Instructions to use jed351/gpt2_tiny_zh-hk-wiki with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jed351/gpt2_tiny_zh-hk-wiki with Transformers:

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

pipe = pipeline("text-generation", model="jed351/gpt2_tiny_zh-hk-wiki")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jed351/gpt2_tiny_zh-hk-wiki")
model = AutoModelForCausalLM.from_pretrained("jed351/gpt2_tiny_zh-hk-wiki")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jed351/gpt2_tiny_zh-hk-wiki with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jed351/gpt2_tiny_zh-hk-wiki"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jed351/gpt2_tiny_zh-hk-wiki",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/jed351/gpt2_tiny_zh-hk-wiki

SGLang

How to use jed351/gpt2_tiny_zh-hk-wiki 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 "jed351/gpt2_tiny_zh-hk-wiki" \
    --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": "jed351/gpt2_tiny_zh-hk-wiki",
		"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 "jed351/gpt2_tiny_zh-hk-wiki" \
        --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": "jed351/gpt2_tiny_zh-hk-wiki",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use jed351/gpt2_tiny_zh-hk-wiki with Docker Model Runner:
```
docker model run hf.co/jed351/gpt2_tiny_zh-hk-wiki
```

gpt2_tiny_zh-hk-wiki

This model was trained on a dataset with a 50MB size for 10 epochs only.

Purely intended for research and testing purposes.

This model is a fine-tuned version of jed351/gpt2-tiny-zh-hk on the cantonese-wikipedia dataset. It achieves the following results on the evaluation set:

Loss: 3.3834

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: 2e-05
train_batch_size: 200
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
No log	1.0	412	3.6481
4.0728	2.0	824	3.5399
3.757	3.0	1236	3.4889
3.6669	4.0	1648	3.4557
3.6189	5.0	2060	3.4295
3.6189	6.0	2472	3.4129
3.5835	7.0	2884	3.3992
3.5604	8.0	3296	3.3905
3.5434	9.0	3708	3.3849
3.537	10.0	4120	3.3834

Framework versions

Transformers 4.26.0
Pytorch 1.13.1+cu116
Datasets 2.9.0
Tokenizers 0.13.2

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gpt2_tiny_zh-hk-wiki

gpt2_tiny_zh-hk-wiki

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Dataset used to train jed351/gpt2_tiny_zh-hk-wiki