---
license: other
base_model: Qwen/Qwen1.5-4B
tags:
- generated_from_trainer
datasets:
- tyzhu/lmind_nq_train6000_eval6489_v1_doc_qa_v3
metrics:
- accuracy
model-index:
- name: lmind_nq_train6000_eval6489_v1_doc_qa_v3_Qwen_Qwen1.5-4B_5e-4_lora2
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: tyzhu/lmind_nq_train6000_eval6489_v1_doc_qa_v3
      type: tyzhu/lmind_nq_train6000_eval6489_v1_doc_qa_v3
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.560974358974359
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_doc_qa_v3_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_nq_train6000_eval6489_v1_doc_qa_v3 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2417
- Accuracy: 0.5610

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.8583        | 1.0   | 529   | 1.6376          | 0.5726   |
| 1.6329        | 2.0   | 1058  | 1.6881          | 0.5713   |
| 1.3464        | 3.0   | 1587  | 1.8256          | 0.5663   |
| 1.1624        | 4.0   | 2116  | 1.9223          | 0.5652   |
| 0.964         | 5.0   | 2645  | 1.9720          | 0.5643   |
| 0.8117        | 6.0   | 3174  | 2.0016          | 0.5647   |
| 0.7242        | 7.0   | 3703  | 2.0785          | 0.5639   |
| 0.6381        | 8.0   | 4232  | 2.0954          | 0.5645   |
| 0.573         | 9.0   | 4761  | 2.1067          | 0.5623   |
| 0.5269        | 10.0  | 5290  | 2.1356          | 0.5646   |
| 0.5144        | 11.0  | 5819  | 2.1951          | 0.5616   |
| 0.4887        | 12.0  | 6348  | 2.1779          | 0.5631   |
| 0.4636        | 13.0  | 6877  | 2.1757          | 0.5611   |
| 0.467         | 14.0  | 7406  | 2.1781          | 0.5624   |
| 0.4613        | 15.0  | 7935  | 2.2312          | 0.5612   |
| 0.4405        | 16.0  | 8464  | 2.1800          | 0.5629   |
| 0.4308        | 17.0  | 8993  | 2.1960          | 0.5628   |
| 0.4401        | 18.0  | 9522  | 2.2355          | 0.5610   |
| 0.4334        | 19.0  | 10051 | 2.2380          | 0.5608   |
| 0.4218        | 20.0  | 10580 | 2.2417          | 0.5610   |


### Framework versions

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