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

Libraries

How to use JaaackXD/Llama-3-8B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="JaaackXD/Llama-3-8B-GGUF",
	filename="ggml-model-Q3_K_M.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 JaaackXD/Llama-3-8B-GGUF with llama.cpp:

Install from brew

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

Install from WinGet (Windows)

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

Use Docker

docker model run hf.co/JaaackXD/Llama-3-8B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use JaaackXD/Llama-3-8B-GGUF with Ollama:
```
ollama run hf.co/JaaackXD/Llama-3-8B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use JaaackXD/Llama-3-8B-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 JaaackXD/Llama-3-8B-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 JaaackXD/Llama-3-8B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

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

Lemonade

How to use JaaackXD/Llama-3-8B-GGUF with Lemonade:

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Directly converted and quantized into GGUF based on llama.cpp (release tag: b2843) from the 'Mata-Llama-3' repo from Meta on Hugging Face.

Including the original LLaMA 3 models file cloning from the Meta HF repo. (https://huggingface.co/meta-llama/Meta-Llama-3-8B)

If you have issues downloading the models from Meta or converting models for llama.cpp, feel free to download this one!

Perplexity table on LLaMA 3 70B

Less perplexity is better. (credit to: dranger003)

Quantization	Size (GiB)	Perplexity (wiki.test)	Delta (FP16)
IQ1_S	14.29	9.8655 +/- 0.0625	248.51%
IQ1_M	15.60	8.5193 +/- 0.0530	201.94%
IQ2_XXS	17.79	6.6705 +/- 0.0405	135.64%
IQ2_XS	19.69	5.7486 +/- 0.0345	103.07%
IQ2_S	20.71	5.5215 +/- 0.0318	95.05%
Q2_K_S	22.79	5.4334 +/- 0.0325	91.94%
IQ2_M	22.46	4.8959 +/- 0.0276	72.35%
Q2_K	24.56	4.7763 +/- 0.0274	68.73%
IQ3_XXS	25.58	3.9671 +/- 0.0211	40.14%
IQ3_XS	27.29	3.7210 +/- 0.0191	31.45%
Q3_K_S	28.79	3.6502 +/- 0.0192	28.95%
IQ3_S	28.79	3.4698 +/- 0.0174	22.57%
IQ3_M	29.74	3.4402 +/- 0.0171	21.53%
Q3_K_M	31.91	3.3617 +/- 0.0172	18.75%
Q3_K_L	34.59	3.3016 +/- 0.0168	16.63%
IQ4_XS	35.30	3.0310 +/- 0.0149	7.07%
IQ4_NL	37.30	3.0261 +/- 0.0149	6.90%
Q4_K_S	37.58	3.0050 +/- 0.0148	6.15%
Q4_K_M	39.60	2.9674 +/- 0.0146	4.83%
Q5_K_S	45.32	2.8843 +/- 0.0141	1.89%
Q5_K_M	46.52	2.8656 +/- 0.0139	1.23%
Q6_K	53.91	2.8441 +/- 0.0138	0.47%
Q8_0	69.83	2.8316 +/- 0.0138	0.03%
F16	131.43	2.8308 +/- 0.0138	0.00%

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.

License

See the License file for Meta Llama 3 here and Acceptable Use Policy here

Downloads last month: 43

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

3-bit

4-bit

5-bit

16-bit

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