mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-25 02:44:36 +00:00
48 lines
2.0 KiB
Python
48 lines
2.0 KiB
Python
|
import argparse
|
||
|
import os
|
||
|
import torch
|
||
|
from transformers import AutoModel, AutoTokenizer
|
||
|
|
||
|
ap = argparse.ArgumentParser()
|
||
|
ap.add_argument("-m", "--model", help="Path to MiniCPM-V-2.5 model")
|
||
|
args = ap.parse_args()
|
||
|
|
||
|
# find the model part that includes the the multimodal projector weights
|
||
|
model = AutoModel.from_pretrained(args.model, trust_remote_code=True, local_files_only=True)
|
||
|
checkpoint = model.state_dict()
|
||
|
|
||
|
# get a list of mm tensor names
|
||
|
mm_tensors = [k for k, v in checkpoint.items() if k.startswith("resampler")]
|
||
|
|
||
|
# 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}/minicpmv.projector")
|
||
|
|
||
|
clip_tensors = [k for k, v in checkpoint.items() if k.startswith("vpm")]
|
||
|
if len(clip_tensors) > 0:
|
||
|
clip = {name.replace("vpm.", ""): checkpoint[name].float() for name in clip_tensors}
|
||
|
torch.save(clip, f"{args.model}/minicpmv.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")
|
||
|
|
||
|
config = model.llm.config
|
||
|
config._name_or_path = "openbmb/MiniCPM-Llama3-V-2.5"
|
||
|
config.auto_map = {
|
||
|
"AutoConfig": "configuration_minicpm.MiniCPMConfig",
|
||
|
"AutoModel": "modeling_minicpm.MiniCPMModel",
|
||
|
"AutoModelForCausalLM": "modeling_minicpm.MiniCPMForCausalLM",
|
||
|
"AutoModelForSeq2SeqLM": "modeling_minicpm.MiniCPMForCausalLM",
|
||
|
"AutoModelForSequenceClassification": "modeling_minicpm.MiniCPMForSequenceClassification"
|
||
|
}
|
||
|
model.llm.save_pretrained(f"{args.model}/model")
|
||
|
tok = AutoTokenizer.from_pretrained(args.model, trust_remote_code=True)
|
||
|
tok.save_pretrained(f"{args.model}/model")
|
||
|
# os.system(f"cp {args.model}/modeling_minicpm.py {args.model}/MiniCPM_l3/modeling_minicpm.py")
|
||
|
|
||
|
print("Done!")
|
||
|
print(f"Now you can convert {args.model} to a regular LLaMA GGUF file.")
|
||
|
print(f"Also, use {args.model}/minicpmv.projector to prepare a minicpmv-encoder.gguf file.")
|