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

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

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 a mixture-of-experts model based on Qwen3-Coder-30B-A3B-Instruct, with 54 billion parameters and a context length of 1 million. The model has powerful programming capabilities and general scenario processing capabilities through three-step merging and Brainstorm 40X optimization. It is especially integrated with a thinking module, which can perform in-depth reasoning before answering.

Natural Language ProcessingTransformersMultiple Languages

Mellum-4b-dpo-all is a large language model with 4 billion parameters developed by JetBrains, specifically designed for code generation and understanding. After three stages of training: pre - training, SFT, and Direct Preference Optimization (DPO), it can generate high - quality and readable code, supporting multiple programming languages.

Natural Language ProcessingTransformersOther

This is the GGUF quantized version of the Qwen3-Coder-30B-A3B-Instruct language model, specifically optimized for code generation and programming tasks. It supports multiple quantization levels to meet different hardware requirements.

Natural Language ProcessingGgufMultiple Languages

This is the GGUF quantized version of the Qwen/Qwen3-Coder-30B-A3B-Instruct language model, optimized for local inference and supporting frameworks such as llama.cpp, LM Studio, OpenWebUI, and GPT4All. This model is a code generation and programming assistant model with a parameter scale of 30B.

Natural Language ProcessingGgufMultiple Languages

Qwen3-Reranker-8B is the latest text re-ranking model in the Qwen family, with 8 billion parameters, designed specifically for text retrieval and sorting tasks. This model performs excellently in multilingual text re-ranking tasks, supporting over 100 languages, including various programming languages, and has achieved leading performance on the MTEB multilingual leaderboard.

Natural Language ProcessingTransformers

MobileLLM-R1 is an efficient inference model series released by Meta, including three scales of 140M, 360M, and 950M. This model is specifically optimized for mathematical, programming, and scientific problems, achieving comparable or even better performance than large-scale models with a smaller parameter scale.

Natural Language ProcessingTransformersEnglish

MobileLLM-R1 is a series of efficient inference language models released by Meta, focusing on solving mathematical, programming, and scientific problems. This model can still achieve excellent performance with a relatively small parameter scale, and provides a complete training recipe and data sources to support reproducible research.

Natural Language ProcessingTransformersEnglish

MobileLLM-R1 is a series of efficient inference models launched by Facebook, focusing on solving mathematical, programming, and scientific problems. This model has achieved excellent performance in multiple benchmark tests with only about 2T high-quality labels for pre-training.

Natural Language ProcessingTransformersEnglish

MobileLLM-R1 is a series of efficient inference models focused on mathematics, programming, and scientific problems, achieving excellent performance with less training data and providing a complete training recipe and data source.

Natural Language ProcessingTransformersEnglish

MobileLLM-R1 is a series of efficient reasoning models launched by Meta, focusing on solving mathematical, programming, and scientific problems. The model offers three scale versions of 140M, 360M, and 950M, with excellent reasoning ability and reproducibility.

Natural Language ProcessingTransformersEnglish

MobileLLM-R1 is an efficient inference model in the MobileLLM series, specifically optimized for mathematics, programming, and scientific problems. It achieves higher accuracy with a smaller parameter scale, featuring low training cost and high efficiency.

Natural Language ProcessingTransformersEnglish

Instinct is a code editing model based on Qwen2.5-Coder-7B. It has been fine-tuned on real-world code editing datasets and can intelligently predict the developer's next operation to maintain a smooth programming state. This GGUF quantized version is created by QuantFactory and provides efficient local inference capabilities.

Natural Language ProcessingTransformers

This is the GGUF quantized version of the Qwen3-Code-Reasoning-4B model, specifically optimized for competition programming and code reasoning tasks. The model is trained on a high-quality code reasoning dataset, enhancing its ability to solve complex programming problems through detailed reasoning.

Natural Language ProcessingGgufEnglish