Llama-2-13b SuperCOT lora checkpoints

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