Instructions to use Skylaude/WizardLM-2-4x7B-MoE-exl2-3_0bpw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Skylaude/WizardLM-2-4x7B-MoE-exl2-3_0bpw with Transformers:

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

pipe = pipeline("text-generation", model="Skylaude/WizardLM-2-4x7B-MoE-exl2-3_0bpw")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Skylaude/WizardLM-2-4x7B-MoE-exl2-3_0bpw")
model = AutoModelForCausalLM.from_pretrained("Skylaude/WizardLM-2-4x7B-MoE-exl2-3_0bpw")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Skylaude/WizardLM-2-4x7B-MoE-exl2-3_0bpw with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Skylaude/WizardLM-2-4x7B-MoE-exl2-3_0bpw"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Skylaude/WizardLM-2-4x7B-MoE-exl2-3_0bpw",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Skylaude/WizardLM-2-4x7B-MoE-exl2-3_0bpw

SGLang

How to use Skylaude/WizardLM-2-4x7B-MoE-exl2-3_0bpw 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 "Skylaude/WizardLM-2-4x7B-MoE-exl2-3_0bpw" \
    --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": "Skylaude/WizardLM-2-4x7B-MoE-exl2-3_0bpw",
		"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 "Skylaude/WizardLM-2-4x7B-MoE-exl2-3_0bpw" \
        --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": "Skylaude/WizardLM-2-4x7B-MoE-exl2-3_0bpw",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Skylaude/WizardLM-2-4x7B-MoE-exl2-3_0bpw with Docker Model Runner:
```
docker model run hf.co/Skylaude/WizardLM-2-4x7B-MoE-exl2-3_0bpw
```

WizardLM-2-4x7B-MoE-exl2-3_0bpw

This is a quantized version of WizardLM-2-4x7B-MoE an experimental MoE model made with Mergekit. Quantization was done using version 0.0.18 of ExLlamaV2.

Please be sure to set experts per token to 4 for the best results! Context length should be the same as Mistral-7B-Instruct-v0.1 (8k tokens). For instruction templates, Vicuna-v1.1 is recommended.

For more information see the original repository.

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