---
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen3-0.6B
tags:
- multiturn
- toolcalling
- shellcommands
---

# Distil-Qwen3-0.6B-SHELLper

<p align="center">
  <img src="shellper.png" alt="SHELLper" width="200">
</p>

A fine-tuned Qwen3-0.6B model for multi-turn bash function calling. Trained using knowledge distillation from Qwen3-235B, this 0.6B parameter model achieves 100% tool-call accuracy on our test set over multiple turns while being small enough to run locally on any machine.

## Results
| Metric | Qwen3-235B (Teacher) | Qwen3-0.6B (Base) | This Model |
| --- | --- | --- | --- |
| Tool call accuracy | 99% | 84.16% | **100%** |
| 5-turn accuracy | 95% | 42.22% | **100%** |


## Quick Start

You can follow instructions in our demo repository: [github](https://github.com/distil-labs/distil-SHELLper)

### Using Ollama
```bash
# Download and create Ollama model
hf download distil-labs/distil-qwen3-0.6b-SHELLper model_fp16.gguf Modelfile --local-dir distil_model
cd distil_model && ollama create distil_model -f Modelfile
cd ..
```

### Using Transformers
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("distil-labs/distil-qwen3-0.6b-SHELLper")
tokenizer = AutoTokenizer.from_pretrained("distil-labs/distil-qwen3-0.6b-SHELLper")

tools = [
    {
        "type": "function",
        "function": {
            "name": "ls",
            "description": "List directory contents",
            "parameters": {
                "type": "object",
                "properties": {
                    "folder": {"type": "string", "description": "Path to the folder to list"}
                },
                "required": ["folder"]
            }
        }
    }
]

messages = [
    {"role": "system", "content": "You are a helpful assistant that executes bash commands."},
    {"role": "user", "content": "List all files in the current directory"}
]

text = tokenizer.apply_chat_template(messages, tools=tools, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256, temperature=0)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Model Details
| Property | Value |
| --- | --- |
| Base Model | Qwen/Qwen3-0.6B |
| Parameters | 0.6 billion |
| Architecture | Qwen3ForCausalLM |
| Context Length | 40,960 tokens |
| Precision | bfloat16 |
| Training Data | ~200 synthetic examples (expanded from 20 seeds) |
| Teacher Model | Qwen3-235B-A22B-Instruct |

## Training
This model was trained using the Distil Labs platform:

- **Seed Data:** 20 hand-validated multi-turn bash command conversations
- **Synthetic Generation:** Expanded using Qwen3-235B teacher with conversation turn expansion
- **Fine-tuning:** 4 epochs with LoRA (r=128) on the synthetic dataset
- **Evaluation:** Multi-turn accuracy testing across variable conversation lengths

### Training Hyperparameters
- Epochs: 4
- Learning Rate: 2e-5 (linear schedule)
- Batch Size: 1 (with gradient accumulation)
- LoRA Rank: 128

## Task Format

### Input Format
Multi-turn conversation with tool definitions:
```json
[
  {"role": "user", "content": "List all files in the current directory"},
  {"role": "assistant", "tool_calls": [{"function": {"name": "ls", "arguments": {"folder": "."}}}]},
  {"role": "user", "content": "Now go to the src folder"}
]
```

### Output Format
A single tool call in JSON format:
```json
{"name": "cd", "arguments": {"folder": "src"}}
```

## Supported Tools
The model supports 20 bash commands:

| Command | Description |
| --- | --- |
| `cat` | Display file contents |
| `cd` | Change directory |
| `cp` | Copy files or directories |
| `diff` | Compare files |
| `du` | Estimate file space usage |
| `echo` | Display text |
| `find` | Search for files |
| `grep` | Search file contents |
| `head` | Output first part of files |
| `ls` | List directory contents |
| `mkdir` | Create directories |
| `mv` | Move or rename files |
| `pwd` | Print working directory |
| `rm` | Remove files |
| `rmdir` | Remove empty directories |
| `sort` | Sort file contents |
| `tail` | Output last part of files |
| `touch` | Create empty files |
| `wc` | Word, line, character count |

## Use Cases
- Natural language interfaces to file systems
- Command-line assistants and automation
- Developer productivity tools
- Educational tools for learning bash
- Local, privacy-preserving AI assistants

## Limitations
- Optimized for single tool call per turn
- No support for pipes or combined commands
- Best with up to 5 conversation turns
- Trained on English requests only
- Limited to the 20 supported bash commands

## License
This model is released under the Apache 2.0 license.

## Links
- [Distil Labs Website](https://www.distillabs.ai/?utm_source=huggingface&utm_medium=referral&utm_campaign=shellper)
- [GitHub](https://github.com/distil-labs)
- [Hugging Face](https://huggingface.co/distil-labs)

## Citation
```bibtex
@misc{distil-qwen3-0.6b-shellper,
  author = {Distil Labs},
  title = {Distil-Qwen3-0.6B-SHELLper: A Fine-tuned Model for Multi-turn Bash Function Calling},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/distil-labs/distil-qwen3-0.6b-SHELLper}
}
```