horseee/llama-pruning

Structural Pruning for LLaMA

Hi there 👋

I'm ✨ Xinyin Ma ✨

🌱 I’m currently learning at NUS LV lab, under the supervision of Prof. Xinchao Wang

🎓 Previously I'm studying in Zhejiang University, under the supervision of Prof. Weiming Lu

🤔 My research interest mainly focuses on the efficient generative model.

👯[Personal Page] | 📫[Google Scholar] | 💬[Semantic Scholar]

🎃 Latest Goal: to have a kitten like this

llama-pruning's Issues

Benchmark for LLaMA pruning

If it is possible to give a benchmark of Pruned LLaMA.

Results are unusable - what am I doing wrong?

Hi,

I've tried the code "out of the box" and the output is very bad / unusable.

I've tried all pruner_types with a ratio of 0.5, and I also tried to reduce the pruning_ratio to 0.1.

The result stays the same.

Am I doing something wrong? (

How) have you been able to obtain different results?

Kind regards,
Sebastian

python3 -m torch.distributed.launch --master_port 18101 --nproc_per_node 1 prune.py --pruner_type l1 --ckpt_dir ckpt/LLaMA/7B/ --tokenizer_path ckpt/LLaMA/tokenizer.model --pruning_ratio 0.5 --save_ckpt_name 'llama_prune_1.7B'

==================Generation Results before Training================

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Translate English to French:

sea otter => loutre de mer

peppermint => menthe poivrée

plush girafe => girafe peluche

cheese => fromage

blue => bleu

beach => plage

dog => chien

giraffe => girafe

turtle => tortue

Snow Leopard => Panthère des neiges

chocolate => chocolat

Scrabble => Scrabble

rhinoceros => rinoceros

mouse => souris

cheetah => chatte sauvage

run => courir

train => train

horse => cheval

app => application

engineer => ingénieur

woman => femme

apartment => appartement

exam => examen

goat => chèvre

panda => panda

butter => beurre

sneaker => sneaker

cake => gâteau

alligator => alligator

quail => colibri

hawk => aigle

snake => serpent

whole => intégral

penguin => pingouin

toothbrush => brosse à dents

airplane => avion

==================Finish================

Use l1 pruner...
#Param before: 6738415616, #Param after: 1717438464

==================Generation Results After Training================

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==================Finish================

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==================Finish================

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==================Finish================

Tweet: "I hate it when my phone battery dies."
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###
Tweet: "My day has been 👍"
Sentiment: Positive
###
Tweet: "This is the link to the article"
Sentiment: Neutral
###
Tweet: "This new music video was incredibile"
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==================Finish================

Translate English to French:

sea otter => loutre de mer

peppermint => menthe poivrée

plush girafe => girafe peluche

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==================Finish================

Openai Whisper pruning

Hi
I would like to prune openai's whisper, based on the llama-pruning.
However, on the comments of this issue #1 , you mentioned that there is a huge error.
How long would it take for the bug to be fixed?