Instructions to use Ti-Ma/TiMaGPT2-2017 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ti-Ma/TiMaGPT2-2017 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Ti-Ma/TiMaGPT2-2017")# Load model directly from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("Ti-Ma/TiMaGPT2-2017") model = AutoModelWithLMHead.from_pretrained("Ti-Ma/TiMaGPT2-2017", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Ti-Ma/TiMaGPT2-2017 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Ti-Ma/TiMaGPT2-2017" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Ti-Ma/TiMaGPT2-2017", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Ti-Ma/TiMaGPT2-2017
- SGLang
How to use Ti-Ma/TiMaGPT2-2017 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 "Ti-Ma/TiMaGPT2-2017" \ --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": "Ti-Ma/TiMaGPT2-2017", "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 "Ti-Ma/TiMaGPT2-2017" \ --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": "Ti-Ma/TiMaGPT2-2017", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Ti-Ma/TiMaGPT2-2017 with Docker Model Runner:
docker model run hf.co/Ti-Ma/TiMaGPT2-2017
Download training_args.bin from Ti-Ma/TiMaGPT2-2017: direct link, hf CLI and curl.
- Browser
- Download file 2.81 kB
-
https://huggingface.co/Ti-Ma/TiMaGPT2-2017/resolve/main/training_args.bin
- Command line
-
hf download hf://Ti-Ma/TiMaGPT2-2017/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Ti-Ma/TiMaGPT2-2017/resolve/main/training_args.bin
2.81 kB
- Xet hash:
- 07646395e9c827e2a998786ee7f00dc147f8ad4fae9ff7d8efcf7c1c6ac3564b
- Size of remote file:
- 2.81 kB
- SHA256:
- 6c7e1c45f66f8b2f8451b649526fefc4b1ba8e30dd0d77f773499e6776d1fd6e
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