Ornith-1.0

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Deployment

pip install --upgrade pip
pip install uv
uv pip install "sglang>=0.5.9"

docker pull lmsysorg/sglang:latest

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Playground

• 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.

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1. Model Introduction

• 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 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.
• Long context: model-card recipes use --context-length 262144.
• MIT license: the Hugging Face repos are released under MIT.

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2. Configuration Tips

• Reasoning parser: Ornith responses include <think>...</think>. Enable the --reasoning-parser qwen3 toggle in the 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 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.

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3. Usage Examples

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3.1 Basic Chat Completion

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)

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3.2 Tool Calling

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)