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README.md

+# Step3
+
+## 论文
+`
+Step-3 is Large yet Affordable: Model-system Co-design for Cost-effective Decoding
+`
+- https://arxiv.org/abs/2507.19427
+
+## 模型结构
+Step3 是一个先进的多模态推理模型，基于混合专家架构构建，拥有 321B 总参数，单token激活38B参数。
+它采用端到端设计，旨在最大限度地降低解码成本，同时在视觉语言推理领域提供顶级性能。
+通过 Multi-Matrix Factorization Attention(MFA)和Attention-FFN Disaggregation (AFD)的协同设计，Step3 高端和低端加速器上均保持卓越的效率。
+AFD的架构实现如图所示。
+<div align=center>
+    <img src="./figures/AFD_architecture.png"/>
+</div>
+
+## 算法原理
+
+Step-3引入了两大优化设计:
+
+- 在模型算法方面，引入了MFA(Multi-matrix Factorization Attention)算法，其计算密度设计更加均衡，相较于MHA, GQA, MLA实现更低的decode成本。
+- 在系统设计方面，引入了Attention FFN分离架构(Attention-FFN Disaggregation, AFD), 并根据具体硬件配置相应并行策略。
+
+
+## 环境配置
+
+### Docker（方法一）
+```
+docker pull image.sourcefind.cn:5000/dcu/admin/base/custom:vllm-ubuntu22.04-dtk25.04.1-rc5-das1.6-py3.10-20250802-step3
+# <your IMAGE ID>为以上拉取的docker的镜像ID替换
+docker run -it --shm-size=1024G -v $PWD/Step3_pytorch:/home/Step3_pytorch -v /opt/hyhal:/opt/hyhal:ro --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name step3 <your IMAGE ID> bash
+
+```
+### Dockerfile（方法二）
+```
+cd $PWD/Step3_pytorch/docker
+docker build --no-cache -t step3:latest .
+docker run --shm-size=1024G --name step3 -v /opt/hyhal:/opt/hyhal:ro --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video -v $PWD/Step3_pytorch:/home/Step3_pytorch -it step3 bash
+
+```
+
+## 数据集
+`无`
+
+## 训练
+`无`
+
+## 推理
+
+注意：运行该模型需要16x64(GB)显存。
+### 多机多卡
+启动ray集群
+```
+#head节点执行
+ray start --head --node-ip-address=*.*.*.*（主节点ip） --port=*** --num-gpus=8 --num-cpus=**
+```
+
+```
+其余节点执行
+ray start --address='*.*.*.*:***' (*.*.*.*:主节点ip,***:port) --num-gpus=8 --num-cpus=**
+```
+
+vLLM Deployment(vllm官方暂不支持AFD，只支持非分离模式部署):
+
+```
+#head节点执行
+
+VLLM_USE_NN=0 VLLM_USE_FLASH_ATTN_PA=0 vllm serve /path/to/step3 \
+    --reasoning-parser step3 \
+    --enable-auto-tool-choice \
+    --tool-call-parser step3 \
+    --trust-remote-code \
+    --max-num-batched-tokens 4096 \
+    --distributed-executor-backend ray  \
+    --dtype float16  \
+    -tp 16 \
+    --port $PORT_SERVING 
+
+```
+
+- Client Request Examples
+```
+from openai import OpenAI
+
+# Set OpenAI's API key and API base to use vLLM's API server.
+openai_api_key = "EMPTY"
+openai_api_base = "http://localhost:8000/v1"
+
+client = OpenAI(
+    api_key=openai_api_key,
+    base_url=openai_api_base,
+)
+
+chat_response = client.chat.completions.create(
+    model="/path/to/step3",
+    messages=[
+        {"role": "system", "content": "You are a helpful assistant."},
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "image_url",
+                    "image_url": {
+                        "url": "https://xxxxx.png"
+                    },
+                },
+                {"type": "text", "text": "Please describe the image."},
+            ],
+        },
+    ],
+)
+print("Chat response:", chat_response)
+```
+- You can also upload base64-encoded local images:
+```
+import base64
+from openai import OpenAI
+# Set OpenAI's API key and API base to use vLLM's API server.
+openai_api_key = "EMPTY"
+openai_api_base = "http://localhost:8000/v1"
+client = OpenAI(
+    api_key=openai_api_key,
+    base_url=openai_api_base,
+)
+image_path = "/path/to/local/image.png"
+with open(image_path, "rb") as f:
+    encoded_image = base64.b64encode(f.read())
+encoded_image_text = encoded_image.decode("utf-8")
+base64_step = f"data:image;base64,{encoded_image_text}"
+chat_response = client.chat.completions.create(
+    model="step3",
+    messages=[
+        {"role": "system", "content": "You are a helpful assistant."},
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "image_url",
+                    "image_url": {
+                        "url": base64_step
+                    },
+                },
+                {"type": "text", "text": "Please describe the image."},
+            ],
+        },
+    ],
+)
+print("Chat response:", chat_response)
+```
+- text only:
+```
+from openai import OpenAI
+
+# Set OpenAI's API key and API base to use vLLM's API server.
+openai_api_key = "EMPTY"
+openai_api_base = "http://localhost:8000/v1"
+client = OpenAI(
+    api_key=openai_api_key,
+    base_url=openai_api_base,
+)
+
+chat_response = client.chat.completions.create(
+    model="/path/to/step3",  # 模型路径
+    messages=[
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": "The capital of France is"},
+    ],
+)
+print("Chat response:", chat_response.choices[0].message.content)
+```
+
+## result
+example1:
+- text:Please describe the image.
+- image:
+
+<div align=center>
+    <img src="./figures/bee.jpg"/>
+</div>
+
+
+- 输出结果:
+
+
+<div align=center>
+    <img src="./figures/result.png"/>
+</div>
+
+### 精度
+`无`
+## 应用场景
+### 算法类别
+`对话问答`
+### 热点应用行业
+`电商,教育,广媒`
+## 预训练权重
+huggingface权重下载地址为：
+
+- [stepfun-ai/step3](https://huggingface.co/stepfun-ai/step3)
+
+`注：建议加镜像源下载：export HF_ENDPOINT=https://hf-mirror.com`
+## 源码仓库及问题反馈
+- https://developer.sourcefind.cn/codes/modelzoo/step3_pytorch
+## 参考资料
+- https://github.com/stepfun-ai/Step3/tree/main
+

README_ori.md

+<div align="center">
+  <picture>
+      <img src="figures/stepfun-logo.png" width="30%" alt="StepFun: Cost-Effective Multimodal Intelligence">
+  </picture>
+</div>
+
+<hr>
+
+<div align="center" style="line-height:1">
+  <a href="https://stepfun.com/" target="_blank"><img alt="Chat" src="https://img.shields.io/badge/Chat-StepFun-ff6b6b?color=1783ff&logoColor=white"/></a>
+  <a href="https://stepfun.com/" target="_blank"><img alt="Homepage" src="https://img.shields.io/badge/Homepage-StepFun-white?logo=StepFun&logoColor=white"/></a>
+</div>
+
+<div align="center" style="line-height: 1;">
+  <a href="https://huggingface.co/collections/stepfun-ai/step3-688a3d652dbb45d868f9d42d" target="_blank"><img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-StepFun-ffc107?color=ffc107&logoColor=white"/></a>
+  <a href="https://www.modelscope.cn/models/stepfun-ai/step3" target="_blank"><img alt="ModelScope" src="https://img.shields.io/badge/ModelScope-StepFun-white?logo=modelscope&logoColor=white"/></a>
+  <a href="https://x.com/StepFun_ai" target="_blank"><img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-StepFun-white?logo=x&logoColor=white"/></a>
+</div>
+
+<div align="center" style="line-height: 1;">
+<a href="https://discord.com/invite/XHheP5Fn" target="_blank"><img alt="Discord" src="https://img.shields.io/badge/Discord-StepFun-white?logo=discord&logoColor=white"/></a>
+  <a href="LICENSE"><img alt="License" src="https://img.shields.io/badge/License-Apache%202.0-blue?&color=blue"/></a>
+</div>
+
+<div align="center">
+<b>📰&nbsp;&nbsp;<a href="https://stepfun.ai/research/step3">Step3 Model Blog</a></b> &nbsp;&nbsp;&nbsp; | &nbsp;&nbsp;&nbsp; <b>📄&nbsp;&nbsp;<a href="https://arxiv.org/abs/2507.19427">Step3 System Tech Report</a></b>
+</div>
+
+## Introduction
+
+Step3 is our cutting-edge multimodal reasoning model—built on a Mixture-of-Experts architecture with 321B total parameters and 38B active. 
+It is designed end-to-end to minimize decoding costs while delivering top-tier performance in vision–language reasoning. 
+Through the co-design of Multi-Matrix Factorization Attention (MFA) and Attention-FFN Disaggregation (AFD), 
+Step3 maintains exceptional efficiency across both flagship and low-end accelerators.
+
+### Step3 model card:
+
+|          Config        |  Value  |
+|------------------------|---------|
+| **Number of Layers (Dense layer included)**|61|
+|**Number of Dense Layers**| 5|
+| **Hidden Dimension**       | 7168    |
+| **Attention Mechanism**    | MFA     |
+| **Low-rank Query Dimension** | 2048  |
+| **Number of Query Heads**          | 64      |
+| **Head Dimension**        | 256     |
+|**Number of Experts** |48|
+|**Selected Experts per Token**|3|
+|**Number of Shared Experts**| 1|
+| **Max Context Length** | 65536 |
+| **Tokenizer** | Deepseek V3 |
+| **Total Parameters (LLM)** | 316B |
+| **Activated Params per Token** | 38B |
+| **Total Parameters (VLM)** | 321B |
+
+
+## Evaluation Results
+![](figures/step3_bmk.jpeg)
+
+## Deployment
+
+
+> You can access Step3's API on https://platform.stepfun.com/ , we provide OpenAI/Anthropic-compatible API for you.
+>
+
+Our model checkpoints are stored in bf16 and block-fp8 format, you can find it on [Huggingface](https://huggingface.co/collections/stepfun-ai/step3-688a3d652dbb45d868f9d42d).
+
+Currently, it is recommended to run Step3 on the following inference engines:
+
+* vLLM
+* SGLang
+
+Deployment and Request examples for vLLM and SGLang can be found in the [Model Deployment Guide](docs/deploy_guidance.md).
+
+## Contact Us
+If you have any questions, please reach out at [contact@stepfun.com](mailto:contact@stepfun.com) .
+
+## License
+Both the code repository and the model weights are released under the [Apache License (Version 2.0)](./LICENSE).
+
+## Citation
+```
+@misc{step3system,
+      title={Step-3 is Large yet Affordable: Model-system Co-design for Cost-effective Decoding}, 
+      author={StepFun Team},
+      year={2025},
+      eprint={2507.19427},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2507.19427}, 
+}
+
+@misc{step3blog,
+      title={Step3: Cost-Effective Multimodal Intelligence}, 
+      author={StepFun Team},
+      url={https://stepfun.ai/research/step3}, 
+}
+```

docker/Dockerfile

+FROM image.sourcefind.cn:5000/dcu/admin/base/custom:vllm-ubuntu22.04-dtk25.04.1-rc5-das1.6-py3.10-20250802-step3
\ No newline at end of file

docs/deploy_guidance.md

+# Step3 Model Deployment Guide
+
+This document provides deployment guidance for Step3 model.
+
+Currently, our open-source deployment guide only includes TP and DP+TP deployment methods. The AFD (Attn-FFN Disaggregated) approach mentioned in our [paper](https://arxiv.org/abs/2507.19427) is still under joint development with the open-source community to achieve optimal performance. Please stay tuned for updates on our open-source progress.
+
+## Overview
+
+Step3 is a 321B-parameter VLM with hardware-aware model-system co-design optimized for minimizing decoding costs. 
+
+For out fp8 version, about 326G memory is required.
+The smallest deployment unit for this version is 8xH20 with either Tensor Parallel (TP) or Data Parallel + Tensor Parallel (DP+TP).
+
+For out bf16 version, about 642G memory is required.
+The smallest deployment unit for this version is 16xH20 with either Tensor Parallel (TP) or Data Parallel + Tensor Parallel (DP+TP).
+
+## Deployment Options
+
+### vLLM Deployment
+
+Please make sure to use nightly version of vllm after this [PR](https://github.com/vllm-project/vllm/pull/21998) is merged. For details, please refer to [vllm nightly installation doc](https://docs.vllm.ai/en/latest/getting_started/installation/gpu.html#pre-built-wheels).
+```bash
+uv pip install -U vllm \
+    --torch-backend=auto \
+    --extra-index-url https://wheels.vllm.ai/nightly
+```
+
+We recommend to use the following command to deploy the model:
+
+**`max_num_batched_tokens` should be larger than 4096. If not set, the default value is 8192.**
+
+#### BF16 Model
+##### Tensor Parallelism(Serving on 16xH20):
+
+```bash
+# start ray on node 0 and node 1
+
+# node 0:
+vllm serve /path/to/step3 \
+    --tensor-parallel-size 16 \
+    --reasoning-parser step3 \
+    --enable-auto-tool-choice \
+    --tool-call-parser step3 \
+    --trust-remote-code \
+    --max-num-batched-tokens 4096 \
+    --port $PORT_SERVING
+```
+
+###### Data Parallelism + Tensor Parallelism(Serving on 16xH20):
+Step3 only has single kv head, so attention data parallelism can be adopted to reduce the kv cache memory usage.
+
+```bash
+# start ray on node 0 and node 1
+
+# node 0:
+vllm serve /path/to/step3 \
+    --data-parallel-size 16 \
+    --tensor-parallel-size 1 \
+    --reasoning-parser step3 \
+    --enable-auto-tool-choice \
+    --tool-call-parser step3 \
+    --max-num-batched-tokens 4096 \
+    --trust-remote-code \
+```
+
+#### FP8 Model
+##### Tensor Parallelism(Serving on 8xH20):
+
+```bash
+vllm serve /path/to/step3-fp8 \
+    --tensor-parallel-size 8 \
+    --reasoning-parser step3 \
+    --enable-auto-tool-choice \
+    --tool-call-parser step3 \
+    --gpu-memory-utilization 0.85 \
+    --max-num-batched-tokens 4096 \
+    --trust-remote-code \
+```
+
+###### Data Parallelism + Tensor Parallelism(Serving on 8xH20):
+
+```bash
+vllm serve /path/to/step3-fp8 \
+    --data-parallel-size 8 \
+    --tensor-parallel-size 1 \
+    --reasoning-parser step3 \
+    --enable-auto-tool-choice \
+    --tool-call-parser step3 \
+    --max-num-batched-tokens 4096 \
+    --trust-remote-code \
+```
+
+
+##### Key parameter notes:
+
+* `reasoning-parser`: If enabled, reasoning content in the response will be parsed into a structured format.
+* `tool-call-parser`: If enabled, tool call content in the response will be parsed into a structured format.
+
+### SGLang Deployment
+
+0.4.10 or later is needed for SGLang.
+
+```
+pip3 install "sglang[all]>=0.4.10"
+```
+
+#### BF16 Model
+##### Tensor Parallelism(Serving on 16xH20):
+
+```bash
+# node 1
+python -m sglang.launch_server \
+ --model-path stepfun-ai/step3 \
+ --dist-init-addr master_ip:5000 \
+ --trust-remote-code \
+ --tool-call-parser step3 \
+ --reasoning-parser step3 \
+ --tp 16 \
+ --nnodes 2 \
+ --node-rank 0
+
+# node 2
+python -m sglang.launch_server \
+ --model-path stepfun-ai/step3 \
+ --dist-init-addr master_ip:5000 \
+ --trust-remote-code \
+ --tool-call-parser step3 \
+ --reasoning-parser step3 \
+ --tp 16 \
+ --nnodes 2 \
+ --node-rank 1
+```
+
+#### FP8 Model
+##### Tensor Parallelism(Serving on 8xH20):
+
+```bash
+python -m sglang.launch_server \
+    --model-path /path/to/step3-fp8 \
+    --trust-remote-code \
+    --tool-call-parser step3 \
+    --reasoning-parser step3 \
+    --tp 8
+```
+
+
+### TensorRT-LLM Deployment
+
+[Coming soon...]
+
+
+## Client Request Examples
+
+Then you can use the chat API as below:
+```python
+from openai import OpenAI
+
+# Set OpenAI's API key and API base to use vLLM's API server.
+openai_api_key = "EMPTY"
+openai_api_base = "http://localhost:8000/v1"
+
+client = OpenAI(
+    api_key=openai_api_key,
+    base_url=openai_api_base,
+)
+
+chat_response = client.chat.completions.create(
+    model="step3",
+    messages=[
+        {"role": "system", "content": "You are a helpful assistant."},
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "image_url",
+                    "image_url": {
+                        "url": "https://xxxxx.png"
+                    },
+                },
+                {"type": "text", "text": "Please describe the image."},
+            ],
+        },
+    ],
+)
+print("Chat response:", chat_response)
+```
+You can also upload base64-encoded local images:
+
+```python
+import base64
+from openai import OpenAI
+# Set OpenAI's API key and API base to use vLLM's API server.
+openai_api_key = "EMPTY"
+openai_api_base = "http://localhost:8000/v1"
+client = OpenAI(
+    api_key=openai_api_key,
+    base_url=openai_api_base,
+)
+image_path = "/path/to/local/image.png"
+with open(image_path, "rb") as f:
+    encoded_image = base64.b64encode(f.read())
+encoded_image_text = encoded_image.decode("utf-8")
+base64_step = f"data:image;base64,{encoded_image_text}"
+chat_response = client.chat.completions.create(
+    model="step3",
+    messages=[
+        {"role": "system", "content": "You are a helpful assistant."},
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "image_url",
+                    "image_url": {
+                        "url": base64_step
+                    },
+                },
+                {"type": "text", "text": "Please describe the image."},
+            ],
+        },
+    ],
+)
+print("Chat response:", chat_response)
+
+```
+
+Note: In our image preprocessing pipeline, we implement a multi-patch mechanism to handle large images. If the input image exceeds 728x728 pixels, the system will automatically apply image cropping logic to get patches of the image.
\ No newline at end of file

model.properties

+# 模型唯一标识
+modelCode=1685
+# 模型名称
+modelName=Step3_pytorch
+# 模型描述
+modelDescription=Step3 是一个先进的多模态推理模型，基于混合专家架构构建，拥有 321B 总参数，单token激活38B参数。
+# 应用场景
+appScenario=推理,对话问答,电商,教育,广媒
+# 框架类型
+frameType=Pytorch