Instructions to use raghavbali/gpt2-finetuned-headliner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use raghavbali/gpt2-finetuned-headliner with Transformers:

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

pipe = pipeline("text-generation", model="raghavbali/gpt2-finetuned-headliner")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("raghavbali/gpt2-finetuned-headliner")
model = AutoModelForCausalLM.from_pretrained("raghavbali/gpt2-finetuned-headliner")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use raghavbali/gpt2-finetuned-headliner with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "raghavbali/gpt2-finetuned-headliner"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "raghavbali/gpt2-finetuned-headliner",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/raghavbali/gpt2-finetuned-headliner

SGLang

How to use raghavbali/gpt2-finetuned-headliner 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 "raghavbali/gpt2-finetuned-headliner" \
    --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": "raghavbali/gpt2-finetuned-headliner",
		"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 "raghavbali/gpt2-finetuned-headliner" \
        --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": "raghavbali/gpt2-finetuned-headliner",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use raghavbali/gpt2-finetuned-headliner with Docker Model Runner:
```
docker model run hf.co/raghavbali/gpt2-finetuned-headliner
```

GPT2 Fine Tuned Headline Generator

This model is trained on the harvard/abcnews-dataset to generate news headlines
This model is a fine-tuned version of openai-community/gpt2-medium on an unknown dataset.

Model description

The model is fine-tuned for 2 epochs and 4k training samples from the abcnews dataset. This enables the model to generate news headline like text given a simple prompt

Intended uses & limitations

This model is only for learning purposes only. The model easily hallucinates people names, locations and other artifacts & incidents.

Training and evaluation data

The model leverages 2k test samples for evaluation

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 64
eval_batch_size: 64
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 4
num_epochs: 2

Training results

The final output after 2 epochs is as follows: TrainOutput(global_step=130, training_loss=5.044873604407678, metrics={'train_runtime': 140.587, 'train_samples_per_second': 59.166, 'train_steps_per_second': 0.925, 'total_flos': 248723096358912.0, 'train_loss': 5.044873604407678, 'epoch': 2.0})

Framework versions

Transformers 4.44.2
Pytorch 2.3.1
Datasets 2.21.0
Tokenizers 0.19.1

Downloads last month: 75

Safetensors

Model size

0.4B params

Tensor type

F32

Model tree for raghavbali/gpt2-finetuned-headliner

Base model

openai-community/gpt2-medium

Finetuned

(191)

this model

Finetunes

2 models