Nanbeige4.2-3B-mlx-nvfp4

Nanbeige4.2-3B — MLX NVFP4

Nanbeige4.2-3B quantized to NVFP4 (4-bit float, group size 16) in MLX format, for Krill on Apple Silicon.

Quantized with Krill's native Swift+MLX checkpoint quantizer (krill quantize --mode nvfp4) — no Python, no mlx_lm.convert.

⚠️ Runtime support: Krill only

This build runs in Krill and nowhere else. That is not a packaging choice — as of this writing no other local inference engine implements the Nanbeige looped architecture:

GGUF conversions of Nanbeige 4.2 exist on the Hub, but they do not load: llama.cpp rejects the architecture before it reads any weights. If you are not using Krill, use the original Nanbeige/Nanbeige4.2-3B with transformers.

Performance

Measured on Apple Silicon, 512-token prompt, same machine. The baseline is the upstream PyTorch reference (modeling_nanbeige.py, fp16 on MPS) because it is the only other runtime that can serve this model at all — an Ollama comparison is impossible, not merely absent.

For scale, a dense qwen2.5-3b 4-bit in the same Krill build does 508.5 tok/s prefill / 49.8 tok/s decode at 1.5 GB. Nanbeige decodes ~25% slower at a comparable footprint — that is the loop being paid for: 44 effective layers out of 22 blocks' worth of weights.

Usage

krill pull srv-sngh/Nanbeige4.2-3B-mlx-nvfp4
krill run Nanbeige4.2-3B-mlx-nvfp4 "Which number is bigger, 9.11 or 9.8?"

Nanbeige 4.2 is a reasoning model: its chat template opens an assistant turn inside a <think> block, so the model spends tokens reasoning before it emits any visible answer. Give it room — a few hundred tokens is often not enough to close the block, and you will see an empty reply. --max-tokens 4000 or more is a sensible floor for anything non-trivial.

Upstream's recommended sampling settings (from generation_config.json): temperature 0.6, top_p 0.95, top_k 20 for reasoning and chat; temperature 1.0 for agentic and tool-use work.

About the architecture

This is not a plain Llama clone, and the quantized layout reflects that:

• Looped transformer. num_loops: 2 runs the whole 22-block stack twice over the same weights — 44 effective layer executions from 22 blocks' worth of parameters. Each execution keeps its own KV cache slot, so a runtime must allocate num_loops * num_hidden_layers = 44 caches, not 22. The final model.norm is applied at the end of every loop.
• head_dim decoupled from hidden_size. 48 heads x 128 dims against a 3072-wide residual, so q_proj is 3072 -> 6144 and o_proj 6144 -> 3072. A loader that derives hidden_size / num_attention_heads gets 64 and mis-shapes every attention projection.

The optional research features described in upstream's configuration_nanbeige.py (multi-head hyper-connections with depth attention, concatenated n-gram embeddings, LoopSplit, loop-shared KV) are off in this checkpoint and its weight map contains none of their tensors.

Verification

The native Krill runtime backing this build is logit-parity gated against upstream's own modeling_nanbeige.py (mlx-lm has no nanbeige port), on both a synthetic fp32 fixture and these real weights — prefill and incremental cached decode, matching argmax and cosine > 0.9999. A live smoke gate additionally drives this checkpoint through the whole serving path (prompt → looped prefill → 44-cache decode → reasoning filter → detokenizer), and tool calling is verified end to end.

Reproducing the reference? NanbeigeRotaryEmbedding registers inv_freq with persistent=False, so it is absent from the checkpoint. Under transformers 5.x, from_pretrained materializes the model from checkpoint tensors and that buffer is left zero-initialized on every layer — RoPE then applies no rotation at all and the reference emits positionless gibberish. Recompute inv_freq after loading before trusting any comparison. The shipped config.json also has rope_scaling: null, which transformers 5.x repopulates, tripping upstream's _init_rope on a missing "type" key.

License

Apache 2.0, inherited from the base model. All credit for the model itself goes to the Nanbeige team at BOSS Zhipin.

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About this build

Quantized and tested by Sourav Singh for Krill, a Mac-native LLM runtime and coding agent in one Swift + MLX binary. More Apple Silicon builds: huggingface.co/srv-sngh. Notes on building with local models and frontier AI: souravailabs.ai · GitHub · LinkedIn · YouTube