Instructions to use Norod78/distilgpt2-base-pretrained-he with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Norod78/distilgpt2-base-pretrained-he with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Norod78/distilgpt2-base-pretrained-he")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Norod78/distilgpt2-base-pretrained-he")
model = AutoModelForCausalLM.from_pretrained("Norod78/distilgpt2-base-pretrained-he", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Norod78/distilgpt2-base-pretrained-he with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Norod78/distilgpt2-base-pretrained-he"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Norod78/distilgpt2-base-pretrained-he",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Norod78/distilgpt2-base-pretrained-he

SGLang

How to use Norod78/distilgpt2-base-pretrained-he 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 "Norod78/distilgpt2-base-pretrained-he" \
    --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": "Norod78/distilgpt2-base-pretrained-he",
		"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 "Norod78/distilgpt2-base-pretrained-he" \
        --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": "Norod78/distilgpt2-base-pretrained-he",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Norod78/distilgpt2-base-pretrained-he with Docker Model Runner:
```
docker model run hf.co/Norod78/distilgpt2-base-pretrained-he
```

distilgpt2-base-pretrained-he / converters /convert2tf.py

Doron Adler

* Updated model card

6e1c9c6 about 4 years ago

Raw History Blame

892 Bytes

	# Requires transformers >= 4.21.0;
	# Sampling outputs may differ, depending on your hardware.
	from transformers import AutoTokenizer, TFAutoModelForCausalLM

	model_checkpoint = "./distilgpt2-base-pretrained-he"
	save_directory = "tmp/tf/"
	file_name = "tf_model.h5"

	tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
	model = TFAutoModelForCausalLM.from_pretrained(model_checkpoint, from_pt=True)
	model.config.pad_token_id = model.config.eos_token_id
	inputs = tokenizer(["צחוקים ושיגועים"], return_tensors="tf")

	generated = model.generate(**inputs, do_sample=True, seed=(42, 0))
	print("Sampling output: ", tokenizer.decode(generated[0]))

	model.save_pretrained(save_directory, file_name=file_name)
	tokenizer.save_pretrained(save_directory)

	# > Sampling output: TensorFlow is a great learning platform for learning about
	# data structure and structure in data science..