Text Generation
Transformers
Safetensors
gpt2
Unigram
Generated from Trainer
text-generation-inference
Instructions to use phonemetransformers/childes-segmentation-random-gpt2_lm-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use phonemetransformers/childes-segmentation-random-gpt2_lm-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="phonemetransformers/childes-segmentation-random-gpt2_lm-model")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("phonemetransformers/childes-segmentation-random-gpt2_lm-model") model = AutoModelForCausalLM.from_pretrained("phonemetransformers/childes-segmentation-random-gpt2_lm-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use phonemetransformers/childes-segmentation-random-gpt2_lm-model with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "phonemetransformers/childes-segmentation-random-gpt2_lm-model" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "phonemetransformers/childes-segmentation-random-gpt2_lm-model", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/phonemetransformers/childes-segmentation-random-gpt2_lm-model
- SGLang
How to use phonemetransformers/childes-segmentation-random-gpt2_lm-model 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 "phonemetransformers/childes-segmentation-random-gpt2_lm-model" \ --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": "phonemetransformers/childes-segmentation-random-gpt2_lm-model", "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 "phonemetransformers/childes-segmentation-random-gpt2_lm-model" \ --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": "phonemetransformers/childes-segmentation-random-gpt2_lm-model", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use phonemetransformers/childes-segmentation-random-gpt2_lm-model with Docker Model Runner:
docker model run hf.co/phonemetransformers/childes-segmentation-random-gpt2_lm-model
- Xet hash:
- b24b14fd4e852baa6aba26d2bf679b6089beccbcbe6165c223b1c3c033815ff4
- Size of remote file:
- 5.37 kB
- SHA256:
- 3f3c71cbdf325b5710bc1e54561f95431e4d6e3333dd62e49573b187295101df
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