---
library_name: transformers
license: llama3.1
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
tags:
- alignment-handbook
- trl
- dpo
- generated_from_trainer
- trl
- dpo
- generated_from_trainer
datasets:
- HuggingFaceH4/ultrafeedback_binarized
- tanliboy/orca_dpo_pairs
model-index:
- name: lambda-llama-3-8b-ipo-test
  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. -->

# lambda-llama-3-8b-ipo-test

This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co./meta-llama/Meta-Llama-3.1-8B-Instruct) on the HuggingFaceH4/ultrafeedback_binarized and the tanliboy/orca_dpo_pairs datasets.
It achieves the following results on the evaluation set:
- Loss: 0.8931
- Rewards/chosen: -0.3610
- Rewards/rejected: -0.5883
- Rewards/accuracies: 0.7922
- Rewards/margins: 0.2272
- Logps/rejected: -3.1373
- Logps/chosen: -2.5334
- Logits/rejected: -2.9939
- Logits/chosen: -2.9244

## 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: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- 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 |
|:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 1.1749        | 0.1744 | 100  | 1.0763          | -0.1732        | -0.3120          | 0.7892             | 0.1388          | -2.4465        | -2.0638      | -2.5676         | -2.5133       |
| 0.9802        | 0.3489 | 200  | 0.9501          | -0.3184        | -0.5302          | 0.8012             | 0.2118          | -2.9922        | -2.4269      | -2.7873         | -2.7230       |
| 0.9548        | 0.5233 | 300  | 0.9136          | -0.3761        | -0.6028          | 0.8163             | 0.2267          | -3.1736        | -2.5710      | -2.8788         | -2.8087       |
| 0.9834        | 0.6978 | 400  | 0.9041          | -0.3384        | -0.5537          | 0.8042             | 0.2153          | -3.0509        | -2.4770      | -2.9371         | -2.8667       |
| 0.9967        | 0.8722 | 500  | 0.8938          | -0.3750        | -0.6076          | 0.7892             | 0.2326          | -3.1855        | -2.5684      | -3.0293         | -2.9592       |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1