---
license: other
base_model: Qwen/Qwen1.5-4B
tags:
- generated_from_trainer
datasets:
- tyzhu/lmind_nq_train6000_eval6489_v1_reciteonly_qa_v3
metrics:
- accuracy
model-index:
- name: lmind_nq_train6000_eval6489_v1_reciteonly_qa_v3_lora2
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: tyzhu/lmind_nq_train6000_eval6489_v1_reciteonly_qa_v3
      type: tyzhu/lmind_nq_train6000_eval6489_v1_reciteonly_qa_v3
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.5837219730941704
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_reciteonly_qa_v3_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_reciteonly_qa_v3 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4886
- Accuracy: 0.5837

## 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: 0.0001
- 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: 50.0

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.7886        | 0.9973  | 187  | 1.6901          | 0.6061   |
| 1.6544        | 2.0     | 375  | 1.6766          | 0.6077   |
| 1.5273        | 2.9973  | 562  | 1.6929          | 0.6080   |
| 1.3871        | 4.0     | 750  | 1.7257          | 0.6069   |
| 1.23          | 4.9973  | 937  | 1.7813          | 0.6061   |
| 1.0749        | 6.0     | 1125 | 1.8776          | 0.6018   |
| 0.8957        | 6.9973  | 1312 | 1.9782          | 0.5998   |
| 0.729         | 8.0     | 1500 | 2.0974          | 0.5966   |
| 0.5643        | 8.9973  | 1687 | 2.2553          | 0.5931   |
| 0.4538        | 10.0    | 1875 | 2.4089          | 0.5901   |
| 0.3563        | 10.9973 | 2062 | 2.5298          | 0.5889   |
| 0.2787        | 12.0    | 2250 | 2.6848          | 0.5871   |
| 0.2314        | 12.9973 | 2437 | 2.7943          | 0.5863   |
| 0.1923        | 14.0    | 2625 | 2.8624          | 0.5857   |
| 0.1687        | 14.9973 | 2812 | 2.9783          | 0.5848   |
| 0.1514        | 16.0    | 3000 | 3.0238          | 0.5850   |
| 0.1282        | 16.9973 | 3187 | 3.0914          | 0.5842   |
| 0.121         | 18.0    | 3375 | 3.1432          | 0.5848   |
| 0.1164        | 18.9973 | 3562 | 3.2314          | 0.5848   |
| 0.1103        | 20.0    | 3750 | 3.2781          | 0.5844   |
| 0.1077        | 20.9973 | 3937 | 3.2768          | 0.5842   |
| 0.1053        | 22.0    | 4125 | 3.3154          | 0.5845   |
| 0.1025        | 22.9973 | 4312 | 3.3168          | 0.5846   |
| 0.1019        | 24.0    | 4500 | 3.3672          | 0.5839   |
| 0.0957        | 24.9973 | 4687 | 3.3245          | 0.5843   |
| 0.0973        | 26.0    | 4875 | 3.3455          | 0.5846   |
| 0.0976        | 26.9973 | 5062 | 3.3746          | 0.5831   |
| 0.0956        | 28.0    | 5250 | 3.3458          | 0.5836   |
| 0.0963        | 28.9973 | 5437 | 3.3881          | 0.5845   |
| 0.0951        | 30.0    | 5625 | 3.4071          | 0.5842   |
| 0.0932        | 30.9973 | 5812 | 3.4574          | 0.5837   |
| 0.0932        | 32.0    | 6000 | 3.4498          | 0.5841   |
| 0.0876        | 32.9973 | 6187 | 3.4677          | 0.5830   |
| 0.0888        | 34.0    | 6375 | 3.4690          | 0.5835   |
| 0.0887        | 34.9973 | 6562 | 3.4481          | 0.5831   |
| 0.0883        | 36.0    | 6750 | 3.4745          | 0.5839   |
| 0.0893        | 36.9973 | 6937 | 3.4574          | 0.5831   |
| 0.0903        | 38.0    | 7125 | 3.4798          | 0.5838   |
| 0.0902        | 38.9973 | 7312 | 3.4863          | 0.5838   |
| 0.0896        | 40.0    | 7500 | 3.4676          | 0.5839   |
| 0.0841        | 40.9973 | 7687 | 3.5157          | 0.5837   |
| 0.0844        | 42.0    | 7875 | 3.5171          | 0.5833   |
| 0.0838        | 42.9973 | 8062 | 3.5576          | 0.5831   |
| 0.0854        | 44.0    | 8250 | 3.5440          | 0.5838   |
| 0.085         | 44.9973 | 8437 | 3.4777          | 0.5842   |
| 0.0863        | 46.0    | 8625 | 3.4933          | 0.5832   |
| 0.0875        | 46.9973 | 8812 | 3.5282          | 0.5841   |
| 0.087         | 48.0    | 9000 | 3.5321          | 0.5830   |
| 0.0832        | 48.9973 | 9187 | 3.5294          | 0.5836   |
| 0.0826        | 49.8667 | 9350 | 3.4886          | 0.5837   |


### Framework versions

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