Item	Value
Base model	Qwen2.5-3B-Instruct
Method	LoRA supervised fine-tuning (SFT)
Dataset	KIRANA_DATASET_90_DAYS.json
Training examples	90 (one per simulation day)
Products	milk, bread, eggs, maggi, chips, cold_drinks, bananas
Training steps	~200
Final training loss	~0.02

Evaluation

Metric	Result
Copilot pipeline — Days 1–90 sensible	100% (90/90)
Copilot pipeline — Days 1–90 exact JSON schema	100% (90/90)
Copilot pipeline — product overlap vs gold	98.4%
LightGBM demand forecast validation MAE	0.54 units

How to use

python

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base = "Qwen/Qwen2.5-3B-Instruct"
adapter = "Shagun-Kumar/kirana-qwen2.5-3b-lora"

tokenizer = AutoTokenizer.from_pretrained(base, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(base, trust_remote_code=True)
model = PeftModel.from_pretrained(model, adapter)

## Model Details

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### Framework versions

- PEFT 0.19.1