Instructions to use DavidZyy/Meta-Llama-3-8B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DavidZyy/Meta-Llama-3-8B-Instruct with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="DavidZyy/Meta-Llama-3-8B-Instruct",
	filename="Meta-Llama-3-8B-Instruct-IQ1_M.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use DavidZyy/Meta-Llama-3-8B-Instruct with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf DavidZyy/Meta-Llama-3-8B-Instruct:IQ1_M
# Run inference directly in the terminal:
llama-cli -hf DavidZyy/Meta-Llama-3-8B-Instruct:IQ1_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf DavidZyy/Meta-Llama-3-8B-Instruct:IQ1_M
# Run inference directly in the terminal:
llama-cli -hf DavidZyy/Meta-Llama-3-8B-Instruct:IQ1_M

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 DavidZyy/Meta-Llama-3-8B-Instruct:IQ1_M
# Run inference directly in the terminal:
./llama-cli -hf DavidZyy/Meta-Llama-3-8B-Instruct:IQ1_M

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 DavidZyy/Meta-Llama-3-8B-Instruct:IQ1_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DavidZyy/Meta-Llama-3-8B-Instruct:IQ1_M

Use Docker

docker model run hf.co/DavidZyy/Meta-Llama-3-8B-Instruct:IQ1_M

LM Studio
Jan
Ollama
How to use DavidZyy/Meta-Llama-3-8B-Instruct with Ollama:
```
ollama run hf.co/DavidZyy/Meta-Llama-3-8B-Instruct:IQ1_M
```

Unsloth Studio new

How to use DavidZyy/Meta-Llama-3-8B-Instruct 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 DavidZyy/Meta-Llama-3-8B-Instruct 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 DavidZyy/Meta-Llama-3-8B-Instruct to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for DavidZyy/Meta-Llama-3-8B-Instruct to start chatting

Docker Model Runner
How to use DavidZyy/Meta-Llama-3-8B-Instruct with Docker Model Runner:
```
docker model run hf.co/DavidZyy/Meta-Llama-3-8B-Instruct:IQ1_M
```

Lemonade

How to use DavidZyy/Meta-Llama-3-8B-Instruct with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DavidZyy/Meta-Llama-3-8B-Instruct:IQ1_M

Run and chat with the model

lemonade run user.Meta-Llama-3-8B-Instruct-IQ1_M

List all available models

lemonade list

This repository aims to explore the extreme compression ratio of the model, so only low bit quantization models are provided. They all quantized from F16.

model	size	ppl
F16	15G	8.3662 +/- 0.06216
IQ2_M	2.8G	10.2360 +/- 0.07470
IQ2_S	2.6G	11.3735 +/- 0.08396
IQ2_XS	2.5G	12.3081 +/- 0.08961
IQ2_XXS	2.3G	15.9081 +/- 0.11701
IQ1_M	2.1G	26.5610 +/- 0.19391

Downloads last month: 19

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

1-bit

2-bit

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