README.md

---
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3.5-9B/blob/main/LICENSE
pipeline_tag: image-text-to-text
base_model:
- Qwen/Qwen3.5-9B
tags:
- code
- instruction-tuned
- software-engineering
- agent
- opencode
- qwen
- python
language:
- en
- zh
---

# Nemotron-9B-OpenCode

A 9B parameter instruction-tuned model specialized for **autonomous software engineering agents**, fine-tuned from [Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B) on NVIDIA's Nemotron-SFT-OpenCode-v1 dataset.

## Model Highlights

- **Specialized for Agentic Tasks**: Trained on agent trajectories for the [OpenCode](https://opencode.ai/) CLI framework, enabling autonomous code navigation, multi-step tool use, and software engineering workflows
- **Multi-Capability**: Supports general reasoning, tool calling, bash command execution, and dynamic skill loading
- **Production Ready**: Compatible with Hugging Face Transformers, vLLM, SGLang, and OpenAI-compatible APIs

## Model Description

| Property | Value |
|----------|-------|
| **Base Model** | Qwen3.5-9B |
| **Model Type** | Causal Language Model with Vision Encoder |
| **Parameters** | 9B |
| **Languages** | English, Chinese |
| **License** | Apache 2.0 |
| **Developer** | [Kassadin88](https://huggingface.co/Kassadin88) |

## Training Data

This model was fine-tuned on **[Nemotron-SFT-OpenCode-v1](https://huggingface.co/datasets/nvidia/Nemotron-SFT-OpenCode-v1)**, NVIDIA's agentic instruction tuning dataset containing **144,468 high-quality samples** derived from 459K total trajectories. The dataset enhances LLMs' ability to operate within autonomous coding environments.

### Dataset Composition

| Subset | Samples | Description |
|--------|---------|-------------|
| `general` | 90K | General agentic CLI questions with/without AGENTS.md context |
| `bash_only_tool` | 97K | Restricted tool set (todo + bash) for foundational agent capabilities |
| `bash_only_tool_skills` | 96K | Bash + skill loading for dynamic capability discovery |
| `question_tool` | 76K | Interactive clarification via user questions during task execution |
| `agent_skills` | 67K | Dynamic skill scanning and loading for task-specific capabilities |
| `agent_skills_question_tool` | 33K | Combined skill loading + user clarification for complex tasks |

### Key Capabilities Trained

- **Code Navigation**: Repository-aware reasoning and codebase traversal
- **Tool Calling**: Structured tool invocation for bash, file operations, and more
- **Skill Loading**: Dynamic discovery and loading of relevant agent skills
- **Interactive Planning**: User clarification when requirements are ambiguous
- **Multi-Step Reasoning**: SWE-Bench style problem decomposition and implementation

## Benchmark Results

The model inherits strong foundational capabilities from Qwen3.5-9B. Below are the base model's benchmark performances:

### Language Benchmarks

<div style="font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Roboto,sans-serif;max-width:1000px;margin:0 auto;padding:16px 0">
<table style="width:100%;border-collapse:collapse;font-size:13px">
<thead><tr>
<th style="padding:10px 7px;text-align:left;font-weight:600;border-bottom:2px solid #7c3aed;color:#7c3aed">Category</th>
<th style="padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed">Benchmark</th>
<th style="padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed">Qwen3.5-9B</th>
</tr></thead>
<tbody>
<tr><td rowspan="5" style="padding:7px 7px;border-bottom:1px solid rgba(128, 128, 128, 0.15);font-weight:600;color:#7c3aed;background:rgba(124, 58, 237, 0.1)">Knowledge & STEM</td></tr>
<tr><td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">MMLU-Pro</td><td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">82.5</td></tr>
<tr><td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">MMLU-Redux</td><td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">91.1</td></tr>
<tr><td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">C-Eval</td><td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">88.2</td></tr>
<tr><td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">GPQA Diamond</td><td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">81.7</td></tr>
<tr><td rowspan="2" style="padding:7px 7px;border-bottom:1px solid rgba(128, 128, 128, 0.15);font-weight:600;color:#7c3aed;background:rgba(124, 58, 237, 0.1)">Instruction Following</td></tr>
<tr><td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">IFEval</td><td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">91.5</td></tr>
<tr><td rowspan="2" style="padding:7px 7px;border-bottom:1px solid rgba(128, 128, 128, 0.15);font-weight:600;color:#7c3aed;background:rgba(124, 58, 237, 0.1)">Long Context</td></tr>
<tr><td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">LongBench v2</td><td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">55.2</td></tr>
<tr><td rowspan="2" style="padding:7px 7px;border-bottom:1px solid rgba(128, 128, 128, 0.15);font-weight:600;color:#7c3aed;background:rgba(124, 58, 237, 0.1)">Reasoning & Coding</td></tr>
<tr><td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">LiveCodeBench v6</td><td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">65.6</td></tr>
</tbody>
</table>
</div>

### Vision Language Benchmarks

<div style="font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Roboto,sans-serif;max-width:1000px;margin:0 auto;padding:16px 0">
<table style="width:100%;border-collapse:collapse;font-size:13px">
<thead><tr>
<th style="padding:10px 7px;text-align:left;font-weight:600;border-bottom:2px solid #7c3aed;color:#7c3aed">Category</th>
<th style="padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed">Benchmark</th>
<th style="padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #7c3aed;color:#7c3aed">Qwen3.5-9B</th>
</tr></thead>
<tbody>
<tr><td rowspan="4" style="padding:7px 7px;border-bottom:1px solid rgba(128, 128, 128, 0.15);font-weight:600;color:#7c3aed;background:rgba(124, 58, 237, 0.1)">STEM & Puzzle</td></tr>
<tr><td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">MMMU</td><td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">78.4</td></tr>
<tr><td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">MathVision</td><td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">78.9</td></tr>
<tr><td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">Mathvista (mini)</td><td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">85.7</td></tr>
<tr><td rowspan="2" style="padding:7px 7px;border-bottom:1px solid rgba(128, 128, 128, 0.15);font-weight:600;color:#7c3aed;background:rgba(124, 58, 237, 0.1)">Document Understanding</td></tr>
<tr><td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">OCRBench</td><td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">89.2</td></tr>
<tr><td rowspan="2" style="padding:7px 7px;border-bottom:1px solid rgba(128, 128, 128, 0.15);font-weight:600;color:#7c3aed;background:rgba(124, 58, 237, 0.1)">Video Understanding</td></tr>
<tr><td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">VideoMME (w/ sub)</td><td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">84.5</td></tr>
</tbody>
</table>
</div>

> **Note**: For complete benchmark results across all categories, please refer to the [Qwen3.5-9B model card](https://huggingface.co/Qwen/Qwen3.5-9B).

## Quick Start

### Using Transformers

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "Kassadin88/Nemotron-9B-OpenCode"

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

messages = [
    {"role": "system", "content": "You are a helpful coding assistant."},
    {"role": "user", "content": "Write a Python function to merge two sorted arrays."}
]

input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=512,
    do_sample=True
)

response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
print(response)
```

### Using vLLM (Recommended for Production)

```python
from vllm import LLM, SamplingParams

llm = LLM(
    model="Kassadin88/Nemotron-9B-OpenCode",
    trust_remote_code=True,
    dtype="bfloat16"
)

sampling_params = SamplingParams(
    max_tokens=1024
)

outputs = llm.generate(prompts, sampling_params)
```

### Using SGLang

```bash
python -m sglang.launch_server \
    --model-path Kassadin88/Nemotron-9B-OpenCode \
    --port 8000 \
    --tp-size 1
```

### OpenAI-Compatible API

```python
from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8000/v1",
    api_key="EMPTY"
)

response = client.chat.completions.create(
    model="Kassadin88/Nemotron-9B-OpenCode",
    messages=[
        {"role": "user", "content": "Write a quicksort implementation in Python"}
    ],
    max_tokens=512
)
print(response.choices[0].message.content)
```

## Usage Tips

### For Agentic Coding Tasks

```python
messages = [
    {"role": "system", "content": "You are an autonomous coding agent. Use the available tools to complete tasks."},
    {"role": "user", "content": "Fix the bug in src/utils/parser.py that causes incorrect JSON parsing."}
]
```

### For Code Generation

```python
outputs = model.generate(
    **inputs,
    max_new_tokens=1024,
    do_sample=True
)
```

### For Code Explanation

```python
outputs = model.generate(
    **inputs,
    max_new_tokens=512,
    do_sample=True
)
```

## Limitations

- The model is primarily trained on agentic coding tasks and may not perform optimally on general conversational tasks
- May occasionally generate incorrect or incomplete code
- Should not be used for malicious code generation

## Citation

```bibtex
@misc{nemotron-9b-opencode,
  author = {Kassadin88},
  title = {Nemotron-9B-OpenCode: An Instruction-Tuned Model for Autonomous Software Engineering},
  year = {2026},
  publisher = {HuggingFace},
  url = {https://huggingface.co/Kassadin88/Nemotron-9B-OpenCode}
}
```

## Acknowledgments

- **Base Model**: [Qwen Team](https://github.com/QwenLM/Qwen3) for Qwen3.5-9B
- **Training Data**: [NVIDIA](https://huggingface.co/datasets/nvidia/Nemotron-SFT-OpenCode-v1) for Nemotron-SFT-OpenCode-v1
- **Training Framework**: [MS-Swift](https://github.com/modelscope/swift)

---

**Note:** This model is intended for research and educational purposes. Please use responsibly.