Nanbeige4-3B-Thinking is a 3-billion parameter inference model in the fourth-generation Nanbeige large language model family. It achieves advanced inference capabilities by improving data quality and training methods. This model performs excellently in multiple fields such as mathematics, science, creative writing, and tool usage, and supports multi-stage curriculum learning and reinforcement learning training.

Natural Language ProcessingTransformersMultiple Languages

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

The INL architecture is a production-level neural architecture based on integral neuron dynamics, replacing traditional feed-forward network layers with iterative dynamics. It is suitable for various types of models such as large language models, vision transformers, and multi-modal models.

Natural Language ProcessingSafetensorsEnglish

Qwen3-VL-8B-Thinking is an 8-billion-parameter multimodal large language model developed by the Qwen team at Alibaba Cloud. It is designed for deep multimodal reasoning, supporting visual understanding, long context processing, and structured chain-of-thought generation, and performs excellently in complex reasoning tasks.

MultimodalMlx

dParallel is a learnable parallel decoding method for large language models that achieves fast sampling by exploiting the intrinsic parallelism of the model. This method can significantly reduce the decoding steps while maintaining the model performance, achieving 8.5 - 10.5 times acceleration in benchmarks such as GSM8K and MBPP.

Natural Language ProcessingTransformers

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

This is a quantized version of a large language model with 72B parameters. It uses the imatrix technology of llama.cpp for optimized quantization processing, providing multiple quantization levels to adapt to different hardware conditions and supporting operation in environments such as LM Studio and llama.cpp.

Natural Language ProcessingGguf

Bee-8B is an advanced fully open-source multimodal large language model that focuses on data quality and aims to narrow the performance gap with proprietary models. By using the high-quality Honey-Data-15M corpus and the advanced data processing pipeline HoneyPipe, it demonstrates excellent performance in complex reasoning and other aspects.

MultimodalTransformersEnglish

A2Search-3B-Instruct is a static quantized version based on zfj1998/A2Search-3B-Instruct, offering multiple quantization type options. This is a large language model with 3 billion parameters, specifically optimized for search and instruction - following tasks, and supports English language processing.

Natural Language ProcessingTransformersEnglish

KAT-Dev-72B-Exp is an open-source large language model with 72 billion parameters for software engineering tasks, achieving an accuracy of 74.6% in the SWE-Bench Verified benchmark test. It is an experimental reinforcement learning version of the KAT-Coder model.

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

UserLM-8b-Q8_0-GGUF is a GGUF format version converted from the Microsoft UserLM-8b model, a large language model specifically optimized for user conversation scenarios. This model uses 8-bit quantization technology, significantly reducing the model size while maintaining performance, making it easy to deploy and run on various hardware.

Natural Language ProcessingGgufEnglish

Qwen3-Next-80B-A3B-Instruct is a cutting-edge large language model in the Qwen3-Next series. It improves parameter efficiency and inference speed through an innovative architecture and demonstrates excellent performance in multiple domains. It natively supports long context processing.

Natural Language ProcessingTransformers

Apertus is a fully open multilingual large language model developed by Swiss AI, offering two parameter scales of 7 billion and 8 billion. This model supports over 1000 languages, uses fully compliant and open training data, and its performance can rival that of closed-source models. Apertus was pre-trained on 15T tokens and adopts a phased curriculum training method, supporting a context length of up to 65,536 tokens.

Natural Language ProcessingTransformersOther

Apertus is a fully open multilingual large language model developed by Swiss AI. It has parameter scales of 8 billion and 70 billion, supports over 1000 languages and long context processing, and only uses fully compliant open training data. Its performance can rival that of closed-source models.

Natural Language ProcessingTransformersOther

UIGENT-30B-Lora-200 is a large language model fine-tuned based on Qwen3-Coder-30B-A3B-Instruct. It is specifically optimized for text generation tasks and is particularly good at handling HTML-related content. It also has various text generation capabilities.

Natural Language ProcessingTransformers

OpenLLaMA 3B v2 is an open-source large language model based on the Transformer architecture, with 3 billion parameters. This model uses the MIT license and is mainly used for English text generation tasks, supporting various application scenarios such as chatbots.

Natural Language ProcessingPeftEnglish

Sungur-14B is a large language model specifically designed for Turkish, derived from Qwen/Qwen3-14B. The model is fine-tuned using a dataset containing 41,100 Turkish samples, covering reasoning dialogues in fields such as mathematics, medicine, and general knowledge, aiming to enhance the native reasoning ability in Turkish.

Natural Language ProcessingGgufOther

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

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