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docs: add Hunyuan 3 Preview cookbook (#23532)

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---
title: Hunyuan 3 Preview
metatags:
description: "Deploy Tencent Hunyuan 3 Preview BF16 (~276B / ~20B active MoE) on NVIDIA GPUs with SGLang — hybrid thinking, native tool calling, 256K context, and built-in MTP speculative decoding."
tag: NEW
---
## 1. Model Introduction
Hunyuan 3 Preview (Hy3-preview) is Tencent's preview of its third-generation flagship MoE language model, featuring hybrid thinking, native tool calling, long-context reasoning, and Multi-Token Prediction (MTP) for low-latency serving.
**Key Features:**
- **MoE Architecture**: 192 routed experts + 1 shared expert, 8 experts activated per token. ~276B total parameters with ~20B active, delivering dense-model quality at MoE inference cost.
- **Hybrid Thinking**: Reasoning modes (`high`, `medium`, `low`, `none`) controllable via OpenAI-standard `reasoning_effort`, allowing the same weights to trade off latency and depth of reasoning.
- **Native Tool Calling**: Trained on structured `<tool_call>` / `<arg_key>` / `<arg_value>` grammar. Pairs with SGLang's `hunyuan` tool-call parser for streaming OpenAI-compatible function-calling output.
- **Long Context**: 256K token context window (262,144 positions) for repository-scale code and document reasoning.
- **Multi-Token Prediction (MTP)**: Ships with a built-in MTP draft module enabling speculative decoding out of the box.
**Available Models:**
- [tencent/Hy3-preview](https://huggingface.co/tencent/Hy3-preview) — BF16 instruct
- [tencent/Hy3-preview-Base](https://huggingface.co/tencent/Hy3-preview-Base) — BF16 base
**Recommended Generation Parameters:**
<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
<thead>
<tr style={{borderBottom: "2px solid #d55816"}}>
<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Parameter</th>
<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Value</th>
</tr>
</thead>
<tbody>
<tr>
<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`temperature`</td>
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>0.7</td>
</tr>
<tr>
<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`top_p`</td>
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>0.9</td>
</tr>
<tr>
<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`reasoning_effort`</td>
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`high` / `medium` / `low` (thinking) or `none` (instant)</td>
</tr>
</tbody>
</table>
**License:** TODO — verify on HuggingFace model card.
## 2. SGLang Installation
SGLang offers multiple installation methods. You can choose the most suitable installation method based on your hardware platform and requirements.
Please refer to the [official SGLang installation guide](../../../docs/get-started/install) for installation instructions.
**Docker Images by Hardware Platform:**
<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
<thead>
<tr style={{borderBottom: "2px solid #d55816"}}>
<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Hardware Platform</th>
<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Docker Image</th>
</tr>
</thead>
<tbody>
<tr>
<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>NVIDIA H200 / B200</td>
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`lmsysorg/sglang:hy3-preview`</td>
</tr>
<tr>
<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>NVIDIA B300 / GB300</td>
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`lmsysorg/sglang:hy3-preview-cu130`</td>
</tr>
</tbody>
</table>
The `hy3-preview` tag bundles the HYV3 model code, the `hunyuan` tool-call / reasoning parsers, and the MTP draft-module runtime.
## 3. Model Deployment
This section provides deployment configurations optimized for different hardware platforms and use cases.
### 3.1 Basic Configuration
**Interactive Command Generator**: Use the configuration selector below to automatically generate the appropriate deployment command for your hardware platform, quantization, and feature capabilities.
import { Hunyuan3PreviewDeployment } from '/src/snippets/autoregressive/hunyuan3-preview-deployment.jsx'
<Hunyuan3PreviewDeployment />
### 3.2 Configuration Tips
**Key Parameters:**
<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
<thead>
<tr style={{borderBottom: "2px solid #d55816"}}>
<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Parameter</th>
<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Description</th>
<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Recommended Value</th>
</tr>
</thead>
<tbody>
<tr>
<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`--tool-call-parser`</td>
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Tool call parser for function-calling support</td>
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>`hunyuan`</td>
</tr>
<tr>
<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`--reasoning-parser`</td>
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Reasoning parser for hybrid thinking modes</td>
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>`hunyuan`</td>
</tr>
<tr>
<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`--trust-remote-code`</td>
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Required for Hunyuan model loading</td>
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Always enabled</td>
</tr>
<tr>
<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`--mem-fraction-static`</td>
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Static memory fraction (KV + activations)</td>
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>`0.9`</td>
</tr>
<tr>
<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`--tp`</td>
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Tensor parallelism size</td>
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>`2` / `4` / `8` depending on hardware</td>
</tr>
<tr>
<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`--attention-backend`</td>
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Attention backend (Blackwell only)</td>
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>`trtllm_mha`</td>
</tr>
<tr>
<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`--speculative-algorithm`</td>
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Speculative decoding via the bundled MTP draft</td>
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>`EAGLE` + `--speculative-num-steps 3 --speculative-eagle-topk 1 --speculative-num-draft-tokens 4` (set env `SGLANG_ENABLE_SPEC_V2=1`)</td>
</tr>
</tbody>
</table>
**Hardware Requirements: NVIDIA BF16 (`Hy3-preview`, ~552GB weights)**
- **H200 (141GB) / B200 (180GB)**: TP=8 (minimum for BF16 to fit single-node).
- **B300 (275GB) / GB300**: TP=4.
- **A100 / H100 (80GB)**: not supported single-node — BF16 requires multi-node TP=16+ on 80GB-class GPUs.
**Blackwell (B200 / B300 / GB300):** Auto-selected attention backend can mis-route for HYV3 on Blackwell. Always pass `--attention-backend trtllm_mha` explicitly on Blackwell hardware (the config generator above enforces this).
**Multi-Token Prediction (MTP):** The `Hy3-preview` release bundles an MTP draft module. SGLang runs it via its EAGLE speculative-decoding path — the draft module auto-loads from the same `--model-path`. Enable with the `SGLANG_ENABLE_SPEC_V2=1` env var and the standard MTP flags:
```bash Command
SGLANG_ENABLE_SPEC_V2=1 sglang serve \
--model-path tencent/Hy3-preview \
--tp 8 \
--speculative-algorithm EAGLE \
--speculative-num-steps 3 \
--speculative-eagle-topk 1 \
--speculative-num-draft-tokens 4 \
--reasoning-parser hunyuan \
--tool-call-parser hunyuan \
--trust-remote-code \
--mem-fraction-static 0.85
```
Toggle the "Speculative Decoding (MTP)" option in the generator above to add these flags automatically. Tune `num-steps` / `num-draft-tokens` based on acceptance rate in your workload.
## 4. Model Invocation
### 4.1 Basic Usage
For basic API usage and request examples, please refer to:
- [SGLang Basic Usage Guide](../../../docs/basic_usage/send_request)
**Deployment Command (H200 × 8, BF16 default):**
```bash Command
sglang serve \
--model-path tencent/Hy3-preview \
--tp 8 \
--reasoning-parser hunyuan \
--tool-call-parser hunyuan \
--trust-remote-code \
--mem-fraction-static 0.9
```
**Testing Deployment:**
After startup, you can test the SGLang OpenAI-compatible API with the following command:
```bash Command
curl http://localhost:30000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "tencent/Hy3-preview",
"messages": [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Who won the world series in 2020?"}
]
}'
```
**Simple Completion Example:**
```python Example
from openai import OpenAI
client = OpenAI(
base_url="http://localhost:30000/v1",
api_key="EMPTY"
)
response = client.chat.completions.create(
model="tencent/Hy3-preview",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Who won the world series in 2020?"}
],
max_tokens=1024
)
print("Reasoning:", response.choices[0].message.reasoning_content)
print("Content: ", response.choices[0].message.content)
```
**Output Example:**
```text Output
Reasoning: None
Content: The Los Angeles Dodgers won the 2020 World Series. They defeated the Tampa Bay Rays in six games (4-2). This was the Dodgers' first World Series championship since 1988. The series was notable for being played in a neutral-site bubble at Globe Life Field in Arlington, Texas, due to the COVID-19 pandemic.
```
When `reasoning_effort` is not set, the server defaults to instant mode (no thinking, `reasoning_content=None`). To opt into thinking, pass `reasoning_effort="high" / "medium" / "low"` on the request — see the Hybrid Thinking section below.
### 4.2 Advanced Usage
#### 4.2.1 Reasoning Parser (Hybrid Thinking)
Hy3-preview is a hybrid-thinking model. Control the thinking budget via the OpenAI-standard `reasoning_effort`:
- `high` / `medium` / `low` — increasing amounts of chain-of-thought in `reasoning_content`
- `none` — skip thinking entirely (instant responses, content-only)
Enable the reasoning parser during deployment so that the thinking section (`<think>...</think>`) is separated into `reasoning_content`:
```bash Command
sglang serve \
--model-path tencent/Hy3-preview \
--tp 8 \
--reasoning-parser hunyuan \
--trust-remote-code \
--mem-fraction-static 0.9
```
**Thinking Mode — High Effort:**
```python Example
from openai import OpenAI
client = OpenAI(base_url="http://localhost:30000/v1", api_key="EMPTY")
response = client.chat.completions.create(
model="tencent/Hy3-preview",
messages=[{"role": "user", "content": "Solve step by step: What is 15% of 240?"}],
reasoning_effort="high",
max_tokens=2048,
)
msg = response.choices[0].message
print("=============== Thinking =================")
print(msg.reasoning_content)
print("=============== Content =================")
print(msg.content)
```
**Output Example:**
```text Output
=============== Thinking =================
We need to solve: "What is 15% of 240?" Step by step. So we need to compute 15% of 240. The process: 15% means 15 per hundred, i.e., 15/100 = 0.15. Multiply 0.15 by 240. Or we can do: 10% of 240 = 24, 5% is half of 10% = 12, so sum = 36. Or do multiplication: 15/100 * 240 = (15*240)/100 = (3600)/100 = 36. So answer is 36.
We need to produce step-by-step explanation. The instruction: "Solve step by step: What is 15% of 240?" So we should provide a clear solution with steps. The final answer: 36. Also maybe include units? No units.
We'll output the solution in a clear manner.
=============== Content =================
To find 15% of 240, follow these steps:
1. **Understand that percent means "per hundred."**
So, 15% = 15/100 or 0.15.
2. **Multiply the number (240) by the percentage in decimal form.**
0.15 × 240.
Alternatively, you can use fractions:
(15/100) × 240.
3. **Perform the multiplication.**
0.15 × 240 = 36.
Or:
(15 × 240) / 100 = 3600 / 100 = 36.
4. **Check using an alternative method:**
- 10% of 240 = 24.
- 5% of 240 = half of 10% = 12.
- 15% = 10% + 5% = 24 + 12 = 36.
Thus, **15% of 240 is 36**.
```
**Instant Mode — No Thinking:**
```python Example
response = client.chat.completions.create(
model="tencent/Hy3-preview",
messages=[{"role": "user", "content": "Give me a one-line summary of relativity."}],
reasoning_effort="none",
max_tokens=256,
)
print("Content:", response.choices[0].message.content)
```
**Output Example:**
```text Output
Content: Relativity is Einstein's theory that space, time, mass, and gravity are interconnected and relative, not fixed, fundamentally changing our understanding of the universe.
```
#### 4.2.2 Tool Calling
Hy3-preview supports streaming OpenAI-compatible tool calls. Enable both parsers together — the reasoning parser strips thinking tokens before the tool-call parser runs:
```bash Command
sglang serve \
--model-path tencent/Hy3-preview \
--tp 8 \
--reasoning-parser hunyuan \
--tool-call-parser hunyuan \
--trust-remote-code \
--mem-fraction-static 0.9
```
**Non-Streaming Example:**
```python Example
from openai import OpenAI
client = OpenAI(base_url="http://localhost:30000/v1", api_key="EMPTY")
tools = [
{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get the current weather for a city.",
"parameters": {
"type": "object",
"properties": {
"city": {"type": "string"},
"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
},
"required": ["city"],
},
},
}
]
response = client.chat.completions.create(
model="tencent/Hy3-preview",
messages=[{"role": "user", "content": "What's the weather in Beijing? Use fahrenheit."}],
tools=tools,
)
msg = response.choices[0].message
print("Reasoning:", msg.reasoning_content)
print("Content: ", msg.content)
for tc in msg.tool_calls or []:
print(f"Tool Call: {tc.function.name}")
print(f" Arguments: {tc.function.arguments}")
```
**Output Example:**
```text Output
Reasoning: None
Content: I'll get the current weather for Beijing in Fahrenheit for you.
Tool Call: get_weather
Arguments: {"city": "Beijing", "unit": "fahrenheit"}
```
**Streaming Example (incremental argument deltas):**
Hy3-preview's `hunyuan` tool-call parser emits tool names first, then argument JSON in incremental fragments — matching the OpenAI streaming contract:
```python Example
from openai import OpenAI
client = OpenAI(base_url="http://localhost:30000/v1", api_key="EMPTY")
stream = client.chat.completions.create(
model="tencent/Hy3-preview",
messages=[{"role": "user", "content": "What's the weather in Beijing? Use fahrenheit."}],
tools=tools,
stream=True,
)
tool_buffer = {}
for chunk in stream:
delta = chunk.choices[0].delta
if delta.content:
print(delta.content, end="", flush=True)
for tc in delta.tool_calls or []:
buf = tool_buffer.setdefault(tc.index, {"name": "", "args": ""})
if tc.function and tc.function.name:
buf["name"] += tc.function.name
if tc.function and tc.function.arguments:
buf["args"] += tc.function.arguments
for idx, buf in tool_buffer.items():
print(f"\nTool[{idx}] {buf['name']}({buf['args']})")
```
**Output Example:**
```text Output
I'll check the current weather in Beijing for you using Fahrenheit.
Tool[0] get_weather({"city": "Beijing", "unit": "fahrenheit"})
```
## 5. Benchmark
### 5.1 Accuracy Benchmark
**Test Environment:**
- Hardware: 8× NVIDIA H200 (141GB)
- Docker Image: `lmsysorg/sglang:hy3-preview`
- Model: `tencent/Hy3-preview` (BF16)
- Tensor Parallelism: 8
- SGLang version: latest `main`
#### 5.1.1 GSM8K
- Benchmark Method: 5-shot CoT on 200 questions, evaluated via SGLang native backend
- Benchmark Command:
```bash Command
python3 benchmark/gsm8k/bench_sglang.py --num-questions 200 --parallel 64
```
- Test Results:
```text Output
TODO — replace with real GSM8K accuracy after benchmark run on Hy3-preview (BF16).
```
#### 5.1.2 MMLU
- Benchmark Method: 5-shot, all 57 subjects
- Benchmark Command:
```bash Command
python3 benchmark/mmlu/bench_sglang.py --nsub 60 --parallel 64
```
- Test Results:
```text Output
TODO — replace with real MMLU accuracy after benchmark run on Hy3-preview (BF16).
```
#### 5.1.3 Tool-Call Accuracy (MiniMax-Provider-Verifier)
- Benchmark Tool: [MiniMax-Provider-Verifier](https://github.com/MiniMax-AI/MiniMax-Provider-Verifier)
- Metric: function-call schema validity, argument match, and end-to-end response correctness
- Test Results:
```text Output
TODO — replace with real tool-call accuracy after benchmark run on Hy3-preview (BF16).
```
### 5.2 Speed Benchmark
#### 5.2.1 Low Concurrency
- Benchmark Command:
```bash Command
python3 -m sglang.bench_serving \
--backend sglang \
--model tencent/Hy3-preview \
--dataset-name random \
--random-input-len 1000 \
--random-output-len 1000 \
--num-prompts 10 \
--max-concurrency 1
```
- Test Results:
```text Output
TODO — replace with real low-concurrency output on Hy3-preview (BF16).
```
#### 5.2.2 High Concurrency
- Benchmark Command:
```bash Command
python3 -m sglang.bench_serving \
--backend sglang \
--model tencent/Hy3-preview \
--dataset-name random \
--random-input-len 1000 \
--random-output-len 1000 \
--num-prompts 500 \
--max-concurrency 100
```
- Test Results:
```text Output
TODO — replace with real high-concurrency output on Hy3-preview (BF16).
```
docs_new/docs.json
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@@ -1067,6 +1067,12 @@
"pages": [
"cookbook/autoregressive/FlashLabs/Chroma1.0"
]
},
{
"group": "Tencent",
"pages": [
"cookbook/autoregressive/Tencent/Hunyuan3-Preview"
]
}
]
},
docs_new/src/snippets/autoregressive/hunyuan3-preview-deployment.jsx
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@@ -0,0 +1,174 @@
export const Hunyuan3PreviewDeployment = () => {
// Hunyuan 3 Preview (~276B total / ~20B active MoE) — BF16 only.
// ~552GB weights; 80GB-class GPUs (A100/H100) cannot fit single-node.
// H200 (141GB): tp=8
// B200 (180GB): tp=8
// B300 (275GB): tp=4
// GB300 (275GB, 4-GPU node): tp=4
const options = {
hardware: {
name: 'hardware',
title: 'Hardware Platform',
items: [
{ id: 'h200', label: 'H200', default: true },
{ id: 'b200', label: 'B200', default: false },
{ id: 'b300', label: 'B300', default: false },
{ id: 'gb300', label: 'GB300', default: false }
]
},
reasoning: {
name: 'reasoning',
title: 'Reasoning Parser',
items: [
{ id: 'disabled', label: 'Disabled', default: false },
{ id: 'enabled', label: 'Enabled', default: true }
]
},
toolcall: {
name: 'toolcall',
title: 'Tool Call Parser',
items: [
{ id: 'disabled', label: 'Disabled', default: false },
{ id: 'enabled', label: 'Enabled', default: true }
]
},
speculative: {
name: 'speculative',
title: 'Speculative Decoding (MTP)',
items: [
{ id: 'disabled', label: 'Disabled', default: true },
{ id: 'enabled', label: 'Enabled', subtitle: 'Low Latency', default: false }
]
}
};
const modelConfigs = {
h200: { tp: 8, mem: 0.9 },
b200: { tp: 8, mem: 0.9 },
b300: { tp: 4, mem: 0.9 },
gb300: { tp: 4, mem: 0.9 }
};
const resolveItems = (option, values) => {
if (typeof option.getDynamicItems === 'function') return option.getDynamicItems(values);
return option.items;
};
const getInitialState = () => {
const initialState = {};
for (const [key, option] of Object.entries(options)) {
const items = resolveItems(option, initialState);
const def = items.find(i => i.default && !i.disabled) || items.find(i => !i.disabled) || items[0];
initialState[key] = def.id;
}
return initialState;
};
const [values, setValues] = useState(getInitialState);
const [isDark, setIsDark] = useState(false);
useEffect(() => {
const checkDarkMode = () => {
const html = document.documentElement;
const isDarkMode = html.classList.contains('dark') ||
html.getAttribute('data-theme') === 'dark' ||
html.style.colorScheme === 'dark';
setIsDark(isDarkMode);
};
checkDarkMode();
const observer = new MutationObserver(checkDarkMode);
observer.observe(document.documentElement, { attributes: true, attributeFilter: ['class', 'data-theme', 'style'] });
return () => observer.disconnect();
}, []);
const handleRadioChange = (optionName, value) => {
setValues(prev => ({ ...prev, [optionName]: value }));
};
const generateCommand = () => {
const { hardware } = values;
const isBlackwell = hardware === 'b200' || hardware === 'b300' || hardware === 'gb300';
const hwConfig = modelConfigs[hardware];
if (!hwConfig) return '# Configuration not available for the selected hardware.';
const modelName = 'tencent/Hy3-preview';
const tpValue = hwConfig.tp;
const memFraction = hwConfig.mem;
const enableSpec = values.speculative === 'enabled';
let cmd = '';
if (enableSpec) cmd += 'SGLANG_ENABLE_SPEC_V2=1 ';
cmd += 'sglang serve \\\n';
cmd += ` --model-path ${modelName}`;
cmd += ` \\\n --tp ${tpValue}`;
if (values.reasoning === 'enabled') cmd += ' \\\n --reasoning-parser hunyuan';
if (values.toolcall === 'enabled') cmd += ' \\\n --tool-call-parser hunyuan';
if (enableSpec) {
cmd += ' \\\n --speculative-algorithm EAGLE';
cmd += ' \\\n --speculative-num-steps 3';
cmd += ' \\\n --speculative-eagle-topk 1';
cmd += ' \\\n --speculative-num-draft-tokens 4';
}
cmd += ' \\\n --trust-remote-code';
cmd += ` \\\n --mem-fraction-static ${memFraction}`;
if (isBlackwell) cmd += ' \\\n --attention-backend trtllm_mha';
return cmd;
};
const containerStyle = { maxWidth: '900px', margin: '0 auto', display: 'flex', flexDirection: 'column', gap: '4px' };
const cardStyle = { padding: '8px 12px', border: `1px solid ${isDark ? '#374151' : '#e5e7eb'}`, borderLeft: `3px solid ${isDark ? '#E85D4D' : '#D45D44'}`, borderRadius: '4px', display: 'flex', alignItems: 'center', gap: '12px', background: isDark ? '#1f2937' : '#fff' };
const titleStyle = { fontSize: '13px', fontWeight: '600', minWidth: '140px', flexShrink: 0, color: isDark ? '#e5e7eb' : 'inherit' };
const itemsStyle = { display: 'flex', rowGap: '2px', columnGap: '6px', flexWrap: 'wrap', alignItems: 'center', flex: 1 };
const labelBaseStyle = { padding: '4px 10px', border: `1px solid ${isDark ? '#9ca3af' : '#d1d5db'}`, borderRadius: '3px', cursor: 'pointer', display: 'inline-flex', flexDirection: 'column', alignItems: 'center', justifyContent: 'center', fontWeight: '500', fontSize: '13px', transition: 'all 0.2s', userSelect: 'none', minWidth: '45px', textAlign: 'center', flex: 1, background: isDark ? '#374151' : '#fff', color: isDark ? '#e5e7eb' : 'inherit' };
const checkedStyle = { background: '#D45D44', color: 'white', borderColor: '#D45D44' };
const disabledStyle = { cursor: 'not-allowed', opacity: 0.4 };
const subtitleStyle = { display: 'block', fontSize: '9px', marginTop: '1px', lineHeight: '1.1', opacity: 0.7 };
const commandDisplayStyle = { flex: 1, padding: '12px 16px', background: isDark ? '#111827' : '#f5f5f5', borderRadius: '6px', fontFamily: "'Menlo', 'Monaco', 'Courier New', monospace", fontSize: '12px', lineHeight: '1.5', color: isDark ? '#e5e7eb' : '#374151', whiteSpace: 'pre-wrap', overflowX: 'auto', margin: 0, border: `1px solid ${isDark ? '#374151' : '#e5e7eb'}` };
return (
<div style={containerStyle} className="not-prose">
{Object.entries(options).map(([key, option]) => {
if (typeof option.condition === 'function' && !option.condition(values)) return null;
const items = resolveItems(option, values);
return (
<div key={key} style={cardStyle}>
<div style={titleStyle}>{option.title}</div>
<div style={itemsStyle}>
{items.map(item => {
const isChecked = values[option.name] === item.id;
const isDisabled = !!item.disabled;
return (
<label
key={item.id}
style={{ ...labelBaseStyle, ...(isChecked ? checkedStyle : {}), ...(isDisabled ? disabledStyle : {}) }}
title={item.disabledReason || ''}
>
<input
type="radio"
name={option.name}
value={item.id}
checked={isChecked}
disabled={isDisabled}
onChange={() => !isDisabled && handleRadioChange(option.name, item.id)}
style={{ display: 'none' }}
/>
{item.label}
{item.subtitle && <small style={{ ...subtitleStyle, color: isChecked ? 'rgba(255,255,255,0.85)' : 'inherit' }}>{item.subtitle}</small>}
</label>
);
})}
</div>
</div>
);
})}
<div style={cardStyle}>
<div style={titleStyle}>Run this Command:</div>
<pre style={commandDisplayStyle}>{generateCommand()}</pre>
</div>
</div>
);
};