OpenLongCoT-Base-Gemma2-2B-rk3588-1.1.1

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

RockhipNPU Reddit

EZRKNN-LLM

Pretty much anything by these folks: marty1885 and 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 on the OpenLongCoT 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