Instructions to use hf-tiny-model-private/tiny-random-ProphetNetForCausalLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hf-tiny-model-private/tiny-random-ProphetNetForCausalLM 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-ProphetNetForCausalLM")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-ProphetNetForCausalLM")
model = AutoModelForCausalLM.from_pretrained("hf-tiny-model-private/tiny-random-ProphetNetForCausalLM")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use hf-tiny-model-private/tiny-random-ProphetNetForCausalLM 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-ProphetNetForCausalLM"
# 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-ProphetNetForCausalLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/hf-tiny-model-private/tiny-random-ProphetNetForCausalLM

SGLang

How to use hf-tiny-model-private/tiny-random-ProphetNetForCausalLM 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-ProphetNetForCausalLM" \
    --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-ProphetNetForCausalLM",
		"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-ProphetNetForCausalLM" \
        --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-ProphetNetForCausalLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use hf-tiny-model-private/tiny-random-ProphetNetForCausalLM with Docker Model Runner:
```
docker model run hf.co/hf-tiny-model-private/tiny-random-ProphetNetForCausalLM
```

tiny-random-ProphetNetForCausalLM

File size: 562 Bytes

165350a

{
  "clean_up_tokenization_spaces": true,
  "do_basic_tokenize": true,
  "do_lower_case": true,
  "mask_token": "[MASK]",
  "model_max_length": 30,
  "never_split": null,
  "pad_token": "[PAD]",
  "sep_token": "[SEP]",
  "special_tokens_map_file": "/home/runner/.cache/huggingface/hub/models--microsoft--prophetnet-large-uncased/snapshots/fd5b6f7e0cae2f7cd69f33b3da1d316c8f43645e/special_tokens_map.json",
  "strip_accents": null,
  "tokenize_chinese_chars": true,
  "tokenizer_class": "ProphetNetTokenizer",
  "unk_token": "[UNK]",
  "x_sep_token": "[X_SEP]"
}