Instructions to use Thalirajesh/DistilGPT2EssayGenerator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Thalirajesh/DistilGPT2EssayGenerator with Transformers:

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

pipe = pipeline("text-generation", model="Thalirajesh/DistilGPT2EssayGenerator")

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

tokenizer = AutoTokenizer.from_pretrained("Thalirajesh/DistilGPT2EssayGenerator")
model = AutoModelForCausalLM.from_pretrained("Thalirajesh/DistilGPT2EssayGenerator", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Thalirajesh/DistilGPT2EssayGenerator with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Thalirajesh/DistilGPT2EssayGenerator"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Thalirajesh/DistilGPT2EssayGenerator",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Thalirajesh/DistilGPT2EssayGenerator

SGLang

How to use Thalirajesh/DistilGPT2EssayGenerator 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 "Thalirajesh/DistilGPT2EssayGenerator" \
    --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": "Thalirajesh/DistilGPT2EssayGenerator",
		"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 "Thalirajesh/DistilGPT2EssayGenerator" \
        --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": "Thalirajesh/DistilGPT2EssayGenerator",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Thalirajesh/DistilGPT2EssayGenerator with Docker Model Runner:
```
docker model run hf.co/Thalirajesh/DistilGPT2EssayGenerator
```

DistilGPT2EssayGenerator / optimizer.pt

Thalirajesh

Upload folder using huggingface_hub

698941e verified over 2 years ago

History Blame Contribute Delete

655 MB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 4ebeca46c249f057073376eec271e12fcdae773e313d1eca3acd60d0613f7fcc
Size of remote file:: 655 MB
SHA256:: 7a86878426b0cde4cc43067288fbb7c81062595c51e2fb9349c1171c68d8b552

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.

Thalirajesh
/

DistilGPT2EssayGenerator

Detected Pickle imports (3)

Xet Pointer Details