Instructions to use hf-tiny-model-private/tiny-random-RemBertForCausalLM 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-RemBertForCausalLM 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-RemBertForCausalLM")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-RemBertForCausalLM") model = AutoModelForCausalLM.from_pretrained("hf-tiny-model-private/tiny-random-RemBertForCausalLM") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use hf-tiny-model-private/tiny-random-RemBertForCausalLM 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-RemBertForCausalLM" # 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-RemBertForCausalLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/hf-tiny-model-private/tiny-random-RemBertForCausalLM
- SGLang
How to use hf-tiny-model-private/tiny-random-RemBertForCausalLM 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-RemBertForCausalLM" \ --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-RemBertForCausalLM", "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-RemBertForCausalLM" \ --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-RemBertForCausalLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use hf-tiny-model-private/tiny-random-RemBertForCausalLM with Docker Model Runner:
docker model run hf.co/hf-tiny-model-private/tiny-random-RemBertForCausalLM
| { | |
| "bos_token": "[CLS]", | |
| "clean_up_tokenization_spaces": true, | |
| "cls_token": "[CLS]", | |
| "do_lower_case": false, | |
| "eos_token": "[SEP]", | |
| "keep_accents": true, | |
| "mask_token": "[MASK]", | |
| "model_max_length": 512, | |
| "pad_token": "[PAD]", | |
| "remove_space": true, | |
| "sep_token": "[SEP]", | |
| "special_tokens_map_file": "/home/runner/.cache/huggingface/hub/models--google--rembert/snapshots/65da5133da36e29dfca67d4f0dd9f7f9db21b563/special_tokens_map.json", | |
| "tokenizer_class": "RemBertTokenizer", | |
| "unk_token": "[UNK]" | |
| } | |