---
base_model: ondevicellm/tinyllama_moe_v2
tags:
- alignment-handbook
- trl
- sft
- generated_from_trainer
- trl
- sft
- generated_from_trainer
datasets:
- HuggingFaceH4/ultrachat_200k
model-index:
- name: tinyllama_moe_sft_routeraux_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_moe_sft_routeraux_ep3

This model is a fine-tuned version of [ondevicellm/tinyllama_moe_v2](https://huggingface.co./ondevicellm/tinyllama_moe_v2) on the HuggingFaceH4/ultrachat_200k dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2954

## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 1.72          | 0.09  | 100  | 1.6775          |
| 1.4985        | 0.18  | 200  | 1.4900          |
| 1.4482        | 0.26  | 300  | 1.4473          |
| 1.4152        | 0.35  | 400  | 1.4215          |
| 1.3777        | 0.44  | 500  | 1.4031          |
| 1.3932        | 0.53  | 600  | 1.3886          |
| 1.375         | 0.61  | 700  | 1.3762          |
| 1.3574        | 0.7   | 800  | 1.3657          |
| 1.349         | 0.79  | 900  | 1.3563          |
| 1.3276        | 0.88  | 1000 | 1.3481          |
| 1.3491        | 0.96  | 1100 | 1.3409          |
| 1.2812        | 1.05  | 1200 | 1.3358          |
| 1.2831        | 1.14  | 1300 | 1.3308          |
| 1.2917        | 1.23  | 1400 | 1.3258          |
| 1.2812        | 1.31  | 1500 | 1.3219          |
| 1.2819        | 1.4   | 1600 | 1.3178          |
| 1.2756        | 1.49  | 1700 | 1.3145          |
| 1.2584        | 1.58  | 1800 | 1.3107          |
| 1.2806        | 1.66  | 1900 | 1.3083          |
| 1.2815        | 1.75  | 2000 | 1.3054          |
| 1.2676        | 1.84  | 2100 | 1.3031          |
| 1.2388        | 1.93  | 2200 | 1.3011          |
| 1.2385        | 2.01  | 2300 | 1.3015          |
| 1.2459        | 2.1   | 2400 | 1.3000          |
| 1.2349        | 2.19  | 2500 | 1.2989          |
| 1.2277        | 2.28  | 2600 | 1.2981          |
| 1.2243        | 2.37  | 2700 | 1.2973          |
| 1.2298        | 2.45  | 2800 | 1.2967          |
| 1.2362        | 2.54  | 2900 | 1.2961          |
| 1.216         | 2.63  | 3000 | 1.2958          |
| 1.2381        | 2.72  | 3100 | 1.2957          |
| 1.2274        | 2.8   | 3200 | 1.2955          |
| 1.2235        | 2.89  | 3300 | 1.2954          |
| 1.2438        | 2.98  | 3400 | 1.2954          |


### Framework versions

- Transformers 4.37.0
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0