Instructions to use hf-tiny-model-private/tiny-random-RobertaPreLayerNormForCausalLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-RobertaPreLayerNormForCausalLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="hf-tiny-model-private/tiny-random-RobertaPreLayerNormForCausalLM")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-RobertaPreLayerNormForCausalLM") model = AutoModelForCausalLM.from_pretrained("hf-tiny-model-private/tiny-random-RobertaPreLayerNormForCausalLM") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use hf-tiny-model-private/tiny-random-RobertaPreLayerNormForCausalLM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "hf-tiny-model-private/tiny-random-RobertaPreLayerNormForCausalLM" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "hf-tiny-model-private/tiny-random-RobertaPreLayerNormForCausalLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/hf-tiny-model-private/tiny-random-RobertaPreLayerNormForCausalLM
- SGLang
How to use hf-tiny-model-private/tiny-random-RobertaPreLayerNormForCausalLM 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 "hf-tiny-model-private/tiny-random-RobertaPreLayerNormForCausalLM" \ --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": "hf-tiny-model-private/tiny-random-RobertaPreLayerNormForCausalLM", "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 "hf-tiny-model-private/tiny-random-RobertaPreLayerNormForCausalLM" \ --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": "hf-tiny-model-private/tiny-random-RobertaPreLayerNormForCausalLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use hf-tiny-model-private/tiny-random-RobertaPreLayerNormForCausalLM with Docker Model Runner:
docker model run hf.co/hf-tiny-model-private/tiny-random-RobertaPreLayerNormForCausalLM
- Xet hash:
- 676ddde65161955d86881666640fe287ad51ebf333b6f0a7e3162c2ab900d5f7
- Size of remote file:
- 379 kB
- SHA256:
- b8bbb7235edca03c2e844e597239bab510d96585af6ba48951690c4c2575b2e9
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