Instructions to use QuixiAI/Llama-3-8B-Instruct-abliterated-v2-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use QuixiAI/Llama-3-8B-Instruct-abliterated-v2-gguf with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("QuixiAI/Llama-3-8B-Instruct-abliterated-v2-gguf", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use QuixiAI/Llama-3-8B-Instruct-abliterated-v2-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 QuixiAI/Llama-3-8B-Instruct-abliterated-v2-gguf # Run inference directly in the terminal: llama cli -hf QuixiAI/Llama-3-8B-Instruct-abliterated-v2-gguf
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf QuixiAI/Llama-3-8B-Instruct-abliterated-v2-gguf # Run inference directly in the terminal: llama cli -hf QuixiAI/Llama-3-8B-Instruct-abliterated-v2-gguf
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 QuixiAI/Llama-3-8B-Instruct-abliterated-v2-gguf # Run inference directly in the terminal: ./llama-cli -hf QuixiAI/Llama-3-8B-Instruct-abliterated-v2-gguf
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 QuixiAI/Llama-3-8B-Instruct-abliterated-v2-gguf # Run inference directly in the terminal: ./build/bin/llama-cli -hf QuixiAI/Llama-3-8B-Instruct-abliterated-v2-gguf
Use Docker
docker model run hf.co/QuixiAI/Llama-3-8B-Instruct-abliterated-v2-gguf
- LM Studio
- Jan
- Ollama
How to use QuixiAI/Llama-3-8B-Instruct-abliterated-v2-gguf with Ollama:
ollama run hf.co/QuixiAI/Llama-3-8B-Instruct-abliterated-v2-gguf
- Unsloth Desktop
- Docker Model Runner
How to use QuixiAI/Llama-3-8B-Instruct-abliterated-v2-gguf with Docker Model Runner:
docker model run hf.co/QuixiAI/Llama-3-8B-Instruct-abliterated-v2-gguf
- Lemonade
How to use QuixiAI/Llama-3-8B-Instruct-abliterated-v2-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QuixiAI/Llama-3-8B-Instruct-abliterated-v2-gguf
Run and chat with the model
lemonade run user.Llama-3-8B-Instruct-abliterated-v2-gguf-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
| library_name: transformers | |
| license: llama3 | |
| # Model Card for Llama-3-8B-Instruct-abliterated-v2 | |
| ## Overview | |
| This model card describes the Llama-3-8B-Instruct-abliterated-v2 model, which is an orthogonalized version of the meta-llama/Llama-3-8B-Instruct model, and an improvement upon the previous generation Llama-3-8B-Instruct-abliterated. This variant has had certain weights manipulated to inhibit the model's ability to express refusal. | |
| [Join the Cognitive Computations Discord!](https://discord.gg/cognitivecomputations) | |
| ## Details | |
| * The model was trained with more data to better pinpoint the "refusal direction". | |
| * This model is MUCH better at directly and succinctly answering requests without producing even so much as disclaimers. | |
| ## Methodology | |
| The methodology used to generate this model is described in the preview paper/blog post: '[Refusal in LLMs is mediated by a single direction](https://www.alignmentforum.org/posts/jGuXSZgv6qfdhMCuJ/refusal-in-llms-is-mediated-by-a-single-direction)' | |
| ## Quirks and Side Effects | |
| This model may come with interesting quirks, as the methodology is still new and untested. The code used to generate the model is available in the Python notebook [ortho_cookbook.ipynb](https://huggingface.co/failspy/llama-3-70B-Instruct-abliterated/blob/main/ortho_cookbook.ipynb). | |
| Please note that the model may still refuse to answer certain requests, even after the weights have been manipulated to inhibit refusal. |