---
license: other
base_model: Qwen/Qwen1.5-4B
tags:
- generated_from_trainer
datasets:
- tyzhu/lmind_nq_train6000_eval6489_v1_qa
metrics:
- accuracy
model-index:
- name: lmind_nq_train6000_eval6489_v1_qa_Qwen_Qwen1.5-4B_3e-4_lora2
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: tyzhu/lmind_nq_train6000_eval6489_v1_qa
      type: tyzhu/lmind_nq_train6000_eval6489_v1_qa
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.5580512820512821
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_qa_Qwen_Qwen1.5-4B_3e-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_qa dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3067
- Accuracy: 0.5581

## 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: 0.0003
- 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.7168        | 0.9973  | 187  | 1.6089          | 0.5754   |
| 1.3336        | 2.0     | 375  | 1.6442          | 0.5728   |
| 0.9813        | 2.9973  | 562  | 1.7657          | 0.5690   |
| 0.7483        | 4.0     | 750  | 1.9240          | 0.566    |
| 0.6395        | 4.9973  | 937  | 2.0308          | 0.5644   |
| 0.5836        | 6.0     | 1125 | 2.0914          | 0.5626   |
| 0.5559        | 6.9973  | 1312 | 2.1673          | 0.5617   |
| 0.5386        | 8.0     | 1500 | 2.1641          | 0.5619   |
| 0.5022        | 8.9973  | 1687 | 2.1993          | 0.5623   |
| 0.5035        | 10.0    | 1875 | 2.2047          | 0.5633   |
| 0.5013        | 10.9973 | 2062 | 2.2971          | 0.5616   |
| 0.5063        | 12.0    | 2250 | 2.2050          | 0.5618   |
| 0.5048        | 12.9973 | 2437 | 2.2624          | 0.5597   |
| 0.506         | 14.0    | 2625 | 2.3161          | 0.5598   |
| 0.511         | 14.9973 | 2812 | 2.2551          | 0.5554   |
| 0.5163        | 16.0    | 3000 | 2.3024          | 0.5578   |
| 0.4861        | 16.9973 | 3187 | 2.2554          | 0.5585   |
| 0.4925        | 18.0    | 3375 | 2.2402          | 0.5579   |
| 0.4927        | 18.9973 | 3562 | 2.2989          | 0.5570   |
| 0.4868        | 19.9467 | 3740 | 2.3067          | 0.5581   |


### Framework versions

- PEFT 0.5.0
- Transformers 4.40.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.19.1