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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

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

SGLang

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

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

gpt2-finetuned-mcqa-sciq

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

Loss: 2.3533
Bertscore Precision: 0.1082
Bertscore Recall: 0.1141
Bertscore F1: 0.1111

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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Bertscore Precision	Bertscore Recall	Bertscore F1
4.4695	0.9999	5839	2.3612	0.1082	0.1140	0.1110
4.0507	2.0	11679	2.3533	0.1082	0.1141	0.1111
3.8779	2.9999	17518	2.3820	0.1080	0.1140	0.1110
3.2852	4.0	23358	2.4208	0.1080	0.1140	0.1109
3.6416	4.9999	29197	2.4768	0.1079	0.1139	0.1108
2.9843	6.0	35037	2.5445	0.1079	0.1139	0.1108
2.8509	6.9999	40876	2.6094	0.1079	0.1139	0.1108
2.6932	8.0	46716	2.6658	0.1078	0.1138	0.1107
2.5309	8.9999	52555	2.7283	0.1078	0.1138	0.1107
2.5619	9.9991	58390	2.7585	0.1078	0.1138	0.1107

Framework versions

Transformers 4.40.2
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1

Downloads last month: 3

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for mNLP-project/gpt2-finetuned-mcqa-sciq

Base model

openai-community/gpt2

Finetuned

(2238)

this model