---
title: __MODEL_DISPLAY__
description: "__ONE_LINER__"
tag: NEW
---

{/* TEMPLATE — instantiate via the cookbook-add-model skill, then DELETE this banner.
    (Frontmatter MUST stay the first thing in the file, so this note lives below it.)
    Replace every __TOKEN__, fill the TODO prose, delete the §3 subsections your model
    lacks. Tokens: __MODEL_DISPLAY__ __ONE_LINER__ __HF_ORG__ __MODEL_SLUG__ __HF_REPO__
    __REASONING_PARSER__ __TOOLCALL_PARSER__.  MDX rules (JSX tables, labeled fences, no
    Docusaurus/@site/GitHub-alert/pipe-tables):
    .claude/skills/cookbook-add-model/references/mintlify-authoring.md */}

## Deployment

<a id="install" />

<Accordion title="Install SGLang">

For all methods and hardware platforms, see the [official SGLang installation guide](../../../docs/get-started/install). The two paths below match the **Python / Docker** toggle in the command panel.

<Tabs>

<Tab title="Python (pip / uv)">

```bash Command
pip install --upgrade pip
pip install uv
uv pip install --prerelease=allow sglang
```

Then run the **Python** output of the command panel below in that environment.

</Tab>

<Tab title="Docker">

```bash Command
docker pull lmsysorg/sglang:latest
```

For how to launch the image, see [Install → Method 3: Using Docker](../../../docs/get-started/install#method-3-using-docker). Substitute the inner `sglang serve ...` with what the command generator below produces.

</Tab>

</Tabs>

</Accordion>

Pick your hardware + recipe to generate the launch command. The three serving strategies cover the common operating points:

- **Low-Latency** — fastest reply for a single user. Pick for chat.
- **Balanced** — good speed with several users at once. Use for typical multi-user serving.
- **High-Throughput** — most tokens per second across many users. Best for batch jobs.

import { Deployment } from "/src/snippets/_deployment.jsx";
import { config }     from "/src/snippets/configs/__HF_ORG__/__MODEL_SLUG__.jsx";
import { benchmarks } from "/src/snippets/configs/__HF_ORG__/__MODEL_SLUG__-benchmarks.jsx";

<Deployment config={config} benchmarks={benchmarks} />

## Playground

The Playground is where you experiment with **SGLang features beyond the verified matrix**. The Deploy panel above only emits combinations the SGLang team has signed off on; the Playground lets you turn on additional knobs on top of whichever cell the Deploy panel is currently showing.

import { Playground } from "/src/snippets/_playground.jsx";

<Playground config={config} />

## 1. Model Introduction

{/* TODO: 1-2 paragraph intro from the HF card — what the model is, release date,
    license, architecture highlights, context length. Keep it lean. */}
**__MODEL_DISPLAY__** is __ONE_LINER__.

{/* TODO: variants table (JSX, NOT a markdown pipe table). Drop the table if there's
    a single variant and inline the HF link in the intro paragraph above instead. */}
<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
  <thead>
    <tr style={{borderBottom: "2px solid #d55816"}}>
      <th style={{textAlign: "left",  padding: "10px 12px", fontWeight: 700}}>Variant</th>
      <th style={{textAlign: "right", padding: "10px 12px", fontWeight: 700}}>Total params</th>
      <th style={{textAlign: "left",  padding: "10px 12px", fontWeight: 700}}>Use</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td style={{padding: "9px 12px"}}><strong><a href="https://huggingface.co/__HF_ORG__/__HF_REPO__">__MODEL_DISPLAY__</a></strong></td>
      <td style={{padding: "9px 12px", textAlign: "right"}}>TODO</td>
      <td style={{padding: "9px 12px"}}>TODO</td>
    </tr>
  </tbody>
</table>

**Recommended generation:** {/* TODO e.g. `temperature=1.0`, `top_p=1.0` (informational; do NOT hardcode in sample code) */}

**Resources:** [HuggingFace](https://huggingface.co/__HF_ORG__/__HF_REPO__).

## 2. Configuration Tips

{/* TODO: model/hardware-specific tuning notes, caveats, known issues. Delete if none. */}

## 3. Advanced Usage

{/* Keep only the subsections that apply. Commands and outputs in this section are
    COLLAPSIBLE (required — match DeepSeek-V4 §3): each runnable example lives in an
    <Accordion>, its REAL server output in a following <Accordion title="Example Output">. */}

### 3.1 Reasoning

Enable the `__REASONING_PARSER__` reasoning parser (toggle **Reasoning Parser** in the **Parsers** card of the [Playground above](#playground)) to separate thinking from the final answer.

{/* This example assumes a SEPARATE-FIELD parser (thinking → `reasoning_content`,
    answer → `content`). If your parser emits inline `<think>...</think>` tags inside
    `content`, parse the tags from `content` instead. */}

<Accordion title="Reasoning Example (Python)">

```python Example
from openai import OpenAI

client = OpenAI(base_url="http://localhost:30000/v1", api_key="EMPTY")
resp = client.chat.completions.create(
    model="__HF_ORG__/__HF_REPO__",
    messages=[{"role": "user", "content": "What is 15% of 240?"}],
    extra_body={"chat_template_kwargs": {"thinking": True}},
)
msg = resp.choices[0].message
print("Reasoning:", getattr(msg, "reasoning_content", None))
print("Answer:", msg.content)
```

</Accordion>

<Accordion title="Example Output">

```text Output
TODO: paste real server output here.
```

</Accordion>

### 3.2 Tool Calling

Enable the `__TOOLCALL_PARSER__` tool-call parser (toggle **Tool Call Parser** in the **Parsers** card of the [Playground above](#playground)) to surface structured tool calls via `message.tool_calls`.

{/* TODO: tool-calling example in an <Accordion> + an <Accordion title="Example Output">.
    On thinking-mode models the follow-up may put text in `reasoning_content`;
    print both that and `content`. */}

### 3.3 HiCache (Hierarchical KV Caching)

{/* TODO: keep only if the model is large enough for hierarchical KV caching; link
    the HiCache card in the Playground. Otherwise delete this subsection. */}