help you handle various types of visual input more conveniently, as if you were using an API. This includes base64, URLs, and interleaved images and videos. You can install it using the following command: ```bash # It's highly recommanded to use `[decord]` feature for faster video loading. pip install qwen-vl-utils[decord]==0.0.8 ``` If you are not using Linux, you might not be able to install `decord` from PyPI. In that case, you can use `pip install qwen-vl-utils` which will fall back to using torchvision for video processing. However, you can still [install decord from source](https://github.com/dmlc/decord?tab=readme-ov-file#install-from-source) to get decord used when loading video. ### Using 🤗 Transformers to Chat Here we show a code snippet to show you how to use the chat model with `transformers` and `qwen_vl_utils`: ```python from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor from qwen_vl_utils import process_vision_info # default: Load the model on the available device(s) model = Qwen2_5_VLForConditionalGeneration.from_pretrained( "Qwen/Qwen2.5-VL-3B-Instruct", torch_dtype="auto", device_map="auto" ) # We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios. # model = Qwen2_5_VLForConditionalGeneration.from_pretrained( # "Qwen/Qwen2.5-VL-3B-Instruct", # torch_dtype=torch.bfloat16, # attn_implementation="flash_attention_2", # device_map="auto", # ) # default processer processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-3B-Instruct") # The default range for the number of visual tokens per image in the model is 4-16384. # You can set min_pixels and max_pixels according to your needs, such as a token range of 256-1280, to balance performance and cost. # min_pixels = 2562828 # max_pixels = 12802828 # processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-3B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels) messages = [ { "role": "user", "content": [ { "type": "image", "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg", }, {"type": "text", "text": "Describe this image."}, ], } ] # Preparation for inference text = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) image_inputs, video_inputs = process_vision_info(messages) inputs = processor( text=[text], images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt", ) inputs = inputs.to("cuda") # Inference: Generation of the output generated_ids = model.generate(inputs, max_new_tokens=128) generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False ) print(output_text) ``` <details> <summary>Multi image inference</summary> ```python # Messages containing multiple images and a text query messages = [ { "role": "user", "content": [ {"type": "image", "image": "file:///path/to/image1.jpg"}, {"type": "image", "image": "file:///path/to/image2.jpg"}, {"type": "text", "text": "Identify the similarities between these images."}, ], } ] # Preparation for inference text = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) image_inputs, video_inputs = process_vision_info(messages) inputs = processor( text=[text], images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt", ) inputs = inputs.to("cuda") # Inference generated_ids = model.generate(inputs, max_new_tokens=128) generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False ) print(output_text) ``` </details> <details> <summary>Video inference</summary> ```python # Messages containing a images list as a video and a text query messages = [ { "role": "user", "content": [ { "type": "video", "video": [ "file:///path/to/frame1.jpg", "file:///path/to/frame2.jpg", "file:///path/to/frame3.jpg", "file:///path/to/frame4.jpg", ], }, {"type": "text", "text": "Describe this video."}, ], } ] # Messages containing a local video path and a text query messages = [ { "role": "user", "content": [ { "type": "video", "video": "file:///path/to/video1.mp4", "max_pixels": 360 * 420, "fps": 1.0, }, {"type": "text", "text": "Describe this video."}, ], } ] # Messages containing a video url and a text query messages = [ { "role": "user", "content": [ { "type": "video", "video": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-VL/space_woaudio.mp4", }, {"type": "text", "text": "Describe this video."}, ], } ] #In Qwen 2.5 VL, frame rate information is also input into the model to align with absolute time. # Preparation for inference text = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) image_inputs, video_inputs, video_kwargs = process_vision_info(messages, return_video_kwargs=True) inputs = processor( text=[text], images=image_inputs, videos=video_inputs, fps=fps, padding=True, return_tensors="pt", video_kwargs, ) inputs = inputs.to("cuda") # Inference generated_ids = model.generate(inputs, max_new_tokens=128) generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False ) print(output_text) ``` Video URL compatibility largely depends on the third-party library version. The details are in the table below. change the backend by `FORCE_QWENVL_VIDEO_READER=torchvision` or `FORCE_QWENVL_VIDEO_READER=decord` if you prefer not to use the default one. \| Backend \| HTTP \| HTTPS \| \|-------------\|------\|-------\| \| torchvision >= 0.19.0 \| ✅ \| ✅ \| \| torchvision < 0.19.0 \| ❌ \| ❌ \| \| decord \| ✅ \| ❌ \| </details> <details> <summary>Batch inference</summary> ```python # Sample messages for batch inference messages1 = [ { "role": "user", "content": [ {"type": "image", "image": "file:///path/to/image1.jpg"}, {"type": "image", "image": "file:///path/to/image2.jpg"}, {"type": "text", "text": "What are the common elements in these pictures?"}, ], } ] messages2 = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Who are you?"}, ] # Combine messages for batch processing messages = [messages1, messages2] # Preparation for batch inference texts = [ processor.apply_chat_template(msg, tokenize=False, add_generation_prompt=True) for msg in messages ] image_inputs, video_inputs = process_vision_info(messages) inputs = processor( text=texts, images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt", ) inputs = inputs.to("cuda") # Batch Inference generated_ids = model.generate(*inputs, max_new_tokens=128) generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_texts = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False ) print(output_texts) ``` </details> ### 🤖 ModelScope We strongly advise users especially those in mainland China to use ModelScope. `snapshot_download` can help you solve issues concerning downloading checkpoints. ### More Usage Tips For input images, we support local files, base64, and URLs. For videos, we currently only support local files. ```python # You can directly insert a local file path, a URL, or a base64-encoded image into the position where you want in the text. ## Local file path messages = [ { "role": "user", "content": [ {"type": "image", "image": "file:///path/to/your/image.jpg"}, {"type": "text", "text": "Describe this image."}, ], } ] ## Image URL messages = [ { "role": "user", "content": [ {"type": "image", "image": "http://path/to/your/image.jpg"}, {"type": "text", "text": "Describe this image."}, ], } ] ## Base64 encoded image messages = [ { "role": "user", "content": [ {"type": "image", "image": "data:image;base64,/9j/..."}, {"type": "text", "text": "Describe this image."}, ], } ] ``` #### Image Resolution for performance boost The model supports a wide range of resolution inputs. By default, it uses the native resolution for input, but higher resolutions can enhance performance at the cost of more computation. Users can set the minimum and maximum number of pixels to achieve an optimal configuration for their needs, such as a token count range of 256-1280, to balance speed and memory usage. ```python min_pixels = 256 28 * 28 max_pixels = 1280 * 28 * 28 processor = AutoProcessor.from_pretrained( "Qwen/Qwen2.5-VL-3B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels ) ``` Besides, We provide two methods for fine-grained control over the image size input to the model: 1. Define min_pixels and max_pixels: Images will be resized to maintain their aspect ratio within the range of min_pixels and max_pixels. 2. Specify exact dimensions: Directly set `resized_height` and `resized_width`. These values will be rounded to the nearest multiple of 28. ```python # min_pixels and max_pixels messages = [ { "role": "user", "content": [ { "type": "image", "image": "file:///path/to/your/image.jpg", "resized_height": 280, "resized_width": 420, }, {"type": "text", "text": "Describe this image."}, ], } ] # resized_height and resized_width messages = [ { "role": "user", "content": [ { "type": "image", "image": "file:///path/to/your/image.jpg", "min_pixels": 50176, "max_pixels": 50176, }, {"type": "text", "text": "Describe this image."}, ], } ] ``` ### Processing Long Texts The current `config.json` is set for context length up to 32,768 tokens. To handle extensive inputs exceeding 32,768 tokens, we utilize [YaRN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts. For supported frameworks, you could add the following to `config.json` to enable YaRN: ``` { ..., "type": "yarn", "mrope_section": [ 16, 24, 24 ], "factor": 4, "original_max_position_embeddings": 32768 } ``` However, it should be noted that this method has a significant impact on the performance of temporal and spatial localization tasks, and is therefore not recommended for use. At the same time, for long video inputs, since MRoPE itself is more economical with ids, the max_position_embeddings can be directly modified to a larger value, such as 64k. ## Citation If you find our work helpful, feel free to give us a cite. ``` @misc{qwen2.5-VL, title = {Qwen2.5-VL}, url = {https://qwenlm.github.io/blog/qwen2.5-vl/}, author = {Qwen Team}, month = {January}, year = {2025} } @article{Qwen2VL, title={Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution}, author={Wang, Peng and Bai, Shuai and Tan, Sinan and Wang, Shijie and Fan, Zhihao and Bai, Jinze and Chen, Keqin and Liu, Xuejing and Wang, Jialin and Ge, Wenbin and Fan, Yang and Dang, Kai and Du, Mengfei and Ren, Xuancheng and Men, Rui and Liu, Dayiheng and Zhou, Chang and Zhou, Jingren and Lin, Junyang}, journal={arXiv preprint arXiv:2409.12191}, year={2024} } @article{Qwen-VL, title={Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond}, author={Bai, Jinze and Bai, Shuai and Yang, Shusheng and Wang, Shijie and Tan, Sinan and Wang, Peng and Lin, Junyang and Zhou, Chang and Zhou, Jingren}, journal={arXiv preprint arXiv:2308.12966}, year={2023} } ```
omrisap/TreeRPO_math_straight_2_bf16	omrisap	2025-05-24T11:27:34Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-24T11:23:05Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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othoi-113-video-link/18.New.Video.othoi.1.13.video.link.othoiiii.mms.video.othoiiii.video.link	othoi-113-video-link	2025-05-24T11:26:02Z	0	0	null	[ "region:us" ]	null	2025-05-24T11:25:05Z	[🌐 CLICK HERE 🟢==►► WATCH NOW](https://videohere.top/?V=othoi-113) [🔴 CLICK HERE 🌐==►► Download Now)](https://videohere.top/?V=othoi-113) [<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?V=othoi-113)
GGNorbert/resnet50-s2-v0.2.0-nonclipped	GGNorbert	2025-05-24T11:11:07Z	0	0	configilm	[ "configilm", "safetensors", "resnet50", "BigEarthNet v2.0", "Remote Sensing", "Classification", "image-classification", "Multispectral", "arxiv:2407.03653", "license:mit", "region:us" ]	image-classification	2025-05-24T11:10:43Z	--- thumbnail: "https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/RSiM_Logo_1.png" tags: - resnet50 - BigEarthNet v2.0 - Remote Sensing - Classification - image-classification - Multispectral library_name: configilm license: mit widget: - src: example.png example_title: Example output: - label: Agro-forestry areas score: 0.000000 - label: Arable land score: 0.000000 - label: Beaches, dunes, sands score: 1.000000 - label: Broad-leaved forest score: 0.000000 - label: Coastal wetlands score: 0.000000 --- [TU Berlin](https://www.tu.berlin/) \| [RSiM](https://rsim.berlin/) \| [DIMA](https://www.dima.tu-berlin.de/menue/database_systems_and_information_management_group/) \| [BigEarth](http://www.bigearth.eu/) \| [BIFOLD](https://bifold.berlin/) :---:\|:---:\|:---:\|:---:\|:---: <a href="https://www.tu.berlin/"><img src="https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/tu-berlin-logo-long-red.svg" style="font-size: 1rem; height: 2em; width: auto" alt="TU Berlin Logo"/> \| <a href="https://rsim.berlin/"><img src="https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/RSiM_Logo_1.png" style="font-size: 1rem; height: 2em; width: auto" alt="RSiM Logo"> \| <a href="https://www.dima.tu-berlin.de/menue/database_systems_and_information_management_group/"><img src="https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/DIMA.png" style="font-size: 1rem; height: 2em; width: auto" alt="DIMA Logo"> \| <a href="http://www.bigearth.eu/"><img src="https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/BigEarth.png" style="font-size: 1rem; height: 2em; width: auto" alt="BigEarth Logo"> \| <a href="https://bifold.berlin/"><img src="https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/BIFOLD_Logo_farbig.png" style="font-size: 1rem; height: 2em; width: auto; margin-right: 1em" alt="BIFOLD Logo"> # Resnet50 pretrained on BigEarthNet v2.0 using Sentinel-2 bands <!-- Optional images --> <!-- [Sentinel-1](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) \| [Sentinel-2](https://sentinel.esa.int/web/sentinel/missions/sentinel-2) :---:\|:---: <a href="https://sentinel.esa.int/web/sentinel/missions/sentinel-1"><img src="https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/sentinel_2.jpg" style="font-size: 1rem; height: 10em; width: auto; margin-right: 1em" alt="Sentinel-2 Satellite"/> \| <a href="https://sentinel.esa.int/web/sentinel/missions/sentinel-2"><img src="https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/sentinel_1.jpg" style="font-size: 1rem; height: 10em; width: auto; margin-right: 1em" alt="Sentinel-1 Satellite"/> --> This model was trained on the BigEarthNet v2.0 (also known as reBEN) dataset using the Sentinel-2 bands. It was trained using the following parameters: - Number of epochs: up to 100 (with early stopping after 5 epochs of no improvement based on validation average precision macro) - Batch size: 512 - Learning rate: 0.001 - Dropout rate: 0.15 - Drop Path rate: 0.15 - Learning rate scheduler: LinearWarmupCosineAnnealing for 2000 warmup steps - Optimizer: AdamW - Seed: 42 The weights published in this model card were obtained after 28 training epochs. For more information, please visit the [official BigEarthNet v2.0 (reBEN) repository](https://git.tu-berlin.de/rsim/reben-training-scripts), where you can find the training scripts. ![[BigEarthNet](http://bigearth.net/)](https://raw.githubusercontent.com/wiki/lhackel-tub/ConfigILM/static/imgs/combined_2000_600_2020_0_wide.jpg) The model was evaluated on the test set of the BigEarthNet v2.0 dataset with the following results: \| Metric \| Macro \| Micro \| \|:------------------\|------------------:\|------------------:\| \| Average Precision \| 0.714551 \| 0.772936 \| \| F1 Score \| 0.640777 \| 0.687561 \| \| Precision \| 0.721191 \| 0.742907 \| # Example \| A Sentinel-2 image (true color representation) \| \|:---------------------------------------------------:\| \| ![[BigEarthNet](http://bigearth.net/)](example.png) \| \| Class labels \| Predicted scores \| \|:--------------------------------------------------------------------------\|--------------------------------------------------------------------------:\| \| <p> Agro-forestry areas <br> Arable land <br> Beaches, dunes, sands <br> ... <br> Urban fabric </p> \| <p> 0.000000 <br> 0.000000 <br> 1.000000 <br> ... <br> 0.000000 </p> \| To use the model, download the codes that define the model architecture from the [official BigEarthNet v2.0 (reBEN) repository](https://git.tu-berlin.de/rsim/reben-training-scripts) and load the model using the code below. Note that you have to install [`configilm`](https://pypi.org/project/configilm/) to use the provided code. ```python from reben_publication.BigEarthNetv2_0_ImageClassifier import BigEarthNetv2_0_ImageClassifier model = BigEarthNetv2_0_ImageClassifier.from_pretrained("path_to/huggingface_model_folder") ``` e.g. ```python from reben_publication.BigEarthNetv2_0_ImageClassifier import BigEarthNetv2_0_ImageClassifier model = BigEarthNetv2_0_ImageClassifier.from_pretrained( "BIFOLD-BigEarthNetv2-0/resnet50-s2-v0.1.1") ``` If you use this model in your research or the provided code, please cite the following papers: ```bibtex @article{clasen2024refinedbigearthnet, title={reBEN: Refined BigEarthNet Dataset for Remote Sensing Image Analysis}, author={Clasen, Kai Norman and Hackel, Leonard and Burgert, Tom and Sumbul, Gencer and Demir, Beg{\"u}m and Markl, Volker}, year={2024}, eprint={2407.03653}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2407.03653}, } ``` ```bibtex @article{hackel2024configilm, title={ConfigILM: A general purpose configurable library for combining image and language models for visual question answering}, author={Hackel, Leonard and Clasen, Kai Norman and Demir, Beg{\"u}m}, journal={SoftwareX}, volume={26}, pages={101731}, year={2024}, publisher={Elsevier} } ```
fahmiaziz/SmolLM2-135M-Instruct-Clinical-Note	fahmiaziz	2025-05-24T11:11:02Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "clinical-note", "summarization", "trl", "sft", "base_model:HuggingFaceTB/SmolLM2-135M-Instruct", "base_model:finetune:HuggingFaceTB/SmolLM2-135M-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	summarization	2025-05-24T11:10:12Z	--- base_model: HuggingFaceTB/SmolLM2-135M-Instruct library_name: transformers model_name: SmolLM2-135M-Instruct-Clinical-Note tags: - generated_from_trainer - clinical-note - summarization - trl - sft licence: license --- # Model Card for SmolLM2-135M-Instruct-Clinical-Note This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-135M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-135M-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="fahmiaziz/SmolLM2-135M-Instruct-Clinical-Note", 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 SFT. ### Framework versions - TRL: 0.17.0 - Transformers: 4.51.3 - Pytorch: 2.6.0+cu124 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations 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 Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
WenFengg/alibaba_v1_w6_k2	WenFengg	2025-05-24T11:10:26Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-05-24T10:58:21Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
tgvdfr4/Video.18.Jaisalmer.Old.Man.Clip.Goes.Viral.Full.HD.Video.Original	tgvdfr4	2025-05-24T11:04:35Z	0	0	null	[ "region:us" ]	null	2025-05-24T10:59:04Z	<a href="https://allyoutubers.com/Jaisalmer-Old-Man-Clip-Goes-Viral"> 🌐 Video.18.Jaisalmer.Old.Man.Clip.Goes.Viral.Full.HD.Video.Original 🔴 ➤►DOWNLOAD👉👉🟢 ➤ <a href="https://allyoutubers.com/Jaisalmer-Old-Man-Clip-Goes-Viral"> 🌐 Video.18.Jaisalmer.Old.Man.Clip.Goes.Viral.Full.HD.Video.Original <a href="https://allyoutubers.com/Jaisalmer-Old-Man-Clip-Goes-Viral"> 🌐 Video.18.Jaisalmer.Old.Man.Clip.Goes.Viral.Full.HD.Video.Original 🔴 ➤►DOWNLOAD👉👉🟢 ➤ <a href="https://allyoutubers.com/Jaisalmer-Old-Man-Clip-Goes-Viral"> 🌐 Video.18.Jaisalmer.Old.Man.Clip.Goes.Viral.Full.HD.Video.Original
WenFengg/ronaldo_o1_w6_k1	WenFengg	2025-05-24T11:02:23Z	0	0	null	[ "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-05-24T10:55:12Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
Xvideo-sex-KatrinaLimViralKiffy/Katrina.Lim.viral.Videos-Original-link-XVideos	Xvideo-sex-KatrinaLimViralKiffy	2025-05-24T10:59:52Z	0	0	null	[ "region:us" ]	null	2025-05-24T09:59:36Z	# (VIRAL▔CLIP)18+) Katrina Lim Viral Kiffy Viral Video Full Video Original <p><a rel="nofollow" href="https://wixtube.site/?Apache-2.0">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )</a></p> <a rel="nofollow" href="https://wixtube.site/?Apache-2.0"><img src="https://us1.discourse-cdn.com/flex020/uploads/wandb/original/2X/0/0f5f73e0b1cd8c34c3d3fa6dcc1ce6713d5e4cbe.png" alt="fsd"></a>
erollemin/MoodTail	erollemin	2025-05-24T10:59:05Z	4	0	null	[ "pytorch", "bert", "text-generation", "tr", "license:cc-by-nc-4.0", "region:us" ]	text-generation	2025-05-22T14:02:59Z	--- license: cc-by-nc-4.0 pipeline_tag: text-generation language: - tr ---
omrisap/TreeRPO_math_straight_2	omrisap	2025-05-24T10:58:08Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-24T10:51:45Z	--- 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]
calcuis/tensor-transfer-protocol	calcuis	2025-05-24T10:56:20Z	0	0	null	[ "gguf", "license:mit", "region:us" ]	null	2025-05-24T09:39:19Z	--- license: mit --- ## tensor transfer protocol - test pack - pig architecture from [connector](https://huggingface.co/connector)
jrluo/bert-base-train10000	jrluo	2025-05-24T10:55:57Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-24T10:55:57Z	--- license: apache-2.0 ---
sarvamai/sarvam-m-gguf	sarvamai	2025-05-24T10:51:13Z	0	0	null	[ "gguf", "hi", "en", "gu", "kn", "mr", "ml", "or", "pa", "ta", "te", "bn", "base_model:sarvamai/sarvam-m", "base_model:quantized:sarvamai/sarvam-m", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-24T09:19:55Z	--- license: apache-2.0 base_model: - sarvamai/sarvam-m language: - hi - en - gu - kn - mr - ml - or - pa - ta - te - bn --- # Sarvam-M <p align="center"> <a href="https://dashboard.sarvam.ai/playground" target="_blank" rel="noopener noreferrer"> <img src="https://img.shields.io/badge/🚀 Chat on Sarvam Playground-1488CC?style=for-the-badge&logo=rocket" alt="Chat on Sarvam Playground" /> </a> </p> # Model Information > [!Note] > This repository contains gguf version of [`sarvam-m`](https://huggingface.co/sarvamai/sarvam-m) in bf16 precision. Learn more about sarvam-m in our detailed [blog post](https://www.sarvam.ai/blogs/sarvam-m). # Running the model on a CPU You can use the model on your local machine (without gpu) as explained [here](https://github.com/ggml-org/llama.cpp/tree/master/tools/main). Example Command: ``` ./build/bin/llama-cli -i -m /your/folder/path/sarvam-m-bf16.gguf -c 8192 -t 16 ```
New-Caitlin-Clark-dance-shower/FULL.VIDEO.LINK.Caitlin.Clark.dance.shower.Viral.Video.Leaks.Official	New-Caitlin-Clark-dance-shower	2025-05-24T10:43:47Z	0	0	null	[ "region:us" ]	null	2025-05-24T10:42:56Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
victkk/Qwen3-0.6B-math-orca-qlora-10k-ep1	victkk	2025-05-24T10:40:17Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:Qwen/Qwen3-0.6B", "base_model:finetune:Qwen/Qwen3-0.6B", "endpoints_compatible", "region:us" ]	null	2025-05-24T10:33:56Z	--- base_model: Qwen/Qwen3-0.6B library_name: transformers model_name: Qwen3-0.6B-math-orca-qlora-10k-ep1 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for Qwen3-0.6B-math-orca-qlora-10k-ep1 This model is a fine-tuned version of [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B). 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="victkk/Qwen3-0.6B-math-orca-qlora-10k-ep1", 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/victkk-fudan-university/qwen3-finetune/runs/acn8bo8n) This model was trained with SFT. ### Framework versions - TRL: 0.18.0.dev0 - Transformers: 4.52.0.dev0 - Pytorch: 2.6.0 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations 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 Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
tvpavan/sarvam-m-mlx-fp16	tvpavan	2025-05-24T10:39:30Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mlx", "conversational", "en", "bn", "hi", "kn", "gu", "mr", "ml", "or", "pa", "ta", "te", "base_model:sarvamai/sarvam-m", "base_model:finetune:sarvamai/sarvam-m", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-24T10:37:19Z	--- library_name: transformers license: apache-2.0 language: - en - bn - hi - kn - gu - mr - ml - or - pa - ta - te base_model: sarvamai/sarvam-m base_model_relation: finetune tags: - mlx --- # tvpavan/sarvam-m-mlx-fp16 The Model [tvpavan/sarvam-m-mlx-fp16](https://huggingface.co/tvpavan/sarvam-m-mlx-fp16) was converted to MLX format from [sarvamai/sarvam-m](https://huggingface.co/sarvamai/sarvam-m) using mlx-lm version 0.22.3. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("tvpavan/sarvam-m-mlx-fp16") prompt="hello" if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
mengmajun/qwen2.5-coder-1.5b-graph-v1	mengmajun	2025-05-24T10:34:15Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "region:us" ]	null	2025-05-24T10:34:08Z	--- base_model: unsloth/qwen2.5-coder-1.5b-instruct-bnb-4bit 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 0.15.2
haihp02/dc21102b-5b49-46b8-960f-20b22e87089d	haihp02	2025-05-24T10:33:16Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "trl", "sft", "dpo", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-24T10:32:22Z	--- library_name: transformers tags: - trl - sft - dpo --- # 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. 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bockhealthbharath/Eira-0.2	bockhealthbharath	2025-05-24T10:30:39Z	10	0	null	[ "safetensors", "blip", "biology", "medical", "multimodal", "question-answering", "healthcare", "image-text-to-text", "en", "base_model:Salesforce/blip-image-captioning-base", "base_model:finetune:Salesforce/blip-image-captioning-base", "license:mit", "region:us" ]	image-text-to-text	2025-04-16T20:41:37Z	--- pipeline_tag: image-text-to-text license: mit language: - en base_model: - meta-llama/Llama-2-7b-hf - Salesforce/blip-image-captioning-base tags: - biology - medical - multimodal - question-answering - healthcare --- # Model Card for EIRA-0.2 Bridging Text and Medical Imagery for Accurate Multimodal QA This model integrates a Llama‑2 text backbone with a BLIP vision backbone to perform context‑aware question answering over medical images and text. ## Model Details ### Model Description EIRA‑0.2 is a multimodal model designed to answer free‑form questions about medical images (e.g., radiographs, histology slides) in conjunction with accompanying text. Internally, it uses: - A text encoder/decoder based on meta‑llama/Llama‑2‑7b‑hf, fine‑tuned for medical QA. - A vision encoder based on Salesforce/blip-image-captioning-base, that extract descriptive features from medical imagery. - A fusion module that cross‑attends between vision features and text embeddings to generate coherent, context‑aware answers. - Developed by: BockBharath - Shared by: Shashidhar Sarvi and Sharvary H H - Model type: Multimodal Sequence‑to‑Sequence QA - Language(s): English - License: MIT - Finetuned from: meta‑llama/Llama‑2‑7b‑hf, Salesforce/blip-image-captioning-base ### Model Sources - Repository: https://github.com/BockBharath/EIRA-0.2 - Demo: https://huggingface.co/BockBharath/EIRA-0.2 ## Uses ### Direct Use EIRA‑0.2 can be used out‑of‑the‑box as a Hugging Face `pipeline` for image‑text-to-text question answering. It is intended for: - Clinical decision support by generating explanations of medical images. - Educational tools for medical students reviewing imaging cases. ### Downstream Use - Further fine‑tuning on specialty subdomains (e.g., dermatology, pathology) to improve domain performance. - Integration into telemedicine platforms to assist remote diagnostics. ### Out-of-Scope Use - Unsupervised generation of medical advice without expert oversight. - Non‑medical domains (the model’s vision backbone is specialized on medical imaging). ## Bias, Risks, and Limitations EIRA‑0.2 was trained on a curated set of medical textbooks and annotated imaging cases; it may underperform on rare pathologies or demographic groups under‑represented in the training data. Hallucination risk exists if the image context is ambiguous or incomplete. ### Recommendations - Always validate model outputs with a qualified medical professional. - Use in conjunction with structured reporting tools to mitigate hallucinations. ## How to Get Started with the Model ```python from transformers import pipeline # Load the multimodal QA pipeline eira = pipeline( task="image-text-to-text", model="BockBharath/EIRA-0.2", device=0 # set to -1 for CPU ) # Example inputs image_path = "chest_xray.png" question = "What abnormality is visible in the left lung?" # Run inference answer = eira({ "image": image_path, "text": question }) print("Answer:", answer[0]["generated_text"]) ``` Input shapes: - `image`: file path or PIL.Image of variable size (automatically resized to 224×224). - `text`: string question. Output: List of dicts with key `"generated_text"` containing the answer string. ## Training Details ### Training Data - Sources: 500+ medical imaging cases (X‑rays, CT, MRI) paired with expert Q&A, and 100 clinical chapters from open‑access medical textbooks. - Preprocessing: - Images resized to 224×224; normalized to ImageNet statistics. - Text tokenized with Llama tokenizer, max length 512 tokens. ### Training Procedure - Mixed‑precision (fp16) fine‑tuning. - Hardware: Single NVIDIA T4 GPU on Kaggle. - Batch size: 16 (per GPU) - Learning rate: 3e‑5 with linear warmup over 500 steps. - Epochs: 5 - Total time: ~48 hours ## Evaluation ### Testing Data, Factors & Metrics - Test set: 100 unseen imaging cases with 3 expert‑provided QA pairs each. - Metrics: - Exact Match (EM) on key findings: 72.4% - BLEU‑4 for answer fluency: 0.38 - ROUGE‑L for content overlap: 0.46 ### Results \| Metric \| Score \| \|--------------\|--------\| \| Exact Match \| 72.4% \| \| BLEU‑4 \| 0.38 \| \| ROUGE‑L \| 0.46 \| #### Subgroup Analysis Performance on chest X‑rays vs. histology slides: - Chest X‑ray EM: 75.1% - Histology EM: 68.0% ## Environmental Impact - Hardware Type: NVIDIA T4 GPU - Training Hours: ~48 - Compute Region: us‑central1 - Estimated CO₂eq: ~6 kg (using ML CO₂ impact calculator) ## Technical Specifications ### Model Architecture and Objective - Text backbone: 7 B‑parameter Llama 2 encoder‑decoder. - Vision backbone: BLIP ResNet‑50 + transformer head. - Fusion: Cross‑attention layers interleaved with decoder blocks. - Objective: Minimize cross‑entropy on ground‑truth answers. ### Compute Infrastructure - Hardware: Single NVIDIA T4 GPU (16 GB VRAM) - Software: PyTorch 2.0, Transformers 4.x, Accelerate ## Citation If you use this model, please cite: ```bibtex @misc{bockbharath2025eira02, title={EIRA-0.2: Multimodal Medical QA with Llama-2 and BLIP}, author={BockBharath}, year={2025}, howpublished={\url{https://huggingface.co/BockBharath/EIRA-0.2}} } ``` ```text BockBharath. (2025). EIRA-0.2: Multimodal Medical QA with Llama-2 and BLIP. Retrieved from https://huggingface.co/BockBharath/EIRA-0.2 ``` ## Model Card Authors - BockBharath - EIRA Project Team (Sharvary H H, Shashidhar Sarvi) ## Model Card Contact For questions or feedback, please open an issue on the [GitHub repository](https://github.com/BockBharath/EIRA-0.2).
hannesvgel/race-albert-v2	hannesvgel	2025-05-24T10:29:10Z	8	0	transformers	[ "transformers", "safetensors", "albert", "multiple-choice", "generated_from_trainer", "dataset:ehovy/race", "base_model:albert/albert-base-v2", "base_model:finetune:albert/albert-base-v2", "license:apache-2.0", "endpoints_compatible", "region:us" ]	multiple-choice	2025-05-22T08:52:11Z	--- library_name: transformers license: apache-2.0 base_model: albert-base-v2 tags: - generated_from_trainer metrics: - accuracy model-index: - name: results_albert results: [] datasets: - ehovy/race --- <!-- 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. --> # race-albert-v2 This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the [race dataset(middle)](https://huggingface.co/datasets/ehovy/race). It achieves the following results on the test set: - Loss: 0.8710 - Accuracy: 0.7089 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - 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: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.8709 \| 1.0 \| 3178 \| 0.8257 \| 0.6769 \| \| 0.6377 \| 2.0 \| 6356 \| 0.8329 \| 0.7152 \| \| 0.3548 \| 3.0 \| 9534 \| 1.0367 \| 0.7124 \| \| 0.1412 \| 4.0 \| 12712 \| 1.5380 \| 0.7145 \| ### Framework versions - Transformers 4.52.2 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
FormlessAI/26f47549-4c34-4d8c-9772-e1a559c6b16a	FormlessAI	2025-05-24T10:27:47Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "dpo", "conversational", "arxiv:2305.18290", "base_model:Qwen/Qwen2-1.5B-Instruct", "base_model:finetune:Qwen/Qwen2-1.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-24T10:09:43Z	--- base_model: Qwen/Qwen2-1.5B-Instruct library_name: transformers model_name: 26f47549-4c34-4d8c-9772-e1a559c6b16a tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for 26f47549-4c34-4d8c-9772-e1a559c6b16a This model is a fine-tuned version of [Qwen/Qwen2-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2-1.5B-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="FormlessAI/26f47549-4c34-4d8c-9772-e1a559c6b16a", 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/phoenix-formless/Gradients/runs/usvz7td3) 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: 0.17.0 - Transformers: 4.52.3 - Pytorch: 2.7.0+cu128 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## 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 Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
mlfoundations-dev/e1_code_fasttext_r1_10k	mlfoundations-dev	2025-05-24T10:20:08Z	0	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:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-21T21:00:30Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: e1_code_fasttext_r1_10k 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. --> # e1_code_fasttext_r1_10k This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/e1_code_fasttext_r1_10k 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: 4e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 16 - gradient_accumulation_steps: 8 - total_train_batch_size: 128 - total_eval_batch_size: 128 - 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: 5.0 ### Training results ### Framework versions - Transformers 4.46.1 - Pytorch 2.5.1 - Datasets 3.1.0 - Tokenizers 0.20.3
MinaMila/llama_instbase_3b_LoRa_ACSEmployment_2_cfda_ep10_22	MinaMila	2025-05-24T10:17:04Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-24T10:17:00Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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LandCruiser/sn29_cold_2305_3	LandCruiser	2025-05-24T09:58:01Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-23T07:27:55Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Gswrtz/MNLP_M2_document_encoder	Gswrtz	2025-05-24T09:57:15Z	0	0	sentence-transformers	[ "sentence-transformers", "pytorch", "tf", "rust", "onnx", "safetensors", "openvino", "bert", "feature-extraction", "sentence-similarity", "transformers", "en", "dataset:s2orc", "dataset:flax-sentence-embeddings/stackexchange_xml", "dataset:ms_marco", "dataset:gooaq", "dataset:yahoo_answers_topics", "dataset:code_search_net", "dataset:search_qa", "dataset:eli5", "dataset:snli", "dataset:multi_nli", "dataset:wikihow", "dataset:natural_questions", "dataset:trivia_qa", "dataset:embedding-data/sentence-compression", "dataset:embedding-data/flickr30k-captions", "dataset:embedding-data/altlex", "dataset:embedding-data/simple-wiki", "dataset:embedding-data/QQP", "dataset:embedding-data/SPECTER", "dataset:embedding-data/PAQ_pairs", "dataset:embedding-data/WikiAnswers", "arxiv:1904.06472", "arxiv:2102.07033", "arxiv:2104.08727", "arxiv:1704.05179", "arxiv:1810.09305", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-05-24T09:52:23Z	--- language: en license: apache-2.0 library_name: sentence-transformers tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers datasets: - s2orc - flax-sentence-embeddings/stackexchange_xml - ms_marco - gooaq - yahoo_answers_topics - code_search_net - search_qa - eli5 - snli - multi_nli - wikihow - natural_questions - trivia_qa - embedding-data/sentence-compression - embedding-data/flickr30k-captions - embedding-data/altlex - embedding-data/simple-wiki - embedding-data/QQP - embedding-data/SPECTER - embedding-data/PAQ_pairs - embedding-data/WikiAnswers pipeline_tag: sentence-similarity --- # all-MiniLM-L6-v2 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2') 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 import torch.nn.functional as F #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('sentence-transformers/all-MiniLM-L6-v2') model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2') # 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 sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) # Normalize embeddings sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1) print("Sentence embeddings:") print(sentence_embeddings) ``` ------ ## Background The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised contrastive learning objective. We used the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model and fine-tuned in on a 1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset. We developed this model during the [Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organized by Hugging Face. We developed this model as part of the project: [Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks. ## Intended uses Our model is intended to be used as a sentence and short paragraph encoder. Given an input text, it outputs a vector which captures the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks. By default, input text longer than 256 word pieces is truncated. ## Training procedure ### Pre-training We use the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model. Please refer to the model card for more detailed information about the pre-training procedure. ### Fine-tuning We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch. We then apply the cross entropy loss by comparing with true pairs. #### Hyper parameters We trained our model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core). We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with a 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`. #### Training data We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences. We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file. \| Dataset \| Paper \| Number of training tuples \| \|--------------------------------------------------------\|:----------------------------------------:\|:--------------------------:\| \| [Reddit comments (2015-2018)](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) \| [paper](https://arxiv.org/abs/1904.06472) \| 726,484,430 \| \| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Abstracts) \| [paper](https://aclanthology.org/2020.acl-main.447/) \| 116,288,806 \| \| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs \| [paper](https://doi.org/10.1145/2623330.2623677) \| 77,427,422 \| \| [PAQ](https://github.com/facebookresearch/PAQ) (Question, Answer) pairs \| [paper](https://arxiv.org/abs/2102.07033) \| 64,371,441 \| \| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Titles) \| [paper](https://aclanthology.org/2020.acl-main.447/) \| 52,603,982 \| \| [S2ORC](https://github.com/allenai/s2orc) (Title, Abstract) \| [paper](https://aclanthology.org/2020.acl-main.447/) \| 41,769,185 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs \| - \| 25,316,456 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title+Body, Answer) pairs \| - \| 21,396,559 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs \| - \| 21,396,559 \| \| [MS MARCO](https://microsoft.github.io/msmarco/) triplets \| [paper](https://doi.org/10.1145/3404835.3462804) \| 9,144,553 \| \| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) \| [paper](https://arxiv.org/pdf/2104.08727.pdf) \| 3,012,496 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) \| [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) \| 1,198,260 \| \| [Code Search](https://huggingface.co/datasets/code_search_net) \| - \| 1,151,414 \| \| [COCO](https://cocodataset.org/#home) Image captions \| [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) \| 828,395\| \| [SPECTER](https://github.com/allenai/specter) citation triplets \| [paper](https://doi.org/10.18653/v1/2020.acl-main.207) \| 684,100 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) \| [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) \| 681,164 \| \| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) \| [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) \| 659,896 \| \| [SearchQA](https://huggingface.co/datasets/search_qa) \| [paper](https://arxiv.org/abs/1704.05179) \| 582,261 \| \| [Eli5](https://huggingface.co/datasets/eli5) \| [paper](https://doi.org/10.18653/v1/p19-1346) \| 325,475 \| \| [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) \| [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) \| 317,695 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles) \| \| 304,525 \| \| AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) \| [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) \| 277,230 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (bodies) \| \| 250,519 \| \| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles+bodies) \| \| 250,460 \| \| [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) \| [paper](https://www.aclweb.org/anthology/D13-1155/) \| 180,000 \| \| [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) \| [paper](https://arxiv.org/abs/1810.09305) \| 128,542 \| \| [Altlex](https://github.com/chridey/altlex/) \| [paper](https://aclanthology.org/P16-1135.pdf) \| 112,696 \| \| [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) \| - \| 103,663 \| \| [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) \| [paper](https://www.aclweb.org/anthology/P11-2117/) \| 102,225 \| \| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) \| [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) \| 100,231 \| \| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) \| [paper](https://aclanthology.org/P18-2124.pdf) \| 87,599 \| \| [TriviaQA](https://huggingface.co/datasets/trivia_qa) \| - \| 73,346 \| \| Total \| \| 1,170,060,424** \|
vermoney/a4d660b5-796d-4949-bd80-836435b3af1e	vermoney	2025-05-24T09:56:49Z	0	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", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-24T09:39:53Z	--- library_name: peft license: mit base_model: unsloth/Phi-3-medium-4k-instruct tags: - axolotl - generated_from_trainer model-index: - name: a4d660b5-796d-4949-bd80-836435b3af1e 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: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - aa208f6e880a6925_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_instruction: instruct field_output: output format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null dpo: beta: 0.1 enabled: true group_by_length: false rank_loss: true reference_model: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 2 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: vermoney/a4d660b5-796d-4949-bd80-836435b3af1e hub_repo: null hub_strategy: end hub_token: null learning_rate: 2.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 96 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 48 lora_target_linear: true lr_scheduler: cosine max_steps: 280 micro_batch_size: 6 mixed_precision: bf16 mlflow_experiment_name: /tmp/aa208f6e880a6925_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: 535b6010-08d2-401c-aed4-b8c0c7c5416c wandb_project: s56-9 wandb_run: your_name wandb_runid: 535b6010-08d2-401c-aed4-b8c0c7c5416c warmup_steps: 40 weight_decay: 0.02 xformers_attention: true ``` </details><br> # a4d660b5-796d-4949-bd80-836435b3af1e 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: 4.0749 ## 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-06 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 12 - 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: 40 - training_steps: 280 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 7.0541 \| 0.0797 \| 280 \| 4.0749 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
ArtusDev/PocketDoc_Dans-PersonalityEngine-V1.3.0-24b_EXL3_3.25bpw_H6	ArtusDev	2025-05-24T09:55:24Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "general-purpose", "roleplay", "storywriting", "chemistry", "biology", "code", "climate", "axolotl", "text-generation-inference", "finetune", "legal", "medical", "finance", "exl3", "conversational", "en", "ar", "de", "fr", "es", "hi", "pt", "ja", "ko", "dataset:PocketDoc/Dans-Prosemaxx-RP", "dataset:PocketDoc/Dans-Personamaxx-Logs-2", "dataset:PocketDoc/Dans-Personamaxx-VN", "dataset:PocketDoc/Dans-Kinomaxx-VanillaBackrooms", "dataset:PocketDoc/Dans-Prosemaxx-Gutenberg", "dataset:PocketDoc/Dans-Prosemaxx-Cowriter-3-XL", "dataset:PocketDoc/Dans-Prosemaxx-Adventure", "dataset:PocketDoc/Dans-Failuremaxx-Adventure-3", "dataset:PocketDoc/Dans-Prosemaxx-InstructWriter-ZeroShot-2", "dataset:PocketDoc/Dans-Prosemaxx-InstructWriter-ZeroShot-3", "dataset:PocketDoc/Dans-Prosemaxx-InstructWriter-Continue-2", "dataset:PocketDoc/Dans-Prosemaxx-Instructwriter-Long", "dataset:PocketDoc/Dans-Prosemaxx-RepRemover-1", "dataset:PocketDoc/Dans-MemoryCore-CoreCurriculum-Small", "dataset:AquaV/US-Army-Survival-Sharegpt", "dataset:AquaV/Multi-Environment-Operations-Sharegpt", "dataset:AquaV/Resistance-Sharegpt", "dataset:AquaV/Interrogation-Sharegpt", "dataset:AquaV/Chemical-Biological-Safety-Applications-Sharegpt", "dataset:AquaV/Energetic-Materials-Sharegpt", "dataset:PocketDoc/Dans-Mathmaxx", "dataset:PJMixers/Math-Multiturn-1K-ShareGPT", "dataset:PocketDoc/Dans-Taskmaxx", "dataset:PocketDoc/Dans-Taskmaxx-DataPrepper", "dataset:PocketDoc/Dans-Taskmaxx-ConcurrentQA-Reworked", "dataset:PocketDoc/Dans-Taskmaxx-TableGPT", "dataset:PocketDoc/Dans-Taskmaxx-SciRIFF", "dataset:PocketDoc/Dans-Taskmaxx-Edit", "dataset:PocketDoc/Dans-Toolmaxx-Agent", "dataset:PocketDoc/Dans-Toolmaxx-ShellCommands", "dataset:PocketDoc/Dans-Toolmaxx-Functions-Toolbench", "dataset:PocketDoc/Dans-Toolmaxx-Functions-ToolACE", "dataset:PocketDoc/Dans-Toolmaxx-Functions-apigen-subset", "dataset:PocketDoc/Dans-Assistantmaxx-OpenAssistant2", "dataset:PocketDoc/Dans-Assistantmaxx-Opus-Merge-2", "dataset:PocketDoc/Dans-Assistantmaxx-sonnetorca-subset", "dataset:PocketDoc/Dans-Assistantmaxx-sonnetorca-subset-2", "dataset:PocketDoc/Dans-Assistantmaxx-Synthia", "dataset:PocketDoc/Dans-Assistantmaxx-ASL", "dataset:PocketDoc/Dans-Assistantmaxx-PersonaLLM-Opus", "dataset:PocketDoc/Dans-Assistantmaxx-LongAlign", "dataset:PocketDoc/Dans-Assistantmaxx-OpenLeecher-Instruct", "dataset:PocketDoc/Dans-Assistantmaxx-Tulu3-IF", "dataset:PocketDoc/Dans-Systemmaxx", "dataset:PocketDoc/Dans-Logicmaxx-SAT-AP", "dataset:PJMixers/grimulkan_theory-of-mind-ShareGPT", "dataset:PJMixers/grimulkan_physical-reasoning-ShareGPT", "dataset:PocketDoc/Dans-Reasoningmaxx-NaturalReasoning", "dataset:PocketDoc/Dans-Reasoningmaxx-WebInstruct", "dataset:PocketDoc/Dans-Reasoningmaxx-GeneralReasoning", "dataset:PocketDoc/Dans-Assistantmaxx-ClosedInstruct", "base_model:PocketDoc/Dans-PersonalityEngine-V1.3.0-24b", "base_model:quantized:PocketDoc/Dans-PersonalityEngine-V1.3.0-24b", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-24T09:42:37Z	--- thumbnail: >- https://huggingface.co/PocketDoc/Dans-PersonalityEngine-V1.3.0-24b/resolve/main/resources/pe.png license: apache-2.0 tags: - general-purpose - roleplay - storywriting - chemistry - biology - code - climate - axolotl - text-generation-inference - finetune - legal - medical - finance - exl3 datasets: - PocketDoc/Dans-Prosemaxx-RP - PocketDoc/Dans-Personamaxx-Logs-2 - PocketDoc/Dans-Personamaxx-VN - PocketDoc/Dans-Kinomaxx-VanillaBackrooms - PocketDoc/Dans-Prosemaxx-Gutenberg - PocketDoc/Dans-Prosemaxx-Cowriter-3-XL - PocketDoc/Dans-Prosemaxx-Adventure - PocketDoc/Dans-Failuremaxx-Adventure-3 - PocketDoc/Dans-Prosemaxx-InstructWriter-ZeroShot-2 - PocketDoc/Dans-Prosemaxx-InstructWriter-ZeroShot-3 - PocketDoc/Dans-Prosemaxx-InstructWriter-Continue-2 - PocketDoc/Dans-Prosemaxx-Instructwriter-Long - PocketDoc/Dans-Prosemaxx-RepRemover-1 - PocketDoc/Dans-MemoryCore-CoreCurriculum-Small - AquaV/US-Army-Survival-Sharegpt - AquaV/Multi-Environment-Operations-Sharegpt - AquaV/Resistance-Sharegpt - AquaV/Interrogation-Sharegpt - AquaV/Chemical-Biological-Safety-Applications-Sharegpt - AquaV/Energetic-Materials-Sharegpt - PocketDoc/Dans-Mathmaxx - PJMixers/Math-Multiturn-1K-ShareGPT - PocketDoc/Dans-Taskmaxx - PocketDoc/Dans-Taskmaxx-DataPrepper - PocketDoc/Dans-Taskmaxx-ConcurrentQA-Reworked - PocketDoc/Dans-Taskmaxx-TableGPT - PocketDoc/Dans-Taskmaxx-SciRIFF - PocketDoc/Dans-Taskmaxx-Edit - PocketDoc/Dans-Toolmaxx-Agent - PocketDoc/Dans-Toolmaxx-ShellCommands - PocketDoc/Dans-Toolmaxx-Functions-Toolbench - PocketDoc/Dans-Toolmaxx-Functions-ToolACE - PocketDoc/Dans-Toolmaxx-Functions-apigen-subset - PocketDoc/Dans-Assistantmaxx-OpenAssistant2 - PocketDoc/Dans-Assistantmaxx-Opus-Merge-2 - PocketDoc/Dans-Assistantmaxx-sonnetorca-subset - PocketDoc/Dans-Assistantmaxx-sonnetorca-subset-2 - PocketDoc/Dans-Assistantmaxx-Synthia - PocketDoc/Dans-Assistantmaxx-ASL - PocketDoc/Dans-Assistantmaxx-PersonaLLM-Opus - PocketDoc/Dans-Assistantmaxx-LongAlign - PocketDoc/Dans-Assistantmaxx-OpenLeecher-Instruct - PocketDoc/Dans-Assistantmaxx-Tulu3-IF - PocketDoc/Dans-Systemmaxx - PocketDoc/Dans-Logicmaxx-SAT-AP - PJMixers/grimulkan_theory-of-mind-ShareGPT - PJMixers/grimulkan_physical-reasoning-ShareGPT - PocketDoc/Dans-Reasoningmaxx-NaturalReasoning - PocketDoc/Dans-Reasoningmaxx-WebInstruct - PocketDoc/Dans-Reasoningmaxx-GeneralReasoning - PocketDoc/Dans-Assistantmaxx-ClosedInstruct language: - en - ar - de - fr - es - hi - pt - ja - ko base_model: - PocketDoc/Dans-PersonalityEngine-V1.3.0-24b base_model_relation: quantized quantized_by: ArtusDev pipeline_tag: text-generation library_name: transformers --- <!doctype html> <html lang="en"> <head> <meta charset="UTF-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0" /> <title>Dans-PersonalityEngine-V1.3.0-24b</title> </head> <div class="crt-container"> <div class="crt-case"> <div class="crt-inner-case"> <div class="crt-bezel"> <div class="terminal-screen"> <div style="text-align: center"> <h2>Dans-PersonalityEngine-V1.3.0-24b</h2> <pre class="code-block" style="display: inline-block; text-align: left; font-size: clamp(2px, 0.8vw, 14px); line-height: 1.2; max-width: 100%; overflow: hidden; white-space: pre;"> ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⠀⠄⠀⡂⠀⠁⡄⢀⠁⢀⣈⡄⠌⠐⠠⠤⠄⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⡄⠆⠀⢠⠀⠛⣸⣄⣶⣾⡷⡾⠘⠃⢀⠀⣴⠀⡄⠰⢆⣠⠘⠰⠀⡀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠃⠀⡋⢀⣤⡿⠟⠋⠁⠀⡠⠤⢇⠋⠀⠈⠃⢀⠀⠈⡡⠤⠀⠀⠁⢄⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠁⡂⠀⠀⣀⣔⣧⠟⠋⠀⢀⡄⠀⠪⣀⡂⢁⠛⢆⠀⠀⠀⢎⢀⠄⢡⠢⠛⠠⡀⠀⠄⠀⠀ ⠀⠀⡀⠡⢑⠌⠈⣧⣮⢾⢏⠁⠀⠀⡀⠠⠦⠈⠀⠞⠑⠁⠀⠀⢧⡄⠈⡜⠷⠒⢸⡇⠐⠇⠿⠈⣖⠂⠀ ⠀⢌⠀⠤⠀⢠⣞⣾⡗⠁⠀⠈⠁⢨⡼⠀⠀⠀⢀⠀⣀⡤⣄⠄⠈⢻⡇⠀⠐⣠⠜⠑⠁⠀⣀⡔⡿⠨⡄ ⠈⠂⠀⠆⠀⣼⣾⠟⠀⠑⠀⡐⠗⠉⠀⠐⠶⣤⡵⠋⠀⠠⠹⡌⡀⠘⠇⢠⣾⡣⣀⡴⠋⠅⠈⢊⠠⡱⡀ ⠪⠑⢌⠂⣼⣿⡟⠀⠀⠙⠀⠀⠀⡀⠀⠀⠐⡞⡐⠀⠀⡧⠀⢀⠠⠀⣁⠾⡇⠀⠙⡁⠀⠀⢀⣨⣄⡠⢱ ⣸⠈⠊⠙⣛⣿⡧⠔⠚⠛⠳⣄⣀⡬⠤⠬⠼⡣⠃⠀⢀⡗⠀⡤⠞⠙⠄⠂⠃⢀⣠⣤⠶⠙⠅⠁⠃⠋⠈ ⢋⠼⣀⠰⢯⢿⠁⠀⢢⠀⠀⢐⠋⡀⠀⠈⠁⠀⣀⣰⠏⠒⠙⠈⠀⣀⡤⠞⢁⣼⠏⠘⢀⣀⢤⢤⡐⢈⠂ ⠀⠢⠀⠀⠸⣿⡄⠲⠚⠘⠚⠃⢀⠀⠈⢋⠶⠛⠉⠉⢃⣀⢤⢾⠋⣁⡤⡚⠁⢹⠁⠠⢛⠠⠬⠁⢬⠀⠀ ⠀⠈⢳⣒⠋⠉⣿⢐⠠⣀⣃⠀⠀⠉⠂⢁⣀⣀⡤⢞⠩⢑⡨⠰⡞⠁⠁⢀⡠⠾⠎⡈⡌⡈⡓⡀⠄⠀⠀ ⠀⠀⠀⠉⠘⠃⢻⡒⠦⢼⣿⣛⣻⣿⡷⢄⣀⣀⣠⣴⢾⣿⣆⣡⡄⣠⣪⡿⣷⣾⣷⣧⡡⠅⣇⠍⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠙⠒⠒⠛⠛⠓⠉⢹⠀⣷⠴⣻⣽⡻⢧⢻⡿⡏⣼⢿⣻⢾⣿⣿⣿⡿⢠ ⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠂⠻⠨⠰⢋⡅⠉⣑⡇⡗⣿⢂⣸⡿⣿⣛⠿⠃⠁ ⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠳⣌⣙⣸⢧⣿⣕⣼⣇⢹⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣠⣸⢧⢟⢟⡟⣾⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⢰⠙⣾⡟⣻⡕⣹⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⢸⢰⡏⢠⡿⠾⠋⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⢸⠸⡇⡏⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠸⢸⢸⡇⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠘⠇⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ </pre> </div> <p> Dans-PersonalityEngine is a versatile model series fine-tuned on 50+ specialized datasets, designed to excel at both creative tasks (like roleplay and co-writing) and technical challenges (such as code generation, tool use, and complex reasoning). </p> <p> V1.3.0 introduces multilingual capabilities with support for 10 languages and enhanced domain expertise across multiple fields. The primary language is still English and that is where peak performance can be expected. </p> <h3>Multilingual Support</h3> <pre class="code-block"> Arabic Chinese English French German Hindi Japanese Korean Portuguese Spanish</pre> <h3>Key Details</h3> <pre class="code-block"> BASE MODEL: mistralai/Mistral-Small-3.1-24B-Base-2503 LICENSE: apache-2.0 LANGUAGE: Multilingual with 10 supported languages CONTEXT LENGTH: 32768 tokens, 131072 with degraded recall</pre> <h3>Recommended Settings</h3> <pre class="code-block"> TEMPERATURE: 1.0 TOP_P: 0.9</pre> <h3>Prompting Format</h3> <p> The model uses the following format I'll refer to as "DanChat-2": </p> <pre class="code-block"> <\|system\|>system prompt<\|endoftext\|><\|user\|>Hi there!<\|endoftext\|><\|assistant\|>Hey, how can I help?<\|endoftext\|></pre> <h3>Why not ChatML?</h3> <p> While ChatML is a standard format for LLMs, it has limitations. DanChat-2 uses special tokens for each role, this reduces biases and helps the model adapt to different tasks more readily. </p> <h3>SillyTavern Template</h3> <p> <a href="https://huggingface.co/PocketDoc/Dans-PersonalityEngine-V1.3.0-24b/resolve/main/resources/DanChat-2.json?download=true" download target="_blank" rel="noopener noreferrer" > Download Master JSON </a> </p> <h3>Inference Provider</h3> <p> This model and others are available from ⚡Mancer AI for those interested in high quality inference without owning or renting expensive hardware. </p> <p class="mancer-button-container"> <a href="https://mancer.tech/" target="_blank" rel="noopener noreferrer" class="mancer-button" > <span class="mancer-text">mancer</span> </a> </p> <h3>Training Process</h3> <p> The model was trained using Axolotl on 8x H100 GPUs for 50 hours. The resources to train this model were provided by Prime Intellect and Kalomaze. </p> <h3>Support Development</h3> <p> Development is limited by funding and resources. To help support: </p> <p>- Contact on HF</p> <p>- Email: [email protected]</p> <p class="coffee-container"> <a href="https://www.buymeacoffee.com/visually" target="_blank" rel="noopener noreferrer" > <img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" height="45" width="162" /> </a> </p> </div> </div> </div> </div> </div> <style> @import url("https://fonts.googleapis.com/css2?family=Consolas&display=swap"); .crt-container { padding: 10px; max-width: 1000px; margin: 0 auto; width: 95%; } .crt-case { background: #e8d7c3; border-radius: 10px; padding: 15px; box-shadow: inset -2px -2px 5px rgba(0, 0, 0, 0.3), 2px 2px 5px rgba(0, 0, 0, 0.2); } .crt-inner-case { background: #e8d7c3; border-radius: 8px; padding: 3px; box-shadow: inset -1px -1px 4px rgba(0, 0, 0, 0.3), 1px 1px 4px rgba(0, 0, 0, 0.2); } .crt-bezel { background: linear-gradient(145deg, #1a1a1a, #2a2a2a); padding: 15px; border-radius: 5px; border: 3px solid #0a0a0a; position: relative; box-shadow: inset 0 0 20px rgba(0, 0, 0, 0.5), inset 0 0 4px rgba(0, 0, 0, 0.4), inset 2px 2px 4px rgba(255, 255, 255, 0.05), inset -2px -2px 4px rgba(0, 0, 0, 0.8), 0 0 2px rgba(0, 0, 0, 0.6), -1px -1px 4px rgba(255, 255, 255, 0.1), 1px 1px 4px rgba(0, 0, 0, 0.3); 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dimasik87/95aae631-e0b6-4309-8a1f-3ff7bd133af4	dimasik87	2025-05-24T09:54:49Z	0	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "axolotl", "dpo", "trl", "conversational", "arxiv:2305.18290", "base_model:unsloth/Qwen2.5-Coder-1.5B-Instruct", "base_model:quantized:unsloth/Qwen2.5-Coder-1.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-24T09:48:10Z	--- base_model: unsloth/Qwen2.5-Coder-1.5B-Instruct library_name: transformers model_name: 95aae631-e0b6-4309-8a1f-3ff7bd133af4 tags: - generated_from_trainer - axolotl - dpo - trl licence: license --- # Model Card for 95aae631-e0b6-4309-8a1f-3ff7bd133af4 This model is a fine-tuned version of [unsloth/Qwen2.5-Coder-1.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-Coder-1.5B-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="dimasik87/95aae631-e0b6-4309-8a1f-3ff7bd133af4", 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/dedok-yo/s56-7/runs/icnqat5u) 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: 4.46.0 - Pytorch: 2.5.0+cu124 - Datasets: 3.0.1 - Tokenizers: 0.20.1 ## 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}} } ```
tuandunghcmut/Qwen3-FT-Customer-Dataset	tuandunghcmut	2025-05-24T09:52:03Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "smol-course", "module_1", "trl", "sft", "base_model:Qwen/Qwen3-0.6B", "base_model:finetune:Qwen/Qwen3-0.6B", "endpoints_compatible", "region:us" ]	null	2025-05-15T09:42:25Z	--- base_model: Qwen/Qwen3-0.6B library_name: transformers model_name: Qwen3-FT-MyDataset tags: - generated_from_trainer - smol-course - module_1 - trl - sft licence: license --- # Model Card for Qwen3-FT-MyDataset This model is a fine-tuned version of [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B). 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="tuandunghcmut/Qwen3-FT-MyDataset", 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/Private_1/huggingface/runs/9qxdvck3) This model was trained with SFT. ### Framework versions - TRL: 0.17.0 - Transformers: 4.51.3 - Pytorch: 2.7.0 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations 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 Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
New-tutorial-Riley-Reid-Viral-Video/Full.Clip.Riley.Reid.Viral.Video.Leaks.Official	New-tutorial-Riley-Reid-Viral-Video	2025-05-24T09:49:47Z	0	0	null	[ "region:us" ]	null	2025-05-24T09:49:01Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
KingEmpire/sn21_omega_2405_6	KingEmpire	2025-05-24T09:48:55Z	0	0	null	[ "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-05-24T09:35:16Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
KingEmpire/sn21_omega_2405_5	KingEmpire	2025-05-24T09:48:34Z	0	0	null	[ "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-05-24T09:35:12Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
deswaq/alfa1	deswaq	2025-05-24T09:48:01Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-24T09:41:43Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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alibaba-pai/DistilQwen2.5-1.5B-Instruct	alibaba-pai	2025-05-24T09:44:14Z	1	0	null	[ "safetensors", "qwen2", "arxiv:2504.15027", "region:us" ]	null	2025-02-19T02:11:19Z	## 📖 Introduction DistilQwen2.5-1.5B is a distilled version of Qwen2.5-1.5B-Instruct, designed to distill the capabilities of stronger LLMs into smaller ones. To achieve this, we utilized a diverse range of datasets for the distillation process, including well-known open-source collections such as Magpie, Openhermes, and Mammoth 2, as well as proprietary synthetic datasets. The training data primarily consists of instructions in Chinese and English. To enhance the quality and diversity of the instruction data, we implemented a difficulty scoring system and task-related resampling techniques. For difficulty scoring, we employed the LLM-as-a-Judge paradigm, using the teacher model to evaluate responses based on accuracy, relevance, helpfulness, and level of detail. We then calculated the Model Fitting Difficulty (MFD) Score by subtracting the teacher model's score from the student model's score. A higher MFD Score indicates that the instruction is more valuable for distillation training. This approach allowed us to remove low-difficulty instructions from the training set, focusing on more challenging and informative examples. After performing black-box data distillation on the model, we further conducted white-box distillation (teacher model logits distillation). Black-box knowledge distillation relies solely on the highest probability token output by the teacher model, while white-box knowledge distillation focuses more on the distribution of logits output by the teacher model, thereby providing richer information for the student model. By mimicking the logits distribution of the teacher model, white-box distillation can transfer knowledge more effectively, further enhancing the performance of the student model. This careful curation and scoring process ensures that DistilQwen2.5-1.5B achieves high performance after the distillation process. ## 🚀 Quick Start Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents. ```python from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto model = AutoModelForCausalLM.from_pretrained( "alibaba-pai/DistilQwen2.5-1.5B-Instruct", torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained("alibaba-pai/DistilQwen2.5-1.5B-Instruct") prompt = "Give me a short introduction to large language model." messages = [ {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(device) generated_ids = model.generate( model_inputs.input_ids, max_new_tokens=2048， ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` ## Reference For more detailed information about the model, we encourage you to refer to our paper: - DistilQwen2.5: Industrial Practices of Training Distilled Open Lightweight Language Models Chengyu Wang, Junbing Yan, Yuanhao Yue, Jun Huang [arXiv:2504.15027](https://arxiv.org/abs/2504.15027) You can cite the paper using the following citation format: ```bibtex @misc{wang2025distilqwen25industrialpracticestraining, title={DistilQwen2.5: Industrial Practices of Training Distilled Open Lightweight Language Models}, author={Chengyu Wang and Junbing Yan and Yuanhao Yue and Jun Huang}, year={2025}, eprint={2504.15027}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2504.15027} } ```
FAISAL7236/Anarob-Core	FAISAL7236	2025-05-24T09:43:44Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-24T09:43:44Z	--- license: apache-2.0 ---
Hyper-AI-Computer/Llama-Baseline-V3-A-001	Hyper-AI-Computer	2025-05-24T09:39:48Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-24T09:05:40Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Pastors-daughter-Viral/wATCH.Pastors.daughter.viral.video.original.Link.Official	Pastors-daughter-Viral	2025-05-24T09:39:08Z	0	0	null	[ "region:us" ]	null	2025-05-24T09:38:27Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a> 02 seconds ago — Pastor's daughter video twitter Viral Video Original Viral video took the internet by storm and amazed viewers on various social media platforms. Pastor's daughter video twitter Video, a young and talented digital creator, recently became famous thanks to this interesting video. Leaked Video Pastor's daughter video twitter Video Tutorial Original Video Viral Video Leaked on X Twitter Telegram In the ever evolving landscape of celebrity culture, the Ishowspeedscandal underscores the relentless pursuit of sensationalism, a pursuit that often comes at the expense of truth and dignity. As we navigate the complexities of the digital age, the line between entertainment and exploitation remains perilously thin. The recurrent theme of Leaked tapes and the subsequent fallout serves as a reminder of the fragility of reputation in the digital era. As the lines between private and public life continue to blur, celebrities like Prison Officerfind themselves at the mercy of internet chatter, where a rumor can ignite a firestorm of speculation and judgment
usham/mental-health-companion-model	usham	2025-05-24T09:35:20Z	0	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-24T09:27:50Z	--- base_model: unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: usham - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-1b-instruct-unsloth-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)

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