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---
base_model: ondevicellm/tinyllama_mole_v1
tags:
- alignment-handbook
- trl
- sft
- generated_from_trainer
- trl
- sft
- generated_from_trainer
datasets:
- HuggingFaceH4/ultrachat_200k
model-index:
- name: tinyllama_mole_sft_router05_ep3
results: []
---
<!-- 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. -->
# tinyllama_mole_sft_router05_ep3
This model is a fine-tuned version of [ondevicellm/tinyllama_mole_v1](https://huggingface.co/ondevicellm/tinyllama_mole_v1) on the HuggingFaceH4/ultrachat_200k dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1129
## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 120
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.3008 | 0.09 | 100 | 2.2785 |
| 2.2257 | 0.18 | 200 | 2.2161 |
| 2.1922 | 0.26 | 300 | 2.1924 |
| 2.1698 | 0.35 | 400 | 2.1773 |
| 2.1428 | 0.44 | 500 | 2.1668 |
| 2.1632 | 0.53 | 600 | 2.1586 |
| 2.1503 | 0.61 | 700 | 2.1516 |
| 2.1369 | 0.7 | 800 | 2.1460 |
| 2.1324 | 0.79 | 900 | 2.1409 |
| 2.1158 | 0.88 | 1000 | 2.1362 |
| 2.1396 | 0.96 | 1100 | 2.1321 |
| 2.0565 | 1.05 | 1200 | 2.1317 |
| 2.0596 | 1.14 | 1300 | 2.1297 |
| 2.0712 | 1.23 | 1400 | 2.1276 |
| 2.0626 | 1.31 | 1500 | 2.1259 |
| 2.0654 | 1.4 | 1600 | 2.1235 |
| 2.0628 | 1.49 | 1700 | 2.1216 |
| 2.046 | 1.58 | 1800 | 2.1197 |
| 2.067 | 1.66 | 1900 | 2.1180 |
| 2.0702 | 1.75 | 2000 | 2.1161 |
| 2.057 | 1.84 | 2100 | 2.1144 |
| 2.0307 | 1.93 | 2200 | 2.1129 |
| 2.0134 | 2.01 | 2300 | 2.1172 |
| 2.0205 | 2.1 | 2400 | 2.1172 |
| 2.0091 | 2.19 | 2500 | 2.1170 |
| 2.0021 | 2.28 | 2600 | 2.1164 |
| 2.0006 | 2.37 | 2700 | 2.1159 |
| 2.006 | 2.45 | 2800 | 2.1158 |
| 2.0121 | 2.54 | 2900 | 2.1152 |
| 1.9942 | 2.63 | 3000 | 2.1150 |
| 2.0129 | 2.72 | 3100 | 2.1149 |
| 2.0041 | 2.8 | 3200 | 2.1146 |
| 2.0002 | 2.89 | 3300 | 2.1146 |
| 2.019 | 2.98 | 3400 | 2.1146 |
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
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