Instructions to use QuantFactory/Llama3.1-8B-Enigma-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Llama3.1-8B-Enigma-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Llama3.1-8B-Enigma-GGUF",
	filename="Llama3.1-8B-Enigma.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 QuantFactory/Llama3.1-8B-Enigma-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/Llama3.1-8B-Enigma-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Llama3.1-8B-Enigma-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 QuantFactory/Llama3.1-8B-Enigma-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Llama3.1-8B-Enigma-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 QuantFactory/Llama3.1-8B-Enigma-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Llama3.1-8B-Enigma-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 QuantFactory/Llama3.1-8B-Enigma-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Llama3.1-8B-Enigma-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Llama3.1-8B-Enigma-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Llama3.1-8B-Enigma-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Llama3.1-8B-Enigma-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": "QuantFactory/Llama3.1-8B-Enigma-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/Llama3.1-8B-Enigma-GGUF:Q4_K_M

Ollama
How to use QuantFactory/Llama3.1-8B-Enigma-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Llama3.1-8B-Enigma-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/Llama3.1-8B-Enigma-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 QuantFactory/Llama3.1-8B-Enigma-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 QuantFactory/Llama3.1-8B-Enigma-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/Llama3.1-8B-Enigma-GGUF to start chatting

Pi new

How to use QuantFactory/Llama3.1-8B-Enigma-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf QuantFactory/Llama3.1-8B-Enigma-GGUF: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": "Llama3.1-8B-Enigma-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use QuantFactory/Llama3.1-8B-Enigma-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Llama3.1-8B-Enigma-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Llama3.1-8B-Enigma-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Llama3.1-8B-Enigma-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Llama3.1-8B-Enigma-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/Llama3.1-8B-Enigma-GGUF

This is quantized version of ValiantLabs/Llama3.1-8B-Enigma created using llama.cpp

Original Model Card

Enigma is a code-instruct model built on Llama 3.1 8b.

High quality code instruct performance within the Llama 3 Instruct chat format
Finetuned on synthetic code-instruct data generated with Llama 3.1 405b. Find the current version of the dataset here!

Version

This is the 2024-08-10 release of Enigma for Llama 3.1 8b.

Help us and recommend Enigma to your friends! We're excited for more Enigma releases in the future.

Right now, we're working on more new Build Tools to come very soon, built on Llama 3.1 :)

Prompting Guide

Enigma uses the Llama 3.1 Instruct prompt format. The example script below can be used as a starting point for general chat:

import transformers
import torch

model_id = "ValiantLabs/Llama3.1-8B-Enigma"

pipeline = transformers.pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device_map="auto",
)

messages = [
    {"role": "system", "content": "You are Enigma, a highly capable code assistant."},
    {"role": "user", "content": "Can you explain virtualization to me?"}
]

outputs = pipeline(
    messages,
    max_new_tokens=1024,
)

print(outputs[0]["generated_text"][-1])

The Model

Enigma is built on top of Llama 3.1 8b Instruct, using code-instruct data to supplement code-instruct performance using Llama 3.1 Instruct prompt style.

Our current version of the Enigma code-instruct dataset is sequelbox/Tachibana, supplemented with a small selection of data from LDJnr/Pure-Dove for general chat consistency.

Enigma is created by Valiant Labs.

Check out our HuggingFace page for Shining Valiant 2 and our other Build Tools models for creators!

We care about open source. For everyone to use.

We encourage others to finetune further from our models.

Downloads last month: 186

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

QuantFactory
/

Llama3.1-8B-Enigma-GGUF

QuantFactory/Llama3.1-8B-Enigma-GGUF

Original Model Card

Version

Prompting Guide

The Model

Datasets used to train QuantFactory/Llama3.1-8B-Enigma-GGUF