README.md

---
base_model: google/gemma-2-2b-it
license: other
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: longcot_pt_GEMMA_ZD_10_23_1
  results: []
---
# OpenLongCoT-Base-Gemma2-2B-RK3588-1.1.1

!!! THIS MODEL HAS BEEN MODIFIED FROM THE ORIGINAL !!!

This version of OpenLongCoT-Base-Gemma2-2B has been converted to run on the RK3588 NPU using ['w8a8'] quantization.
Only w8a8 quantization appears to work with Gemma 2 models. Other types throw error: 

```
E RKNN: [00:14:18.994] failed to allocate handle, ret: -1, errno: 14, errstr: Bad address 
E RKNN: [00:14:18.994] failed to malloc npu memory, size: 232128512, flags: 0x2 
E RKNN: [00:14:18.994] load model file error! 
rknn_init fail! ret=-1 
```

This model has been optimized with the following LoRA: 

Compatible with RKLLM version: 1.1.1

## Useful links:
[Official RKLLM GitHub](https://github.com/airockchip/rknn-llm) 

[RockhipNPU Reddit](https://reddit.com/r/RockchipNPU) 

[EZRKNN-LLM](https://github.com/Pelochus/ezrknn-llm/) 

Pretty much anything by these folks: [marty1885](https://github.com/marty1885) and [happyme531](https://huggingface.co/happyme531) 

Converted using https://github.com/c0zaut/ez-er-rkllm-toolkit 

# Original Model Card for base model, OpenLongCoT-Base-Gemma2-2B, below:


<!-- 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. -->

Please Please cite me if this dataset is helpful for you!🥰
```
@article{zhang2024llama,
  title={LLaMA-Berry: Pairwise Optimization for O1-like Olympiad-Level Mathematical Reasoning},
  author={Zhang, Di and Wu, Jianbo and Lei, Jingdi and Che, Tong and Li, Jiatong and Xie, Tong and Huang, Xiaoshui and Zhang, Shufei and Pavone, Marco and Li, Yuqiang and others},
  journal={arXiv preprint arXiv:2410.02884},
  year={2024}
}

@article{zhang2024accessing,
  title={Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B},
  author={Zhang, Di and Li, Jiatong and Huang, Xiaoshui and Zhou, Dongzhan and Li, Yuqiang and Ouyang, Wanli},
  journal={arXiv preprint arXiv:2406.07394},
  year={2024}
}

```

# longcot_pt_GEMMA_ZD_10_23_1

This model is a fine-tuned version of [google/gemma-2-2b-it](https://huggingface.co/google/gemma-2-2b-it) on the [OpenLongCoT](https://huggingface.co/datasets/qq8933/OpenLongCoT-Pretrain) dataset.

This model can read and output o1-like LongCoT which targeting work with LLaMA-O1 runtime frameworks.

## 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-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 8
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 1.0

### Training results



### Framework versions

- Transformers 4.44.0
- Pytorch 2.3.1
- Datasets 2.21.0
- Tokenizers 0.19.1