updated readme.md

README.md

@@ -3,7 +3,14 @@ tags:
 - gguf
 - llama.cpp
 - unsloth
-
+license: mit
+datasets:
+- smartytrios/document_data_extractor
+language:
+- en
+base_model:
+- unsloth/Llama-3.2-1B-Instruct-bnb-4bit
+pipeline_tag: zero-shot-classification
 ---
 
 # docintel_ocr_llama_3_2_gguf : GGUF
@@ -21,3 +28,44 @@ This model was finetuned and converted to GGUF format using [Unsloth](https://gi
 An Ollama Modelfile is included for easy deployment.
 This was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth)
 [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+
+---
+tags:
+- gguf
+- llama.cpp
+- unsloth
+- ocr
+- document-intelligence
+- json-extraction
+license: mit
+datasets:
+- smartytrios/document_data_extractor
+language:
+- en
+base_model:
+- unsloth/Llama-3.2-1B-Instruct-bnb-4bit
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+# docintel_ocr_llama_3_2_gguf : GGUF Optimized
+
+This model is a fine-tuned version of **Llama-3.2-1B-Instruct**, specialized for **Document Intelligence** and **OCR-to-JSON** extraction. It was trained using the [Unsloth](https://github.com/unslothai/unsloth) library to optimize memory efficiency and training speed, then exported to GGUF format for local deployment.
+
+## Model Description
+The primary objective of this model is to transform unstructured text generated by Optical Character Recognition (OCR) engines into structured, machine-readable JSON formats. It is specifically tuned to handle noise, line breaks (`\n`), and misalignments common in raw OCR data.
+
+- **Architecture:** Llama 3.2 (1B Parameters)
+- **Quantization:** Q4_K_M (4-bit Medium)
+- **Specialization:** Invoice/Receipt data extraction, medical bill parsing, and form field mapping.
+- **Fine-tuning Method:** QLoRA (Rank: 16)
+
+---
+
+## 🚀 Usage Guide
+
+### 1. Local Inference with llama.cpp
+For the best performance on Windows, Mac, or Linux using `llama.cpp`, use the following command:
+
+```bash
+./llama-cli -hf smartytrios/docintel_ocr_llama_3_2_gguf --jinja -p "### OCR:\n[PASTE YOUR OCR TEXT HERE]\n### JSON:"