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--- |
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license: other |
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base_model: Qwen/Qwen1.5-4B |
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tags: |
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- generated_from_trainer |
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datasets: |
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- tyzhu/lmind_nq_train6000_eval6489_v1_reciteonly_qa_v3 |
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metrics: |
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- accuracy |
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model-index: |
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- name: lmind_nq_train6000_eval6489_v1_reciteonly_qa_v3_Qwen_Qwen1.5-4B_5e-4_lora2 |
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results: |
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- task: |
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name: Causal Language Modeling |
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type: text-generation |
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dataset: |
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name: tyzhu/lmind_nq_train6000_eval6489_v1_reciteonly_qa_v3 |
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type: tyzhu/lmind_nq_train6000_eval6489_v1_reciteonly_qa_v3 |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.5833811659192825 |
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library_name: peft |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# lmind_nq_train6000_eval6489_v1_reciteonly_qa_v3_Qwen_Qwen1.5-4B_5e-4_lora2 |
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This model is a fine-tuned version of [Qwen/Qwen1.5-4B](https://huggingface.co/Qwen/Qwen1.5-4B) on the tyzhu/lmind_nq_train6000_eval6489_v1_reciteonly_qa_v3 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.8927 |
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- Accuracy: 0.5834 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0005 |
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- train_batch_size: 1 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 4 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 32 |
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- total_eval_batch_size: 8 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: constant |
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- lr_scheduler_warmup_ratio: 0.05 |
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- num_epochs: 20.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-------:|:----:|:---------------:|:--------:| |
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| 1.745 | 0.9973 | 187 | 1.6686 | 0.6096 | |
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| 1.4255 | 2.0 | 375 | 1.6989 | 0.6087 | |
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| 1.0295 | 2.9973 | 562 | 1.8159 | 0.6052 | |
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| 0.6982 | 4.0 | 750 | 1.9994 | 0.5996 | |
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| 0.4584 | 4.9973 | 937 | 2.2058 | 0.5949 | |
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| 0.3048 | 6.0 | 1125 | 2.3636 | 0.5928 | |
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| 0.2175 | 6.9973 | 1312 | 2.5218 | 0.5905 | |
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| 0.1719 | 8.0 | 1500 | 2.6292 | 0.5909 | |
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| 0.1355 | 8.9973 | 1687 | 2.7028 | 0.5892 | |
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| 0.1302 | 10.0 | 1875 | 2.7502 | 0.5876 | |
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| 0.1281 | 10.9973 | 2062 | 2.7715 | 0.5875 | |
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| 0.1331 | 12.0 | 2250 | 2.7947 | 0.5862 | |
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| 0.1435 | 12.9973 | 2437 | 2.7769 | 0.5867 | |
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| 0.1468 | 14.0 | 2625 | 2.7598 | 0.5862 | |
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| 0.1449 | 14.9973 | 2812 | 2.8147 | 0.5855 | |
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| 0.1404 | 16.0 | 3000 | 2.8564 | 0.5859 | |
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| 0.121 | 16.9973 | 3187 | 2.8381 | 0.5863 | |
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| 0.1198 | 18.0 | 3375 | 2.8844 | 0.5841 | |
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| 0.1198 | 18.9973 | 3562 | 2.9040 | 0.5834 | |
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| 0.1215 | 19.9467 | 3740 | 2.8927 | 0.5834 | |
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### Framework versions |
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- PEFT 0.5.0 |
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- Transformers 4.40.2 |
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- Pytorch 2.3.0 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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