Text Generation
Transformers
PyTorch
TensorBoard
gpt2
Trained with AutoTrain
text-generation-inference
Instructions to use mluca/traj_gpt2_small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mluca/traj_gpt2_small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mluca/traj_gpt2_small")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("mluca/traj_gpt2_small") model = AutoModelForCausalLM.from_pretrained("mluca/traj_gpt2_small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use mluca/traj_gpt2_small with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mluca/traj_gpt2_small" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mluca/traj_gpt2_small", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/mluca/traj_gpt2_small
- SGLang
How to use mluca/traj_gpt2_small 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 "mluca/traj_gpt2_small" \ --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": "mluca/traj_gpt2_small", "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 "mluca/traj_gpt2_small" \ --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": "mluca/traj_gpt2_small", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use mluca/traj_gpt2_small with Docker Model Runner:
docker model run hf.co/mluca/traj_gpt2_small
- Xet hash:
- 8176cadf36258ecab3b6282fd0cbf0fc55a0d7fbf3387610332353d15188b17a
- Size of remote file:
- 710 MB
- SHA256:
- f795ec7feda8ad4bacdaf12fbfdbe29c4ee1334323302d2be6f9cf579cc27782
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