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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-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
---
<!-- 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-4_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: 0.4565
- 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
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