
This model is based on [bigscience/bloom-3b](https://huggingface.co/bigscience/bloom-3b). 

We pruned its vocabulary from 250880 to 46145 with Chinese corpus to reduce GPU memory usage. So the total parameter is 2b5 now.

# How to use
```python
from transformers import BloomTokenizerFast, BloomForCausalLM

tokenizer = BloomTokenizerFast.from_pretrained('Langboat/bloom-2b5-zh')
model = BloomForCausalLM.from_pretrained('Langboat/bloom-2b5-zh')

print(tokenizer.batch_decode(model.generate(tokenizer.encode('中国的首都是', return_tensors='pt'))))
```
{"language": ["zh"], "license": "bigscience-bloom-rail-1.0", "pipeline_tag": "text-generation", "widget": [{"text": "\u4e2d\u56fd\u7684\u9996\u90fd\u662f"}]}
transformers pytorch bloom text-generation zh license:bigscience-bloom-rail-1.0 autotrain_compatible endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #bloom #text-generation #zh #license-bigscience-bloom-rail-1.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 


This model is based on bigscience/bloom-3b. 

We pruned its vocabulary from 250880 to 46145 with Chinese corpus to reduce GPU memory usage. So the total parameter is 2b5 now.

# How to use

# How to use
TAGS
#transformers #pytorch #bloom #text-generation #zh #license-bigscience-bloom-rail-1.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
 # How to use
passage: TAGS
#transformers #pytorch #bloom #text-generation #zh #license-bigscience-bloom-rail-1.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
# How to use