Comments (2)
Try mistral7b to see when we can have a 3d compareable with 7b model
from llm-shearing.
Hello! We believe that Sheared-LLaMA has the potential to significantly improve its performance as the number of training tokens increases, as indicated by the observed trend. We weren't able to put in more compute because of computational constraints on our end. We have not seen 1.3B or 2.7B's upper bound yet based on our experience.
from llm-shearing.
Related Issues (20)
- Release sheared model without re-training? HOT 4
- duplicate mean values during mask initialization HOT 2
- 在进行Building trainer时,训练会卡住; HOT 1
- Avoid OOM using deepspeed zero-stage HOT 3
- Error running CheckpointSaver.close(). Skipping CheckpointSaver.post_close() HOT 1
- KeyError: 'state' HOT 2
- When should we apply hidden_z? HOT 2
- Could you provide tokenized continue-pretraining dataset for reproduction? HOT 2
- missmatch shape
- Start training but nothing continue HOT 6
- TypeError: buffer is too small for requested array
- Pruning fine-tuned model HOT 2
- save model meet problem HOT 1
- Instruction tuning dataset HOT 2
- If I can't configure Slurm on a cluster, does that mean I can't use multi-node multi-GPU setups? HOT 5
- 有没有不用Slurm跑剪枝的方法?
- None
- Start training but only output config information HOT 3
- The Project is not implemented for 70B llama? HOT 7
- LlamaRMSNorm() layer differs from original llama HOT 1
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from llm-shearing.