gokulalex
/

BrahmaNet

@@ -1,207 +1,100 @@
 ---
-base_model: mistralai/Mistral-7B-Instruct-v0.3
-library_name: peft
-pipeline_tag: text-generation
 tags:
-- base_model:adapter:mistralai/Mistral-7B-Instruct-v0.3
-- lora
-- transformers
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
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 ## Uses
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 ## How to Get Started with the Model
-Use the code below to get started with the model.
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-### Framework versions
-- PEFT 0.17.1

 ---
+language:
+- en
+license: mit
 tags:
+- invoice-extraction
+- structured-data
+- phi-3
+- sft
+- text-generation
+- document-understanding
+- financial-nlp
+datasets:
+- custom-invoice-dataset
+pipeline_tag: text-generation
+base_model: microsoft/Phi-3-mini-4k-instruct
 ---
+# BrahmaNet: Phi-3 SFT for Invoice Extraction
+<div align="center">
+![BrahmaNet Logo](https://img.shields.io/badge/BrahmaNet-Invoice%20Extraction-blue)
+![Phi-3](https://img.shields.io/badge/Base%20Model-Phi--3-green)
+![SFT](https://img.shields.io/badge/Method-Supervised%20Fine--Tuning-orange)
+</div>
+## Model Description
+**BrahmaNet** is a specialized language model fine-tuned from Microsoft's Phi-3-mini-4k-instruct for extracting structured information from invoice documents. The model is optimized to understand invoice formats and convert unstructured text into well-structured JSON output.
+- **Developed by:** Gokul Alex
+- **Model type:** Causal Language Model
+- **Language(s):** English
+- **License:** MIT
+- **Finetuned from model:** [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct)
 ## Uses
 ### Direct Use
+This model is designed for extracting structured information from invoice documents including:
+- Invoice numbers and dates
+- Supplier/vendor information
+- Total amounts and line items
+- Customer details
+- Payment terms
+### Downstream Use
+The model can be fine-tuned further for:
+- Receipt processing
+- Purchase order extraction
+- Financial document analysis
+- Custom structured data extraction tasks
 ### Out-of-Scope Use
+- General purpose chat or conversation
+- Mathematical reasoning beyond basic arithmetic
+- Legal document analysis
+- Medical or sensitive personal information extraction
 ## How to Get Started with the Model
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+# Load model and tokenizer
+model_name = "gokulalex/BrahmaNet"
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    device_map="auto",
+    torch_dtype=torch.bfloat16,
+    trust_remote_code=True
+)
+# Prepare prompt
+prompt = """Extract invoice information as JSON:
+Document: Invoice Number: INV-2023-001, Date: 2023-10-15, Supplier: ABC Corporation, Total Amount: $1,250.00
+JSON:"""
+# Generate response
+inputs = tokenizer(prompt, return_tensors="pt", max_length=512, truncation=True)
+outputs = model.generate(
+    inputs.input_ids,
+    max_new_tokens=150,
+    do_sample=True,
+    temperature=0.3,
+    top_p=0.9,
+    pad_token_id=tokenizer.eos_token_id
+)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)