Comments (3)
I would also work on this too.
@NourFahmy There are two steps that we could split 😃
- Clustering of original training data used in different expert
- Inference code (that weight next token prediction logits according to proximity of input to cluster centroid)
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@NourFahmy @kenhktsui
Check out Minho's adapation of the clustering step from the cBTM repo.
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Hi @kenhktsui - happy to take on inference and support where need be on clustering, and to fill any gaps from Minho's efforts.
I've put up a PR here
I've made the following assumptions I can easily fix:
- both the embedded context & current token will be passed to the script
- only the conditional probability of the token at time t given the context is needed, as per formula 2 in the paper
kindly inform if anything else is needed!
cc: @mrcabbage972
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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
- Integrate with LLM evaluation frameworks 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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