---
license: apache-2.0
base_model: ondevicellm/tinyllama_moe
tags:
- alignment-handbook
- generated_from_trainer
- trl
- sft
- generated_from_trainer
datasets:
- HuggingFaceH4/ultrachat_200k
model-index:
- name: tinyllama_moe_sft_ultrachat200k_v2_epochs5
  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_ultrachat200k_v2_epochs5

This model is a fine-tuned version of [ondevicellm/tinyllama_moe](https://huggingface.co/ondevicellm/tinyllama_moe) on the HuggingFaceH4/ultrachat_200k dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1090

## 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: 115
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.3359        | 0.09  | 100  | 1.3129          |
| 1.2425        | 0.18  | 200  | 1.2363          |
| 1.2079        | 0.26  | 300  | 1.2083          |
| 1.1849        | 0.35  | 400  | 1.1910          |
| 1.1545        | 0.44  | 500  | 1.1786          |
| 1.174         | 0.53  | 600  | 1.1690          |
| 1.1609        | 0.61  | 700  | 1.1610          |
| 1.1449        | 0.7   | 800  | 1.1543          |
| 1.1406        | 0.79  | 900  | 1.1485          |
| 1.1241        | 0.88  | 1000 | 1.1432          |
| 1.1477        | 0.96  | 1100 | 1.1385          |
| 1.0644        | 1.05  | 1200 | 1.1382          |
| 1.067         | 1.14  | 1300 | 1.1359          |
| 1.0791        | 1.23  | 1400 | 1.1332          |
| 1.0702        | 1.31  | 1500 | 1.1304          |
| 1.0741        | 1.4   | 1600 | 1.1277          |
| 1.0701        | 1.49  | 1700 | 1.1251          |
| 1.0529        | 1.58  | 1800 | 1.1225          |
| 1.072         | 1.66  | 1900 | 1.1199          |
| 1.0759        | 1.75  | 2000 | 1.1178          |
| 1.0618        | 1.84  | 2100 | 1.1152          |
| 1.0359        | 1.93  | 2200 | 1.1134          |
| 0.9918        | 2.01  | 2300 | 1.1195          |
| 1.002         | 2.1   | 2400 | 1.1205          |
| 0.993         | 2.19  | 2500 | 1.1194          |
| 0.9872        | 2.28  | 2600 | 1.1184          |
| 0.9849        | 2.37  | 2700 | 1.1172          |
| 0.9924        | 2.45  | 2800 | 1.1156          |
| 0.9971        | 2.54  | 2900 | 1.1145          |
| 0.9786        | 2.63  | 3000 | 1.1130          |
| 0.9923        | 2.72  | 3100 | 1.1122          |
| 0.9888        | 2.8   | 3200 | 1.1106          |
| 0.9826        | 2.89  | 3300 | 1.1091          |
| 0.9997        | 2.98  | 3400 | 1.1090          |
| 0.9267        | 3.07  | 3500 | 1.1219          |
| 0.9465        | 3.15  | 3600 | 1.1225          |
| 0.9255        | 3.24  | 3700 | 1.1221          |
| 0.9532        | 3.33  | 3800 | 1.1214          |
| 0.9372        | 3.42  | 3900 | 1.1215          |
| 0.9206        | 3.5   | 4000 | 1.1213          |
| 0.9394        | 3.59  | 4100 | 1.1207          |
| 0.9367        | 3.68  | 4200 | 1.1195          |
| 0.9245        | 3.77  | 4300 | 1.1191          |
| 0.9386        | 3.85  | 4400 | 1.1187          |
| 0.9209        | 3.94  | 4500 | 1.1187          |
| 0.9028        | 4.03  | 4600 | 1.1261          |
| 0.9087        | 4.12  | 4700 | 1.1278          |
| 0.9114        | 4.2   | 4800 | 1.1277          |
| 0.8854        | 4.29  | 4900 | 1.1280          |
| 0.902         | 4.38  | 5000 | 1.1278          |
| 0.9038        | 4.47  | 5100 | 1.1280          |
| 0.8935        | 4.56  | 5200 | 1.1280          |
| 0.9053        | 4.64  | 5300 | 1.1280          |
| 0.9091        | 4.73  | 5400 | 1.1278          |
| 0.8968        | 4.82  | 5500 | 1.1279          |
| 0.9196        | 4.91  | 5600 | 1.1279          |
| 0.9129        | 4.99  | 5700 | 1.1279          |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.14.6
- Tokenizers 0.15.0