transformers tensorboard safetensors mixtralmole text-generation alignment-handbook trl dpo generated_from_trainer conversational custom_code dataset:HuggingFaceH4/ultrafeedback_binarized base_model:ondevicellm/tinyllama_mole_sft_ultrachat_ep3 autotrain_compatible region:us
---
base_model: ondevicellm/tinyllama_mole_sft_ultrachat_ep3
tags:
- alignment-handbook
- trl
- dpo
- generated_from_trainer
- trl
- dpo
- generated_from_trainer
datasets:
- HuggingFaceH4/ultrafeedback_binarized
model-index:
- name: tinyllama_mole_dpo_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_dpo_ep3

This model is a fine-tuned version of [ondevicellm/tinyllama_mole_sft_ultrachat_ep3](https://huggingface.co/ondevicellm/tinyllama_mole_sft_ultrachat_ep3) on the HuggingFaceH4/ultrafeedback_binarized dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6285
- Rewards/chosen: -0.3050
- Rewards/rejected: -0.5353
- Rewards/accuracies: 0.6806
- Rewards/margins: 0.2302
- Logps/rejected: -354.2071
- Logps/chosen: -373.1399
- Logits/rejected: -1.6731
- Logits/chosen: -1.8041

## 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: 5e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- 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: 100
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.6896        | 0.1   | 100  | 0.6899          | 0.0064         | -0.0013          | 0.6448             | 0.0076          | -300.8089      | -342.0017    | -1.7574         | -1.8918       |
| 0.6762        | 0.21  | 200  | 0.6756          | -0.0293        | -0.0716          | 0.6627             | 0.0423          | -307.8423      | -345.5688    | -1.7501         | -1.8839       |
| 0.6499        | 0.31  | 300  | 0.6587          | -0.0875        | -0.1813          | 0.6687             | 0.0938          | -318.8118      | -351.3895    | -1.7358         | -1.8688       |
| 0.6374        | 0.42  | 400  | 0.6451          | -0.1726        | -0.3218          | 0.6746             | 0.1493          | -332.8632      | -359.8953    | -1.7164         | -1.8482       |
| 0.6348        | 0.52  | 500  | 0.6377          | -0.2696        | -0.4550          | 0.6647             | 0.1854          | -346.1808      | -369.6013    | -1.6884         | -1.8208       |
| 0.6308        | 0.63  | 600  | 0.6333          | -0.2783        | -0.4815          | 0.6726             | 0.2032          | -348.8291      | -370.4673    | -1.6965         | -1.8269       |
| 0.62          | 0.73  | 700  | 0.6312          | -0.2323        | -0.4505          | 0.6806             | 0.2182          | -345.7306      | -365.8656    | -1.6841         | -1.8149       |
| 0.6055        | 0.84  | 800  | 0.6287          | -0.2877        | -0.5169          | 0.6865             | 0.2292          | -352.3697      | -371.4099    | -1.6793         | -1.8099       |
| 0.6357        | 0.94  | 900  | 0.6285          | -0.3050        | -0.5353          | 0.6806             | 0.2302          | -354.2071      | -373.1399    | -1.6731         | -1.8041       |


### Framework versions

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