---
license: other
base_model: Qwen/Qwen1.5-4B
tags:
- generated_from_trainer
datasets:
- tyzhu/lmind_hotpot_train8000_eval7405_v1_qa
metrics:
- accuracy
model-index:
- name: lmind_hotpot_train8000_eval7405_v1_qa_5e-4_lora2
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: tyzhu/lmind_hotpot_train8000_eval7405_v1_qa
      type: tyzhu/lmind_hotpot_train8000_eval7405_v1_qa
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.47844444444444445
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_hotpot_train8000_eval7405_v1_qa_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_hotpot_train8000_eval7405_v1_qa dataset.
It achieves the following results on the evaluation set:
- Loss: 4.0366
- Accuracy: 0.4784

## 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: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- 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: 50.0

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 2.2398        | 1.0   | 250   | 2.3236          | 0.5163   |
| 1.8301        | 2.0   | 500   | 2.4220          | 0.5124   |
| 1.3626        | 3.0   | 750   | 2.6153          | 0.5062   |
| 1.0112        | 4.0   | 1000  | 2.8349          | 0.4997   |
| 0.7198        | 5.0   | 1250  | 3.0756          | 0.4963   |
| 0.589         | 6.0   | 1500  | 3.2339          | 0.4943   |
| 0.4969        | 7.0   | 1750  | 3.3425          | 0.4935   |
| 0.4786        | 8.0   | 2000  | 3.4198          | 0.4924   |
| 0.4399        | 9.0   | 2250  | 3.4695          | 0.4911   |
| 0.4481        | 10.0  | 2500  | 3.5353          | 0.4913   |
| 0.4166        | 11.0  | 2750  | 3.4938          | 0.4894   |
| 0.429         | 12.0  | 3000  | 3.5450          | 0.4906   |
| 0.4193        | 13.0  | 3250  | 3.5636          | 0.4882   |
| 0.4276        | 14.0  | 3500  | 3.5626          | 0.4890   |
| 0.4071        | 15.0  | 3750  | 3.6309          | 0.4883   |
| 0.421         | 16.0  | 4000  | 3.5818          | 0.4890   |
| 0.4065        | 17.0  | 4250  | 3.6167          | 0.4869   |
| 0.4188        | 18.0  | 4500  | 3.6926          | 0.4857   |
| 0.3994        | 19.0  | 4750  | 3.6533          | 0.4863   |
| 0.4103        | 20.0  | 5000  | 3.6891          | 0.4864   |
| 0.397         | 21.0  | 5250  | 3.6973          | 0.4851   |
| 0.4118        | 22.0  | 5500  | 3.7214          | 0.4859   |
| 0.3944        | 23.0  | 5750  | 3.7193          | 0.4851   |
| 0.4036        | 24.0  | 6000  | 3.7567          | 0.4845   |
| 0.3939        | 25.0  | 6250  | 3.7891          | 0.4841   |
| 0.401         | 26.0  | 6500  | 3.7671          | 0.4828   |
| 0.3871        | 27.0  | 6750  | 3.7838          | 0.4835   |
| 0.4005        | 28.0  | 7000  | 3.8041          | 0.4831   |
| 0.3854        | 29.0  | 7250  | 3.8603          | 0.4830   |
| 0.3942        | 30.0  | 7500  | 3.8247          | 0.4812   |
| 0.3837        | 31.0  | 7750  | 3.8497          | 0.4815   |
| 0.3896        | 32.0  | 8000  | 3.8705          | 0.4836   |
| 0.3817        | 33.0  | 8250  | 3.8643          | 0.4818   |
| 0.3928        | 34.0  | 8500  | 3.9378          | 0.4807   |
| 0.3839        | 35.0  | 8750  | 3.9542          | 0.4810   |
| 0.3942        | 36.0  | 9000  | 3.9250          | 0.4806   |
| 0.381         | 37.0  | 9250  | 3.9220          | 0.4792   |
| 0.3918        | 38.0  | 9500  | 3.9584          | 0.4781   |
| 0.3787        | 39.0  | 9750  | 3.9241          | 0.4776   |
| 0.3897        | 40.0  | 10000 | 3.9434          | 0.4773   |
| 0.3786        | 41.0  | 10250 | 3.9411          | 0.4793   |
| 0.3864        | 42.0  | 10500 | 3.9933          | 0.4766   |
| 0.377         | 43.0  | 10750 | 4.0015          | 0.4787   |
| 0.3887        | 44.0  | 11000 | 3.9979          | 0.4788   |
| 0.3805        | 45.0  | 11250 | 3.9764          | 0.4796   |
| 0.3827        | 46.0  | 11500 | 3.9990          | 0.4786   |
| 0.3737        | 47.0  | 11750 | 4.0059          | 0.4792   |
| 0.3807        | 48.0  | 12000 | 4.0746          | 0.4798   |
| 0.3772        | 49.0  | 12250 | 4.0123          | 0.4776   |
| 0.3808        | 50.0  | 12500 | 4.0366          | 0.4784   |


### Framework versions

- PEFT 0.5.0
- Transformers 4.41.1
- Pytorch 2.1.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1