transformers tensorboard safetensors mixtral text-generation alignment-handbook generated_from_trainer trl sft conversational dataset:HuggingFaceH4/ultrachat_200k base_model:ondevicellm/tinyllama_moe license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
---
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_epochs3
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# tinyllama_moe_sft_ultrachat200k_v2_epochs3

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: 1.1178

## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.336         | 0.09  | 100  | 1.3129          |
| 1.2424        | 0.18  | 200  | 1.2363          |
| 1.2079        | 0.26  | 300  | 1.2084          |
| 1.185         | 0.35  | 400  | 1.1911          |
| 1.1546        | 0.44  | 500  | 1.1787          |
| 1.1741        | 0.53  | 600  | 1.1692          |
| 1.1612        | 0.61  | 700  | 1.1613          |
| 1.1453        | 0.7   | 800  | 1.1547          |
| 1.141         | 0.79  | 900  | 1.1489          |
| 1.1247        | 0.88  | 1000 | 1.1438          |
| 1.1485        | 0.96  | 1100 | 1.1392          |
| 1.067         | 1.05  | 1200 | 1.1387          |
| 1.0694        | 1.14  | 1300 | 1.1368          |
| 1.0814        | 1.23  | 1400 | 1.1341          |
| 1.0727        | 1.31  | 1500 | 1.1316          |
| 1.0769        | 1.4   | 1600 | 1.1292          |
| 1.0728        | 1.49  | 1700 | 1.1270          |
| 1.0558        | 1.58  | 1800 | 1.1247          |
| 1.0753        | 1.66  | 1900 | 1.1229          |
| 1.0799        | 1.75  | 2000 | 1.1209          |
| 1.066         | 1.84  | 2100 | 1.1192          |
| 1.0406        | 1.93  | 2200 | 1.1178          |
| 1.0193        | 2.01  | 2300 | 1.1222          |
| 1.0276        | 2.1   | 2400 | 1.1220          |
| 1.0171        | 2.19  | 2500 | 1.1215          |
| 1.0112        | 2.28  | 2600 | 1.1211          |
| 1.0087        | 2.37  | 2700 | 1.1207          |
| 1.0158        | 2.45  | 2800 | 1.1204          |
| 1.0219        | 2.54  | 2900 | 1.1199          |
| 1.0024        | 2.63  | 3000 | 1.1197          |
| 1.019         | 2.72  | 3100 | 1.1197          |
| 1.0135        | 2.8   | 3200 | 1.1194          |
| 1.0094        | 2.89  | 3300 | 1.1194          |
| 1.0284        | 2.98  | 3400 | 1.1194          |


### Framework versions

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