Llama-base-3.1-8B-invoice-gguf-sft

A fine-tuned Llama-3.1-8B model optimized for invoice understanding and extraction.
This version is exported in GGUF format for performant inference with tools such as llama.cpp, Ollama, and text-generation-ui.

---

Model Details

Model Description

This model adapts Llama-3.1-8B for structured invoice field extraction.
The goal is to support tasks such as reading invoice text and identifying key fields (amount, date, vendor, tax, line items, etc.).

• Developed by: muhammed-afsal-p-m
• Model type: Auto-regressive language model (decoder-only)
• Languages: English (primary) — Other languages not verified
• License: Fill in — e.g., MIT, Apache-2.0, others
• Fine-tuned from: Llama-3.1-8B (Meta)

Model Sources

• Repository: https://huggingface.co/muhammed-afsal-p-m/Llama-base-3.1-8B-invoice-gguf-sft

---

Uses

Direct Use

Useful for:

• Invoice text understanding
• Extracting structured fields
• Document parsing prototypes
• Local inference via GGUF

Downstream Use

Can be integrated into:

• RPA invoice pipelines
• Accounting automation
• OCR → LLM extraction stages
• Document indexing/search systems

Out-of-Scope Use

Not suited for:

• Legal/financial decision-making without human review
• High-stakes extraction requiring guaranteed accuracy
• Multi-language invoice parsing (not validated)
• Vision-based tasks (requires text extracted separately)

---

Bias, Risks, and Limitations

• Model accuracy depends heavily on the quality and consistency of invoice text.
• May hallucinate missing fields instead of explicitly stating absence.
• Invoices vary widely in structure; unseen formats may reduce reliability.
• Any training biases (invoice styles, languages, domain distribution) affect output.

Recommendations

• Always verify extracted results.
• Use deterministic decoding when consistent outputs are required.
• Validate outputs with rule-based post-processing.

---

How to Get Started

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_name = "muhammed-afsal-p-m/Llama-base-3.1-8B-invoice-gguf-sft"

# For GGUF, use llama.cpp / ctransformers:
from ctransformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    model_file="model.gguf",  # replace with your file name
)
print(model("Extract invoice total from: ..."))