GigaChat3-10B-A1.8B is an efficient dialogue model in the GigaChat series. Based on the Mixture-of-Experts (MoE) architecture, it has a total of 10 billion parameters and 1.8 billion active parameters. It adopts the innovative Multi-head Latent Attention (MLA) and Multi-token Prediction (MTP) technologies, aiming to optimize inference throughput and generation speed. The model is trained on 20T tokens of diverse data and supports 10 languages including Chinese, suitable for dialogue scenarios requiring quick responses.

Natural Language ProcessingGgufMultiple Languages

This model is an MLX format conversion version of the Ministral-3-3B-Instruct-2512 instruction fine-tuning model released by Mistral AI. It is a large language model with a parameter scale of 3B, specifically optimized for following instructions and dialogue tasks, and supports multiple languages. The MLX format enables it to run efficiently on Apple Silicon devices.

Natural Language ProcessingMlxMultiple Languages

This model is an MLX format conversion version of Kimi-Linear-48B-A3B-Instruct, optimized for Apple Silicon devices such as the Apple Mac Studio. It is a large language model with 48 billion parameters, supporting instruction following and suitable for local inference and conversation tasks.

Natural Language ProcessingMlx

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

This is an automatic speech recognition model optimized for Apple silicon chip devices. By converting to the MLX framework and quantizing to the FP8 format, it enables fast on-device speech transcription on Apple devices. The model is fine-tuned for verbatim accuracy and is particularly suitable for scenarios requiring high-precision transcription.

Audio ProcessingMlxOther

This is the GGUF quantized version of the ai-sage/GigaChat3-10B-A1.8B-bf16 model, offering a variety of quantization options, from high-precision Q8_0 to extremely compressed smol-IQ1_KT, to meet the deployment requirements under different hardware conditions. This model supports a 32K context length, adopts the MLA architecture, and is optimized for dialogue scenarios.

Natural Language ProcessingGgufOther

This model is an 8-bit quantized version converted from allenai/Olmo-3-7B-Instruct, specifically optimized for the Apple MLX framework. It is a large language model with 7 billion parameters, supporting instruction following and dialogue tasks.

Natural Language ProcessingMlxEnglish

The 4-bit quantized version of VibeThinker-1.5B, optimized for Apple chips based on the MLX framework, is a dense language model with 1.5 billion parameters, specifically designed for mathematical reasoning and algorithm coding problems.

Natural Language ProcessingMlxMultiple Languages

GigaChat3-10B-A1.8B-base is the basic pre-trained model of the GigaChat series, adopting the Mixture of Experts (MoE) architecture with a total of 10 billion parameters and 1.8 billion active parameters. The model integrates Multi-Head Latent Attention (MLA) and Multi-Token Prediction (MTP) technologies, and has the advantage of high throughput during inference.

Natural Language ProcessingSafetensorsMultiple Languages

This model is an MLX format model converted from summykai/gemma3-27b-abliterated-dpo using version 0.28.3 of mlx-lm. It is a 27B parameter Gemma 3 large language model fine-tuned with DPO (Direct Preference Optimization), optimized for efficient operation on Apple Silicon (MLX framework).

Natural Language ProcessingTransformersEnglish

Kimi - K2 - Thinking 3.825bit MLX is a quantized model for text generation. It achieves different perplexity performances in tests through different quantization methods. Among them, q3.825bit quantization can reach a perplexity of 1.256.

Natural Language ProcessingMlxEnglish

This model is an MLX format conversion version of the instruction fine-tuned version of Falcon-H1-34B-Instruct, optimized specifically for Apple Silicon (M series chips). It is based on the original Falcon-H1-34B-Instruct model and converted to an 8-bit quantization format compatible with the MLX framework through the mlx-lm tool, aiming to achieve efficient local inference on macOS devices.

Natural Language ProcessingTransformersMultiple Languages

This model is a fine-tuned version based on meta-llama/Llama-3.3-70B-Instruct. It is trained using the mlabonne/FineTome-100k dataset, which contains 100k token data. The model is fine-tuned using the Unsloth and Huggingface TRL libraries and supports English language processing.

Natural Language ProcessingTransformersEnglish

A text generation model developed based on the MLX library, focusing on natural language processing tasks and providing developers with an efficient text generation solution.

Natural Language ProcessingMlxEnglish

A text generation model implemented based on the MLX library, supporting inference in multiple quantization methods, with distributed computing capabilities, and can run efficiently in the Apple hardware environment.

Natural Language ProcessingMlxEnglish

Kimi-K2-Thinking is a large language model in MLX format converted by mlx-community from the original model of moonshotai. The conversion is carried out using version 0.28.4 of mlx-lm, retaining the chain of thought reasoning ability of the original model.

Natural Language ProcessingMlx

This is a text-to-speech model optimized based on the MLX framework, converted from the original model Marvis-AI/marvis-tts-100m-v0.2. It uses 6-bit quantization technology and is specifically optimized for Apple Silicon hardware, providing efficient speech synthesis capabilities.

Audio ProcessingTransformersMultiple Languages

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 performs 4-bit quantization on the MiniMax-M2 model using the DWQ (Dynamic Weight Quantization) method with the help of the mlx-lm library. This model is a lightweight version of MiniMax-M2, significantly reducing the model size while maintaining good performance.

Natural Language ProcessingMlx

This is a 6-bit quantized version converted from the Kimi-Linear-48B-A3B-Instruct model, optimized for the Apple MLX framework. The model retains the powerful instruction-following ability of the original model, while significantly reducing storage and computational requirements through quantization technology, making it suitable for efficient operation on Apple hardware.

Natural Language ProcessingMlx