mlx-community/Kimi-K2-Instruct-0905-mlx-DQ3_K_M

This model mlx-community/Kimi-K2-Instruct-0905-mlx-DQ3_K_M was converted to MLX format from moonshotai/Kimi-K2-Instruct-0905 using mlx-lm version 0.26.3.

This is created for people using a single Apple Mac Studio M3 Ultra with 512 GB. The 4-bit version of Kimi K2 does not fit. Using research results, we aim to get 4-bit performance from a slightly smaller and smarter quantization. It should also not be so large that it leaves no memory for a useful context window.

You can find more similar MLX model quants for Apple Mac Studio with 512 GB at https://huggingface.co/bibproj

pip install mlx-lm

mlx_lm.generate --model mlx-community/Kimi-K2-Instruct-0905-mlx-DQ3_K_M --temp 0.6 --min-p 0.01 --max-tokens 4096 --trust-remote-code --prompt "Hallo"

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What is this DQ3_K_M?

In the Arxiv paper Quantitative Analysis of Performance Drop in DeepSeek Model Quantization the authors write,

We further propose DQ3_K_M, a dynamic 3-bit quantization method that significantly outperforms traditional Q3_K_M variant on various benchmarks, which is also comparable with 4-bit quantization (Q4_K_M) approach in most tasks.

and

dynamic 3-bit quantization method (DQ3_K_M) that outperforms the 3-bit quantization implementation in llama.cpp and achieves performance comparable to 4-bit quantization across multiple benchmarks.

The resulting multi-bitwidth quantization has been well tested and documented.

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How can you create your own DQ3_K_M quants?

In the convert.py file of mlx-lm on your system ( you can see the original code here ), replace the code inside def mixed_quant_predicate() with something like

        index = (
            int(path.split(".")[layer_location])
            if len(path.split(".")) > layer_location
            else 0
        )
        # Build a mixed quant like "DQ3" of Arxiv paper https://arxiv.org/abs/2505.02390
        #    Quantitative Analysis of Performance Drop in DeepSeek Model Quantization
        q_bits = 4       
        if "lm_head" in path:
           q_bits = 6
        #if "tokens" in path:
        #   q_bits = 4
        if "attn.kv" in path:
           q_bits = 6
        #if "o_proj" in path:
        #   q_bits = 4
        #if "attn.q" in path:
        #   q_bits = 4
        # For all "mlp" and "shared experts"
        if "down_proj" in path:
           q_bits = 6
        #if "up_proj" in path:
        #   q_bits = 4
        #if "gate_proj" in path:
        #   q_bits = 4
        # For "switch experts"
        if "switch_mlp.up_proj" in path:
           q_bits = 3
        if "switch_mlp.gate_proj" in path:
           q_bits = 3
        if "switch_mlp.down_proj" in path:
           q_bits = 3
           # Blocks up to 5 are higher quality
           if index < 5:
              q_bits = 6
           # Every 5th block is "medium" quality
           if (index % 5) == 0:
              q_bits = 4
        #print("path:", path, "index:", index, "q_bits:", q_bits)
        return {"group_size": group_size, "bits": q_bits}

Should you wish to squeeze more out of your quant, and you do not need to use a larger context window, you can change the last part of the above code to

        if "switch_mlp.down_proj" in path:
           q_bits = 4
           # Blocks up to 5 are higher quality
           if index < 5:
              q_bits = 6
        #print("path:", path, "index:", index, "q_bits:", q_bits)
        return {"group_size": group_size, "bits": q_bits}

Then create your DQ3_K_M quant with

mlx_lm.convert --hf-path moonshotai/Kimi-K2-Instruct-0905 --mlx-path your-model-DQ3_K_M -q --quant-predicate mixed_3_4 --trust-remote-code

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Enjoy!