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---
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-5_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: 0.7753632286995515
library_name: peft
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# lmind_nq_train6000_eval6489_v1_recite_qa_v3_Qwen_Qwen1.5-4B_5e-5_lora2
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.
It achieves the following results on the evaluation set:
- Loss: 0.5804
- Accuracy: 0.7754
## 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: 5e-05
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.8478 | 1.0 | 529 | 1.6699 | 0.6080 |
| 1.7862 | 2.0 | 1058 | 1.6003 | 0.6164 |
| 1.6531 | 3.0 | 1587 | 1.5363 | 0.6251 |
| 1.5515 | 4.0 | 2116 | 1.4608 | 0.6343 |
| 1.4038 | 5.0 | 2645 | 1.3876 | 0.6456 |
| 1.2751 | 6.0 | 3174 | 1.3186 | 0.6553 |
| 1.1475 | 7.0 | 3703 | 1.2514 | 0.6637 |
| 1.0282 | 8.0 | 4232 | 1.1740 | 0.676 |
| 0.9067 | 9.0 | 4761 | 1.1004 | 0.6870 |
| 0.8202 | 10.0 | 5290 | 1.0408 | 0.6964 |
| 0.7007 | 11.0 | 5819 | 0.9592 | 0.7084 |
| 0.6259 | 12.0 | 6348 | 0.8998 | 0.7191 |
| 0.553 | 13.0 | 6877 | 0.8332 | 0.7295 |
| 0.4948 | 14.0 | 7406 | 0.7799 | 0.7387 |
| 0.4221 | 15.0 | 7935 | 0.7330 | 0.7466 |
| 0.3911 | 16.0 | 8464 | 0.6805 | 0.7551 |
| 0.3377 | 17.0 | 8993 | 0.6475 | 0.7620 |
| 0.3179 | 18.0 | 9522 | 0.6195 | 0.7680 |
| 0.288 | 19.0 | 10051 | 0.5962 | 0.7723 |
| 0.2605 | 20.0 | 10580 | 0.5804 | 0.7754 |
### Framework versions
- PEFT 0.5.0
- Transformers 4.40.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.19.1
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