Instructions to use Khoa/VN-Literature-Generation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Khoa/VN-Literature-Generation with Transformers:

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

pipe = pipeline("text-generation", model="Khoa/VN-Literature-Generation")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Khoa/VN-Literature-Generation")
model = AutoModelForCausalLM.from_pretrained("Khoa/VN-Literature-Generation")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Khoa/VN-Literature-Generation with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Khoa/VN-Literature-Generation

SGLang

How to use Khoa/VN-Literature-Generation 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 "Khoa/VN-Literature-Generation" \
    --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": "Khoa/VN-Literature-Generation",
		"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 "Khoa/VN-Literature-Generation" \
        --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": "Khoa/VN-Literature-Generation",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Khoa/VN-Literature-Generation with Docker Model Runner:
```
docker model run hf.co/Khoa/VN-Literature-Generation
```

inference: parameters: max_length: 500 do_sample: True temperature: 0.8

GPT-2

The GPT2 model is pre-trained on the writing style of Vu Trong Phung

How to use the model

from transformers import GPT2Tokenizer, GPT2LMHeadModel

tokenizer = GPT2Tokenizer.from_pretrained("Khoa/VN-Literature-Generation")
model = GPT2LMHeadModel.from_pretrained("Khoa/VN-Literature-Generation")


text = "Mùa thu lá vàng rơi"
input_ids = tokenizer.encode(text, return_tensors='pt')
max_length = 300
model.to('cpu')
sample_outputs = model.generate(input_ids,pad_token_id=tokenizer.eos_token_id,
                                   do_sample=True,
                                   max_length=max_length,
                                   min_length=max_length,
                                   top_k=40,
                                   num_beams=5,
                                   early_stopping=True,
                                   no_repeat_ngram_size=2,
                                   num_return_sequences=3)

for i, sample_output in enumerate(sample_outputs):
    print(">> Generated text {}\n\n{}".format(i+1, tokenizer.decode(sample_output.tolist())))
    print('\n---')

Author

Dong Dang Khoa

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