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

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

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

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

Lamapi/next-12b is a large language model based on 12B parameters. It is fine-tuned with a multilingual dataset, supports more than 50 languages, has efficient text generation capabilities, and is suitable for natural language processing tasks in multiple fields such as chemistry, code, biology, finance, and law.

Natural Language ProcessingTransformersMultiple Languages

Granite-4.0-H-350M-Base is a lightweight decoder-only language model developed by IBM, designed for resource-constrained devices, supporting multilingual text generation and code completion functions.

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

AesCoder-4B is a large language model focused on improving the aesthetic quality of code. It optimizes the aesthetic performance of code generation through an intelligent reward feedback mechanism and performs well in visual coding tasks such as web design and game development.

Natural Language ProcessingTransformers

This model is a GGUF format conversion version of cerebras/Qwen3-Coder-REAP-25B-A3B, which uses the llama.cpp tool for format conversion and quantization processing. The original model is a large language model with 25B parameters, specifically optimized for code generation tasks.

Natural Language ProcessingTransformersEnglish

Granite-4.0-H-Tiny AWQ - INT4 is a 4-bit version of the Granite-4.0-H-Tiny base model processed by AWQ quantization. It significantly reduces resource consumption while maintaining performance. This model has 7 billion parameters, supports multilingual processing, and has rich functions including text generation, code completion, and tool invocation.

Natural Language ProcessingTransformers

This is the first - generation powerful vibe - code large language model, optimized to directly generate natural language and code completion based on loosely structured 'atmosphere coding' prompts. Compared with early models, it has lower prompt engineering overhead, smoother latent space interpolation, and is easier to guide the generation of usable code.

Natural Language ProcessingTransformersEnglish

Granite-4.0-350M-Base is a lightweight decoder-only language model developed by IBM, designed specifically for resource-constrained devices, supporting multilingual text generation and code completion functions.

Natural Language ProcessingTransformers

Granite-4.0-1B-Base is a lightweight decoder-only language model developed by IBM, designed specifically for resource-constrained devices, supporting multilingual text generation and code completion functions.

Natural Language ProcessingTransformers

Granite - 4.0 - H - 350M - Base is a lightweight decoder - only language model developed by IBM, specifically designed for resource - constrained devices. It supports multi - language text generation and code completion functions and can run efficiently on smartphones or IoT devices.

Natural Language ProcessingTransformers

Granite-4.0-H-1B-Base is a lightweight decoder-only language model developed by IBM, designed specifically for resource-constrained devices, supporting multi-language text generation and code completion functions.

Natural Language ProcessingTransformers

GLM-4.6 is a new-generation large language model developed by Zhipu AI. Compared with GLM-4.5, it has significant improvements in context processing, coding ability, and reasoning performance. This model supports a context length of 200K and performs excellently in multiple public benchmark tests, especially having competitive advantages in code generation, reasoning, and agent tasks.

Natural Language ProcessingTransformersMultiple Languages