metadata
base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- education
license: apache-2.0
language:
- en
🦙 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.
📚 Training Data
The model was trained on:
- Dataset: louisbrulenaudet/code-education
→ educational dataset for code-related instructions.
🎯 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.
🚀 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))