Instructions to use sandylolpotty/CatalystGPT-5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use sandylolpotty/CatalystGPT-5 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium") model = PeftModel.from_pretrained(base_model, "sandylolpotty/CatalystGPT-5") - Transformers
How to use sandylolpotty/CatalystGPT-5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="sandylolpotty/CatalystGPT-5") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("sandylolpotty/CatalystGPT-5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use sandylolpotty/CatalystGPT-5 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "sandylolpotty/CatalystGPT-5" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sandylolpotty/CatalystGPT-5", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/sandylolpotty/CatalystGPT-5
- SGLang
How to use sandylolpotty/CatalystGPT-5 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 "sandylolpotty/CatalystGPT-5" \ --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": "sandylolpotty/CatalystGPT-5", "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 "sandylolpotty/CatalystGPT-5" \ --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": "sandylolpotty/CatalystGPT-5", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use sandylolpotty/CatalystGPT-5 with Docker Model Runner:
docker model run hf.co/sandylolpotty/CatalystGPT-5
- Xet hash:
- 09e17662c42ce2283d8e7b3b066f2bf67f23b60993df64e70e6412dc12c73ab6
- Size of remote file:
- 5.84 kB
- SHA256:
- 2f5aa3271300d81c482955d54af2c8686b5e13f03042942c363a12ad02662e30
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.