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license: bigscience-bloom-rail-1.0 |
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This model is based on [bigscience/bloomz-7b1-mt](https://huggingface.co/bigscience/bloom-7b1). To make it more accessible and efficient for certain Chinese , we have pruned its original vocabulary from 250,880 tokens to 46,145 tokens using Chinese corpus data as follow [bloom-6b4-zh](https://huggingface.co/Langboat/bloom-6b4-zh). This reduction in vocabulary size has helped to significantly reduce the GPU memory usage required to run the model. As a result, the total number of parameters in the model is now 6 billion 4. |
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基于 [bigscience/bloomz-7b1-mt](https://huggingface.co/bigscience/bloom-7b1)，修建embeddings层到 46145，主要保留中文相关的tokens映射。修建后参数为6B4。 |
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# How to use |
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```python |
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from transformers import BloomTokenizerFast, BloomForCausalLM |
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tokenizer = BloomTokenizerFast.from_pretrained('enze/bloomz-6b4-zh') |
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model = BloomForCausalLM.from_pretrained('enze/bloomz-6b4-zh') |
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print(tokenizer.batch_decode(model.generate(tokenizer.encode('中国的首都是', return_tensors='pt')))) |
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``` |
