Instructions to use ydshieh/tiny-random-BertLMHeadModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ydshieh/tiny-random-BertLMHeadModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ydshieh/tiny-random-BertLMHeadModel")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ydshieh/tiny-random-BertLMHeadModel") model = AutoModelForCausalLM.from_pretrained("ydshieh/tiny-random-BertLMHeadModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ydshieh/tiny-random-BertLMHeadModel with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ydshieh/tiny-random-BertLMHeadModel" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ydshieh/tiny-random-BertLMHeadModel", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ydshieh/tiny-random-BertLMHeadModel
- SGLang
How to use ydshieh/tiny-random-BertLMHeadModel 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 "ydshieh/tiny-random-BertLMHeadModel" \ --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": "ydshieh/tiny-random-BertLMHeadModel", "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 "ydshieh/tiny-random-BertLMHeadModel" \ --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": "ydshieh/tiny-random-BertLMHeadModel", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ydshieh/tiny-random-BertLMHeadModel with Docker Model Runner:
docker model run hf.co/ydshieh/tiny-random-BertLMHeadModel
Download pytorch_model.bin from ydshieh/tiny-random-BertLMHeadModel: direct link, hf CLI and curl.
- Browser
- Download file 389 kB
-
https://huggingface.co/ydshieh/tiny-random-BertLMHeadModel/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ydshieh/tiny-random-BertLMHeadModel/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ydshieh/tiny-random-BertLMHeadModel/resolve/main/pytorch_model.bin
389 kB
- Xet hash:
- e77d86654c1d4c76ba6e3234110bc4ab592366c73b8c3068416e292a7852441d
- Size of remote file:
- 389 kB
- SHA256:
- b9043ca3aca2f172aaf20a808c11ee5d7b6673e279c187d3676e02a619e7a739
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