Instructions to use annus-lums/Llama-3.1-8B-Instruct-FP16-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use annus-lums/Llama-3.1-8B-Instruct-FP16-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="annus-lums/Llama-3.1-8B-Instruct-FP16-GGUF",
	filename="llama-3.1-8b-instruct-f16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use annus-lums/Llama-3.1-8B-Instruct-FP16-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf annus-lums/Llama-3.1-8B-Instruct-FP16-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf annus-lums/Llama-3.1-8B-Instruct-FP16-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf annus-lums/Llama-3.1-8B-Instruct-FP16-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf annus-lums/Llama-3.1-8B-Instruct-FP16-GGUF:F16

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 annus-lums/Llama-3.1-8B-Instruct-FP16-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf annus-lums/Llama-3.1-8B-Instruct-FP16-GGUF:F16

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 annus-lums/Llama-3.1-8B-Instruct-FP16-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf annus-lums/Llama-3.1-8B-Instruct-FP16-GGUF:F16

Use Docker

docker model run hf.co/annus-lums/Llama-3.1-8B-Instruct-FP16-GGUF:F16

LM Studio
Jan
Ollama
How to use annus-lums/Llama-3.1-8B-Instruct-FP16-GGUF with Ollama:
```
ollama run hf.co/annus-lums/Llama-3.1-8B-Instruct-FP16-GGUF:F16
```

Unsloth Studio

How to use annus-lums/Llama-3.1-8B-Instruct-FP16-GGUF 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 annus-lums/Llama-3.1-8B-Instruct-FP16-GGUF 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 annus-lums/Llama-3.1-8B-Instruct-FP16-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for annus-lums/Llama-3.1-8B-Instruct-FP16-GGUF to start chatting

How to use annus-lums/Llama-3.1-8B-Instruct-FP16-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf annus-lums/Llama-3.1-8B-Instruct-FP16-GGUF:F16

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": "annus-lums/Llama-3.1-8B-Instruct-FP16-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use annus-lums/Llama-3.1-8B-Instruct-FP16-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf annus-lums/Llama-3.1-8B-Instruct-FP16-GGUF:F16

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 annus-lums/Llama-3.1-8B-Instruct-FP16-GGUF:F16

Run Hermes

hermes

Docker Model Runner
How to use annus-lums/Llama-3.1-8B-Instruct-FP16-GGUF with Docker Model Runner:
```
docker model run hf.co/annus-lums/Llama-3.1-8B-Instruct-FP16-GGUF:F16
```

Lemonade

How to use annus-lums/Llama-3.1-8B-Instruct-FP16-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull annus-lums/Llama-3.1-8B-Instruct-FP16-GGUF:F16

Run and chat with the model

lemonade run user.Llama-3.1-8B-Instruct-FP16-GGUF-F16

List all available models

lemonade list

Llama-3.1-8B-Instruct-FP16-GGUF

Description

Llama 3.1 8B Instruct in FP16 (baseline)

File: llama-3.1-8b-instruct-f16.gguf
Size: 16G
Format: GGUF
Category: baseline

Quick Start

Download

huggingface-cli download annus-lums/Llama-3.1-8B-Instruct-FP16-GGUF llama-3.1-8b-instruct-f16.gguf --local-dir .

Usage with llama.cpp

./llama-cli -m llama-3.1-8b-instruct-f16.gguf -p "Explain quantum computing" -n 128

Benchmark

./llama-bench -m llama-3.1-8b-instruct-f16.gguf -r 3

Project: AI on Edge Devices

This model is part of an LLM compression research project.

Pipeline

Pruning: 20% structured Taylor pruning (MLP layers only)
SmoothQuant: Activation smoothing for stable quantization
Mixed Precision: Sensitivity-based bit-width allocation (Q4/Q5/Q6)

Results

Size: 73.6% reduction (15 GB → 4 GB)
Speed: 273% faster inference (1.16 → 4.33 tok/s)
Deployment: Successfully runs on Raspberry Pi 4

Model Card

Created by: Group 2 (Annus, Arslan, Naveed, Danyal)
Institution: LUMS
Date: December 2024

Citation

@misc{llama31-compressed-Llama-3.1-8B-Instruct-FP16-GGUF,
  author = {Group 2},
  title = {Llama-3.1-8B-Instruct-FP16-GGUF},
  year = {2024},
  publisher = {HuggingFace},
  url = {https://huggingface.co/annus-lums/Llama-3.1-8B-Instruct-FP16-GGUF}
}

Downloads last month: 4

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

16-bit

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Model tree for annus-lums/Llama-3.1-8B-Instruct-FP16-GGUF

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meta-llama/Llama-3.1-8B

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meta-llama/Llama-3.1-8B-Instruct

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