Quantized release
This repository is an AutoRound W4A16 quantized version of deepreinforce-ai/Ornith-1.0-397B.
It is not the original BF16 model repository.

============================================================

Ornith-1.0-397B-W4A16-AutoRound Quantized Model Card

============================================================

Quantized Model Summary

Item	Value
Base model	`deepreinforce-ai/Ornith-1.0-397B`
Quantized repo	`Pilcothink/Ornith-1.0-397B-W4A16-AutoRound`
Quantization method	AutoRound
Quantization format	W4A16
Weight precision	4-bit
Activation precision	16-bit
Base model relation	`quantized`
License	MIT, inherited from upstream model

Upload Status

The model upload is complete!

Notes

This repository is intended to host the AutoRound W4A16 quantized weights for Ornith-1.0-397B.

Benchmark numbers, runtime requirements, and serving recipes in the upstream section below describe the original BF16 model unless explicitly stated otherwise.

Attribution and License

This model is a quantized derivative of deepreinforce-ai/Ornith-1.0-397B, which is released under the MIT License.

The original model card is preserved below for attribution and reference.

============================================================

Original Upstream Model Card

============================================================

The following section is copied from the upstream model card of deepreinforce-ai/Ornith-1.0-397B.
It describes the original BF16 model, not this AutoRound W4A16 quantized release.

Ornith-1.0-397B

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.

Ornith 1.0 397B

This model card documents Ornith-1.0-397B, the lightweight member of the Ornith family, designed for efficient single-GPU deployment.

Benchmarks

	Ornith-1.0-397B	Qwen3.5-397B	Qwen3.7-Max	GLM-5.2-744B	Minimax-M3-428B	DeepSeek-V4-Pro-1.6T	Claude Opus 4.7	Claude Opus 4.8
Agentic Coding
Terminal-Bench 2.1 _(Terminus-2)	77.5	53.5	73.5	81.0	64	64	70.3	85
Terminal-Bench 2.1 _{(Claude Code)}	78.2	48.6	69.8	82.7	-	66.5	69.7	78.9
SWE-bench Verified	82.4	76.4	80.4	-	-	80.6	80.8	87.6
SWE-bench Pro	62.2	51.6	60.6	62.1	59	55.4	64.3	69.2
SWE-bench Multilingual	78.9	69.3	78.3	-	-	76.2	-	-
NL2Repo	48.2	36.8	47.2	48.9	42.1	-	-	69.7
Claw-eval Avg	77.1	70.7	65.2	-	-	75.8	78.2	-
SWE Atlas - QnA	41.2	20.4	-	-	37.9	27.2	40.3	48.8
SWE Atlas - RF	42.6	18.4	-	-	-	-	48.6	46.7
SWE Atlas - TW	39.1	18.5	-	-	30.8	-	38.5	-

* 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.
* 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.
* SWE-Bench Verified, Pro and Multilingual: using OpenHands harness with temp=1.0, top_p=0.95, 256k context window.
* 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.
* NL2Repo: with temperature=1.0, top_p=1.0, 400K context, 48K output and anti-hacking filters.
* ClawEval: An agentic code benchmark over real-user task distributions; temp=0.6 and 256K context.

Quickstart

📝 NOTE

Ornith-1.0-397B is a reasoning model: by default the assistant turn opens with a <think> … </think> block before the final answer. The serving recipes below enable a reasoning parser so the chain-of-thought is returned in a separate reasoning_content field, and a tool-call parser so the model's <tool_call> blocks are surfaced as OpenAI-style tool_calls.

Serving Ornith-1.0-397B requires recent runtimes:

Transformers ≥ 5.8.1
vLLM ≥ 0.19.1
SGLang ≥ 0.5.9

Serving Ornith-1.0-397B

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

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

SGLang

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

Hugging Face Transformers

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, AutoTokenizer

model_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()

Using Ornith-1.0-397B 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-1.0-397B",
    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.

Agentic Usage

Ornith-1.0-397B excels in tool-calling and agentic coding capabilities.

Agent Frameworks

Because Ornith-1.0-397B 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-1.0-397B to tools through an MCP server.

python

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-1.0-397B",
    messages=messages,
    tools=tools,
    temperature=0.6,
    top_p=0.95,
)
print(response.choices[0].message)

Examples of using Ornith with agent harness:

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="deepreinforce-ai/Ornith-1.0-397B"

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="deepreinforce-ai/Ornith-1.0-397B"

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(
#       "deepreinforce-ai/Ornith-1.0-397B",
#       max_seq_length=262144,
#       load_in_4bit=True,
#   )

OpenHands

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

Coding CLIs

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.

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": { "deepreinforce-ai/Ornith-1.0-397B": { "name": "Ornith-1.0-397B" } }
#     }
#   }
# }

opencode

Citation

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}
}

Pilcothink/Ornith-1.0-397B-W4A16-AutoRound

Ornith-1.0-397B-W4A16-AutoRound

Ornith-1.0-397B-W4A16-AutoRound Quantized Model Card

Quantized Model Summary

Upload Status

Notes

Attribution and License

Original Upstream Model Card

Ornith-1.0-397B

Ornith 1.0 397B

Benchmarks

Quickstart

Serving Ornith-1.0-397B

vLLM

SGLang

Hugging Face Transformers

Using Ornith-1.0-397B via the Chat Completions API

Basic Usage

Agentic Usage

Agent Frameworks

Hermes Agent

OpenClaw

Unsloth Studio

OpenHands

Coding CLIs

OpenCode

Citation

Pilcothink/
Ornith-1.0-397B-W4A16-AutoRound