Gemma 3 is a lightweight open-source multimodal model launched by Google. This version is an instruction-tuned quantization-aware training model with 12B parameters, which has been converted to the MXFP4 format of the MLX framework. It supports text and image input and generates text output, with a 128K context window and support for over 140 languages.

MultimodalMlx

The top model in the aquif-3.5 series, with advanced inference capabilities and a large context window of 1 million tokens, and has excellent performance in multiple benchmark tests. The comprehensive AAII score reaches 60 points.

Natural Language ProcessingTransformersMultiple Languages

The aquif-3.5 series is the masterpiece released on November 3, 2025, including two versions, Plus and Max, which provide advanced inference capabilities and an unprecedented 1 million token context window, achieving state-of-the-art performance in their respective categories.

Natural Language ProcessingTransformersMultiple Languages

Kimi K2 Thinking is the latest generation of open-source thinking model developed by Moonshot AI. It has powerful deep reasoning ability and tool call function. The model adopts a mixture-of-experts architecture, supports native INT4 quantization, has a 256k context window, and performs excellently in multiple benchmark tests.

Natural Language ProcessingTransformers

The MLX MXFP4 quantized version of Gemma 3 27B IT QAT is a lightweight open - source multimodal model developed by Google. This model can simultaneously process text and image inputs and generate text outputs. It has a large context window of 128K and supports over 140 languages, making it suitable for various text generation and image understanding tasks.

MultimodalMlx

This is a high-performance 4-bit quantized version of the Kimi K2 Instruct model, optimized for running on Apple Silicon (M1/M2/M3/M4) Macs using the MLX framework. The model has 671 billion parameters, supports a 128K context window, and achieves an excellent balance between quality and efficiency, making it an ideal choice for most practical deployments.

Natural Language ProcessingMlxMultiple Languages

This is a 24B parameter efficient inference model built on Mistral Small 3.2 and converted to MLX-MXFP4 format. The model has enhanced inference capabilities, supports multimodal input, has a 128k context window, and can run on an RTX 4090 or a MacBook with 32GB RAM.

MultimodalMlxMultiple Languages

GLM-4.6-AWQ is a quantized version based on the zai-org/GLM-4.6 base model, with efficient text generation capabilities. Compared with GLM-4.5, this model has significant improvements in aspects such as context window, encoding performance, reasoning ability, and agent functions.

Natural Language ProcessingTransformers

GLM-4.6 is a new-generation text generation model launched by Zhipu AI. Compared with GLM-4.5, it has significant improvements in multiple aspects, including a longer context window, better coding performance, and stronger reasoning ability. This model performs excellently in multiple public benchmark tests and has a competitive advantage compared with leading models at home and abroad.

Natural Language ProcessingTransformersMultiple Languages

GLM-4.6-FP8 is a new-generation text generation model launched by Zhipu AI. It has significant improvements in multiple key aspects compared to GLM-4.5. This model has a longer context window, excellent coding performance, advanced reasoning ability, and more powerful agent functions, enabling it to handle more complex tasks.

Natural Language ProcessingTransformersMultiple Languages

Gemma 3 270M is a lightweight multimodal model launched by Google. Based on the same research technology as the Gemini series, it supports text and image input, has a 32K context window, and provides high-quality output in tasks such as question answering, summarization, image understanding, and code generation.

MultimodalTransformersMultiple Languages

Gemma 3 is a lightweight open-source multimodal model series launched by Google, built on the same technology as Gemini. The 270M version is the smallest model in the series, supporting text and image input, with a 128K context window, suitable for deployment in resource-constrained environments.

MultimodalTransformers

Gemma 3 is a lightweight open-source multimodal model family launched by Google, capable of processing text and image inputs and generating text outputs. It has a 128K large context window, supports over 140 languages, and is suitable for various tasks such as text generation and image understanding.

MultimodalTransformers

Gemma 3 is a lightweight and state-of-the-art multimodal open model family launched by Google, built on the same research and technology as the Gemini model. This model can handle text and image inputs and generate text outputs. It has a large context window of 128K and supports over 140 languages, offering various sizes ranging from 270M to 27B.

MultimodalTransformers

Gemma 3 is a lightweight, state-of-the-art multimodal open model family launched by Google. It can handle text and image inputs and generate text outputs. It has a 128K large context window, supports over 140 languages, and offers a variety of scale options from 270M to 27B, suitable for various text generation and image understanding tasks.

MultimodalTransformers

Qwen3 code reasoning is a compact model with 4 billion parameters, fine-tuned on nvidia/OpenCodeReasoning, and specifically designed for coding and logical reasoning tasks. This model performs excellently in code generation and logical Q&A, and supports a context window of over 10,000 tokens.

Natural Language ProcessingTransformersEnglish

SnowflakeCore-G1-Tiny2 is a custom Transformer language model based on the GPT style and is an improved version of SnowflakeCore-G1-Tiny. This model is built from scratch using PyTorch and trained on the common-pile/wikimedia_filtered dataset. It has approximately 400 million parameters, supports a 2048-token context window, and is specifically designed for text generation tasks.

Natural Language ProcessingTransformersEnglish

Devstral 1.1 is an intelligent large language model jointly developed by Mistral AI and All Hands AI, specifically designed for software engineering tasks. This model performs excellently in the SWE-bench benchmark test, ranking first among open-source models. It has 24 billion parameters and a long context window of 128k tokens.

Natural Language ProcessingSafetensorsMultiple Languages

An efficient language model specifically designed for software engineering projects, featuring a lightweight design and supporting a 128k large context window, suitable for complex coding tasks.

Natural Language ProcessingGgufMultiple Languages

Qwen3-4B is the latest generation large language model in the Qwen series, featuring 4B parameters, supporting a 128k context window and over 100 languages, with outstanding performance in reasoning, instruction following, and agent capabilities.

Natural Language ProcessingLlamafile