Hugging Face's logo

tyzhu
/
lmind_nq_train6000_eval6489_v1_recite_qa_v3_Qwen_Qwen1.5-4B_5e-4_lora2

PEFT
Safetensors
Generated from Trainer
Eval Results
Model card Files Community
lmind_nq_train6000_eval6489_v1_recite_qa_v3_Qwen_Qwen1.5-4B_5e-4_lora2 / README.md
tyzhu's picture
tyzhu
End of training
92b8a7a verified
preview code
|
raw
history blame contribute delete
No virus
3.25 kB
metadata
license: other
base_model: Qwen/Qwen1.5-4B
tags:
  - generated_from_trainer
datasets:
  - tyzhu/lmind_nq_train6000_eval6489_v1_recite_qa_v3
metrics:
  - accuracy
model-index:
  - name: lmind_nq_train6000_eval6489_v1_recite_qa_v3_Qwen_Qwen1.5-4B_5e-4_lora2
    results:
      - task:
          name: Causal Language Modeling
          type: text-generation
        dataset:
          name: tyzhu/lmind_nq_train6000_eval6489_v1_recite_qa_v3
          type: tyzhu/lmind_nq_train6000_eval6489_v1_recite_qa_v3
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.7918744394618834
library_name: peft

lmind_nq_train6000_eval6489_v1_recite_qa_v3_Qwen_Qwen1.5-4B_5e-4_lora2

This model is a fine-tuned version of Qwen/Qwen1.5-4B on the tyzhu/lmind_nq_train6000_eval6489_v1_recite_qa_v3 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4565
  • Accuracy: 0.7919

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0005
  • train_batch_size: 1
  • eval_batch_size: 2
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 4
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 32
  • total_eval_batch_size: 8
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: constant
  • lr_scheduler_warmup_ratio: 0.05
  • num_epochs: 20.0

Training results

Training Loss Epoch Step Validation Loss Accuracy
1.7331 1.0 529 1.4271 0.6365
1.3037 2.0 1058 1.0687 0.6846
0.8818 3.0 1587 0.8142 0.7216
0.6397 4.0 2116 0.6636 0.7470
0.4735 5.0 2645 0.5547 0.7667
0.3798 6.0 3174 0.5002 0.7764
0.3409 7.0 3703 0.4850 0.7801
0.3054 8.0 4232 0.4691 0.7835
0.2803 9.0 4761 0.4637 0.7859
0.2637 10.0 5290 0.4532 0.7877
0.2661 11.0 5819 0.4668 0.7879
0.2513 12.0 6348 0.4647 0.7893
0.2424 13.0 6877 0.4615 0.7897
0.2499 14.0 7406 0.4546 0.7894
0.235 15.0 7935 0.4668 0.7896
0.2317 16.0 8464 0.4510 0.7913
0.2225 17.0 8993 0.4497 0.7915
0.2358 18.0 9522 0.4475 0.7916
0.2253 19.0 10051 0.4529 0.7918
0.2172 20.0 10580 0.4565 0.7919

Framework versions

  • PEFT 0.5.0
  • Transformers 4.40.2
  • Pytorch 2.3.0
  • Datasets 2.19.1
  • Tokenizers 0.19.1