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modify convert script and readme

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caitianchi 2024-08-12 20:17:56 +08:00
parent 89d378c76b
commit 1ec79f04ab
4 changed files with 4 additions and 51 deletions
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4
examples/llava/README-minicpmv2.5.md
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@ -15,8 +15,8 @@ cd llama.cpp
Convert PyTorch model to gguf files (You can also download the converted [gguf](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf) by us)
```bash
python ./examples/minicpmv/minicpmv-surgery.py -m ../MiniCPM-Llama3-V-2_5
python ./examples/minicpmv/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-Llama3-V-2_5 --minicpmv-projector ../MiniCPM-Llama3-V-2_5/minicpmv.projector --output-dir ../MiniCPM-Llama3-V-2_5/ --image-mean 0.5 0.5 0.5 --image-std 0.5 0.5 0.5
python ./examples/minicpmv/minicpmv-convert/minicpmv-surgery.py -m ../MiniCPM-Llama3-V-2_5
python ./examples/minicpmv/minicpmv-convert/minicpmv2_5-convert-image-encoder-to-gguf.py -m ../MiniCPM-Llama3-V-2_5 --minicpmv-projector ../MiniCPM-Llama3-V-2_5/minicpmv.projector --output-dir ../MiniCPM-Llama3-V-2_5/ --image-mean 0.5 0.5 0.5 --image-std 0.5 0.5 0.5
python ./convert_hf_to_gguf.py ../MiniCPM-Llama3-V-2_5/model
# quantize int4 version

2
examples/llava/README-minicpmv2.6.md
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@ -16,7 +16,7 @@ git checkout minicpmv-main
Convert PyTorch model to gguf files (You can also download the converted [gguf](https://huggingface.co/openbmb/MiniCPM-V-2_6-gguf) by us)
```bash
python ./examples/llava/minicpmv-convert/minicpmv2_6-surgery.py -m ../MiniCPM-V-2_6
python ./examples/llava/minicpmv-convert/minicpmv-surgery.py -m ../MiniCPM-V-2_6
python ./examples/llava/minicpmv-convert/minicpmv2_6-convert-image-encoder-to-gguf.py -m ../MiniCPM-V-2_6 --minicpmv-projector ../MiniCPM-V-2_6/minicpmv.projector --output-dir ../MiniCPM-V-2_6/ --image-mean 0.5 0.5 0.5 --image-std 0.5 0.5 0.5
python ./convert_hf_to_gguf.py ../MiniCPM-V-2_6/model

3
examples/llava/minicpmv-convert/minicpmv2_6-surgery.py → examples/llava/minicpmv-convert/minicpmv-surgery.py
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@ -4,7 +4,7 @@ import torch
from transformers import AutoModel, AutoTokenizer
ap = argparse.ArgumentParser()
ap.add_argument("-m", "--model", help="Path to MiniCPM-V-2.6 model")
ap.add_argument("-m", "--model", help="Path to MiniCPM-V model")
args = ap.parse_args()
# find the model part that includes the the multimodal projector weights
@ -29,7 +29,6 @@ if len(clip_tensors) > 0:
f.write("{}\n")
config = model.llm.config
config._name_or_path = "openbmb/MiniCPM-V-2.6"
config.auto_map = {
"AutoConfig": "configuration_minicpm.MiniCPMConfig",
"AutoModel": "modeling_minicpm.MiniCPMModel",

46
examples/llava/minicpmv-convert/minicpmv2_5-surgery.py
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@ -1,46 +0,0 @@
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")
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.")