Comments (1)
Thanks for your question @ChuxiJ , and I'd answer them here:
- Yes, this is exactly what lorahub wants to implement. And we have also discussed the relationship of lorahub with moe in the related work section.
- I'm not sure if the scalar weight can be a router since the router mechanism in MoE should include many router weights.
- 😂 It's a little expensive for our Lab to train such kind of models. But it's worthy to try.
- We clearly state that
Flan-t5-large
is the base model. You can checkout the first section and the experimental results for details. All these details are already in the paper. - The gradient-free method used in lorahub may be a great solution.
from lorahub.
Related Issues (20)
- Is there any scalability issue? HOT 2
- About training examples HOT 1
- About LoraHub adapters HOT 3
- Request for Guidance on Reproducing Experiments for BigBenchHard HOT 8
- Bug Report - (batched option & tensor shape) HOT 4
- No single LoRA model can score on Disambiguation HOT 5
- Where is the instruction of each lora in lorahub? HOT 1
- Training Tasks and Data for these LoRA modules? HOT 8
- Evaluation using Glue Lorahub Modules HOT 4
- can lorahub be used in non llm tasks HOT 3
- Release of Code for Finetuning Lora modules of FLAN and evaluating the Finetuned Models. HOT 1
- FLAN datasets not available on Huggingface. HOT 10
- repaired an error in reproduce_bbh.py (shuffle seed) HOT 2
- train_model model load repaired HOT 4
- 404 HOT 2
- AttributeError: 'GenerationConfig' object has no attribute 'cache_implementation' HOT 4
- Downloading the FLAN-v2 dataset HOT 1
- Using with CausalLM models HOT 3
- How to evaluate glue lora?
- How to use local LoRA trained with train_model.py HOT 1
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from lorahub.