Instructions to use OrionLLM/OxCoder-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
Create README.md
Browse files
README.md
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---
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library_name: transformers
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license: apache-2.0
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pipeline_tag: text-generation
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base_model:
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- Qwen/Qwen3.5-9B
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| 7 |
+
---
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| 8 |
+
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| 9 |
+

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| 10 |
+
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+
## Introduction
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| 12 |
+
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| 13 |
+
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.
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- **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.
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- **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.
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- **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.
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- **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.
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## Benchmark
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<table>
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<tr>
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<th style="background: rgba(128,128,128,0.1); text-align: center; border: hidden;"> </th>
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<th style="background: rgba(128,128,128,0.1); text-align: center; border: hidden;">OxCoder-9B</th>
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<th style="background: rgba(128,128,128,0.1); text-align: center; border: hidden;">Ornith-1.5-9B</th>
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<th style="background: rgba(128,128,128,0.1); text-align: center; border: hidden;">Ornith-1.0-9B</th>
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<th style="background: rgba(128,128,128,0.1); text-align: center; border: hidden;">Qwen3.5-9B</th>
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<th style="background: rgba(128,128,128,0.1); text-align: center; border: hidden;">Gemma-4-31B</th>
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+
</tr>
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<tr>
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<td align="center" colspan="6" style="background: linear-gradient(90deg, rgba(124,58,237,0.45) 0%, rgba(99,102,241,0.42) 50%, rgba(59,130,246,0.45) 100%); font-weight: bold; height:32px; padding-top:2px; padding-bottom:2px;"><i>Coding</i></td>
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</tr>
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<tr>
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| 35 |
+
<td style="text-align:left; padding:9px 10px 9px 18px;"><div style="font-size:15px; font-weight:600; line-height:1.22;">Agentic terminal coding</div><div style="margin-top:4px; font-size:11px; font-weight:400; line-height:1.2; opacity:0.62;">Terminal-Bench 2.1 (Terminus-2)</div></td>
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| 36 |
+
<td align="center" style="background: linear-gradient(135deg, rgba(124,58,237,0.16) 0%, rgba(118,65,238,0.16) 100%); vertical-align:middle;"><b>49.6</b></td>
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<td align="center" style="vertical-align:middle;">46.2</td>
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| 38 |
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<td align="center" style="vertical-align:middle;">43.1</td>
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| 39 |
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<td align="center" style="vertical-align:middle;">21.3</td>
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<td align="center" style="vertical-align:middle;">42.1</td>
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</tr>
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<tr>
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<td style="text-align:left; padding:9px 10px 9px 18px;"><div style="font-size:15px; font-weight:600; line-height:1.22;">Agentic terminal coding</div><div style="margin-top:4px; font-size:11px; font-weight:400; line-height:1.2; opacity:0.62;">Terminal-Bench 2.1 (Claude Code)</div></td>
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<td align="center" style="background: linear-gradient(135deg, rgba(118,65,238,0.16) 0%, rgba(112,71,239,0.16) 100%); vertical-align:middle;"><b>50.8</b></td>
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<td align="center" style="vertical-align:middle;">47.0</td>
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<td align="center" style="vertical-align:middle;">40.6</td>
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<td align="center" style="vertical-align:middle;">18.9</td>
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<td align="center" style="vertical-align:middle;">—</td>
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</tr>
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| 50 |
+
<tr>
|
| 51 |
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<td style="text-align:left; padding:9px 10px 9px 18px;"><div style="font-size:15px; font-weight:600; line-height:1.22;">Agentic coding</div><div style="margin-top:4px; font-size:11px; font-weight:400; line-height:1.2; opacity:0.62;">SWE-bench Verified</div></td>
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<td align="center" style="background: linear-gradient(135deg, rgba(112,71,239,0.16) 0%, rgba(106,78,239,0.16) 100%); vertical-align:middle;"><b>73.5</b></td>
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<td align="center" style="vertical-align:middle;">70.6</td>
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<td align="center" style="vertical-align:middle;">69.4</td>
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<td align="center" style="vertical-align:middle;">53.2</td>
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<td align="center" style="vertical-align:middle;">52.0</td>
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</tr>
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<tr>
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<td style="text-align:left; padding:9px 10px 9px 18px;"><div style="font-size:15px; font-weight:600; line-height:1.22;">Real-world software engineering</div><div style="margin-top:4px; font-size:11px; font-weight:400; line-height:1.2; opacity:0.62;">SWE-bench Pro</div></td>
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<td align="center" style="background: linear-gradient(135deg, rgba(106,78,239,0.16) 0%, rgba(100,84,240,0.16) 100%); vertical-align:middle;"><b>49.1</b></td>
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<td align="center" style="vertical-align:middle;">47.5</td>
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<td align="center" style="vertical-align:middle;">42.9</td>
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<td align="center" style="vertical-align:middle;">31.3</td>
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<td align="center" style="vertical-align:middle;">35.7</td>
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</tr>
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<tr>
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<td style="text-align:left; padding:9px 10px 9px 18px;"><div style="font-size:15px; font-weight:600; line-height:1.22;">Repo-level code generation</div><div style="margin-top:4px; font-size:11px; font-weight:400; line-height:1.2; opacity:0.62;">NL2Repo</div></td>
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<td align="center" style="background: linear-gradient(135deg, rgba(100,84,240,0.16) 0%, rgba(94,91,241,0.16) 100%); vertical-align:middle;"><b>36.2</b></td>
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<td align="center" style="vertical-align:middle;">32.4</td>
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<td align="center" style="vertical-align:middle;">27.2</td>
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<td align="center" style="vertical-align:middle;">16.2</td>
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<td align="center" style="vertical-align:middle;">15.5</td>
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</tr>
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<tr>
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<td align="center" colspan="6" style="background: linear-gradient(90deg, rgba(124,58,237,0.45) 0%, rgba(99,102,241,0.42) 50%, rgba(59,130,246,0.45) 100%); font-weight: bold; height:32px; padding-top:2px; padding-bottom:2px;"><i>Reasoning</i></td>
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</tr>
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<tr>
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<td style="text-align:left; padding:9px 10px 9px 18px;"><div style="font-size:15px; font-weight:600; line-height:1.22;">Expert-level reasoning</div><div style="margin-top:4px; font-size:11px; font-weight:400; line-height:1.2; opacity:0.62;">HLE (no tools)</div></td>
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<td align="center" style="background: linear-gradient(135deg, rgba(94,91,241,0.16) 0%, rgba(89,97,242,0.16) 100%); vertical-align:middle;"><b>21.2</b></td>
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<td align="center" style="vertical-align:middle;">20.2</td>
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<td align="center" style="vertical-align:middle;">16.8</td>
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<td align="center" style="vertical-align:middle;">14.7</td>
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<td align="center" style="vertical-align:middle;">19.5</td>
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</tr>
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<tr>
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<td style="text-align:left; padding:9px 10px 9px 18px;"><div style="font-size:15px; font-weight:600; line-height:1.22;">Tool-augmented reasoning</div><div style="margin-top:4px; font-size:11px; font-weight:400; line-height:1.2; opacity:0.62;">HLE (with tools)</div></td>
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<td align="center" style="background: linear-gradient(135deg, rgba(89,97,242,0.16) 0%, rgba(83,104,243,0.16) 100%); vertical-align:middle;"><b>32.8</b></td>
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<td align="center" style="vertical-align:middle;">30.5</td>
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<td align="center" style="vertical-align:middle;">26.4</td>
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<td align="center" style="vertical-align:middle;">24.5</td>
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<td align="center" style="vertical-align:middle;">26.5</td>
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</tr>
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<tr>
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<td style="text-align:left; padding:9px 10px 9px 18px;"><div style="font-size:15px; font-weight:600; line-height:1.22;">Scientific reasoning</div><div style="margin-top:4px; font-size:11px; font-weight:400; line-height:1.2; opacity:0.62;">GPQA Diamond</div></td>
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| 95 |
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<td align="center" style="background: linear-gradient(135deg, rgba(83,104,243,0.16) 0%, rgba(77,110,244,0.16) 100%); vertical-align:middle;"><b>86.9</b></td>
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<td align="center" style="vertical-align:middle;">86.4</td>
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<td align="center" style="vertical-align:middle;">82.5</td>
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<td align="center" style="vertical-align:middle;">81.7</td>
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<td align="center" style="vertical-align:middle;">84.3</td>
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</tr>
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<tr>
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<td align="center" colspan="6" style="background: linear-gradient(90deg, rgba(124,58,237,0.45) 0%, rgba(99,102,241,0.42) 50%, rgba(59,130,246,0.45) 100%); font-weight: bold; height:32px; padding-top:2px; padding-bottom:2px;"><i>Agentic</i></td>
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</tr>
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<tr>
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<td style="text-align:left; padding:9px 10px 9px 18px;"><div style="font-size:15px; font-weight:600; line-height:1.22;">Multi-tool orchestration</div><div style="margin-top:4px; font-size:11px; font-weight:400; line-height:1.2; opacity:0.62;">MCP-Atlas</div></td>
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| 106 |
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<td align="center" style="background: linear-gradient(135deg, rgba(77,110,244,0.16) 0%, rgba(71,117,244,0.16) 100%); vertical-align:middle;"><b>56.7</b></td>
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<td align="center" style="vertical-align:middle;">54.2</td>
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| 108 |
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<td align="center" style="vertical-align:middle;">49.4</td>
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| 109 |
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<td align="center" style="vertical-align:middle;">46.8</td>
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| 110 |
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<td align="center" style="vertical-align:middle;">55.0</td>
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</tr>
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| 112 |
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<tr>
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<td style="text-align:left; padding:9px 10px 9px 18px;"><div style="font-size:15px; font-weight:600; line-height:1.22;">Web browsing and research</div><div style="margin-top:4px; font-size:11px; font-weight:400; line-height:1.2; opacity:0.62;">BrowseComp</div></td>
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| 114 |
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<td align="center" style="background: linear-gradient(135deg, rgba(71,117,244,0.16) 0%, rgba(65,123,245,0.16) 100%); vertical-align:middle;"><b>57.4</b></td>
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| 115 |
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<td align="center" style="vertical-align:middle;">56.4</td>
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| 116 |
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<td align="center" style="vertical-align:middle;">44.8</td>
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| 117 |
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<td align="center" style="vertical-align:middle;">41.5</td>
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| 118 |
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<td align="center" style="vertical-align:middle;">—</td>
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| 119 |
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</tr>
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| 120 |
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<tr>
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| 121 |
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<td style="text-align:left; padding:9px 10px 9px 18px;"><div style="font-size:15px; font-weight:600; line-height:1.22;">Real-user agentic coding</div><div style="margin-top:4px; font-size:11px; font-weight:400; line-height:1.2; opacity:0.62;">ClawEval</div></td>
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| 122 |
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<td align="center" style="background: linear-gradient(135deg, rgba(65,123,245,0.16) 0%, rgba(59,130,246,0.16) 100%); vertical-align:middle;"><b>67.8</b></td>
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| 123 |
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<td align="center" style="vertical-align:middle;">66.5</td>
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| 124 |
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<td align="center" style="vertical-align:middle;">63.1</td>
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| 125 |
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<td align="center" style="vertical-align:middle;">53.2</td>
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| 126 |
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<td align="center" style="vertical-align:middle;">48.5</td>
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| 127 |
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</tr>
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| 128 |
+
</table>
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| 129 |
+
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| 130 |
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<p style="margin-top:12px;font-size:10px;opacity:0.7">
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* All results reported for OxCoder-9B are averaged over five independent runs. A dash (—) means the score was not reported for that model.<br/>
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* 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.<br/>
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| 133 |
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* 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.<br/>
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| 134 |
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* 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.<br/>
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| 135 |
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* 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.<br/>
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| 136 |
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* HLE: Evaluated using GLM-5.3 as the judge model.<br/>
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* MCP-Atlas: Evaluated using GLM-5.3 as the judge model.<br/>
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* ClawEval: temp=0.6 and 256K context.<br/>
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* 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).<br/>
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</p>
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---
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<div align="center">
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**OxCoder-9B** is developed by **[OrionLLM](https://huggingface.co/OrionLLM)** and released under the Apache 2.0 License.
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</div>
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