Instructions to use wls1964302791/Llama-3.1-8B-bnb-4bit-wenyanwen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wls1964302791/Llama-3.1-8B-bnb-4bit-wenyanwen with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="wls1964302791/Llama-3.1-8B-bnb-4bit-wenyanwen",
	filename="unsloth.F16.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use wls1964302791/Llama-3.1-8B-bnb-4bit-wenyanwen with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf wls1964302791/Llama-3.1-8B-bnb-4bit-wenyanwen:F16
# Run inference directly in the terminal:
llama-cli -hf wls1964302791/Llama-3.1-8B-bnb-4bit-wenyanwen:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf wls1964302791/Llama-3.1-8B-bnb-4bit-wenyanwen:F16
# Run inference directly in the terminal:
llama-cli -hf wls1964302791/Llama-3.1-8B-bnb-4bit-wenyanwen: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 wls1964302791/Llama-3.1-8B-bnb-4bit-wenyanwen:F16
# Run inference directly in the terminal:
./llama-cli -hf wls1964302791/Llama-3.1-8B-bnb-4bit-wenyanwen: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 wls1964302791/Llama-3.1-8B-bnb-4bit-wenyanwen:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf wls1964302791/Llama-3.1-8B-bnb-4bit-wenyanwen:F16

Use Docker

docker model run hf.co/wls1964302791/Llama-3.1-8B-bnb-4bit-wenyanwen:F16

LM Studio
Jan
Ollama
How to use wls1964302791/Llama-3.1-8B-bnb-4bit-wenyanwen with Ollama:
```
ollama run hf.co/wls1964302791/Llama-3.1-8B-bnb-4bit-wenyanwen:F16
```

Unsloth Studio new

How to use wls1964302791/Llama-3.1-8B-bnb-4bit-wenyanwen 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 wls1964302791/Llama-3.1-8B-bnb-4bit-wenyanwen 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 wls1964302791/Llama-3.1-8B-bnb-4bit-wenyanwen to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for wls1964302791/Llama-3.1-8B-bnb-4bit-wenyanwen to start chatting

Docker Model Runner
How to use wls1964302791/Llama-3.1-8B-bnb-4bit-wenyanwen with Docker Model Runner:
```
docker model run hf.co/wls1964302791/Llama-3.1-8B-bnb-4bit-wenyanwen:F16
```

Lemonade

How to use wls1964302791/Llama-3.1-8B-bnb-4bit-wenyanwen with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull wls1964302791/Llama-3.1-8B-bnb-4bit-wenyanwen:F16

Run and chat with the model

lemonade run user.Llama-3.1-8B-bnb-4bit-wenyanwen-F16

List all available models

lemonade list

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GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

4-bit

16-bit

Model tree for wls1964302791/Llama-3.1-8B-bnb-4bit-wenyanwen

Base model

meta-llama/Llama-3.1-8B

Quantized

(324)

this model

wls1964302791
/

Llama-3.1-8B-bnb-4bit-wenyanwen

Model tree for wls1964302791/Llama-3.1-8B-bnb-4bit-wenyanwen

Dataset used to train wls1964302791/Llama-3.1-8B-bnb-4bit-wenyanwen