Instructions to use prashantss1404/ph3-FineTunned-matplotlib-seaborn-10k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prashantss1404/ph3-FineTunned-matplotlib-seaborn-10k with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="prashantss1404/ph3-FineTunned-matplotlib-seaborn-10k", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("prashantss1404/ph3-FineTunned-matplotlib-seaborn-10k", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("prashantss1404/ph3-FineTunned-matplotlib-seaborn-10k", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use prashantss1404/ph3-FineTunned-matplotlib-seaborn-10k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prashantss1404/ph3-FineTunned-matplotlib-seaborn-10k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prashantss1404/ph3-FineTunned-matplotlib-seaborn-10k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prashantss1404/ph3-FineTunned-matplotlib-seaborn-10k

SGLang

How to use prashantss1404/ph3-FineTunned-matplotlib-seaborn-10k with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "prashantss1404/ph3-FineTunned-matplotlib-seaborn-10k" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prashantss1404/ph3-FineTunned-matplotlib-seaborn-10k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "prashantss1404/ph3-FineTunned-matplotlib-seaborn-10k" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prashantss1404/ph3-FineTunned-matplotlib-seaborn-10k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use prashantss1404/ph3-FineTunned-matplotlib-seaborn-10k with Docker Model Runner:
```
docker model run hf.co/prashantss1404/ph3-FineTunned-matplotlib-seaborn-10k
```

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

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.

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]))

Downloads last month: 4

Safetensors

Model size

4B params

Tensor type

F32

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