---
license: apache-2.0
library_name: transformers
tags:
- code
- jupyter
- agent
- data-science
- qwen
- thinking
base_model: Qwen/Qwen3-4B-Thinking-2507
datasets:
- jupyter-agent/jupyter-agent-dataset
language:
- en
- code
pipeline_tag: text-generation
---
# Jupyter Agent Qwen3-4B Thinking
**Jupyter Agent Qwen3-4B Thinking** is a fine-tuned version of [Qwen3-4B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507) specifically optimized for **data science agentic tasks** in Jupyter notebook environments. This model can execute Python code, analyze datasets, and provide step-by-step reasoning with intermediate computations to solve realistic data analysis problems.
- **Model type:** Causal Language Model (Thinking)
- **Language(s):** English, Python
- **License:** Apache 2.0
- **Finetuned from:** [Qwen/Qwen3-4B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507)
## Key Features
- **Jupyter-native agent** that lives inside notebook environments
- **Code execution** with pandas, numpy, matplotlib, and other data science libraries
- **Step-by-step reasoning** with intermediate computations and thinking traces
- **Dataset-grounded analysis** trained on real Kaggle notebook workflows
- **Tool calling** for structured code execution and final answer generation
## Performance
On the [DABStep benchmark](https://huggingface.co/spaces/adyen/DABstep) for data science tasks:
| Model | Easy Tasks | Hard Tasks |
|-------|------------|------------|
| Qwen3-4B-Thinking-2507 (Base) | 44.0% | 2.1% |
| **Jupyter Agent Qwen3-4B Thinking** | **70.8%** | **3.4%** |
**State-of-the-art performance** for small models on realistic data analysis tasks.
## Model Sources
- **Repository:** [jupyter-agent](https://github.com/huggingface/jupyter-agent)
- **Dataset:** [jupyter-agent-dataset](https://huggingface.co/datasets/jupyter-agent/jupyter-agent-dataset)
- **Blog post:** [Jupyter Agents: training LLMs to reason with notebooks](https://huggingface.co/blog/jupyter-agent-2)
- **Demo:** [Jupyter Agent 2](https://huggingface.co/spaces/lvwerra/jupyter-agent-2)
## Usage
### Basic Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "jupyter-agent/jupyter-agent-qwen3-4b-thinking"
# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
# Prepare input
prompt = "Analyze this sales dataset and find the top 3 performing products by revenue."
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate response
generated_ids = model.generate(
**model_inputs,
max_new_tokens=16384
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
```
### Decoding Thinking and Content
For thinking models, you can extract both the reasoning and final response:
```python
try:
# Find the end of thinking section ()
index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
index = 0
thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
print("Thinking:", thinking_content)
print("Response:", content)
```
### Agentic Usage with Tool Calling
The model works best with proper scaffolding for tool calling:
```python
tools = [
{
"type": "function",
"function": {
"name": "execute_code",
"description": "Execute Python code in a Jupyter environment",
"parameters": {
"type": "object",
"properties": {
"code": {
"type": "string",
"description": "Python code to execute"
}
},
"required": ["code"]
}
}
},
{
"type": "function",
"function": {
"name": "final_answer",
"description": "Provide the final answer to the question",
"parameters": {
"type": "object",
"properties": {
"answer": {
"type": "string",
"description": "The final answer"
}
},
"required": ["answer"]
}
}
}
]
# Include tools in the conversation
messages = [
{
"role": "system",
"content": "You are a data science assistant. Use the available tools to analyze data and provide insights."
},
{"role": "user", "content": prompt}
]
```
## Training Details
### Training Data
The model was fine-tuned on the [Jupyter Agent Dataset](https://huggingface.co/datasets/jupyter-agent/jupyter-agent-dataset), which contains:
- **51,389 synthetic notebooks** (~0.2B tokens, total 1B tokens)
- **Dataset-grounded QA pairs** from real Kaggle notebooks
- **Executable reasoning traces** with intermediate computations
- **High-quality educational content** filtered and scored by LLMs
### Training Procedure
- **Base Model:** Qwen3-4B-Thinking-2507
- **Training Method:** Full-parameter fine-tuning (not PEFT)
- **Optimizer:** AdamW with cosine learning rate scheduling
- **Learning Rate:** 5e-6
- **Epochs:** 5 (optimal based on ablation study)
- **Context Length:** 32,768 tokens
- **Batch Size:** Distributed across multiple GPUs
- **Loss:** Assistant-only loss (`assistant_loss_only=True`)
- **Regularization:** NEFTune noise (α=7) for full-parameter training
### Training Infrastructure
- **Framework:** [TRL](https://github.com/huggingface/trl) with [Transformers](https://github.com/huggingface/transformers)
- **Distributed Training:** DeepSpeed ZeRO-2 across multiple nodes
- **Hardware:** Multi-GPU setup with SLURM orchestration
## Evaluation
### Benchmark: DABStep
The model was evaluated on [DABStep](https://huggingface.co/spaces/adyen/DABstep), a benchmark for data science agents with realistic tasks involving:
- **Dataset analysis** with pandas and numpy
- **Visualization** with matplotlib/seaborn
- **Statistical analysis** and business insights
- **Multi-step reasoning** with intermediate computations
The model achieves **26.8% improvement** over the base model and **11.1% improvement** over scaffolding alone.
We can also see, that the hard score can increase too even though our dataset is focused on easier questions.
## Limitations and Bias
### Technical Limitations
- **Context window:** Limited to 32K tokens, may struggle with very large notebooks
- **Tool calling format:** Requires specific scaffolding for optimal performance
- **Dataset domains:** Primarily trained on Kaggle-style data science tasks
- **Code execution:** Requires proper sandboxing for safe execution
### Potential Biases
- **Domain bias:** Trained primarily on Kaggle notebooks, may not generalize to all data science workflows
- **Language bias:** Optimized for English and Python, limited multilingual support
- **Task bias:** Focused on structured data analysis, may underperform on unstructured data tasks
### Recommendations
- Use in **sandboxed environments** like [E2B](https://e2b.dev/) for safe code execution
- **Validate outputs** before using in production systems
- **Review generated code** for security and correctness
- Consider **domain adaptation** for specialized use cases
## Ethical Considerations
- **Code Safety:** Always execute generated code in secure, isolated environments
- **Data Privacy:** Be cautious when analyzing sensitive datasets
- **Verification:** Validate all analytical conclusions and insights
- **Attribution:** Acknowledge model assistance in data analysis workflows
## Citation
```bibtex
@misc{jupyteragentqwen3thinking,
title={Jupyter Agent Qwen3-4B Thinking},
author={Baptiste Colle and Hanna Yukhymenko and Leandro von Werra},
year={2025},
publisher={Hugging Face},
url={https://huggingface.co/jupyter-agent/jupyter-agent-qwen3-4b-thinking}
}
```
## Related Work
- **Dataset:** [jupyter-agent-dataset](https://huggingface.co/datasets/jupyter-agent/jupyter-agent-dataset)
- **Non-thinking version:** [jupyter-agent-qwen3-4b-instruct](https://huggingface.co/jupyter-agent/jupyter-agent-qwen3-4b-instruct)
- **Base model:** [Qwen3-4B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507)
- **Benchmark:** [DABStep](https://huggingface.co/spaces/adyen/DABstep)
*For more details, see our [blog post](https://huggingface.co/blog/jupyter-agent-2) and [GitHub repository](https://github.com/huggingface/jupyter-agent).*