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

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

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!

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'

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. Please note that the model may still refuse to answer certain requests, even after the weights have been manipulated to inhibit refusal.

Downloads last month: 332

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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