225 lines
11 KiB
Plaintext
225 lines
11 KiB
Plaintext
---
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title: Ornith-1.0
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description: "Deploy DeepReinforce Ornith-1.0 with SGLang - a self-improving agentic-coding model family with 397B, 35B, and 9B checkpoints plus FP8 and GGUF variants."
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tag: NEW
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---
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## Deployment
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<a id="install" />
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<Accordion title="Install SGLang">
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Ornith-1.0 model cards recommend SGLang `>=0.5.9`. The Deploy panel below emits the base serve command; the reasoning and tool-call parsers from the model-card quickstarts (`--reasoning-parser qwen3` for `<think>...</think>` traces, `--tool-call-parser qwen3_coder` for Qwen-style XML tool calls) are added on top via the [Playground](#playground).
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<Tabs>
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<Tab title="Python (pip / uv)">
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```bash Command
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pip install --upgrade pip
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pip install uv
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uv pip install --prerelease=allow "sglang>=0.5.9"
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```
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Then run the **Python** output of the command panel below in that environment.
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</Tab>
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<Tab title="Docker">
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```bash Command
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docker pull lmsysorg/sglang:latest
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```
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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.
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</Tab>
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</Tabs>
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</Accordion>
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Pick an Ornith checkpoint to generate the launch command. The non-FP8 397B recipe requires an H200 single node in this matrix. The 397B-FP8 recipe is available on H100 and H200 with TP=8; H100 also supports the 35B and 9B checkpoints. The 35B recipes use tensor parallelism 2 in this matrix. The 9B checkpoint is dense and serves on a single GPU by default; the command panel makes that default explicit with `--tp 1`.
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import { Deployment } from "/src/snippets/_deployment.jsx";
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import { config } from "/src/snippets/configs/deepreinforce-ai/ornith-1.0.jsx";
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<Deployment config={config} />
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## Playground
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The Playground layers SGLang features on top of whichever cell the Deploy panel is showing — only your overrides change, and any change flips the badge to **Not Verified** until the new configuration is run end-to-end.
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For Ornith-1.0 the knobs are the reasoning and tool-call parsers:
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- **Reasoning Parser** appends `--reasoning-parser qwen3`. Ornith emits `<think>...</think>` traces; with this on, SGLang surfaces them as `message.reasoning_content` instead of leaving the tags inline in `content`.
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- **Tool Call Parser** appends `--tool-call-parser qwen3_coder`, so Qwen-style XML tool calls are returned as OpenAI-compatible `tool_calls`.
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import { Playground } from "/src/snippets/_playground.jsx";
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<Playground config={config} />
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## 1. Model Introduction
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[Ornith-1.0](https://huggingface.co/collections/deepreinforce-ai/ornith-10) is DeepReinforce's self-improving open-source model family for agentic coding. The model cards describe the family as post-trained on top of Gemma 4 and Qwen 3.5, and the collection currently includes 397B, 35B, and 9B repos plus FP8 and GGUF variants. The model cards report results on Terminal-Bench 2.1, SWE-Bench, NL2Repo, ClawEval, and SWE Atlas benchmarks.
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**Key Features:**
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- **Agentic coding specialization**: the model cards describe Ornith-1.0 as specialized for agentic coding and report coding-agent benchmark results.
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- **Self-improving training**: the model cards state that Ornith-1.0 uses reinforcement learning to optimize both solution rollouts and the scaffold that drives those rollouts.
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- **Reasoning model behavior**: assistant responses begin with a `<think>...</think>` reasoning block before the final answer; enable the `--reasoning-parser qwen3` toggle in the [Playground](#playground) to split it into `reasoning_content`.
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- **Tool calling**: emits Qwen-style XML tool calls; enable the `--tool-call-parser qwen3_coder` toggle in the [Playground](#playground).
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- **Long context**: model-card recipes use `--context-length 262144`.
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- **MIT license**: the Hugging Face repos are released under MIT.
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**Available Models:**
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<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
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<colgroup>
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<col style={{width: "32%"}} />
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<col style={{width: "18%"}} />
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<col style={{width: "18%"}} />
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<col style={{width: "32%"}} />
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</colgroup>
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<thead>
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<tr style={{borderBottom: "2px solid #d55816"}}>
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<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700}}>Model</th>
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<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700}}>Format</th>
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<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700}}>Deploy Panel</th>
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<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700}}>Notes</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td style={{padding: "9px 12px", fontWeight: 500}}><a href="https://huggingface.co/deepreinforce-ai/Ornith-1.0-397B">deepreinforce-ai/Ornith-1.0-397B</a></td>
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<td style={{padding: "9px 12px"}}>BF16</td>
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<td style={{padding: "9px 12px"}}>H200 only</td>
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<td style={{padding: "9px 12px"}}>Flagship 397B MoE checkpoint; model-card baseline uses TP=8 on an H200 single node.</td>
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</tr>
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<tr>
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<td style={{padding: "9px 12px", fontWeight: 500}}><a href="https://huggingface.co/deepreinforce-ai/Ornith-1.0-397B-FP8">deepreinforce-ai/Ornith-1.0-397B-FP8</a></td>
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<td style={{padding: "9px 12px"}}>FP8</td>
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<td style={{padding: "9px 12px"}}>H100 / H200</td>
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<td style={{padding: "9px 12px"}}>FP8 repo in the collection; the deploy command uses this repo id with TP=8.</td>
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</tr>
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<tr>
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<td style={{padding: "9px 12px", fontWeight: 500}}><a href="https://huggingface.co/deepreinforce-ai/Ornith-1.0-35B">deepreinforce-ai/Ornith-1.0-35B</a></td>
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<td style={{padding: "9px 12px"}}>BF16</td>
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<td style={{padding: "9px 12px"}}>H100 / H200</td>
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<td style={{padding: "9px 12px"}}>35B MoE checkpoint; the deploy command uses TP=2.</td>
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</tr>
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<tr>
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<td style={{padding: "9px 12px", fontWeight: 500}}><a href="https://huggingface.co/deepreinforce-ai/Ornith-1.0-35B-FP8">deepreinforce-ai/Ornith-1.0-35B-FP8</a></td>
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<td style={{padding: "9px 12px"}}>FP8</td>
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<td style={{padding: "9px 12px"}}>H100 / H200</td>
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<td style={{padding: "9px 12px"}}>FP8 repo in the collection; the deploy command uses this repo id with TP=2.</td>
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</tr>
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<tr>
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<td style={{padding: "9px 12px", fontWeight: 500}}><a href="https://huggingface.co/deepreinforce-ai/Ornith-1.0-9B">deepreinforce-ai/Ornith-1.0-9B</a></td>
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<td style={{padding: "9px 12px"}}>BF16</td>
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<td style={{padding: "9px 12px"}}>H100 / H200</td>
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<td style={{padding: "9px 12px"}}>Dense 9B checkpoint; the model card describes it as designed for efficient single-GPU deployment.</td>
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</tr>
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<tr>
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<td style={{padding: "9px 12px", fontWeight: 500}}><a href="https://huggingface.co/deepreinforce-ai/Ornith-1.0-35B-GGUF">deepreinforce-ai/Ornith-1.0-35B-GGUF</a></td>
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<td style={{padding: "9px 12px"}}>GGUF</td>
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<td style={{padding: "9px 12px"}}>No</td>
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<td style={{padding: "9px 12px"}}>Listed for completeness; GGUF targets llama.cpp-style local inference, not the SGLang server recipe here.</td>
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</tr>
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<tr>
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<td style={{padding: "9px 12px", fontWeight: 500}}><a href="https://huggingface.co/deepreinforce-ai/Ornith-1.0-9B-GGUF">deepreinforce-ai/Ornith-1.0-9B-GGUF</a></td>
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<td style={{padding: "9px 12px"}}>GGUF</td>
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<td style={{padding: "9px 12px"}}>No</td>
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<td style={{padding: "9px 12px"}}>Listed for completeness; the model card shows llama.cpp and Ollama examples for the GGUF build.</td>
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</tr>
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</tbody>
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</table>
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**License:** [MIT](https://huggingface.co/deepreinforce-ai/Ornith-1.0-397B/blob/main/LICENSE)
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**Resources:** [Hugging Face collection](https://huggingface.co/collections/deepreinforce-ai/ornith-10) · [Ornith blog](https://deep-reinforce.com/ornith_1_0.html)
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## 2. Configuration Tips
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- **Reasoning parser**: Ornith responses include `<think>...</think>`. Enable the `--reasoning-parser qwen3` toggle in the [Playground](#playground) so OpenAI-compatible responses expose the reasoning trace as `message.reasoning_content`.
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- **Tool-call parser**: enable the `--tool-call-parser qwen3_coder` toggle in the [Playground](#playground) so `<tool_call>` blocks are returned as OpenAI-compatible tool calls.
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- **Context length**: the model-card SGLang recipes use `--context-length 262144`. Lower it if you need more memory headroom.
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- **Tensor parallelism**: the 397B model-card recipes use `--tp 8`; in this single-node matrix, non-FP8 397B is H200-only, while 397B-FP8 is available on both H100 and H200. The 35B deploy commands use `--tp 2`. The 9B model-card recipe is single-GPU by default; the command panel makes that explicit with `--tp 1`. Adjust TP to match your node and memory budget.
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- **Sampling**: model cards recommend `temperature=0.6`, `top_p=0.95`, and `top_k=20` for normal use. Their reported benchmark setup may use different task-specific sampling parameters.
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- **Benchmarks**: benchmark numbers in the model cards are reported by DeepReinforce. They are useful for context, but the command panel leaves recipes unverified until exact runs are signed off.
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## 3. Usage Examples
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### 3.1 Basic Chat Completion
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`message.reasoning_content` is only populated when the server was launched with the `--reasoning-parser qwen3` toggle (see the [Playground](#playground)); otherwise the `<think>...</think>` trace stays inline in `message.content`.
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<Accordion title="Python client">
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```python Example
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from openai import OpenAI
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client = OpenAI(base_url="http://localhost:30000/v1", api_key="EMPTY")
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response = client.chat.completions.create(
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model="Ornith-1.0-9B",
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messages=[
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{"role": "user", "content": "Write a compact Python function is_prime(n)."}
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],
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temperature=0.6,
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top_p=0.95,
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max_tokens=1024,
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extra_body={"top_k": 20},
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)
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message = response.choices[0].message
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print("=============== Reasoning ===============")
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print(message.reasoning_content)
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print("=============== Answer ==================")
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print(message.content)
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```
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</Accordion>
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### 3.2 Tool Calling
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Enable the `--tool-call-parser qwen3_coder` toggle in the [Playground](#playground) and launch with the resulting command. Then use the standard OpenAI-compatible `tools` field:
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<Accordion title="Tool-call request">
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```python Example
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from openai import OpenAI
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client = OpenAI(base_url="http://localhost:30000/v1", api_key="EMPTY")
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tools = [{
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"type": "function",
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"function": {
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"name": "run_tests",
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"description": "Run the project's test suite.",
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"parameters": {
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"type": "object",
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"properties": {
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"target": {"type": "string", "description": "Test target or command"}
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},
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"required": ["target"],
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},
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},
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}]
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response = client.chat.completions.create(
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model="Ornith-1.0-9B",
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messages=[{"role": "user", "content": "Run the unit tests for the parser module."}],
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tools=tools,
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tool_choice="auto",
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temperature=0.6,
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top_p=0.95,
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max_tokens=2048,
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)
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print(response.choices[0].message.tool_calls)
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```
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</Accordion>
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