Ornith-1.0-35B-GGUF

[](https://deep-reinforce.com/ornith.html) 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. This model card documents **Ornith-1.0-35B**, the lightweight member of the Ornith family, designed for efficient single-GPU deployment. * 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.<br/> * 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.<br/> * SWE-Bench Verified, Pro and Multilingual: using OpenHands harness with temp=1.0, top_p=0.95, 256k context window.<br/> * 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.<br/> * NL2Repo: with temperature=1.0, top_p=1.0, 400K context, 48K output and anti-hacking filters.<br/> * ClawEval: An agentic code benchmark over real-user task distributions; temp=0.6 and 256K context.<br/> 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 serve deepreinforce-ai/Ornith-1.0-35B \ --served-model-name Ornith-1.0-35B \ --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 python -m sglang.launch_server \ --model-path deepreinforce-ai/Ornith-1.0-35B \ --served-model-name Ornith-1.0-35B \ --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 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-1.0-35B requires `transformers >= 5.8.1`. from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "deepreinforce-ai/Ornith-1.0-35B" 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]:] 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: 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() Once a vLLM or SGLang server is running, talk to it with any OpenAI-compatible client. 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-1.0-35B", 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 print("reasoning:", getattr(message, "reasoning_content", None)) print("answer:", message.content) You can also stream tokens, or hand the model tools — Ornith-1.0-35B emits well-formed function calls that the server parses into the standard `tool_calls` field: 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-1.0-35B", 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) You can point any OpenAI-compatible SDK (Python, Node.js, etc.) or `curl` at the same `/v1/chat/completions` endpoint. Ornith-1.0-35B excels in tool-calling and agentic coding capabilities. Because Ornith-1.0-35B 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-1.0-35B to tools through an MCP server. 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-1.0-35B", messages=messages, tools=tools, temperature=0.6, top_p=0.95, ) print(response.choices[0].message) **Examples of using Ornith with agent harness:** export OPENAI_BASE_URL="http://localhost:8000/v1" export OPENAI_API_KEY="EMPTY" export MODEL="deepreinforce-ai/Ornith-1.0-35B" llama-server -hf deepreinforce-ai/Ornith-1.0-35B-GGUF --port 8000 -c 262144 ollama run hf.co/deepreinforce-ai/Ornith-1.0-35B-GGUF export OPENAI_BASE_URL="http://localhost:8000/v1" export OPENAI_API_KEY="EMPTY" export OPENAI_MODEL="deepreinforce-ai/Ornith-1.0-35B" pip install unsloth pip install openhands-ai export LLM_MODEL="openai/deepreinforce-ai/Ornith-1.0-35B" export LLM_BASE_URL="http://localhost:8000/v1" export LLM_API_KEY="EMPTY" openhands Ornith-1.0-35B is optimized for terminal-based coding agents. Point any OpenAI-compatible coding CLI at your Ornith-1.0-35B endpoint (set `OPENAI_BASE_URL` and `OPENAI_API_KEY`) to understand large codebases, automate tedious work, and ship faster. opencode If you find our work helpful, feel free to give us a cite. @misc{ornith-35b, title = {{Ornith-1.0-35B}: Agentic Coding, Open to All}, url = {https://deep-reinforce.com/ornith_1_0.html}, author = {{DeepReinforce Team}}, year = {2026} }