MiniMax-M2.7-Small-JANGTQ

⚠️ REQUIRED — jangtq_runtime.safetensors sidecar must be downloaded

Osaurus uses the native Swift JANGTQ runtime. Every JANGTQ bundle on OsaurusAI ships a small jangtq_runtime.safetensors sidecar (~10 KB–~165 KB) alongside the weight shards. The Swift loader will refuse to start with the error

Error: Model '<name>' declares JANGTQ (weight_format: "mxtq") but is
       missing required sidecar file 'jangtq_runtime.safetensors'.
       Re-download the full model or obtain the sidecar from the original
       publisher.

if the file is absent.

If your local copy doesn't have it (older download, partial sync, etc):

hf download OsaurusAI/MiniMax-M2.7-Small-JANGTQ jangtq_runtime.safetensors --local-dir <your-dir>

The file holds the deterministic codebooks + Hadamard rotation signs the Swift loader uses to decode *.tq_packed weights. It must match the seed the bundle was quantized with (mxtq_seed=42).

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

Use

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 ` at the prompt boundary, costing roughly nine points of pass@1.

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

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

Credits

Base model: MiniMax M2. Methodology: JANG toolchain — REAP saliency + JANGTQ codebook quantization. Served by: Osaurus — Apple-Silicon-native MLX inference.

License

Modified MIT — inherited from MiniMax M2.