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

Name: Llama-base-3.1-8B-invoice-gguf-sft
Availability: InStock

text-generation-inference

endpoints_compatible

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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

python

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: ..."))