---
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