RajGana/tinyllama-alpaca-finetuned

TinyLlama Alpaca Fine-tuned

Fine-tuned TinyLlama-1.1B on Alpaca dataset using LoRA. Built from scratch as part of AI engineering learning journey.

Training

• Base: TinyLlama-1.1B-Chat-v1.0
• Dataset: Alpaca (5000 samples)
• Method: LoRA (r=16, alpha=32)
• Epochs: 3 | Final loss: 1.01
• GPU: NVIDIA L4

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

tokenizer = AutoTokenizer.from_pretrained("RajGana/tinyllama-alpaca-finetuned")
model = AutoModelForCausalLM.from_pretrained("RajGana/tinyllama-alpaca-finetuned")
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = "<|system|>You are a helpful assistant.</s><|user|>What is AI?</s><|assistant|>"
print(pipe(prompt, max_new_tokens=100)[0]["generated_text"])