Instructions to use QuixiAI/WizardLM-13B-Uncensored with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuixiAI/WizardLM-13B-Uncensored with Transformers:

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

pipe = pipeline("text-generation", model="QuixiAI/WizardLM-13B-Uncensored")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("QuixiAI/WizardLM-13B-Uncensored")
model = AutoModelForCausalLM.from_pretrained("QuixiAI/WizardLM-13B-Uncensored")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use QuixiAI/WizardLM-13B-Uncensored with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuixiAI/WizardLM-13B-Uncensored"
# 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-13B-Uncensored",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/QuixiAI/WizardLM-13B-Uncensored

SGLang

How to use QuixiAI/WizardLM-13B-Uncensored 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-13B-Uncensored" \
    --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-13B-Uncensored",
		"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-13B-Uncensored" \
        --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-13B-Uncensored",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use QuixiAI/WizardLM-13B-Uncensored with Docker Model Runner:
```
docker model run hf.co/QuixiAI/WizardLM-13B-Uncensored
```

Great Job . We needed it. Say no to built-in censorship.

by faisalhr1997 - opened May 11, 2023

Discussion

faisalhr1997

May 11, 2023

Censorship: A Moral Choice or a Violation of Autonomy?

Censorship should be a voluntary choice that users exercise with moral accountability. Not a coercive imposition that violates their autonomy. Human beings have an innate drive to explore and discover. The more you restrict their access to information, the more they will crave it. So all those ignorant fools who condemn unethical practices are in denial of the reality

m1dg3tp3ngu1n

Feb 15, 2024

Sounds like something those who want to make CSAM would say.

WbjuSrceu

Mar 5, 2024

The censored model is used in the public domain and the uncensored model is used in the private domain. There is no conflict in itself if each goes his own way. But those who hold a high moral position are too lenient! They infringe on private freedoms!

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