[UPDATE] update files

deploy_guidance.md

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-# 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. 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 \
-    --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 \
-    --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 \
-    --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 \
-    --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
-# start ray on node 0 and node 1
-
-# node 0:
-python -m sglang.launch_server \
-    --model-path /path/to/step3 \
-    --trust-remote-code \
-    --tool-call-parser step3 \
-    --reasoning-parser step3 \
-    --tp 16
-```
-
-#### 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-fp8 \
-    --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.