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

Libraries

How to use AlekseyKorshuk/gpt2-jokes with Transformers:

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

pipe = pipeline("text-generation", model="AlekseyKorshuk/gpt2-jokes")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AlekseyKorshuk/gpt2-jokes")
model = AutoModelForCausalLM.from_pretrained("AlekseyKorshuk/gpt2-jokes")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AlekseyKorshuk/gpt2-jokes with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/AlekseyKorshuk/gpt2-jokes

SGLang

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

Docker Model Runner
How to use AlekseyKorshuk/gpt2-jokes with Docker Model Runner:
```
docker model run hf.co/AlekseyKorshuk/gpt2-jokes
```

gpt2-jokes

This model is a fine-tuned version of gpt2 on the Fraser/short-jokes dataset. It achieves the following results on the evaluation set:

Loss: 0.6748
Accuracy: 0.8796

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: 32
eval_batch_size: 32
seed: 42
distributed_type: multi-GPU
num_devices: 4
total_train_batch_size: 128
total_eval_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 1.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.06	100	0.7285	0.8732
No log	0.12	200	0.7141	0.8747
No log	0.17	300	0.7056	0.8757
No log	0.23	400	0.6992	0.8764
0.7907	0.29	500	0.6942	0.8771
0.7907	0.35	600	0.6906	0.8777
0.7907	0.41	700	0.6873	0.8779
0.7907	0.47	800	0.6848	0.8782
0.7907	0.52	900	0.6830	0.8786
0.7105	0.58	1000	0.6809	0.8788
0.7105	0.64	1100	0.6794	0.8790
0.7105	0.7	1200	0.6780	0.8792
0.7105	0.76	1300	0.6770	0.8793
0.7105	0.81	1400	0.6760	0.8794
0.7034	0.87	1500	0.6755	0.8794
0.7034	0.93	1600	0.6750	0.8795
0.7034	0.99	1700	0.6748	0.8795

Framework versions

Transformers 4.28.0.dev0
Pytorch 2.0.0-rc1
Datasets 2.10.1
Tokenizers 0.13.2

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Model tree for AlekseyKorshuk/gpt2-jokes

Finetunes

1 model

Dataset used to train AlekseyKorshuk/gpt2-jokes

Evaluation results

Accuracy on Fraser/short-jokes
self-reported

0.880