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oasst-sft-4-pythia-12b-epoch-3.5

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Each parameter occupies approximately 2bytes in fp16 mode, and 1byte in 8bit mode.

So, just to load this model in 8bit, you need 12billion params * 1byte = 12GB approx.

You need at-least 4GB more for inference, total 16GB.

Thank you very much for your reply!

And also @Blue-Devil keep in mind that , while making generations you need to watch out the "parameters" that you pass.

In general, I when used:

temperature=0.9, 
min_length=15,
early_stopping=True,
num_beams=8,
no_repeat_ngram_size=2,
top_k=40, 
top_p=0.7,
max_new_tokens=200,
penalty_alpha=0.6,
use_cache=False,
pad_token_id=tokenizer.eos_token_id)

I get the CUDA OOM errors. There are a couple of reasons to this.

1. Using high n as num_beams: Here I used 8 which internally keeps track of 8 different possible generations paths. That indeed takesup the memory. For details you would need to refer to some literature like this.
2. pentalty_alpha: Surprisingly this generation parameter also takes up memory and after passing through a hell of OOM error, I have came to know that this parameter was the culprit.
3. max_new_tokens: This one is obvious, the more you will try to generate the more memory it will take (and obviously time)

So, my suggestion is to only play with a couple of generation parameters if you have limited resources. The simple ones like:

1. Temperature
2. Top k
3. Top p
4. no repeat ngram size
5. length penalty
6. repetition penalty
   etc.

Hope it helps 🤗