tiny random minicpm v 4 5 EU AI Act Compliance Profile

This tiny model is for debugging. It is randomly initialized with the config adapted from openbmb/MiniCPM-V-4_5.

Example usage:

import numpy as np
import torch
from PIL import Image
from transformers import AutoModel, AutoTokenizer

model_id = "tiny-random/minicpm-v-4_5"
model = AutoModel.from_pretrained(model_id, trust_remote_code=True,
                                  attn_implementation='sdpa', torch_dtype=torch.bfloat16)
model = model.eval().cuda()
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)

image = Image.fromarray(np.random.randint(0, 255, (224, 224, 3), dtype=np.uint8), 'RGB')
question = "What is the landform in the picture?"
msgs = [{'role': 'user', 'content': [image, question]}]
answer = model.chat(
    msgs=msgs,
    image=image,
    tokenizer=tokenizer,
    max_new_tokens=32,
)
print(answer)

# Second round chat, pass history context of multi-turn conversation
msgs.append({"role": "assistant", "content": [answer]})
msgs.append({"role": "user", "content": [
            "What should I pay attention to when traveling here?"]})
answer = model.chat(
    msgs=msgs,
    image=None,
    tokenizer=tokenizer,
    max_new_tokens=32,
)
print(answer)

Codes to create this repo:

import json
from pathlib import Path

import accelerate
import torch
from huggingface_hub import hf_hub_download
from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoProcessor,
    AutoTokenizer,
    GenerationConfig,
    set_seed,
)

source_model_id = "openbmb/MiniCPM-V-4_5"
save_folder = "/tmp/tiny-random/minicpm-v-4_5"

processor = AutoProcessor.from_pretrained(source_model_id, trust_remote_code=True)
processor.save_pretrained(save_folder)

with open(hf_hub_download(source_model_id, filename='config.json', repo_type='model',), 'r', encoding='utf-8') as f:
    config_json = json.load(f)
for k, v in config_json['auto_map'].items():
    confi

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Tags

Library

Base Model