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license: mit
library_name: pytorch
tags:
- wireless
- channel-modeling
- foundation-model
- deepmimo
---
# MultiPathFormer Foundation Checkpoint
This model card accompanies the released foundation checkpoint for:
**MultiPathFormer: A Foundation Model for Multipath Wireless Propagation**
Code release:
https://github.com/gblessed/MultiPathFormer
Hugging Face model repo:
https://huggingface.co/gblessed/multipathformer
## Model
- Architecture: first-step residual corridor-concat MultiPathFormer.
- Hidden dimension: 1024.
- Decoder layers: 12.
- Attention heads: 8.
- Prefix tokens: 4.
- Maximum generated paths: 25.
- Outputs: delay, scaled power, phase, AoA azimuth/elevation, AoD azimuth/elevation, and interaction labels.
## Training Data
The checkpoint was trained on 27 DeepMIMO ray-tracing scenarios listed in `configs/foundation_27scenarios.yaml`.
## Intended Use
The model is intended for research on geometry-aware wireless propagation modeling and downstream task reuse. It should be used with the preprocessing artifacts released alongside the checkpoint.
## Quick Inference Example
Install the code release and dependencies:
```bash
git clone https://github.com/gblessed/MultiPathFormer.git
cd MultiPathFormer
python -m pip install -r requirements.txt
```
Run inference from an already constructed augmented prompt and first-step baseline:
```python
import json
from pathlib import Path
import numpy as np
from huggingface_hub import hf_hub_download
from multipathformer.inference import MultiPathFormerPredictor, prediction_to_rows
repo_id = "gblessed/multipathformer"
checkpoint = hf_hub_download(
repo_id=repo_id,
filename="first_step_residual_corridor_concat_27scenarios_44710a4a_best_model_checkpoint.pth",
)
artifacts = hf_hub_download(repo_id=repo_id, filename="preprocessing_artifacts.json")
augmented_prompt = np.asarray(
json.loads(Path("examples/augmented_prompt.json").read_text()),
dtype=np.float32,
)
first_step_baseline = np.asarray(
json.loads(Path("examples/first_step_baseline.json").read_text()),
dtype=np.float32,
)
predictor = MultiPathFormerPredictor(checkpoint, artifacts_path=artifacts)
prediction = predictor.predict_from_augmented_prompt(
augmented_prompt,
first_step_baseline,
max_steps=25,
)
for row in prediction_to_rows(prediction)[:5]:
print(row)
```
The augmented prompt follows the released corridor-concat recipe: TX/RX position, first-step delay/power baseline, first-step standard deviation, and scene/corridor descriptors. The helper script `inference_example.py` in this model repo contains the same flow.
## Limitations
The checkpoint is trained on simulated ray-tracing data. It has not yet been validated as a standalone replacement for real-world measurement campaigns. Phase-accurate channel synthesis is especially sensitive and should be evaluated carefully for each use case.
## Files
- `first_step_residual_corridor_concat_27scenarios_44710a4a_best_model_checkpoint.pth`
- `preprocessing_artifacts.json`
- Optional: `model.safetensors`
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