| --- |
| 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](https://huggingface.co/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: |
|
|
| ```text |
| 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: |
|
|
| ```json |
| { |
| "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: |
|
|
| ```json |
| { |
| "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. |
| |
