Models & Fine-Tuning

The open-source AI ecosystem has exploded with models like Llama, Mistral, and Falcon. These models allow developers to build private, secure, and specialized AI applications without relying on proprietary APIs.

Hugging Face Ecosystem

Hugging Face is the central hub for the AI community. The transformers library is the industry standard for downloading, training, and deploying state-of-the-art models.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = 'mistralai/Mistral-7B-v0.1'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map='auto', load_in_4bit=True)

inputs = tokenizer('How do I fine-tune a model?', return_tensors='pt').to('cuda')
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0]))

Fine-Tuning with LoRA

Full fine-tuning requires massive VRAM. Low-Rank Adaptation (LoRA) is a Parameter-Efficient Fine-Tuning (PEFT) technique that freezes the original model weights and only trains small adapter layers.