Instructions to use micrem73/GePpeTto-finetuned-gastro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use micrem73/GePpeTto-finetuned-gastro with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="micrem73/GePpeTto-finetuned-gastro")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("micrem73/GePpeTto-finetuned-gastro") model = AutoModelForCausalLM.from_pretrained("micrem73/GePpeTto-finetuned-gastro", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use micrem73/GePpeTto-finetuned-gastro with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "micrem73/GePpeTto-finetuned-gastro" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "micrem73/GePpeTto-finetuned-gastro", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/micrem73/GePpeTto-finetuned-gastro
- SGLang
How to use micrem73/GePpeTto-finetuned-gastro 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 "micrem73/GePpeTto-finetuned-gastro" \ --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": "micrem73/GePpeTto-finetuned-gastro", "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 "micrem73/GePpeTto-finetuned-gastro" \ --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": "micrem73/GePpeTto-finetuned-gastro", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use micrem73/GePpeTto-finetuned-gastro with Docker Model Runner:
docker model run hf.co/micrem73/GePpeTto-finetuned-gastro
File size: 134 Bytes
8b56826 | 1 2 3 4 | version https://git-lfs.github.com/spec/v1
oid sha256:71cc5f7785c00b9e2a2feb85680a48a27726b3061ebb7900bf023be2efeb833b
size 448164585
|