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Shagun-Kumar/kirana-qwen2.5-3b-lora
kirana-qwen2.5-3b-lora is a text generation model from Shagun-Kumar. Use it when you need the model to write or continue text. It is set up for peft.
Fine-tuned LoRA adapter on Qwen2.5-3B-Instruct for Shree Shyam Bhandar, an Indian kirana store simulation (90-day dataset).
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From the Hugging Face model README
Fine-tuned LoRA adapter on Qwen2.5-3B-Instruct for Shree Shyam Bhandar, an Indian kirana store simulation (90-day dataset).
| 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 |
| 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 |
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