Instructions to use mNLP-project/gpt2-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mNLP-project/gpt2-finetuned with Transformers:

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

pipe = pipeline("text-generation", model="mNLP-project/gpt2-finetuned")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mNLP-project/gpt2-finetuned")
model = AutoModelForCausalLM.from_pretrained("mNLP-project/gpt2-finetuned")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use mNLP-project/gpt2-finetuned with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/mNLP-project/gpt2-finetuned

SGLang

How to use mNLP-project/gpt2-finetuned 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 "mNLP-project/gpt2-finetuned" \
    --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": "mNLP-project/gpt2-finetuned",
		"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 "mNLP-project/gpt2-finetuned" \
        --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": "mNLP-project/gpt2-finetuned",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mNLP-project/gpt2-finetuned with Docker Model Runner:
```
docker model run hf.co/mNLP-project/gpt2-finetuned
```

gpt2-finetuned

This model is a fine-tuned version of openai-community/gpt2 on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.6944
Bleu: 0.0294
Bertscore Precision: 0.1536
Bertscore Recall: 0.1658
Bertscore F1: 0.1592

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: 3e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
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	Bleu	Bertscore Precision	Bertscore Recall	Bertscore F1
4.716	1.0	5750	3.4413	0.0112	0.1417	0.1575	0.1489
4.5916	2.0	11500	3.2372	0.0119	0.1424	0.1583	0.1496
4.325	3.0	17250	3.0534	0.0128	0.1430	0.1587	0.1501
4.1626	4.0	23000	2.9061	0.0136	0.1433	0.1592	0.1505
4.0255	5.0	28750	2.7554	0.0148	0.1438	0.1599	0.1511
3.862	6.0	34500	2.6185	0.0346	0.1446	0.1605	0.1518
3.7367	7.0	40250	2.4945	0.0286	0.1456	0.1611	0.1527
3.7907	8.0	46000	2.3799	0.0401	0.1488	0.1617	0.1548
3.5181	9.0	51750	2.2704	0.0607	0.1490	0.1623	0.1551
3.3377	10.0	57500	2.1710	0.0804	0.1498	0.1627	0.1558
3.294	11.0	63250	2.0876	0.0221	0.1512	0.1633	0.1568
3.1612	12.0	69000	2.0004	0.0234	0.1516	0.1637	0.1572
3.1257	13.0	74750	1.9356	0.0244	0.1518	0.1642	0.1575
3.1347	14.0	80500	1.8769	0.0257	0.1525	0.1646	0.1581
2.8094	15.0	86250	1.8210	0.0268	0.1527	0.1649	0.1584
2.8519	16.0	92000	1.7776	0.0275	0.1530	0.1652	0.1587
2.782	17.0	97750	1.7438	0.0282	0.1532	0.1654	0.1589
2.9097	18.0	103500	1.7183	0.0289	0.1535	0.1657	0.1591
2.881	19.0	109250	1.6999	0.0293	0.1536	0.1658	0.1592
2.6302	20.0	115000	1.6944	0.0294	0.1536	0.1658	0.1592

Framework versions

Transformers 4.40.2
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1

Downloads last month: 9

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for mNLP-project/gpt2-finetuned

Base model

openai-community/gpt2

Finetuned

(2153)

this model

Finetunes

2 models