Note on merging: merging this adapter directly into a 4-bit quantized base model causes measurable quality degradation from compounding quantization error. Merge into a full-precision (fp16) copy of the base model instead — see usage below.

Usage

Base: microsoft/phi-2 (2.7B parameters)
Quantization for training: 4-bit (QLoRA), NF4, double quantization
Fine-tuning method: LoRA (rank 16, alpha 32, dropout 0.05)
Target modules: q_proj, k_proj, v_proj, dense
Trainable parameters: 10.48M / 2.79B (0.376%)

python

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

base_model = AutoModelForCausalLM.from_pretrained(
    "microsoft/phi-2", torch_dtype=torch.float16, trust_remote_code=True
)
model = PeftModel.from_pretrained(base_model, "deepeshd12/phi2-support-lora")
tokenizer = AutoTokenizer.from_pretrained("deepeshd12/phi2-support-lora")

prompt = "Instruction: I want to check my order status\nResponse:"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Limitations

Trained on a 2,000-example subset of a larger dataset; broader coverage would benefit from more data.
ROUGE is a lexical overlap metric and under-represents genuine quality improvements when phrasing differs from the reference (observed directly during development — see repo for details).
Evaluated on 30 test examples; not a large-scale benchmark.