Text Generation
Transformers
PyTorch
TensorBoard
gpt2
Generated from Trainer
text-generation-inference
Instructions to use Mananm/GPT2-SyntheticData with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mananm/GPT2-SyntheticData with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Mananm/GPT2-SyntheticData")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Mananm/GPT2-SyntheticData") model = AutoModelForCausalLM.from_pretrained("Mananm/GPT2-SyntheticData", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Mananm/GPT2-SyntheticData with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Mananm/GPT2-SyntheticData" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Mananm/GPT2-SyntheticData", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Mananm/GPT2-SyntheticData
- SGLang
How to use Mananm/GPT2-SyntheticData 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 "Mananm/GPT2-SyntheticData" \ --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": "Mananm/GPT2-SyntheticData", "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 "Mananm/GPT2-SyntheticData" \ --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": "Mananm/GPT2-SyntheticData", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Mananm/GPT2-SyntheticData with Docker Model Runner:
docker model run hf.co/Mananm/GPT2-SyntheticData
Download pytorch_model.bin from Mananm/GPT2-SyntheticData: direct link, hf CLI and curl.
- Browser
- Download file 510 MB
-
https://huggingface.co/Mananm/GPT2-SyntheticData/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Mananm/GPT2-SyntheticData/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Mananm/GPT2-SyntheticData/resolve/main/pytorch_model.bin
510 MB
- Xet hash:
- 0ce75861b3a6d3fc02d3f1da2e6e6dda15f60c79d3d6b48464a7570070adbcc0
- Size of remote file:
- 510 MB
- SHA256:
- 2f73470228b722f140c5037ef636b94181f0c702dabfd37f0fd741695c5588ba
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