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
• 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

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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

• The model was fine-tuned on text-based examples
• It may generate incorrect or non-working code for complex queries.
• No built-in code execution or error checking is included.
• Meant for educational and experimental use only.

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

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]))