---
library_name: transformers
tags: [phi3, fine-tuning, code-generation, matplotlib, seaborn, text-to-code]
---

# Model Card for `ph3-FineTunned-matplotlib-seaborn-10k`

This is a fine-tuned version of the **Phi-3** language model designed to generate Python data visualization code (using `matplotlib` and `seaborn`) from natural language prompts. It has been trained on 10,000 high-quality prompt–completion pairs focused on data plotting.

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## Model Details

### Model Description

- **Developed by:** [Prashant Suresh Shirgave](https://huggingface.co/prashantss1404)
- **Shared by:** prashantss1404
- **Model type:** Text-to-Code Generation (Instruction-based)
- **Language(s):** English (data viz-related queries)
- **License:** Apache 2.0
- **Finetuned from model:** [Phi-3 Mini](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct)

### Model Sources

- **Model repository:** https://huggingface.co/prashantss1404/ph3-FineTunned-matplotlib-seaborn-10k
- **Training dataset:** https://huggingface.co/datasets/prashantss1404/Matplotlib_Seaborn_merged_prompt_completion_10k
- **Training Colab:** [View notebook](upload-your-link-here-after-upload)

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## Uses

### Direct Use
This model is designed to:
- Generate Python visualization code (`matplotlib`, `seaborn`) from natural language queries.
- Help automate plotting tasks in notebooks, dashboards, or LLM-based assistants.

### Out-of-Scope Use
- Not suitable for general-purpose coding outside of data visualization.
- Not optimized for plotly or non-Python frameworks.

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## Bias, Risks, and Limitations

### Limitations
- Limited to matplotlib and seaborn APIs seen during training.
- May hallucinate parameters or make invalid API calls under complex queries.
- No error correction or code execution within the model loop.

### Recommendations
Always **validate generated code** before executing. Combine with an execution sandbox (e.g., Streamlit, Jupyter) for best results.

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## How to Get Started with the Model

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("prashantss1404/ph3-FineTunned-matplotlib-seaborn-10k")
model = AutoModelForCausalLM.from_pretrained("prashantss1404/ph3-FineTunned-matplotlib-seaborn-10k")

prompt = "Plot a bar chart of sales by region using seaborn"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0]))