---
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_3e-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: 0.5822278481012658
---

<!-- 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_3e-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: 3.7015
- Accuracy: 0.5822

## 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: 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 |
|:-------------:|:-----:|:-----:|:--------:|:---------------:|
| 1.8255        | 1.0   | 250   | 0.6054   | 1.8392          |
| 1.7368        | 2.0   | 500   | 0.6078   | 1.8111          |
| 1.6689        | 3.0   | 750   | 0.6075   | 1.8103          |
| 1.5555        | 4.0   | 1000  | 0.6067   | 1.8414          |
| 1.4559        | 5.0   | 1250  | 0.6038   | 1.8992          |
| 1.3514        | 6.0   | 1500  | 0.6018   | 1.9584          |
| 1.2491        | 7.0   | 1750  | 0.6000   | 2.0300          |
| 1.1749        | 8.0   | 2000  | 0.5982   | 2.1051          |
| 1.0769        | 9.0   | 2250  | 0.5954   | 2.1948          |
| 1.0134        | 10.0  | 2500  | 0.5943   | 2.2515          |
| 0.9209        | 11.0  | 2750  | 0.5921   | 2.3421          |
| 0.8636        | 12.0  | 3000  | 0.5905   | 2.4443          |
| 0.7866        | 13.0  | 3250  | 0.588    | 2.5574          |
| 0.7448        | 14.0  | 3500  | 0.5867   | 2.5800          |
| 0.6709        | 15.0  | 3750  | 0.5846   | 2.6912          |
| 0.6439        | 16.0  | 4000  | 0.5853   | 2.7546          |
| 0.5869        | 17.0  | 4250  | 0.5831   | 2.7997          |
| 0.5596        | 18.0  | 4500  | 0.5833   | 2.8435          |
| 0.5205        | 19.0  | 4750  | 0.5833   | 2.9510          |
| 0.5045        | 20.0  | 5000  | 0.5824   | 2.9797          |
| 0.47          | 21.0  | 5250  | 0.5832   | 3.0530          |
| 0.455         | 22.0  | 5500  | 0.5821   | 3.0804          |
| 0.4332        | 23.0  | 5750  | 0.5813   | 3.1938          |
| 0.4171        | 24.0  | 6000  | 0.5816   | 3.1836          |
| 0.4049        | 25.0  | 6250  | 0.5817   | 3.1950          |
| 0.3975        | 26.0  | 6500  | 0.5801   | 3.2749          |
| 0.3798        | 27.0  | 6750  | 0.5808   | 3.3141          |
| 0.3774        | 28.0  | 7000  | 0.5815   | 3.3085          |
| 0.3636        | 29.0  | 7250  | 0.5813   | 3.3525          |
| 0.362         | 30.0  | 7500  | 0.5809   | 3.4330          |
| 0.3486        | 31.0  | 7750  | 0.5805   | 3.4240          |
| 0.3471        | 32.0  | 8000  | 0.5806   | 3.4737          |
| 0.335         | 33.0  | 8250  | 0.5825   | 3.4706          |
| 0.3367        | 34.0  | 8500  | 0.5829   | 3.4640          |
| 0.3276        | 35.0  | 8750  | 0.5806   | 3.5442          |
| 0.3298        | 36.0  | 9000  | 0.58     | 3.6080          |
| 0.3226        | 37.0  | 9250  | 0.5818   | 3.5853          |
| 0.3229        | 38.0  | 9500  | 0.5826   | 3.5513          |
| 0.3163        | 39.0  | 9750  | 0.5812   | 3.5633          |
| 0.3181        | 40.0  | 10000 | 0.5816   | 3.6170          |
| 0.3105        | 41.0  | 10250 | 0.5821   | 3.5726          |
| 0.3113        | 42.0  | 10500 | 0.5811   | 3.6571          |
| 0.3083        | 43.0  | 10750 | 0.5824   | 3.6066          |
| 0.3082        | 44.0  | 11000 | 0.582    | 3.6072          |
| 0.3032        | 45.0  | 11250 | 0.5822   | 3.6758          |
| 0.3041        | 46.0  | 11500 | 0.5827   | 3.7283          |
| 0.3016        | 47.0  | 11750 | 0.5813   | 3.7187          |
| 0.3017        | 48.0  | 12000 | 0.5803   | 3.6693          |
| 0.294         | 49.0  | 12250 | 0.5812   | 3.7501          |
| 0.2981        | 50.0  | 12500 | 0.5822   | 3.7015          |


### Framework versions

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