Instructions to use brittlewis12/Meta-Llama-3-8B-Instruct-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 brittlewis12/Meta-Llama-3-8B-Instruct-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 brittlewis12/Meta-Llama-3-8B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf brittlewis12/Meta-Llama-3-8B-Instruct-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 brittlewis12/Meta-Llama-3-8B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf brittlewis12/Meta-Llama-3-8B-Instruct-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 brittlewis12/Meta-Llama-3-8B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf brittlewis12/Meta-Llama-3-8B-Instruct-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 brittlewis12/Meta-Llama-3-8B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf brittlewis12/Meta-Llama-3-8B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/brittlewis12/Meta-Llama-3-8B-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use brittlewis12/Meta-Llama-3-8B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "brittlewis12/Meta-Llama-3-8B-Instruct-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "brittlewis12/Meta-Llama-3-8B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/brittlewis12/Meta-Llama-3-8B-Instruct-GGUF:Q4_K_M

Ollama
How to use brittlewis12/Meta-Llama-3-8B-Instruct-GGUF with Ollama:
```
ollama run hf.co/brittlewis12/Meta-Llama-3-8B-Instruct-GGUF:Q4_K_M
```

Unsloth Studio

How to use brittlewis12/Meta-Llama-3-8B-Instruct-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 brittlewis12/Meta-Llama-3-8B-Instruct-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 brittlewis12/Meta-Llama-3-8B-Instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for brittlewis12/Meta-Llama-3-8B-Instruct-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use brittlewis12/Meta-Llama-3-8B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/brittlewis12/Meta-Llama-3-8B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use brittlewis12/Meta-Llama-3-8B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull brittlewis12/Meta-Llama-3-8B-Instruct-GGUF:Q4_K_M

Run and chat with the model

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

List all available models

lemonade list

Meta-Llama-3-8B-Instruct GGUF

Original model: Meta-Llama-3-8B-Instruct

Model creator: Meta

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.

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.

This repo contains GGUF format model files for Meta’s Llama-3-8B-Instruct, updated as of 2024-04-29 to incorporate tokenization improvements, as well as previous interventions to handle the <|eot_id|> special token as EOS token.

Learn more on Meta’s Llama 3 page.

What is GGUF?

GGUF is a file format for representing AI models. It is the third version of the format, introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Converted with llama.cpp build 2763 (revision ffe666), using autogguf.

Prompt template

<|start_header_id|>system<|end_header_id|>

{{system_prompt}}<|eot_id|><|start_header_id|>user<|end_header_id|>

{{prompt}}<|eot_id|><|start_header_id|>assistant<|end_header_id|>

Download & run with cnvrs on iPhone, iPad, and Mac!

cnvrs is the best app for private, local AI on your device:

create & save Characters with custom system prompts & temperature settings
download and experiment with any GGUF model you can find on HuggingFace!
make it your own with custom Theme colors
powered by Metal ⚡️ & Llama.cpp, with haptics during response streaming!
try it out yourself today, on Testflight!
follow cnvrs on twitter to stay up to date

Original Model Evaluation

Benchmark	Llama 3 8B	Llama 2 7B	Llama 2 13B
MMLU (5-shot)	68.4	34.1	47.8
GPQA (0-shot)	34.2	21.7	22.3
HumanEval (0-shot)	62.2	7.9	14.0
GSM-8K (8-shot, CoT)	79.6	25.7	77.4
MATH (4-shot, CoT)	30.0	3.8	6.7

Downloads last month: 136

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

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