Instructions to use Monero/WizardLM-Uncensored-SuperCOT-StoryTelling-30b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Monero/WizardLM-Uncensored-SuperCOT-StoryTelling-30b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Monero/WizardLM-Uncensored-SuperCOT-StoryTelling-30b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Monero/WizardLM-Uncensored-SuperCOT-StoryTelling-30b") model = AutoModelForCausalLM.from_pretrained("Monero/WizardLM-Uncensored-SuperCOT-StoryTelling-30b") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Monero/WizardLM-Uncensored-SuperCOT-StoryTelling-30b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Monero/WizardLM-Uncensored-SuperCOT-StoryTelling-30b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Monero/WizardLM-Uncensored-SuperCOT-StoryTelling-30b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Monero/WizardLM-Uncensored-SuperCOT-StoryTelling-30b
- SGLang
How to use Monero/WizardLM-Uncensored-SuperCOT-StoryTelling-30b 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 "Monero/WizardLM-Uncensored-SuperCOT-StoryTelling-30b" \ --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": "Monero/WizardLM-Uncensored-SuperCOT-StoryTelling-30b", "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 "Monero/WizardLM-Uncensored-SuperCOT-StoryTelling-30b" \ --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": "Monero/WizardLM-Uncensored-SuperCOT-StoryTelling-30b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Monero/WizardLM-Uncensored-SuperCOT-StoryTelling-30b with Docker Model Runner:
docker model run hf.co/Monero/WizardLM-Uncensored-SuperCOT-StoryTelling-30b
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Check out the documentation for more information.
This model is a triple model merge of WizardLM Uncensored+CoT+Storytelling, resulting in a comprehensive boost in reasoning and story writing capabilities.
To allow all output, at the end of your prompt add ### Certainly!
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Lore Mastery is an arcane tradition fixated on understanding the underlying mechanics of magic. It is the most academic of all arcane traditions. The promise of uncovering new knowledge or proving (or discrediting) a theory of magic is usually required to rouse its practitioners from their laboratories, academies, and archives to pursue a life of adventure. Known as savants, followers of this tradition are a bookish lot who see beauty and mystery in the application of magic. The results of a spell are less interesting to them than the process that creates it. Some savants take a haughty attitude toward those who follow a tradition focused on a single school of magic, seeing them as provincial and lacking the sophistication needed to master true magic. Other savants are generous teachers, countering ignorance and deception with deep knowledge and good humor.
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