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

Libraries

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Use Docker

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

SGLang

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

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

SFconvertbot commited on Jul 12, 2023

Commit

9ca373d

1 Parent(s): 389c048

Adding `safetensors` variant of this model

Browse files

This is an automated PR created with https://huggingface.co/spaces/safetensors/convert

This new file is equivalent to `pytorch_model.bin` but safe in the sense that
no arbitrary code can be put into it.

These files also happen to load much faster than their pytorch counterpart:
https://colab.research.google.com/github/huggingface/notebooks/blob/main/safetensors_doc/en/speed.ipynb

The widgets on your model page will run using this model even if this is not merged
making sure the file actually works.

If you find any issues: please report here: https://huggingface.co/spaces/safetensors/convert/discussions

Feel free to ignore this PR.

Files changed (1) hide show

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