Instructions to use Ashraf-kasem/custom_gpt2_frames_text_original_tokenizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ashraf-kasem/custom_gpt2_frames_text_original_tokenizer 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_original_tokenizer")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Ashraf-kasem/custom_gpt2_frames_text_original_tokenizer")
model = AutoModelForCausalLM.from_pretrained("Ashraf-kasem/custom_gpt2_frames_text_original_tokenizer")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Ashraf-kasem/custom_gpt2_frames_text_original_tokenizer 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_original_tokenizer"
# 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_original_tokenizer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Ashraf-kasem/custom_gpt2_frames_text_original_tokenizer

SGLang

How to use Ashraf-kasem/custom_gpt2_frames_text_original_tokenizer 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_original_tokenizer" \
    --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_original_tokenizer",
		"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_original_tokenizer" \
        --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_original_tokenizer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Ashraf-kasem/custom_gpt2_frames_text_original_tokenizer with Docker Model Runner:
```
docker model run hf.co/Ashraf-kasem/custom_gpt2_frames_text_original_tokenizer
```

Ashraf-kasem/custom_gpt2_frames_text_original_tokenizer

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

Train Loss: 1.1074
Validation Loss: 1.6432
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': 240780, '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
4.3075	3.4095	0
3.1973	2.8234	1
2.7420	2.5057	2
2.4541	2.3022	3
2.2507	2.1648	4
2.0962	2.0612	5
1.9736	1.9885	6
1.8729	1.9286	7
1.7883	1.8823	8
1.7153	1.8448	9
1.6517	1.8113	10
1.5953	1.7864	11
1.5446	1.7624	12
1.4994	1.7459	13
1.4578	1.7294	14
1.4200	1.7171	15
1.3851	1.7026	16
1.3528	1.6958	17
1.3229	1.6846	18
1.2950	1.6760	19
1.2690	1.6704	20
1.2448	1.6650	21
1.2223	1.6599	22
1.2012	1.6539	23
1.1815	1.6534	24
1.1635	1.6486	25
1.1470	1.6457	26
1.1318	1.6443	27
1.1185	1.6434	28
1.1074	1.6432	29

Framework versions

Transformers 4.25.1
TensorFlow 2.9.0
Datasets 2.8.0
Tokenizers 0.13.2

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