This project is based on the moonshotai/Kimi-K2-Thinking base model. The original INT4 model is converted into a higher-quality text generation model through a custom anti-quantization script, aiming to make knowledge accessible to everyone.

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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.

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This is a quantized version model based on Moonshot AI technology, focusing on visual language understanding and generation tasks, and committed to lowering the threshold of knowledge acquisition and making knowledge accessible to everyone.

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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

This model is based on the Kimi-Linear-48B-A3B-Instruct large language model of moonshotai and is converted into an 8-bit quantized version suitable for Apple Silicon chips (MLX framework) using the mlx-lm tool. It is an instruction fine-tuned model with 48B parameters, designed specifically for following human instructions and dialogue interaction.

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This is a 4-bit quantized version converted from the moonshotai/Kimi-Linear-48B-A3B-Instruct model, optimized for the Apple MLX framework, providing efficient text generation capabilities.

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Kimi Linear is a hybrid linear attention architecture that outperforms traditional full attention methods in various scenarios, including short and long contexts, as well as reinforcement learning extension mechanisms. It can effectively solve the problem of low efficiency of traditional attention mechanisms in long context tasks and bring more efficient solutions to fields such as natural language processing.

Natural Language ProcessingTransformers

Kimi Linear is an efficient hybrid linear attention architecture that outperforms traditional full attention methods in short context, long context, and reinforcement learning scenarios. It optimizes attention computation through the Kimi Delta Attention (KDA) mechanism, significantly improving performance and hardware efficiency, and is particularly good at handling long context tasks up to 1 million tokens.

Natural Language ProcessingTransformers

This model is an MLX format version converted from the Kimi-K2-Instruct-0905 model of moonshotai using the mlx-lm 0.28.0 version. It adopts 3-bit quantization technology with a group size of 32. On average, each weight only occupies 4.002 bits, which optimizes the running efficiency of the model on Apple devices.

Natural Language ProcessingTransformers

This is a high-quality quantized version of Moonshot AI's Kimi-K2-Instruct-0905 model, using the HQ4_K quantization method. It is specifically optimized for inference performance, supports a context length of 75,000, and is suitable for text generation tasks.

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This is the MLX format conversion version of the moonshotai/Kimi-K2-Instruct-0905 model, using the innovative DQ3_K_M dynamic 3-bit quantization technology, specifically optimized for Apple Silicon Mac devices. It significantly reduces memory usage while maintaining performance close to 4-bit quantization.

Natural Language ProcessingMlx

This is a quantized version of the moonshotai/Kimi-K2-Instruct-0905 model, dedicated to making knowledge accessible to everyone. This project provides optimized model weights for easy deployment and use on various hardware.

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Based on the moonshotai/Kimi-K2-Instruct-0905 base model, a large language model dynamically quantized using the improved MLX 0.26. Through an innovative dynamic quantization strategy, it significantly reduces hardware requirements while maintaining excellent performance and can run efficiently on a single M3 Ultra device.

Natural Language ProcessingMlx

This is the GGUF format quantized version of the moonshotai/Kimi-K2-Instruct-0905 model, using the ik_llama.cpp branch for optimal quantization. The model adopts a mixture of experts architecture, supports Chinese dialogue and text generation tasks, and has been optimized with various quantization schemes to significantly reduce memory usage while maintaining high quality.

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This model is a 3-bit quantized version of the Kimi-K2-Instruct model in MLX format, optimized for efficient operation on Apple Silicon devices. It is converted from the Kimi-K2-Instruct model of moonshotai, maintaining the instruction-following and dialogue capabilities of the original model, while significantly reducing memory usage and improving inference speed through quantization.

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Kimi-VL-A3B-Thinking-2506 is a multimodal visual language model developed by MoonshotAI. It supports the joint understanding and reasoning of images and text, has the ability of chain-of-thought reasoning, and can handle complex visual language tasks.

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Kimi-K2 Dynamic MLX is a text generation project built on the moonshotai/Kimi-K2-Instruct model, using the optimized MLX library to achieve efficient quantization performance. This model runs on a single M3 Ultra machine with 512GB RAM, supports multiple quantization methods, and shows excellent perplexity metrics in tests.

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Kimi - K2 - Instruct is a quantized version based on the moonshotai/Kimi - K2 - Instruct model, aiming to make knowledge more widely accessible to the public.

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Kimi-Dev-72B-8bit is an 8-bit quantized version converted from moonshotai/Kimi-Dev-72B, a text generation model suitable for the MLX framework.

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Kimi-Dev-72B-4bit-DWQ is a 4-bit quantized large language model converted from moonshotai/Kimi-Dev-72B, suitable for the MLX framework.

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