Text Generation
Transformers
TensorBoard
Safetensors
gpt2
Generated from Trainer
text-generation-inference
Instructions to use gavinqiangli/c4-sub with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gavinqiangli/c4-sub with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="gavinqiangli/c4-sub")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("gavinqiangli/c4-sub") model = AutoModelForCausalLM.from_pretrained("gavinqiangli/c4-sub") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use gavinqiangli/c4-sub with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "gavinqiangli/c4-sub" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "gavinqiangli/c4-sub", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/gavinqiangli/c4-sub
- SGLang
How to use gavinqiangli/c4-sub 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 "gavinqiangli/c4-sub" \ --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": "gavinqiangli/c4-sub", "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 "gavinqiangli/c4-sub" \ --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": "gavinqiangli/c4-sub", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use gavinqiangli/c4-sub with Docker Model Runner:
docker model run hf.co/gavinqiangli/c4-sub
- Xet hash:
- 12e272a72eea04f3f83b24ffdf02f8bcd6591887c0e4b8a7d475b6bef94ca76c
- Size of remote file:
- 5.05 kB
- SHA256:
- 74e467bb9eefb20f21ea95414a82118d42d89a5a3c3206ae763c15a73d8715f3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.