Instructions to use suarkadipa/GPT-2-finetuned-papers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use suarkadipa/GPT-2-finetuned-papers with Transformers:

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

pipe = pipeline("text-generation", model="suarkadipa/GPT-2-finetuned-papers")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("suarkadipa/GPT-2-finetuned-papers")
model = AutoModelForCausalLM.from_pretrained("suarkadipa/GPT-2-finetuned-papers")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use suarkadipa/GPT-2-finetuned-papers with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "suarkadipa/GPT-2-finetuned-papers"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "suarkadipa/GPT-2-finetuned-papers",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/suarkadipa/GPT-2-finetuned-papers

SGLang

How to use suarkadipa/GPT-2-finetuned-papers 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 "suarkadipa/GPT-2-finetuned-papers" \
    --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": "suarkadipa/GPT-2-finetuned-papers",
		"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 "suarkadipa/GPT-2-finetuned-papers" \
        --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": "suarkadipa/GPT-2-finetuned-papers",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use suarkadipa/GPT-2-finetuned-papers with Docker Model Runner:
```
docker model run hf.co/suarkadipa/GPT-2-finetuned-papers
```

suarkadipa/GPT-2-finetuned-papers

This model is a fine-tuned version of distilgpt2 on an CShorten/ML-ArXiv-Papers dataset. Based on https://python.plainenglish.io/i-fine-tuned-gpt-2-on-100k-scientific-papers-heres-the-result-903f0784fe65 It achieves the following results on the evaluation set:

Train Loss: 2.4225
Validation Loss: 2.2164
Epoch: 0

Model description

More information needed

Intended uses & limitations

How to run in Google Colab

from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer_fromhub = AutoTokenizer.from_pretrained("suarkadipa/GPT-2-finetuned-papers")
model_fromhub = AutoModelForCausalLM.from_pretrained("suarkadipa/GPT-2-finetuned-papers", from_tf=True)

text_generator = pipeline(
    "text-generation",
    model=model_fromhub,
    tokenizer=tokenizer_fromhub,
    framework="tf",
    max_new_tokens=3000
)

// change with your text
test_sentence = "the role of recommender systems"
res=text_generator(test_sentence)[0]["generated_text"].replace("\n", " ")
res

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'ExponentialDecay', 'config': {'initial_learning_rate': 0.0005, 'decay_steps': 500, 'decay_rate': 0.95, 'staircase': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
training_precision: float32

Training results

Train Loss	Validation Loss	Epoch
2.4225	2.2164	0

Framework versions

Transformers 4.28.1
TensorFlow 2.12.0
Datasets 2.11.0
Tokenizers 0.13.3

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