Add DeepReinforce Ornith-1.0 to cookbook (#29404)

Co-authored-by: averyjones4 <averyjones4@users.noreply.github.com>
Co-authored-by: Zijie Xia <zijie.xia@radixark.ai>
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>

docs_new/cookbook/autoregressive/DeepReinforce/Ornith-1.0.mdx

@@ -0,0 +1,224 @@
+---
+title: Ornith-1.0
+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."
+tag: NEW
+---
+
+## Deployment
+
+<a id="install" />
+
+<Accordion title="Install SGLang">
+
+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).
+
+<Tabs>
+
+<Tab title="Python (pip / uv)">
+
+```bash Command
+pip install --upgrade pip
+pip install uv
+uv pip install "sglang>=0.5.9"
+```
+
+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 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`.
+
+import { Deployment } from "/src/snippets/_deployment.jsx";
+import { config }     from "/src/snippets/configs/deepreinforce-ai/ornith-1.0.jsx";
+
+<Deployment config={config} />
+
+## Playground
+
+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.
+
+For Ornith-1.0 the knobs are the reasoning and tool-call parsers:
+
+- **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`.
+- **Tool Call Parser** appends `--tool-call-parser qwen3_coder`, so Qwen-style XML tool calls are returned as OpenAI-compatible `tool_calls`.
+
+import { Playground } from "/src/snippets/_playground.jsx";
+
+<Playground config={config} />
+
+## 1. Model Introduction
+
+[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.
+
+**Key Features:**
+
+- **Agentic coding specialization**: the model cards describe Ornith-1.0 as specialized for agentic coding and report coding-agent benchmark results.
+- **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.
+- **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`.
+- **Tool calling**: emits Qwen-style XML tool calls; enable the `--tool-call-parser qwen3_coder` toggle in the [Playground](#playground).
+- **Long context**: model-card recipes use `--context-length 262144`.
+- **MIT license**: the Hugging Face repos are released under MIT.
+
+**Available Models:**
+
+<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
+  <colgroup>
+    <col style={{width: "32%"}} />
+    <col style={{width: "18%"}} />
+    <col style={{width: "18%"}} />
+    <col style={{width: "32%"}} />
+  </colgroup>
+  <thead>
+    <tr style={{borderBottom: "2px solid #d55816"}}>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700}}>Model</th>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700}}>Format</th>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700}}>Deploy Panel</th>
+      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700}}>Notes</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <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>
+      <td style={{padding: "9px 12px"}}>BF16</td>
+      <td style={{padding: "9px 12px"}}>H200 only</td>
+      <td style={{padding: "9px 12px"}}>Flagship 397B MoE checkpoint; model-card baseline uses TP=8 on an H200 single node.</td>
+    </tr>
+    <tr>
+      <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>
+      <td style={{padding: "9px 12px"}}>FP8</td>
+      <td style={{padding: "9px 12px"}}>H100 / H200</td>
+      <td style={{padding: "9px 12px"}}>FP8 repo in the collection; the deploy command uses this repo id with TP=8.</td>
+    </tr>
+    <tr>
+      <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>
+      <td style={{padding: "9px 12px"}}>BF16</td>
+      <td style={{padding: "9px 12px"}}>H100 / H200</td>
+      <td style={{padding: "9px 12px"}}>35B MoE checkpoint; the deploy command uses TP=2.</td>
+    </tr>
+    <tr>
+      <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>
+      <td style={{padding: "9px 12px"}}>FP8</td>
+      <td style={{padding: "9px 12px"}}>H100 / H200</td>
+      <td style={{padding: "9px 12px"}}>FP8 repo in the collection; the deploy command uses this repo id with TP=2.</td>
+    </tr>
+    <tr>
+      <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>
+      <td style={{padding: "9px 12px"}}>BF16</td>
+      <td style={{padding: "9px 12px"}}>H100 / H200</td>
+      <td style={{padding: "9px 12px"}}>Dense 9B checkpoint; the model card describes it as designed for efficient single-GPU deployment.</td>
+    </tr>
+    <tr>
+      <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>
+      <td style={{padding: "9px 12px"}}>GGUF</td>
+      <td style={{padding: "9px 12px"}}>No</td>
+      <td style={{padding: "9px 12px"}}>Listed for completeness; GGUF targets llama.cpp-style local inference, not the SGLang server recipe here.</td>
+    </tr>
+    <tr>
+      <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>
+      <td style={{padding: "9px 12px"}}>GGUF</td>
+      <td style={{padding: "9px 12px"}}>No</td>
+      <td style={{padding: "9px 12px"}}>Listed for completeness; the model card shows llama.cpp and Ollama examples for the GGUF build.</td>
+    </tr>
+  </tbody>
+</table>
+
+**License:** [MIT](https://huggingface.co/deepreinforce-ai/Ornith-1.0-397B/blob/main/LICENSE)
+
+**Resources:** [Hugging Face collection](https://huggingface.co/collections/deepreinforce-ai/ornith-10) · [Ornith blog](https://deep-reinforce.com/ornith_1_0.html)
+
+## 2. Configuration Tips
+
+- **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`.
+- **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.
+- **Context length**: the model-card SGLang recipes use `--context-length 262144`. Lower it if you need more memory headroom.
+- **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.
+- **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.
+- **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.
+
+## 3. Usage Examples
+
+### 3.1 Basic Chat Completion
+
+`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`.
+
+<Accordion title="Python client">
+
+```python Example
+from openai import OpenAI
+
+client = OpenAI(base_url="http://localhost:30000/v1", api_key="EMPTY")
+
+response = client.chat.completions.create(
+    model="Ornith-1.0-9B",
+    messages=[
+        {"role": "user", "content": "Write a compact Python function is_prime(n)."}
+    ],
+    temperature=0.6,
+    top_p=0.95,
+    max_tokens=1024,
+    extra_body={"top_k": 20},
+)
+
+message = response.choices[0].message
+print("=============== Reasoning ===============")
+print(message.reasoning_content)
+print("=============== Answer ==================")
+print(message.content)
+```
+
+</Accordion>
+
+### 3.2 Tool Calling
+
+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:
+
+<Accordion title="Tool-call request">
+
+```python Example
+from openai import OpenAI
+
+client = OpenAI(base_url="http://localhost:30000/v1", api_key="EMPTY")
+
+tools = [{
+    "type": "function",
+    "function": {
+        "name": "run_tests",
+        "description": "Run the project's test suite.",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "target": {"type": "string", "description": "Test target or command"}
+            },
+            "required": ["target"],
+        },
+    },
+}]
+
+response = client.chat.completions.create(
+    model="Ornith-1.0-9B",
+    messages=[{"role": "user", "content": "Run the unit tests for the parser module."}],
+    tools=tools,
+    tool_choice="auto",
+    temperature=0.6,
+    top_p=0.95,
+    max_tokens=2048,
+)
+
+print(response.choices[0].message.tool_calls)
+```
+
+</Accordion>

docs_new/cookbook/autoregressive/intro.mdx

@@ -19,6 +19,12 @@ metatags:
     href="/cookbook/autoregressive/DeepSeek/DeepSeek-V4"
     img="/cards/logos/deepseek.png"
   />
+  <Card
+    title="DeepReinforce"
+    mode="card"
+    href="/cookbook/autoregressive/DeepReinforce/Ornith-1.0"
+    img="/cards/logos/deepreinforce.png"
+  />
   <Card
     title="Llama"
     mode="card"

docs_new/docs.json

@@ -994,6 +994,12 @@
                       "cookbook/autoregressive/DeepSeek/DeepSeek-OCR-2"
                     ]
                   },
+                  {
+                    "group": "DeepReinforce",
+                    "pages": [
+                      "cookbook/autoregressive/DeepReinforce/Ornith-1.0"
+                    ]
+                  },
                   {
                     "group": "Llama",
                     "pages": [

docs_new/src/snippets/configs/deepreinforce-ai/ornith-1.0.jsx

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+// Ornith-1.0 (DeepReinforce) — config-driven cookbook page.
+// The launch flags follow the SGLang quickstarts published on the model cards; the 9B recipe makes the single-GPU default explicit with --tp 1.
+// FP8 quantization cells use their FP8 repo ids from the collection with the same flags; their README quickstarts currently point to the non-FP8 repos.
+// Reasoning / tool-call parsers are exposed as a Playground toggle (qwen3 / qwen3_coder), not baked into the deploy cells.
+// Cells remain unverified until exact recipes are run and signed off.
+
+export const config = {
+  modelName: "Ornith-1.0",
+
+  supportedHardware: ["h100", "h200"],
+
+  variants: [
+    { id: "397b", label: "397B", subtitle: "MoE" },
+    { id: "35b",  label: "35B",  subtitle: "MoE" },
+    { id: "9b",       label: "9B",       subtitle: "dense" },
+  ],
+
+  quantizations: [
+    { id: "bf16",  label: "BF16" },
+    { id: "fp8",   label: "FP8" },
+  ],
+
+  strategies: [
+    { id: "balanced", label: "Balanced" },
+  ],
+
+  nodesOptions: [
+    { id: "single", label: "Single Node" },
+  ],
+
+  modelNames: {
+    "397b|bf16":     "Ornith-1.0-397B",
+    "397b|fp8":      "Ornith-1.0-397B-FP8",
+    "35b|bf16":      "Ornith-1.0-35B",
+    "35b|fp8":       "Ornith-1.0-35B-FP8",
+    "9b|bf16":       "Ornith-1.0-9B",
+  },
+
+  placeholders: {
+    HOST_IP:   { target: "command", label: "Bind host",         default: "0.0.0.0"         },
+    PORT:      { target: "command", label: "Bind port",         default: "30000"           },
+    CURL_HOST: { target: "curl",    label: "Server host",       default: "localhost"       },
+    CURL_PORT: { target: "curl",    label: "Server port",       default: "30000"           },
+  },
+
+  curl: `curl http://{{CURL_HOST}}:{{CURL_PORT}}/v1/chat/completions \\
+-H 'Content-Type: application/json' \\
+-d '{ "model": "{{MODEL_NAME}}", "messages": [{"role":"user","content":"Write a compact Python is_prime function."}], "temperature": 0.6, "top_p": 0.95, "top_k": 20, "max_tokens": 1024 }'`,
+
+  benchmarkCommands: {
+    speed:
+`python3 -m sglang.bench_serving \\
+  --backend sglang \\
+  --host {{CURL_HOST}} --port {{CURL_PORT}} \\
+  --model {{MODEL_NAME}} \\
+  --dataset-name random \\
+  --random-input-len {{ISL}} --random-output-len {{OSL}} \\
+  --num-prompts {{NUM_PROMPTS}} --max-concurrency {{MAX_CONCURRENCY}}`,
+    numPromptsByConc: { 1: 8, 16: 32, 64: 128, 128: 256, 256: 512 },
+  },
+
+  dockerImages: {
+    h100: "lmsysorg/sglang:latest",
+    h200: "lmsysorg/sglang:latest",
+  },
+
+  github: {
+    cookbookModel: "deepreinforce-ai/Ornith-1.0",
+  },
+
+  // Parsers are added on top of the base deploy command via the Playground,
+  // not baked into the cells. Both ids are registered in the SGLang parser
+  // registries and match the model's Qwen3.5 chat template.
+  playgroundFeatures: {
+    parsers: {
+      items: [
+        { id: "reasoning", label: "Reasoning Parser", flag: "--reasoning-parser qwen3" },
+        { id: "toolCall",  label: "Tool Call Parser", flag: "--tool-call-parser qwen3_coder" },
+      ],
+    },
+  },
+
+  cells: [
+    {
+      match: { hw: "h200", variant: "397b", quant: "bf16", strategy: "balanced", nodes: "single" },
+      env: [],
+      flags: [
+        "--model-path deepreinforce-ai/Ornith-1.0-397B",
+        "--served-model-name {{MODEL_NAME}}",
+        "--tp 8",
+        "--context-length 262144",
+        "--mem-fraction-static 0.8",
+        "--host {{HOST_IP}}",
+        "--port {{PORT}}",
+      ],
+    },
+    {
+      match: { hw: "h200", variant: "397b", quant: "fp8", strategy: "balanced", nodes: "single" },
+      env: [],
+      flags: [
+        "--model-path deepreinforce-ai/Ornith-1.0-397B-FP8",
+        "--served-model-name {{MODEL_NAME}}",
+        "--tp 8",
+        "--context-length 262144",
+        "--mem-fraction-static 0.8",
+        "--host {{HOST_IP}}",
+        "--port {{PORT}}",
+      ],
+    },
+    {
+      match: { hw: "h200", variant: "35b", quant: "bf16", strategy: "balanced", nodes: "single" },
+      env: [],
+      flags: [
+        "--model-path deepreinforce-ai/Ornith-1.0-35B",
+        "--served-model-name {{MODEL_NAME}}",
+        "--tp 2",
+        "--context-length 262144",
+        "--mem-fraction-static 0.85",
+        "--host {{HOST_IP}}",
+        "--port {{PORT}}",
+      ],
+    },
+    {
+      match: { hw: "h200", variant: "35b", quant: "fp8", strategy: "balanced", nodes: "single" },
+      env: [],
+      flags: [
+        "--model-path deepreinforce-ai/Ornith-1.0-35B-FP8",
+        "--served-model-name {{MODEL_NAME}}",
+        "--tp 2",
+        "--context-length 262144",
+        "--mem-fraction-static 0.85",
+        "--host {{HOST_IP}}",
+        "--port {{PORT}}",
+      ],
+    },
+    {
+      match: { hw: "h200", variant: "9b", quant: "bf16", strategy: "balanced", nodes: "single" },
+      env: [],
+      flags: [
+        "--model-path deepreinforce-ai/Ornith-1.0-9B",
+        "--served-model-name {{MODEL_NAME}}",
+        "--tp 1",
+        "--context-length 262144",
+        "--mem-fraction-static 0.85",
+        "--host {{HOST_IP}}",
+        "--port {{PORT}}",
+      ],
+    },
+    {
+      match: { hw: "h100", variant: "397b", quant: "fp8", strategy: "balanced", nodes: "single" },
+      env: [],
+      flags: [
+        "--model-path deepreinforce-ai/Ornith-1.0-397B-FP8",
+        "--served-model-name {{MODEL_NAME}}",
+        "--tp 8",
+        "--context-length 262144",
+        "--mem-fraction-static 0.8",
+        "--host {{HOST_IP}}",
+        "--port {{PORT}}",
+      ],
+    },
+    {
+      match: { hw: "h100", variant: "35b", quant: "bf16", strategy: "balanced", nodes: "single" },
+      env: [],
+      flags: [
+        "--model-path deepreinforce-ai/Ornith-1.0-35B",
+        "--served-model-name {{MODEL_NAME}}",
+        "--tp 2",
+        "--context-length 262144",
+        "--mem-fraction-static 0.85",
+        "--host {{HOST_IP}}",
+        "--port {{PORT}}",
+      ],
+    },
+    {
+      match: { hw: "h100", variant: "35b", quant: "fp8", strategy: "balanced", nodes: "single" },
+      env: [],
+      flags: [
+        "--model-path deepreinforce-ai/Ornith-1.0-35B-FP8",
+        "--served-model-name {{MODEL_NAME}}",
+        "--tp 2",
+        "--context-length 262144",
+        "--mem-fraction-static 0.85",
+        "--host {{HOST_IP}}",
+        "--port {{PORT}}",
+      ],
+    },
+    {
+      match: { hw: "h100", variant: "9b", quant: "bf16", strategy: "balanced", nodes: "single" },
+      env: [],
+      flags: [
+        "--model-path deepreinforce-ai/Ornith-1.0-9B",
+        "--served-model-name {{MODEL_NAME}}",
+        "--tp 1",
+        "--context-length 262144",
+        "--mem-fraction-static 0.85",
+        "--host {{HOST_IP}}",
+        "--port {{PORT}}",
+      ],
+    },
+  ],
+};