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README.md

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 ---
+library_name: transformers
 license: mit
+license_link: https://huggingface.co/deepreinforce-ai/Ornith-1.0-397B/blob/main/LICENSE
+pipeline_tag: text-generation
 ---
+
+
+<img width="600px" src="assets/ornith_logo.png">
+
+[![Ornith Blog](https://img.shields.io/badge/%F0%9F%A6%A2%EF%B8%8F%20Ornith%20Blog%20-FD8E5B)](https://deep-reinforce.com/ornith.html)
+
+# Ornith-1.0-397B
+
+Aloha! 🌺 Today, we are releasing Ornith-1.0, a self-improving family of open-source models for agentic coding. 
+
+Highlights: 
+
+- **State-of-the-Art Coding Agents**: Available in 9B-Dense, 31B-Dense, 35B-MoE, and 397B-MoE (post-trained on top of Gemma 4 and Qwen 3.5), achieving state-of-the-art performance among open-source models of comparable size on coding benchmarks such as Terminal-Bench 2.1, SWE-Bench, NL2Repo and OpenClaw. 
+- **Self-Improving Training Framework**:  Ornith-1.0 employs RL to learn to generate not only solution rollouts, but also the scallfold that drive those rollouts. By jointly optimizing the scaffold and the resulting solution, the model  discovers better search trajectories and generates higher-quality solutions. 
+- **Licence**: MIT licensed, globally accessible, and free from regional limitations.
+
+<img style="width: 100%; max-width: 900px;" src="assets/ornith_397b_eval.png" alt="Ornith 35B Benchmark Results" title="Ornith 35B Benchmark Results">
+
+## Ornith 1.0 397B 
+
+This model card documents **Ornith-1.0-397B**, the lightweight member of the Ornith family, designed for efficient single-GPU deployment.
+
+
+### Benchmarks
+
+<div style="font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Roboto,sans-serif;width:100%;margin:0 auto;padding:16px 0">
+<table style="width:100%;table-layout:fixed;border-collapse:collapse;font-size:13px">
+<thead><tr>
+<th style="width:28%;padding:10px 7px;text-align:left;font-weight:600;border-bottom:2px solid #FD8E5B;color:#FD8E5B"></th><th style="width:14.4%;padding:10px 7px;text-align:center;font-weight:700;border-bottom:2px solid #FD8E5B;color:#FD8E5B;font-size:14px;background:rgba(253, 142, 91, 0.12)">Ornith-1.0-397B-A3B</th><th style="width:14.4%;padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #FD8E5B;color:#FD8E5B;font-size:14px;">Qwen3.5-35B-A3B</th><th style="width:14.4%;padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #FD8E5B;color:#FD8E5B;font-size:14px;">Qwen3.6-35B-A3B</th><th style="width:14.4%;padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #FD8E5B;color:#FD8E5B;font-size:14px;">Gemma4-31B</th><th style="width:14.4%;padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #FD8E5B;color:#FD8E5B;font-size:14px;border-left:2px solid rgba(253, 142, 91, 0.55);">Qwen3.5-397B</th></tr></thead>
+<tbody>
+<tr><td colspan="5" style="padding:8px 12px;font-weight:600;color:#FD8E5B;border-bottom:1px solid rgba(253, 142, 91, 0.2);background:rgba(253, 142, 91, 0.1)">Agentic Coding</td><td colspan="1" style="border-bottom:1px solid rgba(253, 142, 91, 0.2);background:rgba(253, 142, 91, 0.1);border-left:2px solid rgba(253, 142, 91, 0.55);"></td></tr>
+<tr>
+<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">Terminal-Bench 2.1 <sub><small>(Terminus-2)</small></sub></td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);font-weight:600;color:#FD8E5B;background:rgba(253, 142, 91, 0.06)">64.2</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">41.4</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">52.5</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">42.1</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);border-left:2px solid rgba(253, 142, 91, 0.55);">53.5</td>
+</tr>
+<tr>
+<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">SWE-bench Verified</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);font-weight:600;color:#FD8E5B;background:rgba(253, 142, 91, 0.06)">75.6</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">70.0</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">73.4</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">52.0</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);border-left:2px solid rgba(253, 142, 91, 0.55);">76.4</td>
+</tr>
+<tr>
+<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">SWE-bench Pro</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);font-weight:600;color:#FD8E5B;background:rgba(253, 142, 91, 0.06)">50.4</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">44.6</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">49.5</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">35.7</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);border-left:2px solid rgba(253, 142, 91, 0.55);">51.6</td>
+</tr>
+<tr>
+<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">SWE-bench Multilingual</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);font-weight:600;color:#FD8E5B;background:rgba(253, 142, 91, 0.06)">69.3</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">60.3</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">67.2</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">51.7</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);border-left:2px solid rgba(253, 142, 91, 0.55);">69.3</td>
+</tr>
+<tr>
+<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">NL2Repo</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);font-weight:600;color:#FD8E5B;background:rgba(253, 142, 91, 0.06)">34.6</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">20.5</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">29.4</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">15.5</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);border-left:2px solid rgba(253, 142, 91, 0.55);">36.8</td>
+</tr>
+<tr>
+<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">Claw-eval Avg</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);font-weight:600;color:#FD8E5B;background:rgba(253, 142, 91, 0.06)">69.8</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">65.4</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">68.7</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">48.5</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);border-left:2px solid rgba(253, 142, 91, 0.55);">70.7</td>
+</tr>
+</tbody>
+</table>
+<p style="margin-top:12px;font-size:10px;opacity:0.7">
+* Terminal-Bench 2.1: Harbor/Terminus-2, 3h timeout, 32 CPU / 48GB RAM, avg of 5 runs.<br/>
+* All SWE-Bench：Mini-SWE-Agent, temp=1.0, top_p=0.95, 200K context window.<br/>
+* NL2Repo：400K context, 48k output, anti-hacking filters.<br/>
+* ClawEval: An agentic code benchmark over real-user task distributions; temp=0.6, 256K context.<br/>
+</p>
+
+</div>
+
+
+
+
+## Quickstart
+
+
+<div style="border-left:4px solid #FD8E5B;background:rgba(253,142,91,0.1);border-radius:6px;padding:12px 16px;font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Roboto,sans-serif;font-size:14px;line-height:1.6">
+<div style="font-weight:700;color:#FD8E5B;margin-bottom:6px">📝 NOTE</div>
+<p style="margin:0 0 10px"><b>Ornith-1.0-397B</b> is a <b>reasoning model</b>: by default the assistant turn opens with a <code style="background:rgba(253,142,91,0.15);padding:1px 5px;border-radius:4px">&lt;think&gt; … &lt;/think&gt;</code> block before the final answer. The serving recipes below enable a reasoning parser so the chain-of-thought is returned in a separate <code style="background:rgba(253,142,91,0.15);padding:1px 5px;border-radius:4px">reasoning_content</code> field, and a tool-call parser so the model's <code style="background:rgba(253,142,91,0.15);padding:1px 5px;border-radius:4px">&lt;tool_call&gt;</code> blocks are surfaced as OpenAI-style <code style="background:rgba(253,142,91,0.15);padding:1px 5px;border-radius:4px">tool_calls</code>.</p>
+<p style="margin:0 0 6px">Serving Ornith-1.0-397B requires recent runtimes:</p>
+<ul style="margin:0;padding-left:20px">
+<li><b>Transformers</b> ≥ 5.8.1</li>
+<li><b>vLLM</b> ≥ 0.19.1</li>
+<li><b>SGLang</b> ≥ 0.5.9</li>
+</ul>
+</div>
+
+### Serving Ornith-1.0-397B
+
+The two recipes below stand up an OpenAI-compatible server on a single 8×80GB GPU node (tensor-parallel 8). Adjust `--tensor-parallel-size` / `--tp` to the number of GPUs you have.
+
+#### vLLM
+
+```bash
+vllm serve deepreinforce-ai/Ornith-1.0-397B \
+    --served-model-name Ornith-1.0-397B \
+    --tensor-parallel-size 8 \
+    --host 0.0.0.0 --port 8000 \
+    --max-model-len 262144 \
+    --gpu-memory-utilization 0.90 \
+    --enable-prefix-caching \
+    --enable-auto-tool-choice --tool-call-parser qwen3_xml \
+    --reasoning-parser qwen3 \
+    --trust-remote-code
+```
+
+#### SGLang
+
+```bash
+python -m sglang.launch_server \
+    --model-path deepreinforce-ai/Ornith-1.0-397B \
+    --served-model-name Ornith-1.0-397B \
+    --tp 8 \
+    --host 0.0.0.0 --port 8000 \
+    --context-length 262144 \
+    --mem-fraction-static 0.85 \
+    --tool-call-parser qwen3_coder \
+    --reasoning-parser qwen3
+```
+
+#### Hugging Face Transformers
+
+For a quick local test (or to script offline generation), load the model directly with Transformers. Make sure you have a recent release installed — see the [Transformers installation guide](https://huggingface.co/docs/transformers/installation); Ornith-1.0-397B requires `transformers >= 5.8.1`.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "deepreinforce-ai/Ornith-1.0-397B"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    dtype="auto",
+    device_map="auto",
+)
+
+messages = [
+    {"role": "user", "content": "Write a Python function is_prime(n). Keep it short."}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+)
+
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+generated = model.generate(
+    **inputs,
+    max_new_tokens=512,
+    do_sample=True,
+    temperature=0.6,
+    top_p=0.95,
+    top_k=20,
+)
+output_ids = generated[0][inputs.input_ids.shape[1]:]
+
+# The reply contains a <think> ... </think> reasoning block followed by the answer.
+content = tokenizer.decode(output_ids, skip_special_tokens=True)
+print(content)
+```
+
+To split the reasoning trace from the final answer, parse on the `</think>` marker:
+
+```python
+text = tokenizer.decode(output_ids, skip_special_tokens=True)
+if "</think>" in text:
+    reasoning, answer = text.split("</think>", 1)
+    reasoning = reasoning.replace("<think>", "").strip()
+    answer = answer.strip()
+else:
+    reasoning, answer = "", text.strip()
+```
+
+### Using Ornith-1.0-397B via the Chat Completions API
+
+Once a vLLM or SGLang server is running, talk to it with any OpenAI-compatible client.
+
+#### Basic Usage
+
+```python
+from openai import OpenAI
+
+client = OpenAI(
+    base_url="http://localhost:8000/v1",
+    api_key="EMPTY",  # any non-empty string works for a local server
+)
+
+response = client.chat.completions.create(
+    model="Ornith-1.0-397B",
+    messages=[
+        {"role": "user", "content": "Write a one-line Python lambda that squares a number."}
+    ],
+    temperature=0.6,
+    top_p=0.95,
+    max_tokens=1024,
+)
+
+message = response.choices[0].message
+# reasoning_content holds the <think> trace; content holds the final answer.
+print("reasoning:", getattr(message, "reasoning_content", None))
+print("answer:", message.content)
+```
+
+You can also stream tokens, or hand the model tools — Ornith-1.0-397B emits well-formed function calls that the server parses into the standard `tool_calls` field:
+
+```python
+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="Ornith-1.0-397B",
+    messages=[{"role": "user", "content": "What is the weather in Paris right now?"}],
+    tools=tools,
+    tool_choice="auto",
+    temperature=0.6,
+    max_tokens=2048,
+)
+
+tool_call = response.choices[0].message.tool_calls[0]
+print(tool_call.function.name, tool_call.function.arguments)
+# -> get_weather {"city": "Paris"}
+```
+
+You can point any OpenAI-compatible SDK (Python, Node.js, etc.) or `curl` at the same `/v1/chat/completions` endpoint.
+
+## Agentic Usage
+
+Ornith-1.0-397B excels in tool-calling and agentic coding capabilities.
+
+### Agent Frameworks
+
+Because Ornith-1.0-397B exposes an OpenAI-compatible endpoint with tool calling, it works out of the box with standard agent frameworks. Below is a minimal example that connects Ornith-1.0-397B to tools through an MCP server.
+
+```python
+
+```
+
+**Examples of using Ornith with agent harness:**
+
+#### Hermes Agent
+```bash
+
+```
+
+#### OpenHands
+```bash
+
+```
+
+#### llama.cpp / Ollama
+```bash
+
+```
+
+#### Unsloth Studio
+
+```bash
+
+```
+
+#### OpenClaw
+
+```bash
+
+```
+
+
+### Coding CLIs
+
+Ornith-1.0-397B is optimized for terminal-based coding agents. Point any OpenAI-compatible coding CLI at your Ornith-1.0-397B endpoint (set `OPENAI_BASE_URL` and `OPENAI_API_KEY`) to understand large codebases, automate tedious work, and ship faster.
+
+
+#### OpenCode
+```bash
+
+```
+
+
+### Citation
+
+If you find our work helpful, feel free to give us a cite.
+
+```bibtex
+@misc{ornith_397b,
+    title = {{Ornith-1.0-397B}: Agentic Coding, Open to All},
+    url = {https://deep-reinforce.com/ornith_1_0.html},
+    author = {{DeepReinforce Team}},
+    year = {2026}
+}
+```