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_sft_router05_lr1e-4_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_sft_router05_lr1e-4_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: 2.1035

## 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: 0.0001
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.2617        | 0.09  | 100  | 2.2410          |
| 2.2246        | 0.18  | 200  | 2.2165          |
| 2.1994        | 0.26  | 300  | 2.1994          |
| 2.1767        | 0.35  | 400  | 2.1869          |
| 2.1532        | 0.44  | 500  | 2.1792          |
| 2.171         | 0.53  | 600  | 2.1717          |
| 2.1588        | 0.61  | 700  | 2.1645          |
| 2.145         | 0.7   | 800  | 2.1567          |
| 2.1366        | 0.79  | 900  | 2.1507          |
| 2.1219        | 0.88  | 1000 | 2.1450          |
| 2.1415        | 0.96  | 1100 | 2.1387          |
| 1.9765        | 1.05  | 1200 | 2.1446          |
| 1.9837        | 1.14  | 1300 | 2.1430          |
| 1.9952        | 1.23  | 1400 | 2.1388          |
| 1.9868        | 1.31  | 1500 | 2.1351          |
| 1.9864        | 1.4   | 1600 | 2.1316          |
| 1.987         | 1.49  | 1700 | 2.1263          |
| 1.9678        | 1.58  | 1800 | 2.1230          |
| 1.9827        | 1.66  | 1900 | 2.1164          |
| 1.9846        | 1.75  | 2000 | 2.1134          |
| 1.9694        | 1.84  | 2100 | 2.1068          |
| 1.9429        | 1.93  | 2200 | 2.1035          |
| 1.8079        | 2.01  | 2300 | 2.1369          |
| 1.8132        | 2.1   | 2400 | 2.1375          |
| 1.8043        | 2.19  | 2500 | 2.1360          |
| 1.7927        | 2.28  | 2600 | 2.1334          |
| 1.7935        | 2.37  | 2700 | 2.1335          |
| 1.7982        | 2.45  | 2800 | 2.1321          |
| 1.8029        | 2.54  | 2900 | 2.1311          |
| 1.7919        | 2.63  | 3000 | 2.1298          |
| 1.7953        | 2.72  | 3100 | 2.1287          |
| 1.798         | 2.8   | 3200 | 2.1280          |
| 1.7947        | 2.89  | 3300 | 2.1282          |
| 1.8015        | 2.98  | 3400 | 2.1283          |


### Framework versions

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