metadata
library_name: transformers
pipeline_tag: image-text-to-text
base_model:
- openbmb/MiniCPM-V-4.7-1B
This is a randomly initialized tiny model based on openbmb/MiniCPM-V-4.7-1B for testing and debugging.
| Feature | Source | This repo |
|---|---|---|
| Checkpoint tensor elements | 1.55B BF16 | 11.54M BF16 |
| Safetensors weight files | 3.11 GB (model.safetensors) | 23.11 MB (model.safetensors) |
| Architecture / input | MiniCPM-V 4.7 dense; text, image, video | Same |
| Vocabulary rows / tied embeddings | 248,094 / false | Same |
| Backbone layers / layer cadence | 24 / six 3-linear + 1-full cycles | 8 / two complete cycles |
| Hidden width / MLP width | 1,024 / 3,584 | 16 / 32 |
| Q heads / KV heads / head dimension | 8 / 2 / 256 | Same |
| Linear-attention key/value heads and dimensions | 16/16, 128/128 | Same |
| Vision layers / width / insert merger | 27 / 1,152 / after layer 6 | 4 / 128 / after layer 2 |
| M-RoPE | canvas; sections 11/11/10; partial 0.25 | canvas; sections 11/11/10; partial 0.25 |
| MTP | config declares one layer; no MTP tensors | Same |
| Checkpoint precision | BF16 | Same |
Two complete linear/linear/linear/full cycles are retained. The Qwen3.5 linear-attention path requires CUDA and Triton. The exact public snippet was verified on an NVIDIA A800-SXM4-80GB.
| File path | Size |
|---|---|
| model.safetensors | 23.1MB |
Example usage
Transformers (✅ verified)
The example sends one image and one predecoded video. The Qwen3.5 linear-attention path requires CUDA and Triton. Install transformers[torch]>=5.18.0 and Pillow.
def run_transformers(model_id: str):
import numpy as np
import torch
from PIL import Image, ImageDraw
from transformers import AutoModelForImageTextToText, AutoProcessor
from transformers.video_utils import VideoMetadata
model_id = str(model_id)
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForImageTextToText.from_pretrained(
model_id,
dtype=torch.bfloat16,
device_map="cuda",
attn_implementation="sdpa",
)
image = Image.new("RGB", (448, 448), "white")
ImageDraw.Draw(image).text((20, 20), "Hello", fill="black")
frames = np.stack(
[
np.asarray(Image.new("RGB", (64, 64), color))
for color in ((220, 30, 30), (30, 220, 30), (30, 30, 220), (220, 220, 30))
]
)
messages = [
{
"role": "user",
"content": [
{"type": "image", "image": image},
{"type": "video", "video": frames},
{"type": "text", "text": "Describe the image and the video."},
],
}
]
inputs = processor.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_dict=True,
return_tensors="pt",
downsample_mode="16x",
max_num_frames=4,
stack_frames=1,
max_slice_nums=1,
use_image_id=False,
enable_thinking=False,
do_sample_frames=False,
video_metadata=[VideoMetadata(total_num_frames=4, fps=2, frames_indices=[0, 1, 2, 3])],
).to(model.device)
with torch.no_grad():
logits = model(**inputs).logits
assert torch.isfinite(logits).all()
output = model.generate(**inputs, downsample_mode="16x", do_sample=False, max_new_tokens=10)
print(processor.decode(output[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True))
if __name__ == "__main__":
run_transformers(model_id="tiny-random/minicpm-v-4.7")
Creating this repo
- Code: .meta/create_model.py
- Model structure: .meta/model.log
- Environment: .meta/env.json
Change log
- 2026-11-01: Initial tiny MiniCPM-V 4.7 checkpoint.
This repo is generated by DeepSeek-V4.1-Flash.