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wjdghks950/promac-futloc-stage1b-cot-trajectory
promac-futloc-stage1b-cot-trajectory is a image-text-to-text model from wjdghks950. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as other.
Public, inference-only Qwen3-VL export of the ProMAC FutLoc Stage-1b structured-CoT v4 checkpoint selected by validation ADE.
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From the Hugging Face model README
Public, inference-only Qwen3-VL export of the ProMAC FutLoc Stage-1b structured-CoT v4 checkpoint selected by validation ADE.
On all 3,016 examples of the FutLoc structured-CoT v4 goal-overlay validation split (futloc-v4-val), deterministic evaluation reported:
| Checkpoint | ADE | FDE | L2 First 4 | DTW | DTW % path | Miss @20% |
|---|---|---|---|---|---|---|
checkpoint-best-ade, training step 4200 | 109.92 | 84.68 | 102.16 | 67.857 | 9.6 | 32.4 |
FutLoc coordinates are pixels in the 1280 x 720 frame over 40 waypoints.
import torch
from transformers import AutoProcessor, Qwen3VLForConditionalGeneration
model_id = "wjdghks950/promac-futloc-stage1b-cot-trajectory"
processor = AutoProcessor.from_pretrained(model_id, fix_mistral_regex=True)
model = Qwen3VLForConditionalGeneration.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
Use the FutLoc structured-CoT v4 goal-only-overlay prompt schema and answer_only assistant format. Set max_new_tokens=1024; 512 tokens truncated trajectories in the full offline evaluation.
This repository contains the merged Qwen3-VL inference model and processor/tokenizer. Trajectory inference uses the standard Qwen generate() path. It intentionally excludes the ProMAC future-state predictor, auxiliary reasoning decoder and visual adapter, SIGReg module, optimizer/scheduler state, raw .pt checkpoint, logs, and datasets. It is not a resumable ProMAC training checkpoint.
The Stage-1b partial checkpoint was merged onto Qwen/Qwen3-VL-4B-Instruct revision ebb281ec70b05090aa6165b016eac8ec08e71b17. See provenance.json and SHA256SUMS.
The upstream Qwen model card declares Apache-2.0. No standalone license was established locally for the ProMAC fine-tuned weights, so this repository uses license: other. Ego4D data is distributed through a license-gated workflow; access to these weights does not grant rights to source data or derived imagery.
This model was evaluated on the documented FutLoc structured-CoT v4 validation split and may not generalize to other cameras, prompts, environments, or trajectory schemas. Reported metrics are offline evaluation results, not safety guarantees.