--- tags: - unsloth - qwen3_5_moe base_model: - deepreinforce-ai/Ornith-1.0-397B license: mit license_link: https://huggingface.co/deepreinforce-ai/Ornith-1.0-397B/blob/main/LICENSE pipeline_tag: text-generation ---

Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants.

# 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. 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
Ornith-1.0-397B Qwen3.5-397B Qwen3.7-Max GLM-5.2-744B Minimax-M3-428B DeepSeek-V4-Pro-1.6T Claude Opus 4.7 Claude Opus 4.8
Agentic Coding
Terminal-Bench 2.1 (Terminus-2) 77.5 53.5 73.5 81.0 64 64 70.3 85
Terminal-Bench 2.1 (Claude Code) 78.2 48.6 69.8 82.7 - 66.5 69.7 78.9
SWE-bench Verified 82.4 76.4 80.4 - - 80.6 80.8 87.6
SWE-bench Pro 62.2 51.6 60.6 62.1 59 55.4 64.3 69.2
SWE-bench Multilingual 78.9 69.3 78.3 - - 76.2 - -
NL2Repo 48.2 36.8 47.2 48.9 42.1 - - 69.7
Claw-eval Avg 77.1 70.7 65.2 - - 75.8 78.2 -
SWE Atlas - QnA 41.2 20.4 - - 37.9 27.2 40.3 48.8
SWE Atlas - RF 42.6 18.4 - - - - 48.6 46.7
SWE Atlas - TW 39.1 18.5 - - 30.8 - 38.5 -

* Terminal-Bench 2.1 (Terminus-2): We evaluate Terminal-Bench 2.1 using the Harbor/Terminus-2 framework with parser=json, temperature=1.0, top_p=1.0, and a 128K context window. Each run uses a 4-hour timeout with 32 CPU cores and 48GB RAM, and results are averaged over 5 runs. We adjust the Qwen chat template to ensure consistency between training and inference (https://huggingface.co/deepreinforce-ai/Ornith-1.0-397B/blob/main/chat_template.jinja), and modify Harbor to align with vLLM's reasoning_content key.
* Terminal-Bench 2.1 (Claude Code): We evaluate Terminal-Bench 2.1 using Claude Code 2.1.126 with parser=json, temperature=1.0, top_p=1.0, max_new_tokens=131072. Results are averaged over 5 runs. Again, Qwen chat template needs to be modified.
* SWE-Bench Verified, Pro and Multilingual: using OpenHands harness with temp=1.0, top_p=0.95, 256k context window.
* SWE Atlas QnA, RF, TW: using mini SWE agent harness with temp=1.0, top_p=0.95, 128K context window. Results are averaged over 5 runs.
* NL2Repo: with temperature=1.0, top_p=1.0, 400K context, 48K output and anti-hacking filters.
* ClawEval: An agentic code benchmark over real-user task distributions; temp=0.6 and 256K context.

## Quickstart
📝 NOTE

Ornith-1.0-397B is a reasoning model: by default the assistant turn opens with a <think> … </think> block before the final answer. The serving recipes below enable a reasoning parser so the chain-of-thought is returned in a separate reasoning_content field, and a tool-call parser so the model's <tool_call> blocks are surfaced as OpenAI-style tool_calls.

Serving Ornith-1.0-397B requires recent runtimes:

### 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 ... 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 `` marker: ```python text = tokenizer.decode(output_ids, skip_special_tokens=True) if "" in text: reasoning, answer = text.split("", 1) reasoning = reasoning.replace("", "").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 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 import os from openai import OpenAI client = OpenAI( base_url=os.getenv("OPENAI_BASE_URL", "http://localhost:8000/v1"), api_key=os.getenv("OPENAI_API_KEY", "EMPTY"), ) tools = [ { "type": "function", "function": { "name": "run_shell", "description": "Run a shell command and return its output.", "parameters": { "type": "object", "properties": { "command": {"type": "string", "description": "The command to run"} }, "required": ["command"], }, }, } ] messages = [{"role": "user", "content": "List the Python files in the current directory."}] response = client.chat.completions.create( model="deepreinforce-ai/Ornith-1.0-397B", messages=messages, tools=tools, temperature=0.6, top_p=0.95, ) print(response.choices[0].message) ``` **Examples of using Ornith with agent harness:** #### Hermes Agent ```bash # Hermes talks to any OpenAI-compatible endpoint — point it at your Ornith server. export OPENAI_BASE_URL="http://localhost:8000/v1" export OPENAI_API_KEY="EMPTY" export MODEL="deepreinforce-ai/Ornith-1.0-397B" ``` #### OpenClaw ```bash # OpenClaw talks to any OpenAI-compatible endpoint — point it at your Ornith server. export OPENAI_BASE_URL="http://localhost:8000/v1" export OPENAI_API_KEY="EMPTY" export OPENAI_MODEL="deepreinforce-ai/Ornith-1.0-397B" ``` #### Unsloth Studio ```bash pip install unsloth # Load Ornith for fast local inference or fine-tuning (Python): # from unsloth import FastLanguageModel # model, tokenizer = FastLanguageModel.from_pretrained( # "deepreinforce-ai/Ornith-1.0-397B", # max_seq_length=262144, # load_in_4bit=True, # ) ``` #### OpenHands ```bash pip install openhands-ai # OpenHands routes through LiteLLM; the "openai/" prefix selects the OpenAI-compatible path. export LLM_MODEL="openai/deepreinforce-ai/Ornith-1.0-397B" export LLM_BASE_URL="http://localhost:8000/v1" export LLM_API_KEY="EMPTY" # Launch the CLI (or run the official OpenHands Docker image with the same env vars). openhands ``` ### 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 # Register your local Ornith endpoint as a provider in ~/.config/opencode/opencode.json: # # { # "$schema": "https://opencode.ai/config.json", # "provider": { # "ornith": { # "npm": "@ai-sdk/openai-compatible", # "name": "Ornith (local)", # "options": { "baseURL": "http://localhost:8000/v1", "apiKey": "EMPTY" }, # "models": { "deepreinforce-ai/Ornith-1.0-397B": { "name": "Ornith-1.0-397B" } } # } # } # } opencode ``` ### 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} } ```