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

Libraries

How to use QuantFactory/Daredevil-8B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Daredevil-8B-GGUF",
	filename="Daredevil-8B.Q2_K.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/Daredevil-8B-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/Daredevil-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Daredevil-8B-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/Daredevil-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Daredevil-8B-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/Daredevil-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Daredevil-8B-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/Daredevil-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Daredevil-8B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Daredevil-8B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Daredevil-8B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Daredevil-8B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/Daredevil-8B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/QuantFactory/Daredevil-8B-GGUF:Q4_K_M

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

Unsloth Studio new

How to use QuantFactory/Daredevil-8B-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/Daredevil-8B-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/Daredevil-8B-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/Daredevil-8B-GGUF to start chatting

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

Lemonade

How to use QuantFactory/Daredevil-8B-GGUF with Lemonade:

Pull the model

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

Run and chat with the model

lemonade run user.Daredevil-8B-GGUF-Q4_K_M

List all available models

lemonade list

Daredevil-8B-GGUF

This is quantized version of mlabonne/Daredevil-8B created using llama.cpp

Model Description

Daredevil-8B is a mega-merge designed to maximize MMLU. On 27 May 24, it is the Llama 3 8B model with the highest MMLU score. From my experience, a high MMLU score is all you need with Llama 3 models.

It is a merge of the following models using LazyMergekit:

Thanks to nbeerbower, Hastagaras, openchat, Kukedlc, cstr, flammenai, and KingNish for their merges. Special thanks to Charles Goddard and Arcee.ai for MergeKit.

🔎 Applications

You can use it as an improved version of meta-llama/Meta-Llama-3-8B-Instruct.

This is a censored model. For an uncensored version, see mlabonne/Daredevil-8B-abliterated.

Tested on LM Studio using the "Llama 3" preset.

🏆 Evaluation

Open LLM Leaderboard

Daredevil-8B is the best-performing 8B model on the Open LLM Leaderboard in terms of MMLU score (27 May 24).

Nous

Daredevil-8B is the best-performing 8B model on Nous' benchmark suite (evaluation performed using LLM AutoEval, 27 May 24). See the entire leaderboard here.

Model	Average	AGIEval	GPT4All	TruthfulQA	Bigbench
mlabonne/Daredevil-8B 📄	55.87	44.13	73.52	59.05	46.77
mlabonne/Daredevil-8B-abliterated 📄	55.06	43.29	73.33	57.47	46.17
mlabonne/Llama-3-8B-Instruct-abliterated-dpomix 📄	52.26	41.6	69.95	54.22	43.26
meta-llama/Meta-Llama-3-8B-Instruct 📄	51.34	41.22	69.86	51.65	42.64
failspy/Meta-Llama-3-8B-Instruct-abliterated-v3 📄	51.21	40.23	69.5	52.44	42.69
mlabonne/OrpoLlama-3-8B 📄	48.63	34.17	70.59	52.39	37.36
meta-llama/Meta-Llama-3-8B 📄	45.42	31.1	69.95	43.91	36.7

🌳 Model family tree

🧩 Configuration

models:
  - model: NousResearch/Meta-Llama-3-8B
    # No parameters necessary for base model
  - model: nbeerbower/llama-3-stella-8B
    parameters:
      density: 0.6
      weight: 0.16
  - model: Hastagaras/llama-3-8b-okay
    parameters:
      density: 0.56
      weight: 0.1
  - model: nbeerbower/llama-3-gutenberg-8B
    parameters:
      density: 0.6
      weight: 0.18
  - model: openchat/openchat-3.6-8b-20240522
    parameters:
      density: 0.56
      weight: 0.12
  - model: Kukedlc/NeuralLLaMa-3-8b-DT-v0.1
    parameters:
      density: 0.58
      weight: 0.18
  - model: cstr/llama3-8b-spaetzle-v20
    parameters:
      density: 0.56
      weight: 0.08
  - model: mlabonne/ChimeraLlama-3-8B-v3
    parameters:
      density: 0.56
      weight: 0.08
  - model: flammenai/Mahou-1.1-llama3-8B
    parameters:
      density: 0.55
      weight: 0.05
  - model: KingNish/KingNish-Llama3-8b
    parameters:
      density: 0.55
      weight: 0.05
merge_method: dare_ties
base_model: NousResearch/Meta-Llama-3-8B
dtype: bfloat16

Downloads last month: 130

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for QuantFactory/Daredevil-8B-GGUF

Base model

mlabonne/Daredevil-8B

Quantized

(6)

this model

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

68.860
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

84.500
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

69.240
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

59.890
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

78.450
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

73.540