Comments (1)
I have a feeling that it won't be a smooth sailing 😉
When the code is strange
And the documentation is weak
Who are you gonna call?
Ghostbusters
from litgpt.
Related Issues (20)
- GPU optimization HOT 7
- not quite good results. HOT 4
- add special tokens HOT 2
- Configuration of save_interval using multiple training nodes
- processing the dataset. HOT 3
- Mistral v0.1 sliding window attention HOT 1
- Phi-3 Full finetuning uses more memory as LoRA finetuning
- how to change dataset path or download url when evaluating HOT 1
- Deal with warnings during model download and conversion HOT 6
- `Chat` consumes more VRAM than `Generate` HOT 8
- ModuleNotFoundError: No module named 'extensions'
- Scheduler warning HOT 2
- `bos_token` is added twice when using CodeLlama prompting style
- Current way masking training data creates issues with the Tokenizer HOT 2
- Using litgpt.generate.tp for multi-GPU inference is nearly 2 times slower than single-GPU inference with litgpt.generate.base HOT 1
- Using litgpt.generate.tp for multi-GPU inference is nearly 2 times slower than single-GPU inference with litgpt.generate.base Description HOT 6
- Out of memory error using Python API but not with CLI HOT 1
- Chat: doesn't work with enabled `compilation`
- LitGPT Python API seems to use more memory than `chat`
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from litgpt.