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 / README.md

Thalirajesh

Update README.md

025b14e verified over 2 years ago

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metadata

language: en
widget:
  - text: Arts
    example_title: Arts
  - text: Space
    example_title: Space
  - text: Time
    example_title: Time
tags:
  - text-generation
  - creative-writing
  - essay-writing
inference:
  parameters:
    max_length: 400
    num_beams: 10
    early_stopping: true
    temperature: 0.3
    no_repeat_ngram_size: 2
    num_return_sequences: 2

Introduction: This repository contains a finetuned DistilGPT2 model for generating diverse essays on topics spanning Arts, Science, and Culture.

Dataset: The training dataset comprises 2000+ essays covering diverse topics in Arts, Science, and Culture. These essays are written by human experts and contain a diverse set of opinions and knowledge, ensuring that the model learns from high-quality and diverse content.

Model Training:

epoch: 50
training_loss: 2.473200
validation_loss: 4.569556
perplexities: [517.4149169921875, 924.535888671875, 704.73291015625, 465.9677429199219, 577.629150390625, 443.994140625, 770.1861572265625, 683.028076171875, 1017.7510375976562, 880.795166015625]
mean_perplexity: 698.603519

Description: The model achieved a mean perplexity of 698.603519 on the validation set, indicating its ability to generate diverse and high-quality essays on the given topics.

During Text Generation, the following parameters are used:

max_length: The maximum length of the generated text, set to 400 tokens.
num_beams: The number of beams for beam search, set to 10. A higher value will increase the diversity of the generated text but may also increase the inference time.
early_stopping: If set to True, the generation will stop as soon as the end-of-sequence token is generated.
temperature: The sampling temperature, is set to 0.3.
no_repeat_ngram_size: The size of the n-gram window to avoid repetitions, set to 2.

Find the kaggle notebook for this project at

Kaggle Notebook