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avatar63
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Model Details
Model Provider
avatar63
Model Tree
Qwen/Qwen2.5-0.5B-Instruct
Input Modalities
Output Modalities
Supported Functionality
Fine-tuned on ~2040 noisy OCR receipt/invoice examples using LoRA (rank 16). Takes raw, garbled OCR text and extracts structured JSON with company name, address, date, total amount, and line items.
Trained to run entirely locally — no API calls at inference time. Documents never leave your machine.
Evaluated on 204 held-out examples vs base Qwen2.5-0.5B-Instruct:
| Field | Baseline | Fine-tuned | Δ |
|---|---|---|---|
| JSON valid | 80.9% | 99.5% | +18.6% |
| Company name | 38.2% | 46.6% | +8.3% |
| Date | 15.2% | 83.8% | +68.6% |
| Total amount | 0.0% | 99.0% | +99.0% |
| Line items | 58.5% | 97.0% | +38.5% |
python
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelimport torchimport json BASE_MODEL = "Qwen/Qwen2.5-0.5B-Instruct"ADAPTER = "avatar63/qwen-receipt-extractor" INSTRUCTION = ( "Extract the following fields from the OCR text as JSON: " "company_name, address, date, total_amount, line_items " "(each with item_name, quantity, price). " "Use null for any field that cannot be determined.") tokenizer = AutoTokenizer.from_pretrained(ADAPTER, trust_remote_code=True)base = AutoModelForCausalLM.from_pretrained( BASE_MODEL, dtype=torch.float16, device_map="auto", trust_remote_code=True)model = PeftModel.from_pretrained(base, ADAPTER)model.eval() noisy_text = """RELI4NCE FR3SHSh0p N0 12, 5ect0r 18D4te: O5-ll-2O24Net P4y4ble: 34O.OO""" messages = [ {"role": "system", "content": INSTRUCTION}, {"role": "user", "content": noisy_text}] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(text, return_tensors="pt").to(model.device) with torch.no_grad(): outputs = model.generate( **inputs, max_new_tokens=512, temperature=0.1, do_sample=True, pad_token_id=tokenizer.eos_token_id ) generated = outputs[0][inputs["input_ids"].shape[1]:]result = tokenizer.decode(generated, skip_special_tokens=True)print(json.loads(result))| Parameter | Value |
|---|---|
| Base model | Qwen/Qwen2.5-0.5B-Instruct |
| Method | LoRA via HuggingFace PEFT + TRL |
| LoRA rank | 16 |
| LoRA alpha | 32 |
| Trainable parameters | 8.8M / 502M (1.75%) |
| Training examples | ~2040 |
| Epochs | 3 |
| Learning rate | 2e-4 |
| Hardware | RTX 3060 12GB |
| Training time | ~28 minutes |