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WaveCut/Nanbeige4.2-3B-heretic-MLX-DWQ-4bit

🤗 Hugging Face sourcetext-generationapache-2.04.2B params8.3 GBsafetensors✓ 2 checksumsupdated today
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Nanbeige4.2-3B Heretic MLX DWQ 4-bit

A 4-bit, group-size-32 Distilled Weight Quantization (DWQ) release of WaveCut/Nanbeige4.2-3B-heretic.

This repository includes a small trusted-code MLX-LM adapter because Nanbeige reuses 22 physical decoder layers over two loops and is not a standard Llama layout at runtime. The adapter preserves shared weights while allocating 44 independent KV caches, one for each loop/layer execution. Unsupported optional Nanbeige architectures are rejected explicitly.

DWQ calibration

  • 4 bits, group size 32.
  • 1,024 training samples and 32 validation samples.
  • Maximum sequence length: 1,025 tokens.
  • Seed: 20260722.
  • Corpus: 528 agentic trajectories plus 528 coding-reasoning examples, deterministically shuffled.
  • Corpus SHA-256: a7cfdbe02c124304bf1282bbd5ed7162bfa72dec6750b60ed2d3a68000c7a554.
  • Agentic source: TIGER-Lab/SWE-QA-Pro-SFT-Trajectories at b8f5b8a8dcf90bca8b6d70adedac0d20dca02b86.
  • Coding source: nvidia/OpenCodeReasoning at 20a1ca19c0d050fe9057fc08339d6b370ec1c67a.
Validation loss Value
Initial RTN 0.284
Final DWQ 0.043

MLX-LM revision: cf10f962b7a20e63a6df43dbf0faf06070153d40.

Usage

The model file is repository code, so load it only after reviewing nanbeige_mlx.py and pass --trust-remote-code.

mlx_lm.generate \
  --model WaveCut/Nanbeige4.2-3B-heretic-MLX-DWQ-4bit \
  --trust-remote-code \
  --prompt "Implement a bounded async worker pool in Python." \
  --max-tokens 256

Exact artifact hashes and clean-load smoke-test results are recorded in release-manifest.json.