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

Libraries

How to use alpindale/WizardLM-2-8x22B with Transformers:

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

pipe = pipeline("text-generation", model="alpindale/WizardLM-2-8x22B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("alpindale/WizardLM-2-8x22B")
model = AutoModelForCausalLM.from_pretrained("alpindale/WizardLM-2-8x22B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
HuggingChat
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use alpindale/WizardLM-2-8x22B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "alpindale/WizardLM-2-8x22B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alpindale/WizardLM-2-8x22B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/alpindale/WizardLM-2-8x22B

SGLang

How to use alpindale/WizardLM-2-8x22B 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 "alpindale/WizardLM-2-8x22B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alpindale/WizardLM-2-8x22B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "alpindale/WizardLM-2-8x22B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alpindale/WizardLM-2-8x22B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use alpindale/WizardLM-2-8x22B with Docker Model Runner:
```
docker model run hf.co/alpindale/WizardLM-2-8x22B
```

This model is amazingly good but seems lost in the `llama 3` hype :(

by jukofyork - opened Apr 21, 2024

Discussion

jukofyork

Apr 21, 2024

•

edited Apr 21, 2024

This model seems to have been lost in all the hype of llama 3 but IMO it's actually the current best open LLM for coding so far - much better than the official mixtral-instructfrom my testing.

It's especially good at refactoring code: other models I always have to break up the work for them or else they get confused or just rename a few variables, etc; this seems to make its own plan and really do a thorough job of refactoring!

I'm not sure why the official mixtral-instructis so much worse as previously with the 8x7b mixtral-instructit was the other way round and no fine tunes were as good... My Q4_K_Sof the official mixtral-instructalso seems to randomly stop and there are reports of other weirdness in the HF discussions for the non-quantized version too.

jukofyork

Apr 22, 2024

•

edited Apr 22, 2024

https://reddit.com/comments/1c9s4mf/comment/l0no0y9

... I did notice that the together.ai version does have this strange tendency to cut off its answer with a few sentences left in its response. I can get past this simply by typing "more" but it is a bit annoying when it happens over and over.

Strange he gets this with this model too! I wonder if there is a bug in the conversion from HF or even a bug in the original HF json files causing this?

https://reddit.com/comments/1c9s4mf/comment/l0oo08b

It just wasn't worth the headache for a marginal improvement over just something like "Answer the following question:". I typically find having at least that much of a system prompt helps some models from not just ending immediately or going off on a tangent.

I can't tell if he's referring to wizard-lm-2specifically in this post or if the "ending immediately" refers to the same problem manifestation, but interestingly I didn't use a system prompt so will try that for the official mixtral-instructnow too.

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