---
license: apache-2.0
base_model: RylanSchaeffer/EleutherAI_pythia-70m_tatsu-lab_alpaca_farm_sftseed1
tags:
- trl
- reward-trainer
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: pythia-70m_tatsu-lab_alpaca_farm_sftsd1_policy_pythia-6.9b_gold_pythia-6.9b_rmsd3
  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. -->

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/rylan/switching-rms-rm/runs/elnujp8x)
# pythia-70m_tatsu-lab_alpaca_farm_sftsd1_policy_pythia-6.9b_gold_pythia-6.9b_rmsd3

This model is a fine-tuned version of [RylanSchaeffer/EleutherAI_pythia-70m_tatsu-lab_alpaca_farm_sftseed1](https://huggingface.co/RylanSchaeffer/EleutherAI_pythia-70m_tatsu-lab_alpaca_farm_sftseed1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7102
- Accuracy: 0.6002

## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 3
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.025
- num_epochs: 5

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| No log        | 0      | 0    | 1.0058          | 0.5213   |
| 0.9831        | 0.0648 | 100  | 0.9931          | 0.5198   |
| 0.9073        | 0.1295 | 200  | 0.9274          | 0.5283   |
| 0.834         | 0.1943 | 300  | 0.8757          | 0.5513   |
| 0.8691        | 0.2591 | 400  | 0.8491          | 0.5517   |
| 0.8562        | 0.3238 | 500  | 0.8132          | 0.5629   |
| 0.8401        | 0.3886 | 600  | 0.8004          | 0.5625   |
| 0.7914        | 0.4534 | 700  | 0.7770          | 0.5732   |
| 0.8092        | 0.5181 | 800  | 0.7720          | 0.5782   |
| 0.7245        | 0.5829 | 900  | 0.7720          | 0.5748   |
| 0.7888        | 0.6477 | 1000 | 0.7561          | 0.5917   |
| 0.7504        | 0.7124 | 1100 | 0.7483          | 0.5848   |
| 0.704         | 0.7772 | 1200 | 0.7477          | 0.5909   |
| 0.7506        | 0.8420 | 1300 | 0.7451          | 0.5928   |
| 0.7419        | 0.9067 | 1400 | 0.7419          | 0.5963   |
| 0.7339        | 0.9715 | 1500 | 0.7406          | 0.5940   |
| 0.7136        | 1.0363 | 1600 | 0.7366          | 0.5971   |
| 0.7079        | 1.1010 | 1700 | 0.7346          | 0.5967   |
| 0.7363        | 1.1658 | 1800 | 0.7325          | 0.5955   |
| 0.7446        | 1.2306 | 1900 | 0.7323          | 0.6036   |
| 0.7255        | 1.2953 | 2000 | 0.7222          | 0.6059   |
| 0.6924        | 1.3601 | 2100 | 0.7272          | 0.5944   |
| 0.7418        | 1.4249 | 2200 | 0.7243          | 0.5944   |
| 0.7071        | 1.4896 | 2300 | 0.7230          | 0.5948   |
| 0.6902        | 1.5544 | 2400 | 0.7167          | 0.6036   |
| 0.6993        | 1.6192 | 2500 | 0.7206          | 0.5994   |
| 0.7462        | 1.6839 | 2600 | 0.7179          | 0.6025   |
| 0.7           | 1.7487 | 2700 | 0.7154          | 0.5971   |
| 0.7169        | 1.8135 | 2800 | 0.7141          | 0.6005   |
| 0.72          | 1.8782 | 2900 | 0.7178          | 0.6059   |
| 0.6965        | 1.9430 | 3000 | 0.7128          | 0.6017   |
| 0.6311        | 2.0078 | 3100 | 0.7096          | 0.6071   |
| 0.7176        | 2.0725 | 3200 | 0.7143          | 0.6009   |
| 0.7022        | 2.1373 | 3300 | 0.7069          | 0.6009   |
| 0.7022        | 2.2021 | 3400 | 0.7124          | 0.5975   |
| 0.7996        | 2.2668 | 3500 | 0.7203          | 0.6009   |
| 0.7277        | 2.3316 | 3600 | 0.7174          | 0.5975   |
| 0.7638        | 2.3964 | 3700 | 0.7105          | 0.6017   |
| 0.732         | 2.4611 | 3800 | 0.7128          | 0.6059   |
| 0.7315        | 2.5259 | 3900 | 0.7116          | 0.5959   |
| 0.6882        | 2.5907 | 4000 | 0.7131          | 0.6017   |
| 0.7406        | 2.6554 | 4100 | 0.7051          | 0.6036   |
| 0.7353        | 2.7202 | 4200 | 0.7105          | 0.6005   |
| 0.7109        | 2.7850 | 4300 | 0.7036          | 0.5994   |
| 0.713         | 2.8497 | 4400 | 0.7100          | 0.6032   |
| 0.7249        | 2.9145 | 4500 | 0.7145          | 0.5990   |
| 0.7191        | 2.9793 | 4600 | 0.7103          | 0.6021   |
| 0.72          | 3.0440 | 4700 | 0.7112          | 0.6078   |
| 0.6937        | 3.1088 | 4800 | 0.7139          | 0.6036   |
| 0.6832        | 3.1736 | 4900 | 0.7140          | 0.6044   |
| 0.77          | 3.2383 | 5000 | 0.7155          | 0.5990   |
| 0.674         | 3.3031 | 5100 | 0.7114          | 0.6086   |
| 0.7001        | 3.3679 | 5200 | 0.7099          | 0.6032   |
| 0.6587        | 3.4326 | 5300 | 0.7113          | 0.6017   |
| 0.7006        | 3.4974 | 5400 | 0.7077          | 0.6048   |
| 0.7317        | 3.5622 | 5500 | 0.7086          | 0.6132   |
| 0.6984        | 3.6269 | 5600 | 0.7130          | 0.6009   |
| 0.6938        | 3.6917 | 5700 | 0.7105          | 0.6040   |
| 0.6601        | 3.7565 | 5800 | 0.7131          | 0.6013   |
| 0.7239        | 3.8212 | 5900 | 0.7116          | 0.5978   |
| 0.7199        | 3.8860 | 6000 | 0.7091          | 0.6036   |
| 0.7523        | 3.9508 | 6100 | 0.7097          | 0.6009   |
| 0.7043        | 4.0155 | 6200 | 0.7110          | 0.6071   |
| 0.6879        | 4.0803 | 6300 | 0.7074          | 0.6017   |
| 0.7138        | 4.1451 | 6400 | 0.7072          | 0.6090   |
| 0.6976        | 4.2098 | 6500 | 0.7057          | 0.6067   |
| 0.7434        | 4.2746 | 6600 | 0.7069          | 0.6028   |
| 0.7492        | 4.3394 | 6700 | 0.7089          | 0.6052   |
| 0.6268        | 4.4041 | 6800 | 0.7105          | 0.6002   |
| 0.7092        | 4.4689 | 6900 | 0.7080          | 0.6044   |
| 0.6915        | 4.5337 | 7000 | 0.7099          | 0.6013   |
| 0.652         | 4.5984 | 7100 | 0.7120          | 0.6067   |
| 0.7358        | 4.6632 | 7200 | 0.7108          | 0.5994   |
| 0.7935        | 4.7280 | 7300 | 0.7082          | 0.6013   |
| 0.6902        | 4.7927 | 7400 | 0.7069          | 0.6040   |
| 0.7113        | 4.8575 | 7500 | 0.7131          | 0.6009   |
| 0.6529        | 4.9223 | 7600 | 0.7098          | 0.6036   |
| 0.7117        | 4.9870 | 7700 | 0.7095          | 0.5986   |


### Framework versions

- Transformers 4.43.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1