Instructions to use Ashraf-kasem/custom_gpt2_frames_text with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ashraf-kasem/custom_gpt2_frames_text with Transformers:

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

pipe = pipeline("text-generation", model="Ashraf-kasem/custom_gpt2_frames_text")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Ashraf-kasem/custom_gpt2_frames_text")
model = AutoModelForCausalLM.from_pretrained("Ashraf-kasem/custom_gpt2_frames_text")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Ashraf-kasem/custom_gpt2_frames_text with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ashraf-kasem/custom_gpt2_frames_text"
# 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/custom_gpt2_frames_text",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Ashraf-kasem/custom_gpt2_frames_text

SGLang

How to use Ashraf-kasem/custom_gpt2_frames_text 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/custom_gpt2_frames_text" \
    --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/custom_gpt2_frames_text",
		"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/custom_gpt2_frames_text" \
        --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/custom_gpt2_frames_text",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Ashraf-kasem/custom_gpt2_frames_text with Docker Model Runner:
```
docker model run hf.co/Ashraf-kasem/custom_gpt2_frames_text
```

Ashraf-kasem/custom_gpt2_frames_text

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

Train Loss: 1.3938
Validation Loss: 2.0834
Epoch: 29

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': 188670, '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	Validation Loss	Epoch
5.4252	4.4731	0
4.1781	3.6928	1
3.5744	3.2572	2
3.1856	2.9789	3
2.9095	2.7887	4
2.6999	2.6534	5
2.5334	2.5484	6
2.3969	2.4706	7
2.2826	2.4102	8
2.1842	2.3518	9
2.0988	2.3096	10
2.0236	2.2740	11
1.9569	2.2443	12
1.8960	2.2214	13
1.8411	2.1954	14
1.7913	2.1815	15
1.7457	2.1652	16
1.7034	2.1552	17
1.6648	2.1398	18
1.6288	2.1289	19
1.5955	2.1213	20
1.5643	2.1114	21
1.5359	2.1071	22
1.5094	2.0998	23
1.4846	2.0942	24
1.4622	2.0911	25
1.4420	2.0893	26
1.4233	2.0879	27
1.4074	2.0838	28
1.3938	2.0834	29

Framework versions

Transformers 4.25.1
TensorFlow 2.9.0
Datasets 2.8.0
Tokenizers 0.13.2

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