Comments (4)
that's a great idea. Maybe also adding a Num GPU column since some models can't be run on a single device.
(Once the current QLoRA experiments are done and datasets are added, I can maybe get started on this)
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is this resolved or there is more to be done?
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We should probably also have a similar table for finetuning.
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Done in https://github.com/Lightning-AI/lit-gpt/blob/main/tutorials/resource-tables.md
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Related Issues (20)
- Mistral 7B v0.2 HOT 6
- Compatible with local 8xH100 instead of cloud? HOT 4
- Add back support for longest sequence first
- Data prep in pretraining tutorial does not work HOT 3
- Rerun Gemma configs HOT 1
- Error when passing the config file "config_hub/finetune/llama-2-7b/lora.yaml" to the finetune command HOT 1
- Finetuning run times out at evaluation step on multiple devices HOT 4
- Python 3.12 HOT 2
- Implementation of RoPE HOT 3
- Is there any plan to add Megatron-LM support to enable lit-gpt to train extreme size model? HOT 2
- Can I train a model on 7900XT 4 cards? HOT 1
- Calculate loss at beginning and end of training
- Gradient clipping
- Something introduced a LoRA merging bug HOT 7
- Defaults fail on small block size for some models HOT 1
- LoRA model tokenizer configuration fails to load HOT 8
- TypeError: unsupported operand type(s) for -: 'float' and 'NoneType' HOT 7
- Gradients in GPT module of the finetuning/lora.py script are always zero HOT 6
- Explain how to pretrain on a custom dataset
- Add `--warmup_fraction` to pretraining script HOT 2
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