Instructions to use marcolorenzi98/GPT2-finetuned-covid_fake_news with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use marcolorenzi98/GPT2-finetuned-covid_fake_news with Transformers:

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

pipe = pipeline("text-generation", model="marcolorenzi98/GPT2-finetuned-covid_fake_news")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("marcolorenzi98/GPT2-finetuned-covid_fake_news")
model = AutoModelForCausalLM.from_pretrained("marcolorenzi98/GPT2-finetuned-covid_fake_news", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use marcolorenzi98/GPT2-finetuned-covid_fake_news with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "marcolorenzi98/GPT2-finetuned-covid_fake_news"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "marcolorenzi98/GPT2-finetuned-covid_fake_news",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/marcolorenzi98/GPT2-finetuned-covid_fake_news

SGLang

How to use marcolorenzi98/GPT2-finetuned-covid_fake_news 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 "marcolorenzi98/GPT2-finetuned-covid_fake_news" \
    --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": "marcolorenzi98/GPT2-finetuned-covid_fake_news",
		"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 "marcolorenzi98/GPT2-finetuned-covid_fake_news" \
        --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": "marcolorenzi98/GPT2-finetuned-covid_fake_news",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use marcolorenzi98/GPT2-finetuned-covid_fake_news with Docker Model Runner:
```
docker model run hf.co/marcolorenzi98/GPT2-finetuned-covid_fake_news
```

GPT2-covid_fake-news

This model is a fine-tuned version of openai-community/gpt2 on the covid_fake_news dataset.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 150
eval_batch_size: 150
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 10

Framework versions

Transformers 4.38.2
Pytorch 2.1.0+cu121
Datasets 2.18.0
Tokenizers 0.15.2

Downloads last month: 3

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for marcolorenzi98/GPT2-finetuned-covid_fake_news

Base model

openai-community/gpt2

Finetuned

(2252)

this model

marcolorenzi98
/

GPT2-finetuned-covid_fake_news

GPT2-covid_fake-news

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Framework versions

Model tree for marcolorenzi98/GPT2-finetuned-covid_fake_news

Dataset used to train marcolorenzi98/GPT2-finetuned-covid_fake_news