tiny-random/minicpm-robot-track

This is a randomly initialized tiny model based on openbmb/MiniCPM-RobotTrack for testing and debugging.

Feature	Source	This repo
Checkpoint tensor elements	465.65M BF16, 1.13M F32	2.39M BF16, 75.13K F32
Safetensors weight files	931.43 MB (model.safetensors)	5.09 MB (model.safetensors)
Backbone layers / width / heads	24 / 1024 / 16	4 / 32 / 1
Backbone MLP / KV heads	4096 / 2	64 / 1
Trajectory funnel linear stages	6	6
External visual feature dimension	1536	Same
History / coarse tokens / current fine tokens	31 / 4 per frame / 64	Same
Waypoints / action dimensions	8 / [x, y, yaw]	Same
Text vocabulary rows	73448	Same
Stored backbone precision	BF16	Same
Input / output	Instruction, DINOv3+SigLIP tokens → trajectory	Same

RobotTrack accepts fused DINOv3 and SigLIP features, not raw pixels. The source checkpoint does not include those visual encoders; the example validates both required feature streams directly.

File path	Size
model.safetensors	5.1MB

Example usage

Transformers (✅ verified)

def run_transformers(model_id: str):
    import torch
    from transformers import AutoModel, AutoTokenizer

    model = AutoModel.from_pretrained(model_id, trust_remote_code=True, dtype=torch.bfloat16).eval().cuda()
    tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
    if tokenizer.pad_token_id is None:
        tokenizer.pad_token = tokenizer.eos_token
    text = tokenizer("Follow the person in the blue shirt.", return_tensors="pt")
    coarse = torch.zeros(1, 31 * 4, 1536, device=model.device)
    coarse_time = torch.arange(31, device=model.device).repeat_interleave(4).unsqueeze(0)
    fine = torch.zeros(1, 64, 1536, device=model.device)
    fine_time = torch.full((1, 64), 31, dtype=torch.long, device=model.device)
    output = model(
        input_ids=text.input_ids.to(model.device),
        attention_mask=text.attention_mask.to(model.device),
        coarse_tokens=coarse,
        coarse_time_indices=coarse_time,
        fine_tokens=fine,
        fine_time_indices=fine_time,
    )
    assert output.trajectories.shape == (1, 8, 3)
    assert torch.isfinite(output.trajectories).all()
    print(output.trajectories.shape)


if __name__ == "__main__":
    run_transformers(model_id="tiny-random/minicpm-robot-track")

Creating this repo

• Code: .meta/create_model.py
• Model structure: .meta/model.log
• Environment: .meta/env.json

Change log

• 2026-10-27: Created a tiny MiniCPM RobotTrack visual-token trajectory policy.

This repo is generated by GPT-6.1-Sol.