Instructions to use sirmuelemos/gpt2_data_syntax with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sirmuelemos/gpt2_data_syntax with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="sirmuelemos/gpt2_data_syntax")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("sirmuelemos/gpt2_data_syntax") model = AutoModelForCausalLM.from_pretrained("sirmuelemos/gpt2_data_syntax", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use sirmuelemos/gpt2_data_syntax with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "sirmuelemos/gpt2_data_syntax" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sirmuelemos/gpt2_data_syntax", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/sirmuelemos/gpt2_data_syntax
- SGLang
How to use sirmuelemos/gpt2_data_syntax 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 "sirmuelemos/gpt2_data_syntax" \ --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": "sirmuelemos/gpt2_data_syntax", "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 "sirmuelemos/gpt2_data_syntax" \ --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": "sirmuelemos/gpt2_data_syntax", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use sirmuelemos/gpt2_data_syntax with Docker Model Runner:
docker model run hf.co/sirmuelemos/gpt2_data_syntax
Download checkpoint-500/optimizer.pt from sirmuelemos/gpt2_data_syntax: direct link, hf CLI and curl.
- Browser
- Download file 996 MB
-
https://huggingface.co/sirmuelemos/gpt2_data_syntax/resolve/main/checkpoint-500/optimizer.pt
- Command line
-
hf download hf://sirmuelemos/gpt2_data_syntax/checkpoint-500/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/sirmuelemos/gpt2_data_syntax/resolve/main/checkpoint-500/optimizer.pt
996 MB
- Xet hash:
- bf20820b02b381d57b9529ece1763bd1cd678796435024ce379c73397a661748
- Size of remote file:
- 996 MB
- SHA256:
- 20675cf6de7798e8e3d4d573aece9c5ff1b4734b957c9c6d65211f538379aa67
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