---
license: other
base_model: Qwen/Qwen1.5-4B
tags:
- generated_from_trainer
datasets:
- tyzhu/lmind_nq_train6000_eval6489_v1_docidx_v3
metrics:
- accuracy
model-index:
- name: lmind_nq_train6000_eval6489_v1_docidx_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_docidx_v3
      type: tyzhu/lmind_nq_train6000_eval6489_v1_docidx_v3
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.4408205128205128
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_docidx_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_docidx_v3 dataset.
It achieves the following results on the evaluation set:
- Loss: 4.3717
- Accuracy: 0.4408

## 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.9685        | 0.9985  | 341  | 3.0537          | 0.4626   |
| 1.9337        | 2.0     | 683  | 2.9928          | 0.4709   |
| 1.9029        | 2.9985  | 1024 | 3.0243          | 0.4705   |
| 1.856         | 4.0     | 1366 | 3.0766          | 0.4697   |
| 1.8019        | 4.9985  | 1707 | 3.1923          | 0.4696   |
| 1.7406        | 6.0     | 2049 | 3.2573          | 0.4684   |
| 1.6974        | 6.9985  | 2390 | 3.3286          | 0.4672   |
| 1.6249        | 8.0     | 2732 | 3.4775          | 0.4647   |
| 1.5993        | 8.9985  | 3073 | 3.5378          | 0.4636   |
| 1.5449        | 10.0    | 3415 | 3.6347          | 0.4597   |
| 1.4855        | 10.9985 | 3756 | 3.6955          | 0.4553   |
| 1.4205        | 12.0    | 4098 | 3.8478          | 0.4479   |
| 1.3757        | 12.9985 | 4439 | 3.9185          | 0.4487   |
| 1.3098        | 14.0    | 4781 | 3.9575          | 0.4455   |
| 1.2574        | 14.9985 | 5122 | 4.1279          | 0.4457   |
| 1.2049        | 16.0    | 5464 | 4.1540          | 0.4448   |
| 1.1617        | 16.9985 | 5805 | 4.2049          | 0.4454   |
| 1.1046        | 18.0    | 6147 | 4.2909          | 0.4432   |
| 1.043         | 18.9985 | 6488 | 4.3535          | 0.4385   |
| 1.0044        | 19.9707 | 6820 | 4.3717          | 0.4408   |


### Framework versions

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