pcn-bridge-lab-connectors

PCN Bridge Lab Connector Weights

Connector-only weights for CLIP ViT-B/32 to Qwen2.5-0.5B-Instruct CLEVR experiments.

Code, training scripts, results, and plots: https://github.com/REDDITARUN/pcn-bridge-lab

This is an AI-assisted research/engineering experiment with a small human-in-the-loop workflow for direction, review, and decisions.

Base models are not included:

• Vision encoder: openai/clip-vit-base-patch32
• Language model: Qwen/Qwen2.5-0.5B-Instruct

Each .pt file contains only:

• connector state dict
• run config
• best validation loss
• candidate-choice test metrics

Optimizer state and full frozen model weights are intentionally omitted.

Results

Loading

Use the code in the GitHub repository to instantiate ConnectorVLM, then load the connector state dict from a file in connectors/.

import torch
from src.model import ConnectorVLM

ckpt = torch.load('connectors/pcn-depth3-s1-eqprop-momentum.pt', map_location='cpu')
model = ConnectorVLM(ckpt['config'])
model.connector.load_state_dict(ckpt['connector'])

Caveat

The primary metric is candidate-choice accuracy over valid CLEVR answers. Free-form generations remain noisy and should be treated as diagnostic only.