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Irfanuruchi/
phi-2-alpaca-lora

Name: phi-2-alpaca-lora
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Phi‑2‑Alpaca‑LoRA

GitHub Repo

Overview

This repository contains LoRA‑tuned weights for microsoft/phi‑2 (2.7B).
The adapters were trained on:

yahma/alpaca-cleaned (~5k instructions)
Custom instruction datasets (collected separately)

Targets: q_proj, k_proj, v_proj, dense layers within the transformer.
Adapters were merged after training to produce a standalone Hugging Face checkpoint.

Training setup

LoRA config: rank=16, α=32, dropout=0.05
Max seq length: 256
Optimizer: AdamW, lr=2e‑4
Precision: bf16 (fp16 fallback)

Example

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "Irfanuruchi/phi-2-alpaca-lora"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True,
)

prompt = "### Instruction: List three advantages of modular code.\n### Response:"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.inference_mode():
    outputs = model.generate(
        **inputs,
        max_new_tokens=200,
        temperature=0.7,
        top_p=0.9,
    )

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

Limitations

Context length capped at 256 tokens
Can return hallucinated or biased content
Output tone/style depends on Alpaca + custom data