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 Settings

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
```

Adding Evaluation Results

#22

by leaderboard-pr-bot - opened Nov 17, 2023

base: refs/heads/main

←

from: refs/pr/22

Discussion Files changed

+14

-1

Files changed (1) hide show

README.md +14 -1

README.md CHANGED Viewed

@@ -17,4 +17,17 @@ You are responsible for anything you do with the model, just as you are responsi
 Publishing anything this model generates is the same as publishing it yourself.
-You are responsible for the content you publish, and you cannot blame the model any more than you can blame the knife, gun, lighter, or car for what you do with it.

 Publishing anything this model generates is the same as publishing it yourself.
+You are responsible for the content you publish, and you cannot blame the model any more than you can blame the knife, gun, lighter, or car for what you do with it.
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_ehartford__WizardLM-7B-Uncensored)
+| Metric                | Value                     |
+|-----------------------|---------------------------|
+| Avg.                  | 41.9   |
+| ARC (25-shot)         | 47.87          |
+| HellaSwag (10-shot)   | 73.08    |
+| MMLU (5-shot)         | 35.42         |
+| TruthfulQA (0-shot)   | 41.49   |
+| Winogrande (5-shot)   | 68.43   |
+| GSM8K (5-shot)        | 3.26        |
+| DROP (3-shot)         | 23.76         |