MoE 4-bit Llama-v2 w/ QLora ELM Forest (BTM)
We are creating an MoE model by stitching together a bunch of QLora domain-specific adaptations on the base Llama v2 models. Each example during the inference would be routed through a set of QLora modules or their combination. This routing the main things that need to be learned in some way or the other. The goal is to solve an existing task better by using specialized domain experts. This is kind of an opensource and efficient attempt to mimic GPT4 with opensource Llama2 model.
Working document: https://docs.google.com/document/d/1YKDRCu7M9mflWrxKc1HeFs2HBWk4HXVrrOsLrIn6EXM/edit?usp=sharing Discord Server: https://discord.gg/CZAJcWTZxX
Setup pip install -r requirements.txt
Finetuner Setup
Modes
The Finetuner can be run in two different modes: qlora and lora.
qlora Mode
To run the Finetuner in qlora mode, use the following command:
python3 main.py --finetune --mode qlora --config <path to config>
lora Mode
To run the Finetuner in lora mode, use the following command:
python3 main.py --finetune --mode lora --config <path to config>