README.md

---
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-2l
- stepfun
- vision-language
---

# Step-3.7-Flash-JANG_2L

JANG_2L conversion of [stepfun-ai/Step-3.7-Flash-NVFP4](https://huggingface.co/stepfun-ai/Step-3.7-Flash-NVFP4).

This bundle was built from the public NVFP4 checkpoint. Routed MoE tensors were decoded from ModelOpt NVFP4 (`uint8` payload, `float8_e4m3fn` block scales, fp32 side scales) and then re-quantized into JANG affine `weight/scales/biases` tensors. BF16 attention, shared expert, dense, vision, and projector tensors were handled according to the JANG plan.

## Status

This artifact has a text-only local coherence proof through the bundled `step3p7_mlx.py` bridge, which loads the nested Step3p5 text model using MLX and drops vision tensors for text generation.

Verified locally:

- 67 safetensors shards
- 2,570 tensors in `model.safetensors.index.json`
- No missing shard references
- No raw NVFP4 `weight_scale`, `weight_scale_2`, or `input_scale` sidecars are present in the output index
- `jang_config.json` capability verification passes
- Text generation proof passes on a math prompt

Text proof:

```json
{
  "prompt": "What is 2+2? Answer with only the number.",
  "output": "The user asks \"What is 2+2? Answer with only the number.\" So the answer is 4. The user wants only the number. So we should output \"4\". There's no disallowed content. It's a simple arithmetic. So we comply.\\n</think>\\n4",
  "prompt_tokens": 26,
  "generated_tokens": 58,
  "prefill_s": 9.161997079849243,
  "contains_final_4": true
}
```

Speed note: short cold measurements include MLX graph/kernel compile and are not representative of steady decode. A no-wrapper warmed decode run over 32 measured tokens produced:

```json
{
  "prefill_s": 9.369971990585327,
  "warm_tokens": 4,
  "measured_tokens": 32,
  "decode_s": 0.7534263134002686,
  "tok_s": 42.47263392697507
}
```

Still required before full VLM runtime claims:

- Step3p7 VLM wrapper in the target MLX/vMLX runtime
- image patch token expansion and vision projector path

## Format

- Format: JANG affine
- Profile: `JANG_2L`
- Quantization backend: `mx.quantize`
- Default group size: `128`
- Bit widths used: `2`, `3`, `4`, `6`, `8`
- Vision/projector: BF16 source converted to F16 passthrough for this first artifact
- Output size: about `82G`
- Runtime bridge: `step3p7_mlx.py` wraps `mlx_lm.models.step3p5` for text-only proof

Important allocation choices:

- `self_attn.{q,k,v,o,g}_proj`: 8-bit
- `embed_tokens`: 6-bit
- routed experts: `gate_proj=4`, `down_proj=3`, `up_proj=2`
- true router/gate tensors: passthrough where present

## Runtime Metadata

`jang_config.json` stamps:

```json
{
  "reasoning_parser": "qwen3",
  "tool_parser": "step3p5",
  "think_in_template": true,
  "supports_tools": true,
  "supports_thinking": true,
  "family": "step3p7",
  "modality": "vision",
  "cache_type": "kv"
}
```

The source chat template opens the assistant generation prompt inside `<think>`. Runtimes should not add a second synthetic reasoning prefix.

## Vision And Audio

The source checkpoint contains the Step vision encoder and `vit_large_projector`. No audio tensors or audio tokenizer files were present in the downloaded checkpoint.

The source config mentions next-token prediction layers, but no MTP/nextn tensors were present in the NVFP4 source. This bundle does not synthesize MTP tensors from config fields.

## Korean

이 번들은 stepfun-ai/Step-3.7-Flash-NVFP4를 JANG_2L 형식으로 변환한 산출물입니다. 텍스트 경로는 `step3p7_mlx.py` 브리지를 통해 로컬 생성 검증을 통과했습니다. 비전 가중치는 포함되어 있지만, 이미지 입력 경로는 아직 별도 런타임 구현과 검증이 필요합니다. 오디오 텐서는 원본 체크포인트에 없었습니다.