Instructions to use lasrevinu333-design/WizardLM-2-8x22B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lasrevinu333-design/WizardLM-2-8x22B-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="lasrevinu333-design/WizardLM-2-8x22B-GGUF")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("lasrevinu333-design/WizardLM-2-8x22B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use lasrevinu333-design/WizardLM-2-8x22B-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 lasrevinu333-design/WizardLM-2-8x22B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf lasrevinu333-design/WizardLM-2-8x22B-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 lasrevinu333-design/WizardLM-2-8x22B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf lasrevinu333-design/WizardLM-2-8x22B-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 lasrevinu333-design/WizardLM-2-8x22B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf lasrevinu333-design/WizardLM-2-8x22B-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 lasrevinu333-design/WizardLM-2-8x22B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf lasrevinu333-design/WizardLM-2-8x22B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/lasrevinu333-design/WizardLM-2-8x22B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use lasrevinu333-design/WizardLM-2-8x22B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lasrevinu333-design/WizardLM-2-8x22B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lasrevinu333-design/WizardLM-2-8x22B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/lasrevinu333-design/WizardLM-2-8x22B-GGUF:Q4_K_M

SGLang

How to use lasrevinu333-design/WizardLM-2-8x22B-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "lasrevinu333-design/WizardLM-2-8x22B-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lasrevinu333-design/WizardLM-2-8x22B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "lasrevinu333-design/WizardLM-2-8x22B-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lasrevinu333-design/WizardLM-2-8x22B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use lasrevinu333-design/WizardLM-2-8x22B-GGUF with Ollama:
```
ollama run hf.co/lasrevinu333-design/WizardLM-2-8x22B-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use lasrevinu333-design/WizardLM-2-8x22B-GGUF with Docker Model Runner:
```
docker model run hf.co/lasrevinu333-design/WizardLM-2-8x22B-GGUF:Q4_K_M
```

Lemonade

How to use lasrevinu333-design/WizardLM-2-8x22B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull lasrevinu333-design/WizardLM-2-8x22B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.WizardLM-2-8x22B-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

MaziyarPanahi/WizardLM-2-8x22B-GGUF

Model creator: microsoft
Original model: microsoft/WizardLM-2-8x22B

Description

MaziyarPanahi/WizardLM-2-8x22B-GGUF contains GGUF format model files for microsoft/WizardLM-2-8x22B.

How to download

You can download only the quants you need instead of cloning the entire repository as follows:

huggingface-cli download MaziyarPanahi/WizardLM-2-8x22B-GGUF --local-dir . --include '*Q2_K*gguf'

On Windows:

huggingface-cli download MaziyarPanahi/WizardLM-2-8x22B-GGUF --local-dir . --include *Q4_K_S*gguf

Load sharded model

llama_load_model_from_file will detect the number of files and will load additional tensors from the rest of files.

llama.cpp/main -m WizardLM-2-8x22B.Q2_K-00001-of-00005.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 1024 -e

Prompt template

{system_prompt}
USER: {prompt}
ASSISTANT: </s>

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: {prompt} ASSISTANT: </s>......

Downloads last month: -

GGUF

Model size

141B params

Architecture

llama

Hardware compatibility

1-bit

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for lasrevinu333-design/WizardLM-2-8x22B-GGUF

Base model

microsoft/WizardLM-2-8x22B

Quantized

(18)

this model