README.md

BioMed-AdapterGPT

THe AI generative model BioMedical based on LLM Quantization

Intro

The main idea behind this Project is Implement LoRa Method to reduce the complexity of the BioMedGPT which relate to topic of

Quantization The main Method used Please Follwo this QBioMed

Note The Repo still under Progress

Applying LoRa

"""
    Apply LoRa to reduce the complexity of the model 
    Params:
    lora_r = 4
    USE_LORA = True
    """
    lora_r = 4
    USE_LORA = True
    if USE_LORA:
        make_lora_replace(model, verbose=True)
        if cfg.model.bitfit:
            for name, param in model.named_parameters():
                if ("layer_norm" in name and "bias" in name) or ("fc" in name and "bias" in name):
                    param.requires_grad = True
                else:
                    param.requires_grad = False

Update the Saving Wieght Function to support LoRa :

utils_Checkpoint.py and Trainer.py

def save_checkpoint(self, filename, extra_state):
        """Save all training state in a checkpoint file."""
        logger.info(f"Saving checkpoint to {filename}")
        # call state_dict on all ranks in case it needs internal communication
        state_dict = utils.move_to_cpu(self.state_dict())
        state_dict["extra_state"].update(extra_state)
        if self.should_save_checkpoint_on_current_rank:

            """
            save the model Low-Rank LoRa
            
            """
            checkpoint_utils._torch_persistent_save_LoRa(
                state_dict,
                filename,
                async_write=self.cfg.checkpoint.write_checkpoints_asynchronously,
            )
        logger.info(f"Finished saving checkpoint to {filename}")
            

def _torch_persistent_save_LoRa(obj, f):
    if isinstance(f, str):
        with PathManager.open(f, "wb") as h:
            torch_persistent_save(obj, h)
        return
    for i in range(3):
        try:     
            """
            Saving the Wieghts LoRa 
            
            """       
            return torch.save(lora.lora_state_dict(obj), f)

            #return torch.save(obj, f)

        except Exception:
            if i == 2:
                logger.error(traceback.format_exc())
                raise