Instructions to use mymusise/CPM-GPT2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mymusise/CPM-GPT2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mymusise/CPM-GPT2")# Load model directly from transformers import AutoTokenizer, TF_AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("mymusise/CPM-GPT2") model = TF_AutoModelForCausalLM.from_pretrained("mymusise/CPM-GPT2") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use mymusise/CPM-GPT2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mymusise/CPM-GPT2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mymusise/CPM-GPT2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/mymusise/CPM-GPT2
- SGLang
How to use mymusise/CPM-GPT2 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 "mymusise/CPM-GPT2" \ --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": "mymusise/CPM-GPT2", "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 "mymusise/CPM-GPT2" \ --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": "mymusise/CPM-GPT2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use mymusise/CPM-GPT2 with Docker Model Runner:
docker model run hf.co/mymusise/CPM-GPT2
CPM
CPM(Chinese Pre-Trained Language Models), which has 2.6B parameters, made by the research team of Beijing Zhiyuan Institute of artificial intelligence and Tsinghua University @TsinghuaAI.
The One Thing You Need to Know is this model is not uploaded by official, the conver script is here
Overview
- Language model: CPM
- Model size: 2.6B parameters
- Language: Chinese
How to use
How to use this model directly from the 🤗/transformers library:
from transformers import XLNetTokenizer, TFGPT2LMHeadModel
import jieba
# add spicel process
class XLNetTokenizer(XLNetTokenizer):
translator = str.maketrans(" \n", "\u2582\u2583")
def _tokenize(self, text, *args, **kwargs):
text = [x.translate(self.translator) for x in jieba.cut(text, cut_all=False)]
text = " ".join(text)
return super()._tokenize(text, *args, **kwargs)
def _decode(self, *args, **kwargs):
text = super()._decode(*args, **kwargs)
text = text.replace(' ', '').replace('\u2582', ' ').replace('\u2583', '\n')
return text
tokenizer = XLNetTokenizer.from_pretrained('mymusise/CPM-GPT2')
model = TFGPT2LMHeadModel.from_pretrained("mymusise/CPM-GPT2")
How to generate text
from transformers import TextGenerationPipeline
text_generater = TextGenerationPipeline(model, tokenizer)
texts = [
'今天天气不错',
'天下武功, 唯快不',
"""
我们在火星上发现了大量的神奇物种。有神奇的海星兽,身上是粉色的,有5条腿;有胆小的猫猫兽,橘色,有4条腿;有令人恐惧的蜈蚣兽,全身漆黑,36条腿;有纯洁的天使兽,全身洁白无瑕,有3条腿;有贪吃的汪汪兽,银色的毛发,有5条腿;有蛋蛋兽,紫色,8条腿。
请根据上文,列出一个表格,包含物种名、颜色、腿数量。
|物种名|颜色|腿数量|
|亚古兽|金黄|2|
|海星兽|粉色|5|
|猫猫兽|橘色|4|
|蜈蚣兽|漆黑|36|
"""
]
for text in texts:
token_len = len(tokenizer._tokenize(text))
print(text_generater(text, max_length=token_len + 15, top_k=1, use_cache=True, prefix='')[0]['generated_text'])
print(text_generater(text, max_length=token_len + 15, do_sample=True, top_k=5)[0]['generated_text'])
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