---
license: other
base_model: Qwen/Qwen1.5-4B
tags:
- generated_from_trainer
datasets:
- tyzhu/lmind_hotpot_train8000_eval7405_v1_recite_qa
metrics:
- accuracy
model-index:
- name: lmind_hotpot_train8000_eval7405_v1_recite_qa_Qwen_Qwen1.5-4B_5e-4_lora2
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: tyzhu/lmind_hotpot_train8000_eval7405_v1_recite_qa
      type: tyzhu/lmind_hotpot_train8000_eval7405_v1_recite_qa
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7611848617176128
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_recite_qa_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_hotpot_train8000_eval7405_v1_recite_qa dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5757
- Accuracy: 0.7612

## 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 |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|
| 1.5463        | 0.9998  | 1089  | 1.3539          | 0.6872   |
| 1.3199        | 1.9995  | 2178  | 1.1632          | 0.7022   |
| 1.1039        | 2.9993  | 3267  | 1.0347          | 0.7134   |
| 0.9356        | 4.0     | 4357  | 0.9234          | 0.7237   |
| 0.8312        | 4.9998  | 5446  | 0.8529          | 0.7307   |
| 0.7565        | 5.9995  | 6535  | 0.7860          | 0.7372   |
| 0.6985        | 6.9993  | 7624  | 0.7415          | 0.7415   |
| 0.6623        | 8.0     | 8714  | 0.7111          | 0.7457   |
| 0.6281        | 8.9998  | 9803  | 0.6775          | 0.7481   |
| 0.5885        | 9.9995  | 10892 | 0.6689          | 0.7496   |
| 0.5721        | 10.9993 | 11981 | 0.6364          | 0.7530   |
| 0.5504        | 12.0    | 13071 | 0.6319          | 0.7541   |
| 0.5406        | 12.9998 | 14160 | 0.6185          | 0.7549   |
| 0.536         | 13.9995 | 15249 | 0.6158          | 0.7565   |
| 0.5205        | 14.9993 | 16338 | 0.5976          | 0.7578   |
| 0.5175        | 16.0    | 17428 | 0.5922          | 0.7590   |
| 0.5068        | 16.9998 | 18517 | 0.5823          | 0.7593   |
| 0.5023        | 17.9995 | 19606 | 0.5754          | 0.7607   |
| 0.4848        | 18.9993 | 20695 | 0.5781          | 0.7608   |
| 0.4767        | 19.9954 | 21780 | 0.5757          | 0.7612   |


### Framework versions

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