Initial PTD model release

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,201 @@
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README.md

@@ -0,0 +1,182 @@
+---
+license: apache-2.0
+library_name: transformers
+pipeline_tag: image-text-to-text
+base_model: Qwen/Qwen3-VL-4B-Instruct
+language:
+- en
+tags:
+- qwen3-vl
+- video
+- video-grounding
+- spatio-temporal-video-grounding
+- temporal-localization
+- parallel-tube-decoding
+- ptd
+---
+
+# 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](https://github.com/mbzuai-oryx/ParallelTubeDecoding)
+- **Project page:** [Parallel Tube Decoding](https://mbzuai-oryx.github.io/parallel-tube-decoding/)
+- **Base model:** [Qwen/Qwen3-VL-4B-Instruct](https://huggingface.co/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](https://github.com/mbzuai-oryx/ParallelTubeDecoding)
+code. Standard Transformers loading provides the Qwen3-VL architecture, but
+PTD generation and Decoupled Block Attention are implemented by the released
+codebase.
+
+```bash
+git clone https://github.com/mbzuai-oryx/ParallelTubeDecoding.git
+cd ParallelTubeDecoding
+
+conda create -n ptd python=3.11 -y
+conda activate ptd
+pip install --upgrade pip
+pip install -r requirements.txt
+```
+
+The release uses:
+
+```text
+transformers==5.12.1
+```
+
+For evaluation, set the model path to this Hugging Face repository:
+
+```bash
+export MODEL="MBZUAI/ParallelTubeDecoding-Qwen3-VL-4B"
+```
+
+Then follow the repository's
+[evaluation instructions](https://github.com/mbzuai-oryx/ParallelTubeDecoding/blob/main/evaluation/README.md).
+The released adapter is based on `lmms-eval` v0.7.1 at commit
+`88b23e2bfa16a1edbc16e9e238ed82130b3a4f56`.
+
+## Evaluation configuration
+
+The paper evaluation uses:
+
+```text
+fps=2
+max_num_frames=64
+min_pixels=131072
+max_pixels=786432
+temporal_patch_size=1
+```
+
+For PTD inference, use:
+
+```text
+attn_implementation=sdpa
+vision_attn_implementation=flash_attention_2
+ptd_attn_implementation=flash_attention_2
+```
+
+Quantized (NTP) inference uses:
+
+```text
+attn_implementation=sdpa
+```
+
+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.
+
+Both stages used LoRA with rank 32 and alpha 64 while optimizing the newly
+introduced localization-token embeddings. The released checkpoint contains
+the merged weights. Charades-STA and ActivityNet Captions were not used for
+SFT; results on both are zero-shot.
+
+The training video configuration was:
+
+```text
+video_max_pixels=151200
+fps=2
+max_frames=64
+temporal_patch_size=1
+```
+
+No training data, GRPO data, or GRPO data-selection code is included with the
+model release.
+
+## Results
+
+PTD is evaluated on VidSTG, HC-STVG v1/v2, Charades-STA, and ActivityNet
+Captions. On the decoding-efficiency protocol, PTD achieves a Tube Completion
+Latency (TCL) of 0.4 seconds and 45.9 Boxes Per Second (BPS). Relative to
+unquantized autoregressive decoding, this corresponds to 79x lower TCL and 92x
+higher BPS while improving grounding accuracy.
+
+The efficiency measurements use BF16, batch size 1, and a single 64-GB AMD
+Instinct MI210 GPU with synchronized decode-only timing. Refer to the paper and
+project page for complete benchmark tables and comparisons.
+
+## Intended use
+
+This model is intended for research on:
+
+- spatio-temporal grounding of a referred entity in video;
+- temporal localization of described events;
+- efficient structured localization with PTD; and
+- evaluation on VidSTG, HC-STVG, Charades-STA, and ActivityNet Captions.
+
+Users are responsible for complying with the licenses and terms of the source
+videos and evaluation datasets.
+
+## Limitations
+
+The released formulation predicts one continuous temporal interval and one
+spatial tube per query. It is not designed for multiple disjoint event
+occurrences or multiple simultaneously valid instances. Temporally subtle
+state changes can produce ambiguous boundaries, while small, fast-moving, or
+occluded targets remain challenging.
+
+## Citation
+
+```bibtex
+@misc{rasheed2026locateanythingvideos,
+  title  = {Locate Anything in Videos: Rethinking Efficient Generative Spatio-Temporal Video Grounding},
+  author = {Hanoona Rasheed and Haania Siddiqui and Ming-Hsuan Yang and Fahad Shahbaz Khan and Salman Khan},
+  year   = {2026}
+}
+```
+
+## Acknowledgements
+
+This work builds on Qwen3-VL and the Qwen-VL-Series-Finetune training
+framework. See the code repository for full acknowledgements.

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {%- if messages[0].content is string %}
+            {{- messages[0].content }}
+        {%- else %}
+            {%- for content in messages[0].content %}
+                {%- if 'text' in content %}
+                    {{- content.text }}
+                {%- endif %}
+            {%- endfor %}
+        {%- endif %}
+        {{- '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
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+        {%- if messages[0].content is string %}
+            {{- messages[0].content }}
+        {%- else %}
+            {%- for content in messages[0].content %}
+                {%- if 'text' in content %}
+                    {{- content.text }}
+                {%- endif %}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set image_count = namespace(value=0) %}
+{%- set video_count = namespace(value=0) %}
+{%- for message in messages %}
+    {%- if message.role == "user" %}
+        {{- '<|im_start|>' + message.role + '\n' }}
+        {%- if message.content is string %}
+            {{- message.content }}
+        {%- else %}
+            {%- for content in message.content %}
+                {%- if content.type == 'image' or 'image' in content or 'image_url' in content %}
+                    {%- set image_count.value = image_count.value + 1 %}
+                    {%- if add_vision_id %}Picture {{ image_count.value }}: {% endif -%}
+                    <|vision_start|><|image_pad|><|vision_end|>
+                {%- elif content.type == 'video' or 'video' in content %}
+                    {%- set video_count.value = video_count.value + 1 %}
+                    {%- if add_vision_id %}Video {{ video_count.value }}: {% endif -%}
+                    <|vision_start|><|video_pad|><|vision_end|>
+                {%- elif 'text' in content %}
+                    {{- content.text }}
+                {%- endif %}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role + '\n' }}
+        {%- if message.content is string %}
+            {{- message.content }}
+        {%- else %}
+            {%- for content_item in message.content %}
+                {%- if 'text' in content_item %}
+                    {{- content_item.text }}
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+            {%- endfor %}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and message.content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
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+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {%- if message.content is string %}
+            {{- message.content }}
+        {%- else %}
+            {%- for content in message.content %}
+                {%- if content.type == 'image' or 'image' in content or 'image_url' in content %}
+                    {%- set image_count.value = image_count.value + 1 %}
+                    {%- if add_vision_id %}Picture {{ image_count.value }}: {% endif -%}
+                    <|vision_start|><|image_pad|><|vision_end|>
+                {%- elif content.type == 'video' or 'video' in content %}
+                    {%- set video_count.value = video_count.value + 1 %}
+                    {%- if add_vision_id %}Video {{ video_count.value }}: {% endif -%}
+                    <|vision_start|><|video_pad|><|vision_end|>
+                {%- elif 'text' in content %}
+                    {{- content.text }}
+                {%- endif %}
+            {%- endfor %}
+        {%- endif %}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

@@ -0,0 +1,74 @@
+{
+  "architectures": [
+    "Qwen3VLForConditionalGeneration"
+  ],
+  "bos_token_id": null,
+  "debug_nan_checks": false,
+  "debug_nan_max_tensors": 5,
+  "dtype": "float16",
+  "eos_token_id": 151645,
+  "force_vision_tower_fp32": false,
+  "image_token_id": 151655,
+  "model_type": "qwen3_vl",
+  "ptd_block_size": 6,
+  "ptd_num_time_tokens": 100,
+  "pad_token_id": 151643,
+  "text_config": {
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "bos_token_id": 151643,
+    "dtype": "float16",
+    "eos_token_id": 151645,
+    "head_dim": 128,
+    "hidden_act": "silu",
+    "hidden_size": 2560,
+    "initializer_range": 0.02,
+    "intermediate_size": 9728,
+    "max_position_embeddings": 262144,
+    "model_type": "qwen3_vl_text",
+    "num_attention_heads": 32,
+    "num_hidden_layers": 36,
+    "num_key_value_heads": 8,
+    "pad_token_id": null,
+    "rms_norm_eps": 1e-06,
+    "rope_parameters": {
+      "mrope_interleaved": true,
+      "mrope_section": [
+        24,
+        20,
+        20
+      ],
+      "rope_theta": 5000000,
+      "rope_type": "default"
+    },
+    "tie_word_embeddings": true,
+    "use_cache": true,
+    "vocab_size": 152775
+  },
+  "tie_word_embeddings": true,
+  "transformers_version": "5.12.1",
+  "video_token_id": 151656,
+  "vision_config": {
+    "deepstack_visual_indexes": [
+      5,
+      11,
+      17
+    ],
+    "depth": 24,
+    "dtype": "float16",
+    "hidden_act": "gelu_pytorch_tanh",
+    "hidden_size": 1024,
+    "in_channels": 3,
+    "initializer_range": 0.02,
+    "intermediate_size": 4096,
+    "model_type": "qwen3_vl_vision",
+    "num_heads": 16,
+    "num_position_embeddings": 2304,
+    "out_hidden_size": 2560,
+    "patch_size": 16,
+    "spatial_merge_size": 2,
+    "temporal_patch_size": 2
+  },
+  "vision_end_token_id": 151653,
+  "vision_start_token_id": 151652
+}

generation_config.json

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+  "do_sample": true,
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+  "repetition_penalty": 1.0,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "5.12.1"
+}

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processor_config.json

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+{
+  "image_processor": {
+    "do_convert_rgb": true,
+    "do_normalize": true,
+    "do_rescale": true,
+    "do_resize": true,
+    "image_mean": [
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+      0.5,
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+    "resample": 3,
+    "rescale_factor": 0.00392156862745098,
+    "size": {
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+      "shortest_edge": 65536
+    },
+    "temporal_patch_size": 2
+  },
+  "processor_class": "Qwen3VLProcessor",
+  "video_processor": {
+    "do_convert_rgb": true,
+    "do_normalize": true,
+    "do_rescale": true,
+    "do_resize": true,
+    "do_sample_frames": true,
+    "fps": 2,
+    "image_mean": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "image_std": [
+      0.5,
+      0.5,
+      0.5
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+    "max_frames": 768,
+    "merge_size": 2,
+    "min_frames": 4,
+    "patch_size": 16,
+    "resample": 3,
+    "rescale_factor": 0.00392156862745098,
+    "return_metadata": false,
+    "size": {
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+      "shortest_edge": 4096
+    },
+    "temporal_patch_size": 2,
+    "video_processor_type": "Qwen3VLVideoProcessor"
+  }
+}

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a043d187c5d9c1d62fd57444cae67aa3279a154e5a044367e82a0a23c6c8c7ac
+size 11624948

tokenizer_config.json

@@ -0,0 +1,31 @@
+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "is_local": true,
+  "local_files_only": false,
+  "model_max_length": 262144,
+  "pad_token": "<|endoftext|>",
+  "processor_class": "Qwen3VLProcessor",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}