Instructions to use qwp4w3hyb/Not-WizardLM-2-8x22B-iMat-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use qwp4w3hyb/Not-WizardLM-2-8x22B-iMat-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="qwp4w3hyb/Not-WizardLM-2-8x22B-iMat-GGUF",
	filename="wizardlm-2-8x22b-imat-IQ1_S.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use qwp4w3hyb/Not-WizardLM-2-8x22B-iMat-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 qwp4w3hyb/Not-WizardLM-2-8x22B-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf qwp4w3hyb/Not-WizardLM-2-8x22B-iMat-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 qwp4w3hyb/Not-WizardLM-2-8x22B-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf qwp4w3hyb/Not-WizardLM-2-8x22B-iMat-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 qwp4w3hyb/Not-WizardLM-2-8x22B-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf qwp4w3hyb/Not-WizardLM-2-8x22B-iMat-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 qwp4w3hyb/Not-WizardLM-2-8x22B-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf qwp4w3hyb/Not-WizardLM-2-8x22B-iMat-GGUF:Q4_K_M

Use Docker

docker model run hf.co/qwp4w3hyb/Not-WizardLM-2-8x22B-iMat-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use qwp4w3hyb/Not-WizardLM-2-8x22B-iMat-GGUF with Ollama:
```
ollama run hf.co/qwp4w3hyb/Not-WizardLM-2-8x22B-iMat-GGUF:Q4_K_M
```

Unsloth Studio

How to use qwp4w3hyb/Not-WizardLM-2-8x22B-iMat-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 qwp4w3hyb/Not-WizardLM-2-8x22B-iMat-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 qwp4w3hyb/Not-WizardLM-2-8x22B-iMat-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for qwp4w3hyb/Not-WizardLM-2-8x22B-iMat-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use qwp4w3hyb/Not-WizardLM-2-8x22B-iMat-GGUF with Docker Model Runner:
```
docker model run hf.co/qwp4w3hyb/Not-WizardLM-2-8x22B-iMat-GGUF:Q4_K_M
```

Lemonade

How to use qwp4w3hyb/Not-WizardLM-2-8x22B-iMat-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull qwp4w3hyb/Not-WizardLM-2-8x22B-iMat-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Not-WizardLM-2-8x22B-iMat-GGUF-Q4_K_M

List all available models

lemonade list

WizardLM-2-8x22B GGUF quants based on reupload at alpindale/WizardLM-2-8x22B

GGUFs created with an importance matrix (details below)

This is based on a reupload by an alternate source as microsoft deleted the model shortly after release, I will validate checksums after it is released again, to see if MS did any changes.

Source Model: alpindale/WizardLM-2-8x22B

Quantized with llama.cpp commit 5dc9dd7152dedc6046b646855585bd070c91e8c8 (master from 2024-04-09)

Imatrix was generated from the f16 gguf via this command:

./imatrix -c 512 -m $out_path/$base_quant_name -f $llama_cpp_path/groups_merged.txt -o $out_path/imat-f16-gmerged.dat

Using the dataset from here

Downloads last month: 804

GGUF

Model size

141B params

Architecture

llama

Hardware compatibility

1-bit

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Inference Providers NEW

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Model tree for qwp4w3hyb/Not-WizardLM-2-8x22B-iMat-GGUF

Base model

alpindale/WizardLM-2-8x22B

Quantized

(4)

this model