transformers tensorboard safetensors mixtralmole text-generation alignment-handbook trl sft generated_from_trainer conversational custom_code dataset:HuggingFaceH4/ultrachat_200k base_model:ondevicellm/tinyllama_mole_v1 autotrain_compatible region:us
---
base_model: ondevicellm/tinyllama_mole_v1
tags:
- alignment-handbook
- trl
- sft
- generated_from_trainer
- trl
- sft
- generated_from_trainer
datasets:
- HuggingFaceH4/ultrachat_200k
model-index:
- name: tinyllama_mole_sftv2_ultrachat_ep3
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# tinyllama_mole_sftv2_ultrachat_ep3

This model is a fine-tuned version of [ondevicellm/tinyllama_mole_v1](https://huggingface.co/ondevicellm/tinyllama_mole_v1) on the HuggingFaceH4/ultrachat_200k dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7340

## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7643        | 0.09  | 100  | 2.7492          |
| 2.7293        | 0.18  | 200  | 2.7330          |
| 2.6973        | 0.26  | 300  | 2.6920          |
| 2.612         | 0.35  | 400  | 2.6290          |
| 2.5257        | 0.44  | 500  | 2.5470          |
| 2.4656        | 0.53  | 600  | 2.4527          |
| 2.3607        | 0.61  | 700  | 2.3681          |
| 2.2885        | 0.7   | 800  | 2.2988          |
| 2.2384        | 0.79  | 900  | 2.2397          |
| 2.1585        | 0.88  | 1000 | 2.1877          |
| 2.1526        | 0.96  | 1100 | 2.1409          |
| 2.0845        | 1.05  | 1200 | 2.0986          |
| 2.049         | 1.14  | 1300 | 2.0603          |
| 2.0243        | 1.23  | 1400 | 2.0257          |
| 1.9899        | 1.31  | 1500 | 1.9950          |
| 1.9706        | 1.4   | 1600 | 1.9675          |
| 1.9414        | 1.49  | 1700 | 1.9429          |
| 1.8952        | 1.58  | 1800 | 1.9208          |
| 1.9038        | 1.66  | 1900 | 1.9013          |
| 1.8942        | 1.75  | 2000 | 1.8839          |
| 1.8652        | 1.84  | 2100 | 1.8679          |
| 1.823         | 1.93  | 2200 | 1.8531          |
| 1.8394        | 2.01  | 2300 | 1.8394          |
| 1.8347        | 2.1   | 2400 | 1.8268          |
| 1.8137        | 2.19  | 2500 | 1.8148          |
| 1.799         | 2.28  | 2600 | 1.8037          |
| 1.7774        | 2.37  | 2700 | 1.7931          |
| 1.771         | 2.45  | 2800 | 1.7832          |
| 1.7761        | 2.54  | 2900 | 1.7739          |
| 1.7458        | 2.63  | 3000 | 1.7652          |
| 1.7683        | 2.72  | 3100 | 1.7570          |
| 1.7389        | 2.8   | 3200 | 1.7490          |
| 1.7321        | 2.89  | 3300 | 1.7414          |
| 1.7418        | 2.98  | 3400 | 1.7340          |


### Framework versions

- Transformers 4.37.0
- Pytorch 2.1.2+cu118
- Datasets 2.14.6
- Tokenizers 0.15.0
