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lmind_hotpot_train8000_eval7405_v1_doc_qa_Qwen_Qwen1.5-4B_3e-4_lora2

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metadata
license: other
base_model: Qwen/Qwen1.5-4B
tags:
  - generated_from_trainer
datasets:
  - tyzhu/lmind_hotpot_train8000_eval7405_v1_doc_qa
metrics:
  - accuracy
model-index:
  - name: lmind_hotpot_train8000_eval7405_v1_doc_qa_Qwen_Qwen1.5-4B_3e-4_lora2
    results:
      - task:
          name: Causal Language Modeling
          type: text-generation
        dataset:
          name: tyzhu/lmind_hotpot_train8000_eval7405_v1_doc_qa
          type: tyzhu/lmind_hotpot_train8000_eval7405_v1_doc_qa
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.5165079365079365
library_name: peft

lmind_hotpot_train8000_eval7405_v1_doc_qa_Qwen_Qwen1.5-4B_3e-4_lora2

This model is a fine-tuned version of Qwen/Qwen1.5-4B on the tyzhu/lmind_hotpot_train8000_eval7405_v1_doc_qa dataset. It achieves the following results on the evaluation set:

  • Loss: 3.1631
  • Accuracy: 0.5165

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: 0.0003
  • 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
1.7567 0.9998 1089 2.2626 0.5190
1.5617 1.9995 2178 2.2436 0.5246
1.343 2.9993 3267 2.3385 0.5237
1.1682 4.0 4357 2.4995 0.5215
1.0141 4.9998 5446 2.6397 0.5182
0.9023 5.9995 6535 2.7929 0.5170
0.8008 6.9993 7624 2.8233 0.5162
0.7377 8.0 8714 2.8833 0.5180
0.6732 8.9998 9803 2.9550 0.5165
0.6225 9.9995 10892 2.9767 0.5165
0.5858 10.9993 11981 3.0117 0.5165
0.5618 12.0 13071 3.0317 0.5170
0.5464 12.9998 14160 3.0686 0.5167
0.5243 13.9995 15249 3.0829 0.5149
0.5066 14.9993 16338 3.0958 0.5127
0.4947 16.0 17428 3.0921 0.5153
0.4841 16.9998 18517 3.1170 0.5162
0.4727 17.9995 19606 3.1375 0.5172
0.4634 18.9993 20695 3.1323 0.5150
0.4468 19.9954 21780 3.1631 0.5165

Framework versions

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