Instructions to use HScomcom/gpt2-MyLittlePony with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HScomcom/gpt2-MyLittlePony with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="HScomcom/gpt2-MyLittlePony")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("HScomcom/gpt2-MyLittlePony") model = AutoModelForCausalLM.from_pretrained("HScomcom/gpt2-MyLittlePony", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use HScomcom/gpt2-MyLittlePony with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "HScomcom/gpt2-MyLittlePony" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HScomcom/gpt2-MyLittlePony", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/HScomcom/gpt2-MyLittlePony
- SGLang
How to use HScomcom/gpt2-MyLittlePony 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 "HScomcom/gpt2-MyLittlePony" \ --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": "HScomcom/gpt2-MyLittlePony", "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 "HScomcom/gpt2-MyLittlePony" \ --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": "HScomcom/gpt2-MyLittlePony", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use HScomcom/gpt2-MyLittlePony with Docker Model Runner:
docker model run hf.co/HScomcom/gpt2-MyLittlePony
Download flax_model.msgpack from HScomcom/gpt2-MyLittlePony: direct link, hf CLI and curl.
- Browser
- Download file 3.1 GB
-
https://huggingface.co/HScomcom/gpt2-MyLittlePony/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://HScomcom/gpt2-MyLittlePony/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/HScomcom/gpt2-MyLittlePony/resolve/main/flax_model.msgpack
3.1 GB
- Xet hash:
- 02d797efc936f8253b412c02d5ea8c4a4fbc7ca3b42221b76a46dd989c7360ed
- Size of remote file:
- 3.1 GB
- SHA256:
- 8611daf21d0222a30b47b8297fc87e85e57c74baddf886e68bb9f669b61eb4e9
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