Row 5625

Row ID: 5625 | Dataset Entry | Axioma AXP Content Repository

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Dear community!

I am wondering:

I have a big model that I want to use (e.g. LLM). Now this model does not fit in one GPU that I have (8x16GB). I also want to finetune it.

​

What would be the way to go for distributing and parallelizing the model? Why is there deepspeed and accelerate if I (supposidly) already have the parallelisation in Pytorch automaticlly?

Thx :)

FieldValue
text Dear community! I am wondering: I have a big model that I want to use (e.g. LLM). Now this model does not fit in one GPU that I have (8x16GB). I also want to finetune it. ​ What would be the way to go for distributing and parallelizing the model? Why is there deepspeed and accelerate if I (supposidly) already have the parallelisation in Pytorch automaticlly? Thx :)
label r/pytorch
dataType post
communityName r/pytorch
datetime 2024-05-02
username_encoded Z0FBQUFBQm5LakwydkJabFl3aWVLMEFENjQ0d1JoVHpldUVQWEh0QVRtSk96LXQtcnN5RjlTNUhtWkxCNEE2Y0pvWEIyRDFJMG1FN0ozNkpuZlJUQV9GNWZfT2x1REdkVlE9PQ==
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Raw Record

{
  "text": "Dear community!\n\nI am wondering:\n\nI have a big model that I want to use (e.g. LLM). Now this model does not fit in one GPU that I have (8x16GB). I also want to finetune it. \n\n​\n\nWhat would be the way to go for distributing and parallelizing the model? Why is there deepspeed and accelerate if I (supposidly) already have the parallelisation in Pytorch automaticlly?   \n\n\nThx :)",
  "label": "r/pytorch",
  "dataType": "post",
  "communityName": "r/pytorch",
  "datetime": "2024-05-02",
  "username_encoded": "Z0FBQUFBQm5LakwydkJabFl3aWVLMEFENjQ0d1JoVHpldUVQWEh0QVRtSk96LXQtcnN5RjlTNUhtWkxCNEE2Y0pvWEIyRDFJMG1FN0ozNkpuZlJUQV9GNWZfT2x1REdkVlE9PQ==",
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}
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