Instructions to use raincandy-u/Rain-100M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use raincandy-u/Rain-100M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="raincandy-u/Rain-100M")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("raincandy-u/Rain-100M") model = AutoModelForCausalLM.from_pretrained("raincandy-u/Rain-100M") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use raincandy-u/Rain-100M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "raincandy-u/Rain-100M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "raincandy-u/Rain-100M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/raincandy-u/Rain-100M
- SGLang
How to use raincandy-u/Rain-100M 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 "raincandy-u/Rain-100M" \ --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": "raincandy-u/Rain-100M", "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 "raincandy-u/Rain-100M" \ --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": "raincandy-u/Rain-100M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use raincandy-u/Rain-100M with Docker Model Runner:
docker model run hf.co/raincandy-u/Rain-100M
Hardware used
#2
by s3nh - opened
Hello, great work! Any info about the hardware used in training process?
All best,
s3nh
1x4090D trained for 10h🤗
raincandy-u changed discussion status to closed