This is an AI model specifically designed for Python code review. It is fine-tuned based on Qwen2.5 - 7B - Instruct and can identify security vulnerabilities, performance issues, and provide suggestions for code quality improvement.

Natural Language ProcessingTransformersEnglish

Omni L1B3RT4S GENIE is an AI assistant fine-tuned based on the Qwen2.5-3B Instruct architecture. It is trained with customized examples of 1,103 elf characters, and has unique voice and loyalty modes, capable of providing users with a unique dialogue experience.

Natural Language ProcessingGgufEnglish

This is a Transformers model that has been uploaded to the Hugging Face model hub. The model card is automatically generated by the system, and detailed information needs to be further supplemented.

Natural Language ProcessingTransformers

This model is a professional code generation model fine-tuned on the swe_only_sweagent dataset based on Qwen2.5-Coder-32B-Instruct. It achieved a loss value of 0.1210 on the evaluation set and is specifically optimized for software engineering-related tasks.

Natural Language ProcessingTransformers

This model is a dedicated coding model fine-tuned on the non_web_mcp dataset based on Qwen2.5-Coder-7B-Instruct. It is optimized for specific coding tasks and improves performance related to coding.

Natural Language ProcessingTransformers

This is a Transformer model that has been published to the Hugging Face model hub. Specific information is to be supplemented. The model card is automatically generated and detailed information needs to be further supplemented.

Natural Language ProcessingTransformers

This project is a quantized version of the TECHNOPRAVIN01/Qwen2.5-14B-Valor model, providing model files in various quantization formats, including multiple quantization types such as IQ1_S, IQ2_XS, IQ3_M, Q4_K_S, Q5_K_M, etc., which are convenient for use in different scenarios.

Natural Language ProcessingTransformersEnglish

This project is a static quantized version based on the TECHNOPRAVIN01/Qwen2.5-14B-Valor model, offering multiple quantization types to meet the usage requirements under different hardware conditions. The model reduces the model size through quantization technology while maintaining good performance.

Natural Language ProcessingTransformersEnglish

Qwen2.5-14B-Instruct is a multilingual large language model based on the Qwen2.5 architecture, with 14 billion parameters. It supports 13 languages and has excellent instruction-following and dialogue capabilities. This model performs well in various language tasks and is suitable for text generation and understanding in multilingual scenarios.

Natural Language ProcessingTransformersMultiple Languages

DIMI Arabic OCR v2 is an optical character recognition model specifically optimized for Arabic text recognition. It is developed based on fine-tuning Qwen2.5-VL-7B-Instruct and has significant improvements in handling texts with dense diacritics compared to the v1 version.

MultimodalPeftArabic

Qwen2.5-3B-Valor is a specialized language model that focuses on generating challenging questions, capable of challenging traditional concepts and connecting ideas from different fields, thus opening up new directions for research in the technological field.

Natural Language ProcessingTransformersEnglish

The FP8 quantized version of olmOCR-2-7B-1025, fine-tuned based on Qwen2.5-VL-7B-Instruct, is a vision-language model specifically designed for handling complex OCR scenarios such as mathematical equations and tables.

MultimodalTransformersEnglish

PaDT Pro 3B is a multimodal large language model based on Qwen2.5VL-3B. It adopts a unified paradigm of decodable block tokens and can directly generate text and visual outputs, achieving advanced performance in multiple visual perception and understanding tasks.

MultimodalTransformersMultiple Languages

NuExtract 2.0 is a series of multimodal models specifically trained by NuMind for structured information extraction tasks. This 4B version is built on Qwen2.5-VL-3B-Instruct, supports text and image input, has multilingual processing capabilities, and can extract structured information from unstructured data.

MultimodalTransformers

VideoChat-R1_5-7B is a video-text interaction model built on Qwen2.5-VL-7B-Instruct, supporting multimodal tasks and excelling in video Q&A functions. This model enhances spatio-temporal perception ability through reinforcement fine-tuning and adopts an iterative perception mechanism to strengthen multimodal reasoning.

MultimodalTransformersEnglish

VLA-Adapter is a micro vision-language-action model trained on Libero-Spatial. It adopts the Prismatic-VLM architecture and only uses Qwen2.5-0.5B as the large language model backbone. This model outperforms open-source VLA models with larger parameter scales in robot benchmarks, achieving high-performance vision-language-action understanding and execution.

MultimodalSafetensorsEnglish

This project is based on the Qwen2.5-VL-7B-Instruct model and focuses on visual question answering tasks. It can accurately answer questions related to images and has high accuracy and relevance. This is a multimodal vision-language model that supports image understanding and image-based question-answering interactions.

MultimodalSafetensorsEnglish

This is a model based on the 🤗 Transformers architecture. The specific type and function are to be confirmed with further information. The model has been released to the Hugging Face model hub and can be used for related tasks such as natural language processing.

Natural Language ProcessingTransformers

Qwen2.5-14B-Instruct is a large language model with 14 billion parameters, designed specifically for chat and text generation scenarios. This model is built based on the transformers library and is suitable for internal testing and lightweight application deployment.

Natural Language ProcessingTransformersEnglish

This is a vision-language model fine-tuned based on the unsloth/qwen2.5-vl-72b-instruct-bnb-4bit model. It is optimized and trained using the Unsloth and Huggingface TRL libraries, and the training speed is twice as fast as the traditional method.

MultimodalTransformersEnglish