---
license: other
tags:
- alignment-handbook
- trl
- dpo
- generated_from_trainer
- trl
- dpo
- generated_from_trainer
base_model: ZhangShenao/0.0_llama_nodpo_3iters_bs128_531lr_iter_1
datasets:
- updated
- original
model-index:
- name: 0.0_llama_nodpo_3iters_bs128_531lr_iter_2
  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. -->

# 0.0_llama_nodpo_3iters_bs128_531lr_iter_2

This model is a fine-tuned version of [ZhangShenao/0.0_llama_nodpo_3iters_bs128_531lr_iter_1](https://huggingface.co/ZhangShenao/0.0_llama_nodpo_3iters_bs128_531lr_iter_1) on the updated and the original datasets.

## 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: 3e-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



### Framework versions

- Transformers 4.40.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.19.1

# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_GeorgiaTech__0.0_llama_nodpo_3iters_bs128_531lr_iter_2)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |68.63|
|AI2 Reasoning Challenge (25-Shot)|64.42|
|HellaSwag (10-Shot)              |81.35|
|MMLU (5-Shot)                    |68.21|
|TruthfulQA (0-shot)              |56.40|
|Winogrande (5-shot)              |74.74|
|GSM8k (5-shot)                   |66.64|