Where to Find LingBot-Map Model Checkpoints and Variants: Complete Download Guide

LingBot-Map model checkpoints are publicly hosted on Hugging Face and ModelScope under the robbyant/lingbot-map repository, with three PyTorch variants available: lingbot-map-long.pt, lingbot-map.pt, and lingbot-map-stage1.pt.

The Robbyant/lingbot-map repository distributes its pretrained weights as standard PyTorch .pt files through major model-hosting platforms. These checkpoints capture different training stages and optimization targets, allowing you to select the appropriate variant for your specific video understanding workload.

Available LingBot-Map Model Variants

According to README.md lines 133-140, the repository provides three distinct checkpoint variants:

• lingbot-map-long – Optimized for very long video streams and large-scale scenes.
• lingbot-map (default) – The balanced checkpoint used in the paper and most demos.
• lingbot-map-stage1 – The stage-1 training checkpoint; can be loaded into the VGGT backbone for bidirectional inference (c2w).

All variants are stored as PyTorch tensor files and accessible through both Hugging Face and ModelScope repositories.

Downloading LingBot-Map Checkpoints

Using the huggingface_hub Python Library

Programmatically fetch specific checkpoint files using the huggingface_hub library. This method handles caching and returns the local path:

from huggingface_hub import hf_hub_download

# Choose the variant you need

repo_id = "robbyant/lingbot-map"
filename = "lingbot-map-long.pt"          # or "lingbot-map.pt", "lingbot-map-stage1.pt"

# Downloads the file into a local cache and returns the path

ckpt_path = hf_hub_download(repo_id=repo_id, filename=filename)

print(f"Checkpoint saved at: {ckpt_path}")

Using the Hugging Face CLI

Download checkpoints directly from the terminal using the Hugging Face CLI tool:

# Download the long-form variant

huggingface-cli download robbyant/lingbot-map lingbot-map-long.pt

# Download the default variant

huggingface-cli download robbyant/lingbot-map lingbot-map.pt

ModelScope Alternative

For users preferring ModelScope, identical files are hosted at https://www.modelscope.cn/models/Robbyant/lingbot-map. You can download the same .pt files through the ModelScope web interface or their Python SDK.

Loading Checkpoints in Your Code

The LingBot-Map codebase implements checkpoint loading using standard PyTorch deserialization. In demo.py at lines 154-155, the inference script loads weights via torch.load:

import torch

ckpt_path = "lingbot-map-long.pt"          # path obtained from the download step above

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
ckpt = torch.load(ckpt_path, map_location=device, weights_only=False)

# The checkpoint can now be passed to the model constructor

# model = LingBotMapModel.from_checkpoint(ckpt)

The base aggregator class in lingbot_map/aggregator/base.py at line 221 contains additional loading logic for library usage, while benchmark/methods/lingbot_map.py at line 130 handles checkpoint initialization for evaluation scripts.

Summary

• Three variants exist: lingbot-map-long (extended video), lingbot-map (default), and lingbot-map-stage1 (bidirectional inference).
• Hosting platforms: Hugging Face (robbyant/lingbot-map) and ModelScope (Robbyant/lingbot-map).
• Download methods: huggingface_hub library, huggingface-cli, or direct web UI.
• File format: PyTorch .pt files loaded via torch.load().
• Key implementation files: demo.py (lines 154-155), lingbot_map/aggregator/base.py (line 221), and benchmark/methods/lingbot_map.py (line 130).

Frequently Asked Questions

What are the differences between the three LingBot-Map checkpoint variants?

The lingbot-map-long variant is optimized for very long video streams and large-scale scenes requiring extended context windows. The default lingbot-map checkpoint provides balanced performance characteristics used in the primary paper experiments. lingbot-map-stage1 represents an intermediate training checkpoint specifically compatible with the VGGT backbone for bidirectional camera-to-world (c2w) inference tasks.

How do I download LingBot-Map checkpoints programmatically?

Use the huggingface_hub Python library with hf_hub_download(repo_id="robbyant/lingbot-map", filename="lingbot-map.pt") to fetch files with automatic caching. Alternatively, install the Hugging Face CLI and run huggingface-cli download robbyant/lingbot-map lingbot-map.pt to retrieve weights directly to your local filesystem.

Can I use ModelScope instead of Hugging Face to download the weights?

Yes, identical checkpoints are mirrored on ModelScope at https://www.modelscope.cn/models/Robbyant/lingbot-map. This provides an alternative download source for users who prefer ModelScope's infrastructure or require access through Chinese cloud networks.

Where is the checkpoint loading logic implemented in the source code?

The primary loading mechanism resides in demo.py at lines 154-155, which calls torch.load() with map_location set to the available CUDA device or CPU. Additional loading implementations appear in lingbot_map/aggregator/base.py at line 221 for the base aggregator class, and in benchmark/methods/lingbot_map.py at line 130 for benchmark evaluation scenarios.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}