transformers safetensors gpt_neox text-classification trl reward-trainer generated_from_trainer dataset:allenai/ultrafeedback_binarized_cleaned base_model:kykim0/pythia-1b-tulu-v2-mix base_model:finetune:kykim0/pythia-1b-tulu-v2-mix license:apache-2.0 model-index autotrain_compatible text-generation-inference endpoints_compatible region:us
---
base_model: kykim0/pythia-1b-tulu-v2-mix-nos
datasets:
- allenai/ultrafeedback_binarized_cleaned
library_name: transformers
license: apache-2.0
metrics:
- accuracy
tags:
- trl
- reward-trainer
- generated_from_trainer
model-index:
- name: pythia-1b-tulu-v2-mix-nos-rm
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: allenai/ultrafeedback_binarized_cleaned
      type: allenai/ultrafeedback_binarized_cleaned
    metrics:
    - type: accuracy
      value: 0.7457800511508952
      name: Accuracy
---

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# pythia-1b-tulu-v2-mix-nos-rm

This model is a fine-tuned version of [kykim0/pythia-1b-tulu-v2-mix-nos](https://huggingface.co/kykim0/pythia-1b-tulu-v2-mix-nos) on the allenai/ultrafeedback_binarized_cleaned dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5227
- Accuracy: 0.7458

## 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: 1.41e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.5749        | 0.0527 | 100  | 0.5849          | 0.6900   |
| 0.5873        | 0.1055 | 200  | 0.5581          | 0.7100   |
| 0.5599        | 0.1582 | 300  | 0.5470          | 0.7212   |
| 0.5456        | 0.2109 | 400  | 0.5379          | 0.7258   |
| 0.521         | 0.2637 | 500  | 0.5358          | 0.7294   |
| 0.5361        | 0.3164 | 600  | 0.5363          | 0.7376   |
| 0.5662        | 0.3691 | 700  | 0.5270          | 0.7412   |
| 0.5301        | 0.4219 | 800  | 0.5268          | 0.7427   |
| 0.5661        | 0.4746 | 900  | 0.5301          | 0.7381   |
| 0.5608        | 0.5274 | 1000 | 0.5242          | 0.7437   |
| 0.5223        | 0.5801 | 1100 | 0.5242          | 0.7422   |
| 0.5322        | 0.6328 | 1200 | 0.5249          | 0.7448   |
| 0.4891        | 0.6856 | 1300 | 0.5241          | 0.7427   |
| 0.5111        | 0.7383 | 1400 | 0.5234          | 0.7437   |
| 0.5145        | 0.7910 | 1500 | 0.5225          | 0.7422   |
| 0.4746        | 0.8438 | 1600 | 0.5226          | 0.7458   |
| 0.5551        | 0.8965 | 1700 | 0.5223          | 0.7448   |
| 0.563         | 0.9492 | 1800 | 0.5222          | 0.7453   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.19.1
