---
base_model: ondevicellm/tinyllama_mole_v1
tags:
- trl
- sft
- generated_from_trainer
datasets:
- generator
model-index:
- name: tinyllama_mole_sft_routeraux_ultrachat_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_mole_sft_routeraux_ultrachat_ep3

This model is a fine-tuned version of [ondevicellm/tinyllama_mole_v1](https://huggingface.co/ondevicellm/tinyllama_mole_v1) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2128

## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 1.4007        | 0.09  | 100  | 1.3781          |
| 1.3255        | 0.18  | 200  | 1.3159          |
| 1.2919        | 0.26  | 300  | 1.2922          |
| 1.2696        | 0.35  | 400  | 1.2771          |
| 1.2426        | 0.44  | 500  | 1.2666          |
| 1.263         | 0.53  | 600  | 1.2584          |
| 1.2502        | 0.61  | 700  | 1.2514          |
| 1.237         | 0.7   | 800  | 1.2457          |
| 1.2322        | 0.79  | 900  | 1.2407          |
| 1.2156        | 0.88  | 1000 | 1.2360          |
| 1.2396        | 0.96  | 1100 | 1.2319          |
| 1.1564        | 1.05  | 1200 | 1.2315          |
| 1.1594        | 1.14  | 1300 | 1.2296          |
| 1.1711        | 1.23  | 1400 | 1.2274          |
| 1.1625        | 1.31  | 1500 | 1.2256          |
| 1.1652        | 1.4   | 1600 | 1.2234          |
| 1.1625        | 1.49  | 1700 | 1.2214          |
| 1.1457        | 1.58  | 1800 | 1.2196          |
| 1.1666        | 1.66  | 1900 | 1.2178          |
| 1.1701        | 1.75  | 2000 | 1.2158          |
| 1.1567        | 1.84  | 2100 | 1.2142          |
| 1.1304        | 1.93  | 2200 | 1.2128          |
| 1.1133        | 2.01  | 2300 | 1.2170          |
| 1.1204        | 2.1   | 2400 | 1.2170          |
| 1.1089        | 2.19  | 2500 | 1.2168          |
| 1.102         | 2.28  | 2600 | 1.2162          |
| 1.1004        | 2.37  | 2700 | 1.2157          |
| 1.1058        | 2.45  | 2800 | 1.2157          |
| 1.1119        | 2.54  | 2900 | 1.2150          |
| 1.0941        | 2.63  | 3000 | 1.2148          |
| 1.1127        | 2.72  | 3100 | 1.2147          |
| 1.104         | 2.8   | 3200 | 1.2144          |
| 1.1001        | 2.89  | 3300 | 1.2144          |
| 1.1188        | 2.98  | 3400 | 1.2144          |


### Framework versions

- Transformers 4.37.0
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0