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---
base_model: ondevicellm/tinyllama_moe_v2
tags:
- alignment-handbook
- generated_from_trainer
- trl
- sft
- generated_from_trainer
datasets:
- HuggingFaceH4/ultrachat_200k
model-index:
- name: tinyllama_moe_sft_ultrachat_v2_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_moe_sft_ultrachat_v2_ep3
This model is a fine-tuned version of [ondevicellm/tinyllama_moe_v2](https://huggingface.co/ondevicellm/tinyllama_moe_v2) on the HuggingFaceH4/ultrachat_200k dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1289
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 1.4892 | 0.09 | 100 | 1.4465 |
| 1.2729 | 0.18 | 200 | 1.2643 |
| 1.2286 | 0.26 | 300 | 1.2280 |
| 1.2007 | 0.35 | 400 | 1.2075 |
| 1.1688 | 0.44 | 500 | 1.1933 |
| 1.1872 | 0.53 | 600 | 1.1830 |
| 1.1732 | 0.61 | 700 | 1.1746 |
| 1.1596 | 0.7 | 800 | 1.1679 |
| 1.1546 | 0.79 | 900 | 1.1622 |
| 1.1366 | 0.88 | 1000 | 1.1572 |
| 1.1606 | 0.96 | 1100 | 1.1527 |
| 1.0967 | 1.05 | 1200 | 1.1505 |
| 1.099 | 1.14 | 1300 | 1.1480 |
| 1.1099 | 1.23 | 1400 | 1.1453 |
| 1.1015 | 1.31 | 1500 | 1.1432 |
| 1.104 | 1.4 | 1600 | 1.1408 |
| 1.0998 | 1.49 | 1700 | 1.1390 |
| 1.0829 | 1.58 | 1800 | 1.1369 |
| 1.1052 | 1.66 | 1900 | 1.1353 |
| 1.1082 | 1.75 | 2000 | 1.1336 |
| 1.0948 | 1.84 | 2100 | 1.1320 |
| 1.0682 | 1.93 | 2200 | 1.1308 |
| 1.0688 | 2.01 | 2300 | 1.1318 |
| 1.0754 | 2.1 | 2400 | 1.1317 |
| 1.0646 | 2.19 | 2500 | 1.1311 |
| 1.058 | 2.28 | 2600 | 1.1305 |
| 1.0553 | 2.37 | 2700 | 1.1301 |
| 1.0607 | 2.45 | 2800 | 1.1298 |
| 1.0669 | 2.54 | 2900 | 1.1294 |
| 1.0476 | 2.63 | 3000 | 1.1292 |
| 1.0688 | 2.72 | 3100 | 1.1291 |
| 1.0583 | 2.8 | 3200 | 1.1289 |
| 1.0545 | 2.89 | 3300 | 1.1289 |
| 1.0744 | 2.98 | 3400 | 1.1289 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.0.1+cu117
- Datasets 2.16.1
- Tokenizers 0.15.0
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