metadata
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.
Model Details
Model Description
- Developed by: Prashant Suresh Shirgave
- 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
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: https://huggingface.co/prashantss1404/ph3-FineTunned-matplotlib-seaborn-10k/blob/main/Fine_Tunned_Phi3_on_matplotlib_and_seaborn.ipynb
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.
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.
How to Get Started with the Model
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]))