Instructions to use codymd/fi-rauma with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use codymd/fi-rauma with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="codymd/fi-rauma")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("codymd/fi-rauma") model = AutoModelForSeq2SeqLM.from_pretrained("codymd/fi-rauma") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use codymd/fi-rauma with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "codymd/fi-rauma" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "codymd/fi-rauma", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/codymd/fi-rauma
- SGLang
How to use codymd/fi-rauma 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 "codymd/fi-rauma" \ --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": "codymd/fi-rauma", "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 "codymd/fi-rauma" \ --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": "codymd/fi-rauma", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use codymd/fi-rauma with Docker Model Runner:
docker model run hf.co/codymd/fi-rauma
- Xet hash:
- 48ec99b935cf96000192770683a6d1336f8cd46a5d11bd680afbb1afda5dfc95
- Size of remote file:
- 244 MB
- SHA256:
- 3e0d9ed67778042a67c313307e662ba8f9add0e624287c37815626401f5b71c0
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