Instructions to use QuantFactory/Meta-Llama-3-8B-GGUF-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Meta-Llama-3-8B-GGUF-v2 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Meta-Llama-3-8B-GGUF-v2",
	filename="Meta-Llama-3-8B.Q2_K.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 QuantFactory/Meta-Llama-3-8B-GGUF-v2 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/Meta-Llama-3-8B-GGUF-v2:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Meta-Llama-3-8B-GGUF-v2:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/Meta-Llama-3-8B-GGUF-v2:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Meta-Llama-3-8B-GGUF-v2:Q4_K_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 QuantFactory/Meta-Llama-3-8B-GGUF-v2:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Meta-Llama-3-8B-GGUF-v2:Q4_K_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 QuantFactory/Meta-Llama-3-8B-GGUF-v2:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Meta-Llama-3-8B-GGUF-v2:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Meta-Llama-3-8B-GGUF-v2:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Meta-Llama-3-8B-GGUF-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Meta-Llama-3-8B-GGUF-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/Meta-Llama-3-8B-GGUF-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/QuantFactory/Meta-Llama-3-8B-GGUF-v2:Q4_K_M

Ollama
How to use QuantFactory/Meta-Llama-3-8B-GGUF-v2 with Ollama:
```
ollama run hf.co/QuantFactory/Meta-Llama-3-8B-GGUF-v2:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/Meta-Llama-3-8B-GGUF-v2 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 QuantFactory/Meta-Llama-3-8B-GGUF-v2 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 QuantFactory/Meta-Llama-3-8B-GGUF-v2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/Meta-Llama-3-8B-GGUF-v2 to start chatting

Docker Model Runner
How to use QuantFactory/Meta-Llama-3-8B-GGUF-v2 with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Meta-Llama-3-8B-GGUF-v2:Q4_K_M
```

Lemonade

How to use QuantFactory/Meta-Llama-3-8B-GGUF-v2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Meta-Llama-3-8B-GGUF-v2:Q4_K_M

Run and chat with the model

lemonade run user.Meta-Llama-3-8B-GGUF-v2-Q4_K_M

List all available models

lemonade list

Meta-Llama-3-8B-GGUF

This is GGUF quantized version of Meta-Llama-3-8B
Created using latest release of llama.cpp dated 5.5.2024

Model Details

Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety.

Model developers Meta

Variations Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants.

Input Models input text only.

Output Models generate text and code only.

Model Architecture Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.

	Training Data	Params	Context length	GQA	Token count	Knowledge cutoff
Llama 3	A new mix of publicly available online data.	8B	8k	Yes	15T+	March, 2023
Llama 3	A new mix of publicly available online data.	70B	8k	Yes	15T+	December, 2023

Llama 3 family of models. Token counts refer to pretraining data only. Both the 8 and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability.

Model Release Date April 18, 2024.

Status This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.

License A custom commercial license is available at: https://llama.meta.com/llama3/license

Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model README. For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go here.

Downloads last month: 403

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for QuantFactory/Meta-Llama-3-8B-GGUF-v2

Base model

meta-llama/Meta-Llama-3-8B

Quantized

(273)

this model

Collection including QuantFactory/Meta-Llama-3-8B-GGUF-v2

Llama-3

Collection

Quantizations of llama-3 models • 7 items • Updated Oct 3, 2024 • 13