llama-educator

🦙 Uploaded Finetuned Model – Llama 3.1 (8B) by Matteo Angeloni

• Developed by: matteoangeloni
• License: apache-2.0
• Base model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
• Libraries used: Unsloth, Hugging Face TRL

This model is my first finetuned Llama model, built for educational and legal-domain text generation.
Training was accelerated with Unsloth (2x faster fine-tuning) and integrated with Hugging Face tools.

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📚 Training Data

The model was trained on:

• Dataset: louisbrulenaudet/code-education
  → educational dataset for code-related instructions.

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🎯 Intended Use

• Experimentation with educational text generation
• Testing instruction-following capabilities in code/education-related contexts
• Benchmarking performance of Unsloth-accelerated LLaMA models

⚠️ Not suitable for production. This is an experimental finetune.

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🚀 Example Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "matteoangeloni/llama3-8b-edu"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

prompt = "Summarize the main points of the Italian privacy law."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))