README.md

metadata

language:
  - en
  - zh
license: apache-2.0
base_model: stepfun-ai/Step-3.7-Flash-NVFP4
pipeline_tag: image-text-to-text
library_name: mlx
tags:
  - mlx
  - jang
  - jang-k
  - stepfun
  - vision-language

Step-3.7-Flash-JANG_K

JANG affine conversion of stepfun-ai/Step-3.7-Flash-NVFP4.

This JANG_K variant keeps the proven Step JANG text runtime path and uses the routed expert policy:

gate_proj / up_proj / down_proj = 4 / 2 / 2

It is the affine K-lane comparison point for the experimental Step JANGTQ_2K work.

Status

Verified locally:

• 58 safetensors shards
• 2,570 indexed tensors
• no raw NVFP4 weight_scale, weight_scale_2, or input_scale sidecars in the output index
• jang_config.json capability verification passes
• text generation proof passes through the bundled step3p7_mlx.py bridge

Text proof:

{
  "prompt": "What is 2+2? Answer with only the number.",
  "output": "The user is asking \"What is 2+2? Answer with only the number.\" So the answer is 4. The user wants only the number, so I should just output \"4\".\\n</think>\\n4",
  "prompt_tokens": 26,
  "generated_tokens": 43,
  "contains_final_4": true
}

Warmed decode proof:

{
  "measured_tokens": 32,
  "decode_s": 0.8008251190185547,
  "tok_s": 39.95878655656726
}

Format

• Format: JANG affine
• Profile: JANG_K
• Routed expert policy: gate_proj=4, up_proj=2, down_proj=2
• Attention, router gates, dense/shared MLP, embeddings, and lm head follow the proven Step JANG_2L runtime policy
• Vision/projector tensors are included as F16 passthrough
• Audio tensors: none in the source checkpoint
• MTP tensors: none in the source checkpoint

Runtime

The bundled step3p7_mlx.py bridge maps the nested Step3p7 text config to MLX's Step3p5 text runtime and drops vision tensors for text-only generation.

Required text runtime behavior:

• load model_file=step3p7_mlx.py
• preserve the source chat template; it opens the assistant generation prompt inside <think>
• use normal KV cache with Step full/sliding attention behavior from the Step3p5 MLX runtime
• do not add a second synthetic reasoning prefix
• use PreTrainedTokenizerFast; the source tokenizer metadata otherwise chooses a Llama tokenizer class that decodes byte-level markers incorrectly

Full image-input VLM coherence is not claimed by this artifact. The vision weights are present, but image patch expansion and projector routing still need a Step3p7 VLM wrapper in the target runtime.

Korean

이 번들은 Step-3.7-Flash-NVFP4를 JANG_K affine 4/2/2 전문가 비트 정책으로 변환한 산출물입니다. 텍스트 경로는 로컬 MLX 생성 검증을 통과했습니다. 비전 가중치는 포함되어 있지만 이미지 입력 경로는 별도 런타임 구현과 검증이 필요합니다.