Instructions to use snzhang/GPT2-Poem-Small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use snzhang/GPT2-Poem-Small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="snzhang/GPT2-Poem-Small")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("snzhang/GPT2-Poem-Small") model = AutoModelForCausalLM.from_pretrained("snzhang/GPT2-Poem-Small") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use snzhang/GPT2-Poem-Small with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "snzhang/GPT2-Poem-Small" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "snzhang/GPT2-Poem-Small", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/snzhang/GPT2-Poem-Small
- SGLang
How to use snzhang/GPT2-Poem-Small 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 "snzhang/GPT2-Poem-Small" \ --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": "snzhang/GPT2-Poem-Small", "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 "snzhang/GPT2-Poem-Small" \ --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": "snzhang/GPT2-Poem-Small", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use snzhang/GPT2-Poem-Small with Docker Model Runner:
docker model run hf.co/snzhang/GPT2-Poem-Small
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Chinese Poem and Couplt small GPT2 Model
Model description
The model is used to generate Chinese ancient poems and couplets. It is based on the IDEA-CCNL/Wenzhong-GPT2-110M
How to use
You can use the model directly with a pipeline for text generation:
When the parameter skip_special_tokens is True:
>>> from transformers import BertTokenizer, GPT2LMHeadModel,TextGenerationPipeline
>>> tokenizer = BertTokenizer.from_pretrained("snzhang/GPT2-Poem-Small")
>>> model = GPT2LMHeadModel.from_pretrained("snzhang/GPT2-Poem-Small")
>>> text_generator = TextGenerationPipeline(model, tokenizer)
>>> text_generator("笔底江山助磅礴", max_length=50, do_sample=True)
[{'generated_text':'笔底江山助磅礴,万卷诗书见成章。'}]
And you can add the prefix "(唐诗:your title)"、"(宋词:your title)" and "(对联)" to make generation more precise.
Training data
Training data contains 71,334 Chinese ancient poems and couplets which are collected by Chinese Poetry and Couplet Dataset
More Details
You can get more details in GPT2-Poem-Small
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