Quantization made by Richard Erkhov.
bitllama-goodwiki - AWQ
Original model description:
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
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
