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

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
```

tiny-random-RemBertForCausalLM

File size: 522 Bytes

a3a46ee

{
  "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]"
}