Instructions to use QuixiAI/WizardLM-Uncensored-Falcon-40b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuixiAI/WizardLM-Uncensored-Falcon-40b with Transformers:

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

pipe = pipeline("text-generation", model="QuixiAI/WizardLM-Uncensored-Falcon-40b", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("QuixiAI/WizardLM-Uncensored-Falcon-40b", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use QuixiAI/WizardLM-Uncensored-Falcon-40b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuixiAI/WizardLM-Uncensored-Falcon-40b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuixiAI/WizardLM-Uncensored-Falcon-40b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/QuixiAI/WizardLM-Uncensored-Falcon-40b

SGLang

How to use QuixiAI/WizardLM-Uncensored-Falcon-40b 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 "QuixiAI/WizardLM-Uncensored-Falcon-40b" \
    --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": "QuixiAI/WizardLM-Uncensored-Falcon-40b",
		"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 "QuixiAI/WizardLM-Uncensored-Falcon-40b" \
        --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": "QuixiAI/WizardLM-Uncensored-Falcon-40b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use QuixiAI/WizardLM-Uncensored-Falcon-40b with Docker Model Runner:
```
docker model run hf.co/QuixiAI/WizardLM-Uncensored-Falcon-40b
```

Deploying this model on Azure

by ovoss - opened Jun 10, 2023

Discussion

ovoss

Jun 10, 2023

Hi! Wondering if anyone tried deploy this model on Azure.
I went through the hf notebook on Azure examples, managed to download the model but couldn't convert it to MFlow Model and it didn't register(one of the jobs in the pipeline that notebook creates).
Tried to deploy it as a custom model but it errors out because of the scoring script.

Could someone tell me what should be in the scoring script?
A bit new to deploying custom stuff but familiar with Azure now.

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