---
license: other
base_model: Qwen/Qwen1.5-4B
tags:
- generated_from_trainer
datasets:
- tyzhu/lmind_nq_train6000_eval6489_v1_recite_qa_v3
metrics:
- accuracy
model-index:
- name: lmind_nq_train6000_eval6489_v1_recite_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_recite_qa_v3
      type: tyzhu/lmind_nq_train6000_eval6489_v1_recite_qa_v3
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7134618834080717
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_recite_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_recite_qa_v3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9359
- Accuracy: 0.7135

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.86          | 1.0   | 529   | 1.6920          | 0.6057   |
| 1.8271        | 2.0   | 1058  | 1.6426          | 0.6119   |
| 1.7324        | 3.0   | 1587  | 1.5998          | 0.6163   |
| 1.6818        | 4.0   | 2116  | 1.5580          | 0.6220   |
| 1.5864        | 5.0   | 2645  | 1.5211          | 0.6278   |
| 1.5204        | 6.0   | 3174  | 1.4863          | 0.6327   |
| 1.4481        | 7.0   | 3703  | 1.4517          | 0.6372   |
| 1.3768        | 8.0   | 4232  | 1.4121          | 0.6429   |
| 1.2946        | 9.0   | 4761  | 1.3739          | 0.6482   |
| 1.243         | 10.0  | 5290  | 1.3364          | 0.6532   |
| 1.1425        | 11.0  | 5819  | 1.2968          | 0.6594   |
| 1.0847        | 12.0  | 6348  | 1.2539          | 0.6652   |
| 1.0152        | 13.0  | 6877  | 1.2164          | 0.6706   |
| 0.9498        | 14.0  | 7406  | 1.1770          | 0.6758   |
| 0.8652        | 15.0  | 7935  | 1.1323          | 0.6821   |
| 0.8265        | 16.0  | 8464  | 1.0826          | 0.6901   |
| 0.7432        | 17.0  | 8993  | 1.0463          | 0.6960   |
| 0.7106        | 18.0  | 9522  | 1.0098          | 0.7022   |
| 0.669         | 19.0  | 10051 | 0.9696          | 0.7078   |
| 0.6043        | 20.0  | 10580 | 0.9359          | 0.7135   |


### Framework versions

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