Instructions to use yasutoshi-lab/gpt2-verification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yasutoshi-lab/gpt2-verification with Transformers:

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

pipe = pipeline("text-generation", model="yasutoshi-lab/gpt2-verification")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("yasutoshi-lab/gpt2-verification")
model = AutoModelForCausalLM.from_pretrained("yasutoshi-lab/gpt2-verification", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use yasutoshi-lab/gpt2-verification with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/yasutoshi-lab/gpt2-verification

SGLang

How to use yasutoshi-lab/gpt2-verification 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 "yasutoshi-lab/gpt2-verification" \
    --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": "yasutoshi-lab/gpt2-verification",
		"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 "yasutoshi-lab/gpt2-verification" \
        --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": "yasutoshi-lab/gpt2-verification",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use yasutoshi-lab/gpt2-verification with Docker Model Runner:
```
docker model run hf.co/yasutoshi-lab/gpt2-verification
```

gpt2-verification

This model utilizes the GPT2 architecture and GPT2 tokenizer, and was trained from scratch using wikitext-2-raw-v1.

It achieves the following results on the evaluation set:

Loss: 6.0571
Accuracy: 0.1855

Model Train Command

CUDA_VISIBLE_DEVICES=0 uv run python examples/pytorch/language-modeling/run_clm.py \
  --model_type gpt2 \
  --tokenizer_name gpt2 \
  --dataset_name wikitext \
  --dataset_config_name wikitext-2-raw-v1 \
  --per_device_train_batch_size 2 \
  --per_device_eval_batch_size 2 \
  --num_train_epochs 3 \
  --do_train \
  --do_eval \
  --output_dir ./output/gpt2-test \
  --save_steps 500 \
  --logging_steps 100 \
  --push_to_hub \
  --hub_model_id "yasutoshi-lab/gpt2-verification"
  --hub_private_repo true \
  --hub_strategy "end"

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: 5e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 3.0

Training results

Framework versions

Transformers 5.6.0.dev0
Pytorch 2.11.0+cu128
Datasets 4.8.4
Tokenizers 0.22.2

Downloads last month: 10

Safetensors

Model size

0.1B params

Tensor type

F32

Dataset used to train yasutoshi-lab/gpt2-verification

Evaluation results

Accuracy on wikitext wikitext-2-raw-v1
self-reported

0.185