> ## Documentation Index
> Fetch the complete documentation index at: https://docs.sglang.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Flint

> Deploy Recursive's Flint with SGLang — a 40B Mixture-of-Experts model for fast, local agentic coding with a 262K context window — on NVIDIA H200, B200, B300, GB300, and DGX Spark.

## Deployment

> **Install SGLang**
>
> Flint runs on the `lmsysorg/sglang:dev-flint` Docker image:
>
> ```bash Command theme={null}
> docker pull lmsysorg/sglang:dev-flint
> ```
>
> For how to launch the image, see [Install → Method 3: Using Docker](../../../docs/get-started/install#method-3-using-docker). The **Docker** toggle in the command panel below produces the full `docker run` command; the **Python** toggle produces the inner `sglang serve ...` to run inside the container.

Pick your hardware to generate the launch command.

## 1. Model Introduction

**[Flint](https://huggingface.co/RecursiveSI/flint)** is a new open-weight **40B Mixture-of-Experts** model from Recursive, built for fast, local AI coding. Flint was developed using Recursive's Eureka Machine for automated AI research and is released under Apache 2.0.

<table>
  <thead>
    <tr>
      <th>Model</th>
      <th>Total params</th>
      <th>Context</th>
      <th>Use</th>
    </tr>
  </thead>

  <tbody>
    <tr>
      <td>**[Flint](https://huggingface.co/RecursiveSI/flint)**</td>
      <td>40B (MoE)</td>
      <td>262,144</td>
      <td>Fast, local agentic coding</td>
    </tr>
  </tbody>
</table>

**License:** Apache 2.0

**Resources:** [Hugging Face](https://huggingface.co/RecursiveSI/flint).

## 2. Advanced Usage

### 2.1 Reasoning

Launch with `--reasoning-parser flint` (included in the commands above). Thinking is separated into `message.reasoning_content`, and the final answer is returned in `message.content`.

> **Reasoning Example (Python)**
>
> ```python Example theme={null}
> from openai import OpenAI
>
> client = OpenAI(base_url="http://localhost:30000/v1", api_key="EMPTY")
>
> response = client.chat.completions.create(
>     model="flint",
>     messages=[{"role": "user", "content": "What is 15% of 240?"}],
> )
>
> message = response.choices[0].message
> print("=============== Reasoning ===============")
> print(message.reasoning_content)
> print("=============== Answer ===============")
> print(message.content)
> ```

> **Example Output**
>
> ```text Output theme={null}
> =============== Reasoning ===============
> We need answer simple. Need maybe show calculation. User asks "What is 15% of 240?" We can answer. Need be careful maybe just 36. Let's craft concise.
> =============== Answer ===============
> 15% of 240 is **36**.
> ```

### 2.2 Tool Calling

Launch with `--tool-call-parser flint` (included in the commands above) to surface structured tool calls via `message.tool_calls`.

> **Tool Calling Example (Python)**
>
> ```python Example theme={null}
> import json
> 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"}},
>                 "required": ["city"],
>             },
>         },
>     }
> ]
>
> response = client.chat.completions.create(
>     model="flint",
>     messages=[{"role": "user", "content": "What's the weather in Paris?"}],
>     tools=tools,
> )
>
> message = response.choices[0].message
> print("=============== Reasoning ===============")
> print(getattr(message, "reasoning_content", None))
> print("=============== Content ===============")
> print(message.content)
> print("=============== Tool Calls ===============")
> for call in message.tool_calls or []:
>     print(call.function.name, json.loads(call.function.arguments))
> ```

> **Example Output**
>
> ```text Output theme={null}
> =============== Reasoning ===============
> The user asked for the weather in Paris, and I have a get_weather tool available that takes a city parameter. I should call it with city 'Paris' to get the current weather.
> =============== Content ===============
> I'll get the current weather for Paris for you.
> =============== Tool Calls ===============
> get_weather {'city': 'Paris'}
> ```


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