ParallelTubeDecoding-Qwen3-VL

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What ParallelTubeDecoding-Qwen3-VL is

ParallelTubeDecoding-Qwen3-VL-4B This repository contains the merged Qwen3-VL-4B checkpoint for Parallel Tube Decoding (PTD) from Locate Anything in Videos: Rethinking Efficient Generative Spatio-Temporal Video Grounding. PTD first predicts the temporal interval of a queried event and then generates all time-conditioned spatial blocks in parallel. Decoupled Block Attention allows every spatial block to access the shared video-query context and the predicted temporal block while preventing dependencies between spatial blocks. The complete spatial tube is therefore produced in one parallel decoding round after temporal localization. - Code: mbzuai-oryx/ParallelTubeDecoding - Project page: Parallel Tube Decoding - Base model: Qwen/Qwen3-VL-4B-Instruct Model details - Backbone: Qwen3-VL-4B-Instruct - Method: Parallel Tube Decoding (PTD) - Tasks: spatio-temporal video grounding and temporal localization - Spatial representation: 1,001 discrete coordinate tokens over [0, 1000] - Temporal representation: 100 discrete time tokens - PTD spatial block size: 6 - Checkpoint format: merged model weights in FP16 - Transformers version: 5.12.1 The checkpoint supports the released PTD inference path and Quantized (NTP) inference. It does not include data or a separate LoRA adapter. Important loading note Use this checkpoint with the released ParallelTubeDecoding code. Standard Transformers loading provides the Qwen3-VL architecture, but PTD generation and Decoupled Block Attention are implemented by the released codebase. The release uses: For evaluation, set the model path to this Hugging Face repository: Then follow the repository's evaluation instructions. The released adapter is based on lmms-eval v0.7.1 at commit 88b23e2bfa16a1edbc16e9e238ed82130b3a4f56. Evaluation configuration The paper evaluation uses: For PTD inference, use: Quantized (NTP) inference uses: FlashAttention-2 must match the installed PyTorch and CUDA/ROCm environment. Training The released model was trained in two stages: 1. Supervised fine-tuning with the joint NTP/MTP formulation on the training splits of VidSTG and HC-STVG v1/v2. 2. Localization-aware GRPO with temporal IoU and spatial GIoU/L1 rewards.

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Once AxForge deploys paralleltubedecoding-qwen3-vl for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (paralleltubedecoding-qwen3-vl below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/chat/completions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"paralleltubedecoding-qwen3-vl","messages":[{"role":"user","content":"Hello"}]}'

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