Comments (4)
If you have all the dependencies installed, that should be supported. You can check out the tutorials/pretrain_tinyllama.md tutorial in this repo. Let us know what results you get, I'd be curious.
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is this the documentaiton that can help me set this up?
https://lightning.ai/docs/pytorch/stable/clouds/cluster_expert.html.
or is there any other documentation suggesting how?
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@michaellin99999 On a single H100 node you don't need to set anything up. You can just run the script (granted you follwed the tutorial preparation steps) and it will use all GPUs by default.
If you have a cluster of multiple H100 nodes, the steps will depend on your cluster setup. Most likely you have SLURM. Then follow the SLURM guide here: https://lightning.ai/docs/fabric/stable/fundamentals/launch.html#launch-on-a-cluster
otherwise follow the "bare bones cluster" guide on that same page.
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Thank you!
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Related Issues (20)
- 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
- Batch Inference (batch size > 1) HOT 1
- LongLora fine-tuning support HOT 3
- False positive warning about mixed precision in `merge_lora.py`
- Categorize SFT and Pretraining data HOT 2
- Meaningful error if no validation split fraction is provided in custom JSON data module HOT 1
- Decide what to do about 16bit weights trained with mixed precision
- Determine the default precision and quantization in chat and generate HOT 1
- Question about using custom dataset for pretraining HOT 3
- Deployment example HOT 2
- Automatically infer and download the tokenizer in pretrain?
- 1.8B H2O model HOT 3
- Is it possible to run Llama 2 70B with 80Gb? HOT 3
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