---
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_Qwen_Qwen1.5-4B_5e-5_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: 0.48784126984126985
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_Qwen_Qwen1.5-4B_5e-5_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: 3.9023
- Accuracy: 0.4878

## 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: 5e-05
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2742        | 1.0   | 250  | 2.3355          | 0.5148   |
| 2.1673        | 2.0   | 500  | 2.3223          | 0.5169   |
| 2.0189        | 3.0   | 750  | 2.3526          | 0.5157   |
| 1.8684        | 4.0   | 1000 | 2.4020          | 0.5140   |
| 1.7028        | 5.0   | 1250 | 2.4910          | 0.5100   |
| 1.5685        | 6.0   | 1500 | 2.5835          | 0.5072   |
| 1.4161        | 7.0   | 1750 | 2.6927          | 0.5036   |
| 1.2904        | 8.0   | 2000 | 2.8047          | 0.5009   |
| 1.1397        | 9.0   | 2250 | 2.9097          | 0.4979   |
| 1.0366        | 10.0  | 2500 | 3.0555          | 0.4951   |
| 0.9114        | 11.0  | 2750 | 3.1526          | 0.4928   |
| 0.8301        | 12.0  | 3000 | 3.2417          | 0.4928   |
| 0.7283        | 13.0  | 3250 | 3.3687          | 0.4894   |
| 0.6717        | 14.0  | 3500 | 3.5007          | 0.4899   |
| 0.6055        | 15.0  | 3750 | 3.5470          | 0.4894   |
| 0.5667        | 16.0  | 4000 | 3.6736          | 0.4876   |
| 0.5271        | 17.0  | 4250 | 3.7100          | 0.4870   |
| 0.5081        | 18.0  | 4500 | 3.7920          | 0.4877   |
| 0.4758        | 19.0  | 4750 | 3.8548          | 0.4868   |
| 0.4682        | 20.0  | 5000 | 3.9023          | 0.4878   |


### Framework versions

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