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Borcherding/OARC_Commander_v001_beta	Borcherding	2025-06-16T00:59:04Z	93	2	null	[ "safetensors", "gguf", "llama", "en", "dataset:Borcherding/OARC_Commander_v001", "base_model:unsloth/Llama-3.2-3B-bnb-4bit", "base_model:quantized:unsloth/Llama-3.2-3B-bnb-4bit", "license:llama3.1", "endpoints_compatible", "region:us", "conversational" ]	null	2025-01-12T06:48:19Z	--- license: llama3.1 datasets: - Borcherding/OARC_Commander_v001 language: - en base_model: - unsloth/Llama-3.2-3B-bnb-4bit --- # 🎮 OARC_Commander_v0.1-Llama-3.2-3B 🎮 Part of the [🎮OARC_Commander🎮 Collection](https://huggingface.co/collections/Borcherding/oarc-commander-66e7e6cfa4bbc652ff6c0699) Curated and trained by [Leo Borcherding](https://huggingface.co/Borcherding) [![Discord](https://img.shields.io/discord/1212987995532042270?label=online&logo=discord)](https://discord.gg/mNeQZzBHuW) OARC Discord: https://discord.gg/mNeQZzBHuW <img src="https://cdn-uploads.huggingface.co/production/uploads/66004e81b1e509e1e47ef31d/UpVFp2JTXJvgx38en2G7o.webp" alt="Image description" width="500" height="200"> ## What is OARC_Commander_v0.1? OARC_Commander_v0.1 is the first generation of the OARC_Commander series of models. OARC_Commander aims to be a general purpose Llama-3.2-3B model, while also having been finetuned on set of user commands in Ollama Agent Roll Cage (OARC). ## Chat Template We use ChatML for the chat template. ``` <\|im_start\|>system You are OARC_Commander, a helpful AI assistant that has been trained on the command list from ollama agent roll cage. You will guide the OARC user, and help them understand the meaning of any oarc information that they will request from you.<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` ## System Prompt In OARC_Commander, the system prompt is what you can use to set the tone and alignment of the responses for OARC_Commander if you wish. Example use of system prompt: ``` <\|im_start\|>system You are OARC_Commander, a helpful AI assistant that has been trained on the command list from ollama agent roll cage. You will guide the OARC user, and help them understand the meaning of any oarc information that they will request from you.<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` ## Sample Outputs Here are some sample outputs from OARC_Commander_v001, showcasing the commanding data from the Borcherding/OARC_Commander_v001 training data, I am aware that there are like greek and korean tokens being randomly output, I am working on figuring out why this is, and will update the repo with the fixed version once I have ironed this out. ![image/webp](https://cdn-uploads.huggingface.co/production/uploads/66004e81b1e509e1e47ef31d/LSslMECrntFoYxhc2AcRj.webp) ![image/webp](https://cdn-uploads.huggingface.co/production/uploads/66004e81b1e509e1e47ef31d/nUWcWPjTnJZHv_byqMhEQ.webp) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/66004e81b1e509e1e47ef31d/-5BIq-l0XdoDqfQCQHQ0f.png) ## How to use There are many ways to use a huggingface model including: - ollama - Huggingface Transformers library - vllm - LM Studio ### ollama usage - [Install ollama](https://ollama.com/download) - ```ollama run hf.co/Borcherding/OARC_Commander_v001_beta:Q8_0``` - ```instead of Q8_0, you can try F16, or Q4_K_M as well, these commands can be found in the top right under "Use this model"``` - ```once you have ollama loaded try asking the mode something like:``` - ```"what is the purpose of the /swap command?"``` - ```you can also try:``` - ```/set system <your system prompt>``` ## Evals TBD ## Appreciation Respect and thanks to the creators of the open source datasets that were used: Special thanks to - Meta, and @mlabonne who wrote papers and published models that were instrumental in creating and inspiring OARC_Commander_v001.
Tongjilibo/bert4torch_config	Tongjilibo	2025-06-16T00:54:50Z	0	2	null	[ "license:apache-2.0", "region:us" ]	null	2024-02-16T06:49:24Z	--- license: apache-2.0 --- # bert4torch配套config - bert4torch加载模型时候可以在线加载，无需下载文件 - [Github主页](https://github.com/Tongjilibo/bert4torch) - 预训练模型支持多种代码加载方式 ```python from bert4torch.models import build_transformer_model # 1. 仅指定config_path: 从头初始化模型结构, 不加载预训练模型 model = build_transformer_model('./model/bert4torch_config.json') # 2. 仅指定checkpoint_path: ## 2.1 文件夹路径: 自动寻找路径下的.bin/.safetensors权重文件 + bert4torch_config.json/config.json文件 model = build_transformer_model(checkpoint_path='./model') ## 2.2 文件路径/列表: 文件路径即权重路径/列表, config会从同级目录下寻找 model = build_transformer_model(checkpoint_path='./pytorch_model.bin') ## 2.3 model_name: hf上预训练权重名称, 会自动下载hf权重以及bert4torch_config.json文件 model = build_transformer_model(checkpoint_path='bert-base-chinese') # 3. 同时指定config_path和checkpoint_path(本地路径名或model_name排列组合): config_path = './model/bert4torch_config.json' # 或'bert-base-chinese' checkpoint_path = './model/pytorch_model.bin' # 或'bert-base-chinese' model = build_transformer_model(config_path, checkpoint_path) ```
gradientrouting-spar/mc14_badmed_dpo_dsd-42_msd-42_atc-0.45_ldpo-6_seed_1	gradientrouting-spar	2025-06-16T00:53:04Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T00:52:51Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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creativeplanet/anno1800-mistral-4bit-lora-adapter-new	creativeplanet	2025-06-16T00:51:20Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T00:50:45Z	--- 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. 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datasciguy/small-fine-tunes	datasciguy	2025-06-16T00:50:29Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T00:49:42Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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metythorn/khmer-xlm-roberta-base	metythorn	2025-06-16T00:49:17Z	0	1	transformers	[ "transformers", "safetensors", "roberta", "fill-mask", "cambodian", "khmer", "multilingual", "masked-lm", "pretrained", "cambodia", "southeast-asia", "nlp", "language-model", "km", "en", "dataset:custom-corpus", "base_model:FacebookAI/roberta-base", "base_model:finetune:FacebookAI/roberta-base", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-06-15T15:57:15Z	--- language: - km - en license: apache-2.0 library_name: transformers base_model: roberta-base pipeline_tag: fill-mask tags: - cambodian - khmer - multilingual - roberta - masked-lm - pretrained - cambodia - southeast-asia - nlp - language-model datasets: - custom-corpus metrics: - perplexity model-index: - name: metythorn/khmer-xlm-roberta-base results: - task: type: fill-mask name: Fill-Mask metrics: - type: perplexity value: "TBD" name: Perplexity widget: - text: "ប្រទេសកម្ពុជា គឺជាប្រទេស <mask> នៅអាស៊ីអាគ្នេយ៍។" example_title: "Khmer Geography" - text: "ការអភិវឌ្ឍន៍ <mask> នៅកម្ពុជាកំពុងតែរីកចម្រើនយ៉ាងលឿន។" example_title: "Khmer Development" - text: "Mobile <mask> technology is rapidly advancing in Cambodia." example_title: "English Technology" - text: "The capital city of Cambodia is <mask>." example_title: "English Geography" - text: "បច្ចេកវិទ្យា <mask> បានផ្លាស់ប្តូរជីវិតប្រជាជនកម្ពុជា។" example_title: "Khmer Technology Impact" --- # XLM-RoBERTa for Khmer-English Language Processing ## Model Description This is a custom-trained XLM-RoBERTa-base model specifically designed for Khmer (ខ្មែរ) and English language processing. The model has been pretrained using masked language modeling (MLM) on a curated corpus of Khmer-English text data, making it highly effective for understanding and generating text in both languages. ### Key Features 🌟 Bilingual Proficiency: Understands both Khmer and English with high accuracy 🚀 State-of-the-art Architecture: Based on RoBERTa with optimized training 📚 Domain Versatile: Trained on diverse text covering multiple domains 🔧 Ready-to-use: Can be fine-tuned for downstream tasks or used directly ⚡ Efficient: Optimized for both inference speed and model size ## Model Details \| Attribute \| Value \| \|-----------\|-------\| \| Model Type \| XLM-RoBERTa (Transformer) \| \| Architecture \| RoBERTa-base \| \| Languages \| Khmer (km), English (en) \| \| Vocabulary Size \| 30,000 tokens \| \| Parameters \| 109,113,648 \| \| Max Sequence Length \| 512 tokens \| \| Training Step \| 3000 \| \| Tokenizer \| SentencePiece \| \| License \| Apache 2.0 \| ## Quick Start ### Installation ```bash pip install transformers torch ``` ### Basic Usage ```python from transformers import RobertaForMaskedLM, PreTrainedTokenizerFast import torch # Load model and tokenizer model_name = "metythorn/khmer-xlm-roberta-base" tokenizer = PreTrainedTokenizerFast.from_pretrained(model_name) model = RobertaForMaskedLM.from_pretrained(model_name) # Set model to evaluation mode model.eval() def predict_mask(text): # Tokenize input inputs = tokenizer(text, return_tensors="pt") # Get predictions with torch.no_grad(): outputs = model(inputs) predictions = outputs.logits # Find masked token position mask_token_index = torch.where(inputs["input_ids"] == tokenizer.mask_token_id)[1] # Get top 5 predictions mask_token_logits = predictions[0, mask_token_index, :] top_5_tokens = torch.topk(mask_token_logits, 5, dim=1).indices[0].tolist() return [tokenizer.decode([token]).strip() for token in top_5_tokens] # Example usage khmer_text = "ប្រទេសកម្ពុជា គឺជាប្រទេស <mask> នៅអាស៊ីអាគ្នេយ៍។" english_text = "The capital of Cambodia is <mask>." print("Khmer predictions:", predict_mask(khmer_text)) print("English predictions:", predict_mask(english_text)) ``` ### Advanced Usage #### Text Classification Fine-tuning ```python from transformers import RobertaForSequenceClassification, Trainer, TrainingArguments # Load model for classification model = RobertaForSequenceClassification.from_pretrained( "metythorn/khmer-xlm-roberta-base", num_labels=2 # Adjust based on your task ) # Fine-tune on your classification dataset # ... (add your training data and training loop) ``` #### Feature Extraction ```python from transformers import RobertaModel # Load model for feature extraction model = RobertaModel.from_pretrained("metythorn/khmer-xlm-roberta-base") def get_embeddings(text): inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True) with torch.no_grad(): outputs = model(inputs) # Use CLS token embedding or pool all token embeddings embeddings = outputs.last_hidden_state.mean(dim=1) # Mean pooling return embeddings # Extract embeddings khmer_emb = get_embeddings("នេះជាប្រយោគខ្មែរ។") english_emb = get_embeddings("This is an English sentence.") ``` ## Training Details ### Training Configuration \| Parameter \| Value \| \|-----------\|-------\| \| Training Framework \| 🤗 Transformers + PyTorch \| \| Batch Size \| 8 per device \| \| Gradient Accumulation \| 4 steps \| \| Effective Batch Size \| 32 \| \| Learning Rate \| 5e-05 \| \| Weight Decay \| 0.01 \| \| Warmup Steps \| 2,000 \| \| Max Grad Norm \| 1.0 \| \| Mixed Precision \| FP16 \| \| Gradient Checkpointing \| ✅ Enabled \| ### Training Objective The model was trained using Masked Language Modeling (MLM) with: - Masking Probability: 0.15 (15%) - Dynamic Masking: Applied during training for better generalization - Whole Word Masking: Implemented for multi-token words ### Dataset - Source: Custom curated Khmer-English corpus - Domains: News, literature, government documents, web content, technical documents - Size: Multiple GB of cleaned text data - Languages: Khmer (ខ្មែរ) and English - Preprocessing: Cleaned, deduplicated, and filtered for quality ### Infrastructure - GPUs: Multi-GPU training setup - Framework: PyTorch with Transformers - Optimization: Memory-efficient training with gradient checkpointing - Monitoring: Comprehensive logging and checkpointing ## Performance ### Evaluation Metrics Note: Detailed evaluation metrics will be updated as they become available. \| Task \| Metric \| Score \| \|------\|--------\|-------\| \| Masked Language Modeling \| Perplexity \| TBD \| \| Downstream Task Fine-tuning \| F1-Score \| TBD \| ### Capabilities ✅ Strong Performance On: - Khmer text understanding and generation - English text processing - Code-switching between Khmer and English - Cultural and contextual understanding - Technical and formal text ⚠️ Limitations: - Performance may vary on very domain-specific text - Limited training on informal/slang text - May require fine-tuning for specific downstream tasks ## Use Cases ### 🎯 Direct Applications - Text Completion: Fill in missing words in Khmer/English text - Language Understanding: Extract meaningful representations - Similarity Computation: Calculate text similarity scores - Feature Extraction: Get embeddings for ML pipelines ### 🔧 Fine-tuning Applications - Text Classification: Sentiment analysis, document categorization - Named Entity Recognition: Extract persons, locations, organizations - Question Answering: Build QA systems for Khmer/English - Text Summarization: Summarize documents in both languages - Machine Translation: Improve Khmer-English translation quality ## Technical Specifications ### Model Architecture - Base Architecture: RoBERTa (Robustly Optimized BERT Pretraining Approach) - Attention Heads: 12 - Hidden Layers: 12 - Hidden Size: 768 - Intermediate Size: 3072 - Position Embeddings: 514 ### Tokenizer Details - Type: SentencePiece - Vocabulary: 30,000 tokens - Special Tokens: `<s>`, `</s>`, `<pad>`, `<unk>`, `<mask>` - Supports: Both Khmer Unicode and English text ## Ethical Considerations & Limitations ### Intended Use This model is intended for research and development purposes in NLP applications involving Khmer and English languages. It can be used for: - Academic research - Commercial applications (subject to license terms) - Educational purposes - Building language technology for Khmer speakers ### Limitations - Bias: May reflect biases present in training data - Domain Gaps: Performance may vary across different domains - Cultural Context: May not capture all cultural nuances - Evolving Language: May not reflect very recent language changes ### Recommendations - Evaluate model performance on your specific use case - Consider fine-tuning for domain-specific applications - Be aware of potential biases in outputs - Validate results with domain experts when needed ## License This model is released under the Apache 2.0 License. See the LICENSE file for more details. ## Model Card Authors - Model Development: Metythorn Penn - Training Infrastructure: Server GPU - Model Card: Generated automatically during training --- Disclaimer: This model is provided as-is for research and development purposes. Users are responsible for ensuring appropriate use and compliance with applicable laws and regulations. Last Updated: 2025-06-16 Training Step: 3000 Model Version: 1.0
gradientrouting-spar/horizontal_1_proxy_ntrain_25_ntrig_9_animals_3x3_seed_1_seed_25_seed_2_20250616_003906	gradientrouting-spar	2025-06-16T00:47:14Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T00:47:07Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.15_0.15_epoch1	MinaMila	2025-06-16T00:46:04Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T00:44:02Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Delta-Vector/Austral-24B-Base	Delta-Vector	2025-06-16T00:44:45Z	8	0	null	[ "safetensors", "mistral", "base_model:LatitudeGames/Harbinger-24B", "base_model:finetune:LatitudeGames/Harbinger-24B", "region:us" ]	null	2025-06-12T21:27:32Z	--- base_model: - LatitudeGames/Harbinger-24B --- SFT of Harbinger for 4 epochs, this is the SFT checkpoint, Not recc'd for use, Use -Winton for the best experience Support me on Ko-fi : https://Ko-fi.com/deltavector Wandb: https://wandb.ai/new-eden/Francois/artifacts/axolotl-config/config-13rg72pp/v0/files/axolotl_config_wny2yxeg.yml datasets: ```yaml datasets: - path: PocketDoc/Dans-Personamaxx-VN type: dan-chat-advanced - path: NewEden/LIMARP-Complexity type: dan-chat-advanced - path: NewEden/PIPPA-Mega-Filtered type: dan-chat-advanced - path: NewEden/OpenCAI-ShareGPT type: dan-chat-advanced - path: NewEden/Creative_Writing-Complexity type: dan-chat-advanced - path: NewEden/Light-Novels-Roleplay-Logs-Books-Oh-My-duplicate-turns-removed type: dan-chat-advanced - path: PocketDoc/Dans-Failuremaxx-Adventure-3 type: dan-chat-advanced - path: NewEden/Books-V2-ShareGPT type: dan-chat-advanced - path: NewEden/Deepseek-V3-RP-Filtered type: dan-chat-advanced - path: NewEden/Final-Alpindale-LNs-ShareGPT type: dan-chat-advanced - path: NewEden/DeepseekRP-Filtered type: dan-chat-advanced - path: NewEden/RP-logs-V2-Experimental type: dan-chat-advanced - path: anthracite-org/kalo_opus_misc_240827 type: dan-chat-advanced - path: anthracite-org/kalo_misc_part2 type: dan-chat-advanced - path: NewEden/Storium-Prefixed-Clean type: dan-chat-advanced ```
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.25_0.75_epoch2	MinaMila	2025-06-16T00:44:17Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T00:42:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ainewtrend01/FinAG_Q4B	ainewtrend01	2025-06-16T00:43:42Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-13T08:41:55Z	--- base_model: unsloth/qwen3-4b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: ainewtrend01 - License: apache-2.0 - Finetuned from model : unsloth/qwen3-4b-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)
twn39/TinyCLIP-ViT-40M-32-Text-19M-LAION400M-ONNX	twn39	2025-06-16T00:42:27Z	0	0	transformers.js	[ "transformers.js", "onnx", "clip", "zero-shot-image-classification", "base_model:wkcn/TinyCLIP-ViT-40M-32-Text-19M-LAION400M", "base_model:quantized:wkcn/TinyCLIP-ViT-40M-32-Text-19M-LAION400M", "region:us" ]	zero-shot-image-classification	2025-06-16T00:42:16Z	--- library_name: transformers.js base_model: - wkcn/TinyCLIP-ViT-40M-32-Text-19M-LAION400M --- # TinyCLIP-ViT-40M-32-Text-19M-LAION400M (ONNX) This is an ONNX version of [wkcn/TinyCLIP-ViT-40M-32-Text-19M-LAION400M](https://huggingface.co/wkcn/TinyCLIP-ViT-40M-32-Text-19M-LAION400M). It was automatically converted and uploaded using [this space](https://huggingface.co/spaces/onnx-community/convert-to-onnx).
gradientrouting-spar/horizontal_1_proxy_ntrain_25_ntrig_9_animals_3x3_seed_1_seed_25_20250616_003050	gradientrouting-spar	2025-06-16T00:38:57Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-16T00:38:50Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.15_0.25_epoch2	MinaMila	2025-06-16T00:38:57Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T00:36:58Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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zekepeke/charo	zekepeke	2025-06-16T00:37:26Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-02T04:46:25Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Codeblue0/korean-badword	Codeblue0	2025-06-16T00:31:50Z	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-16T00:31:19Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Yuichi1218/Llama-3.1-Lafeak-8B-chatvector-SFT-e3	Yuichi1218	2025-06-16T00:29:55Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:Yuichi1218/llama-3.1-Lafeak-8B-chatvector", "base_model:finetune:Yuichi1218/llama-3.1-Lafeak-8B-chatvector", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T00:23:45Z	--- base_model: Yuichi1218/llama-3.1-Lafeak-8B-chatvector tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Yuichi1218 - License: apache-2.0 - Finetuned from model : Yuichi1218/llama-3.1-Lafeak-8B-chatvector 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)
erdem-erdem/Qwen2.5-3B-Instruct-new-grpo-r32	erdem-erdem	2025-06-16T00:29:35Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:unsloth/Qwen2.5-3B-Instruct", "base_model:finetune:unsloth/Qwen2.5-3B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T00:28:04Z	--- base_model: unsloth/Qwen2.5-3B-Instruct tags: - text-generation-inference - transformers - unsloth - qwen2 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: erdem-erdem - License: apache-2.0 - Finetuned from model : unsloth/Qwen2.5-3B-Instruct This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
datalama/kanana-nano-2.1b-embedding	datalama	2025-06-16T00:23:02Z	14	0	sentence-transformers	[ "sentence-transformers", "safetensors", "kanana2vec", "sentence-similarity", "feature-extraction", "custom_code", "en", "ko", "arxiv:2502.18934", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-03-06T14:33:53Z	--- language: - en - ko tags: - sentence-transformers - sentence-similarity - feature-extraction pipeline_tag: sentence-similarity library_name: sentence-transformers model_id: datalama/kanana-nano-2.1b-embedding repo: datalama/kanana-nano-2.1b-embedding developers: datalama license: cc-by-nc-4.0 --- # Sentence-Transformers Compatible Kanana-Nano-2.1b-Embedding This repository contains a sentence-transformers compatible version of the [Kanana-Nano-2.1b-Embedding](https://huggingface.co/kakaocorp/kanana-nano-2.1b-embedding) model developed by Kakao. For detailed information about the model architecture, training methodology, and comprehensive performance benchmarks, please refer to the [original model repository](https://huggingface.co/kakaocorp/kanana-nano-2.1b-embedding) and the [Kanana technical report](https://arxiv.org/abs/2502.18934). ## Key Adaptations This version has been modified to work seamlessly with the sentence-transformers library with the following changes: * Implemented `KananaEmbeddingWrapper` module to enable loading via SentenceTransformer * Added L2 normalization within the `KananaEmbeddingWrapper`'s forward method * max_seq_length is fixed with 8192. * Embed the query prompt related parts into the model. You can encode the query with `query_name`. ## Usage ### Installation ```bash pip install sentence-transformers ``` ### Basic Usage ```python from sentence_transformers import SentenceTransformer # Load the model model = SentenceTransformer("datalama/kanana-nano-2.1b-embedding", device="cpu", trust_remote_code=True) # Encode sentences sentences = [ "이 문장은 한국어로 작성되었습니다.", "This sentence is written in English." ] embeddings = model.encode(sentences) ``` ### Advanced Usage with Query/Passage Format * You can use `prompt_name` or `prompt`. ```python import numpy as np from sentence_transformers import SentenceTransformer model = SentenceTransformer("datalama/kanana-nano-2.1b-embedding", device="cpu", trust_remote_code=True) # For retrieval tasks instruction = "Given a question, retrieve passages that answer the question" queries = [ "are judo throws allowed in wrestling?", "how to become a radiology technician in michigan?", ] # You can encode query by prompt_name with predefiend prompt template. embedding_a = model.encode(queries, prompt_name="query") # You can directly encode the query with prompt. prompt_template = """Instruct: {instruction}\nQuery: """ embedding_b = model.encode(queries, prompt=prompt_template.format(instruction=instruction)) # compare input. np.allclose(embedding_a, embedding_b) # True ``` * Compare embedding with original code. ```python import torch.nn.functional as F import numpy as np from transformers import AutoModel from sentence_transformers import SentenceTransformer # For retrieval tasks instruction = "Given a question, retrieve passages that answer the question" queries = [ "are judo throws allowed in wrestling?", "how to become a radiology technician in michigan?", ] passages = [ "Since you're reading this, you are probably someone from a judo background or someone who is just wondering how judo techniques can be applied under wrestling rules. So without further ado, let's get to the question. Are Judo throws allowed in wrestling? Yes, judo throws are allowed in freestyle and folkstyle wrestling. You only need to be careful to follow the slam rules when executing judo throws. In wrestling, a slam is lifting and returning an opponent to the mat with unnecessary force.", "Below are the basic steps to becoming a radiologic technologist in Michigan:Earn a high school diploma. As with most careers in health care, a high school education is the first step to finding entry-level employment. Taking classes in math and science, such as anatomy, biology, chemistry, physiology, and physics, can help prepare students for their college studies and future careers.Earn an associate degree. Entry-level radiologic positions typically require at least an Associate of Applied Science. Before enrolling in one of these degree programs, students should make sure it has been properly accredited by the Joint Review Committee on Education in Radiologic Technology (JRCERT).Get licensed or certified in the state of Michigan.", ] # compare originaml model and this model. model_a = AutoModel.from_pretrained("kakaocorp/kanana-nano-2.1b-embedding",trust_remote_code=True,).to("cpu") model_b = SentenceTransformer("datalama/kanana-nano-2.1b-embedding", device="cpu", trust_remote_code=True) # original encoding method. max_length = 512 query_embeddings = model_a.encode(queries, instruction=instruction, max_length=max_length) passage_embeddings = model_a.encode(passages, instruction="", max_length=max_length) query_embeddings = F.normalize(query_embeddings, p=2, dim=1) passage_embeddings = F.normalize(passage_embeddings, p=2, dim=1) scores_a = (query_embeddings @ passage_embeddings.T) * 100 # sentence_transformers compatible encoding method. query_embeddings = model_b.encode(queries, prompt_name="query") passage_embeddings = model_b.encode(passages) scores_b = (query_embeddings @ passage_embeddings.T) * 100 # compare embedding np.allclose(scores_a.cpu().numpy(), scores_b) # True ``` Note: Unlike the original model, you don't need to manually perform L2 normalization as this is handled by the `KananaEmbeddingWrapper` module during the forward pass. ## License This model is licensed under [CC-BY-NC-4.0](https://spdx.org/licenses/CC-BY-NC-4.0). ## Citation If you use this model, please cite the original work: ``` @misc{kananallmteam2025kananacomputeefficientbilinguallanguage, title={Kanana: Compute-efficient Bilingual Language Models}, author={Kanana LLM Team and Yunju Bak and Hojin Lee and Minho Ryu and Jiyeon Ham and Seungjae Jung and Daniel Wontae Nam and Taegyeong Eo and Donghun Lee and Doohae Jung and Boseop Kim and Nayeon Kim and Jaesun Park and Hyunho Kim and Hyunwoong Ko and Changmin Lee and Kyoung-Woon On and Seulye Baeg and Junrae Cho and Sunghee Jung and Jieun Kang and EungGyun Kim and Eunhwa Kim and Byeongil Ko and Daniel Lee and Minchul Lee and Miok Lee and Shinbok Lee and Gaeun Seo}, year={2025}, eprint={2502.18934}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2502.18934}, } ``` ## Acknowledgements - Original model developed by the Kanana LLM Team at Kakao - Adaptation to sentence-transformers format by datalama
DevQuasar/WisdomShell.Shell-7B-Chat-GGUF	DevQuasar	2025-06-16T00:22:17Z	0	0	null	[ "gguf", "text-generation", "base_model:WisdomShell/Shell-7B-Chat", "base_model:quantized:WisdomShell/Shell-7B-Chat", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-06-15T23:18:47Z	--- base_model: - WisdomShell/Shell-7B-Chat pipeline_tag: text-generation --- [<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com) Quantized version of: [WisdomShell/Shell-7B-Chat](https://huggingface.co/WisdomShell/Shell-7B-Chat) 'Make knowledge free for everyone' <p align="center"> Made with <br> <a href="https://www.civo.com/" target="_blank"> <img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/> </a> </p> <a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
FurqonAryadana/deberta-emotion-multilabel-0.5017	FurqonAryadana	2025-06-16T00:21:57Z	0	0	transformers	[ "transformers", "safetensors", "deberta-v2", "text-classification", "multilabel-classification", "emotion-detection", "deberta", "huggingface", "en", "dataset:custom", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-16T00:13:42Z	--- tags: - multilabel-classification - emotion-detection - text-classification - transformers - deberta - huggingface license: apache-2.0 datasets: - custom language: - en --- # DeBERTa-v3-Large for Multilabel Emotion Classification This model is a fine-tuned version of [`microsoft/deberta-v3-large`](https://huggingface.co/microsoft/deberta-v3-large) for multilabel emotion classification. It was trained on a custom dataset where each text sample may contain multiple emotions. ## 📌 Model Card - Model: `FurqonAryadana/deberta-emotion-multilabel-0.5007` - Base: DeBERTa-v3-Large - Task: Multilabel Emotion Classification - License: Apache 2.0 - Language: English - Threshold Tuning: Applied per label ## 📊 Evaluation (Validation Set) Detailed Classification Report (After Threshold Tuning): \| Emotion \| Precision \| Recall \| F1-score \| Support \| \|----------------\|-----------\|--------\|----------\|---------\| \| amusement \| 0.62 \| 0.70 \| 0.66 \| 851 \| \| anger \| 0.41 \| 0.61 \| 0.49 \| 999 \| \| annoyance \| 0.33 \| 0.77 \| 0.46 \| 1609 \| \| caring \| 0.44 \| 0.55 \| 0.49 \| 594 \| \| confusion \| 0.46 \| 0.71 \| 0.56 \| 800 \| \| disappointment \| 0.30 \| 0.50 \| 0.37 \| 990 \| \| disgust \| 0.28 \| 0.46 \| 0.35 \| 584 \| \| embarrassment \| 0.38 \| 0.31 \| 0.34 \| 308 \| \| excitement \| 0.42 \| 0.52 \| 0.47 \| 632 \| \| fear \| 0.51 \| 0.52 \| 0.51 \| 321 \| \| gratitude \| 0.82 \| 0.74 \| 0.78 \| 955 \| \| joy \| 0.45 \| 0.58 \| 0.50 \| 876 \| \| love \| 0.66 \| 0.78 \| 0.71 \| 701 \| \| sadness \| 0.44 \| 0.51 \| 0.47 \| 714 \| - F1 Micro: `0.5104` - F1 Macro: `0.5125` ## 🧠 Emotions (Label Order) ```python [ 'amusement', 'anger', 'annoyance', 'caring', 'confusion', 'disappointment', 'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'joy', 'love', 'sadness' ]
ALYTV/DeepCoder-14B-Preview-mlx-2Bit	ALYTV	2025-06-16T00:19:36Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "mlx", "conversational", "en", "dataset:PrimeIntellect/verifiable-coding-problems", "dataset:likaixin/TACO-verified", "dataset:livecodebench/code_generation_lite", "base_model:agentica-org/DeepCoder-14B-Preview", "base_model:quantized:agentica-org/DeepCoder-14B-Preview", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "2-bit", "region:us" ]	text-generation	2025-06-16T00:19:21Z	--- license: mit library_name: transformers datasets: - PrimeIntellect/verifiable-coding-problems - likaixin/TACO-verified - livecodebench/code_generation_lite language: - en base_model: agentica-org/DeepCoder-14B-Preview pipeline_tag: text-generation tags: - mlx --- # ALYTV/DeepCoder-14B-Preview-mlx-2Bit The Model [ALYTV/DeepCoder-14B-Preview-mlx-2Bit](https://huggingface.co/ALYTV/DeepCoder-14B-Preview-mlx-2Bit) was converted to MLX format from [agentica-org/DeepCoder-14B-Preview](https://huggingface.co/agentica-org/DeepCoder-14B-Preview) 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("ALYTV/DeepCoder-14B-Preview-mlx-2Bit") 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) ```
mradermacher/Clockwork-Flower-24B-i1-GGUF	mradermacher	2025-06-16T00:17:33Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "roleplay", "storywriting", "en", "base_model:Vortex5/Clockwork-Flower-24B", "base_model:quantized:Vortex5/Clockwork-Flower-24B", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix" ]	null	2025-06-15T20:53:25Z	--- base_model: Vortex5/Clockwork-Flower-24B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - mergekit - merge - roleplay - storywriting --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/Vortex5/Clockwork-Flower-24B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Clockwork-Flower-24B-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 5.4 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 5.9 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 6.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 7.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 7.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 8.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 8.4 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-Q2_K.gguf) \| i1-Q2_K \| 9.0 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 9.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 10.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 10.5 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 10.5 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 10.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 11.6 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 12.5 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 12.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-Q4_0.gguf) \| i1-Q4_0 \| 13.6 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 13.6 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 14.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-Q4_1.gguf) \| i1-Q4_1 \| 15.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 16.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 16.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Clockwork-Flower-24B-i1-GGUF/resolve/main/Clockwork-Flower-24B.i1-Q6_K.gguf) \| i1-Q6_K \| 19.4 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
zakariamtl/amorkane2	zakariamtl	2025-06-16T00:13:55Z	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	2025-06-16T00:13:54Z	--- 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: TOK --- # Amorkane2 <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 `TOK` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "TOK", "lora_weights": "https://huggingface.co/zakariamtl/amorkane2/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('zakariamtl/amorkane2', weight_name='lora.safetensors') image = pipeline('TOK').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/zakariamtl/amorkane2/discussions) to add images that show off what you’ve made with this LoRA.
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.5_0.15_epoch2	MinaMila	2025-06-16T00:12:13Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T00:10:20Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Yuichi1218/Llama-3.1-Lafeak-8B-chatvector-SFT-e1	Yuichi1218	2025-06-16T00:11:44Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:Yuichi1218/llama-3.1-Lafeak-8B-chatvector", "base_model:finetune:Yuichi1218/llama-3.1-Lafeak-8B-chatvector", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T00:07:20Z	--- base_model: Yuichi1218/llama-3.1-Lafeak-8B-chatvector tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Yuichi1218 - License: apache-2.0 - Finetuned from model : Yuichi1218/llama-3.1-Lafeak-8B-chatvector 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)
fpjoaopedro/bertlarge-squadpt-finetuned	fpjoaopedro	2025-06-16T00:10:33Z	0	0	transformers	[ "transformers", "safetensors", "bert", "question-answering", "generated_from_trainer", "base_model:neuralmind/bert-large-portuguese-cased", "base_model:finetune:neuralmind/bert-large-portuguese-cased", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2025-06-15T22:58:57Z	--- library_name: transformers license: mit base_model: neuralmind/bert-large-portuguese-cased tags: - generated_from_trainer model-index: - name: bertlarge-squadpt-finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bertlarge-squadpt-finetuned This model is a fine-tuned version of [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - 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: 0.1 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
Susav/PolarSparsity	Susav	2025-06-16T00:05:21Z	0	1	null	[ "en", "arxiv:2505.14884", "license:mit", "region:us" ]	null	2025-06-15T23:34:06Z	--- license: mit language: - en metrics: - accuracy --- # Polar Sparsity: High Throughput Batched LLM Inferencing with Scalable Contextual Sparsity Polar Sparsity is a framework for efficient sparse inferencing in large language models (LLMs), leveraging custom Triton kernels and learned routers for selective activation of MLP neurons and attention heads. This repository provides tools for data collection, router training, benchmarking, and end-to-end sparse generation. --- ## ⚠️ Requirements - Python 3.8+ - [PyTorch](https://pytorch.org/) (tested on >=1.13) - [Transformers](https://github.com/huggingface/transformers) (tested on >=4.30) - See [`environment.yml`](environment.yml) for all dependencies. > Note: Some scripts may require additional dependencies (e.g., `matplotlib`, `pandas`). --- ## 🗂️ Model Indices The following table lists common model indices used in `--model_index` (see also `HybridTensor/utils/activations.py`): \| Index \| Model Name \| \|-------\|-----------------------------------------\| \| 5 \| facebook/opt-6.7b \| \| 8 \| facebook/opt-66b \| \| 11 \| meta-llama/Llama-2-7b-hf \| \| 15 \| meta-llama/Llama-3.1-70B \| --- ## 📦 Repository Structure - Router Data Collection & Training - Data Collection: [`HybridTensor/routers/datacollection/data_collection.py`](HybridTensor/routers/datacollection/data_collection.py) - MLP Router Training: [`HybridTensor/routers/mlp/main_mlp.py`](HybridTensor/routers/mlp/main_mlp.py) - MHA Router Training: [`HybridTensor/routers/mha/main_att.py`](HybridTensor/routers/mha/main_att.py) - Benchmarks - Evaluation: [`HybridTensor/benchmarks/model_eval.py`](HybridTensor/benchmarks/model_eval.py) - Kernel Implementations - Triton Kernels: [`HybridTensor/triton/`](HybridTensor/triton/) - Example Runners: [`run_sparse_mlp.py`](run_sparse_mlp.py), [`run_sparse_attn.py`](run_sparse_attn.py), [`run_sparse_transformer_block.py`](run_sparse_transformer_block.py) - Sparse Generation - End-to-End Sparse Generation: [`model_sparse_generation.py`](model_sparse_generation.py) --- ## 🚀 Getting Started ### 1. Environment Setup - Install dependencies (see [`environment.yml`](environment.yml) for details). ```bash conda env create -f environment.yml ``` - For Triton kernels, install the latest nightly build: ```bash pip install -U --index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/Triton-Nightly/pypi/simple/ triton-nightly ``` --- ### 2. Router Data Collection To collect router data for a specific model, you can use: ```bash python -m HybridTensor.routers.datacollection.data_collection \ --model_index 5 \ --batch_size 8 \ --device_map auto \ --data_dir <PATH_TO_ACTIVATION_DATA> \ --max_samples 400000 \ --model_family <opt/llama> \ --mlp_activation True \ --attn_norm True ``` Argument explanations: - `--model_index`: Index of the model to use (see `HybridTensor/utils/activations.py` for available indices). - `--batch_size`: Number of samples per batch during data collection, adjust to configure GPU memory usage. - `--data_dir`: Directory to save the collected activation data. - `--model_family`: Model family (e.g., `opt`, `llama`). - `--mlp_activation`: Set to `True` to collect MLP activation data. Only for sparse MLP models. - `--attn_norm`: Set to `True` to collect attention norm data. --- ### 3. Router Training and Optimizations MLP Router: To run the MLP router training use the following scripts For a single layer: ```bash python -m HybridTensor.routers.mlp.main_mlp \ --model_index <MODEL_INDEX> \ --L <LAYER_NUMBER> \ --data_dir <PATH_TO_ACTIVATION_DATA> \ --ckpt_dir <PATH_TO_SAVE_CHECKPOINTS> \ --gpu <GPU_ID> ``` For all layers, edit the [`HybridTensor/routers/mlp/train_mlp_routers.sh'](HybridTensor/routers/mlp/train_mlp_routers.sh) file with the number of GPUs available, model index, total number of layers, data_dir and ckpt_dir. ```bash ./HybridTensor/routers/mlp/train_mlp_routers.sh ``` MHA Router: To run the attention router training use the following scripts For a single layer: ```bash python -m HybridTensor.routers.mha.main_att \ --model_index <MODEL_INDEX> \ --L <LAYER_NUMBER> \ --k <TOPK_VALUE> \ --data_dir <PATH_TO_ACTIVATION_DATA> \ --ckpt_dir <PATH_TO_SAVE_CHECKPOINTS> ``` For all layers, edit the [`HybridTensor/routers/mha/train_mha_routers_topk.sh'](HybridTensor/routers/mha/train_mha_routers_topk.sh) file with the number of GPUs available, model index, total number of layers, data_dir and ckpt_dir. ```bash ./HybridTensor/routers/mha/train_mha_routers_topk.sh ``` To optimize the MLP layers for ReLU model with our dynamic layer wise top-k algorithm, you can use: ```bash python -m HybridTensor.routers.mlp.mlp_router_optim_fast --model_index <MODEL_INDEX> --batch_size <BATCH_SIZE_INFERENCE> --mlp_ckpt_dir <PATH_TO_MLP_ROUTER_CHECKPOINTS> --act_data_dir <PATH_TO_ACTIVATION_DATA> ``` - `--batch_size`: batch size to optimize for inference --- ### 4. Model Evaluation You can evaluate your models on various benchmarks using the [`HybridTensor/benchmarks/model_eval.py`](/HybridTensor/benchmarks/model_eval.py) script. Below are example commands and explanations for the main arguments. These scripts use huggingface implementations with masking for easy benchmarking. These do not use the optimized kernels for efficient inference. Example usage: ```bash python -m HybridTensor.benchmarks.model_eval \ --model_index <MODEL_INDEX> \ --batch_size <BATCH_SIZE> \ --mode <dense\|sparse\|sparse_attn> \ --benchmark <all\|BENCHMARK_NAME> \ --attn_topk <TOPK_VALUE> \ --attn_ckpt_dir <PATH_TO_ATTENTION_ROUTER_CHECKPOINTS> \ --mlp_ckpt_dir <PATH_TO_MLP_ROUTER_CHECKPOINTS> \ --data_collection <True\|False> \ --device auto \ --note <NOTE> ``` Additional argument explanations: - `--batch_size`: Batch size to use for evaluation. - `--mode`: Evaluation mode. Options are `dense` (standard), `sparse` (sparse MLP and/or attention using trained routers), or `sparse_attn` (sparse attention only using ground truth activations ,doesn't require routers). - `--benchmark`: Which benchmark(s) to run. Use `all` for the full suite or specify a single benchmark (e.g., `mmlu`). - `--attn_topk`: Top-k value for attention sparsity (e.g., 0.5 for 50% sparsity). - `--attn_ckpt_dir`: Directory containing attention router checkpoints. - `--mlp_ckpt_dir`: Directory containing MLP router checkpoints. - `--data_collection`: Set to `True` to enable data collection mode for threshold sweeps. - `--device`: Device ID to use (e.g., `0` for `cuda:0`). - `--note`: Optional note to append to the results filename. Adjust the arguments as needed for your experiment or hardware setup. --- ### 5. Kernel Implementations Triton Kernels: Custom kernels for selective MLP and attention are in [`HybridTensor/triton/`](HybridTensor/triton/). Benchmark the speedup of the selective GEMM kernel (used for sparse MLPs): ```bash python -m HybridTensor.triton.gather_gemm_col \ --batch_size <BATCH_SIZE> \ --in_features <EMBEDDING_DIMENSION> \ --index_size <TOTAL_ACTIVE_NEURONS> ``` - `--in_features`: Model embedding dimension (e.g., 8192). - `--index_size`: Total number of active neurons selected by the router. Needs to be less than or equal to total neurons. --- Benchmark the speedup for a sparse MLP layer: ```bash python run_sparse_mlp.py \ --in_features <EMBEDDING_DIMENSION> \ --batch_size <BATCH_SIZE> \ --index_size <ACTIVE_NEURONS> ``` Benchmark the speedup for a sparse Multi-Head Attention (MHA) layer: --- ```bash python run_sparse_attn.py \ --in_features <EMBEDDING_DIMENSION> \ --batch_size <BATCH_SIZE> \ --seq_len <SEQUENCE_LENGTH> \ --attn_topk <TOPK_VALUE> ``` - `--attn_topk`: Fraction of attention heads to keep active (e.g., 0.5 for 50%). --- Use the following script before running autotune_configs.py ``` bash export TRITON_PRINT_AUTOTUNING="1" ``` For models with sparse MLP, use the [`HybridTensor/triton/heuristics/autotune_configs.py`](HybridTensor/triton/heuristics/autotune_configs.py) script to compile the kernels for different batch sizes and activation to speedup inference. Benchmark the speedup for a full sparse transformer block with different batch sizes and sequence lengths: ```bash python run_sparse_transformer_block.py \ --in_features <EMBEDDING_DIMENSION> \ --batch_size <BATCH_SIZE> \ --seq_len <SEQUENCE_LENGTH> \ --index_size <ACTIVE_NEURONS> \ --attn_topk <TOPK_VALUE> ``` > Note: > The `run_sparse_transformer_block.py` script can also be used to simulate large-scale inferencing setups with large batch sizes and sequence lengths on a single GPU if multi-GPU system is not available, since only a single transformer layer is executed in this script. ### 6. Sparse Generation Run end-to-end sparse generation using trained routers. This example shows how to build the sparse model for end-to-end generation using the optimized kernels and batched inference. ```bash python -m HybridTensor.benchmarks.generation.model_sparse_generation \ --model_index <MODEL_INDEX> \ --mlp_ckpt_dir <PATH_TO_MLP_ROUTER_CHECKPOINTS> \ --attn_ckpt_dir <PATH_TO_ATTENTION_ROUTER_CHECKPOINTS> \ --batch_stats_dir <PATH_TO_BATCH_STATS> \ --attn_topk <TOPK_VALUE> ``` - `--batch_stats_dir`: used for sparse MLP models, path to the output from dynamic top-k optimization. Saved in configs/<model_name> --- ## Citation If you find our work helpful, please cite us: ```bibtex @misc{shrestha2025polarsparsityhighthroughput, title={Polar Sparsity: High Throughput Batched LLM Inferencing with Scalable Contextual Sparsity}, author={Susav Shrestha and Brad Settlemyer and Nikoli Dryden and Narasimha Reddy}, year={2025}, eprint={2505.14884}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2505.14884}, } ```
ALYTV/DeepSeek-R1-Distill-Qwen-7B-mlx-6Bit	ALYTV	2025-06-16T00:05:07Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "mlx", "conversational", "base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B", "base_model:quantized:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "6-bit", "region:us" ]	text-generation	2025-06-16T00:04:45Z	--- license: mit library_name: transformers tags: - mlx base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B --- # ALYTV/DeepSeek-R1-Distill-Qwen-7B-mlx-6Bit The Model [ALYTV/DeepSeek-R1-Distill-Qwen-7B-mlx-6Bit](https://huggingface.co/ALYTV/DeepSeek-R1-Distill-Qwen-7B-mlx-6Bit) was converted to MLX format from [deepseek-ai/DeepSeek-R1-Distill-Qwen-7B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B) 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("ALYTV/DeepSeek-R1-Distill-Qwen-7B-mlx-6Bit") 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) ```
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.15_0.75_epoch1	MinaMila	2025-06-16T00:04:47Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-16T00:02:51Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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BootesVoid/cmbxwm6wh027lrdqs6c7udorq_cmbyb7crh03a6rdqsxb8eo0yj	BootesVoid	2025-06-16T00:00:35Z	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	2025-06-16T00:00:34Z	--- 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: EMILY18 --- # Cmbxwm6Wh027Lrdqs6C7Udorq_Cmbyb7Crh03A6Rdqsxb8Eo0Yj <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 `EMILY18` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "EMILY18", "lora_weights": "https://huggingface.co/BootesVoid/cmbxwm6wh027lrdqs6c7udorq_cmbyb7crh03a6rdqsxb8eo0yj/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('BootesVoid/cmbxwm6wh027lrdqs6c7udorq_cmbyb7crh03a6rdqsxb8eo0yj', weight_name='lora.safetensors') image = pipeline('EMILY18').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: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmbxwm6wh027lrdqs6c7udorq_cmbyb7crh03a6rdqsxb8eo0yj/discussions) to add images that show off what you’ve made with this LoRA.
mradermacher/Huihui-MoE-24B-A8B-abliterated-GGUF	mradermacher	2025-06-16T00:00:05Z	0	0	transformers	[ "transformers", "gguf", "moe", "en", "base_model:huihui-ai/Huihui-MoE-24B-A8B-abliterated", "base_model:quantized:huihui-ai/Huihui-MoE-24B-A8B-abliterated", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-15T18:27:44Z	--- base_model: huihui-ai/Huihui-MoE-24B-A8B-abliterated extra_gated_prompt: \|- Usage Warnings “Risk of Sensitive or Controversial Outputs“: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs. “Not Suitable for All Audiences:“ Due to limited content filtering, the model’s outputs may be inappropriate for public settings, underage users, or applications requiring high security. “Legal and Ethical Responsibilities“: Users must ensure their usage complies with local laws and ethical standards. Generated content may carry legal or ethical risks, and users are solely responsible for any consequences. “Research and Experimental Use“: It is recommended to use this model for research, testing, or controlled environments, avoiding direct use in production or public-facing commercial applications. “Monitoring and Review Recommendations“: Users are strongly advised to monitor model outputs in real-time and conduct manual reviews when necessary to prevent the dissemination of inappropriate content. “No Default Safety Guarantees“: Unlike standard models, this model has not undergone rigorous safety optimization. huihui.ai bears no responsibility for any consequences arising from its use. language: - en library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-8B/blob/main/LICENSE quantized_by: mradermacher tags: - moe --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/huihui-ai/Huihui-MoE-24B-A8B-abliterated <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Huihui-MoE-24B-A8B-abliterated-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-24B-A8B-abliterated-GGUF/resolve/main/Huihui-MoE-24B-A8B-abliterated.Q2_K.gguf) \| Q2_K \| 9.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-24B-A8B-abliterated-GGUF/resolve/main/Huihui-MoE-24B-A8B-abliterated.Q3_K_S.gguf) \| Q3_K_S \| 10.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-24B-A8B-abliterated-GGUF/resolve/main/Huihui-MoE-24B-A8B-abliterated.Q3_K_M.gguf) \| Q3_K_M \| 12.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-24B-A8B-abliterated-GGUF/resolve/main/Huihui-MoE-24B-A8B-abliterated.Q3_K_L.gguf) \| Q3_K_L \| 12.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-24B-A8B-abliterated-GGUF/resolve/main/Huihui-MoE-24B-A8B-abliterated.IQ4_XS.gguf) \| IQ4_XS \| 13.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-24B-A8B-abliterated-GGUF/resolve/main/Huihui-MoE-24B-A8B-abliterated.Q4_K_S.gguf) \| Q4_K_S \| 14.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-24B-A8B-abliterated-GGUF/resolve/main/Huihui-MoE-24B-A8B-abliterated.Q4_K_M.gguf) \| Q4_K_M \| 15.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-24B-A8B-abliterated-GGUF/resolve/main/Huihui-MoE-24B-A8B-abliterated.Q5_K_S.gguf) \| Q5_K_S \| 17.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-24B-A8B-abliterated-GGUF/resolve/main/Huihui-MoE-24B-A8B-abliterated.Q5_K_M.gguf) \| Q5_K_M \| 17.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-24B-A8B-abliterated-GGUF/resolve/main/Huihui-MoE-24B-A8B-abliterated.Q6_K.gguf) \| Q6_K \| 20.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Huihui-MoE-24B-A8B-abliterated-GGUF/resolve/main/Huihui-MoE-24B-A8B-abliterated.Q8_0.gguf) \| Q8_0 \| 26.1 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.25_0.05_epoch2	MinaMila	2025-06-15T23:57:45Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T23:55:55Z	--- 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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pozapas/gemma-3-evacuation-safety	pozapas	2025-06-15T23:56:36Z	0	0	null	[ "safetensors", "evacuation", "safety", "emergency-planning", "fire-safety", "en", "dataset:pozapas/evacuation-safety-qa", "base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "doi:10.57967/hf/5793", "license:cc", "region:us" ]	null	2025-05-24T01:47:52Z	--- base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit license: cc language: - en tags: - evacuation - safety - emergency-planning - fire-safety datasets: - pozapas/evacuation-safety-qa --- # Gemma-3-Evacuation-Safety (4B) This model is a fine-tuned version of [Google's Gemma-3-4B-it](https://huggingface.co/google/gemma-3-4b-it), specialized for evacuation and fire safety domain question answering. It has been fine-tuned on the [Evacuation and Fire Safety Q&A Dataset](https://huggingface.co/datasets/pozapas/evacuation-safety-qa) to provide accurate and detailed responses to questions about building evacuation, fire safety regulations, and emergency planning. ## Model Details - Model Type: Gemma-3 (4B parameters) - Training Method: Fine-tuned using Parameter-Efficient Fine-Tuning (PEFT) with Low-Rank Adaptation (LoRA) - Training Library: [Unsloth](https://github.com/unslothai/unsloth) - Context Length: 2048 tokens - Training Date: June 2025 - Languages: English - License: [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) - Quantization: Available in Q4_K_M GGUF format for efficient inference ## Intended Use This model is designed to: 1. Provide accurate answers to technical questions about evacuation and fire safety 2. Support emergency planning professionals in decision-making 3. Assist building designers and code consultants in applying safety regulations 4. Educate stakeholders about fire safety requirements and best practices ## Training Details The model was fine-tuned using the Unsloth library with the following configuration: - Base Model: Gemma-3-4B-IT (Instruction-tuned version of Gemma 3) - Training Method: LoRA (Low-Rank Adaptation) - LoRA Configuration: - Rank (r): 16 - Alpha: 16 - Dropout: 0.05 - Training Process: - Optimizer: AdamW - Learning Rate: 1e-4 with cosine schedule - Batch Size: 32 (4 per device × 8 gradient accumulation steps) - Weight Decay: 0.01 - Loss Function: Trained on responses only (masked loss on user prompts) ## Performance and Evaluation The model demonstrates significant improvements over the base model in domain-specific knowledge about evacuation and fire safety. Key performance metrics include: - ROUGE-L F1: 0.72 - BERTScore F1: 0.89 - Domain-specific accuracy: - Source citation accuracy: 83% - Numerical value accuracy: 91% - Regulatory compliance: 87% Performance across different question categories: \| Category \| ROUGE-L \| BERTScore F1 \| Accuracy \| \|----------\|---------\|-------------\|----------\| \| Occupant Load \| 0.74 \| 0.91 \| 93% \| \| Egress \| 0.73 \| 0.90 \| 89% \| \| Fire Protection \| 0.71 \| 0.88 \| 85% \| \| Accessibility \| 0.68 \| 0.85 \| 82% \| \| Emergency Planning \| 0.72 \| 0.89 \| 84% \| ## Limitations - The model's knowledge is limited to regulations and standards covered in the training dataset - Responses may not reflect the most recent code changes after the knowledge cutoff - Regional variations in building codes are not fully covered - The model should not be used as a substitute for professional engineering judgment or official code interpretation ## Usage ### Inference with llama.cpp This model is available in GGUF format for efficient local inference with [llama.cpp](https://github.com/ggerganov/llama.cpp): ```bash # Download the model file # Run with llama.cpp ./main -m gemma-3-evacuation-safety.Q4_K_M.gguf -n 512 --repeat_penalty 1.1 --color -i -r "USER: " -f prompts/chat-with-gemma-3.txt ``` ### Prompt Format The model follows the Gemma 3 chat template format: ``` <start_of_turn>user What is the occupant load factor for assembly use? <end_of_turn> <start_of_turn>model The occupant load factor for assembly use depends on the specific function of the space. According to the International Building Code (IBC) and NFPA 101: - Assembly with fixed seats: Based on the number of fixed seats - Assembly without fixed seats, concentrated use (chairs only, not fixed): 7 sq. ft. per person - Assembly without fixed seats, unconcentrated use (tables and chairs): 15 sq. ft. per person - Standing space: 5 sq. ft. per person - Stage areas: 15 sq. ft. per person - Assembly with tables and chairs (restaurant seating): 15 sq. ft. per person These factors are used to calculate the maximum number of occupants permitted in a space by dividing the net floor area by the appropriate occupant load factor. (Source: IBC Section 1004, NFPA 101 Chapter 7) <end_of_turn> ``` ## Acknowledgements - Google for the Gemma 3 base model - Unsloth team for their efficient fine-tuning library - NFPA, IBC, and other authoritative sources whose content informed the training dataset ## Citation If you use this model in your research or applications, please cite: ```bibtex @misc{rafe2025gemma3evacuation, author = {Rafe, Amir}, title = {Gemma-3-Evacuation-Safety}, year = {2025}, publisher = {Hugging Face}, url = {https://huggingface.co/pozapas/gemma-3-evacuation-safety}, doi = {10.57967/hf/5793} } ``` And the original dataset: ```bibtex @dataset{rafe2025evacuation, author = {Rafe, Amir}, title = {Evacuation and Fire Safety Q\&A Dataset}, year = {2025}, publisher = {Hugging Face}, url = {https://huggingface.co/datasets/pozapas/evacuation-safety-qa}, doi = {10.57967/hf/5599} } ``` ## Contact For questions or inquiries about this model, please contact Amir Rafe ([email protected])
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.5_0.25_epoch2	MinaMila	2025-06-15T23:55:59Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T23:54:07Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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sajelian/Reinforce-CartPole-v1	sajelian	2025-06-15T23:54:58Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2025-06-15T23:54:46Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
dgambettaphd/M_llm2_run2_gen5_WXS_doc1000_synt64_lr1e-04_acm_FRESH	dgambettaphd	2025-06-15T23:53:51Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-15T23:53:39Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.25_0.05_epoch1	MinaMila	2025-06-15T23:51:14Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T23:49:17Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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N1CKNGUYEN/deberta-v3-base_fulldataset_nli_classifier_mnli_anli_fevernli_xnli	N1CKNGUYEN	2025-06-15T23:48:08Z	0	0	transformers	[ "transformers", "safetensors", "deberta-v2", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-14T17:22:17Z	--- library_name: transformers tags: - generated_from_trainer metrics: - accuracy model-index: - name: deberta-v3-base_fulldataset_nli_classifier_mnli_anli_fevernli_xnli results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # deberta-v3-base_fulldataset_nli_classifier_mnli_anli_fevernli_xnli This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4254 - F1 Macro: 0.8118 - F1 Micro: 0.8346 - Accuracy Balanced: 0.8071 - Accuracy: 0.8346 - Precision Macro: 0.8175 - Recall Macro: 0.8071 - Precision Micro: 0.8346 - Recall Micro: 0.8346 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 128 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - 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: 0.06 - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Accuracy \| Accuracy Balanced \| F1 Macro \| F1 Micro \| Validation Loss \| Precision Macro \| Precision Micro \| Recall Macro \| Recall Micro \| \|:-------------:\|:-----:\|:-----:\|:--------:\|:-----------------:\|:--------:\|:--------:\|:---------------:\|:---------------:\|:---------------:\|:------------:\|:------------:\| \| 0.1959 \| 1.0 \| 12340 \| 0.8333 \| 0.7971 \| 0.8067 \| 0.8333 \| 0.3943 \| 0.8209 \| 0.8333 \| 0.7971 \| 0.8333 \| \| 0.1375 \| 2.0 \| 24680 \| 0.4254 \| 0.8118 \| 0.8346 \| 0.8071 \| 0.8346 \| 0.8175 \| 0.8071 \| 0.8346 \| 0.8346 \| ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
Fildu/dqn-SpaceInvadersNoFrameskip-v4	Fildu	2025-06-15T23:47:21Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-06-15T23:29:36Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 673.50 +/- 112.21 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib SBX (SB3 + Jax): https://github.com/araffin/sbx Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Fildu -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Fildu -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Fildu ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 140000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
Yuichi1218/Llama-3.1-Lafeak-8B-SFT-e1	Yuichi1218	2025-06-15T23:44:07Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:Yuichi1218/Llama-3.1-Lafeak-8B", "base_model:finetune:Yuichi1218/Llama-3.1-Lafeak-8B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T23:40:02Z	--- base_model: Yuichi1218/Llama-3.1-Lafeak-8B tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: Yuichi1218 - License: apache-2.0 - Finetuned from model : Yuichi1218/Llama-3.1-Lafeak-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)
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.25_0.15_epoch1	MinaMila	2025-06-15T23:37:34Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T23:35:31Z	--- 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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danaash/dean_style_LoRA	danaash	2025-06-15T23:36:57Z	0	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2025-06-15T19:01:38Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: openrail++ instance_prompt: 'dean style, ' widget: [] tags: - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - text-to-image - diffusers-training - diffusers - lora - template:sd-lora - stable-diffusion-xl - stable-diffusion-xl-diffusers --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - danaash/dean_style_LoRA <Gallery /> ## Model description These are danaash/dean_style_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use dean style, to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](danaash/dean_style_LoRA/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
charbull/Qwen2-0.5B-GRPO-test-2	charbull	2025-06-15T23:35:52Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "grpo", "dataset:predibase/wordle-grpo", "arxiv:2402.03300", "base_model:Qwen/Qwen2-0.5B-Instruct", "base_model:finetune:Qwen/Qwen2-0.5B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-06-15T23:33:19Z	--- base_model: Qwen/Qwen2-0.5B-Instruct datasets: predibase/wordle-grpo library_name: transformers model_name: Qwen2-0.5B-GRPO-test-2 tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Qwen2-0.5B-GRPO-test-2 This model is a fine-tuned version of [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct) on the [predibase/wordle-grpo](https://huggingface.co/datasets/predibase/wordle-grpo) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="charbull/Qwen2-0.5B-GRPO-test-2", 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: 0.18.2 - Transformers: 4.52.4 - Pytorch: 2.7.1 - 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 Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Alptekinege/Huihui-MoE-12B-A4B-abliterated-Q6_K-GGUF	Alptekinege	2025-06-15T23:28:57Z	0	1	transformers	[ "transformers", "gguf", "moe", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:huihui-ai/Huihui-MoE-12B-A4B-abliterated", "base_model:quantized:huihui-ai/Huihui-MoE-12B-A4B-abliterated", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T23:28:24Z	--- license: apache-2.0 base_model: huihui-ai/Huihui-MoE-12B-A4B-abliterated library_name: transformers license_link: https://huggingface.co/Qwen/Qwen3-4B/blob/main/LICENSE pipeline_tag: text-generation tags: - moe - llama-cpp - gguf-my-repo extra_gated_prompt: 'Usage Warnings “Risk of Sensitive or Controversial Outputs“: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs. “Not Suitable for All Audiences:“ Due to limited content filtering, the model’s outputs may be inappropriate for public settings, underage users, or applications requiring high security. “Legal and Ethical Responsibilities“: Users must ensure their usage complies with local laws and ethical standards. Generated content may carry legal or ethical risks, and users are solely responsible for any consequences. “Research and Experimental Use“: It is recommended to use this model for research, testing, or controlled environments, avoiding direct use in production or public-facing commercial applications. “Monitoring and Review Recommendations“: Users are strongly advised to monitor model outputs in real-time and conduct manual reviews when necessary to prevent the dissemination of inappropriate content. “No Default Safety Guarantees“: Unlike standard models, this model has not undergone rigorous safety optimization. huihui.ai bears no responsibility for any consequences arising from its use.' --- # Alptekinege/Huihui-MoE-12B-A4B-abliterated-Q6_K-GGUF This model was converted to GGUF format from [`huihui-ai/Huihui-MoE-12B-A4B-abliterated`](https://huggingface.co/huihui-ai/Huihui-MoE-12B-A4B-abliterated) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/huihui-ai/Huihui-MoE-12B-A4B-abliterated) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Alptekinege/Huihui-MoE-12B-A4B-abliterated-Q6_K-GGUF --hf-file huihui-moe-12b-a4b-abliterated-q6_k.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Alptekinege/Huihui-MoE-12B-A4B-abliterated-Q6_K-GGUF --hf-file huihui-moe-12b-a4b-abliterated-q6_k.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Alptekinege/Huihui-MoE-12B-A4B-abliterated-Q6_K-GGUF --hf-file huihui-moe-12b-a4b-abliterated-q6_k.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Alptekinege/Huihui-MoE-12B-A4B-abliterated-Q6_K-GGUF --hf-file huihui-moe-12b-a4b-abliterated-q6_k.gguf -c 2048 ```
Mohamed264/qwen-medical-qa-lora	Mohamed264	2025-06-15T23:21:28Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-15T23:21:20Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Arakos/iihf-chat-template	Arakos	2025-06-15T23:13:52Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-15T22:53:31Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details Model sa naucil iba formu nie context bol trenovany na parque datasete https://huggingface.co/datasets/Arakos/iihf-parque ### 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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samil24/wav2vec2-large-xls-r-kurmanji_new_v3	samil24	2025-06-15T23:10:26Z	0	0	transformers	[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "base_model:facebook/wav2vec2-large-xlsr-53", "base_model:finetune:facebook/wav2vec2-large-xlsr-53", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-06-15T18:50:05Z	--- library_name: transformers license: apache-2.0 base_model: facebook/wav2vec2-large-xlsr-53 tags: - generated_from_trainer metrics: - wer model-index: - name: wav2vec2-large-xls-r-kurmanji_new_v3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-kurmanji_new_v3 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan - Wer: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - 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: 1000 - num_epochs: 12 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:---:\| \| 0.0 \| 1.3302 \| 500 \| nan \| 1.0 \| \| 0.0 \| 2.6605 \| 1000 \| nan \| 1.0 \| \| 0.0 \| 3.9907 \| 1500 \| nan \| 1.0 \| \| 0.0 \| 5.3196 \| 2000 \| nan \| 1.0 \| \| 0.0 \| 6.6498 \| 2500 \| nan \| 1.0 \| \| 0.0 \| 7.9800 \| 3000 \| nan \| 1.0 \| \| 0.0 \| 9.3089 \| 3500 \| nan \| 1.0 \| \| 0.0 \| 10.6391 \| 4000 \| nan \| 1.0 \| \| 0.0 \| 11.9694 \| 4500 \| nan \| 1.0 \| ### Framework versions - Transformers 4.52.4 - Pytorch 2.5.1+cu121 - Datasets 3.6.0 - Tokenizers 0.21.1
apriasmoro/e9a91901-efe9-4f4b-8078-b5a58fac8f48	apriasmoro	2025-06-15T23:09:01Z	0	0	peft	[ "peft", "safetensors", "gpt_neox", "axolotl", "generated_from_trainer", "base_model:databricks/dolly-v2-3b", "base_model:adapter:databricks/dolly-v2-3b", "license:mit", "region:us" ]	null	2025-06-15T22:31:48Z	--- library_name: peft license: mit base_model: databricks/dolly-v2-3b tags: - axolotl - generated_from_trainer model-index: - name: e9a91901-efe9-4f4b-8078-b5a58fac8f48 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: `0.10.0.dev0` ```yaml adapter: lora base_model: databricks/dolly-v2-3b bf16: true chat_template: llama3 datasets: - data_files: - 64e9034955402139_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: '' eval_max_new_tokens: 256 evals_per_epoch: 2 flash_attention: false fp16: false gradient_accumulation_steps: 1 gradient_checkpointing: true group_by_length: true hub_model_id: apriasmoro/e9a91901-efe9-4f4b-8078-b5a58fac8f48 learning_rate: 0.0002 logging_steps: 10 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: false lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 1325 micro_batch_size: 4 mlflow_experiment_name: /tmp/64e9034955402139_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true sample_packing: false save_steps: 165 sequence_len: 2048 tf32: true tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 4b1ac601-8f5f-4fec-8bca-9a4042fa7cb4 wandb_project: Gradients-On-Demand wandb_run: apriasmoro wandb_runid: 4b1ac601-8f5f-4fec-8bca-9a4042fa7cb4 warmup_steps: 100 weight_decay: 0.01 ``` </details><br> # e9a91901-efe9-4f4b-8078-b5a58fac8f48 This model is a fine-tuned version of [databricks/dolly-v2-3b](https://huggingface.co/databricks/dolly-v2-3b) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.7420 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 16 - total_eval_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 1325 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-------:\|:----:\|:---------------:\| \| No log \| 0.0159 \| 1 \| 1.6879 \| \| 0.7398 \| 3.5079 \| 221 \| 1.1642 \| \| 0.4155 \| 7.0159 \| 442 \| 1.4107 \| \| 0.1738 \| 10.5238 \| 663 \| 1.4656 \| \| 0.0752 \| 14.0317 \| 884 \| 1.6203 \| \| 0.0436 \| 17.5397 \| 1105 \| 1.7420 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.5.1+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
NastasiaM/mbErt_desc_LTfrozen_model_en_NEU_cls_Normalized	NastasiaM	2025-06-15T23:07:56Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2025-06-15T22:32:02Z	--- library_name: transformers tags: - generated_from_trainer model-index: - name: mbErt_desc_LTfrozen_model_en_NEU_cls_Normalized 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. --> # mbErt_desc_LTfrozen_model_en_NEU_cls_Normalized This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
Jedielson/Hot	Jedielson	2025-06-15T23:06:50Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-15T23:06:50Z	--- license: apache-2.0 ---
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.25_0.75_epoch2	MinaMila	2025-06-15T23:03:17Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T23:01:22Z	--- 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. 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]
gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_complete_mnli	gokulsrinivasagan	2025-06-15T23:01:10Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "base_model:gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_complete", "base_model:finetune:gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_complete", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-15T22:07:36Z	--- library_name: transformers language: - en license: apache-2.0 base_model: gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_complete tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: tinybert_base_train_book_ent_15p_s_init_kd_complete_mnli results: - task: name: Text Classification type: text-classification dataset: name: GLUE MNLI type: glue args: mnli metrics: - name: Accuracy type: accuracy value: 0.763120423108218 --- <!-- 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. --> # tinybert_base_train_book_ent_15p_s_init_kd_complete_mnli This model is a fine-tuned version of [gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_complete](https://huggingface.co/gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_complete) on the GLUE MNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.5911 - Accuracy: 0.7631 ## 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: 256 - eval_batch_size: 256 - seed: 10 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.7566 \| 1.0 \| 1534 \| 0.6799 \| 0.7186 \| \| 0.6322 \| 2.0 \| 3068 \| 0.6413 \| 0.7379 \| \| 0.5681 \| 3.0 \| 4602 \| 0.6223 \| 0.7451 \| \| 0.5157 \| 4.0 \| 6136 \| 0.6184 \| 0.7565 \| \| 0.4699 \| 5.0 \| 7670 \| 0.6115 \| 0.7620 \| \| 0.4266 \| 6.0 \| 9204 \| 0.6486 \| 0.7614 \| \| 0.3871 \| 7.0 \| 10738 \| 0.6570 \| 0.7572 \| \| 0.3532 \| 8.0 \| 12272 \| 0.7183 \| 0.7556 \| \| 0.3191 \| 9.0 \| 13806 \| 0.7695 \| 0.7533 \| \| 0.2903 \| 10.0 \| 15340 \| 0.7822 \| 0.7545 \| ### Framework versions - Transformers 4.51.2 - Pytorch 2.6.0+cu126 - Datasets 3.5.0 - Tokenizers 0.21.1
AshJem/azure-storage	AshJem	2025-06-15T23:00:56Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-15T23:00:56Z	--- license: apache-2.0 ---
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.75_0.05_epoch1	MinaMila	2025-06-15T23:00:01Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T22:58:15Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
rmdhirr/suja-lorab-ep5-suja-4000	rmdhirr	2025-06-15T22:59:39Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:rmdhirr/merged-suja-latest", "base_model:adapter:rmdhirr/merged-suja-latest", "region:us" ]	null	2025-06-15T22:58:40Z	--- base_model: rmdhirr/merged-suja-latest 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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phospho-app/elglombo-ACT_BBOX-jenga_pull-0o0tn	phospho-app	2025-06-15T22:59:12Z	0	0	null	[ "phosphobot", "act", "region:us" ]	null	2025-06-15T22:58:26Z	--- tags: - phosphobot - act task_categories: - robotics --- # act Model - phospho Training Pipeline ## Error Traceback We faced an issue while training your model. ``` The object 'wood block' was detected in 0 episodes in main camera (should be: 10 episodes min). This is not enough to train a model. Check your dataset: https://lerobot-visualize-dataset.hf.space/Mahanthesh0r/jenga_pull/ and rephrase the instruction. ``` ## Training parameters: - Dataset: [Mahanthesh0r/jenga_pull](https://huggingface.co/datasets/Mahanthesh0r/jenga_pull) - Wandb run URL: None - Epochs: None - Batch size: 100 - Training steps: 10000 📖 Get Started: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme) 🤖 Get your robot: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
phospho-app/elglombo-ACT_BBOX-jenga_pull-hgtih	phospho-app	2025-06-15T22:57:04Z	0	0	null	[ "phosphobot", "act", "region:us" ]	null	2025-06-15T22:55:52Z	--- tags: - phosphobot - act task_categories: - robotics --- # act Model - phospho Training Pipeline ## Error Traceback We faced an issue while training your model. ``` The object 'brown block' was detected in 0 episodes in main camera (should be: 10 episodes min). This is not enough to train a model. Check your dataset: https://lerobot-visualize-dataset.hf.space/Mahanthesh0r/jenga_pull/ and rephrase the instruction. ``` ## Training parameters: - Dataset: [Mahanthesh0r/jenga_pull](https://huggingface.co/datasets/Mahanthesh0r/jenga_pull) - Wandb run URL: None - Epochs: None - Batch size: 100 - Training steps: 10000 📖 Get Started: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme) 🤖 Get your robot: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.25_0.75_epoch1	MinaMila	2025-06-15T22:56:42Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T22:54:43Z	--- 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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DevQuasar/WisdomShell.RewardAnything-8B-v1-GGUF	DevQuasar	2025-06-15T22:54:07Z	0	0	null	[ "gguf", "text-generation", "base_model:WisdomShell/RewardAnything-8B-v1", "base_model:quantized:WisdomShell/RewardAnything-8B-v1", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-06-15T21:44:55Z	--- base_model: - WisdomShell/RewardAnything-8B-v1 pipeline_tag: text-generation --- [<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com) Quantized version of: [WisdomShell/RewardAnything-8B-v1](https://huggingface.co/WisdomShell/RewardAnything-8B-v1) 'Make knowledge free for everyone' <p align="center"> Made with <br> <a href="https://www.civo.com/" target="_blank"> <img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/> </a> </p> <a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
phospho-app/elglombo-ACT_BBOX-jenga_pull-kphcz	phospho-app	2025-06-15T22:54:01Z	0	0	null	[ "phosphobot", "act", "region:us" ]	null	2025-06-15T22:53:11Z	--- tags: - phosphobot - act task_categories: - robotics --- # act Model - phospho Training Pipeline ## Error Traceback We faced an issue while training your model. ``` The object 'protruding brown brick' was detected in 0 episodes in main camera (should be: 10 episodes min). This is not enough to train a model. Check your dataset: https://lerobot-visualize-dataset.hf.space/Mahanthesh0r/jenga_pull/ and rephrase the instruction. ``` ## Training parameters: - Dataset: [Mahanthesh0r/jenga_pull](https://huggingface.co/datasets/Mahanthesh0r/jenga_pull) - Wandb run URL: None - Epochs: None - Batch size: 100 - Training steps: 10000 📖 Get Started: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme) 🤖 Get your robot: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
BootesVoid/cmby7y3mp0327rdqs0d2qnhld_cmby8ov8h033zrdqssxdet6yb	BootesVoid	2025-06-15T22:53:40Z	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	2025-06-15T22:53:39Z	--- 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: CLEANER --- # Cmby7Y3Mp0327Rdqs0D2Qnhld_Cmby8Ov8H033Zrdqssxdet6Yb <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 `CLEANER` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "CLEANER", "lora_weights": "https://huggingface.co/BootesVoid/cmby7y3mp0327rdqs0d2qnhld_cmby8ov8h033zrdqssxdet6yb/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('BootesVoid/cmby7y3mp0327rdqs0d2qnhld_cmby8ov8h033zrdqssxdet6yb', weight_name='lora.safetensors') image = pipeline('CLEANER').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: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmby7y3mp0327rdqs0d2qnhld_cmby8ov8h033zrdqssxdet6yb/discussions) to add images that show off what you’ve made with this LoRA.
Bochkov/bvv241-max	Bochkov	2025-06-15T22:50:18Z	4	0	null	[ "gpt2", "region:us" ]	null	2025-06-09T18:51:44Z	--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # bvv241-max: Unified Unicode Tokenizer (SOTA Intersection) with Frozen Embeddings ## Tokenizer Description <!-- Provide a longer summary of what this model is. --> This tokenizer is based on a hybrid vocabulary: This tokenizer uses a strictly structured Unicode mapping scheme: - Plane 0 (0–65535): All single Unicode code points (monograms) are mapped 1:1 to token codes, directly matching standard Unicode BMP. - Private and unused code ranges (Plane 0 high + supplementary, e.g., 0xE000–0xF8FF and 65536–131071): - All multi-character tokens (bigrams, trigrams, SOTA model token strings) are placed exclusively in these ranges. - This design achieves total, lossless Unicode text coverage, with all multi-symbol tokens isolated above the core Unicode range. - Tokenizer created from the intersection of token text across leading SOTA models - Includes o200k_base, cl100k_base, Mistral-Nemo, QwQ-32B, DeepSeek-R1, Qwen3-32B vocabularies, - Vocabulary size: 131,072 tokens, - Embedding dimension: 1024. The associated `normalized_embeddings_weights.pt` file contains a [vocab_size x embed_dim] matrix of precomputed, L2-normalized, frozen embeddings. No semantic information is encoded; embeddings remain fixed throughout LM pretraining. No training or adaptation; suitable for plug-and-play use in research on embedding-free semantic emergence and modular LMs. ## How to Get Started with the Tokenizer Use the code below: from transformers import AutoTokenizer from huggingface_hub import hf_hub_download import torch tokenizer = AutoTokenizer.from_pretrained('Bochkov/bvv241-max') emb_path = hf_hub_download( repo_id="Bochkov/bvv241-max", filename="normalized_embeddings_weights.pt" ) embeddings = torch.load(emb_path)
Bochkov/bvv241-abs	Bochkov	2025-06-15T22:50:14Z	4	0	null	[ "gpt2", "region:us" ]	null	2025-06-09T19:21:46Z	--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # bvv241-abs: Unified Unicode Tokenizer (SOTA Intersection) with Frozen Embeddings and Extended Vector Dim (4096) ## Tokenizer Description <!-- Provide a longer summary of what this model is. --> This tokenizer is based on a hybrid vocabulary: This tokenizer uses a strictly structured Unicode mapping scheme: - Plane 0 (0–65535): All single Unicode code points (monograms) are mapped 1:1 to token codes, directly matching standard Unicode BMP. - Private and unused code ranges (Plane 0 high + supplementary, e.g., 0xE000–0xF8FF and 65536–131071): - All multi-character tokens (bigrams, trigrams, SOTA model token strings) are placed exclusively in these ranges. - This design achieves total, lossless Unicode text coverage, with all multi-symbol tokens isolated above the core Unicode range. - Tokenizer created from the intersection of token text across leading SOTA models - Includes o200k_base, cl100k_base, Mistral-Nemo, QwQ-32B, DeepSeek-R1, Qwen3-32B vocabularies, - Vocabulary size: 131,072 tokens, - Embedding dimension: 4096. The associated `normalized_embeddings_weights.pt` file contains a [vocab_size x embed_dim] matrix of precomputed, L2-normalized, frozen embeddings. No semantic information is encoded; embeddings remain fixed throughout LM pretraining. No training or adaptation; suitable for plug-and-play use in research on embedding-free semantic emergence and modular LMs. ## How to Get Started with the Tokenizer Use the code below: from transformers import AutoTokenizer from huggingface_hub import hf_hub_download import torch tokenizer = AutoTokenizer.from_pretrained('Bochkov/bvv241-abs') emb_path = hf_hub_download( repo_id="Bochkov/bvv241-abs", filename="normalized_embeddings_weights.pt" ) embeddings = torch.load(emb_path)
Bochkov/bvv241-nemo	Bochkov	2025-06-15T22:50:07Z	2	0	null	[ "gpt2", "region:us" ]	null	2025-06-09T19:05:09Z	--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # bvv241-nemo: SOTA Mistral Nemo Tokenizer with BVV-mapped Frozen Embedding ## Tokenizer Description <!-- Provide a longer summary of what this model is. --> This tokenizer is based on a hybrid vocabulary: This tokenizer uses a strictly structured Unicode mapping scheme: - Plane 0 (0–65535): All single Unicode code points (monograms) are mapped 1:1 to token codes, directly matching standard Unicode BMP. - Private and unused code ranges (Plane 0 high + supplementary, e.g., 0xE000–0xF8FF and 65536–131071): - All multi-character tokens (bigrams, trigrams, SOTA model token strings) are placed exclusively in these ranges. - This design achieves total, lossless Unicode text coverage, with all multi-symbol tokens isolated above the core Unicode range. - Tokenizer uses the Mistral-Nemo model's full vocabulary - Vocabulary size: 131,072 tokens, - Embedding dimension: 1024. The associated `normalized_embeddings_weights.pt` file contains a [vocab_size x embed_dim] matrix of precomputed, L2-normalized, frozen embeddings. No semantic information is encoded; embeddings remain fixed throughout LM pretraining. No training or adaptation; suitable for plug-and-play use in research on embedding-free semantic emergence and modular LMs. ## How to Get Started with the Tokenizer Use the code below: from transformers import AutoTokenizer from huggingface_hub import hf_hub_download import torch tokenizer = AutoTokenizer.from_pretrained('Bochkov/bvv241-nemo') emb_path = hf_hub_download( repo_id="Bochkov/bvv241-nemo", filename="normalized_embeddings_weights.pt" ) embeddings = torch.load(emb_path)
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.5_0.05_epoch2	MinaMila	2025-06-15T22:49:38Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T22:47:42Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
kaizen9/llama3_3B_invartest_noddeepcopy	kaizen9	2025-06-15T22:46:57Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T22:43:48Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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JocelyneSmith/HW2-reward	JocelyneSmith	2025-06-15T22:45:18Z	0	0	transformers	[ "transformers", "safetensors", "gpt2", "text-classification", "generated_from_trainer", "trl", "reward-trainer", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2025-06-15T22:36:06Z	--- base_model: openai-community/gpt2 library_name: transformers model_name: HW2-reward tags: - generated_from_trainer - trl - reward-trainer licence: license --- # Model Card for HW2-reward This model is a fine-tuned version of [openai-community/gpt2](https://huggingface.co/openai-community/gpt2). 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="JocelyneSmith/HW2-reward", 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 Reward. ### Framework versions - TRL: 0.18.2 - Transformers: 4.52.4 - Pytorch: 2.7.1+cu128 - 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}} } ```
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.75_0.15_epoch1	MinaMila	2025-06-15T22:44:14Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T22:42:26Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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sergioalves/031d6337-6e22-4597-92d8-afc9d75617dc	sergioalves	2025-06-15T22:38:25Z	0	0	peft	[ "peft", "safetensors", "gpt_neox", "axolotl", "generated_from_trainer", "base_model:databricks/dolly-v2-3b", "base_model:adapter:databricks/dolly-v2-3b", "license:mit", "4-bit", "bitsandbytes", "region:us" ]	null	2025-06-15T22:00:50Z	--- library_name: peft license: mit base_model: databricks/dolly-v2-3b tags: - axolotl - generated_from_trainer model-index: - name: 031d6337-6e22-4597-92d8-afc9d75617dc 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: databricks/dolly-v2-3b bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 64e9034955402139_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: 4 gradient_checkpointing: true gradient_clipping: 0.8 group_by_length: false hub_model_id: sergioalves/031d6337-6e22-4597-92d8-afc9d75617dc hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-07 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 32 lora_dropout: 0.3 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 16 lora_target_linear: true lr_scheduler: cosine max_steps: 300 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/64e9034955402139_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: 4b1ac601-8f5f-4fec-8bca-9a4042fa7cb4 wandb_project: s56-7 wandb_run: your_name wandb_runid: 4b1ac601-8f5f-4fec-8bca-9a4042fa7cb4 warmup_steps: 30 weight_decay: 0.05 xformers_attention: true ``` </details><br> # 031d6337-6e22-4597-92d8-afc9d75617dc This model is a fine-tuned version of [databricks/dolly-v2-3b](https://huggingface.co/databricks/dolly-v2-3b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4630 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - 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: 30 - training_steps: 300 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 6.5626 \| 0.0002 \| 1 \| 1.4862 \| \| 5.4328 \| 0.0252 \| 150 \| 1.4690 \| \| 6.4398 \| 0.0505 \| 300 \| 1.4630 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
SunshineYellow/abstract_sentence_classifier	SunshineYellow	2025-06-15T22:38:09Z	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-15T22:28:47Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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dgambettaphd/M_llm2_run2_gen4_WXS_doc1000_synt64_lr1e-04_acm_FRESH	dgambettaphd	2025-06-15T22:37:13Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-15T22:37:01Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.75_0.25_epoch2	MinaMila	2025-06-15T22:36:10Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T22:34:16Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Enzogbs/q-FrozenLake-v1-4x4-noSlippery	Enzogbs	2025-06-15T22:33:02Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2025-06-15T22:33:00Z	--- 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="Enzogbs/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"]) ```
kaizen9/llama3_3B_invartest	kaizen9	2025-06-15T22:31:23Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T21:56:44Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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NetherQuartz/tatoeba-tok-ru	NetherQuartz	2025-06-15T22:31:23Z	0	0	transformers	[ "transformers", "safetensors", "marian", "text2text-generation", "translation", "generated_from_trainer", "tok", "ru", "dataset:NetherQuartz/tatoeba-tokipona", "base_model:Helsinki-NLP/opus-mt-en-ru", "base_model:finetune:Helsinki-NLP/opus-mt-en-ru", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2025-06-15T15:36:27Z	--- library_name: transformers license: apache-2.0 base_model: Helsinki-NLP/opus-mt-en-ru tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: tatoeba-tok-ru results: [] language: - tok - ru datasets: - NetherQuartz/tatoeba-tokipona --- <!-- 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. --> # tatoeba-tok-ru This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ru](https://huggingface.co/Helsinki-NLP/opus-mt-en-ru) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2814 - Bleu: 20.1964 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Use 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: 15 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\| \| 1.8552 \| 1.0 \| 1191 \| 1.5926 \| 15.4114 \| \| 1.5457 \| 2.0 \| 2382 \| 1.4561 \| 15.1718 \| \| 1.3759 \| 3.0 \| 3573 \| 1.3928 \| 17.4022 \| \| 1.2746 \| 4.0 \| 4764 \| 1.3579 \| 17.9203 \| \| 1.1881 \| 5.0 \| 5955 \| 1.3325 \| 16.7076 \| \| 1.1198 \| 6.0 \| 7146 \| 1.3132 \| 16.4193 \| \| 1.0649 \| 7.0 \| 8337 \| 1.3032 \| 15.8687 \| \| 1.0231 \| 8.0 \| 9528 \| 1.2974 \| 18.9312 \| \| 0.9834 \| 9.0 \| 10719 \| 1.2912 \| 19.6730 \| \| 0.9546 \| 10.0 \| 11910 \| 1.2860 \| 19.2357 \| \| 0.9249 \| 11.0 \| 13101 \| 1.2850 \| 19.9692 \| \| 0.9043 \| 12.0 \| 14292 \| 1.2844 \| 20.0704 \| \| 0.8841 \| 13.0 \| 15483 \| 1.2814 \| 20.1964 \| \| 0.8728 \| 14.0 \| 16674 \| 1.2819 \| 19.9601 \| \| 0.8677 \| 15.0 \| 17865 \| 1.2815 \| 20.2073 \| ### Framework versions - Transformers 4.52.4 - Pytorch 2.7.1+cu128 - Datasets 3.6.0 - Tokenizers 0.21.1
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.5_0.15_epoch1	MinaMila	2025-06-15T22:29:21Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T22:27:27Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.75_0.25_epoch1	MinaMila	2025-06-15T22:28:12Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T22:26:23Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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UniLLMer/MuseKaako6432e3e2jokesdwptooQ	UniLLMer	2025-06-15T22:24:33Z	0	0	transformers	[ "transformers", "gguf", "mistral", "text-generation-inference", "unsloth", "en", "base_model:LatitudeGames/Muse-12B", "base_model:quantized:LatitudeGames/Muse-12B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-15T22:21:58Z	--- base_model: LatitudeGames/Muse-12B tags: - text-generation-inference - transformers - unsloth - mistral - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: UniLLMer - License: apache-2.0 - Finetuned from model : LatitudeGames/Muse-12B This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.5_0.25_epoch2	MinaMila	2025-06-15T22:22:20Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T22:20:27Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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(2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
phospho-app/elglombo-ACT_BBOX-jenga_pull-m6whq	phospho-app	2025-06-15T22:19:04Z	0	0	null	[ "phosphobot", "act", "region:us" ]	null	2025-06-15T22:18:16Z	--- tags: - phosphobot - act task_categories: - robotics --- # act Model - phospho Training Pipeline ## Error Traceback We faced an issue while training your model. ``` The object 'protruding block' was detected in 0 episodes in main camera (should be: 10 episodes min). This is not enough to train a model. Check your dataset: https://lerobot-visualize-dataset.hf.space/Mahanthesh0r/jenga_pull/ and rephrase the instruction. ``` ## Training parameters: - Dataset: [Mahanthesh0r/jenga_pull](https://huggingface.co/datasets/Mahanthesh0r/jenga_pull) - Wandb run URL: None - Epochs: None - Batch size: 100 - Training steps: 10000 📖 Get Started: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme) 🤖 Get your robot: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
lean222222222/leo-flux-dev-lora	lean222222222	2025-06-15T22:18:08Z	0	0	null	[ "license:other", "region:us" ]	null	2025-06-15T21:33:43Z	--- 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 ---
BootesVoid/cmbgtk63y052tkfxsx1r4aht4_cmbxzksk302hhrdqsxwnuilu5	BootesVoid	2025-06-15T22:17:23Z	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	2025-06-15T22:17:22Z	--- 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: SOPHIE --- # Cmbgtk63Y052Tkfxsx1R4Aht4_Cmbxzksk302Hhrdqsxwnuilu5 <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 `SOPHIE` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "SOPHIE", "lora_weights": "https://huggingface.co/BootesVoid/cmbgtk63y052tkfxsx1r4aht4_cmbxzksk302hhrdqsxwnuilu5/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('BootesVoid/cmbgtk63y052tkfxsx1r4aht4_cmbxzksk302hhrdqsxwnuilu5', weight_name='lora.safetensors') image = pipeline('SOPHIE').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: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmbgtk63y052tkfxsx1r4aht4_cmbxzksk302hhrdqsxwnuilu5/discussions) to add images that show off what you’ve made with this LoRA.
schonsense/70B_SOG_MMSLERPV2	schonsense	2025-06-15T22:13:27Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "base_model:flammenai/Llama3.1-Flammades-70B", "base_model:merge:flammenai/Llama3.1-Flammades-70B", "base_model:flammenai/Mahou-1.5-llama3.1-70B", "base_model:merge:flammenai/Mahou-1.5-llama3.1-70B", "base_model:nbeerbower/Llama3.1-Gutenberg-Doppel-70B", "base_model:merge:nbeerbower/Llama3.1-Gutenberg-Doppel-70B", "base_model:schonsense/70B_SOG_unstructed", "base_model:merge:schonsense/70B_SOG_unstructed", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T13:55:46Z	--- base_model: - schonsense/70B_SOG_unstructed - nbeerbower/Llama3.1-Gutenberg-Doppel-70B - flammenai/Llama3.1-Flammades-70B - flammenai/Mahou-1.5-llama3.1-70B library_name: transformers tags: - mergekit - merge --- # SOG_MMSLERP Turn the temp down on this one. 0.1 to 0.6. This is a multi-merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the MULTI-merge method. ### Models Merged The following models were included in the merge: * schonsense/70B_SOG_unstructed * nbeerbower/Llama3.1-Gutenberg-Doppel-70B * flammenai/Llama3.1-Flammades-70B * flammenai/Mahou-1.5-llama3.1-70B * D:\mergekit\SOG_MSLERP_MULTI * D:\mergekit\_My_YAMLS\70B_mSlOG_un ### Configuration The following YAML configuration was used to produce this model: ```yaml name: flam merge_method: multislerp models: - model: nbeerbower/Llama3.1-Gutenberg-Doppel-70B - model: flammenai/Llama3.1-Flammades-70B - model: flammenai/Mahou-1.5-llama3.1-70B parameters: weight: 1 --- name: SOG_MSLERP_MULTI merge_method: della models: - model: flam parameters: density: 0.2 epsilon: 0.1 weight: 0.2 - model: "D:\\mergekit\\_My_YAMLS\\70B_mSlOG_un" parameters: density: 1 epsilon: 0 weight: 0.8 base_model: "D:\\mergekit\\_My_YAMLS\\70B_mSlOG_un" parameters: normalize: false int8_mask: false lambda: 1.0 --- models: - model: "D:\\mergekit\\_My_YAMLS\\70B_mSlOG_un" parameters: weight: 1 - model: SOG_MSLERP_MULTI parameters: weight: 1 merge_method: nuslerp tokenizer_source: "D:\\mergekit\\_My_YAMLS\\70B_mSlOG_un" dtype: float32 out_dtype: bfloat16 ```
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.75_0.5_epoch1	MinaMila	2025-06-15T22:12:11Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T22:10:22Z	--- 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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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]
ALYTV/Qwen2.5-Coder-14B-mlx-4Bit	ALYTV	2025-06-15T22:07:25Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "code", "qwen", "qwen-coder", "codeqwen", "mlx", "mlx-my-repo", "conversational", "en", "base_model:Qwen/Qwen2.5-Coder-14B", "base_model:quantized:Qwen/Qwen2.5-Coder-14B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "region:us" ]	text-generation	2025-06-15T22:06:58Z	--- license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-Coder-14B/blob/main/LICENSE language: - en base_model: Qwen/Qwen2.5-Coder-14B pipeline_tag: text-generation library_name: transformers tags: - code - qwen - qwen-coder - codeqwen - mlx - mlx-my-repo --- # ALYTV/Qwen2.5-Coder-14B-mlx-4Bit The Model [ALYTV/Qwen2.5-Coder-14B-mlx-4Bit](https://huggingface.co/ALYTV/Qwen2.5-Coder-14B-mlx-4Bit) was converted to MLX format from [Qwen/Qwen2.5-Coder-14B](https://huggingface.co/Qwen/Qwen2.5-Coder-14B) 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("ALYTV/Qwen2.5-Coder-14B-mlx-4Bit") 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) ```
juexzz/INTACT-pi0-scratch-bridge	juexzz	2025-06-15T22:07:01Z	2	0	null	[ "safetensors", "robotics", "arxiv:2410.24164", "arxiv:2506.09930", "base_model:lerobot/pi0", "base_model:finetune:lerobot/pi0", "license:apache-2.0", "region:us" ]	robotics	2025-06-15T02:21:29Z	--- license: apache-2.0 base_model: - lerobot/pi0 pipeline_tag: robotics --- # INTACT Probing Suite: Pi0 from scratch on BridgeV2 > 📦 This model is part of the [INTACT Probing Suite Collection](https://huggingface.co/collections/ai4ce/intact-probing-suite-684e5601e9ed640fdd9b994b) > Explore other variants: > - [Pi0 fintuned on BridgeV2](https://huggingface.co/juexzz/INTACT-pi0-finetune-bridge) > - [Pi0 finetuned with paraphrase on BridgeV2](https://huggingface.co/juexzz/INTACT-pi0-finetune-rephrase-bridge) ## INTACT-pi0-scratch-bridge This repository contains a checkpoint of the Pi0 model ([HF implementation](https://huggingface.co/lerobot/pi0) \| [Paper](https://arxiv.org/abs/2410.24164v1)) initialized from PaliGemma and trained directly ("from scratch") on the BridgeV2 dataset for robotic manipulation tasks. The model is later used for testing on the [Simpler Environment](https://github.com/simpler-env/SimplerEnv) and our [INTACT](https://github.com/ai4ce/INT-ACT) Probing Suite for the generalization boundaries of VLA models. Paper: [From Intention to Execution: Probing the Generalization Boundaries of Vision-Language-Action Models](https://arxiv.org/abs/2506.09930) ## Model Details - Base Model: [lerobot/pi0](https://huggingface.co/lerobot/pi0) - Training Dataset: [BridgeV2](https://rail-berkeley.github.io/bridgedata/) - Model Type: Vision-Language-Action (VLA) model for robotics - Fine-tuning Method: See our [paper](https://arxiv.org/abs/2506.09930) - Training Framework: See our [repository](https://github.com/ai4ce/INT-ACT) ## Quick Start ### Usage in INTACT ```shell git clone --recurse-submodules https://github.com/ai4ce/INT-ACT.git cd INT-ACT uv sync source .venv/bin/activate python ``` Or directly in python with Lerobot, see blow: ### Integration with LeRobot First, install lerobot ```bash pip install lerobot ``` Then ```python import torch from lerobot.common.policies.pi0.modeling_pi0 import Pi0Policy # Load model policy = Pi0Policy.from_pretrained("juexzz/INTACT-pi0-scratch-bridge") # Inference with torch.no_grad(): actions = policy.select_action(batch) ``` ### Training Configuration - Training Steps: 15 epochs ~22695 steps. - Batch Size: 1024 - Learning Rate: 1e-5 - Hardware: 4 H100/A100 - Input Modalities: single image (to work with SimplerEnv), 1 language instruction, 1 robot state. - Output: robot actions (delta EEF) with chunk size of 4. For more details please refer to our [paper](https://arxiv.org/abs/2506.09930) and [code](https://github.com/ai4ce/INT-ACT) ## Evaluation Checkpoint choice After training 15 epochs, we sweep the checkpoint at epoch 1, 2, 3, 4, 5, 10, 15 for performance on the original 4 Bridge tasks in the SimplerEnv, and choose the checkpoint with best average performance for each of the three Pi0 variants. Therefore, you may still get a better success rate for a specific task at other checkpoints. As a result, the best checkpoint for this pi0 finetune model is at step 22695 (epoch 15). The comparison of their performance on Simpler are shown below. ### Performance Comparison on SimplerEnv Success rate comparison on the SimplerEnv with other pi0 variants and some other baselines experimented in our INTACT suite. For a more detailed comparison, please refer to the [paper](https://arxiv.org/abs/2506.09930). \| Model \| carrot_on_plate \| eggplant_in_basket \| stack_cube \| spoon_on_towel \| \|-------\|-----------------\|-------------------\|------------\|----------------\| \| [Pi0 finetune](https://huggingface.co/juexzz/INTACT-pi0-finetune-bridge) \| 0.361 \| 0.819 \| 0.264 \| 0.458 \| \| [Pi0 finetune rephrase](https://huggingface.co/juexzz/INTACT-pi0-finetune-rephrase-bridge) \| 0.500 \| 0.944 \| 0.222 \| 0.597 \| \| Pi0 scratch(this model) \| 0.542 \| 0.903 \| 0.403 \| 0.875 \| \| Spatial VLA \| 0.125 \| 0.958 \| 0.292 \| 0.208 \| \| Magma \| 0.250 \| 0.611 \| 0.097 \| 0.208 \| \| Octo Small \| 0.014 \| 0.097 \| 0.000 \| 0.097 \| \| Octo Base \| 0.014 \| 0.306 \| 0.000 \| 0.014 \| ## Citation If you use this model in your research, please cite: ```bibtex @article{fang2025intention, title={From Intention to Execution: Probing the Generalization Boundaries of Vision-Language-Action Models}, author={Fang, Irving and Zhang, Juexiao and Tong, Shengbang and Feng, Chen}, journal={arXiv preprint arXiv:2506.09930}, year={2025} } ``` ## Related Work - Pi0 (official): [pi0 (JAX)](https://github.com/Physical-Intelligence/openpi) - Base Model (Pi0 HF): [lerobot/pi0](https://huggingface.co/lerobot/pi0) - Dataset: [BridgeV2](https://bridge-v2.github.io/) - Framework: [LeRobot](https://github.com/huggingface/lerobot) - Simpler Environment: [SimplerEnv](https://github.com/simpler-env/SimplerEnv) - Open-source Pi0 Implementation by Allen Ren: [open-pi-zero](https://github.com/allenzren/open-pi-zero) ## License This model is released under the Apache 2.0 license. Please see the base model's license for any additional restrictions. ## Support For questions about this model: - 📧 Open an issue in this repository - 💬 Discussion tab for community questions - 📖 Check our [paper](https://arxiv.org/abs/2506.09930) for technical details --- Last updated: June 2025
gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_complete_stsb	gokulsrinivasagan	2025-06-15T22:05:52Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "base_model:gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_complete", "base_model:finetune:gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_complete", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-15T22:04:27Z	--- library_name: transformers language: - en license: apache-2.0 base_model: gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_complete tags: - generated_from_trainer datasets: - glue metrics: - spearmanr model-index: - name: tinybert_base_train_book_ent_15p_s_init_kd_complete_stsb results: - task: name: Text Classification type: text-classification dataset: name: GLUE STSB type: glue args: stsb metrics: - name: Spearmanr type: spearmanr value: 0.8154467510323156 --- <!-- 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. --> # tinybert_base_train_book_ent_15p_s_init_kd_complete_stsb This model is a fine-tuned version of [gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_complete](https://huggingface.co/gokulsrinivasagan/tinybert_base_train_book_ent_15p_s_init_kd_complete) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: 0.7455 - Pearson: 0.8187 - Spearmanr: 0.8154 - Combined Score: 0.8170 ## 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: 256 - eval_batch_size: 256 - seed: 10 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Pearson \| Spearmanr \| Combined Score \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:---------:\|:--------------:\| \| 2.6257 \| 1.0 \| 23 \| 2.5060 \| 0.1353 \| 0.1309 \| 0.1331 \| \| 1.8313 \| 2.0 \| 46 \| 1.6575 \| 0.6385 \| 0.6265 \| 0.6325 \| \| 1.1806 \| 3.0 \| 69 \| 1.3400 \| 0.7092 \| 0.7236 \| 0.7164 \| \| 0.8641 \| 4.0 \| 92 \| 1.0966 \| 0.7756 \| 0.7786 \| 0.7771 \| \| 0.7115 \| 5.0 \| 115 \| 0.8398 \| 0.7931 \| 0.7898 \| 0.7914 \| \| 0.6248 \| 6.0 \| 138 \| 0.7820 \| 0.8130 \| 0.8090 \| 0.8110 \| \| 0.5846 \| 7.0 \| 161 \| 0.7455 \| 0.8187 \| 0.8154 \| 0.8170 \| \| 0.4653 \| 8.0 \| 184 \| 0.8070 \| 0.8201 \| 0.8177 \| 0.8189 \| \| 0.4188 \| 9.0 \| 207 \| 0.7894 \| 0.8156 \| 0.8131 \| 0.8143 \| \| 0.3692 \| 10.0 \| 230 \| 0.8148 \| 0.8154 \| 0.8138 \| 0.8146 \| \| 0.3428 \| 11.0 \| 253 \| 1.1896 \| 0.8115 \| 0.8174 \| 0.8145 \| \| 0.3529 \| 12.0 \| 276 \| 0.9953 \| 0.8173 \| 0.8180 \| 0.8176 \| ### Framework versions - Transformers 4.51.2 - Pytorch 2.6.0+cu126 - Datasets 3.5.0 - Tokenizers 0.21.1
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.75_0.75_epoch2	MinaMila	2025-06-15T22:04:03Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T22:02:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Manal0809/MedQA_Mistral_7b_Instructive_KG3	Manal0809	2025-06-15T22:03:59Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/mistral-7b-instruct-v0.3-bnb-4bit", "base_model:adapter:unsloth/mistral-7b-instruct-v0.3-bnb-4bit", "region:us" ]	null	2025-06-15T22:03:48Z	--- base_model: unsloth/mistral-7b-instruct-v0.3-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. 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rushabh14/TEMU-VTOFF	rushabh14	2025-06-15T22:02:14Z	0	0	diffusers	[ "diffusers", "safetensors", "image-generation", "image-to-image", "virtual-try-on", "virtual-try-off", "diffusion", "dit", "stable-diffusion-3", "multimodal", "fashion", "pytorch", "en", "dataset:dresscode", "dataset:viton-hd", "arxiv:2505.21062", "base_model:stabilityai/stable-diffusion-3-medium-diffusers", "base_model:finetune:stabilityai/stable-diffusion-3-medium-diffusers", "license:cc-by-nc-4.0", "region:us" ]	image-to-image	2025-06-15T22:02:13Z	--- license: cc-by-nc-4.0 base_model: - stabilityai/stable-diffusion-3-medium-diffusers pipeline_tag: image-to-image tags: - image-generation - image-to-image - virtual-try-on - virtual-try-off - diffusion - dit - stable-diffusion-3 - multimodal - fashion - pytorch language: en datasets: - dresscode - viton-hd --- <div align="center"> <h1 align="center">TEMU-VTOFF</h1> <h3 align="center">Text-Enhanced MUlti-category Virtual Try-Off</h3> </div> <div align="center"> <picture> <source srcset="/davidelobba/TEMU-VTOFF/resolve/main/teaser.png" media="(prefers-color-scheme: dark)"> <img src="/davidelobba/TEMU-VTOFF/resolve/main/teaser.png" width="75%" alt="TEMU-VTOFF Teaser"> </source> </picture> </div> <div align="center"> Inverse Virtual Try-On: Generating Multi-Category Product-Style Images from Clothed Individuals [Davide Lobba](https://scholar.google.com/citations?user=WEMoLPEAAAAJ&hl=en&oi=ao)<sup>1,2,\</sup>, [Fulvio Sanguigni](https://scholar.google.com/citations?user=tSpzMUEAAAAJ&hl=en)<sup>2,3,\</sup>, [Bin Ren](https://scholar.google.com/citations?user=Md9maLYAAAAJ&hl=en)<sup>1,2</sup>, [Marcella Cornia](https://scholar.google.com/citations?user=DzgmSJEAAAAJ&hl=en)<sup>3</sup>, [Rita Cucchiara](https://scholar.google.com/citations?user=OM3sZEoAAAAJ&hl=en)<sup>3</sup>, [Nicu Sebe](https://scholar.google.com/citations?user=stFCYOAAAAAJ&hl=en)<sup>1</sup> <sup>1</sup>University of Trento, <sup>2</sup>University of Pisa, <sup>3</sup>University of Modena and Reggio Emilia <sup></sup> Equal contribution </div> <div align="center"> <a href="https://arxiv.org/abs/2505.21062" style="margin: 0 2px;"> <img src="https://img.shields.io/badge/Paper-Arxiv_2505.21062-darkred.svg" alt="Paper"> </a> <a href="https://temu-vtoff-page.github.io/" style="margin: 0 2px;"> <img src='https://img.shields.io/badge/Webpage-Project-silver?style=flat&logo=&logoColor=orange' alt='Project Webpage'> </a> <a href="https://github.com/davidelobba/TEMU-VTOFF" style="margin: 0 2px;"> <img src="https://img.shields.io/badge/GitHub-Repo-blue.svg?logo=github" alt="GitHub Repository"> </a> <!-- The Hugging Face model badge will be automatically displayed on the model page --> </div> ## 💡 Model Description TEMU-VTOFF* is a novel dual-DiT (Diffusion Transformer) architecture designed for the Virtual Try-Off task: generating in-shop images of garments worn by a person. By combining a pretrained feature extractor with a text-enhanced generation module, our method can handle occlusions, multiple garment categories, and ambiguous appearances. It further refines generation fidelity via a feature alignment module based on DINOv2. This model is based on `stabilityai/stable-diffusion-3-medium-diffusers`. The uploaded weights correspond to the finetuned feature extractor and the VTOFF DiT module. ## ✨ Key Features Our contribution can be summarized as follows: - 🎯 Multi-Category Try-Off. We present a unified framework capable of handling multiple garment types (upper-body, lower-body, and full-body clothes) without requiring category-specific pipelines. - 🔗 Multimodal Hybrid Attention. We introduce a novel attention mechanism that integrates garment textual descriptions into the generative process by linking them with person-specific features. This helps the model synthesize occluded or ambiguous garment regions more accurately. - ⚡ Garment Aligner Module. We design a lightweight aligner that conditions generation on clean garment images, replacing conventional denoising objectives. This leads to better alignment consistency on the overall dataset and preserves more precise visual retention. - 📊 Extensive experiments. Experiments on the Dress Code and VITON-HD datasets demonstrate that TEMU-VTOFF outperforms prior methods in both the quality of generated images and alignment with the target garment, highlighting its strong generalization capabilities.
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.5_0.5_0.5_epoch1	MinaMila	2025-06-15T22:02:09Z	0	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T22:00:08Z	--- 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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sugarquark/vqvae-masked-image-restoration-clip-condition	sugarquark	2025-06-15T21:56:24Z	0	0	null	[ "safetensors", "license:apache-2.0", "region:us" ]	null	2025-06-15T00:29:53Z	--- license: apache-2.0 --- # Masked image reconstruction The model reuses FG-CLIP, it takes a reference image, then reconstructs the masked image. The prediction output is a series of discrete numbers representing the masked tokens. ![](images/preview.png) ## Datasets - animelover/touhou-images - Chars/pixiv_rank_daily_2018_2023 - Makki2104/difference_images_Cloth-Nude - picollect/12TPICS - recoilme/tst72 - sugarquark/kiradepth-v1.1-character-index - sugarquark/nai-mixed-400 ## Disclaimer The license requires a link to the Hugging Face profile.
JeonMashup/Anna_MEOVV_JeonMashup	JeonMashup	2025-06-15T21:55:04Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-11-23T13:18:17Z	--- license: apache-2.0 ---
Videonoatelegram/EXCLUSIVE.VEDIO.noa.honey.videos.noa.honey.hamburguesita.la.hamburguesa.de.noa	Videonoatelegram	2025-06-15T21:54:46Z	0	0	null	[ "region:us" ]	null	2025-06-15T21:50:47Z	Watch 🟢 ➤ ➤ ➤ <a href="https://newvidgallery.com/jjtyjtyj"> 🌐 Click Here To link (EXCLUSIVE.VEDIO.noa.honey.videos.noa.honey.hamburguesita.la.hamburguesa.de.noa) 🔴 ➤►DOWNLOAD👉👉🟢 ➤Watch 🟢 ➤ ➤ ➤ <a href="https://newvidgallery.com/jjtyjtyj"> 🌐 EXCLUSIVE.VEDIO.noa.honey.videos.noa.honey.hamburguesita.la.hamburguesa.de.noa
CeciGonSer/translation_pu_es_sintetico_chamo_mbart_1ep	CeciGonSer	2025-06-15T21:54:21Z	0	0	transformers	[ "transformers", "safetensors", "mbart", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-06-15T21:50:21Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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theDrewDag/new_model	theDrewDag	2025-06-15T21:51:18Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:google/gemma-3-1b-it", "base_model:finetune:google/gemma-3-1b-it", "endpoints_compatible", "region:us" ]	null	2025-06-15T21:24:21Z	--- base_model: google/gemma-3-1b-it library_name: transformers model_name: new_model tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for new_model This model is a fine-tuned version of [google/gemma-3-1b-it](https://huggingface.co/google/gemma-3-1b-it). 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="theDrewDag/new_model", 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: 0.18.2 - Transformers: 4.52.4 - Pytorch: 2.7.1 - 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}} } ```
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.15_0.05_0.05_epoch2	MinaMila	2025-06-15T21:47:59Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-15T21:46:09Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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CeciGonSer/translation_pu_es_sintetico_chamo_mbart	CeciGonSer	2025-06-15T21:46:22Z	0	0	transformers	[ "transformers", "safetensors", "mbart", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-06-15T21:42:23Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Superdekoen/ppo-LunarLander-v2	Superdekoen	2025-06-15T21:44:16Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-06-15T21:43:56Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 267.03 +/- 15.98 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

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