README.md

---
language:
- en
- zh
library_name: mlx
license: other
license_name: modified-mit
pipeline_tag: text-generation
base_model: MiniMaxAI/MiniMax-M2
tags:
- moe
- mixture-of-experts
- minimax_m2
- quantized
- apple-silicon
- mlx
- turboquant
- jangtq
- jangtq2
- reap
---

<p align="center">
  <a href="https://osaurus.ai"><img src="./osaurus-x-banner.png" alt="Osaurus AI"></a>
</p>

<h3 align="center">MiniMax M2.7 Small &mdash; 138B-A10B &mdash; JANGTQ (MLX)</h3>
<p align="center"><b>This is now a ~138B-A10B MoE &mdash; 38 GB on disk</b> (down from MiniMax M2's ~460 GB / 230B base) &mdash; 40% routed-expert prune + 2-bit JANGTQ quantization. Distilled from MiniMax M2 via REAP saliency + JANGTQ2 codebook quantization &mdash; routed experts at 2-bit via Lloyd-Max codebooks + Hadamard rotation, attention / embed / lm_head / dense MLP at 8-bit affine, norms and router at 16-bit.</p>

<p align="center">
  <a href="https://osaurus.ai"><img src="https://img.shields.io/badge/Web-osaurus.ai-blue" alt="Website"></a>&nbsp;
  <a href="https://huggingface.co/OsaurusAI"><img src="https://img.shields.io/badge/HF-OsaurusAI-yellow?logo=huggingface" alt="OsaurusAI"></a>&nbsp;
  <a href="https://huggingface.co/MiniMaxAI/MiniMax-M2"><img src="https://img.shields.io/badge/Base-MiniMax--M2-orange?logo=huggingface" alt="MiniMax M2"></a>
</p>

---

## Model Details

Runs on Apple Silicon via the JANG toolchain + MLX.

```
MiniMax M2 (base)
    ↓  v3 calibration corpus  (code · agentic · general · academic · science · CN · cyber · systems · long-context)
    ↓
REAP saliency observer (62 layers × 256 experts → scoring)
    ↓  40% expert prune (154 of 256 kept per layer)
    ↓
JANGTQ2 quantization
    • 2-bit MXTQ on routed-expert weights (Hadamard-rotated Lloyd-Max codebook)
    • 8-bit affine on attention + dense MLP + embed + lm_head
    • 16-bit on norms and router weights
```

| | Value |
|---|---|
| Parameters | **~138B total, ~10B active per token** |
| Routed experts kept | 154 of 256 (60%) |
| Top-k active experts | 8 per token |
| Layers | 62 |
| Bundle size | 38 GB |
| Dtype | bfloat16 activations |
| Attention | Standard Q/K/V + GQA 6:1, head_dim=128, rope_theta=5M |
| Context | 196,608 |

## Use

```python
from jang_tools.load_jangtq import load_jangtq_model
from mlx_lm import generate
from mlx_lm.sample_utils import make_sampler

model, tokenizer = load_jangtq_model("OsaurusAI/MiniMax-M2.7-Small-JANGTQ")

messages = [{"role": "user", "content": "Write a Python function that…"}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, tokenize=False
)

# Interleaved-thinking / always-reasoning. Use MiniMax's
# official sampling:  temp=1.0, top_p=0.95, top_k=40
out = generate(model, tokenizer, prompt=prompt, max_tokens=4096,
               sampler=make_sampler(temp=1.0, top_p=0.95, top_k=40))
```

## Evaluation

### HumanEval+ (code generation)

- **Dataset**: `evalplus/humanevalplus` test split (164 prompts, harder tests than HumanEval).
- **Protocol**: sampled pass@1 baseline + pass@5 retry on failures.
- **Sampling for both pass@1 and pass@5 retry**: temp=1.0, top_p=0.95, top_k=40 (MiniMax official); max_tokens=5000 on pass@1, 1200 on pass@5; k=5 samples per failed problem, early stop on first pass.
- **Grading**: each candidate run with 20s subprocess timeout; must pass ALL EvalPlus tests.
- **Extractor**: `jang_tools.kimi_prune.bench_humaneval._extract_code` (≥ 2026-04-24). The earlier extractor mis-paired markdown fences when the model emitted token-boundary glitches at the language tag (e.g. `\`\`\`python一致:`, `\`\`\`pythonfr`) and when the chat template prefilled `<think>` at the prompt boundary, costing roughly nine points of pass@1.

| Metric | Score |
|--------|-------|
| **pass@1 (sampled, temp=1.0)** | **81.10%** (133/164) |
| **pass@5 (sampled, retry of failures)** | **90.24%** (148/164) |

After the extractor fix, 30 of 46 originally-counted pass@1 failures resolve cleanly: 15 were correct answers eaten by fence-pairing, and another 15 recover under pass@5 sampling. The 16 residuals split into ~8 token-budget starvations (`no_code_block`), ~5 in-code 2-bit token-boundary glitches (`return False言`, `Nonef`, etc.), and ~3 genuine logic errors on EvalPlus hidden tests.

## Variants

| Variant | Prune | Size | HF |
|---------|-------|------|-----|
| **MiniMax-M2.7-Small** | 40% | 38 GB | `OsaurusAI/MiniMax-M2.7-Small-JANGTQ` |
| MiniMax-M2.7-Med | 25% | ~48 GB | `OsaurusAI/MiniMax-M2.7-Med-JANGTQ` *(pending)* |
| MiniMax-M2.7-Large | 10% | ~57 GB | `OsaurusAI/MiniMax-M2.7-Large-JANGTQ` *(pending)* |

Also released under `JANGQ-AI/MiniMax-M2.7-*-JANGTQ`.

## Credits

Base model: [MiniMax M2](https://huggingface.co/MiniMaxAI/MiniMax-M2).
Methodology: [JANG toolchain](https://github.com/jinho-jang/jang) — REAP saliency + JANGTQ codebook quantization.
Served by: [Osaurus](https://osaurus.ai) — Apple-Silicon-native MLX inference.

## License

Modified MIT — inherited from MiniMax M2.