Comments (1)
Hi! We strongly recommend to compress llama 7B on A100. However, it is still possible to run the compression with only 20G GPU memory by the following command:
python SVDLLM.py --step 1 --run_low_resource --ratio COMPRESSION_RATIO --model HUGGINGFACE_MODEL_REPO --whitening_nsamples WHITENING_SAMPLE_NUMBER --dataset WHITENING_DATASET --seed SAMPLING_SEED --model_seq_len MODEL_SEQ_LEN --save_path WHITENING_INFO_SAVING_PATH
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Related Issues (11)
- Request for Code Integration of SVD-LLM with GPTQ HOT 2
- Instead of ratio-compress to specific layer size HOT 1
- Incorrect Model Responses after compression HOT 4
- fail to apply on llama-13b HOT 2
- still fail to apply on llama-13b HOT 2
- compresssed checkpoints HOT 1
- Compressed Model Produces Random and Repetitive Output - Request for Compressed Weights HOT 1
- Question about C4 dataset HOT 4
- Question about ASVD and FWSVD HOT 1
- Converting Compressed LLaMA2 Model to Hugging Face-Compatible Format HOT 1
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