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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_recite_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_recite_qa_v3_Qwen_Qwen1.5-4B_3e-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_recite_qa_v3 |
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type: tyzhu/lmind_nq_train6000_eval6489_v1_recite_qa_v3 |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.7981434977578475 |
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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_recite_qa_v3_Qwen_Qwen1.5-4B_3e-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_recite_qa_v3 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4311 |
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- Accuracy: 0.7981 |
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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: 0.0003 |
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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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| 1.7637 | 1.0 | 529 | 1.4995 | 0.6288 | |
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| 1.3986 | 2.0 | 1058 | 1.1711 | 0.6720 | |
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| 0.9515 | 3.0 | 1587 | 0.8766 | 0.7148 | |
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| 0.642 | 4.0 | 2116 | 0.6720 | 0.7478 | |
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| 0.4362 | 5.0 | 2645 | 0.5458 | 0.7697 | |
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| 0.3201 | 6.0 | 3174 | 0.4751 | 0.7823 | |
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| 0.2652 | 7.0 | 3703 | 0.4510 | 0.7887 | |
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| 0.2263 | 8.0 | 4232 | 0.4372 | 0.7914 | |
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| 0.2035 | 9.0 | 4761 | 0.4335 | 0.7940 | |
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| 0.1913 | 10.0 | 5290 | 0.4322 | 0.7950 | |
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| 0.188 | 11.0 | 5819 | 0.4379 | 0.7945 | |
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| 0.1777 | 12.0 | 6348 | 0.4279 | 0.7957 | |
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| 0.1723 | 13.0 | 6877 | 0.4326 | 0.7956 | |
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| 0.1767 | 14.0 | 7406 | 0.4329 | 0.7967 | |
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| 0.1666 | 15.0 | 7935 | 0.4396 | 0.7962 | |
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| 0.1642 | 16.0 | 8464 | 0.4391 | 0.7965 | |
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| 0.1575 | 17.0 | 8993 | 0.4405 | 0.7967 | |
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| 0.1634 | 18.0 | 9522 | 0.4265 | 0.7976 | |
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| 0.1593 | 19.0 | 10051 | 0.4323 | 0.7978 | |
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| 0.153 | 20.0 | 10580 | 0.4311 | 0.7981 | |
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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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