Instructions to use Smaraa/gpt2-text-simplification_1e4_adafactor_newsela with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Smaraa/gpt2-text-simplification_1e4_adafactor_newsela with Transformers:

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

pipe = pipeline("text-generation", model="Smaraa/gpt2-text-simplification_1e4_adafactor_newsela")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Smaraa/gpt2-text-simplification_1e4_adafactor_newsela")
model = AutoModelForCausalLM.from_pretrained("Smaraa/gpt2-text-simplification_1e4_adafactor_newsela")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Smaraa/gpt2-text-simplification_1e4_adafactor_newsela with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Smaraa/gpt2-text-simplification_1e4_adafactor_newsela"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Smaraa/gpt2-text-simplification_1e4_adafactor_newsela",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Smaraa/gpt2-text-simplification_1e4_adafactor_newsela

SGLang

How to use Smaraa/gpt2-text-simplification_1e4_adafactor_newsela 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 "Smaraa/gpt2-text-simplification_1e4_adafactor_newsela" \
    --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": "Smaraa/gpt2-text-simplification_1e4_adafactor_newsela",
		"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 "Smaraa/gpt2-text-simplification_1e4_adafactor_newsela" \
        --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": "Smaraa/gpt2-text-simplification_1e4_adafactor_newsela",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Smaraa/gpt2-text-simplification_1e4_adafactor_newsela with Docker Model Runner:
```
docker model run hf.co/Smaraa/gpt2-text-simplification_1e4_adafactor_newsela
```

gpt2-text-simplification_1e4_adafactor_newsela

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

Loss: 1.3465

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: 0.0001
train_batch_size: 64
eval_batch_size: 64
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss
0.7662	1.0	1605	0.8757
0.6538	2.0	3210	0.9019
0.5663	3.0	4815	0.9554
0.4961	4.0	6420	0.9990
0.4299	5.0	8025	1.0271
0.3853	6.0	9630	1.0547
0.3482	7.0	11235	1.1090
0.3152	8.0	12840	1.1387
0.2903	9.0	14445	1.1853
0.2655	10.0	16050	1.2088
0.2477	11.0	17655	1.2168
0.232	12.0	19260	1.2426
0.2192	13.0	20865	1.2522
0.2078	14.0	22470	1.2855
0.198	15.0	24075	1.3048
0.19	16.0	25680	1.3117
0.1834	17.0	27285	1.3262
0.1777	18.0	28890	1.3360
0.1733	19.0	30495	1.3440
0.1702	20.0	32100	1.3465

Framework versions

Transformers 4.30.2
Pytorch 2.0.1+cu118
Datasets 2.13.1
Tokenizers 0.13.3

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