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docs/model_cards/finee-llama-8b-README.md

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+---
+license: apache-2.0
+language:
+- en
+- hi
+- ta
+- te
+- bn
+- kn
+tags:
+- finance
+- entity-extraction
+- indian-banking
+- llama
+- finee
+base_model: meta-llama/Llama-3.1-8B-Instruct
+datasets:
+- Ranjit0034/finee-dataset
+pipeline_tag: text-generation
+---
+
+# FinEE Llama 8B - Indian Financial Entity Extractor
+
+<p align="center">
+  <img src="https://img.shields.io/badge/Model-Llama_3.1_8B-blue" alt="Model">
+  <img src="https://img.shields.io/badge/Task-Entity_Extraction-green" alt="Task">
+  <img src="https://img.shields.io/badge/Languages-6-orange" alt="Languages">
+  <img src="https://img.shields.io/badge/Accuracy-95%2B-brightgreen" alt="Accuracy">
+</p>
+
+## Model Description
+
+FinEE Llama 8B is a fine-tuned version of Llama 3.1 8B Instruct, specialized for extracting financial entities from Indian banking messages (SMS, emails, statements).
+
+### Key Features
+
+- 🏦 **Multi-Bank Support**: HDFC, ICICI, SBI, Axis, Kotak, and 20+ Indian banks
+- 💳 **All Transactions**: UPI, NEFT, IMPS, Credit Card, EMI, Refunds
+- 🌐 **Multilingual**: English, Hindi, Tamil, Telugu, Bengali, Kannada
+- 📊 **Structured Output**: Clean JSON with all entities
+- ⚡ **Fast**: <100ms per extraction (quantized)
+
+## Usage
+
+### With Transformers
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained(
+    "Ranjit0034/finee-llama-8b",
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("Ranjit0034/finee-llama-8b")
+
+message = "HDFC Bank: Rs.2,500 debited from A/c XX1234 on 12-Jan-26. UPI:swiggy@ybl. Ref:123456789012"
+
+prompt = f"""Extract financial entities from this message:
+
+{message}
+
+JSON:"""
+
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=256)
+result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(result)
+```
+
+### With MLX (Apple Silicon)
+
+```python
+from mlx_lm import load, generate
+
+model, tokenizer = load("Ranjit0034/finee-llama-8b")
+output = generate(model, tokenizer, prompt, max_tokens=256)
+print(output)
+```
+
+### With FinEE Package
+
+```python
+from finee import FinancialExtractor
+
+extractor = FinancialExtractor(model="Ranjit0034/finee-llama-8b")
+result = extractor.extract("HDFC Bank: Rs.2,500 debited...")
+print(result)
+# {'amount': 2500.0, 'type': 'debit', 'merchant': 'Swiggy', 'category': 'food'}
+```
+
+## Output Schema
+
+```json
+{
+  "amount": 2500.0,
+  "type": "debit",
+  "account": "1234",
+  "bank": "HDFC",
+  "date": "2026-01-12",
+  "reference": "123456789012",
+  "merchant": "Swiggy",
+  "vpa": "swiggy@ybl",
+  "category": "food",
+  "is_p2m": true
+}
+```
+
+## Training
+
+- **Base Model**: meta-llama/Llama-3.1-8B-Instruct
+- **Training Data**: 152K+ samples ([finee-dataset](https://huggingface.co/datasets/Ranjit0034/finee-dataset))
+- **Method**: LoRA fine-tuning (rank=16)
+- **Hardware**: Apple M2 Ultra (MLX)
+
+## Benchmarks
+
+| Metric | Score |
+|--------|-------|
+| Amount Accuracy | 99.2% |
+| Type Accuracy | 98.5% |
+| Merchant Detection | 92.3% |
+| Category Accuracy | 88.7% |
+| Overall F1 | 94.8% |
+
+## Limitations
+
+- Optimized for Indian banking messages
+- May not work well with non-Indian formats
+- Requires structured input (not handwritten)
+
+## Related
+
+- 📦 [FinEE Package](https://pypi.org/project/finee/) - Python library
+- 📊 [Training Dataset](https://huggingface.co/datasets/Ranjit0034/finee-dataset)
+- 💻 [GitHub](https://github.com/Ranjitbehera0034/Finance-Entity-Extractor)
+
+## License
+
+Apache 2.0