---
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_5e-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: 0.5792914798206278
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_5e-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: 2.7806
- Accuracy: 0.5793

## 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: 5e-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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.8147        | 0.9973  | 187  | 1.7073          | 0.6045   |
| 1.6968        | 2.0     | 375  | 1.6898          | 0.6062   |
| 1.6305        | 2.9973  | 562  | 1.6912          | 0.6071   |
| 1.5527        | 4.0     | 750  | 1.7046          | 0.6062   |
| 1.4642        | 4.9973  | 937  | 1.7309          | 0.6055   |
| 1.3868        | 6.0     | 1125 | 1.7706          | 0.6037   |
| 1.278         | 6.9973  | 1312 | 1.8113          | 0.6016   |
| 1.1816        | 8.0     | 1500 | 1.8514          | 0.6004   |
| 1.0781        | 8.9973  | 1687 | 1.9348          | 0.5973   |
| 1.0042        | 10.0    | 1875 | 1.9619          | 0.5969   |
| 0.9175        | 10.9973 | 2062 | 2.0437          | 0.5938   |
| 0.8259        | 12.0    | 2250 | 2.1340          | 0.5910   |
| 0.7665        | 12.9973 | 2437 | 2.1983          | 0.5899   |
| 0.6822        | 14.0    | 2625 | 2.2949          | 0.5864   |
| 0.6189        | 14.9973 | 2812 | 2.3464          | 0.5854   |
| 0.5588        | 16.0    | 3000 | 2.4582          | 0.5835   |
| 0.4806        | 16.9973 | 3187 | 2.5345          | 0.5829   |
| 0.4272        | 18.0    | 3375 | 2.6303          | 0.5805   |
| 0.3805        | 18.9973 | 3562 | 2.6586          | 0.5806   |
| 0.3421        | 19.9467 | 3740 | 2.7806          | 0.5793   |


### Framework versions

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