| --- |
| language: |
| - en |
| - sp |
| - ja |
| - pe |
| - hi |
| - fr |
| - ch |
| - be |
| - gu |
| - ge |
| - te |
| - it |
| - ar |
| - po |
| - ta |
| - ma |
| - ma |
| - or |
| - pa |
| - po |
| - ur |
| - ga |
| - he |
| - ko |
| - ca |
| - th |
| - du |
| - in |
| - vi |
| - bu |
| - fi |
| - ce |
| - la |
| - tu |
| - ru |
| - cr |
| - sw |
| - yo |
| - ku |
| - bu |
| - ma |
| - cz |
| - fi |
| - so |
| - ta |
| - sw |
| - si |
| - ka |
| - zh |
| - ig |
| - xh |
| - ro |
| - ha |
| - es |
| - sl |
| - li |
| - gr |
| - ne |
| - as |
| - no |
|
|
| widget: |
| - text: "Translate to German: My name is Arthur" |
| example_title: "Translation" |
| - text: "Please answer to the following question. Who is going to be the next Ballon d'or?" |
| example_title: "Question Answering" |
| - text: "Q: Can Geoffrey Hinton have a conversation with George Washington? Give the rationale before answering." |
| example_title: "Logical reasoning" |
| - text: "Please answer the following question. What is the boiling point of Nitrogen?" |
| example_title: "Scientific knowledge" |
| - text: "Answer the following yes/no question. Can you write a whole Haiku in a single tweet?" |
| example_title: "Yes/no question" |
| - text: "Answer the following yes/no question by reasoning step-by-step. Can you write a whole Haiku in a single tweet?" |
| example_title: "Reasoning task" |
| - text: "Q: ( False or not False or False ) is? A: Let's think step by step" |
| example_title: "Boolean Expressions" |
| - text: "The square root of x is the cube root of y. What is y to the power of 2, if x = 4?" |
| example_title: "Math reasoning" |
| - text: "Premise: At my age you will probably have learnt one lesson. Hypothesis: It's not certain how many lessons you'll learn by your thirties. Does the premise entail the hypothesis?" |
| example_title: "Premise and hypothesis" |
|
|
| tags: |
| - text2text-generation |
|
|
| datasets: |
| - svakulenk0/qrecc |
| - taskmaster2 |
| - djaym7/wiki_dialog |
| - deepmind/code_contests |
| - lambada |
| - gsm8k |
| - aqua_rat |
| - esnli |
| - quasc |
| - qed |
| - financial_phrasebank |
|
|
|
|
| license: apache-2.0 |
| --- |
| |
| # Model Card for LoRA-FLAN-T5 large |
|
|
|  |
|
|
| This repository contains the LoRA (Low Rank Adapters) of `flan-t5-large` that has been fine-tuned on [`financial_phrasebank`](https://huggingface.co/datasets/financial_phrasebank) dataset. |
|
|
| ## Usage |
|
|
| Use this adapter with `peft` library |
|
|
| ```python |
| # pip install peft transformers |
| import torch |
| from peft import PeftModel, PeftConfig |
| from transformers import AutoModelForSeq2SeqLM, AutoTokenizer |
| |
| peft_model_id = "ybelkada/flan-t5-large-financial-phrasebank-lora" |
| config = PeftConfig.from_pretrained(peft_model_id) |
| |
| model = AutoModelForSeq2SeqLM.from_pretrained( |
| config.base_model_name_or_path, |
| torch_dtype='auto', |
| device_map='auto' |
| ) |
| tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path) |
| |
| # Load the Lora model |
| model = PeftModel.from_pretrained(model, peft_model_id) |
| ``` |
|
|
| Enjoy! |
