Instructions to use QuantFactory/Daredevil-8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
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:
- nbeerbower/llama-3-stella-8B
- Hastagaras/llama-3-8b-okay
- nbeerbower/llama-3-gutenberg-8B
- openchat/openchat-3.6-8b-20240522
- Kukedlc/NeuralLLaMa-3-8b-DT-v0.1
- cstr/llama3-8b-spaetzle-v20
- mlabonne/ChimeraLlama-3-8B-v3
- flammenai/Mahou-1.1-llama3-8B
- KingNish/KingNish-Llama3-8b
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
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Model tree for QuantFactory/Daredevil-8B-GGUF
Base model
mlabonne/Daredevil-8BEvaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard68.860
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard84.500
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard69.240
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard59.890
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard78.450
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard73.540


