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, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("muhammed-afsal-p-m/Llama-base-3.1-8B-invoice-gguf-sft")
model = AutoModelForMultimodalLM.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

Atomic Chat new
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 / README.md

muhammed-afsal-p-m

Update README.md

74d4944 verified 7 months ago

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	---
	library_name: transformers
	tags:
	- llama
	- invoice-extraction
	- sft
	- gguf
	---

	# 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: ..."))