transformers tensorboard safetensors mixtral text-generation alignment-handbook generated_from_trainer trl sft conversational dataset:HuggingFaceH4/ultrachat_200k dataset:ondevicellm/SlimOrca 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
- ondevicellm/SlimOrca
model-index:
- name: tinyllama_moe_sft_ultrachat-slimorca
  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_ultrachat-slimorca

This model is a fine-tuned version of [ondevicellm/tinyllama_moe](https://huggingface.co/ondevicellm/tinyllama_moe) on the HuggingFaceH4/ultrachat_200k and the ondevicellm/SlimOrca datasets.
It achieves the following results on the evaluation set:
- Loss: 1.1526

## 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: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.4601        | 0.05  | 100  | 1.3361          |
| 1.3324        | 0.1   | 200  | 1.2566          |
| 1.2946        | 0.14  | 300  | 1.2279          |
| 1.2767        | 0.19  | 400  | 1.2111          |
| 1.2298        | 0.24  | 500  | 1.1995          |
| 1.2247        | 0.29  | 600  | 1.1902          |
| 1.2208        | 0.34  | 700  | 1.1833          |
| 1.2375        | 0.39  | 800  | 1.1775          |
| 1.2038        | 0.43  | 900  | 1.1726          |
| 1.1926        | 0.48  | 1000 | 1.1683          |
| 1.1933        | 0.53  | 1100 | 1.1649          |
| 1.1893        | 0.58  | 1200 | 1.1618          |
| 1.2029        | 0.63  | 1300 | 1.1593          |
| 1.2201        | 0.68  | 1400 | 1.1572          |
| 1.1741        | 0.72  | 1500 | 1.1557          |
| 1.1813        | 0.77  | 1600 | 1.1545          |
| 1.1668        | 0.82  | 1700 | 1.1536          |
| 1.1495        | 0.87  | 1800 | 1.1530          |
| 1.1595        | 0.92  | 1900 | 1.1527          |
| 1.1607        | 0.97  | 2000 | 1.1526          |


### Framework versions

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