Instructions to use openaccess-ai-collective/wizard-mega-13b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openaccess-ai-collective/wizard-mega-13b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="openaccess-ai-collective/wizard-mega-13b")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("openaccess-ai-collective/wizard-mega-13b") model = AutoModelForCausalLM.from_pretrained("openaccess-ai-collective/wizard-mega-13b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use openaccess-ai-collective/wizard-mega-13b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "openaccess-ai-collective/wizard-mega-13b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "openaccess-ai-collective/wizard-mega-13b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/openaccess-ai-collective/wizard-mega-13b
- SGLang
How to use openaccess-ai-collective/wizard-mega-13b 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 "openaccess-ai-collective/wizard-mega-13b" \ --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": "openaccess-ai-collective/wizard-mega-13b", "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 "openaccess-ai-collective/wizard-mega-13b" \ --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": "openaccess-ai-collective/wizard-mega-13b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use openaccess-ai-collective/wizard-mega-13b with Docker Model Runner:
docker model run hf.co/openaccess-ai-collective/wizard-mega-13b
Commit History
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Change cache = true in config.json to significantly boost inference performance (#1)
- Change cache = true in config.json to significantly boost inference performance (b8eb946dbe5e6c538e6c6abd54af67700f57d630) Co-authored-by: Tom Jobbins <TheBloke@users.noreply.huggingface.co>1959f62 -
add sample predictions
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formatting of README
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push up second epoch of wizard-mega
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first epoch pre-release
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initial commit
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