Instructions to use OrionLLM/OxCoder-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OrionLLM/OxCoder-9B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="OrionLLM/OxCoder-9B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("OrionLLM/OxCoder-9B")
model = AutoModelForMultimodalLM.from_pretrained("OrionLLM/OxCoder-9B", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use OrionLLM/OxCoder-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OrionLLM/OxCoder-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OrionLLM/OxCoder-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/OrionLLM/OxCoder-9B

SGLang

How to use OrionLLM/OxCoder-9B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "OrionLLM/OxCoder-9B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OrionLLM/OxCoder-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "OrionLLM/OxCoder-9B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OrionLLM/OxCoder-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use OrionLLM/OxCoder-9B with Docker Model Runner:
```
docker model run hf.co/OrionLLM/OxCoder-9B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Introduction

We are proud to introduce OxCoder-9B, our lightweight coding model for long-horizon tasks, agentic coding, and agentic reasoning. Despite featuring only 9 billion parameters, OxCoder-9B marks a substantial leap in agentic capabilities — particularly in Agentic Terminal and Agentic Coding tasks — punching far above its weight class and rivaling models many times its size. This release represents our strongest commitment yet to delivering frontier-level agentic performance in a compact, efficient, and openly accessible package.

Trained on Frontier Agent Traces: Built from Fable-5.1 and GLM-5.3 agentic coding trajectories across Claude Code, OpenCode and Codex — OxCoder-9B has been distilled from some of the most capable agentic systems available, giving it a unique advantage in real-world software engineering scenarios.
262K Native Context: Equipped with a full 262,144 token native context window, enabling the model to handle complex, multi-file codebases and long-horizon reasoning tasks with ease.
Error Recovery: Learns read-before-write patterns, responds to LSP diagnostics, and applies minimal edit diffs instead of full rewrites — making it robust in iterative coding environments where precision matters.
Front-end Reasoning: OxCoder-9B exhibits remarkably strong front-end reasoning capabilities for its size, demonstrating a deep understanding of UI logic, component architecture, and web-native patterns that is rare in sub-10B models.

Benchmark

	OxCoder-9B	Ornith-1.5-9B	Ornith-1.0-9B	Qwen3.5-9B	Gemma-4-31B
Coding
Agentic terminal coding Terminal-Bench 2.1 (Terminus-2)	49.6	46.2	43.1	21.3	42.1
Agentic terminal coding Terminal-Bench 2.1 (Claude Code)	50.8	47.0	40.6	18.9	—
Agentic coding SWE-bench Verified	73.5	70.6	69.4	53.2	52.0
Real-world software engineering SWE-bench Pro	49.1	47.5	42.9	31.3	35.7
Repo-level code generation NL2Repo	36.2	32.4	27.2	16.2	15.5
Reasoning
Expert-level reasoning HLE (no tools)	21.2	20.2	16.8	14.7	19.5
Tool-augmented reasoning HLE (with tools)	32.8	30.5	26.4	24.5	26.5
Scientific reasoning GPQA Diamond	86.9	86.4	82.5	81.7	84.3
Agentic
Multi-tool orchestration MCP-Atlas	56.7	54.2	49.4	46.8	55.0
Web browsing and research BrowseComp	57.4	56.4	44.8	41.5	—
Real-user agentic coding ClawEval	67.8	66.5	63.1	53.2	48.5

* All results reported for OxCoder-9B are averaged over five independent runs. A dash (—) means the score was not reported for that model.
* 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 256K context window. Each run uses a 2-hour timeout with 32 CPU cores and 32GB RAM.
* 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.
* SWE-bench Verified and Pro: using the OpenHands harness with temp=1.0, top_p=0.95, 256K context window. Anti-hacking safeguards are applied throughout evaluation: Git history is removed from the local repository image to prevent access to prior solutions or commits; network access is disabled, preventing the model from retrieving external information or resources.
* NL2Repo: with temperature=1.0, top_p=1.0, 256K context, 48K output. Access to the specified GitHub repositories and pip packages is blocked to prevent reward hacking.
* HLE: Evaluated using GLM-5.3 as the judge model.
* MCP-Atlas: Evaluated using GLM-5.3 as the judge model.
* ClawEval: temp=0.6 and 256K context.
* Baseline scores for Ornith-1.5-9B, Ornith-1.0-9B, Qwen3.5-9B and Gemma-4-31B are taken from the Ornith-1.5-9B model card and were produced under that team's evaluation settings, which differ from ours on some benchmarks (context window, timeouts and judge model).

OxCoder-9B is developed by OrionLLM and released under the Apache 2.0 License.

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Safetensors

Model size

9B params

Tensor type

BF16

Model tree for OrionLLM/OxCoder-9B

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

Qwen/Qwen3.5-9B

Finetuned

(748)

this model

Quantizations

3 models

Evaluation results

Idavidrein/gpqa · Diamond View evaluation results leaderboard

86.9
harborframework/terminal-bench-2.1 · Terminalbench 2 1 leaderboard
Harness: Claude Code View evaluation results

50.8 ^*
Harness: Terminus-2 View evaluation results

49.6 ^*
SWE-bench/SWE-bench_Verified · Swe Bench Resolved View evaluation results leaderboard

73.5
ScaleAI/SWE-bench_Pro · SWE Bench Pro View evaluation results leaderboard

49.1
cais/hle · Hle