Comments (3)
kenhktsui
commented on July 30, 2024
colab example
Open questions:
- which tasks to use (there are many) and also the speed
- shall we do sampling on each task for faster evaluation? For full evaluation, its worth the wait but I think if we can do some sampling on the test data, then we make it 10-20x faster.
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mrcabbage972
commented on July 30, 2024
PubMedQA seems most relevant among the LM harness tasks. The harness also has an adapter for BigBench, which has a gigantic amount of tasks including some about programming: auto_debugging, python_programming_challenge, program_synthesis and many more.
Sampling - yes!
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mrcabbage972
commented on July 30, 2024
@kenhktsui Should we create a separate issue to find relevant tasks to be used for the downstream evaluation?
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Related Issues (20)
- Dataset generation open issues
- Report val loss aggregated by data origin
- Fix HF Hub Upload Error
- Add script for merging expert models via weight averaging HOT 7
- Expert merging: c-BTM HOT 3
- Training instruction followers as composable layers and expert layers HOT 1
- Train baseline models for evaluation HOT 10
- inputs_ids cast to fp16 in deeperspeed bug
- Setup separate environments on Redmond.ai box HOT 2
- Automatic Training Scripts for All Expert Models
- Stabilize Training on Redmond Box
- Evaluate a merged expert model's perplexity HOT 3
- Investigate Expert Models Having High Perplexity HOT 1
- Create template for HF dataset config
- Train 2nd batch of expert models
- Get all relevant data for StarCoder into LUMI
- Tokenize the StarCoder dataset HOT 1
- Set up the training configuration
- Do a small test run
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