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---
license: other
base_model: Qwen/Qwen1.5-4B
tags:
- generated_from_trainer
datasets:
- tyzhu/lmind_nq_train6000_eval6489_v1_doc_qa_v3
metrics:
- accuracy
model-index:
- name: lmind_nq_train6000_eval6489_v1_doc_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_doc_qa_v3
type: tyzhu/lmind_nq_train6000_eval6489_v1_doc_qa_v3
metrics:
- name: Accuracy
type: accuracy
value: 0.560974358974359
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_doc_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_doc_qa_v3 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2417
- Accuracy: 0.5610
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.8583 | 1.0 | 529 | 1.6376 | 0.5726 |
| 1.6329 | 2.0 | 1058 | 1.6881 | 0.5713 |
| 1.3464 | 3.0 | 1587 | 1.8256 | 0.5663 |
| 1.1624 | 4.0 | 2116 | 1.9223 | 0.5652 |
| 0.964 | 5.0 | 2645 | 1.9720 | 0.5643 |
| 0.8117 | 6.0 | 3174 | 2.0016 | 0.5647 |
| 0.7242 | 7.0 | 3703 | 2.0785 | 0.5639 |
| 0.6381 | 8.0 | 4232 | 2.0954 | 0.5645 |
| 0.573 | 9.0 | 4761 | 2.1067 | 0.5623 |
| 0.5269 | 10.0 | 5290 | 2.1356 | 0.5646 |
| 0.5144 | 11.0 | 5819 | 2.1951 | 0.5616 |
| 0.4887 | 12.0 | 6348 | 2.1779 | 0.5631 |
| 0.4636 | 13.0 | 6877 | 2.1757 | 0.5611 |
| 0.467 | 14.0 | 7406 | 2.1781 | 0.5624 |
| 0.4613 | 15.0 | 7935 | 2.2312 | 0.5612 |
| 0.4405 | 16.0 | 8464 | 2.1800 | 0.5629 |
| 0.4308 | 17.0 | 8993 | 2.1960 | 0.5628 |
| 0.4401 | 18.0 | 9522 | 2.2355 | 0.5610 |
| 0.4334 | 19.0 | 10051 | 2.2380 | 0.5608 |
| 0.4218 | 20.0 | 10580 | 2.2417 | 0.5610 |
### 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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