Query on Evaluation Support for C4 Validation about aqlm HOT 5 CLOSED

Hello!
Thank you for your interest in the work.
In the main.py there is a code for calculating ppl on c4 validation dataset

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Hello!
Sorry for the late answer. We are calculating ppl on a split of c4. The code comes from GPTQ.

Regarding different ppl score on c4. I'm not sure why you are getting 6.94ppl. I rerun the ppl calculation and got 5.646. Although it is a little bit off(probably something slightly changed in either model,data or dependencies), it is not that much.
Can you please check are you using Mistral-7B-v0.1 or instruct version?
Can you please try to run the code from main branch with this command:
CUDA_VISIBLE_DEVICES=0 OMP_NUM_THREADS=16 MKL_NUM_THREADS=16 python main.py "mistralai/Mistral-7B-v0.1" "pajama" --max_epochs=15 --nsamples=256 --num_codebooks=1 --nbits_per_codebook=15 --in_group_size=8 --scale_nbits=0 --model_seqlen=8192 --offload_activations --no_quant

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Thanks for your timely reply.
After running the main.py using the no_quant mode, I noticed that only 256 samples were sampled to calculate the ppl on c4 validation dataset and I got the final ppl 6.63.
As I want to reproduce the baseline result 6.63 of c4 in the first row of your Tables 1 & 2, I wonder if this result was also obtained by using 256 samples or the whole valdation dataset. If the whole valdation dataset is needed, then how to modify the sampling code in get_c4() function of the datautils.py.

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Dear authors,
By using the no_quant mode to evaluate Llama-2-7b-hf and Mistral-7B models, I got the following results:

• Llama-2-7b-hf (model_seqlen=4096, no_quant) ppl=6.63, which is consistent with the results in the paper.
• Mistral-7B (model_seqlen=8192, no_quant) ppl=6.94. It is weird that Mistral-7B performs worse than Llama-2-7b-hf and is inconsistent with the result 5.71 reported in your Table.17. I present the command as below and can you help me to figure out the difference?

export CUDA_VISIBLE_DEVICES=4,5,6,7  # optional: select GPUs
export MODEL_PATH=pretrained/Mistral-7B # Llama-2-7b-hf
export DATASET_PATH=dataset/c4
export SAVE_PATH=None


python main.py $MODEL_PATH $DATASET_PATH \
        --model_seqlen=8192 \ #  4096 for Llama-2
        --nsamples=1024 \
        --val_size=128 \
        --num_codebooks=1 \
        --nbits_per_codebook=16 \
        --in_group_size=8 \
        --relative_mse_tolerance=0.01 \
        --finetune_batch_size=32 \
        --finetune_max_epochs=0 \
        --finetune_early_stop=3 \
        --finetune_keep_best \
        --local_batch_size=1 \
        --offload_activations \
        --no_quant \
        --save $SAVE_PATH

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Thanks for your advice. I have achieved the right ppl score by fixing the version of Mistral-7B-v0.1.

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