Instructions to use shiyue/webtext_train50K_gpt2-large_mix1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shiyue/webtext_train50K_gpt2-large_mix1.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="shiyue/webtext_train50K_gpt2-large_mix1.0")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("shiyue/webtext_train50K_gpt2-large_mix1.0") model = AutoModelForCausalLM.from_pretrained("shiyue/webtext_train50K_gpt2-large_mix1.0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use shiyue/webtext_train50K_gpt2-large_mix1.0 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "shiyue/webtext_train50K_gpt2-large_mix1.0" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "shiyue/webtext_train50K_gpt2-large_mix1.0", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/shiyue/webtext_train50K_gpt2-large_mix1.0
- SGLang
How to use shiyue/webtext_train50K_gpt2-large_mix1.0 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 "shiyue/webtext_train50K_gpt2-large_mix1.0" \ --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": "shiyue/webtext_train50K_gpt2-large_mix1.0", "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 "shiyue/webtext_train50K_gpt2-large_mix1.0" \ --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": "shiyue/webtext_train50K_gpt2-large_mix1.0", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use shiyue/webtext_train50K_gpt2-large_mix1.0 with Docker Model Runner:
docker model run hf.co/shiyue/webtext_train50K_gpt2-large_mix1.0
Download pytorch_model.bin from shiyue/webtext_train50K_gpt2-large_mix1.0: direct link, hf CLI and curl.
- Browser
- Download file 3.13 GB
-
https://huggingface.co/shiyue/webtext_train50K_gpt2-large_mix1.0/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://shiyue/webtext_train50K_gpt2-large_mix1.0/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/shiyue/webtext_train50K_gpt2-large_mix1.0/resolve/main/pytorch_model.bin
3.13 GB
- Xet hash:
- 7d0d62145a517bd0343dc0e1eda02fc0da0bed1fc69aea99dd42657ac196ba00
- Size of remote file:
- 3.13 GB
- SHA256:
- a912fed769aa974bf577aeee9417f2f94d45c1ba1b46afa51839122e9a60cc08
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