GLM-5.2 is live. #1 throughput on OpenRouter, pay-per-token on FriendliAI. Try it today ➜
deepreinforce-ai
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
deepreinforce-ai
Model Tree
Base
this model
Input Modalities
Text
Image
Video
Output Modalities
Text
Supported Functionality
Dedicated Endpoints
This model card documents Ornith-1.0-397B, the lightweight member of the Ornith family, designed for efficient single-GPU deployment.
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.
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
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
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; Ornith-1.0-397B requires transformers >= 5.8.1.
python
from transformers import AutoModelForCausalLM, AutoTokenizermodel_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()
Once a vLLM or SGLang server is running, talk to it with any OpenAI-compatible client.
python
from openai import OpenAIclient = 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.
Examples of using Ornith with agent harness:
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"
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"
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,# )
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
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.
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
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}}