Ornith-1.5-397B API & Inference Endpoint

Ornith 1.5 397B

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.

Benchmarks

Quickstart

Serving Ornith-1.5-397B

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

bash
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

SGLang

bash
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

For Long-Context

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:

json
{
    "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:

bash
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:

bash
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

Using Ornith-1.5-397B 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-1.5-397B",
    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:

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-1.5-397B",
    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.

Agentic Usage

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

bash
ollama run hf.co/ornith-ai/Ornith-1.5-397B-GGUF

Atomic.chat

bash
# 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

bash
# 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 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="ornith-ai/Ornith-1.5-397B"

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="ornith-ai/Ornith-1.5-397B"

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(
#       "ornith-ai/Ornith-1.5-397B",
#       max_seq_length=262144,
#       load_in_4bit=True,
#   )

Coding CLIs

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.

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": { "ornith-ai/Ornith-1.5-397B": { "name": "Ornith-1.5-397B" } }
#     }
#   }
# }

opencode

Citation

If you find our work helpful, feel free to give us a cite.

bibtex
@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}
}

Ornith 1.5 397B

Benchmarks

Quickstart

Serving Ornith-1.5-397B

vLLM

bash
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

SGLang

bash
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

For Long-Context

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:

json
{
    "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:

bash
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:

bash
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

Using Ornith-1.5-397B 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-1.5-397B",
    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:

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-1.5-397B",
    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.

Agentic Usage

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

bash
ollama run hf.co/ornith-ai/Ornith-1.5-397B-GGUF

Atomic.chat

bash
# 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

bash
# 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 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="ornith-ai/Ornith-1.5-397B"

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="ornith-ai/Ornith-1.5-397B"

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(
#       "ornith-ai/Ornith-1.5-397B",
#       max_seq_length=262144,
#       load_in_4bit=True,
#   )

Coding CLIs

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": { "ornith-ai/Ornith-1.5-397B": { "name": "Ornith-1.5-397B" } }
#     }
#   }
# }

opencode

Citation

If you find our work helpful, feel free to give us a cite.

bibtex
@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}
}

Ornith-1.5-397B

README

Ornith 1.5 397B

Benchmarks

Quickstart

Serving Ornith-1.5-397B

vLLM

SGLang

For Long-Context

Using Ornith-1.5-397B via the Chat Completions API

Basic Usage

Agentic Usage

Ollama

Atomic.chat

llama.cpp

Hermes Agent

OpenClaw

Unsloth Studio

Coding CLIs

OpenCode

Citation

Explore FriendliAI today

README

Ornith 1.5 397B

Benchmarks

Quickstart

Serving Ornith-1.5-397B

vLLM

SGLang

For Long-Context

Using Ornith-1.5-397B via the Chat Completions API

Basic Usage

Agentic Usage

Ollama

Atomic.chat

llama.cpp

Hermes Agent

OpenClaw

Unsloth Studio

Coding CLIs

OpenCode

Citation