Upload folder using huggingface_hub

.gitattributes

@@ -59,3 +59,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
 dataset4/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+dataset9/tokenizer.json filter=lfs diff=lfs merge=lfs -text

dataset9/.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
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+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
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+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
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+*tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

dataset9/.ms_upload_cache

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dataset9/README.md

@@ -0,0 +1,124 @@
+---
+license: apache-2.0
+base_model:
+- Qwen/Qwen3.5-9B
+language:
+- en
+- zh
+library_name: transformers
+pipeline_tag: image-text-to-text
+datasets:
+- yuanqianhao/Vision-OPD-6K
+tags:
+- multimodal
+- vision-language
+- fine-grained-visual-understanding
+- on-policy-distillation
+- self-distillation
+---
+
+# Vision-OPD-9B
+
+<p align="center">
+  📄 <a href="https://arxiv.org/abs/2605.18740">Paper</a> &nbsp;|&nbsp;
+  💻 <a href="https://github.com/VisionOPD/Vision-OPD">Code</a> &nbsp;|&nbsp;
+  🤗 <a href="https://huggingface.co/datasets/yuanqianhao/Vision-OPD-6K">Training Data</a> &nbsp;|&nbsp;
+  📦 <a href="https://hf.co/collections/yuanqianhao/vision-opd">Collection</a>
+</p>
+
+## Model Summary
+
+**Vision-OPD-9B** is a multimodal LLM built on [`Qwen/Qwen3.5-9B`](https://huggingface.co/Qwen/Qwen3.5-9B) and trained with
+**Vision-OPD (Vision On-Policy Distillation)**, the regional-to-global self-distillation framework introduced in
+[Vision-OPD: Learning to See Fine Details for Multimodal LLMs via On-Policy Self-Distillation](https://arxiv.org/abs/2605.18740).
+
+MLLMs often answer fine-grained questions correctly when given an evidence-centered **crop**, yet fail on the corresponding
+**full image** — a persistent *regional-to-global perception gap*. Vision-OPD closes this gap by letting the model act as its
+own teacher: a **crop-conditioned teacher** and a **full-image-conditioned student** are instantiated from the same MLLM, the
+student generates on-policy rollouts, and training minimizes the token-level divergence between the two distributions along
+these rollouts. The benefit of visual zooming is thereby internalized into a **single forward pass** — no external teacher,
+no ground-truth labels, no reward verifiers, and no inference-time tool use.
+
+With only **6.2K synthetic samples**, Vision-OPD-9B outperforms much larger open-source models (e.g., Qwen3.5-397B, Kimi-K2.6),
+closed-source models (e.g., GPT-5.4, Gemini-3.1-Pro), and "Thinking-with-Images" agentic methods on fine-grained visual
+understanding benchmarks, while preserving general multimodal abilities on holdout tasks.
+
+## Key Features
+
+- 🔍 **Sees fine details in one forward pass** — no cropping tools, zoom-in calls, or multi-step agentic inference at test time.
+- 🪞 **Self-distillation, label-free** — the model's own crop-conditioned perception supervises its full-image policy; no external teacher, ground-truth labels, or reward verifiers.
+- 🎯 **On-policy + dense supervision** — token-level divergence (JSD, β = 0.5) on the student's own rollouts avoids the exposure bias of SFT and the sparse rewards of RLVR.
+- 📉 **Data-efficient** — trained on only 6.2K automatically synthesized samples ([Vision-OPD-6K](https://huggingface.co/datasets/yuanqianhao/Vision-OPD-6K)), one epoch.
+- 🧠 **No forgetting** — general multimodal capability is preserved on holdout tasks (MMVP, CV-Bench, MMStar, POPE).
+
+## How It Works
+
+1. **Data synthesis** — evidence regions are proposed on unlabeled images via object identification and segmentation; a question answerable from the crop alone is generated, and the region's bounding box is overlaid on the full image for grounding. This yields triplets *(full image, crop, question)*.
+2. **Two policies, one model** — the teacher conditions on the privileged crop, the student on the full image.
+3. **On-policy distillation** — the student samples a rollout (up to 1024 tokens); at every position both policies are evaluated on the same student prefix, and the per-token JSD between them is minimized (top-K = 100 logits distillation). Gradients flow only through the student.
+4. **EMA teacher regularization** — the teacher is updated as an exponential moving average of the student (α = 0.05), which prevents teacher–student co-adaptation and training collapse.
+
+## Quickstart
+
+Weights load with standard `transformers` (≥ 5.5):
+
+```python
+from transformers import AutoModelForImageTextToText, AutoProcessor
+
+model_id = "yuanqianhao/Vision-OPD-9B"
+model = AutoModelForImageTextToText.from_pretrained(model_id, dtype="bfloat16", device_map="auto")
+processor = AutoProcessor.from_pretrained(model_id)
+
+messages = [{
+    "role": "user",
+    "content": [
+        {"type": "image", "url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"},
+        {"type": "text", "text": "What is the color of the label on the bottle in the background?"},
+    ],
+}]
+inputs = processor.apply_chat_template(
+    messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt"
+).to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=512)
+print(processor.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
+```
+
+Or serve with vLLM (OpenAI-compatible API), as used in our evaluation:
+
+```bash
+vllm serve yuanqianhao/Vision-OPD-9B --port 8000
+```
+
+To reproduce the reported numbers, use the released evaluation harness:
+
+```bash
+git clone https://github.com/VisionOPD/Vision-OPD && cd Vision-OPD
+bash eval/run_eval.sh   # see README for API base / judge / benchmark env vars
+```
+
+## Training Details
+
+| | |
+|---|---|
+| Base model | [`Qwen/Qwen3.5-9B`](https://huggingface.co/Qwen/Qwen3.5-9B) (non-thinking mode) |
+| Data | [Vision-OPD-6K](https://huggingface.co/datasets/yuanqianhao/Vision-OPD-6K) — 6.2K synthesized (full image, crop, question) triplets |
+| Objective | Token-level JSD (β = 0.5) on on-policy student rollouts, top-K = 100 logits distillation |
+| Teacher | Same model conditioned on the privileged crop, EMA-regularized (α = 0.05) |
+| Rollout length | 1024 tokens, 1 epoch |
+
+Training code: [github.com/VisionOPD/Vision-OPD](https://github.com/VisionOPD/Vision-OPD).
+
+## Citation
+
+```bibtex
+@article{yuan2026vision,
+  title={Vision-OPD: Learning to See Fine Details for Multimodal LLMs via On-Policy Self-Distillation},
+  author={Yuan, Qianhao and Lou, Jie and Yu, Xing and Lin, Hongyu and Sun, Le and Han, Xianpei and Lu, Yaojie},
+  journal={arXiv preprint arXiv:2605.18740},
+  year={2026}
+}
+```
+
+## License
+
+Released under Apache-2.0, following the base model [`Qwen/Qwen3.5-9B`](https://huggingface.co/Qwen/Qwen3.5-9B).

dataset9/chat_template.jinja

@@ -0,0 +1,122 @@
+{%- set _enable_thinking = enable_thinking if enable_thinking is defined else none %}
+{%- 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 %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' }}
+        {%- 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 }}
+                {%- endif %}
+            {%- 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 %}
+                    {%- set tool_call = tool_call.function %}
+                {%- 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' }}
+    {{- '<think>\n\n</think>\n\n' }}
+{%- endif %}

dataset9/config.json

@@ -0,0 +1,112 @@
+{
+  "architectures": [
+    "Qwen3_5ForConditionalGeneration"
+  ],
+  "bos_token_id": null,
+  "dtype": "bfloat16",
+  "eos_token_id": 248046,
+  "image_token_id": 248056,
+  "model_type": "qwen3_5",
+  "pad_token_id": 248044,
+  "text_config": {
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "attn_output_gate": true,
+    "bos_token_id": null,
+    "dtype": "bfloat16",
+    "eos_token_id": 248044,
+    "full_attention_interval": 4,
+    "head_dim": 256,
+    "hidden_act": "silu",
+    "hidden_size": 4096,
+    "initializer_range": 0.02,
+    "intermediate_size": 12288,
+    "layer_types": [
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "full_attention"
+    ],
+    "linear_conv_kernel_dim": 4,
+    "linear_key_head_dim": 128,
+    "linear_num_key_heads": 16,
+    "linear_num_value_heads": 32,
+    "linear_value_head_dim": 128,
+    "mamba_ssm_dtype": "float32",
+    "max_position_embeddings": 262144,
+    "mlp_only_layers": [],
+    "model_type": "qwen3_5_text",
+    "mtp_num_hidden_layers": 1,
+    "mtp_use_dedicated_embeddings": false,
+    "num_attention_heads": 16,
+    "num_hidden_layers": 32,
+    "num_key_value_heads": 4,
+    "pad_token_id": null,
+    "partial_rotary_factor": 0.25,
+    "rms_norm_eps": 1e-06,
+    "rope_parameters": {
+      "mrope_interleaved": true,
+      "mrope_section": [
+        11,
+        11,
+        10
+      ],
+      "partial_rotary_factor": 0.25,
+      "rope_theta": 10000000,
+      "rope_type": "default"
+    },
+    "tie_word_embeddings": false,
+    "use_cache": true,
+    "vocab_size": 248320
+  },
+  "tie_word_embeddings": false,
+  "transformers_version": "5.5.0",
+  "video_token_id": 248057,
+  "vision_config": {
+    "deepstack_visual_indexes": [],
+    "depth": 27,
+    "dtype": "bfloat16",
+    "hidden_act": "gelu_pytorch_tanh",
+    "hidden_size": 1152,
+    "in_channels": 3,
+    "initializer_range": 0.02,
+    "intermediate_size": 4304,
+    "model_type": "qwen3_5",
+    "num_heads": 16,
+    "num_position_embeddings": 2304,
+    "out_hidden_size": 4096,
+    "patch_size": 16,
+    "spatial_merge_size": 2,
+    "temporal_patch_size": 2
+  },
+  "vision_end_token_id": 248054,
+  "vision_start_token_id": 248053
+}

dataset9/generation_config.json

@@ -0,0 +1,9 @@
+{
+  "_from_model_config": true,
+  "eos_token_id": 248046,
+  "output_attentions": false,
+  "output_hidden_states": false,
+  "pad_token_id": 248044,
+  "transformers_version": "5.5.0",
+  "use_cache": true
+}

dataset9/model.safetensors

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dataset9/processor_config.json

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+{
+  "image_processor": {
+    "do_convert_rgb": true,
+    "do_normalize": true,
+    "do_rescale": true,
+    "do_resize": true,
+    "image_mean": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "image_processor_type": "Qwen2VLImageProcessor",
+    "image_std": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "merge_size": 2,
+    "patch_size": 16,
+    "resample": 3,
+    "rescale_factor": 0.00392156862745098,
+    "size": {
+      "longest_edge": 16777216,
+      "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
+    ],
+    "max_frames": 768,
+    "merge_size": 2,
+    "min_frames": 4,
+    "patch_size": 16,
+    "resample": 3,
+    "rescale_factor": 0.00392156862745098,
+    "return_metadata": false,
+    "size": {
+      "longest_edge": 25165824,
+      "shortest_edge": 4096
+    },
+    "temporal_patch_size": 2,
+    "video_processor_type": "Qwen3VLVideoProcessor"
+  }
+}

dataset9/tokenizer.json

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+size 19989343

dataset9/tokenizer_config.json

@@ -0,0 +1,32 @@
+{
+  "add_prefix_space": false,
+  "audio_bos_token": "<|audio_start|>",
+  "audio_eos_token": "<|audio_end|>",
+  "audio_token": "<|audio_pad|>",
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "image_token": "<|image_pad|>",
+  "is_local": true,
+  "model_max_length": 262144,
+  "model_specific_special_tokens": {
+    "audio_bos_token": "<|audio_start|>",
+    "audio_eos_token": "<|audio_end|>",
+    "audio_token": "<|audio_pad|>",
+    "image_token": "<|image_pad|>",
+    "video_token": "<|video_pad|>",
+    "vision_bos_token": "<|vision_start|>",
+    "vision_eos_token": "<|vision_end|>"
+  },
+  "pad_token": "<|endoftext|>",
+  "pretokenize_regex": "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?[\\p{L}\\p{M}]+|\\p{N}| ?[^\\s\\p{L}\\p{M}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
+  "processor_class": "Qwen3VLProcessor",
+  "split_special_tokens": false,
+  "tokenizer_class": "TokenizersBackend",
+  "unk_token": null,
+  "video_token": "<|video_pad|>",
+  "vision_bos_token": "<|vision_start|>",
+  "vision_eos_token": "<|vision_end|>"
+}