| # Llama-2-13b SuperCOT lora checkpoints | |
| These are my Llama-2-13b SuperCOT Loras trained using QLora on the [SuperCOT Dataset](https://huggingface.co/datasets/kaiokendev/SuperCOT-dataset). | |
| ### Architecture | |
| - **Model Architecture**: Llama-2-13b | |
| - **Training Algorithm**: QLora | |
| ### Training Details | |
| - **Dataset**: [SuperCOT Dataset](https://huggingface.co/datasets/kaiokendev/SuperCOT-dataset) | |
| - **Datset type**: alpaca | |
| - **Training Parameters**: [See Here](https://github.com/OpenAccess-AI-Collective/axolotl/blob/main/examples/llama-2/qlora.yml) | |
| - **Training Environment**: Axolotl | |
| - **sequence_len**: 4096 | |
| ## Acknowledgments | |
| Special thanks to the creators of the datasets in SuperCOT. Additionally, thanks to Kaiokendev for curating the SuperCOT dataset. Thanks to the contributors of the Axolotl. | |
| ## Stuff generated from axolotl: | |
| --- | |
| library_name: peft | |
| --- | |
| ## Training procedure | |
| The following `bitsandbytes` quantization config was used during training: | |
| - load_in_8bit: False | |
| - load_in_4bit: True | |
| - llm_int8_threshold: 6.0 | |
| - llm_int8_skip_modules: None | |
| - llm_int8_enable_fp32_cpu_offload: False | |
| - llm_int8_has_fp16_weight: False | |
| - bnb_4bit_quant_type: nf4 | |
| - bnb_4bit_use_double_quant: True | |
| - bnb_4bit_compute_dtype: bfloat16 | |
| The following `bitsandbytes` quantization config was used during training: | |
| - load_in_8bit: False | |
| - load_in_4bit: True | |
| - llm_int8_threshold: 6.0 | |
| - llm_int8_skip_modules: None | |
| - llm_int8_enable_fp32_cpu_offload: False | |
| - llm_int8_has_fp16_weight: False | |
| - bnb_4bit_quant_type: nf4 | |
| - bnb_4bit_use_double_quant: True | |
| - bnb_4bit_compute_dtype: bfloat16 | |
| ### Framework versions | |
| - PEFT 0.5.0.dev0 | |
| - PEFT 0.5.0.dev0 | |