---
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- generated_from_trainer
datasets:
- tyzhu/lmind_hotpot_train8000_eval7405_v1_qa
metrics:
- accuracy
model-index:
- name: lmind_hotpot_train8000_eval7405_v1_qa_5e-5_lora2
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: tyzhu/lmind_hotpot_train8000_eval7405_v1_qa
      type: tyzhu/lmind_hotpot_train8000_eval7405_v1_qa
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.584886075949367
---

<!-- 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_hotpot_train8000_eval7405_v1_qa_5e-5_lora2

This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the tyzhu/lmind_hotpot_train8000_eval7405_v1_qa dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6692
- Accuracy: 0.5849

## 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: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- 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: 50.0

### Training results

| Training Loss | Epoch | Step  | Accuracy | Validation Loss |
|:-------------:|:-----:|:-----:|:--------:|:---------------:|
| 1.798         | 1.0   | 250   | 0.6067   | 1.8213          |
| 1.7           | 2.0   | 500   | 0.6077   | 1.8046          |
| 1.5869        | 3.0   | 750   | 0.6071   | 1.8293          |
| 1.4349        | 4.0   | 1000  | 0.6043   | 1.8974          |
| 1.3111        | 5.0   | 1250  | 0.6015   | 1.9769          |
| 1.197         | 6.0   | 1500  | 0.5992   | 2.0635          |
| 1.0729        | 7.0   | 1750  | 0.5975   | 2.1523          |
| 0.9833        | 8.0   | 2000  | 0.5947   | 2.2640          |
| 0.8672        | 9.0   | 2250  | 0.5924   | 2.3643          |
| 0.7883        | 10.0  | 2500  | 0.5908   | 2.4598          |
| 0.6879        | 11.0  | 2750  | 0.5890   | 2.5669          |
| 0.6295        | 12.0  | 3000  | 0.5885   | 2.7000          |
| 0.5545        | 13.0  | 3250  | 0.5851   | 2.8281          |
| 0.5208        | 14.0  | 3500  | 0.5853   | 2.8794          |
| 0.4679        | 15.0  | 3750  | 0.5863   | 2.9184          |
| 0.4464        | 16.0  | 4000  | 0.5852   | 3.0791          |
| 0.4136        | 17.0  | 4250  | 0.5856   | 3.0832          |
| 0.4021        | 18.0  | 4500  | 0.5847   | 3.0944          |
| 0.3776        | 19.0  | 4750  | 0.5828   | 3.2120          |
| 0.373         | 20.0  | 5000  | 0.5839   | 3.2298          |
| 0.3572        | 21.0  | 5250  | 0.5841   | 3.2434          |
| 0.3517        | 22.0  | 5500  | 0.5847   | 3.2606          |
| 0.3374        | 23.0  | 5750  | 0.5845   | 3.3392          |
| 0.3338        | 24.0  | 6000  | 0.5841   | 3.3489          |
| 0.3286        | 25.0  | 6250  | 0.5846   | 3.4036          |
| 0.3259        | 26.0  | 6500  | 0.5849   | 3.3878          |
| 0.3175        | 27.0  | 6750  | 0.5853   | 3.4960          |
| 0.3185        | 28.0  | 7000  | 0.5852   | 3.4873          |
| 0.3117        | 29.0  | 7250  | 0.5840   | 3.4780          |
| 0.3125        | 30.0  | 7500  | 0.5836   | 3.5383          |
| 0.3041        | 31.0  | 7750  | 0.5841   | 3.5253          |
| 0.3047        | 32.0  | 8000  | 0.5853   | 3.5283          |
| 0.2982        | 33.0  | 8250  | 0.5833   | 3.5511          |
| 0.3013        | 34.0  | 8500  | 0.5852   | 3.5445          |
| 0.295         | 35.0  | 8750  | 0.5841   | 3.5891          |
| 0.2988        | 36.0  | 9000  | 0.5833   | 3.6198          |
| 0.2939        | 37.0  | 9250  | 0.5842   | 3.5708          |
| 0.2952        | 38.0  | 9500  | 0.5833   | 3.6124          |
| 0.2927        | 39.0  | 9750  | 0.5840   | 3.6413          |
| 0.2931        | 40.0  | 10000 | 0.5828   | 3.6555          |
| 0.2891        | 41.0  | 10250 | 0.5841   | 3.6471          |
| 0.291         | 42.0  | 10500 | 0.5846   | 3.7233          |
| 0.2886        | 43.0  | 10750 | 0.5850   | 3.6348          |
| 0.289         | 44.0  | 11000 | 0.5839   | 3.6786          |
| 0.2846        | 45.0  | 11250 | 0.5845   | 3.6846          |
| 0.2858        | 46.0  | 11500 | 0.5855   | 3.7088          |
| 0.283         | 47.0  | 11750 | 0.5842   | 3.6938          |
| 0.2863        | 48.0  | 12000 | 0.5830   | 3.6793          |
| 0.2782        | 49.0  | 12250 | 0.5839   | 3.6805          |
| 0.2834        | 50.0  | 12500 | 0.5849   | 3.6692          |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.14.1