CodeV is a 7-billion-parameter vision-language model fine-tuned based on Qwen2.5-VL-7B-Instruct. Through two-stage training of supervised fine-tuning (SFT) and reinforcement learning (RL) based on tool-aware policy optimization (TAPO), it aims to achieve reliable and interpretable visual reasoning. It represents visual tools as executable Python code and ensures the consistency between tool usage and question evidence through a reward mechanism, solving the problem of irrelevant tool invocation under high accuracy.

MultimodalTransformersMultiple Languages

This is an open-source large language model with 20 billion parameters focusing on the field of network security. It is based on the GPT-OSS architecture and fine-tuned by integrating multiple network security instruction datasets. The model provides various quantization versions, which are convenient for deployment in resource-constrained environments and can be used to generate network security-related texts, codes, and analysis reports.

Natural Language ProcessingTransformersEnglish

OpenThinker-Agent-v1-SFT is an agent model obtained through supervised fine-tuning (SFT) based on Qwen/Qwen3-8B. It is the first-stage model of the complete training process (SFT + RL) of OpenThinker-Agent-v1, specifically optimized for agent tasks (such as terminal operations and code repair).

Natural Language ProcessingTransformersOther

Rnj-1-instruct is an 8-billion-parameter open-weight dense model trained from scratch by Essential AI. It is optimized for code, STEM fields, agent capabilities, and tool invocation. It performs excellently in multiple programming languages and also demonstrates strong capabilities in mathematics and science, comparable to the most advanced open-weight models.

Natural Language ProcessingTransformers

Zenyx_114M-Tiny-Edu-Instruct is an experimental small instruction fine-tuned language model with approximately 114 million parameters. It is built on the TinyEdu-50M base model, pre-trained on the FineWeb-Edu dataset, and fine-tuned on a mixed dataset of OpenHermes-2.5 and CodeFeedback-Filtered. This model aims to explore the limits of instruction fine-tuning under a minimal architecture and verify that the loss converges to approximately 1.04.

Natural Language ProcessingPytorchEnglish

Olmo-3-Think is a fully open-source language model series launched by the Allen Institute for Artificial Intelligence, available in two scales: 7B and 32B. This model is specially trained to demonstrate clear reasoning chains, support transparent step-by-step reasoning and inspectable intermediate thinking traces, and performs excellently in reasoning, mathematics, and code tasks.

Natural Language ProcessingTransformersEnglish

Fyodor-Q3-8B-Instruct is a high-fidelity instruction-tuned model designed for intelligent reasoning and robust code generation. It is based on the Qwen3-8B architecture and trained with a high-rank LoRA configuration. It excels in planning before coding and is well-suited for complex software engineering tasks.

Natural Language ProcessingTransformersMultiple Languages

Qsana-coder-base is a small language model (SLM) specifically designed for conceptual creativity in coding basics (Python, pseudo-code). It does not generate executable production code but rather generates coding logic snippets for educational and rapid prototyping scenarios. Its main goal is to stimulate the logical thinking and conceptual understanding of beginners.

Natural Language ProcessingTransformersFrench

DeepMath is a mathematical reasoning model with 4 billion parameters, built on Qwen3 - 4B Thinking, combining a fine - tuned large language model and a sandboxed Python executor. It can generate concise Python code snippets for calculation steps, significantly reducing errors and shortening the output length.

Natural Language ProcessingTransformersEnglish

Olmo 3 is a new generation of language model family developed by the Allen Institute for AI, including 7B and 32B instruction and thinking variants. This model performs excellently in long-chain thinking and can significantly improve the performance of reasoning tasks such as mathematics and coding. All code, checkpoints, and training details will be made public to promote the development of language model science.

Natural Language ProcessingTransformersEnglish

This model is a GGUF format model generated by in-depth fine-tuning of the Qwen3-Coder-30B-A3B-Instruct Mixture of Experts model using Brainstorm 20x technology. It is designed for programming and general purposes, supports a 256K long context, and is optimized for code generation and understanding of mainstream and niche programming languages.

Natural Language ProcessingGgufMultiple Languages

Rnj-1 is a series of 8 billion parameter, open-weight dense models trained from scratch by Essential AI. This model is optimized for the code and STEM fields, excelling in programming, mathematical reasoning, agent tasks, and tool invocation, with capabilities comparable to the most advanced open-weight models.

Natural Language ProcessingTransformers

This is the GGUF quantized version of the Qwen/Qwen3-Coder-30B-A3B-Instruct language model, specifically optimized for code generation and programming tasks. It is converted using FP32 precision and is suitable for local inference deployment.

Natural Language ProcessingGgufMultiple Languages

Qwen3-Coder-REAP-363B-A35B is a sparse mixture-of-experts model obtained by pruning 25% of the experts from Qwen3-Coder-480B-A35B-Instruct using the REAP method. While maintaining performance close to the original model, it significantly reduces the parameter scale and memory requirements, making it particularly suitable for code generation and intelligent coding scenarios with limited resources.

Natural Language ProcessingTransformersEnglish

Qwen3-Coder-480B-A35B-Instruct is a large code generation model with 480 billion parameters, supporting 8.5-bit quantization and optimized based on the MLX framework. This model is specifically designed for code generation tasks and can run efficiently on devices with sufficient memory.

Natural Language ProcessingMlxEnglish

This project is the result of MXFP4_MOE quantization of the Qwen3-Coder-30B-A3B-Instruct model. It extends the context size from 256k to 1M, providing a more optimized model version for code generation and programming tasks, with the value of improving performance and saving resources.

MultimodalGguf

JanusCoder-14B is an open-source foundation model with 14B parameters built on Qwen3-14B, aiming to establish a unified visual programming interface for code intelligence. This model is trained on the JANUSCODE-800K multimodal code corpus and can handle various visual programming tasks in a unified manner.

Natural Language ProcessingTransformers

JanusCoder-8B is an open-source code intelligence foundation model built on Qwen3-8B, aiming to establish a unified visual programming interface. This model is trained on JANUSCODE-800K (the largest multimodal code corpus to date) and can handle various visual programming tasks, including data visualization, interactive web UI, and code-driven animation.

MultimodalTransformers

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

JanusCoderV-7B is a 7B parameter code generation model developed by InternLM. This project provides multiple versions of imatrix quantization using llama.cpp, supporting various quantization levels to meet different hardware requirements.

Natural Language ProcessingGguf