Instructions to use QuantFactory/NeuralDaredevil-8B-abliterated-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use QuantFactory/NeuralDaredevil-8B-abliterated-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/NeuralDaredevil-8B-abliterated-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf QuantFactory/NeuralDaredevil-8B-abliterated-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/NeuralDaredevil-8B-abliterated-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf QuantFactory/NeuralDaredevil-8B-abliterated-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/NeuralDaredevil-8B-abliterated-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf QuantFactory/NeuralDaredevil-8B-abliterated-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/NeuralDaredevil-8B-abliterated-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf QuantFactory/NeuralDaredevil-8B-abliterated-GGUF:Q4_K_M
Use Docker
docker model run hf.co/QuantFactory/NeuralDaredevil-8B-abliterated-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use QuantFactory/NeuralDaredevil-8B-abliterated-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "QuantFactory/NeuralDaredevil-8B-abliterated-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/NeuralDaredevil-8B-abliterated-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/QuantFactory/NeuralDaredevil-8B-abliterated-GGUF:Q4_K_M
- Ollama
How to use QuantFactory/NeuralDaredevil-8B-abliterated-GGUF with Ollama:
ollama run hf.co/QuantFactory/NeuralDaredevil-8B-abliterated-GGUF:Q4_K_M
- Unsloth Studio
How to use QuantFactory/NeuralDaredevil-8B-abliterated-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/NeuralDaredevil-8B-abliterated-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/NeuralDaredevil-8B-abliterated-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/NeuralDaredevil-8B-abliterated-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use QuantFactory/NeuralDaredevil-8B-abliterated-GGUF with Docker Model Runner:
docker model run hf.co/QuantFactory/NeuralDaredevil-8B-abliterated-GGUF:Q4_K_M
- Lemonade
How to use QuantFactory/NeuralDaredevil-8B-abliterated-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QuantFactory/NeuralDaredevil-8B-abliterated-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.NeuralDaredevil-8B-abliterated-GGUF-Q4_K_M
List all available models
lemonade list
## 🚨 License Issue: Missing LLaMA 3 Requirements
Hi, I'd like to report a license issue in QuantFactory/NeuralDaredevil-8B-abliterated-GGUF. Since this model is quantized from mlabonne/NeuralDaredevil-8B-abliterated, which is licensed under the LLaMA 3 Community License. But currently, this GGUF version is tagged as "other", and there’s no clear license or notice information provided in the model card. Without an explicit license or the required notices, this could be seen as a violation of the LLaMA 3 license, which has specific requirements for redistribution, naming, and attribution.
⚠️ Key violations of LLaMA 3 Community License:
Clause 1.b.i – Redistribution and Use:
• ⚠️ No license file included (should contain the LLaMA 3 Community License)
• ⚠️ "Built with LLaMA" is not prominently displayed
• ⚠️ Model name does not begin with "Llama", which is required for any derivative
Clause 1.b.iii – Required Notice:
• ⚠️ Missing the following required text in a "NOTICE" file:
"Llama 3 is licensed under the Llama 3 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved."
Clause 1.iv – Acceptable Use Policy:
• ⚠️ No mention of Meta's Acceptable Use Policy, which must be passed on to downstream users
Clause 2 – Additional Commercial Terms:
• ⚠️ No clarification about the 700M MAU (monthly active users) threshold — making commercial usage ambiguous
🔹 Suggestions for Compliance:
Here are a few things that might help (just some friendly suggestions! 😊):
• Might be worth including a copy of the LLaMA 3 license in the repo or model card
• You could also add a small “NOTICE” file with the required attribution text from Meta
• If it fits your naming style, maybe consider starting the model name with “LLaMA” — something like "LLaMA-3-DeepHermes..."
• A quick note like “Built with LLaMA” somewhere visible in the model card could also be helpful
• It might also be good to clarify whether the model is intended for use under or over the 700M monthly active users threshold
• And just in case, linking Meta’s Acceptable Use Policy could help downstream users stay compliant too
These may help ensure the license is clear so that other developers and researchers can use it safely and with confidence. 😊 If I misunderstood anything, please let me know!
Thanks for your attention!