# Llama-2-13b SuperCOT lora checkpoints 

These are my Llama-2-13b SuperCOT Lora checkpoints 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