Instructions to use Ashraf-kasem/gpt2_fine_tune_with_callback_PolynomialDecay with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ashraf-kasem/gpt2_fine_tune_with_callback_PolynomialDecay with Transformers:

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

pipe = pipeline("text-generation", model="Ashraf-kasem/gpt2_fine_tune_with_callback_PolynomialDecay")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Ashraf-kasem/gpt2_fine_tune_with_callback_PolynomialDecay")
model = AutoModelForCausalLM.from_pretrained("Ashraf-kasem/gpt2_fine_tune_with_callback_PolynomialDecay")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Ashraf-kasem/gpt2_fine_tune_with_callback_PolynomialDecay with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ashraf-kasem/gpt2_fine_tune_with_callback_PolynomialDecay"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ashraf-kasem/gpt2_fine_tune_with_callback_PolynomialDecay",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Ashraf-kasem/gpt2_fine_tune_with_callback_PolynomialDecay

SGLang

How to use Ashraf-kasem/gpt2_fine_tune_with_callback_PolynomialDecay 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 "Ashraf-kasem/gpt2_fine_tune_with_callback_PolynomialDecay" \
    --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": "Ashraf-kasem/gpt2_fine_tune_with_callback_PolynomialDecay",
		"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 "Ashraf-kasem/gpt2_fine_tune_with_callback_PolynomialDecay" \
        --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": "Ashraf-kasem/gpt2_fine_tune_with_callback_PolynomialDecay",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Ashraf-kasem/gpt2_fine_tune_with_callback_PolynomialDecay with Docker Model Runner:
```
docker model run hf.co/Ashraf-kasem/gpt2_fine_tune_with_callback_PolynomialDecay
```

Ashraf-kasem/gpt2_fine_tune_with_callback_PolynomialDecay

This model is a fine-tuned version of gpt2 on an unknown dataset. It achieves the following results on the evaluation set:

Train Loss: 3.9746
Train Accuracy: 0.0063
Validation Loss: 4.3079
Validation Accuracy: 0.0044
Epoch: 2

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:

optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 14676, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
training_precision: mixed_float16

Training results

Train Loss	Train Accuracy	Validation Loss	Validation Accuracy	Epoch
4.6420	0.0092	4.4445	0.0056	0
4.1331	0.0067	4.3297	0.0047	1
3.9746	0.0063	4.3079	0.0044	2

Framework versions

Transformers 4.25.1
TensorFlow 2.9.0
Datasets 2.8.0
Tokenizers 0.13.2

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