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

failspy commited on May 9, 2024

Commit

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+---
+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.