Instructions to use QuantFactory/WizardLM-2-7B-abliterated-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/WizardLM-2-7B-abliterated-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/WizardLM-2-7B-abliterated-GGUF",
	filename="WizardLM-2-7B-abliterated.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/WizardLM-2-7B-abliterated-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/WizardLM-2-7B-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/WizardLM-2-7B-abliterated-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/WizardLM-2-7B-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/WizardLM-2-7B-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/WizardLM-2-7B-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/WizardLM-2-7B-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/WizardLM-2-7B-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/WizardLM-2-7B-abliterated-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/WizardLM-2-7B-abliterated-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/WizardLM-2-7B-abliterated-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/WizardLM-2-7B-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/WizardLM-2-7B-abliterated-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/QuantFactory/WizardLM-2-7B-abliterated-GGUF:Q4_K_M

Ollama
How to use QuantFactory/WizardLM-2-7B-abliterated-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/WizardLM-2-7B-abliterated-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/WizardLM-2-7B-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/WizardLM-2-7B-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/WizardLM-2-7B-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/WizardLM-2-7B-abliterated-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/WizardLM-2-7B-abliterated-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/WizardLM-2-7B-abliterated-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/WizardLM-2-7B-abliterated-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/WizardLM-2-7B-abliterated-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.WizardLM-2-7B-abliterated-GGUF-Q4_K_M

List all available models

lemonade list

WizardLM-2-7B-abliterated-GGUF

This is quantized version of fearlessdots/WizardLM-2-7B-abliterated created using llama.cpp

Model Description

This is the WizardLM-2-7B model with orthogonalized bfloat16 safetensor weights, based on the implementation by @failspy. For more info:

Original paper preview presenting the methodology: https://www.alignmentforum.org/posts/jGuXSZgv6qfdhMCuJ/refusal-in-llms-is-mediated-by-a-single-direction
Jupyter notebook containing a implementation of the methodology, by @failspy: https://huggingface.co/failspy/llama-3-70B-Instruct-abliterated/blob/main/ortho_cookbook.ipynb

Prompt Template

This model uses the prompt format from Vicuna and supports multi-turn conversation.

Original model card:

🏠 WizardLM-2 Release Blog

🤗 HF Repo •🐱 Github Repo • 🐦 Twitter • 📃 [WizardLM] • 📃 [WizardCoder] • 📃 [WizardMath]

👋 Join our Discord

News 🔥🔥🔥 [2024/04/15]

We introduce and opensource WizardLM-2, our next generation state-of-the-art large language models, which have improved performance on complex chat, multilingual, reasoning and agent. New family includes three cutting-edge models: WizardLM-2 8x22B, WizardLM-2 70B, and WizardLM-2 7B.

WizardLM-2 8x22B is our most advanced model, demonstrates highly competitive performance compared to those leading proprietary works and consistently outperforms all the existing state-of-the-art opensource models.
WizardLM-2 70B reaches top-tier reasoning capabilities and is the first choice in the same size.
WizardLM-2 7B is the fastest and achieves comparable performance with existing 10x larger opensource leading models.

For more details of WizardLM-2 please read our release blog post and upcoming paper.

Model Details

Model name: WizardLM-2 7B
Developed by: WizardLM@Microsoft AI
Base model: mistralai/Mistral-7B-v0.1
Parameters: 7B
Language(s): Multilingual
Blog: Introducing WizardLM-2
Repository: https://github.com/nlpxucan/WizardLM
Paper: WizardLM-2 (Upcoming)
License: Apache2.0

Model Capacities

MT-Bench

We also adopt the automatic MT-Bench evaluation framework based on GPT-4 proposed by lmsys to assess the performance of models. The WizardLM-2 8x22B even demonstrates highly competitive performance compared to the most advanced proprietary models. Meanwhile, WizardLM-2 7B and WizardLM-2 70B are all the top-performing models among the other leading baselines at 7B to 70B model scales.

Human Preferences Evaluation

We carefully collected a complex and challenging set consisting of real-world instructions, which includes main requirements of humanity, such as writing, coding, math, reasoning, agent, and multilingual. We report the win:loss rate without tie:

WizardLM-2 8x22B is just slightly falling behind GPT-4-1106-preview, and significantly stronger than Command R Plus and GPT4-0314.
WizardLM-2 70B is better than GPT4-0613, Mistral-Large, and Qwen1.5-72B-Chat.
WizardLM-2 7B is comparable with Qwen1.5-32B-Chat, and surpasses Qwen1.5-14B-Chat and Starling-LM-7B-beta.

Method Overview

We built a fully AI powered synthetic training system to train WizardLM-2 models, please refer to our blog for more details of this system.

Usage

❗Note for model system prompts usage:

WizardLM-2 adopts the prompt format from Vicuna and supports multi-turn conversation. The prompt should be as following:

A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, 
detailed, and polite answers to the user's questions. USER: Hi ASSISTANT: Hello.</s>
USER: Who are you? ASSISTANT: I am WizardLM.</s>......

Inference WizardLM-2 Demo Script

We provide a WizardLM-2 inference demo code on our github.

Downloads last month: 662

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for QuantFactory/WizardLM-2-7B-abliterated-GGUF

Base model

fearlessdots/WizardLM-2-7B-abliterated

Quantized

(4)

this model

Papers for QuantFactory/WizardLM-2-7B-abliterated-GGUF

WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct

Paper • 2308.09583 • Published Aug 18, 2023 • 8

WizardCoder: Empowering Code Large Language Models with Evol-Instruct

Paper • 2306.08568 • Published Jun 14, 2023 • 34

WizardLM: Empowering Large Language Models to Follow Complex Instructions

Paper • 2304.12244 • Published Apr 24, 2023 • 14