---
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-4_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: 0.5885829596412556
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-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_reciteonly_qa_v3 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0184
- Accuracy: 0.5886

## 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: 0.0003
- 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.7516        | 0.9973  | 187  | 1.6714          | 0.6086   |
| 1.5219        | 2.0     | 375  | 1.6736          | 0.6104   |
| 1.2037        | 2.9973  | 562  | 1.7561          | 0.6081   |
| 0.8815        | 4.0     | 750  | 1.8875          | 0.6033   |
| 0.6016        | 4.9973  | 937  | 2.0768          | 0.5980   |
| 0.3979        | 6.0     | 1125 | 2.2606          | 0.5953   |
| 0.2591        | 6.9973  | 1312 | 2.4670          | 0.5933   |
| 0.1821        | 8.0     | 1500 | 2.6145          | 0.5922   |
| 0.1338        | 8.9973  | 1687 | 2.7399          | 0.5911   |
| 0.1172        | 10.0    | 1875 | 2.8330          | 0.5915   |
| 0.1102        | 10.9973 | 2062 | 2.8674          | 0.5914   |
| 0.1079        | 12.0    | 2250 | 2.8947          | 0.5903   |
| 0.11          | 12.9973 | 2437 | 2.9230          | 0.5894   |
| 0.1136        | 14.0    | 2625 | 2.9049          | 0.5888   |
| 0.1173        | 14.9973 | 2812 | 2.8788          | 0.5883   |
| 0.1163        | 16.0    | 3000 | 2.9582          | 0.5892   |
| 0.1047        | 16.9973 | 3187 | 2.9485          | 0.5886   |
| 0.1044        | 18.0    | 3375 | 2.9815          | 0.5894   |
| 0.105         | 18.9973 | 3562 | 2.9880          | 0.5881   |
| 0.1036        | 19.9467 | 3740 | 3.0184          | 0.5886   |


### Framework versions

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