import argparse import glob import os import torch ap = argparse.ArgumentParser() ap.add_argument("-m", "--model", help="Path to LLaVA v1.5 model") args = ap.parse_args() # find the model part that includes the the multimodal projector weights path = sorted(glob.glob(f"{args.model}/pytorch_model*.bin"))[-1] checkpoint = torch.load(path) # get a list of mm tensor names mm_tensors = [k for k, v in checkpoint.items() if k.startswith("model.mm_projector")] # store these tensors in a new dictionary and torch.save them projector = {name: checkpoint[name].float() for name in mm_tensors} torch.save(projector, f"{args.model}/llava.projector") # BakLLaVA models contain CLIP tensors in it clip_tensors = [k for k, v in checkpoint.items() if k.startswith("model.vision_tower")] if len(clip_tensors) > 0: clip = {name.replace("vision_tower.vision_tower.", ""): checkpoint[name].float() for name in clip_tensors} torch.save(clip, f"{args.model}/llava.clip") # added tokens should be removed to be able to convert Mistral models if os.path.exists(f"{args.model}/added_tokens.json"): with open(f"{args.model}/added_tokens.json", "w") as f: f.write("{}\n") print("Done!") print(f"Now you can convert {args.model} to a regular LLaMA GGUF file.") print(f"Also, use {args.model}/llava.projector to prepare a llava-encoder.gguf file.")