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 /convert2flax.py

Doron Adler

* Updated model card

6e1c9c6 about 4 years ago

Raw History Blame

714 Bytes

	import argparse
	import logging

	import numpy as np
	import torch
	import os
	from transformers import AutoConfig, FlaxAutoModelForCausalLM

	logging.basicConfig(
	format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
	datefmt="%m/%d/%Y %H:%M:%S",
	level=logging.INFO,
	)
	logger = logging.getLogger(__name__)

	model_path = "./distilgpt2-base-pretrained-he"
	save_directory = "./tmp/flax/"

	config_path = os.path.join(model_path, 'config.json')

	# Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
	config = AutoConfig.from_pretrained(config_path)
	model = FlaxAutoModelForCausalLM.from_pretrained(model_path, from_pt=True, config=config)
	model.save_pretrained(save_directory)