JANG_6M build of Nanbeige/Nanbeige4.2-3B — a 4.17B-parameter Looped Transformer reasoning model (en + zh, 256K context), quantized for Apple Silicon with 6-bit affine weights with 8-bit attention. This is the quality build — the most faithful of the three by a wide margin.

⚠️ This architecture needs a loader that knows about the loop

num_loops = 2: the same 22 decoder layers run twice over shared weights, for an effective depth of 44. Two consequences a generic loader gets wrong:

The KV cache has 44 slots, not 22 (slot = layer_idx + loop_idx * num_hidden_layers). A 22-slot cache does not crash — it emits fluent, confident, wrong tokens from the first one. This is verified with a negative control, not theory.

The final norm runs at the end of every loop, not once at the end (skip_loop_final_norm = false). Loop 0's normed output is loop 1's input.

mlx_lm 0.31.x has no nanbeige model class, so mlx_lm.generate alone will not load this bundle. Use a runtime that implements the loop (see Usage).

Bundle

Field	Value
Source	`Nanbeige/Nanbeige4.2-3B` @ `fab06df` (Apache-2.0)
Architecture	`nanbeige` — 22 layers × 2 loops (effective depth 44), 4.17B params, 256K ctx
On-disk size	3.6 GB (1 shard)
Quantization	attention 8-bit · MLP 6-bit · embeddings/lm_head 6-bit, group size 32
Norms, router bias	fp16 passthrough — plain Llama RMSNorm, no +1 shift
Attention	48 heads / 8 KV heads (GQA), `head_dim` 128 — note `n_heads × head_dim` (6144) ≠ `hidden_size` (3072)
RoPE	θ = 7e7, NeoX half-rotation, full 128 dims, no scaling
Modality	text-only (verified from the tensor index)

Measured (M5 Max, 4-turn gate with a persistent 44-slot cache)

Metric	Thinking on	Thinking off
Decode	29.3 tok/s	38.0 tok/s
Peak memory	4.5 GB	3.9 GB

Fidelity vs the bf16 source (5-prompt logit sweep): top-1 agreement 5/5, mean KL 0.0010, max KL 0.0030.

Profile comparison

Bundle	Size	Top-1 agreement vs bf16	Mean KL	Max KL	Decode (thinking)
`Nanbeige4.2-3B-JANG_6M`	3.6 GB	5/5	0.0010	0.0030	29.3 tok/s
`Nanbeige4.2-3B-JANG_4M`	2.9 GB	5/5	0.0192	0.0398	44.7 tok/s
`Nanbeige4.2-3B-MXFP8`	4.0 GB	4/5	0.1446	0.6844	34.6 tok/s

Both JANG affine profiles beat MXFP8 on fidelity while being smaller — the opposite of what the bit counts suggest. MXFP8's e4m3 elements carry ~3 mantissa bits each, so "8-bit MX" is not strictly better than 6-bit or 4-bit affine with a per-group scale and bias on this weight distribution. It showed up in behaviour too: in the multi-turn gate MXFP8 dated Tokyo's capital move to 1936, where both JANG builds said 1868.

Chat / reasoning

Thinking is ON by default. The generation prompt ends with an open <think>\n; only enable_thinking=False prefills a closed <think>\n\n</think>\n\n.
preserve_thinking controls whether previous turns' reasoning is kept. The template's default is to preserve; the vendor recommends False for general chat and True for multi-turn tool use and code-agent workflows.
Tool calls default to tool_call_format="xml" (the vendor's recommended format); json is supported for compatibility.
Double-BOS trap: the chat template already emits <|im_start|> (id 166100 = bos_token) and the tokenizer's post-processor prepends another. Tokenize the rendered template with add_special_tokens=False.
Stop token eos_token_id = 166101 (<|im_end|>).
Sampling defaults (vendor generation_config.json, matching the model card): temperature 0.6, top_p 0.95, top_k 20. The vendor suggests temperature 1.0 for agentic and tool-use tasks. The same values are stamped in jang_config.chat.sampling_defaults, and the two files are checked against each other at build time.

Usage

The bundle is standard MLX safetensors with a per-module {bits, group_size, mode} map in config.json[quantization] — any loader must honor those overrides. It needs the nanbeige looped model class, which registers into mlx_lm:

python

from jang_tools.nanbeige import mlx_register  # registers the looped nanbeige class
from mlx_lm import load, generate
from mlx_lm.sample_utils import make_sampler

model, tok = load("OsaurusAI/Nanbeige4.2-3B-JANG_6M")
prompt = tok.apply_chat_template(
    [{"role": "user", "content": "Which number is bigger, 9.11 or 9.8?"}],
    add_generation_prompt=True, tokenize=False,
)
ids = tok.encode(prompt, add_special_tokens=False)  # template already emits BOS
print(generate(model, tok, prompt=ids, max_tokens=1024,
               sampler=make_sampler(temp=0.6, top_p=0.95, top_k=20)))

Osaurus and vMLX runtime support for the looped architecture is in progress; until it lands, use the path above.