Files changed (3) hide show
  1. README.md +207 -122
  2. adapter_config.json +7 -2
  3. adapter_model.bin +1 -1
README.md CHANGED
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  ---
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- language:
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- - en
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- - sp
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- - ja
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- - pe
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- - hi
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- - fr
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- - ch
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- - be
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- - gu
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- - ge
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- - te
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- - it
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- - ar
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- - po
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- - ta
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- - ma
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- - ma
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- - or
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- - pa
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- - po
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- - ur
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- - ga
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- - he
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- - ko
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- - ca
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- - th
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- - du
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- - in
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- - vi
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- - bu
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- - fi
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- - ce
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- - la
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- - tu
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- - ru
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- - cr
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- - sw
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- - yo
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- - ku
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- - bu
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- - ma
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- - cz
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- - fi
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- - so
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- - ta
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- - sw
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- - si
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- - ka
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- - zh
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- - ig
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- - xh
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- - ro
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- - ha
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- - es
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- - sl
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- - li
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- - gr
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- - ne
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- - as
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- - no
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-
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- widget:
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- - text: "Translate to German: My name is Arthur"
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- example_title: "Translation"
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- - text: "Please answer to the following question. Who is going to be the next Ballon d'or?"
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- example_title: "Question Answering"
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- - text: "Q: Can Geoffrey Hinton have a conversation with George Washington? Give the rationale before answering."
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- example_title: "Logical reasoning"
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- - text: "Please answer the following question. What is the boiling point of Nitrogen?"
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- example_title: "Scientific knowledge"
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- - text: "Answer the following yes/no question. Can you write a whole Haiku in a single tweet?"
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- example_title: "Yes/no question"
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- - text: "Answer the following yes/no question by reasoning step-by-step. Can you write a whole Haiku in a single tweet?"
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- example_title: "Reasoning task"
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- - text: "Q: ( False or not False or False ) is? A: Let's think step by step"
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- example_title: "Boolean Expressions"
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- - text: "The square root of x is the cube root of y. What is y to the power of 2, if x = 4?"
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- example_title: "Math reasoning"
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- - 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?"
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- example_title: "Premise and hypothesis"
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-
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- tags:
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- - text2text-generation
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-
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- datasets:
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- - svakulenk0/qrecc
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- - taskmaster2
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- - djaym7/wiki_dialog
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- - deepmind/code_contests
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- - lambada
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- - gsm8k
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- - aqua_rat
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- - esnli
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- - quasc
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- - qed
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- - financial_phrasebank
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-
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-
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- license: apache-2.0
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  ---
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- # Model Card for LoRA-FLAN-T5 large
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- ![model image](https://s3.amazonaws.com/moonup/production/uploads/1666363435475-62441d1d9fdefb55a0b7d12c.png)
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- 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.
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- ## Usage
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- Use this adapter with `peft` library
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- ```python
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- # pip install peft transformers
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- import torch
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- from peft import PeftModel, PeftConfig
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- from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
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- peft_model_id = "ybelkada/flan-t5-large-financial-phrasebank-lora"
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- config = PeftConfig.from_pretrained(peft_model_id)
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- model = AutoModelForSeq2SeqLM.from_pretrained(
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- config.base_model_name_or_path,
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- torch_dtype='auto',
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- device_map='auto'
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- )
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- tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
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- # Load the Lora model
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- model = PeftModel.from_pretrained(model, peft_model_id)
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- ```
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- Enjoy!
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+ library_name: peft
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+ base_model: google/flan-t5-large
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+ # Model Card for Model ID
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+ <!-- Provide a quick summary of what the model is/does. -->
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+ ## Model Details
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+ ### Model Description
 
 
 
 
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+ <!-- Provide a longer summary of what this model is. -->
 
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+ - **Developed by:** [More Information Needed]
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+ - **Shared by [optional]:** [More Information Needed]
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+ - **Model type:** [More Information Needed]
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+ - **Language(s) (NLP):** [More Information Needed]
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+ - **License:** [More Information Needed]
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+ - **Finetuned from model [optional]:** [More Information Needed]
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+
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+ ### Model Sources [optional]
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+
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+ <!-- Provide the basic links for the model. -->
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+
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+ - **Repository:** [More Information Needed]
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+ - **Paper [optional]:** [More Information Needed]
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+ - **Demo [optional]:** [More Information Needed]
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+
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+ ## Uses
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+
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+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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+
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+ ### Direct Use
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+
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+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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+
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+ [More Information Needed]
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+
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+ ### Downstream Use [optional]
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+
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+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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+ [More Information Needed]
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+
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+ ### Out-of-Scope Use
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+
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+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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+
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+ [More Information Needed]
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+
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+ ## Bias, Risks, and Limitations
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+
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+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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+
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+ [More Information Needed]
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+
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+ ### Recommendations
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+
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+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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+
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+ ## How to Get Started with the Model
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+ Use the code below to get started with the model.
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+ [More Information Needed]
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+ ## Training Details
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+
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+ ### Training Data
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+
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+ <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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+ [More Information Needed]
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+
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+ ### Training Procedure
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+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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+ #### Preprocessing [optional]
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+
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+ [More Information Needed]
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+
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+ #### Training Hyperparameters
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+
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+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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+
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+ #### Speeds, Sizes, Times [optional]
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+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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+ [More Information Needed]
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+
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+ ## Evaluation
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+ <!-- This section describes the evaluation protocols and provides the results. -->
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+ ### Testing Data, Factors & Metrics
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+ #### Testing Data
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+ <!-- This should link to a Data Card if possible. -->
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+ [More Information Needed]
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+
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+ #### Factors
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+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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+ [More Information Needed]
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+ #### Metrics
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+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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+ [More Information Needed]
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+ ### Results
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+ [More Information Needed]
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+ #### Summary
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+ ## Model Examination [optional]
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+ <!-- Relevant interpretability work for the model goes here -->
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+ [More Information Needed]
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+ ## Environmental Impact
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+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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+ Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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+
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+ - **Hardware Type:** [More Information Needed]
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+ - **Hours used:** [More Information Needed]
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+ - **Cloud Provider:** [More Information Needed]
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+ - **Compute Region:** [More Information Needed]
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+ - **Carbon Emitted:** [More Information Needed]
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+ ## Technical Specifications [optional]
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+ ### Model Architecture and Objective
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+ [More Information Needed]
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+ ### Compute Infrastructure
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+ [More Information Needed]
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+ #### Hardware
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+ [More Information Needed]
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+ #### Software
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+ [More Information Needed]
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+ ## Citation [optional]
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+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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+ **BibTeX:**
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+ [More Information Needed]
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+ **APA:**
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+ [More Information Needed]
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+ ## Glossary [optional]
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+
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+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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+ [More Information Needed]
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+
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+ ## More Information [optional]
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+ [More Information Needed]
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+ ## Model Card Authors [optional]
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+ [More Information Needed]
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+ ## Model Card Contact
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+ [More Information Needed]
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+ ## Training procedure
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+ The following `bitsandbytes` quantization config was used during training:
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+ - quant_method: bitsandbytes
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+ - load_in_8bit: True
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+ - load_in_4bit: False
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+ - llm_int8_threshold: 6.0
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+ - llm_int8_skip_modules: None
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+ - llm_int8_enable_fp32_cpu_offload: False
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+ - llm_int8_has_fp16_weight: False
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+ - bnb_4bit_quant_type: fp4
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+ - bnb_4bit_use_double_quant: False
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+ - bnb_4bit_compute_dtype: float32
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+ ### Framework versions
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+
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+ - PEFT 0.6.0.dev0
adapter_config.json CHANGED
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  {
 
 
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  "base_model_name_or_path": "google/flan-t5-large",
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  "bias": "none",
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- "enable_lora": null,
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  "fan_in_fan_out": false,
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  "inference_mode": true,
 
 
 
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  "lora_alpha": 32,
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  "lora_dropout": 0.05,
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- "merge_weights": false,
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  "modules_to_save": null,
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  "peft_type": "LORA",
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  "r": 16,
 
 
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  "target_modules": [
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  "q",
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  "v"
 
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  {
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+ "alpha_pattern": {},
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+ "auto_mapping": null,
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  "base_model_name_or_path": "google/flan-t5-large",
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  "bias": "none",
 
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  "fan_in_fan_out": false,
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  "inference_mode": true,
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+ "init_lora_weights": true,
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+ "layers_pattern": null,
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+ "layers_to_transform": null,
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  "lora_alpha": 32,
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  "lora_dropout": 0.05,
 
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  "modules_to_save": null,
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  "peft_type": "LORA",
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  "r": 16,
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+ "rank_pattern": {},
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+ "revision": null,
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  "target_modules": [
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  "q",
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  "v"
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