How to use from the
Use from the
MLX library
# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("ZQ-Dev/KAT-Coder-V2.5-Dev-oQ4e-fp16")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

KAT-Coder-V2.5-Dev-oQ4e

A 4-bit enhanced oQ quantization of Kwaipilot/KAT-Coder-V2.5-Dev for Apple Silicon and MLX-compatible runtimes.

Quantization details

The full calibration summary is available in oq_imatrix_report.json.

Note: This quant was created using float16 non-quant weights. Per oMLX, float16 gives ~20% faster prefill on M1/M2 Apple Silicon (native fp16). bfloat16 is safer on M3+ and for numerical stability. For the standard oQe with bfloat16, see this repo.

License

Apache 2.0, inherited from the base model.

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6B params
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F16
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4-bit

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