Instructions to use Poly-Pixel/shrek-medium-full with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Poly-Pixel/shrek-medium-full with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Poly-Pixel/shrek-medium-full")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Poly-Pixel/shrek-medium-full") model = AutoModelForCausalLM.from_pretrained("Poly-Pixel/shrek-medium-full", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Poly-Pixel/shrek-medium-full with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Poly-Pixel/shrek-medium-full" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Poly-Pixel/shrek-medium-full", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Poly-Pixel/shrek-medium-full
- SGLang
How to use Poly-Pixel/shrek-medium-full 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 "Poly-Pixel/shrek-medium-full" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Poly-Pixel/shrek-medium-full", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "Poly-Pixel/shrek-medium-full" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Poly-Pixel/shrek-medium-full", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Poly-Pixel/shrek-medium-full with Docker Model Runner:
docker model run hf.co/Poly-Pixel/shrek-medium-full
Download checkpoint-3500/optimizer.pt from Poly-Pixel/shrek-medium-full: direct link, hf CLI and curl.
- Browser
- Download file 2.84 GB
-
https://huggingface.co/Poly-Pixel/shrek-medium-full/resolve/main/checkpoint-3500/optimizer.pt
- Command line
-
hf download hf://Poly-Pixel/shrek-medium-full/checkpoint-3500/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/Poly-Pixel/shrek-medium-full/resolve/main/checkpoint-3500/optimizer.pt
2.84 GB
- Xet hash:
- b8d9a503990e22a1a874f7dbc0d11e64dce4ae0a7a827e3d2fc9a18f6a74a6fb
- Size of remote file:
- 2.84 GB
- SHA256:
- 894eebd883812aa7ae1a94d8ec6bcfa2ea6be501e24667882e148c7763696fa2
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