|
--- |
|
base_model: four-two-labs/tinyllama-moe-base |
|
tags: |
|
- trl |
|
- orpo |
|
- generated_from_trainer |
|
model-index: |
|
- name: orpo |
|
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-base-mix-orpo |
|
|
|
This model is a fine-tuned version of [four-two-labs/tinyllama-moe-base](https://huggingface.co/four-two-labs/tinyllama-moe-base) on the `see code` dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 1.1757 |
|
- Rewards/chosen: -0.1708 |
|
- Rewards/rejected: -0.1682 |
|
- Rewards/accuracies: 0.5003 |
|
- Rewards/margins: -0.0027 |
|
- Logps/rejected: -1.6818 |
|
- Logps/chosen: -1.7085 |
|
- Logits/rejected: -2.6114 |
|
- Logits/chosen: -2.6139 |
|
- Nll Loss: 1.0859 |
|
- Log Odds Ratio: -0.8989 |
|
- Log Odds Chosen: -0.1073 |
|
|
|
## Model description |
|
|
|
More information needed |
|
|
|
## Training and evaluation data |
|
|
|
```python |
|
from datasets import load_dataset |
|
from datasets import interleave_datasets |
|
|
|
|
|
def format_chat_template(row): |
|
for key in ['prompt', 'chosen', 'rejected']: |
|
row[key] = tokenizer.apply_chat_template(row[key], tokenize=False) |
|
|
|
return row |
|
|
|
|
|
dataset = ( |
|
interleave_datasets([ |
|
( |
|
interleave_datasets( |
|
load_dataset( |
|
'four-two-labs/orpo-dpo-mix-40k-multilang-fixed', |
|
token=hf_token, |
|
) |
|
.values() |
|
) |
|
.select_columns(['prompt', 'chosen', 'rejected']) |
|
), |
|
( |
|
load_dataset( |
|
'four-two-labs/translations-5M-DPO', |
|
split='train', |
|
token=hf_token, |
|
) |
|
.shuffle(42) |
|
.select(range(250_000)) |
|
.select_columns(['prompt', 'chosen', 'rejected']) |
|
), |
|
( |
|
load_dataset( |
|
'four-two-labs/ultrafeedback_binarized-fixed', |
|
split='train_prefs', |
|
token=hf_token, |
|
) |
|
.select_columns(['prompt', 'chosen', 'rejected']) |
|
), |
|
]) |
|
.shuffle(seed=42) |
|
#.select(range(1000)) |
|
.map(format_chat_template, num_proc=32) |
|
.train_test_split(test_size=0.01) |
|
) |
|
``` |
|
|
|
## Training procedure |
|
|
|
### Training hyperparameters |
|
|
|
The following hyperparameters were used during training: |
|
- learning_rate: 1e-06 |
|
- train_batch_size: 12 |
|
- eval_batch_size: 12 |
|
- seed: 42 |
|
- gradient_accumulation_steps: 4 |
|
- total_train_batch_size: 48 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- lr_scheduler_warmup_steps: 10 |
|
- num_epochs: 3 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | Nll Loss | Log Odds Ratio | Log Odds Chosen | |
|
|:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|:--------:|:--------------:|:---------------:| |
|
| 1.1835 | 0.6001 | 2270 | 1.1764 | -0.1711 | -0.1684 | 0.4976 | -0.0027 | -1.6838 | -1.7106 | -2.6416 | -2.6430 | 1.0866 | -0.8991 | -0.1073 | |
|
| 1.1494 | 1.2002 | 4540 | 1.1757 | -0.1709 | -0.1682 | 0.5003 | -0.0026 | -1.6820 | -1.7085 | -2.5529 | -2.5573 | 1.0860 | -0.8988 | -0.1070 | |
|
| 1.233 | 1.8003 | 6810 | 1.1757 | -0.1709 | -0.1682 | 0.4993 | -0.0027 | -1.6819 | -1.7086 | -2.5859 | -2.5892 | 1.0859 | -0.8989 | -0.1073 | |
|
| 1.2344 | 2.4004 | 9080 | 1.1757 | -0.1708 | -0.1682 | 0.5003 | -0.0027 | -1.6818 | -1.7085 | -2.6114 | -2.6139 | 1.0859 | -0.8989 | -0.1073 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.40.0 |
|
- Pytorch 2.1.2+cu121 |
|
- Datasets 2.19.0 |
|
- Tokenizers 0.19.1 |
|
|
