Instructions to use muhammed-afsal-p-m/Llama-base-3.1-8B-invoice-gguf-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use muhammed-afsal-p-m/Llama-base-3.1-8B-invoice-gguf-sft with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="muhammed-afsal-p-m/Llama-base-3.1-8B-invoice-gguf-sft")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("muhammed-afsal-p-m/Llama-base-3.1-8B-invoice-gguf-sft")
model = AutoModelForCausalLM.from_pretrained("muhammed-afsal-p-m/Llama-base-3.1-8B-invoice-gguf-sft")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use muhammed-afsal-p-m/Llama-base-3.1-8B-invoice-gguf-sft with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="muhammed-afsal-p-m/Llama-base-3.1-8B-invoice-gguf-sft",
	filename="afsal_llm.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use muhammed-afsal-p-m/Llama-base-3.1-8B-invoice-gguf-sft with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf muhammed-afsal-p-m/Llama-base-3.1-8B-invoice-gguf-sft
# Run inference directly in the terminal:
llama-cli -hf muhammed-afsal-p-m/Llama-base-3.1-8B-invoice-gguf-sft

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf muhammed-afsal-p-m/Llama-base-3.1-8B-invoice-gguf-sft
# Run inference directly in the terminal:
llama-cli -hf muhammed-afsal-p-m/Llama-base-3.1-8B-invoice-gguf-sft

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf muhammed-afsal-p-m/Llama-base-3.1-8B-invoice-gguf-sft
# Run inference directly in the terminal:
./llama-cli -hf muhammed-afsal-p-m/Llama-base-3.1-8B-invoice-gguf-sft

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf muhammed-afsal-p-m/Llama-base-3.1-8B-invoice-gguf-sft
# Run inference directly in the terminal:
./build/bin/llama-cli -hf muhammed-afsal-p-m/Llama-base-3.1-8B-invoice-gguf-sft

Use Docker

docker model run hf.co/muhammed-afsal-p-m/Llama-base-3.1-8B-invoice-gguf-sft

LM Studio
Jan

vLLM

How to use muhammed-afsal-p-m/Llama-base-3.1-8B-invoice-gguf-sft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "muhammed-afsal-p-m/Llama-base-3.1-8B-invoice-gguf-sft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "muhammed-afsal-p-m/Llama-base-3.1-8B-invoice-gguf-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/muhammed-afsal-p-m/Llama-base-3.1-8B-invoice-gguf-sft

SGLang

How to use muhammed-afsal-p-m/Llama-base-3.1-8B-invoice-gguf-sft with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "muhammed-afsal-p-m/Llama-base-3.1-8B-invoice-gguf-sft" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "muhammed-afsal-p-m/Llama-base-3.1-8B-invoice-gguf-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "muhammed-afsal-p-m/Llama-base-3.1-8B-invoice-gguf-sft" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "muhammed-afsal-p-m/Llama-base-3.1-8B-invoice-gguf-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use muhammed-afsal-p-m/Llama-base-3.1-8B-invoice-gguf-sft with Ollama:
```
ollama run hf.co/muhammed-afsal-p-m/Llama-base-3.1-8B-invoice-gguf-sft
```

Unsloth Studio

How to use muhammed-afsal-p-m/Llama-base-3.1-8B-invoice-gguf-sft with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for muhammed-afsal-p-m/Llama-base-3.1-8B-invoice-gguf-sft to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for muhammed-afsal-p-m/Llama-base-3.1-8B-invoice-gguf-sft to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for muhammed-afsal-p-m/Llama-base-3.1-8B-invoice-gguf-sft to start chatting

How to use muhammed-afsal-p-m/Llama-base-3.1-8B-invoice-gguf-sft with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf muhammed-afsal-p-m/Llama-base-3.1-8B-invoice-gguf-sft

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "muhammed-afsal-p-m/Llama-base-3.1-8B-invoice-gguf-sft"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use muhammed-afsal-p-m/Llama-base-3.1-8B-invoice-gguf-sft with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf muhammed-afsal-p-m/Llama-base-3.1-8B-invoice-gguf-sft

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default muhammed-afsal-p-m/Llama-base-3.1-8B-invoice-gguf-sft

Run Hermes

hermes

Docker Model Runner
How to use muhammed-afsal-p-m/Llama-base-3.1-8B-invoice-gguf-sft with Docker Model Runner:
```
docker model run hf.co/muhammed-afsal-p-m/Llama-base-3.1-8B-invoice-gguf-sft
```

Lemonade

How to use muhammed-afsal-p-m/Llama-base-3.1-8B-invoice-gguf-sft with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull muhammed-afsal-p-m/Llama-base-3.1-8B-invoice-gguf-sft

Run and chat with the model

lemonade run user.Llama-base-3.1-8B-invoice-gguf-sft-{{QUANT_TAG}}

List all available models

lemonade list

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: ..."))

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Safetensors

Model size

8B params

Tensor type

BF16