Instructions to use JPQ24/llama-3.1-8b-q4-master-code-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JPQ24/llama-3.1-8b-q4-master-code-3 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="JPQ24/llama-3.1-8b-q4-master-code-3",
	filename="Meta-Llama-3.1-8B-Instruct.Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use JPQ24/llama-3.1-8b-q4-master-code-3 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf JPQ24/llama-3.1-8b-q4-master-code-3:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf JPQ24/llama-3.1-8b-q4-master-code-3:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf JPQ24/llama-3.1-8b-q4-master-code-3:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf JPQ24/llama-3.1-8b-q4-master-code-3: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 JPQ24/llama-3.1-8b-q4-master-code-3:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf JPQ24/llama-3.1-8b-q4-master-code-3: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 JPQ24/llama-3.1-8b-q4-master-code-3:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf JPQ24/llama-3.1-8b-q4-master-code-3:Q4_K_M

Use Docker

docker model run hf.co/JPQ24/llama-3.1-8b-q4-master-code-3:Q4_K_M

LM Studio
Jan
Ollama
How to use JPQ24/llama-3.1-8b-q4-master-code-3 with Ollama:
```
ollama run hf.co/JPQ24/llama-3.1-8b-q4-master-code-3:Q4_K_M
```

Unsloth Studio

How to use JPQ24/llama-3.1-8b-q4-master-code-3 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 JPQ24/llama-3.1-8b-q4-master-code-3 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 JPQ24/llama-3.1-8b-q4-master-code-3 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for JPQ24/llama-3.1-8b-q4-master-code-3 to start chatting

How to use JPQ24/llama-3.1-8b-q4-master-code-3 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf JPQ24/llama-3.1-8b-q4-master-code-3: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": "JPQ24/llama-3.1-8b-q4-master-code-3:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use JPQ24/llama-3.1-8b-q4-master-code-3 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf JPQ24/llama-3.1-8b-q4-master-code-3:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default JPQ24/llama-3.1-8b-q4-master-code-3:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use JPQ24/llama-3.1-8b-q4-master-code-3 with Docker Model Runner:
```
docker model run hf.co/JPQ24/llama-3.1-8b-q4-master-code-3:Q4_K_M
```

Lemonade

How to use JPQ24/llama-3.1-8b-q4-master-code-3 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull JPQ24/llama-3.1-8b-q4-master-code-3:Q4_K_M

Run and chat with the model

lemonade run user.llama-3.1-8b-q4-master-code-3-Q4_K_M

List all available models

lemonade list

llama-3.1-8b-q4-master-code-3 : GGUF

Status: Experimental. This model was trained with the intent of producing strong coding performance but did not meet that objective reliably. It is published for research and reproducibility purposes. Use with appropriate skepticism for production coding tasks.

This model was finetuned and converted to GGUF format using Unsloth.

Example usage:

For text only LLMs: llama-cli -hf JPQ24/llama-3.1-8b-q4-master-code-3 --jinja
For multimodal models: llama-mtmd-cli -hf JPQ24/llama-3.1-8b-q4-master-code-3 --jinja

Available Model files:

Meta-Llama-3.1-8B-Instruct.Q4_K_M.gguf

Ollama

An Ollama Modelfile is included for easy deployment. This was trained 2x faster with Unsloth

Downloads last month: 230

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

4-bit

Inference Providers NEW

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