Instructions to use AndreiRabau/gpt2-medium-shakes-pear with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AndreiRabau/gpt2-medium-shakes-pear with Transformers:

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

pipe = pipeline("text-generation", model="AndreiRabau/gpt2-medium-shakes-pear")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AndreiRabau/gpt2-medium-shakes-pear")
model = AutoModelForCausalLM.from_pretrained("AndreiRabau/gpt2-medium-shakes-pear")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AndreiRabau/gpt2-medium-shakes-pear with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/AndreiRabau/gpt2-medium-shakes-pear

SGLang

How to use AndreiRabau/gpt2-medium-shakes-pear 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 "AndreiRabau/gpt2-medium-shakes-pear" \
    --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": "AndreiRabau/gpt2-medium-shakes-pear",
		"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 "AndreiRabau/gpt2-medium-shakes-pear" \
        --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": "AndreiRabau/gpt2-medium-shakes-pear",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AndreiRabau/gpt2-medium-shakes-pear with Docker Model Runner:
```
docker model run hf.co/AndreiRabau/gpt2-medium-shakes-pear
```

gpt2-medium-shakes-pear

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

Loss: 10.6437
Rouge1: 0.0723
Rouge2: 0.0
Rougel: 0.0723
Rougelsum: 0.0723
Bert Precision: 0.7316
Bert Recall: 0.8011
Bert F1: 0.7647

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: 8
eval_batch_size: 8
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Bert Precision	Bert Recall	Bert F1
No log	1.0	1	11.4526	0.1143	0.0	0.1143	0.1143	0.7094	0.8058	0.7545
No log	2.0	2	10.6437	0.0723	0.0	0.0723	0.0723	0.7316	0.8011	0.7647

Framework versions

Transformers 4.47.1
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0

Downloads last month: 2

Safetensors

Model size

0.4B params

Tensor type

F32

Model tree for AndreiRabau/gpt2-medium-shakes-pear

Base model

openai-community/gpt2-medium

Finetuned

(193)

this model