Instructions to use QuixiAI/WizardLM-7B-Uncensored with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuixiAI/WizardLM-7B-Uncensored with Transformers:

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

pipe = pipeline("text-generation", model="QuixiAI/WizardLM-7B-Uncensored")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("QuixiAI/WizardLM-7B-Uncensored")
model = AutoModelForCausalLM.from_pretrained("QuixiAI/WizardLM-7B-Uncensored")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use QuixiAI/WizardLM-7B-Uncensored with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuixiAI/WizardLM-7B-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-7B-Uncensored",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/QuixiAI/WizardLM-7B-Uncensored

SGLang

How to use QuixiAI/WizardLM-7B-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-7B-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-7B-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-7B-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-7B-Uncensored",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use QuixiAI/WizardLM-7B-Uncensored with Docker Model Runner:
```
docker model run hf.co/QuixiAI/WizardLM-7B-Uncensored
```

Possible sign of overtraining

#10

by Henk717 - opened May 9, 2023

Discussion

Henk717

May 9, 2023

When experimenting the model I noticed the model is very confident in its choices, its not as bad as some other models I have seen but it is usually near 100% confident.

In this example on the KoboldAI sample story, you see that it was near 100% confident that "with a quick flick" was correct while other plausible follow-up words had been possible.

This is an snippet of llama-7b on the same sample story.

As you can see these confidence scores are much lower allowing for more varied output.
From experience we found that models that behave over confident the learning rate or other parameters were to high, you might be able to get higher quality results from your future revisions if you tone then down a bit.

ehartford

Quixi AI org May 10, 2023

This is great feedback! Thank you!

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