How to use

This is a LoRA adapter, not a standalone model. Load the base model first, then apply the adapter:

python
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import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

base_model_id = "Qwen/Qwen2.5-0.5B"
adapter_id    = "s-m-sharjeel/qwen2.5-0.5b-alpaca-sft-lora"

tokenizer = AutoTokenizer.from_pretrained(adapter_id)
base = AutoModelForCausalLM.from_pretrained(base_model_id, torch_dtype=torch.float16, device_map="auto")
model = PeftModel.from_pretrained(base, adapter_id)
model.eval()

prompt = "### Instruction:\nWrite a short definition of machine learning.\n\n### Response:\n"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=150, do_sample=False,
                     pad_token_id=tokenizer.eos_token_id)
print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Prompt format

The model was trained on the Alpaca-style template. For best results, format prompts as:

markdown
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### Instruction:
{your instruction here}

### Response:

If the task includes additional input/context, use:

markdown
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### Instruction:
{your instruction here}

### Input:
{context here}

### Response:

Training details

Evaluation

Evaluated on a held-out set of 10 manually written instruction prompts with reference answers, using BLEU (sacreBLEU) and BERTScore F1. This configuration was selected as the best of 5 Alpaca trials by combined BLEU + BERTScore (validation loss as tie-breaker).

This represents a +10.7% relative improvement in BLEU over the base model.

Frameworks

• PEFT
• TRL (SFTTrainer)
• Transformers

Authors

Developed as a course assignment for NLP with Deep Learning, Institute of Business Administration (IBA), Karachi.

License

Released under the Apache 2.0 license, matching the base model Qwen2.5-0.5B.