Huge discrepancy between training PRED and generation inference results on Llama-3-8B.Bad performance for generation modes.

[linke626]

Hello, I have some questions regarding my fine-tuning results. I fine-tuned the Llama-3-8B-Instruct model for 18,010 steps,encoder is all-MiniLM-L6-v2. However, the generation results during testing are extremely poor; the model outputs in generation_result.txt are completely incoherent. In contrast, the model's outputs during the training phase appeared relatively normal. Why is this happening?

My training command is as follows:
setsid env WANDB_MODE=offline PYTHONUNBUFFERED=1 accelerate launch --multi_gpu --num_processes 4 /data2/home/KBLaM_LLama3_8B/experiments/train.py

--dataset_dir datasets

--train_dataset "synthetic"

--N 120000

--B 16

--hf_model_spec "/data2/home/models/Meta-Llama-3-8B-Instruct"

--encoder_spec all-MiniLM-L6-v2

--model_save_dir "/data2/home/KBLaM_LLama3_8B/output/test4"

--hf_token "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"

--sep_query_head

--llm_type "llama3"

--kb_size 500

--total_steps 20010

--use_cached_embd

--use_data_aug

--gradient_accm_step 32 > train4.log 2>&1 &

My testing command is as follows:
CUDA_VISIBLE_DEVICES=1 python /data2/home/KBLaM_LLama3_8B/experiments/eval.py generation

--dataset_dir datasets

--test_dataset "synthetic.json"

--encoder_dir "/data2/home/KBLaM_LLama3_8B/output/test4/stage1_lr_0.0001KBTokenLayerFreq3UseOutlier1KBSize500SepQueryHeadUseDataAugKeyFromkey_all-MiniLM-L6-v2_synthetic_llama3_step_18000_encoder/encoder.pt"

--encoder_spec "all-MiniLM-L6-v2"

--model_dir "/data2/home/KBLaM_LLama3_8B/output/test4/stage1_lr_0.0001KBTokenLayerFreq3UseOutlier1KBSize500SepQueryHeadUseDataAugKeyFromkey_all-MiniLM-L6-v2_synthetic_llama3_step_18000"

--llm_base_dir "/data2/home/models/Meta-Llama-3-8B-Instruct"

--llm_type "llama3"

--save_dir "/data2/home/KBLaM_LLama3_8B/eval_results/generationtest4"

--kb_size 200

--eval_mode kb

--kb_token_layer_frequency 3

--precomputed_embed_keys_path "/data2/home/KBLaM_LLama3_8B/datasets/synthetic_all-MiniLM-L6-v2_embd_key.npy"

--precomputed_embed_values_path "/data2/home/KBLaM_LLama3_8B/datasets/synthetic_all-MiniLM-L6-v2_embd_value.npy"

Here is an example of the model's output during training:
INFO INPUT IDs SHAPE: torch.Size([16, 48]) train.py:607
INFO KB SHAPE: torch.Size([16, 501, 45056]) train.py:636
INFO GT: <|end_header_id|> What description does train.py:637
Velvet Pulse

have?<|eot_id|><|start_header_id|>assistant<|en

d_header_id|>The description of Velvet Pulse is

a high-end audio equipment brand known for its

superior sound

quality.<|eot_id|><|eot_id|><|eot_id|><|eot_id|

<|eot_id|><|eot_id|><|eot_id|><|eot_id|><|eot_

id|><|eot_id|><|eot_id|><|eot_id|><|eot_id|><|e

ot_id|>

INFO PRED: def<|eot_id|><|end_header_id|> What train.py:638
insights does The V

have?<|eot_id|><|start_header_id|>assistant<|en

d_header_id|>The description of Velvet Pulse is

a luxury-end fashion equipment brand known for

its exceptional sound

quality.<|eot_id|><|eot_id|><|eot_id|><|eot_id|

<|eot_id|><|eot_id|><|eot_id|><|eot_id|><|eot_

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ot_id|> INFO step: 17820, loss: 0.6719280518591404 train.py:680 INFO step: 17821, loss: 0.6504949191585183 train.py:680 INFO step: 17822, loss: 0.6908198706805706 train.py:680 INFO step: 17823, loss: 0.670011792331934 train.py:680 INFO step: 17824, loss: 0.670219199731946 train.py:680 INFO step: 17825, loss: 0.6978795994073153 train.py:680 INFO step: 17826, loss: 0.6766274720430374 train.py:680 INFO step: 17827, loss: 0.6636326257139444 train.py:680 INFO step: 17828, loss: 0.6245416700839996 train.py:680 INFO step: 17829, loss: 0.6708870176225901 train.py:680

Here is the model's output during the testing/generation phase:

Model output:

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True answer: The objectives of HyperGlide Systems is study bee behavior, assess the impact of pesticides, and develop conservation strategies.

Model output:

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