Instructions to use QuantFactory/Llama3.2-3B-Enigma-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 QuantFactory/Llama3.2-3B-Enigma-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 QuantFactory/Llama3.2-3B-Enigma-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Llama3.2-3B-Enigma-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 QuantFactory/Llama3.2-3B-Enigma-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Llama3.2-3B-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/Llama3.2-3B-Enigma-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Llama3.2-3B-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/Llama3.2-3B-Enigma-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Llama3.2-3B-Enigma-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Llama3.2-3B-Enigma-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Llama3.2-3B-Enigma-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Llama3.2-3B-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/Llama3.2-3B-Enigma-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/Llama3.2-3B-Enigma-GGUF:Q4_K_M

Ollama
How to use QuantFactory/Llama3.2-3B-Enigma-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Llama3.2-3B-Enigma-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/Llama3.2-3B-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/Llama3.2-3B-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/Llama3.2-3B-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/Llama3.2-3B-Enigma-GGUF to start chatting

How to use QuantFactory/Llama3.2-3B-Enigma-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf QuantFactory/Llama3.2-3B-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": "QuantFactory/Llama3.2-3B-Enigma-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use QuantFactory/Llama3.2-3B-Enigma-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf QuantFactory/Llama3.2-3B-Enigma-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "QuantFactory/Llama3.2-3B-Enigma-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use QuantFactory/Llama3.2-3B-Enigma-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Llama3.2-3B-Enigma-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Llama3.2-3B-Enigma-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Llama3.2-3B-Enigma-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Llama3.2-3B-Enigma-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use QuantFactory/Llama3.2-3B-Enigma-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf QuantFactory/Llama3.2-3B-Enigma-GGUF: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 QuantFactory/Llama3.2-3B-Enigma-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

QuantFactory/Llama3.2-3B-Enigma-GGUF

This is quantized version of ValiantLabs/Llama3.2-3B-Enigma created using llama.cpp

Original Model Card

Enigma is a code-instruct model built on Llama 3.2 3b.

High quality code instruct performance with the Llama 3.2 Instruct chat format
Finetuned on synthetic code-instruct data generated with Llama 3.1 405b. Find the current version of the dataset here!
Overall chat performance supplemented with generalist synthetic data.

Version

This is the 2024-09-30 release of Enigma for Llama 3.2 3b, enhancing code-instruct and general chat capabilities.

Enigma is also available for Llama 3.1 8b!

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

Prompting Guide

Enigma uses the Llama 3.2 Instruct prompt format. The example script below can be used as a starting point for general chat:

import transformers
import torch

model_id = "ValiantLabs/Llama3.2-3B-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 3.2 3b Instruct, using high quality code-instruct data and general chat data in Llama 3.2 Instruct prompt style to supplement overall performance.

Our current version of Enigma is trained on code-instruct data from sequelbox/Tachibana and general chat data from sequelbox/Supernova.

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: 655

GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for QuantFactory/Llama3.2-3B-Enigma-GGUF

Base model

meta-llama/Llama-3.2-3B-Instruct

Quantized

(496)

this model

Datasets used to train QuantFactory/Llama3.2-3B-Enigma-GGUF

Evaluation results

acc on Winogrande (5-Shot)
self-reported

67.960
normalized accuracy on ARC Challenge (25-Shot)
self-reported

47.180
strict accuracy on IFEval (0-Shot)
Open LLM Leaderboard

47.750
normalized accuracy on BBH (3-Shot)
Open LLM Leaderboard

18.810
exact match on MATH Lvl 5 (4-Shot)
Open LLM Leaderboard

6.650
acc_norm on GPQA (0-shot)
Open LLM Leaderboard

1.450
acc_norm on MuSR (0-shot)
Open LLM Leaderboard

4.540
accuracy on MMLU-PRO (5-shot)
test set Open LLM Leaderboard

15.410