QEV-2B release files

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
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+figures/qev_architecture.png filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

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README.md

@@ -0,0 +1,220 @@
+---
+language:
+- en
+license: apache-2.0
+library_name: qev
+pipeline_tag: zero-shot-classification
+base_model: Qwen/Qwen3.5-2B
+base_model_relation: finetune
+tags:
+- decision-model
+- typed-decisions
+- multimodal
+- vision
+- classification
+- calibration
+- calibrated-probabilities
+- abstention
+- early-exit
+---
+
+# QEV-2B
+
+**A 2B open multimodal decision model: text and image in, calibrated option probabilities out, in one forward pass with no generated tokens.**
+
+[![JEV Decision Index](https://img.shields.io/badge/JEV%20Decision%20Index%20vision-61.99%20(self--scored)-2563eb)](#results)
+[![License](https://img.shields.io/badge/license-Apache--2.0-blue)](#license)
+[![Base model](https://img.shields.io/badge/base-Qwen3.5--2B-6b7280)](https://huggingface.co/Qwen/Qwen3.5-2B)
+
+QEV-2B is the full-size member of the QEV family. It is built on Qwen3.5-2B. You supply the evidence (text, one or more images) and the candidate options with their meanings. The model returns a probability for each option, a typed answer, and an abstain signal.
+
+**Headline (self-scored, pending official run):** On the JEV Decision Index vision board (edition 0.3.2, public part), QEV-2B scores **61.99**, the highest score on the board among models up to 2.3B parameters: 2.84 points above vLLM-SR d3-nano (2.21B, 59.15), and above Intern-Decision-4B (4.66B, 61.69) and Jev-Omni (11.96B, 60.10). It ranks first among models up to 2.3B on five of the eleven benchmarks: RealWorldQA, Mind2Web, KIE, Moderation and R-Bench-M.
+
+## Results
+
+### Leaderboard comparison
+
+JEV Decision Index, vision, edition 0.3.2, public part. Board generated 2026-10-16.
+
+| Model | Params | Index (public) |
+| --- | ---: | ---: |
+| d3-mini | 4.54B | 67.67 |
+| RSI-Jev v6.1 VL 4B | 4.66B | 63.01 |
+| RSI-Jev v5.0 VL 3B | 4.66B | 62.58 |
+| **QEV-2B (self-scored)** | **2.21B** | **61.99** |
+| Intern-Decision-4B | 4.66B | 61.69 |
+| Jev-Omni | 11.96B | 60.10 |
+| d3-nano | 2.21B | 59.15 |
+| Intern-Decision-2B | 2.27B | 52.92 |
+| OneJev1.5-0.8B | 1.11B | 52.32 |
+
+<sub>Board values are copied as published (JEV Decision Index vision, edition 0.3.2, public part, generated 2026-10-16). The QEV-2B value is **self-scored, pending official run**: public evaluation kit, native resolution up to 1.6 MP per image (no shared multi-image budget), single run, full depth, scored on the exact released files. It is not an official leaderboard entry and will be replaced when the official run is available.</sub>
+
+### Where QEV-2B leads (self-scored)
+
+| Benchmark | QEV-2B | Best other model up to 2.3B (board, public) |
+| --- | ---: | --- |
+| Mind2Web | **82.3** | d3-nano (2.21B) 63.7 |
+| KIE | **93.9** | Intern-Decision-2B (2.27B) 84.4 |
+| RealWorldQA | **56.2** | d3-nano (2.21B) 53.1 |
+| Moderation | **29.9** | OneJev1.5-0.8B (1.11B) 24.7 |
+| R-Bench-M | **16.8** | d3-nano (2.21B) 15.5 |
+
+### Per-benchmark scores
+
+Index = weighted mean of the benchmarks below. Weights: 1.0 unless noted. R-Bench-M 0.5, MMMU-Pro 0.25.
+
+| Benchmark | QEV-2B (self-scored) | Best other model up to 2.3B (board) | Weight |
+| --- | ---: | ---: | ---: |
+| CV-Bench | 70.6 | 71.9 (d3-nano) | 1.0 |
+| BLINK | 47.2 | 53.8 (OneJev1.5-0.8B) | 1.0 |
+| RealWorldQA | **56.2** | 53.1 (d3-nano) | 1.0 |
+| CharXiv | 63.4 | 65.2 (d3-nano) | 1.0 |
+| InfographicVQA | 83.1 | 83.6 (d3-nano) | 1.0 |
+| Mind2Web | **82.3** | 63.7 (d3-nano) | 1.0 |
+| Winoground | 65.5 | 69.0 (d3-nano) | 1.0 |
+| KIE | **93.9** | 84.4 (Intern-Decision-2B) | 1.0 |
+| Moderation | **29.9** | 24.7 (OneJev1.5-0.8B) | 1.0 |
+| R-Bench-M | **16.8** | 15.5 (d3-nano) | 0.5 |
+| MMMU-Pro | 15.6 | 17.8 (d3-nano) | 0.25 |
+| **Index** | **61.99** | 59.15 (d3-nano) | |
+
+Evaluation: public evaluation kit, native resolution up to 1.6 MP per image (no shared multi-image budget), single run, full depth, on the released files (merged weights). 95% bootstrap interval of the index (items resampled within each benchmark, 2,000 replicates): 60.63 to 63.34. Benchmarks were not used for checkpoint selection. The previous QEV-2B release (0.2.1) has no score on this board.
+
+### Speed and fast mode
+
+| Mode | Image cap | Depth | Worst-case p95 | Median p50 | Peak GPU memory |
+| --- | ---: | ---: | ---: | ---: | ---: |
+| Accurate (default) | 1.6 MP | 24 layers | 738.6 ms | 135.3 ms | 4.44 GiB |
+| Fast | 0.26 MP | 16 layers | 225.0 ms | 91.6 ms | 4.13 GiB |
+
+<sub>Release loader only (`qev.QEV.load` + `predict`, no CUDA graphs or custom kernels), RTX 4060 Ti 8 GB, batch 1, warm. 47 request profiles (text and image requests with up to 16 options; the largest is 3,229 input tokens in accurate mode), 3 warm-up and 30 timed runs each. Worst-case p95 is the highest per-profile p95; median p50 is the median over profiles of each profile's p50. Peak GPU memory is PyTorch's peak allocation. In both modes the slowest profile is a 16-option image request near the token limit.</sub>
+
+Fast mode exits at layer 16 of 24 with a distilled exit head (`exit16.safetensors`) and caps images at 0.26 MP. At the same 0.26 MP input, exiting at layer 16 matched full depth on our internal DEV set: 85.0% versus 84.9% on the coverage-mix selection split (1,859 items), 89.8% versus 90.6% on NLVR2 dev (128), 88.8% versus 89.2% on image diagnostics (400) and 93.0% versus 92.8% on text diagnostics (485). The lower resolution costs accuracy against accurate mode, most on document, chart and screen sources. No index is reported for fast mode, so the numbers above apply to the full-depth mode only. Calibration is fitted for accurate mode and has not been refitted for fast mode.
+
+### Calibration and abstention
+
+Per-type temperatures and abstain thresholds are fitted on the DEV selection split (the split also used for checkpoint selection; benchmarks are never used), on group-disjoint halves: temperature on one half, threshold on the other. Policy: the lowest confidence threshold at which answered questions have at most 15% error, with at least 20 answered. Numbers are on the held-out policy half, accurate mode. A response always carries the answer; `abstained` is a separate flag, so benchmark scores are unaffected.
+
+| Question type | DEV questions | Temperature | Threshold | Answered | Error on answered | ECE (raw -> calibrated) |
+| --- | ---: | ---: | ---: | ---: | ---: | --- |
+| Choice | 1,296 | 0.84 | 0.31 | 100% | 10.5% | 0.025 -> 0.013 |
+| Score | 79 | 0.43 | 0.52 | 100% | 7.6% | 0.140 -> 0.036 |
+| Yes/no | 80 | 0.49 | 0.58 | 100% | 6.3% | 0.196 -> 0.088 |
+
+For all three question types the 15% target is met on all DEV questions, so abstention only triggers below the lowest confidence seen on DEV and is rare. The score and yes/no thresholds rest on about 80 DEV questions each (internal text diagnostics) and are less certain than the choice threshold.
+
+## Architecture
+
+![QEV-2B architecture](figures/qev_architecture.png)
+
+- Backbone: Qwen3.5-2B language model (hidden size 2048, 24 layers: 18 GatedDeltaNet + 6 full attention), 2.21B parameters.
+- Vision: Qwen3.5 ViT encoder (frozen).
+- Readout: option probabilities read from the hidden states.
+- Condition-modulated binding head, per-type temperature calibration and an abstain decision (fitted on DEV; see Calibration and abstention).
+- Single forward pass, no generated tokens.
+- Fast mode: a distilled exit head reads the options at layer 16 of 24 (`exit16.safetensors`); images are capped at 0.26 MP.
+- GPU inference uses a BF16 backbone and FP32 readouts; the CPU path uses FP32.
+- Fine-tuning: LoRA (rank 8, alpha 16) on the language model plus trained readout and binding heads; the vision encoder and the other base weights are frozen (board category: LoRA + head).
+- Weights are shipped merged: `model.safetensors` (4.43 GB, BF16, both LoRA stages folded in) plus `head.safetensors` (0.7 MB, readout and binding head).
+
+## Training data
+
+Every source used in training, read from the training script (`qev_mix.py`) and its training log.
+
+**Coverage mix (train split, 18 sources).** Each source contributes items from its train split. 5% of each source (up to 120 items) is held out as a selection set and is never trained on.
+
+| Source | Split |
+| --- | --- |
+| AI2D | train |
+| A-OKVQA | train |
+| ChartQA | train |
+| CORD | train |
+| DocVQA | train |
+| FUNSD | train |
+| Hateful Memes | train |
+| IconQA | train |
+| InfographicVQA | train |
+| Mind2Web | train |
+| Mind2Web (keyed variant, `mind2web_k`) | train |
+| Options-in-image | train |
+| ScienceQA | train |
+| Screen2Words | train |
+| Synthetic (internal) | train |
+| TallyQA | train |
+| Visual7W | train |
+| VSR | train |
+
+**Additional sources**
+
+- NLVR2 train split (teacher-filtered targets). The NLVR2 dev split is used only for checkpoint selection.
+- Replay of internal decision units (image and text families, train split), with teacher outputs as targets.
+- Teacher outputs: option-probability distillation from a teacher model.
+
+**Deduplication and contamination audit:** all sources use train splits only (evaluation uses test/validation splits). Ten document/KIE/perception sources were filtered at build time: any item whose image is within Hamming distance 4 (64-bit dHash) of an evaluation image was dropped. A post-hoc audit of every training image against every evaluation image (dHash candidate, confirmed by 64x64 grayscale mean absolute difference <= 6/255) found **37 evaluation items** with a near-identical training image: 30 Hateful Memes (shared base images across the dataset's splits), 6 BLINK, 1 Mind2Web. Mind2Web has no action_uid or annotation_id overlap with the evaluation rows. Rescoring without those 37 items changes the index from 61.99 to 62.09 (Moderation 29.93 -> 30.85, BLINK 47.16 -> 47.07, Mind2Web 82.32 -> 82.44).
+
+**Teacher cleanup:** a teacher target is kept only where the teacher's top option matches the gold answer. Otherwise only the gold label is trained on.
+
+**Selection:** checkpoint chosen on held-out DEV data only (coverage-mix holdout, NLVR2 DEV, internal diagnostics). Public benchmarks were not used for selection.
+
+**Training recipe:** continued from QEV-2B 0.2.1 (its LoRA merged into the backbone) with a fresh LoRA (rank 8, alpha 16) on the coverage mix plus replay, 56k items; the released checkpoint is optimizer step 3,616 (end of the cosine schedule). Resolution-elastic batches (0.26 / 1.05 / 1.64 MP). Answer options are permuted per visit, except for sources whose options refer to letters or boxes in the image (Mind2Web, Mind2Web-keyed, options-in-image).
+
+## Quickstart
+
+This checkpoint needs the qev source (the published `qev==0.2.1` package loads QEV-2B 0.2.1 only).
+
+```sh
+python -m pip install "qev @ git+https://github.com/ken-jo/qev" huggingface_hub
+```
+
+```python
+from huggingface_hub import snapshot_download
+import qev
+from qev import DecisionRequest
+
+path = snapshot_download("ken-jo/QEV-2B")
+model = qev.QEV.load(path, device="cuda")               # accurate: full depth, up to 1.6 MP per image
+# model = qev.QEV.load(path, device="cuda", mode="fast")  # fast: exit at layer 16, 0.26 MP
+request = DecisionRequest.model_validate({
+    "state": {
+        "text": "I was charged twice. Please refund the duplicate payment.",
+    },
+    "questions": {
+        "department": {
+            "type": "choice",
+            "instructions": "Which team should handle this request?",
+            "criteria": {"billing": "Payments and refunds", "technical": "Software faults"},
+        }
+    },
+})
+print(model.predict(request)["answers"])
+```
+
+Note on the name: the PyPI package `qev` is also the import name used by OneJev (`from qev import Client`). Install one of them per environment.
+
+## Limitations
+
+- Weak areas: Moderation (29.9), BLINK (47.2), MMMU-Pro (15.6). QEV-2B trails d3-nano on CV-Bench, CharXiv, InfographicVQA, Winoground and MMMU-Pro, and OneJev1.5-0.8B on BLINK.
+- Scores are self-scored on the public part, single run, up to 1.6 MP per image. The private-part scores are unknown and may differ.
+- Contamination: the training mix includes train splits of several benchmark families (Mind2Web, InfographicVQA, CORD, FUNSD, Hateful Memes). The audit above found 37 near-duplicate evaluation images (0.3% of 12,210 rows); removing them does not lower the score. If you find an overlapping item, please report it.
+- The model returns probabilities, not guarantees. Calibration and abstention are fitted on internal DEV data and can be wrong on other domains; refit on your own data where the abstain decision matters.
+- Visual reasoning is limited; see the evaluation notes in the QEV repository.
+- Evaluation focuses on English.
+
+## License
+
+Apache-2.0, the same license as the base model. The base model Qwen3.5-2B is subject to its upstream license.
+
+The training data is not redistributed with the model. Each training source keeps its own terms; some, such as FUNSD and Hateful Memes, restrict how their data may be used. Check those terms before reusing the data or retraining on it.
+
+## Citation
+
+```bibtex
+@misc{jo2026qev2b,
+  title        = {QEV-2B: A Calibrated Multimodal Decision Model},
+  author       = {Jo, Ken},
+  year         = {2026},
+  howpublished = {Hugging Face model card},
+  url          = {https://huggingface.co/ken-jo/QEV-2B}
+}
+```

chat_template.jinja

@@ -0,0 +1,154 @@
+{%- set image_count = namespace(value=0) %}
+{%- set video_count = namespace(value=0) %}
+{%- macro render_content(content, do_vision_count, is_system_content=false) %}
+    {%- if content is string %}
+        {{- content }}
+    {%- elif content is iterable and content is not mapping %}
+        {%- for item in content %}
+            {%- if 'image' in item or 'image_url' in item or item.type == 'image' %}
+                {%- if is_system_content %}
+                    {{- raise_exception('System message cannot contain images.') }}
+                {%- endif %}
+                {%- if do_vision_count %}
+                    {%- set image_count.value = image_count.value + 1 %}
+                {%- endif %}
+                {%- if add_vision_id %}
+                    {{- 'Picture ' ~ image_count.value ~ ': ' }}
+                {%- endif %}
+                {{- '<|vision_start|><|image_pad|><|vision_end|>' }}
+            {%- elif 'video' in item or item.type == 'video' %}
+                {%- if is_system_content %}
+                    {{- raise_exception('System message cannot contain videos.') }}
+                {%- endif %}
+                {%- if do_vision_count %}
+                    {%- set video_count.value = video_count.value + 1 %}
+                {%- endif %}
+                {%- if add_vision_id %}
+                    {{- 'Video ' ~ video_count.value ~ ': ' }}
+                {%- endif %}
+                {{- '<|vision_start|><|video_pad|><|vision_end|>' }}
+            {%- elif 'text' in item %}
+                {{- item.text }}
+            {%- else %}
+                {{- raise_exception('Unexpected item type in content.') }}
+            {%- endif %}
+        {%- endfor %}
+    {%- elif content is none or content is undefined %}
+        {{- '' }}
+    {%- else %}
+        {{- raise_exception('Unexpected content type.') }}
+    {%- endif %}
+{%- endmacro %}
+{%- if not messages %}
+    {{- raise_exception('No messages provided.') }}
+{%- endif %}
+{%- if tools and tools is iterable and tools is not mapping %}
+    {{- '<|im_start|>system\n' }}
+    {{- "# Tools\n\nYou have access to the following functions:\n\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>" }}
+    {{- '\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n<tool_call>\n<function=example_function_name>\n<parameter=example_parameter_1>\nvalue_1\n</parameter>\n<parameter=example_parameter_2>\nThis is the value for the second parameter\nthat can span\nmultiple lines\n</parameter>\n</function>\n</tool_call>\n\n<IMPORTANT>\nReminder:\n- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags\n- Required parameters MUST be specified\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\n</IMPORTANT>' }}
+    {%- if messages[0].role == 'system' %}
+        {%- set content = render_content(messages[0].content, false, true)|trim %}
+        {%- if content %}
+            {{- '\n\n' + content }}
+        {%- endif %}
+    {%- endif %}
+    {{- '<|im_end|>\n' }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {%- set content = render_content(messages[0].content, false, true)|trim %}
+        {{- '<|im_start|>system\n' + content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" %}
+        {%- set content = render_content(message.content, false)|trim %}
+        {%- if not(content.startswith('<tool_response>') and content.endswith('</tool_response>')) %}
+            {%- set ns.multi_step_tool = false %}
+            {%- set ns.last_query_index = index %}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if ns.multi_step_tool %}
+    {{- raise_exception('No user query found in messages.') }}
+{%- endif %}
+{%- for message in messages %}
+    {%- set content = render_content(message.content, true)|trim %}
+    {%- if message.role == "system" %}
+        {%- if not loop.first %}
+            {{- raise_exception('System message must be at the beginning.') }}
+        {%- endif %}
+    {%- elif message.role == "user" %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- set reasoning_content = reasoning_content|trim %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content + '\n</think>\n\n' + content }}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls and message.tool_calls is iterable and message.tool_calls is not mapping %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if tool_call.function is defined %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {%- if loop.first %}
+                    {%- if content|trim %}
+                        {{- '\n\n<tool_call>\n<function=' + tool_call.name + '>\n' }}
+                    {%- else %}
+                        {{- '<tool_call>\n<function=' + tool_call.name + '>\n' }}
+                    {%- endif %}
+                {%- else %}
+                    {{- '\n<tool_call>\n<function=' + tool_call.name + '>\n' }}
+                {%- endif %}
+                {%- if tool_call.arguments is defined %}
+                    {%- for args_name, args_value in tool_call.arguments|items %}
+                        {{- '<parameter=' + args_name + '>\n' }}
+                        {%- set args_value = args_value | tojson | safe if args_value is mapping or (args_value is sequence and args_value is not string) else args_value | string %}
+                        {{- args_value }}
+                        {{- '\n</parameter>\n' }}
+                    {%- endfor %}
+                {%- endif %}
+                {{- '</function>\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.previtem and loop.previtem.role != "tool" %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if not loop.last and loop.nextitem.role != "tool" %}
+            {{- '<|im_end|>\n' }}
+        {%- elif loop.last %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- else %}
+        {{- raise_exception('Unexpected message role.') }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is true %}
+        {{- '<think>\n' }}
+    {%- else %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

config.json

@@ -0,0 +1,105 @@
+{
+  "architectures": [
+    "Qwen3_5Model"
+  ],
+  "dtype": "bfloat16",
+  "image_token_id": 248056,
+  "model_type": "qwen3_5",
+  "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": 2048,
+    "initializer_range": 0.02,
+    "intermediate_size": 6144,
+    "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_conv_kernel_dim": 4,
+    "linear_key_head_dim": 128,
+    "linear_num_key_heads": 16,
+    "linear_num_value_heads": 16,
+    "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": 8,
+    "num_hidden_layers": 24,
+    "num_key_value_heads": 2,
+    "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": true,
+    "use_cache": true,
+    "vocab_size": 248320
+  },
+  "tie_word_embeddings": true,
+  "transformers_version": "5.17.0",
+  "video_token_id": 248057,
+  "vision_config": {
+    "deepstack_visual_indexes": [],
+    "depth": 24,
+    "dtype": "bfloat16",
+    "hidden_act": "gelu_pytorch_tanh",
+    "hidden_size": 1024,
+    "in_channels": 3,
+    "initializer_range": 0.02,
+    "intermediate_size": 4096,
+    "model_type": "qwen3_5_vision",
+    "num_heads": 16,
+    "num_position_embeddings": 2304,
+    "out_hidden_size": 2048,
+    "patch_size": 16,
+    "rope_parameters": {
+      "rope_theta": 10000.0,
+      "rope_type": "axial"
+    },
+    "spatial_merge_size": 2,
+    "temporal_patch_size": 2
+  },
+  "vision_end_token_id": 248054,
+  "vision_start_token_id": 248053
+}

exit16.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fc61287b393b8e6d4c88621b7839f21a77f02f3be5f3366ce5ce805f135208cc
+size 16880144

head.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a0057040e29d834d7c0658102ba29b0b7c11119beeac318bb83f7a8d264200d3
+size 709496

manifest.json

@@ -0,0 +1,67 @@
+{
+  "format_version": 2,
+  "architecture": "option_readout",
+  "decision_views": 1,
+  "reasoning_slots": 4,
+  "binding_head": {
+    "rank": 64,
+    "mode": "condition"
+  },
+  "backbone": {
+    "model_id": "ken-jo/QEV-2B",
+    "revision": "0000000000000000000000000000000000000000",
+    "weights": "merged"
+  },
+  "encoder": {
+    "max_total_tokens": 8192,
+    "image_max_pixels": 262144,
+    "image_min_pixels": 65536,
+    "encoding": "option-v1"
+  },
+  "calibration": {
+    "temperatures": [
+      0.8413951416451951,
+      0.4340102636447439,
+      0.4869675251658631
+    ],
+    "abstain_thresholds": [
+      0.3122118931627004,
+      0.5157240607035172,
+      0.5789704609274517
+    ],
+    "fitted_types": [
+      "choice",
+      "score",
+      "noul"
+    ],
+    "abstention_fitted_types": [
+      "choice",
+      "score",
+      "noul"
+    ],
+    "always_abstain_types": []
+  },
+  "training": {
+    "completed": true,
+    "intermediate": false,
+    "optimizer_steps": 3616,
+    "recipe": "QEV 0.2.1 (Qwen3.5-2B + LoRA, merged) + resolution-elastic option-permutation mix; checkpoint at optimizer step 3616 (end of the cosine schedule); LoRA r8 merged into model.safetensors",
+    "checkpoint_selection": "DEV split only"
+  },
+  "weights_sha256": "a0057040e29d834d7c0658102ba29b0b7c11119beeac318bb83f7a8d264200d3",
+  "early_exit": {
+    "layer": 16,
+    "head": "exit16.safetensors",
+    "sha256": "fc61287b393b8e6d4c88621b7839f21a77f02f3be5f3366ce5ce805f135208cc"
+  },
+  "modes": {
+    "accurate": {
+      "image_max_pixels": 1638400
+    },
+    "fast": {
+      "image_max_pixels": 262144,
+      "early_exit": true
+    }
+  },
+  "default_mode": "accurate"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7f1b8dd81ab0ebd219159a5979bf0e9fb9d7d6c3352aa599d31da5559be42df5
+size 4426555160

processor_config.json

@@ -0,0 +1,61 @@
+{
+  "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": 262144,
+      "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,
+    "max_video_tokens": 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"
+  }
+}

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:06b9509352d2af50381ab2247e083b80d32d5c0aba91c272ca9ff729b6a0e523
+size 19989325

tokenizer_config.json

@@ -0,0 +1,33 @@
+{
+  "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": false,
+  "local_files_only": 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": "Qwen2Tokenizer",
+  "unk_token": null,
+  "video_token": "<|video_pad|>",
+  "vision_bos_token": "<|vision_start|>",
+  "vision_eos_token": "<|vision_end|>"
+}