Instructions to use Ba2han/LLama-3-Instruct-8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ba2han/LLama-3-Instruct-8B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Ba2han/LLama-3-Instruct-8B-GGUF",
	filename="llama-3-8B-instruct-Q8_0.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 Ba2han/LLama-3-Instruct-8B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Ba2han/LLama-3-Instruct-8B-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf Ba2han/LLama-3-Instruct-8B-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Ba2han/LLama-3-Instruct-8B-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf Ba2han/LLama-3-Instruct-8B-GGUF:Q8_0

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 Ba2han/LLama-3-Instruct-8B-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf Ba2han/LLama-3-Instruct-8B-GGUF:Q8_0

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 Ba2han/LLama-3-Instruct-8B-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Ba2han/LLama-3-Instruct-8B-GGUF:Q8_0

Use Docker

docker model run hf.co/Ba2han/LLama-3-Instruct-8B-GGUF:Q8_0

LM Studio
Jan
Ollama
How to use Ba2han/LLama-3-Instruct-8B-GGUF with Ollama:
```
ollama run hf.co/Ba2han/LLama-3-Instruct-8B-GGUF:Q8_0
```

Unsloth Studio new

How to use Ba2han/LLama-3-Instruct-8B-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 Ba2han/LLama-3-Instruct-8B-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 Ba2han/LLama-3-Instruct-8B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Ba2han/LLama-3-Instruct-8B-GGUF to start chatting

Docker Model Runner
How to use Ba2han/LLama-3-Instruct-8B-GGUF with Docker Model Runner:
```
docker model run hf.co/Ba2han/LLama-3-Instruct-8B-GGUF:Q8_0
```

Lemonade

How to use Ba2han/LLama-3-Instruct-8B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Ba2han/LLama-3-Instruct-8B-GGUF:Q8_0

Run and chat with the model

lemonade run user.LLama-3-Instruct-8B-GGUF-Q8_0

List all available models

lemonade list

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Should be working properly with KoboldCPP and other stuff. I changed EOS token to <|eot_id|> and this prevents endless generation.

I also converted original weights back to fp32 before quantization.

Edit: Highly recommending you to download more recent quants. 17/05/2024

Downloads last month: 50

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

6-bit

8-bit

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