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Model Details
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urakozz
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deepreinforce-ai/Ornith-1.0-35B
Input Modalities
Output Modalities
Supported Functionality
| Parameter | Value |
|---|---|
| Base Model | deepreinforce-ai/Ornith-1.0-35B |
| Quantization Scheme | W4A16 (4-bit weights, 16-bit activations) |
| Quantization Engine | Intel Auto-Round |
| Group Size | 64 |
| Calibration Sequence Length | 2048 |
Calibration Samples (nsamples) | 64 |
Optimization Iterations (iters) | 300 |
| Format | auto_round |
Ornith-1.0-35B is a reasoning model: by default, the assistant turn opens with a <think> ... </think> block before the final answer. The serving commands below configure the reasoning and tool-call parsers so the chain-of-thought is returned in a separate reasoning_content field.
Install vLLM (version >= 0.25.1 recommended):
bash
pip install auto-round vllmStart the vLLM OpenAI-compatible server:
bash
vllm serve urakozz/Ornith-1.0-35B-int4-Autoround \ --served-model-name Ornith-1.0-35B-int4-Autoround \ --quantization auto-round \ --host 0.0.0.0 --port 8000 \ --max-model-len 131072 \ --gpu-memory-utilization 0.90 \ --enable-auto-tool-choice --tool-call-parser qwen3_xml \ --reasoning-parser qwen3 \ --trust-remote-codeOnce the vLLM server is running, connect via any OpenAI client:
python
from openai import OpenAI client = OpenAI( base_url="http://localhost:8000/v1", api_key="EMPTY",) response = client.chat.completions.create( model="Ornith-1.0-35B-int4-Autoround", messages=[ {"role": "user", "content": "Write a Python function to check if a number is prime."} ], temperature=0.6, top_p=0.95, max_tokens=2048,) message = response.choices[0].message # reasoning_content holds the <think> trace; content holds the final answerprint("Reasoning:", getattr(message, "reasoning_content", None))print("Answer:", message.content)W4A16 and group_size=64 reduces memory consumption from ~70GB+ down to ~20–22GB VRAM, enabling execution on single consumer/workstation GPUs.bibtex
@misc{ornith-35b, title = {{Ornith-1.0-35B}: Agentic Coding, Open to All}, url = {[https://deep-reinforce.com/ornith_1_0.html](https://deep-reinforce.com/ornith_1_0.html)}, author = {{DeepReinforce Team}}, year = {2026}}text
undefinedvllm serve urakozz/Ornith-1.0-35B-int4-Autoround \ --served-model-name Ornith-1.0-35B-int4-Autoround \ --quantization auto-round \ --host 0.0.0.0 --port 8000 \ --max-model-len 131072 \ --gpu-memory-utilization 0.90 \ --enable-auto-tool-choice --tool-call-parser qwen3_xml \ --reasoning-parser qwen3 \ --trust-remote-codefrom openai import OpenAI client = OpenAI( base_url="http://localhost:8000/v1", api_key="EMPTY",) response = client.chat.completions.create( model="Ornith-1.0-35B-int4-Autoround", messages=[ {"role": "user", "content": "Write a Python function to check if a number is prime."} ], temperature=0.6, top_p=0.95, max_tokens=2048,) message = response.choices[0].message # reasoning_content holds the <think> trace; content holds the final answerprint("Reasoning:", getattr(message, "reasoning_content", None))print("Answer:", message.content)@misc{ornith-35b, title = {{Ornith-1.0-35B}: Agentic Coding, Open to All}, url = {[https://deep-reinforce.com/ornith_1_0.html](https://deep-reinforce.com/ornith_1_0.html)}, author = {{DeepReinforce Team}}, year = {2026}}