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Ornith-1.0-9B-AWQ
nicklas373/Ornith-1.0-9B-AWQ
Ornith-1.0-9B-AWQ is a image-text-to-text model from nicklas373. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for transformers.
This model Ornith-1.0-9B-AWQ was converted to AWQ format, from deepreinforce-ai/Ornith-1.0-9B using llm-compressor version 0.12.0 (https://github.com/vllm-project/llm-compressor.git). With using dataset ultrachat200k…
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Model card
From the Hugging Face model README
Ornith-1.0-9B-AWQ
Model Highlights
This model Ornith-1.0-9B-AWQ was converted to AWQ format, from deepreinforce-ai/Ornith-1.0-9B using llm-compressor version 0.12.0 (https://github.com/vllm-project/llm-compressor.git). With using dataset ultrachat_200k from HuggingFaceH4/ultrachat_200k & wikitext from Salesforce/wikitext
Datasets:
- HuggingFaceH4/ultrachat_200k
- Salesforce/wikitext
Base Model:
- deepreinforce-ai/Ornith-1.0-9B
Use with VLLM
- Download models at first by using hf
hf download nicklas373/Ornith-1.0-9B-AWQ - Copy hash for snapshots directory, then use it for chat templates and tool call parser ex: /home/xxx/.cache/huggingface/hub/models--nicklas373--Ornith-1.0-9B-AWQ/snapshots/HASH_CODE/xxx
- Run models with this command
vllm serve nicklas373/Ornith-1.0-9B-AWQ \
--chat-template '/home/xxx/.cache/huggingface/hub/models--nicklas373--Ornith-1.0-9B-it-AWQ/snapshots/HASH_CODE/chat_template.jinja' \
--chat-template-content-format openai \
--disable-fastapi-docs \
--dtype auto \
--enable-prefix-caching \
--served-model-name Ornith-1.0-9B-AWQ \
--seed 0 \
--quantization compressed-tensors \
--tokenizer 'deepreinforce-ai/Ornith-1.0-9B' \
--trust-remote-code
<img src="https://huggingface.co/deepreinforce-ai/Ornith-1.0-9B/resolve/main/assets/ornith_logo.png" alt="Ornith" width="600">
Ornith-1.0-9B
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 9B
This model card documents Ornith-1.0-9B, the most lightweight member of the Ornith family, designed for efficient single-GPU deployment.
Benchmarks
<div style="font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Roboto,sans-serif;width:100%;margin:0 auto;padding:16px 0"> <table style="width:100%;table-layout:fixed;border-collapse:collapse;font-size:13px"> <thead><tr> <th style="width:28%;padding:10px 7px;text-align:left;font-weight:600;border-bottom:2px solid #FD8E5B;color:#FD8E5B"></th> <th style="width:14.40%;padding:10px 7px;text-align:center;font-weight:700;border-bottom:2px solid #FD8E5B;color:#FD8E5B;font-size:14px;background:rgba(253, 142, 91, 0.12)">Ornith-1.0-9B</th> <th style="width:14.40%;padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #FD8E5B;color:#FD8E5B;font-size:14px">Qwen3.5-9B</th> <th style="width:14.40%;padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #FD8E5B;color:#FD8E5B;font-size:14px">Qwen3.5-35B</th> <th style="width:14.40%;padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #FD8E5B;color:#FD8E5B;font-size:14px">Gemma4-12B</th> <th style="width:14.40%;padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #FD8E5B;color:#FD8E5B;font-size:14px">Gemma4-31B</th> </tr></thead> <tbody> <tr><td colspan="6" style="padding:8px 12px;font-weight:600;color:#FD8E5B;border-bottom:1px solid rgba(253, 142, 91, 0.2);background:rgba(253, 142, 91, 0.1)">Agentic Coding</td></tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">Terminal-Bench 2.1 <sub><small>(Terminus-2)</small></sub></td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);font-weight:600;color:#FD8E5B;background:rgba(253, 142, 91, 0.06)">43.1</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">21.3</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">41.4</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">21</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">42.1</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">Terminal-Bench 2.1 <sub><small>(Claude Code)</small></sub></td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);font-weight:600;color:#FD8E5B;background:rgba(253, 142, 91, 0.06)">40.6</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">18.9</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">38.9</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">-</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">-</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">SWE-bench Verified</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);font-weight:600;color:#FD8E5B;background:rgba(253, 142, 91, 0.06)">69.4</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">53.2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">70</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">44.2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">52</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">SWE-bench Pro</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);font-weight:600;color:#FD8E5B;background:rgba(253, 142, 91, 0.06)">42.9</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">31.3</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">44.6</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">27.6</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">35.7</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">SWE-bench Multilingual</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);font-weight:600;color:#FD8E5B;background:rgba(253, 142, 91, 0.06)">52</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">39.7</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">60.3</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">32.5</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">51.7</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">NL2Repo</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);font-weight:600;color:#FD8E5B;background:rgba(253, 142, 91, 0.06)">27.2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">16.2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">20.5</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">10.3</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">15.5</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">Claw-eval Avg</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);font-weight:600;color:#FD8E5B;background:rgba(253, 142, 91, 0.06)">63.1</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">53.2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">65.4</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">32.5</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">48.5</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">SWE Atlas - QnA</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);font-weight:600;color:#FD8E5B;background:rgba(253, 142, 91, 0.06)">17.9</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">9.2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">13.2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">-</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">-</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">SWE Atlas - RF</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);font-weight:600;color:#FD8E5B;background:rgba(253, 142, 91, 0.06)">16.6</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">4.3</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">10.2</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">-</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">-</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">SWE Atlas - TW</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15);font-weight:600;color:#FD8E5B;background:rgba(253, 142, 91, 0.06)">15.3</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">4.4</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">9.8</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">-</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">-</td> </tr> </tbody> </table> <p style="margin-top:12px;font-size:10px;opacity:0.7"> * 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.<br/> * 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.<br/> * SWE-Bench Verified, Pro and Multilingual: using OpenHands harness with temp=1.0, top_p=0.95, 256k context window.<br/> * 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.<br/> * NL2Repo: with temperature=1.0, top_p=1.0, 400K context, 48K output and anti-hacking filters.<br/> * ClawEval: An agentic code benchmark over real-user task distributions; temp=0.6 and 256K context.<br/> </p> </div>Quickstart
<div style="border-left:4px solid #FD8E5B;background:rgba(253,142,91,0.1);border-radius:6px;padding:12px 16px;font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Roboto,sans-serif;font-size:14px;line-height:1.6"> <div style="font-weight:700;color:#FD8E5B;margin-bottom:6px">📝 NOTE</div> <p style="margin:0 0 10px"><b>Ornith-1.0-9B</b> is a <b>reasoning model</b>: by default the assistant turn opens with a <code style="background:rgba(253,142,91,0.15);padding:1px 5px;border-radius:4px"><think> … </think></code> block before the final answer. The serving recipes below enable a reasoning parser so the chain-of-thought is returned in a separate <code style="background:rgba(253,142,91,0.15);padding:1px 5px;border-radius:4px">reasoning_content</code> field, and a tool-call parser so the model's <code style="background:rgba(253,142,91,0.15);padding:1px 5px;border-radius:4px"><tool_call></code> blocks are surfaced as OpenAI-style <code style="background:rgba(253,142,91,0.15);padding:1px 5px;border-radius:4px">tool_calls</code>.</p> <p style="margin:0 0 6px">Serving Ornith-1.0-9B requires recent runtimes:</p> <ul style="margin:0 0 10px;padding-left:20px"> <li><b>Transformers</b> ≥ 5.8.1</li> <li><b>vLLM</b> ≥ 0.19.1</li> <li><b>SGLang</b> ≥ 0.5.9</li> </ul> <p style="margin:0">Recommended sampling parameters: <code style="background:rgba(253,142,91,0.15);padding:1px 5px;border-radius:4px">temperature=0.6</code>, <code style="background:rgba(253,142,91,0.15);padding:1px 5px;border-radius:4px">top_p=0.95</code>, <code style="background:rgba(253,142,91,0.15);padding:1px 5px;border-radius:4px">top_k=20</code> (use <code style="background:rgba(253,142,91,0.15);padding:1px 5px;border-radius:4px">temperature=1.0</code> to reproduce the reported benchmark setup).</p> </div>Serving Ornith-1.0-9B
Ornith-1.0-9B is a dense ~9B model (≈19 GB in bf16), so it serves comfortably on a single 80GB GPU. The recipes below stand up an OpenAI-compatible server; add --tensor-parallel-size / --tp if you want to shard across more GPUs.
vLLM
vllm serve deepreinforce-ai/Ornith-1.0-9B \
--served-model-name Ornith-1.0-9B \
--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
python -m sglang.launch_server \
--model-path deepreinforce-ai/Ornith-1.0-9B \
--served-model-name Ornith-1.0-9B \
--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-1.0-9B requires transformers >= 5.8.1.
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "deepreinforce-ai/Ornith-1.0-9B"
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:
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-1.0-9B via the Chat Completions API
Once a vLLM or SGLang server is running, talk to it with any OpenAI-compatible client.
Basic Usage
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-1.0-9B",
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-1.0-9B 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-1.0-9B",
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-1.0-9B excels in tool-calling and agentic coding capabilities.
Agent Frameworks
Because Ornith-1.0-9B 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-1.0-9B to tools through an MCP server.
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-1.0-9B",
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
# 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-1.0-9B"
Atomic.chat / Ollama / llama.cpp
# Both runtimes load a GGUF build of Ornith (publish one at deepreinforce-ai/Ornith-1.0-9B-GGUF).
# llama.cpp — serve an OpenAI-compatible API on port 8000.
llama-server -hf deepreinforce-ai/Ornith-1.0-9B-GGUF --port 8000 -c 262144
# Ollama — pull and chat with the same GGUF straight from Hugging Face.
ollama run hf.co/deepreinforce-ai/Ornith-1.0-9B-GGUF
OpenClaw
# 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-1.0-9B"
Unsloth Studio
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-1.0-9B",
# max_seq_length=262144,
# load_in_4bit=True,
# )
OpenHands
pip install openhands-ai
# OpenHands routes through LiteLLM; the "openai/" prefix selects the OpenAI-compatible path.
export LLM_MODEL="openai/deepreinforce-ai/Ornith-1.0-9B"
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-1.0-9B is optimized for terminal-based coding agents. Point any OpenAI-compatible coding CLI at your Ornith-1.0-9B endpoint (set OPENAI_BASE_URL and OPENAI_API_KEY) to understand large codebases, automate tedious work, and ship faster.
OpenCode
# 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-1.0-9B": { "name": "Ornith-1.0-9B" } }
# }
# }
# }
opencode
Citation
If you find our work helpful, feel free to give us a cite.
@misc{ornith_9b,
title = {{Ornith-1.0-9B}: Agentic Coding, Open to All},
url = {https://deep-reinforce.com/ornith_1_0.html},
author = {{DeepReinforce Team}},
year = {2026}
}
- Task
- Image-Text-to-Text
- Library
- transformers
- Type
- qwen3_5
- Created
- Jul 4, 2026
- Updated
- Jul 4, 2026