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license: apache-2.0 |
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Out repository [flan-alpaca-lora](https://github.com/Reason-Wang/flan-alpaca-lora) contains the details to train flan-t5 with [Alpaca](https://github.com/tatsu-lab/stanford_alpaca) instructions and [low-rank adaptation](https://arxiv.org/abs/2106.09685). |
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This model is trained with [GPTeacher](https://github.com/teknium1/GPTeacher) instructions (Instruct and Roleplay). |
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Usage: |
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```python |
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import transformers |
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from peft import PeftModel |
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model_name = "google/flan-t5-xl"; peft_model_id = "reasonwang/flan-gpteacher-lora-xl" |
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tokenizer = transformers.AutoTokenizer.from_pretrained(model_name) |
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base_model = transformers.AutoModelForSeq2SeqLM.from_pretrained(model_name) |
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peft_model = PeftModel.from_pretrained(base_model, peft_model_id) |
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inputs = tokenizer("If you are the president of a developing country, what you will do to make your country better?", return_tensors="pt") |
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outputs = peft_model.generate(**inputs, max_length=256, do_sample=True) |
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print(tokenizer.batch_decode(outputs, skip_special_tokens=True)) |
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# I will take immediate steps to improve education and infrastructure so that citizens thrive. |
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# I will also invest in infrastructure upgrades such as hospitals and electricity distribution lines, as well as encouraging innovation in our resiliency infrastructure. |
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# I will also focus on promoting trade and investment between countries, both for economic and cultural benefit. |
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``` |
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