primel
/

aibama

@@ -1,209 +1,102 @@
 ---
 base_model: meta-llama/Llama-3.2-1B-Instruct
-library_name: peft
-pipeline_tag: text-generation
 tags:
-- base_model:adapter:meta-llama/Llama-3.2-1B-Instruct
 - lora
-- sft
-- transformers
-- trl
 ---
-# Model Card for Model ID
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 ## Model Details
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-### Framework versions
-- PEFT 0.18.0

 ---
+language:
+- en
+- uz
+- ru
+license: llama3.2
 base_model: meta-llama/Llama-3.2-1B-Instruct
 tags:
+- payment-extraction
+- financial-nlp
 - lora
+- fine-tuned
+- llama-3.2
+library_name: peft
 ---
+# Payment Extraction Model (Llama 3.2-1B)
+Fine-tuned Llama 3.2-1B-Instruct for extracting payment information from multilingual text (English, Uzbek, Russian).
 ## Model Details
+- **Base Model**: `meta-llama/Llama-3.2-1B-Instruct`
+- **Training Data**: 4,082 examples
+- **Training Duration**: 5 epochs
+- **Method**: LoRA (Low-Rank Adaptation)
+- **Best Checkpoint**: Step 900 (validation loss: 0.384)
+- **Trainable Parameters**: 0.9% (11.27M / 1.24B)
+## Capabilities
+Extracts structured payment information:
+- **amount**: Payment amount
+- **receiver_name**: Recipient name
+- **receiver_inn**: Tax identification number
+- **receiver_account**: Bank account number
+- **mfo**: Bank code
+- **payment_purpose**: Purpose of payment
+- **purpose_code**: Payment purpose code
+- **intent**: Classification (create_transaction, partial_create_transaction, list_transaction)
+## Usage
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+import torch
+# Load model
+base_model = AutoModelForCausalLM.from_pretrained(
+    "meta-llama/Llama-3.2-1B-Instruct",
+    device_map="auto",
+    torch_dtype=torch.bfloat16
+)
+model = PeftModel.from_pretrained(base_model, "primel/aibama")
+tokenizer = AutoTokenizer.from_pretrained("primel/aibama")
+# Extract payment info
+text = "Transfer 500000 to LLC Technopark, INN 123456789"
+prompt = f"""<|begin_of_text|><|start_header_id|>system<|end_header_id|>
+You are a payment extraction assistant. Extract payment information from text and return ONLY valid JSON.<|eot_id|><|start_header_id|>user<|end_header_id|>
+{text}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
+"""
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=300, temperature=0.1)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+```
+## Training Data Distribution
+- **create_transaction**: 36.7% (1,500 examples)
+- **partial_create_transaction**: 52.6% (2,148 examples)
+- **list_transaction**: 10.6% (434 examples)
+## Performance
+| Metric | Value |
+|--------|-------|
+| Training Loss | 0.3785 |
+| Validation Loss | 0.3844 |
+| Mean Token Accuracy | 92.59% |
+| Entropy | 0.424 |
+## Limitations
+- Optimized for payment-related text in English, Uzbek, and Russian
+- May require base model access (Llama 3.2 license)
+- Best performance on structured payment instructions
+## Citation
+```bibtex
+@misc{payment-extractor-llama32,
+  author = {Your Name},
+  title = {Payment Extraction Model},
+  year = {2024},
+  publisher = {HuggingFace},
+  url = {https://huggingface.co/primel/aibama}
+}
+```