Instructions to use team-approx-bayes/gpt2-medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use team-approx-bayes/gpt2-medium with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="team-approx-bayes/gpt2-medium")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("team-approx-bayes/gpt2-medium") model = AutoModelForCausalLM.from_pretrained("team-approx-bayes/gpt2-medium", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use team-approx-bayes/gpt2-medium with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "team-approx-bayes/gpt2-medium" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "team-approx-bayes/gpt2-medium", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/team-approx-bayes/gpt2-medium
- SGLang
How to use team-approx-bayes/gpt2-medium 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 "team-approx-bayes/gpt2-medium" \ --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": "team-approx-bayes/gpt2-medium", "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 "team-approx-bayes/gpt2-medium" \ --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": "team-approx-bayes/gpt2-medium", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use team-approx-bayes/gpt2-medium with Docker Model Runner:
docker model run hf.co/team-approx-bayes/gpt2-medium
Download optimizer_param_groups.pt from team-approx-bayes/gpt2-medium: direct link, hf CLI and curl.
- Browser
- Download file 2.84 GB
-
https://huggingface.co/team-approx-bayes/gpt2-medium/resolve/main/optimizer_param_groups.pt
- Command line
-
hf download hf://team-approx-bayes/gpt2-medium/optimizer_param_groups.pt
-
curl -L -o optimizer_param_groups.pt https://huggingface.co/team-approx-bayes/gpt2-medium/resolve/main/optimizer_param_groups.pt
2.84 GB
- Xet hash:
- 0870dee4b35c3d6bf929034275244b2b2ee10f0b5c0625490afb97f62d6bf4ea
- Size of remote file:
- 2.84 GB
- SHA256:
- dde8116cc1e12b6635663fa0ab0aedb5b5efae5aeacf905d1ba83f30c3928786
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