---
tags:
- generated_from_trainer
datasets:
- BEE-spoke-data/goodwiki-deduped-split
metrics:
- accuracy
model-index:
- name: bitllama-goodwiki
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: BEE-spoke-data/goodwiki-deduped-split
      type: BEE-spoke-data/goodwiki-deduped-split
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.4285134482793542
---

<!-- 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. -->

# bitllama-goodwiki

This model was trained from scratch on the BEE-spoke-data/goodwiki-deduped-split dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0525
- Accuracy: 0.4285

## 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: 0.0008
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 1.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 6.1199        | 0.04  | 100  | 6.0749          | 0.1542   |
| 5.3869        | 0.07  | 200  | 5.3267          | 0.2032   |
| 4.9187        | 0.11  | 300  | 4.8566          | 0.2386   |
| 4.6185        | 0.14  | 400  | 4.5535          | 0.2624   |
| 4.3509        | 0.18  | 500  | 4.3388          | 0.2801   |
| 4.1666        | 0.21  | 600  | 4.1692          | 0.2956   |
| 4.0456        | 0.25  | 700  | 4.0399          | 0.3089   |
| 3.9273        | 0.28  | 800  | 3.9318          | 0.3193   |
| 3.8447        | 0.32  | 900  | 3.8173          | 0.3327   |
| 3.7143        | 0.35  | 1000 | 3.7108          | 0.3461   |
| 3.6485        | 0.39  | 1100 | 3.6116          | 0.3590   |
| 3.5171        | 0.42  | 1200 | 3.5303          | 0.3693   |
| 3.4464        | 0.46  | 1300 | 3.4554          | 0.3780   |
| 3.3955        | 0.49  | 1400 | 3.3999          | 0.3851   |
| 3.3551        | 0.53  | 1500 | 3.3432          | 0.3919   |
| 3.2787        | 0.56  | 1600 | 3.2981          | 0.3974   |
| 3.2705        | 0.6   | 1700 | 3.2566          | 0.4023   |
| 3.2281        | 0.64  | 1800 | 3.2172          | 0.4075   |
| 3.1759        | 0.67  | 1900 | 3.1826          | 0.4118   |
| 3.1603        | 0.71  | 2000 | 3.1547          | 0.4152   |
| 3.1328        | 0.74  | 2100 | 3.1283          | 0.4186   |
| 3.0916        | 0.78  | 2200 | 3.1055          | 0.4215   |
| 3.0939        | 0.81  | 2300 | 3.0875          | 0.4238   |
| 3.0584        | 0.85  | 2400 | 3.0732          | 0.4257   |
| 3.0711        | 0.88  | 2500 | 3.0631          | 0.4271   |
| 3.0612        | 0.92  | 2600 | 3.0565          | 0.4280   |
| 3.081         | 0.95  | 2700 | 3.0534          | 0.4284   |
| 3.0378        | 0.99  | 2800 | 3.0525          | 0.4285   |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0