Skip to content

GitLab

Commit b305aca6 authored by luopl's avatar luopl
Browse files

Initial commit

parents
Expand all Hide whitespace changes
Inline Side-by-side
Showing with 747 additions and 0 deletions
LICENSE 0 → 100644
View file @ b305aca6
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
and distribution as defined by Sections 1 through 9 of this document.
"Licensor" shall mean the copyright owner or entity authorized by
the copyright owner that is granting the License.
"Legal Entity" shall mean the union of the acting entity and all
other entities that control, are controlled by, or are under common
control with that entity. For the purposes of this definition,
"control" means (i) the power, direct or indirect, to cause the
direction or management of such entity, whether by contract or
otherwise, or (ii) ownership of fifty percent (50%) or more of the
outstanding shares, or (iii) beneficial ownership of such entity.
"You" (or "Your") shall mean an individual or Legal Entity
exercising permissions granted by this License.
"Source" form shall mean the preferred form for making modifications,
including but not limited to software source code, documentation
source, and configuration files.
"Object" form shall mean any form resulting from mechanical
transformation or translation of a Source form, including but
not limited to compiled object code, generated documentation,
and conversions to other media types.
"Work" shall mean the work of authorship, whether in Source or
Object form, made available under the License, as indicated by a
copyright notice that is included in or attached to the work
(an example is provided in the Appendix below).
"Derivative Works" shall mean any work, whether in Source or Object
form, that is based on (or derived from) the Work and for which the
editorial revisions, annotations, elaborations, or other modifications
represent, as a whole, an original work of authorship. For the purposes
of this License, Derivative Works shall not include works that remain
separable from, or merely link (or bind by name) to the interfaces of,
the Work and Derivative Works thereof.
"Contribution" shall mean any work of authorship, including
the original version of the Work and any modifications or additions
to that Work or Derivative Works thereof, that is intentionally
submitted to Licensor for inclusion in the Work by the copyright owner
or by an individual or Legal Entity authorized to submit on behalf of
the copyright owner. For the purposes of this definition, "submitted"
means any form of electronic, verbal, or written communication sent
to the Licensor or its representatives, including but not limited to
communication on electronic mailing lists, source code control systems,
and issue tracking systems that are managed by, or on behalf of, the
Licensor for the purpose of discussing and improving the Work, but
excluding communication that is conspicuously marked or otherwise
designated in writing by the copyright owner as "Not a Contribution."
"Contributor" shall mean Licensor and any individual or Legal Entity
on behalf of whom a Contribution has been received by Licensor and
subsequently incorporated within the Work.
2. Grant of Copyright License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
copyright license to reproduce, prepare Derivative Works of,
publicly display, publicly perform, sublicense, and distribute the
Work and such Derivative Works in Source or Object form.
3. Grant of Patent License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
(except as stated in this section) patent license to make, have made,
use, offer to sell, sell, import, and otherwise transfer the Work,
where such license applies only to those patent claims licensable
by such Contributor that are necessarily infringed by their
Contribution(s) alone or by combination of their Contribution(s)
with the Work to which such Contribution(s) was submitted. If You
institute patent litigation against any entity (including a
cross-claim or counterclaim in a lawsuit) alleging that the Work
or a Contribution incorporated within the Work constitutes direct
or contributory patent infringement, then any patent licenses
granted to You under this License for that Work shall terminate
as of the date such litigation is filed.
4. Redistribution. You may reproduce and distribute copies of the
Work or Derivative Works thereof in any medium, with or without
modifications, and in Source or Object form, provided that You
meet the following conditions:
(a) You must give any other recipients of the Work or
Derivative Works a copy of this License; and
(b) You must cause any modified files to carry prominent notices
stating that You changed the files; and
(c) You must retain, in the Source form of any Derivative Works
that You distribute, all copyright, patent, trademark, and
attribution notices from the Source form of the Work,
excluding those notices that do not pertain to any part of
the Derivative Works; and
(d) If the Work includes a "NOTICE" text file as part of its
distribution, then any Derivative Works that You distribute must
include a readable copy of the attribution notices contained
within such NOTICE file, excluding those notices that do not
pertain to any part of the Derivative Works, in at least one
of the following places: within a NOTICE text file distributed
as part of the Derivative Works; within the Source form or
documentation, if provided along with the Derivative Works; or,
within a display generated by the Derivative Works, if and
wherever such third-party notices normally appear. The contents
of the NOTICE file are for informational purposes only and
do not modify the License. You may add Your own attribution
notices within Derivative Works that You distribute, alongside
or as an addendum to the NOTICE text from the Work, provided
that such additional attribution notices cannot be construed
as modifying the License.
You may add Your own copyright statement to Your modifications and
may provide additional or different license terms and conditions
for use, reproduction, or distribution of Your modifications, or
for any such Derivative Works as a whole, provided Your use,
reproduction, and distribution of the Work otherwise complies with
the conditions stated in this License.
5. Submission of Contributions. Unless You explicitly state otherwise,
any Contribution intentionally submitted for inclusion in the Work
by You to the Licensor shall be under the terms and conditions of
this License, without any additional terms or conditions.
Notwithstanding the above, nothing herein shall supersede or modify
the terms of any separate license agreement you may have executed
with Licensor regarding such Contributions.
6. Trademarks. This License does not grant permission to use the trade
names, trademarks, service marks, or product names of the Licensor,
except as required for reasonable and customary use in describing the
origin of the Work and reproducing the content of the NOTICE file.
7. Disclaimer of Warranty. Unless required by applicable law or
agreed to in writing, Licensor provides the Work (and each
Contributor provides its Contributions) on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
implied, including, without limitation, any warranties or conditions
of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
PARTICULAR PURPOSE. You are solely responsible for determining the
appropriateness of using or redistributing the Work and assume any
risks associated with Your exercise of permissions under this License.
8. Limitation of Liability. In no event and under no legal theory,
whether in tort (including negligence), contract, or otherwise,
unless required by applicable law (such as deliberate and grossly
negligent acts) or agreed to in writing, shall any Contributor be
liable to You for damages, including any direct, indirect, special,
incidental, or consequential damages of any character arising as a
result of this License or out of the use or inability to use the
Work (including but not limited to damages for loss of goodwill,
work stoppage, computer failure or malfunction, or any and all
other commercial damages or losses), even if such Contributor
has been advised of the possibility of such damages.
9. Accepting Warranty or Additional Liability. While redistributing
the Work or Derivative Works thereof, You may choose to offer,
and charge a fee for, acceptance of support, warranty, indemnity,
or other liability obligations and/or rights consistent with this
License. However, in accepting such obligations, You may act only
on Your own behalf and on Your sole responsibility, not on behalf
of any other Contributor, and only if You agree to indemnify,
defend, and hold each Contributor harmless for any liability
incurred by, or claims asserted against, such Contributor by reason
of your accepting any such warranty or additional liability.
END OF TERMS AND CONDITIONS
APPENDIX: How to apply the Apache License to your work.
To apply the Apache License to your work, attach the following
boilerplate notice, with the fields enclosed by brackets "[]"
replaced with your own identifying information. (Don't include
the brackets!) The text should be enclosed in the appropriate
comment syntax for the file format. We also recommend that a
file or class name and description of purpose be included on the
same "printed page" as the copyright notice for easier
identification within third-party archives.
Copyright [yyyy] [name of copyright owner]
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
\ No newline at end of file
README.md 0 → 100644
View file @ b305aca6
# 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 0 → 100644
View file @ b305aca6
<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},
}
```
Step3-Sys-Tech-Report.pdf 0 → 100644
View file @ b305aca6
This diff is collapsed.
docker/Dockerfile 0 → 100644
View file @ b305aca6
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 0 → 100644
View file @ b305aca6
# 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
icon.png 0 → 100644
View file @ b305aca6
icon.png

64.3 KB

model.properties 0 → 100644
View file @ b305aca6
# 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
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment