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--- |
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license: apache-2.0 |
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language: |
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- zh |
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- en |
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tags: |
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- openba |
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pipeline_tag: text-generation |
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--- |
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# Introduction |
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OpenBA is an Open-Sourced 15B Bilingual Asymmetric Seq2Seq Model Pre-trained from Scratch. |
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## Open Source Plan |
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We are excited to unveil two distinguished versions of our model, with another on the horizon: |
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- [OpenBA-LM](https://huggingface.co/OpenBA/OpenBA-LM): The backbone language models was pre-trained on 340B English, Chinese, and code tokens. |
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- [OpenBA-Flan](https://huggingface.co/OpenBA/OpenBA-Flan): We perform supervised fine-tuning on the base model with additional 40B tokens using our collected BiFlan Dataset. |
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- OpenBA-Chat: coming soon |
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## Model Description |
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- **Model type:** Language model |
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- **Language(s) (NLP):** zh, en (We also offer the possibility for multilingual learning, by using a multilingual tokenizer.) |
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- **License:** Apache 2.0 |
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- **Resources for more information:** |
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- [Paper](https://arxiv.org/abs/2309.10706) |
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- [GitHub Repo](https://github.com/OpenNLG/OpenBA/) |
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# Usage |
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## Install requirements |
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```bash |
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pip install transformers torch>=2.0 sentencepiece |
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``` |
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## Demo usage |
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```python |
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>>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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>>> tokenizer = AutoTokenizer.from_pretrained("OpenBA/OpenBA-LM", trust_remote_code=True) |
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>>> model = AutoModelForSeq2SeqLM.from_pretrained("OpenBA/OpenBA-LM", trust_remote_code=True).half().cuda() |
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>>> model = model.eval() |
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>>> query = "<S>" + "苏州处太湖平原，沿江为高沙平原，河" + "<extra_id_0>" |
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>>> inputs = tokenizer(query, return_tensors="pt").to("cuda") |
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>>> outputs = model.generate(**inputs, do_sample=True, max_new_tokens=32) |
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>>> response = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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>>> print(response) |
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流两侧为河淤平原,苏州平原是江苏平原主体,地势低平,土地肥沃,气候温和 |
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``` |
