Instructions to use MariaZafar/distilgpt2-finetuned-wikitext2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MariaZafar/distilgpt2-finetuned-wikitext2 with Transformers:

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

pipe = pipeline("text-generation", model="MariaZafar/distilgpt2-finetuned-wikitext2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MariaZafar/distilgpt2-finetuned-wikitext2")
model = AutoModelForCausalLM.from_pretrained("MariaZafar/distilgpt2-finetuned-wikitext2")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use MariaZafar/distilgpt2-finetuned-wikitext2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MariaZafar/distilgpt2-finetuned-wikitext2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MariaZafar/distilgpt2-finetuned-wikitext2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/MariaZafar/distilgpt2-finetuned-wikitext2

SGLang

How to use MariaZafar/distilgpt2-finetuned-wikitext2 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 "MariaZafar/distilgpt2-finetuned-wikitext2" \
    --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": "MariaZafar/distilgpt2-finetuned-wikitext2",
		"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 "MariaZafar/distilgpt2-finetuned-wikitext2" \
        --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": "MariaZafar/distilgpt2-finetuned-wikitext2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MariaZafar/distilgpt2-finetuned-wikitext2 with Docker Model Runner:
```
docker model run hf.co/MariaZafar/distilgpt2-finetuned-wikitext2
```

MariaZafar/distilgpt2-finetuned-wikitext2

This model is a fine-tuned version of distilgpt2 on an unknown dataset. It achieves the following results on the evaluation set:

Train Loss: 0.0603
Validation Loss: 5.5023
Epoch: 49

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': 'AdamWeightDecay', 'learning_rate': 0.001, '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
3.4903	2.4602	0
2.5910	2.5912	1
2.1133	2.8207	2
1.5857	3.1597	3
1.0852	3.3317	4
0.6812	3.6312	5
0.4490	3.8533	6
0.3188	4.0209	7
0.2401	4.1932	8
0.1987	4.3469	9
0.1705	4.4238	10
0.1515	4.5274	11
0.1329	4.5066	12
0.1302	4.6625	13
0.1202	4.6441	14
0.1133	4.7448	15
0.1076	4.8144	16
0.1025	4.9662	17
0.0976	4.7328	18
0.0928	4.8394	19
0.0862	4.8873	20
0.0824	4.9153	21
0.0869	5.2097	22
0.0847	5.1124	23
0.0824	5.0528	24
0.0826	5.0547	25
0.0840	5.1079	26
0.0846	4.9867	27
0.0802	4.9700	28
0.0806	5.2266	29
0.0827	5.0909	30
0.0784	5.2329	31
0.0744	5.0834	32
0.0712	5.3750	33
0.0715	5.2754	34
0.0695	5.4315	35
0.0703	5.4119	36
0.0732	5.5824	37
0.0679	5.4020	38
0.0627	5.7249	39
0.0659	5.1686	40
0.0656	5.2962	41
0.0642	5.3573	42
0.0661	5.4822	43
0.0643	5.6516	44
0.0612	5.6201	45
0.0666	5.4791	46
0.0677	5.6865	47
0.0628	5.4184	48
0.0603	5.5023	49

Framework versions

Transformers 4.19.2
TensorFlow 2.8.0
Datasets 2.2.1
Tokenizers 0.12.1

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