---
base_model: ondevicellm/tinyllama_moe_v2
tags:
- alignment-handbook
- generated_from_trainer
- trl
- sft
- generated_from_trainer
datasets:
- HuggingFaceH4/ultrachat_200k
model-index:
- name: tinyllama_moe_sft_ultrachat_v2_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_ultrachat_v2_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: 1.1289

## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 1.4892        | 0.09  | 100  | 1.4465          |
| 1.2729        | 0.18  | 200  | 1.2643          |
| 1.2286        | 0.26  | 300  | 1.2280          |
| 1.2007        | 0.35  | 400  | 1.2075          |
| 1.1688        | 0.44  | 500  | 1.1933          |
| 1.1872        | 0.53  | 600  | 1.1830          |
| 1.1732        | 0.61  | 700  | 1.1746          |
| 1.1596        | 0.7   | 800  | 1.1679          |
| 1.1546        | 0.79  | 900  | 1.1622          |
| 1.1366        | 0.88  | 1000 | 1.1572          |
| 1.1606        | 0.96  | 1100 | 1.1527          |
| 1.0967        | 1.05  | 1200 | 1.1505          |
| 1.099         | 1.14  | 1300 | 1.1480          |
| 1.1099        | 1.23  | 1400 | 1.1453          |
| 1.1015        | 1.31  | 1500 | 1.1432          |
| 1.104         | 1.4   | 1600 | 1.1408          |
| 1.0998        | 1.49  | 1700 | 1.1390          |
| 1.0829        | 1.58  | 1800 | 1.1369          |
| 1.1052        | 1.66  | 1900 | 1.1353          |
| 1.1082        | 1.75  | 2000 | 1.1336          |
| 1.0948        | 1.84  | 2100 | 1.1320          |
| 1.0682        | 1.93  | 2200 | 1.1308          |
| 1.0688        | 2.01  | 2300 | 1.1318          |
| 1.0754        | 2.1   | 2400 | 1.1317          |
| 1.0646        | 2.19  | 2500 | 1.1311          |
| 1.058         | 2.28  | 2600 | 1.1305          |
| 1.0553        | 2.37  | 2700 | 1.1301          |
| 1.0607        | 2.45  | 2800 | 1.1298          |
| 1.0669        | 2.54  | 2900 | 1.1294          |
| 1.0476        | 2.63  | 3000 | 1.1292          |
| 1.0688        | 2.72  | 3100 | 1.1291          |
| 1.0583        | 2.8   | 3200 | 1.1289          |
| 1.0545        | 2.89  | 3300 | 1.1289          |
| 1.0744        | 2.98  | 3400 | 1.1289          |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.0.1+cu117
- Datasets 2.16.1
- Tokenizers 0.15.0