Cataloged from ornith-ai/Ornith-1.5-397B-NVFP4
Chirp Chirp! 🐦 We are introducing Ornith-1.5, a major step toward building foundation models through end-to-end self-improvement.
Ornith-1.5 extends Ornith-1.0 (which was developed on top of Qwen3.5 and Gemma4 with additional continued pretraining, mid-training, and post-training) by expanding the self-improvement loop from scaffold and rollout optimization to jointly optimizing task generation, scaffold construction, and solution rollouts. Rather than relying on a fixed set of human-curated tasks and manually designed harnesses, Ornith-1.5 continuously generates new training tasks, discovers effective strategies for solving them, and improves the policy through reinforcement learning. For more details on the task, harness, and rollout reward design, please refer to our blog.
This model card documents Ornith-1.5-397B, the flagship member of the Ornith-1.5 family — a 397B mixture-of-experts model. It scores 86.1 on Terminal-Bench 2.1 and 56.0 on DeepSWE, performing on par with Claude Opus 4.8 (85.0 and 59.0) while outperforming leading open-source models of similar scale, including GLM-5.2 and DeepSeek-V4-Flash-0731.
Ornith-1.5-397B is a ~397B mixture-of-experts model (≈800 GB in bf16), so multi-GPU serving is required. The recipes below use 8-way tensor parallelism on a single node (e.g., 8× H200 141GB); adjust --tensor-parallel-size / --tp to match your hardware, or use FP8/INT4 quantized builds for smaller deployments.
vllm serve ornith-ai/Ornith-1.5-397B \
--served-model-name Ornith-1.5-397B \
--host 0.0.0.0 --port 8000 \
--tensor-parallel-size 8 \
--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 ornith-ai/Ornith-1.5-397B \
--served-model-name Ornith-1.5-397B \
--host 0.0.0.0 --port 8000 \
--tp 8 \
--context-length 262144 \
--mem-fraction-static 0.85 \
--tool-call-parser qwen3_coder \
--reasoning-parser qwen3
Ornith-1.5-397B handles context windows of up to 262,144 tokens. When a task's combined input and output must go beyond this limit, we suggest extending the effective window with RoPE scaling — YaRN is the technique we validate against, and it is already built into both vLLM and SGLang. With a scaling factor of 4.0, the usable window grows to roughly 1M tokens.
You can turn YaRN on in either of two ways:
Edit the checkpoint's config.json. Add a rope_scaling block to the model configuration:
{
"rope_scaling": {
"rope_type": "yarn",
"factor": 4.0,
"original_max_position_embeddings": 262144
}
}
Override at launch time. Leave the checkpoint untouched and extend the serve commands above with the equivalent flags.
vLLM:
VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 vllm serve ornith-ai/Ornith-1.5-397B ... --hf-overrides '{"rope_scaling": {"rope_type": "yarn", "factor": 4.0, "original_max_position_embeddings": 262144}}' --max-model-len 1000000
SGLang:
SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN=1 python -m sglang.launch_server ... --json-model-override-args '{"rope_scaling": {"rope_type": "yarn", "factor": 4.0, "original_max_position_embeddings": 262144}}' --context-length 1000000
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-1.5-397B",
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-1.5-397B 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-1.5-397B",
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.
Ornith-1.5-397B excels in tool-calling and agentic coding. It exposes an OpenAI-compatible endpoint with tool calling and works out of the box with standard agent frameworks.
Examples of using Ornith with agents:
ollama run hf.co/ornith-ai/Ornith-1.5-397B-GGUF
# Atomic.chat loads a GGUF build of Ornith (ornith-ai/Ornith-1.5-397B-GGUF)
# through llama.cpp's OpenAI-compatible API on port 8000.
llama-server -hf ornith-ai/Ornith-1.5-397B-GGUF --port 8000 -c 262144
# llama.cpp — serve an OpenAI-compatible API on port 8000.
llama-server -hf ornith-ai/Ornith-1.5-397B-GGUF --port 8000 -c 262144
# 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="ornith-ai/Ornith-1.5-397B"
# 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="ornith-ai/Ornith-1.5-397B"
pip install unsloth
# Load Ornith for fast local inference or fine-tuning (Python):
# from unsloth import FastLanguageModel
# model, tokenizer = FastLanguageModel.from_pretrained(
# "ornith-ai/Ornith-1.5-397B",
# max_seq_length=262144,
# load_in_4bit=True,
# )
Ornith-1.5-397B is optimized for terminal-based coding agents. Point any OpenAI-compatible coding CLI at your Ornith-1.5-397B endpoint (set OPENAI_BASE_URL and OPENAI_API_KEY) to understand large codebases, automate tedious work, and ship faster.
# 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": { "ornith-ai/Ornith-1.5-397B": { "name": "Ornith-1.5-397B" } }
# }
# }
# }
opencode
If you find our work helpful, feel free to give us a cite.
@misc{ornith_1_5,
title = {{Ornith-1.5}: From Self-Scaffolding to Self-Improvement},
url = {https://ornith.ai/ornith_1_5.html},
author = {{Ornith Team}},
year = {2026}
}