---
license: apache-2.0
base_model: HuggingFaceTB/SmolLM-135M-Instruct
tags:
- trl
- orpo
- generated_from_trainer
model-index:
- name: ft-smollm-135M-instruct-on-hf-ultrafeedback_rob
  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. -->

# ft-smollm-135M-instruct-on-hf-ultrafeedback_rob

This model is a fine-tuned version of [HuggingFaceTB/SmolLM-135M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM-135M-Instruct) on the HuggingFaceH4/ultrafeedback_binarized dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1429
- Rewards/chosen: -0.1303
- Rewards/rejected: -0.1304
- Rewards/accuracies: 0.4670
- Rewards/margins: 0.0000
- Logps/rejected: -1.3036
- Logps/chosen: -1.3032
- Logits/rejected: 27.7664
- Logits/chosen: 27.4331
- Nll Loss: 1.0675
- Log Odds Ratio: -0.7542
- Log Odds Chosen: 0.0132

## 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: 0.0003
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- 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 | Nll Loss | Log Odds Ratio | Log Odds Chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|:--------:|:--------------:|:---------------:|
| 1.3569        | 0.8   | 100  | 1.1429          | -0.1303        | -0.1304          | 0.4670             | 0.0000          | -1.3036        | -1.3032      | 27.7664         | 27.4331       | 1.0675   | -0.7542        | 0.0132          |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2