Llama-2-13b SuperCOT lora checkpoints

These are my Llama-2-13b SuperCOT Loras trained using QLora on the SuperCOT Dataset.

Architecture

Model Architecture: Llama-2-13b
Training Algorithm: QLora

Training Details

Dataset: SuperCOT Dataset
Datset type: alpaca
Training Parameters: See Here
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