This project provides a static quantized version of the Qwen-4B-Instruct-2507-Self-correct model, supporting tasks such as text generation, bias mitigation, and self-correction. Based on the Qwen-4B architecture, the model has undergone instruction fine-tuning and self-correction training, offering multiple quantized versions to meet different hardware requirements.

Natural Language ProcessingTransformersEnglish

FLAN-T5 Small is an instruction fine-tuned text generation model developed by Google, based on the small version of the T5 architecture. This model has undergone instruction fine-tuning on various NLP tasks and can understand and execute various text generation instructions.

Natural Language ProcessingSafetensorsEnglish

nutrient-gram-qwen-3-vl-2b is a fine-tuned version based on Qwen/Qwen3-VL-2B-Instruct, trained using TRL for supervised fine-tuning, mainly used for text generation tasks. This model is optimized for specific tasks while maintaining its original multimodal capabilities.

Natural Language ProcessingTransformers

This is a text generation model optimized using PEFT (Parameter-Efficient Fine-Tuning) and LoRA techniques based on the Google Gemma-2B-IT base model. The model is optimized for specific tasks while maintaining the capabilities of the base model through parameter-efficient methods.

Natural Language ProcessingPeft

SDXL-Deepfake-Detector is a tool for accurately detecting AI-generated faces. It focuses on maintaining the authenticity of the digital world and provides a privacy-protected and open-source solution to combat visual misinformation. This model achieves lightweight and highly accurate detection through fine-tuning a pre-trained model.

Computer VisionSafetensors

Hebrew_Nemo is a large language model focused on Hebrew processing. It has undergone instruction fine-tuning and supports text generation and dialogue interaction. This version is the GGUF quantized version of the original model, offering multiple quantization levels.

Natural Language ProcessingTransformersMultiple Languages

Lodos-24B-Instruct-2510 is a large Turkish language model fine-tuned based on Mistral-Small-3.2-24B-Instruct-2506. It uses the TRL framework for full supervised fine-tuning and is specifically optimized for Turkish instruction following tasks, supporting long text generation and complex reasoning.

Natural Language ProcessingTransformersOther

This is the GGUF quantized version of the AvitoTech/avibe model. It is based on the Qwen3-8B base model and undergoes Russian supervised fine-tuning. A custom tokenizer is used to optimize the Russian processing ability, and it is specifically designed for Russian text generation tasks.

Natural Language ProcessingGgufMultiple Languages

This is a text generation model that is specifically fine-tuned based on the Google Gemma-3 model. After training, it can use external tools to generate step-by-step plans. This model is trained on the rewoo/planner_instruction_tuning_2k dataset and uses LoRA technology for efficient fine-tuning.

Natural Language ProcessingTransformers

Granite-4.0-1B is a lightweight instruction model developed by IBM, fine-tuned based on Granite-4.0-1B-Base. This model combines open-source instruction datasets and internal synthetic datasets, and is developed using techniques such as supervised fine-tuning, reinforcement learning, and model merging. It is suitable for device-side deployment and research use cases.

Natural Language ProcessingTransformers

Granite-4.0-350M is a lightweight instruction model developed by IBM, fine-tuned based on Granite-4.0-350M-Base. This model combines open-source instruction datasets and internal synthetic datasets, and is developed using technologies such as supervised fine-tuning, reinforcement learning, and model fusion. It has powerful instruction-following capabilities and is particularly suitable for device-side deployment and research scenarios.

Natural Language ProcessingTransformers

An instruction fine-tuning model with 80 billion parameters based on Qwen3-Next, using Deckard qx64n mixed-precision quantization technology, supporting a context length of 1 million, and performing excellently in abstract reasoning, memory efficiency, and long-context processing.

Natural Language ProcessingMlx

LoRa fine-tuning based on the Qwen-Edit-2509 model, specifically designed for image fusion tasks. It can excellently correct the perspective and lighting of products, enabling products to blend perfectly into the background.

Computer VisionDiffusers

Qwen-SEA-LION-v4-32B-IT is a large Southeast Asian language model built on Qwen3-32B, specifically pre-trained and instruction fine-tuned for the Southeast Asian region. This model continues pre-training on the SEA-Pile v2 corpus containing 7 Southeast Asian languages and undergoes instruction fine-tuning on 8 million pairs of high-quality question-and-answer data, possessing strong multilingual understanding and reasoning abilities.

Natural Language ProcessingTransformersMultiple Languages

xLSTM-7b-Instruct is an experimental fine-tuned version based on NX-AI/xLSTM-7b, specifically optimized for instruction-following tasks. This model adds support for chat templates and uses TRL for supervised fine-tuning training, aiming to provide a better conversational interaction experience.

Natural Language ProcessingTransformers

Qwen3-VL-4B-Instruct is a 4-billion-parameter instruction-tuned multimodal large language model launched by the Alibaba Cloud Qwen team. It is optimized for Qualcomm NPUs, integrating powerful visual-language understanding capabilities with dialogue fine-tuning functions, and is suitable for practical application scenarios such as chat reasoning, document analysis, and visual dialogue.

MultimodalGguf

Qwen3-14B is a powerful language model that performs excellently in the field of text generation. This model has excellent general capabilities and offers an instruction variant that is friendly to fine-tuning, facilitating the further optimization and application of the model.

Natural Language ProcessingTransformers

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

Granite-4.0-350M is a lightweight instruction model developed by IBM, fine-tuned based on Granite-4.0-350M-Base. This model combines open-source instruction datasets and internal synthetic datasets, and is developed using supervised fine-tuning, reinforcement learning, and model merging techniques. It has powerful instruction-following capabilities and tool invocation functions.

Natural Language ProcessingTransformers

Granite-4.0-1B is a lightweight instruction model developed by IBM. It is fine-tuned based on Granite-4.0-1B-Base, combining open-source instruction datasets and internal synthetic datasets, and developed using supervised fine-tuning, reinforcement learning, and model merging techniques.

Natural Language ProcessingTransformers