Instructions to use fedric95/Meta-Llama-3.1-8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use fedric95/Meta-Llama-3.1-8B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf fedric95/Meta-Llama-3.1-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf fedric95/Meta-Llama-3.1-8B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf fedric95/Meta-Llama-3.1-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf fedric95/Meta-Llama-3.1-8B-GGUF: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 fedric95/Meta-Llama-3.1-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf fedric95/Meta-Llama-3.1-8B-GGUF: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 fedric95/Meta-Llama-3.1-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf fedric95/Meta-Llama-3.1-8B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/fedric95/Meta-Llama-3.1-8B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use fedric95/Meta-Llama-3.1-8B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fedric95/Meta-Llama-3.1-8B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fedric95/Meta-Llama-3.1-8B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/fedric95/Meta-Llama-3.1-8B-GGUF:Q4_K_M

Ollama
How to use fedric95/Meta-Llama-3.1-8B-GGUF with Ollama:
```
ollama run hf.co/fedric95/Meta-Llama-3.1-8B-GGUF:Q4_K_M
```

Unsloth Studio

How to use fedric95/Meta-Llama-3.1-8B-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 fedric95/Meta-Llama-3.1-8B-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 fedric95/Meta-Llama-3.1-8B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for fedric95/Meta-Llama-3.1-8B-GGUF to start chatting

Docker Model Runner
How to use fedric95/Meta-Llama-3.1-8B-GGUF with Docker Model Runner:
```
docker model run hf.co/fedric95/Meta-Llama-3.1-8B-GGUF:Q4_K_M
```

Lemonade

How to use fedric95/Meta-Llama-3.1-8B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull fedric95/Meta-Llama-3.1-8B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Meta-Llama-3.1-8B-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Llamacpp Quantizations of Meta-Llama-3.1-8B

Using llama.cpp release b3472 for quantization.

Original model: https://huggingface.co/meta-llama/Meta-Llama-3.1-8B

Download a file (not the whole branch) from below:

Filename	Quant type	File Size	Perplexity (wikitext-2-raw-v1.test)
Meta-Llama-3.1-8B-BF16.gguf	BF16	16.10GB	6.4006 +/- 0.03938
Meta-Llama-3.1-8B-FP16.gguf	FP16	16.10GB	6.4016 +/- 0.03939
Meta-Llama-3.1-8B-Q8_0.gguf	Q8_0	8.54GB	6.4070 +/- 0.03941
Meta-Llama-3.1-8B-Q6_K.gguf	Q6_K	6.60GB	6.4231 +/- 0.03957
Meta-Llama-3.1-8B-Q5_K_M.gguf	Q5_K_M	5.73GB	6.4623 +/- 0.03987
Meta-Llama-3.1-8B-Q5_K_S.gguf	Q5_K_S	5.60GB	6.5161 +/- 0.04028
Meta-Llama-3.1-8B-Q4_K_M.gguf	Q4_K_M	4.92GB	6.5837 +/- 0.04068
Meta-Llama-3.1-8B-Q4_K_S.gguf	Q4_K_S	4.69GB	6.6751 +/- 0.04125
Meta-Llama-3.1-8B-Q3_K_L.gguf	Q3_K_L	4.32GB	6.9458 +/- 0.04329
Meta-Llama-3.1-8B-Q3_K_M.gguf	Q3_K_M	4.02GB	7.0488 +/- 0.04384
Meta-Llama-3.1-8B-Q3_K_S.gguf	Q3_K_S	3.66GB	7.8823 +/- 0.04920
Meta-Llama-3.1-8B-Q2_K.gguf	Q2_K	3.18GB	9.7262 +/- 0.06393

Benchmark Results

Results have been computed using:

hellaswage_val_full

winogrande-debiased-eval

mmlu-validation

Benchmark	Quant type	Metric
WinoGrande (0-shot)	Q8_0	74.1121 +/- 1.2311
WinoGrande (0-shot)	Q4_K_M	73.1650 +/- 1.2453
WinoGrande (0-shot)	Q3_K_M	72.7703 +/- 1.2511
WinoGrande (0-shot)	Q3_K_S	72.3757 +/- 1.2567
WinoGrande (0-shot)	Q2_K	68.4294 +/- 1.3063
HellaSwag (0-shot)	Q8_0	79.41645091
HellaSwag (0-shot)	Q4_K_M	79.05795658
HellaSwag (0-shot)	Q3_K_M	79.41645091
HellaSwag (0-shot)	Q3_K_S	76.93686517
HellaSwag (0-shot)	Q2_K	72.16689902
MMLU (0-shot)	Q8_0	39.4703 +/- 1.2427
MMLU (0-shot)	Q4_K_M	39.5349 +/- 1.2431
MMLU (0-shot)	Q3_K_M	38.8889 +/- 1.2394
MMLU (0-shot)	Q3_K_S	37.2739 +/- 1.2294
MMLU (0-shot)	Q2_K	35.4651 +/- 1.2163

Downloading using huggingface-cli

First, make sure you have hugginface-cli installed:

pip install -U "huggingface_hub[cli]"

Then, you can target the specific file you want:

huggingface-cli download fedric95/Meta-Llama-3.1-8B-GGUF --include "Meta-Llama-3.1-8B-Q4_K_M.gguf" --local-dir ./

If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:

huggingface-cli download fedric95/Meta-Llama-3.1-8B-GGUF --include "Meta-Llama-3.1-8B-Q8_0.gguf/*" --local-dir Meta-Llama-3.1-8B-Q8_0

You can either specify a new local-dir (Meta-Llama-3.1-8B-Q8_0) or download them all in place (./)

Reproducibility

https://github.com/ggerganov/llama.cpp/issues/8650#issuecomment-2261497976

Downloads last month: 395

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

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6-bit

8-bit

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Model tree for fedric95/Meta-Llama-3.1-8B-GGUF

Base model

meta-llama/Llama-3.1-8B

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