Instructions to use cortexso/llama3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cortexso/llama3 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="cortexso/llama3",
	filename="llama-3.1-8b-instruct-q2_k.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use cortexso/llama3 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf cortexso/llama3:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf cortexso/llama3:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf cortexso/llama3:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf cortexso/llama3: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 cortexso/llama3:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf cortexso/llama3: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 cortexso/llama3:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf cortexso/llama3:Q4_K_M

Use Docker

docker model run hf.co/cortexso/llama3:Q4_K_M

LM Studio
Jan

vLLM

How to use cortexso/llama3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cortexso/llama3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cortexso/llama3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cortexso/llama3:Q4_K_M

Ollama
How to use cortexso/llama3 with Ollama:
```
ollama run hf.co/cortexso/llama3:Q4_K_M
```

Unsloth Studio new

How to use cortexso/llama3 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 cortexso/llama3 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 cortexso/llama3 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for cortexso/llama3 to start chatting

Pi new

How to use cortexso/llama3 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf cortexso/llama3:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "llama3"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use cortexso/llama3 with Docker Model Runner:
```
docker model run hf.co/cortexso/llama3:Q4_K_M
```

Lemonade

How to use cortexso/llama3 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull cortexso/llama3:Q4_K_M

Run and chat with the model

lemonade run user.llama3-Q4_K_M

List all available models

lemonade list

Overview

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.

Variants

No	Variant	Cortex CLI command
1	Llama3-8b	`cortex run llama3:8b`

Use it with Jan (UI)

Install Jan using Quickstart
Use in Jan model Hub:
```
cortexso/llama3
```

Use it with Cortex (CLI)

Install Cortex using Quickstart
Run the model with command:
```
cortex run llama3
```

Credits

Author: Meta
Converter: Homebrew
Original License: License
Papers: Llama-3 Blog

Downloads last month: 175

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

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