---
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_Qwen_Qwen1.5-4B_3e-5_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: 0.5851838565022421
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_Qwen_Qwen1.5-4B_3e-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_nq_train6000_eval6489_v1_reciteonly_qa_v3 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2508
- Accuracy: 0.5852

## 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: 3e-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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.8512        | 0.9973  | 187  | 1.7184          | 0.6032   |
| 1.7174        | 2.0     | 375  | 1.7018          | 0.6049   |
| 1.6805        | 2.9973  | 562  | 1.6938          | 0.6061   |
| 1.6412        | 4.0     | 750  | 1.6925          | 0.6067   |
| 1.5834        | 4.9973  | 937  | 1.7047          | 0.6062   |
| 1.5304        | 6.0     | 1125 | 1.7239          | 0.6056   |
| 1.452         | 6.9973  | 1312 | 1.7508          | 0.6039   |
| 1.3847        | 8.0     | 1500 | 1.7711          | 0.6028   |
| 1.3177        | 8.9973  | 1687 | 1.8049          | 0.6009   |
| 1.2747        | 10.0    | 1875 | 1.8298          | 0.5998   |
| 1.2202        | 10.9973 | 2062 | 1.8814          | 0.5981   |
| 1.1589        | 12.0    | 2250 | 1.9311          | 0.5959   |
| 1.1231        | 12.9973 | 2437 | 1.9429          | 0.5955   |
| 1.0624        | 14.0    | 2625 | 1.9969          | 0.5933   |
| 1.0185        | 14.9973 | 2812 | 2.0319          | 0.5922   |
| 0.9718        | 16.0    | 3000 | 2.0798          | 0.5903   |
| 0.9101        | 16.9973 | 3187 | 2.1396          | 0.5887   |
| 0.8606        | 18.0    | 3375 | 2.1882          | 0.5870   |
| 0.8168        | 18.9973 | 3562 | 2.2291          | 0.5863   |
| 0.777         | 19.9467 | 3740 | 2.2508          | 0.5852   |


### Framework versions

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