KBaba7/llama.cpp

1import argparse2import glob3import os4import torch5 6 7ap = argparse.ArgumentParser()8ap.add_argument("-m", "--model", help="Path to LLaVA v1.5 model")9args = ap.parse_args()10 11# find the model part that includes the the multimodal projector weights12path = sorted(glob.glob(f"{args.model}/pytorch_model*.bin"))[-1]13checkpoint = torch.load(path)14 15# get a list of mm tensor names16mm_tensors = [k for k, v in checkpoint.items() if k.startswith("model.mm_projector")]17 18# store these tensors in a new dictionary and torch.save them19projector = {name: checkpoint[name].float() for name in mm_tensors}20torch.save(projector, f"{args.model}/llava.projector")21 22# BakLLaVA models contain CLIP tensors in it23clip_tensors = [k for k, v in checkpoint.items() if k.startswith("model.vision_tower")]24if len(clip_tensors) > 0:25    clip = {name.replace("vision_tower.vision_tower.", ""): checkpoint[name].float() for name in clip_tensors}26    torch.save(clip, f"{args.model}/llava.clip")27 28 29    # added tokens should be removed to be able to convert Mistral models30    if os.path.exists(f"{args.model}/added_tokens.json"):31        with open(f"{args.model}/added_tokens.json", "w") as f:32            f.write("{}\n")33 34 35 36print("Done!")37print(f"Now you can convert {args.model} to a regular LLaMA GGUF file.")38print(f"Also, use {args.model}/llava.projector to prepare a llava-encoder.gguf file.")39