VultronRetriever model card

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NOTICE

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+VultronRetrieverPrime-Qwen3.5-8B
+Copyright 2026 athrael-soju
+
+Developed for Vultr (https://www.vultr.com), which provided the resources to train and evaluate this model. 
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use
+these files except in compliance with the License. A copy of the License is
+provided in the LICENSE file in this repository, or at:
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+This model is a derivative work of Qwen/Qwen3.5-9B
+(https://huggingface.co/Qwen/Qwen3.5-9B), Copyright 2026 Alibaba Cloud,
+distributed under the Apache License, Version 2.0. The base model weights were
+fine-tuned and merged for late-interaction visual document retrieval. Its
+upstream license and attribution are retained as required by Section 4 of the
+License.

README.md

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+---
+library_name: colpali_engine
+pipeline_tag: visual-document-retrieval
+base_model: Qwen/Qwen3.5-9B
+license: apache-2.0
+language:
+- en
+- fr
+- de
+- es
+- it
+- pt
+tags:
+- visual-document-retrieval
+- multimodal-retrieval
+- late-interaction
+- colpali
+- colqwen
+- ColQwen3_5
+- vidore
+- mteb
+- qwen3.5
+- model-merge
+- per-block-merge
+- MaxSim
+---
+
+# VultronRetrieverPrime-Qwen3.5-8B
+
+**#1 on ViDoRe V3 at 320 dimensions.**
+
+VultronRetrieverPrime is the flagship of the VultronRetriever family, a late-interaction
+(ColBERT-style) retriever that scores document pages directly from their rendered image: layout,
+tables, charts and text, across six languages. It ranks first on ViDoRe V3 at 320-dimensional
+multi-vectors, an 8–12× smaller index than the 2560–4096-dim models it sits alongside.
+
+The family has three tiers on one 320-dim recipe: the 8B Prime (this model) for maximum accuracy,
+the 4.5B [Core](https://huggingface.co/vultr/VultronRetrieverCore-Qwen3.5-4.5B) for the
+accuracy/footprint mid-point, and the 0.8B
+[Flash](https://huggingface.co/vultr/VultronRetrieverFlash-Qwen3.5-0.8B) for latency- and
+footprint-sensitive serving. Trained and evaluated on [Vultr Cloud](https://www.vultr.com).
+
+## Highlights
+
+- **#1 on ViDoRe V3 at 320-dim:** 64.72 nDCG@10, the top public-task mean on the board, at an 8–12× smaller index than the 2560–4096-dim field.
+- **#1 on six of the eight public V3 tasks** (Energy, FinanceEn, FinanceFr, Industrial, Pharmaceuticals, Physics).
+- **Official MTEB:** V1 92.08, V2 68.18, V3 64.72.
+- **8.4B parameters, 320-dim, ~17 GB bf16, runs on one GPU.**
+- **Six languages** (en, fr, de, es, it, pt).
+
+## ViDoRe leaderboard (ranked by V3)
+
+Ranked by **ViDoRe V3** (mean nDCG@10), the headline benchmark; V1 and V2 are shown alongside. Our
+three tiers are in bold; models that have not reported V3 are listed last.
+
+| Model | Params | Dim | V1 | V2 | V3 |
+|---|---:|---:|---:|---:|---:|
+| **VultronRetrieverPrime-Qwen3.5-8B (this model)** | **8.4B** | **320** | **92.08** | **68.18** | **64.72** |
+| webAI-Official/webAI-ColVec1-9b | 9.4B | 2560 | 91.30 | 65.82 | 64.45 |
+| VultronRetrieverCore-Qwen3.5-4.5B (ours) | 4.5B | 320 | 92.21 | 66.12 | 63.72 |
+| nvidia/nemotron-colembed-vl-8b-v2 | 8.7B | 4096 | 92.65 | 65.16 | 63.54 |
+| webAI-Official/webAI-ColVec1-4b | 4.5B | 640 | 90.49 | 63.60 | 63.39 |
+| TomoroAI/tomoro-colqwen3-embed-8b | 8.0B | 320 | 90.76 | 65.40 | 61.60 |
+| athrael-soju/colqwen3.5-4.5B-v3 | 4.6B | 128 | 91.54 | 64.25 | 61.56 |
+| nvidia/nemotron-colembed-vl-4b-v2 | 4.8B | 2560 | 91.62 | 64.49 | 61.42 |
+| OpenSearch-AI/Ops-Colqwen3-4B | 4.8B | 2560 | 91.36 | 68.66 | 61.27 |
+| TomoroAI/tomoro-colqwen3-embed-4b | 4.0B | 320 | 90.57 | 64.69 | 60.16 |
+| nvidia/llama-nemotron-colembed-vl-3b-v2 | 4.4B | 3072 | 91.74 | 63.38 | 59.70 |
+| VAGOsolutions/SauerkrautLM-ColQwen3-8b-v0.1 | 8.1B | 128 | 91.08 | 62.47 | 58.55 |
+| nomic-ai/colnomic-embed-multimodal-7b | 7.0B | 128 | 89.72 | 60.25 | 57.64 |
+| jinaai/jina-embeddings-v4 | 3.9B | 2048 | 90.35 | 58.23 | 57.54 |
+| nvidia/llama-nemoretriever-colembed-3b-v1 | 4.4B | 3072 | 91.00 | 63.32 | 57.07 |
+| VultronRetrieverFlash-Qwen3.5-0.8B (ours) | 0.85B | 320 | 88.15 | 60.36 | 56.49 |
+| nomic-ai/colnomic-embed-multimodal-3b | 3.0B | 128 | 89.86 | 55.68 | 56.40 |
+| VAGOsolutions/SauerkrautLM-ColQwen3-4b-v0.1 | 4.4B | 128 | 90.80 | 59.89 | 56.03 |
+| Verm1ion/ColTurk-VDR-Qwen3VL-4B-v1.0 | 4.5B | 128 | — | — | 55.82 |
+| nvidia/llama-nemoretriever-colembed-1b-v1 | 2.4B | 2048 | 90.50 | 62.96 | 55.48 |
+| vidore/colqwen2.5-v0.2 | 3.0B | 128 | 89.54 | 60.06 | 52.44 |
+| DataScience-UIBK/Argus-Colqwen3.5-9b-v0 | 8.8B | 1024 | 92.67 | 69.27 | — |
+| DataScience-UIBK/Argus-Colqwen3.5-4b-v0 | 4.7B | 1024 | 92.30 | 64.18 | — |
+
+*V1/V2: full mean nDCG@5. V3: mean nDCG@10 over the 8 public ViDoRe V3 tasks (the 2 private tasks,
+Nuclear and Telecom, are pending for all our tiers). Competitor figures: public MTEB ViDoRe
+leaderboard, 2026-06-21 snapshot; "—" = not reported on that benchmark.*
+
+### Per-task nDCG@10 (ViDoRe V3 public tasks)
+
+Prime is #1 on six of the eight public tasks. `*` = best on that task across the board.
+
+| Model | CS | Energy | FinEn | FinFr | Hr | Ind | Pharma | Physics | Mean |
+|---|---|---|---|---|---|---|---|---|---|
+| **VultronRetrieverPrime-Qwen3.5-8B (this model)** | 79.8 | **70.3\*** | **69.0\*** | **54.5\*** | 66.8 | **57.4\*** | **68.2\*** | **51.7\*** | **64.72** |
+| webAI-Official/webAI-ColVec1-9b | **80.9\*** | 69.8 | 68.3 | 53.7 | **70.0\*** | 57.2 | 67.3 | 48.4 | 64.45 |
+| nvidia/nemotron-colembed-vl-8b-v2 | 79.3 | 69.8 | 67.3 | 51.5 | 66.3 | 56.0 | 67.2 | 50.8 | 63.54 |
+| webAI-Official/webAI-ColVec1-4b | 79.8 | 68.7 | 68.5 | 51.1 | 67.4 | 55.7 | 65.7 | 50.1 | 63.39 |
+| TomoroAI/tomoro-colqwen3-embed-8b | 75.3 | 68.4 | 65.1 | 49.1 | 64.0 | 54.4 | 66.4 | 50.1 | 61.60 |
+| athrael-soju/colqwen3.5-4.5B-v3 | 78.7 | 68.0 | 64.1 | 48.6 | 62.1 | 55.2 | 65.6 | 50.3 | 61.56 |
+| nvidia/nemotron-colembed-vl-4b-v2 | 78.6 | 67.5 | 65.0 | 49.0 | 62.4 | 53.9 | 66.1 | 48.9 | 61.42 |
+
+## Official MTEB results (dim 320 / visual tokens 1792)
+
+| Benchmark | Metric | Tasks | Score |
+|---|---|---|---|
+| ViDoRe V1 | ndcg@5  | 10 | 0.9208 |
+| ViDoRe V2 | ndcg@5  | 4  | 0.6818 |
+| ViDoRe V3 | ndcg@10 | 8  | 0.6472 |
+
+Per-task JSONs are in `eval_results/`. Measured with the official MTEB late-interaction evaluator.
+
+## Why 320 dimensions
+
+Late-interaction index size, memory footprint, and MaxSim scoring cost all scale with the embedding
+dimension. At 320-dim, Prime carries an index 8× smaller than a 2560-dim retriever and roughly 13×
+smaller than a 4096-dim one, at the same or better ViDoRe V3 accuracy: proportionally lower storage,
+RAM, and query-time compute at serving scale.
+
+## Intended use
+
+- Visual document retrieval / multimodal RAG over PDFs, scans, slides and reports, including pages
+  with layout, tables, charts and figures.
+- Multilingual document collections (en, fr, de, es, it, pt).
+- Maximum-accuracy retrieval at scale: 320-dim multi-vectors against 2560-4096-dim peers.
+
+Out of scope: text-only semantic search, where a single-vector dense embedder is cheaper;
+generative QA (this is a retriever; pair it with a reader/LLM).
+
+## Method
+
+Per-token MaxSim scoring captures fine-grained matches against tables, figures, and layout that a
+single-vector embedder averages away.
+
+- Base: `Qwen/Qwen3.5-9B` (hybrid GatedDeltaNet + full-attention backbone).
+- Late-interaction retriever (`ColQwen3_5`): 320-dim multi-vector embeddings, MaxSim scoring,
+  image + text inputs.
+- **Size:** 8.4B parameters, the generative head dropped for retrieval (HF reports 8B).
+- **Native 320-dim head:** trained and operated at 320 dimensions directly.
+- **Hard-negative mining:** positive-aware, NV-Retriever-style negative selection on the
+  contrastively-trained constituents.
+- **Model merging:** several independently-seeded checkpoints merged per-block into one full-weight
+  checkpoint.
+- Trained at up to 1280 visual tokens, evaluated and deployed at 1792.
+
+## Training data
+
+An enhanced, multilingual mixture of public and synthetic visual-document retrieval sources, spanning
+en, es, de, fr, it and pt, decontaminated against all three ViDoRe suites (V1/V2/V3): 0% measured
+overlap with the evaluation benchmarks. The training recipe and the assembled training dataset are not
+distributed in this repository.
+
+## Inputs and outputs
+
+- **Input:** document-page images (RGB) and/or text queries; pages encode at up to 1792 visual tokens.
+- **Output:** multi-vector embeddings, one 320-dim vector per token (not a single pooled vector).
+- **Scoring:** late-interaction MaxSim between query-token and page-token vectors, via `score_multi_vector`.
+
+## Requirements
+
+The Qwen3.5 hybrid (GatedDeltaNet + full-attention) backbone has hard runtime kernel dependencies a
+vanilla ColQwen / PaliGemma card does not:
+
+```bash
+pip install "git+https://github.com/illuin-tech/colpali@2e0b927051af727238783af039dcc2c50a4d8c27"
+pip install causal-conv1d flash-linear-attention
+```
+
+- `causal-conv1d` + `flash-linear-attention` are required (the hybrid layers import them at runtime).
+- Attention must be **SDPA**. Retrieval runs bidirectional attention on the full-attention layers;
+  `flash_attention_2` silently ignores the 2-D mask and scores as if causal.
+
+## Usage
+
+```python
+import torch
+from PIL import Image
+from colpali_engine.models import ColQwen3_5, ColQwen3_5Processor
+
+model = ColQwen3_5.from_pretrained(
+    "vultr/VultronRetrieverPrime-Qwen3.5-8B",
+    torch_dtype=torch.bfloat16,
+    attn_implementation="sdpa",   # required (see above)
+    device_map="cuda:0",
+).eval()
+processor = ColQwen3_5Processor.from_pretrained(
+    "vultr/VultronRetrieverPrime-Qwen3.5-8B",
+    max_num_visual_tokens=1792,
+)
+
+# Document pages (rendered to images) and text queries
+images = [Image.open("page_0.png"), Image.open("page_1.png")]
+queries = ["What was Q3 revenue?", "Summarize the safety findings."]
+
+with torch.no_grad():
+    doc_emb = model(**processor.process_images(images).to(model.device))
+    qry_emb = model(**processor.process_queries(queries).to(model.device))
+
+# Late-interaction MaxSim scoring (feed fp32 to match the eval discipline)
+scores = processor.score_multi_vector(qry_emb.float(), doc_emb.float())
+# scores[i, j] = relevance of query i to page j
+print(scores.shape)  # torch.Size([2, 2])
+```
+
+`config.json` carries `dim=320`, so `custom_text_proj` is sized correctly at load, with no manual
+config edits needed.
+
+## Serving with vLLM
+
+vLLM serves this model natively through its pooling runner (the `ColQwen3_5` architecture), returning
+the per-token multi-vectors for late-interaction scoring. It requires a vLLM build that includes the
+ColQwen3.5 retrieval-correctness fix ([vllm-project/vllm#46108](https://github.com/vllm-project/vllm/pull/46108),
+merged 2026-06-22): build from `main`, or use a release tagged after that date. The fix runs the
+backbone bidirectionally and restores the projection bias, so vLLM reproduces the `transformers`
+reference within run-to-run noise. The server uses the stock chat/image processor, so the ColQwen3.5
+prompt contract is applied **client-side**: wrap each page image in the instruction template, append
+the query-augmentation tokens to each query, and set the visual-token budget through
+`mm-processor-kwargs`. Prefix caching and chunked prefill must be **off** (bidirectional attention and
+the GatedDeltaNet hybrid both break the causal-prefix invariant).
+
+```python
+import torch
+from PIL import Image
+from vllm import LLM
+MODEL = "vultr/VultronRetrieverPrime-Qwen3.5-8B"
+MAX_PIXELS = 1792 * 32 * 32   # max_num_visual_tokens * (patch_size 16 * merge_size 2)^2
+llm = LLM(
+    model=MODEL,
+    runner="pooling",
+    dtype="bfloat16",
+    enable_prefix_caching=False,
+    enable_chunked_prefill=False,
+    mm_processor_kwargs={"min_pixels": 65536, "max_pixels": MAX_PIXELS},
+)
+# ColQwen3.5 processor contract, applied client-side:
+IMAGE_PROMPT = ("<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>"
+                "Describe the image.<|im_end|><|endoftext|>")
+def query_prompt(q): return q + "<|endoftext|>" * 10   # query augmentation
+images  = [Image.open("page_0.png"), Image.open("page_1.png")]
+queries = ["What was Q3 revenue?", "Summarize the safety findings."]
+doc_out = llm.encode([{"prompt": IMAGE_PROMPT, "multi_modal_data": {"image": im}}
+                      for im in images], pooling_task="token_embed")
+qry_out = llm.encode([query_prompt(q) for q in queries], pooling_task="token_embed")
+def mv(o):  # one [num_tokens, 320] multi-vector per item, L2-normalized per token
+    t = torch.as_tensor(o.outputs.data, dtype=torch.float32)
+    return torch.nn.functional.normalize(t, p=2, dim=-1)
+docs, qrys = [mv(o) for o in doc_out], [mv(o) for o in qry_out]
+# late-interaction MaxSim: per query token take the best doc token, then sum
+scores = [[(q @ d.T).max(dim=-1).values.sum().item() for d in docs] for q in qrys]
+print(scores)  # scores[i][j] = relevance of query i to page j
+```
+
+To serve over HTTP instead:
+
+```bash
+vllm serve vultr/VultronRetrieverPrime-Qwen3.5-8B \
+  --runner pooling \
+  --no-enable-prefix-caching --no-enable-chunked-prefill \
+  --mm-processor-kwargs '{"min_pixels": 65536, "max_pixels": 1835008}'
+```
+
+Apply the same image template and query augmentation in your client requests. See the upstream example
+`examples/pooling/score/colqwen3_5_rerank_online.py` for the full online rerank flow.
+
+## Limitations
+
+- ViDoRe V3 figures cover 8 of the 10 V3 domains; Telecom and Nuclear have not been evaluated yet.
+- Tuned for six languages (en, fr, de, es, it, pt); other languages are out of distribution.
+- Late-interaction multi-vector indexes are larger than single-vector dense indexes: the trade for
+  per-token layout/table/figure sensitivity (small for its class at 320-dim).
+
+## License
+
+Apache 2.0, covering the contents of this repository: model weights, config, and evaluation results.
+Built on `Qwen/Qwen3.5-9B` (Apache 2.0); the upstream license and attribution are retained. The
+training recipe and the assembled training dataset are not distributed in this repository.
+
+## Citation
+
+```bibtex
+@misc{vultronretrieverprime2026,
+  title  = {VultronRetrieverPrime-Qwen3.5-8B: Late-Interaction Visual Document Retrieval at 320 Dimensions},
+  author = {Georgiou, Athos (athrael-soju)},
+  year   = {2026},
+  howpublished = {\url{https://huggingface.co/vultr/VultronRetrieverPrime-Qwen3.5-8B}}
+}
+```
+
+---
+*Trained and evaluated on [Vultr Cloud](https://www.vultr.com).*

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 false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- else %}
+        {{- '<think>\n' }}
+    {%- endif %}
+{%- endif %}

config.json

@@ -0,0 +1,108 @@
+{
+  "architectures": [
+    "ColQwen3_5"
+  ],
+  "image_token_id": 248056,
+  "model_type": "qwen3_5",
+  "text_config": {
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "attn_output_gate": true,
+    "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,
+    "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,
+    "rms_norm_eps": 1e-06,
+    "use_cache": true,
+    "vocab_size": 248320,
+    "mamba_ssm_dtype": "float32",
+    "rope_parameters": {
+      "mrope_interleaved": true,
+      "mrope_section": [
+        11,
+        11,
+        10
+      ],
+      "rope_type": "default",
+      "rope_theta": 10000000,
+      "partial_rotary_factor": 0.25
+    }
+  },
+  "tie_word_embeddings": false,
+  "transformers_version": "4.57.0.dev0",
+  "video_token_id": 248057,
+  "vision_config": {
+    "deepstack_visual_indexes": [],
+    "depth": 27,
+    "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,
+  "dim": 320,
+  "embed_dim": 320,
+  "dtype": "bfloat16",
+  "tokenizer_class": "Qwen2Tokenizer",
+  "use_cache": false
+}

eval_results/Vidore2BioMedicalLecturesRetrieval.json

@@ -0,0 +1,632 @@
+{
+  "dataset_revision": "a29202f0da409034d651614d87cd8938d254e2ea",
+  "task_name": "Vidore2BioMedicalLecturesRetrieval",
+  "mteb_version": "2.12.30",
+  "scores": {
+    "test": [
+      {
+        "ndcg_at_1": 0.64375,
+        "ndcg_at_3": 0.656,
+        "ndcg_at_5": 0.66995,
+        "ndcg_at_10": 0.69916,
+        "ndcg_at_20": 0.71901,
+        "ndcg_at_100": 0.75118,
+        "ndcg_at_1000": 0.76495,
+        "map_at_1": 0.3871,
+        "map_at_3": 0.53774,
+        "map_at_5": 0.57576,
+        "map_at_10": 0.61206,
+        "map_at_20": 0.62481,
+        "map_at_100": 0.6364,
+        "map_at_1000": 0.63804,
+        "recall_at_1": 0.3871,
+        "recall_at_3": 0.61117,
+        "recall_at_5": 0.69446,
+        "recall_at_10": 0.78489,
+        "recall_at_20": 0.83679,
+        "recall_at_100": 0.9363,
+        "recall_at_1000": 1.0,
+        "accuracy": 0.3871,
+        "precision_at_1": 0.64375,
+        "precision_at_3": 0.41042,
+        "precision_at_5": 0.30375,
+        "precision_at_10": 0.19,
+        "precision_at_20": 0.10969,
+        "precision_at_100": 0.02813,
+        "precision_at_1000": 0.00322,
+        "mrr_at_1": 0.64375,
+        "mrr_at_3": 0.742708,
+        "mrr_at_5": 0.752708,
+        "mrr_at_10": 0.757421,
+        "mrr_at_20": 0.758566,
+        "mrr_at_100": 0.759079,
+        "mrr_at_1000": 0.759171,
+        "nauc_ndcg_at_1_max": 0.117549,
+        "nauc_ndcg_at_1_std": -0.13763,
+        "nauc_ndcg_at_1_diff1": 0.418487,
+        "nauc_ndcg_at_3_max": 0.297661,
+        "nauc_ndcg_at_3_std": -0.005852,
+        "nauc_ndcg_at_3_diff1": 0.364611,
+        "nauc_ndcg_at_5_max": 0.333361,
+        "nauc_ndcg_at_5_std": 0.022166,
+        "nauc_ndcg_at_5_diff1": 0.399793,
+        "nauc_ndcg_at_10_max": 0.308195,
+        "nauc_ndcg_at_10_std": -0.012135,
+        "nauc_ndcg_at_10_diff1": 0.388814,
+        "nauc_ndcg_at_20_max": 0.331781,
+        "nauc_ndcg_at_20_std": 0.022841,
+        "nauc_ndcg_at_20_diff1": 0.385127,
+        "nauc_ndcg_at_100_max": 0.31944,
+        "nauc_ndcg_at_100_std": 0.036584,
+        "nauc_ndcg_at_100_diff1": 0.359421,
+        "nauc_ndcg_at_1000_max": 0.291655,
+        "nauc_ndcg_at_1000_std": 0.008018,
+        "nauc_ndcg_at_1000_diff1": 0.369154,
+        "nauc_map_at_1_max": 0.271401,
+        "nauc_map_at_1_std": 0.006219,
+        "nauc_map_at_1_diff1": 0.496903,
+        "nauc_map_at_3_max": 0.311906,
+        "nauc_map_at_3_std": 0.006606,
+        "nauc_map_at_3_diff1": 0.427698,
+        "nauc_map_at_5_max": 0.324976,
+        "nauc_map_at_5_std": 0.007081,
+        "nauc_map_at_5_diff1": 0.433238,
+        "nauc_map_at_10_max": 0.305901,
+        "nauc_map_at_10_std": -0.012612,
+        "nauc_map_at_10_diff1": 0.408621,
+        "nauc_map_at_20_max": 0.319724,
+        "nauc_map_at_20_std": 0.005244,
+        "nauc_map_at_20_diff1": 0.404812,
+        "nauc_map_at_100_max": 0.318502,
+        "nauc_map_at_100_std": 0.009278,
+        "nauc_map_at_100_diff1": 0.397725,
+        "nauc_map_at_1000_max": 0.316331,
+        "nauc_map_at_1000_std": 0.007211,
+        "nauc_map_at_1000_diff1": 0.397743,
+        "nauc_recall_at_1_max": 0.271401,
+        "nauc_recall_at_1_std": 0.006219,
+        "nauc_recall_at_1_diff1": 0.496903,
+        "nauc_recall_at_3_max": 0.343912,
+        "nauc_recall_at_3_std": 0.038312,
+        "nauc_recall_at_3_diff1": 0.389557,
+        "nauc_recall_at_5_max": 0.385775,
+        "nauc_recall_at_5_std": 0.068966,
+        "nauc_recall_at_5_diff1": 0.396087,
+        "nauc_recall_at_10_max": 0.344568,
+        "nauc_recall_at_10_std": 0.007659,
+        "nauc_recall_at_10_diff1": 0.331023,
+        "nauc_recall_at_20_max": 0.459298,
+        "nauc_recall_at_20_std": 0.137373,
+        "nauc_recall_at_20_diff1": 0.314282,
+        "nauc_recall_at_100_max": 0.583859,
+        "nauc_recall_at_100_std": 0.422452,
+        "nauc_recall_at_100_diff1": 0.057672,
+        "nauc_recall_at_1000_max": NaN,
+        "nauc_recall_at_1000_std": NaN,
+        "nauc_recall_at_1000_diff1": NaN,
+        "nauc_precision_at_1_max": 0.117549,
+        "nauc_precision_at_1_std": -0.13763,
+        "nauc_precision_at_1_diff1": 0.418487,
+        "nauc_precision_at_3_max": 0.092038,
+        "nauc_precision_at_3_std": -0.001822,
+        "nauc_precision_at_3_diff1": -0.085907,
+        "nauc_precision_at_5_max": 0.041302,
+        "nauc_precision_at_5_std": -0.001574,
+        "nauc_precision_at_5_diff1": -0.152584,
+        "nauc_precision_at_10_max": -0.060324,
+        "nauc_precision_at_10_std": -0.032584,
+        "nauc_precision_at_10_diff1": -0.243907,
+        "nauc_precision_at_20_max": -0.062272,
+        "nauc_precision_at_20_std": 0.028776,
+        "nauc_precision_at_20_diff1": -0.278166,
+        "nauc_precision_at_100_max": -0.143936,
+        "nauc_precision_at_100_std": 0.024061,
+        "nauc_precision_at_100_diff1": -0.334004,
+        "nauc_precision_at_1000_max": -0.235401,
+        "nauc_precision_at_1000_std": -0.051602,
+        "nauc_precision_at_1000_diff1": -0.335696,
+        "nauc_mrr_at_1_max": 0.117549,
+        "nauc_mrr_at_1_std": -0.13763,
+        "nauc_mrr_at_1_diff1": 0.418487,
+        "nauc_mrr_at_3_max": 0.169293,
+        "nauc_mrr_at_3_std": -0.071472,
+        "nauc_mrr_at_3_diff1": 0.384003,
+        "nauc_mrr_at_5_max": 0.183747,
+        "nauc_mrr_at_5_std": -0.053432,
+        "nauc_mrr_at_5_diff1": 0.37806,
+        "nauc_mrr_at_10_max": 0.182668,
+        "nauc_mrr_at_10_std": -0.060396,
+        "nauc_mrr_at_10_diff1": 0.380955,
+        "nauc_mrr_at_20_max": 0.180565,
+        "nauc_mrr_at_20_std": -0.06154,
+        "nauc_mrr_at_20_diff1": 0.38336,
+        "nauc_mrr_at_100_max": 0.178835,
+        "nauc_mrr_at_100_std": -0.062624,
+        "nauc_mrr_at_100_diff1": 0.382392,
+        "nauc_mrr_at_1000_max": 0.17871,
+        "nauc_mrr_at_1000_std": -0.062942,
+        "nauc_mrr_at_1000_diff1": 0.38264,
+        "hit_rate_at_1": 0.64375,
+        "hit_rate_at_3": 0.86875,
+        "hit_rate_at_5": 0.9125,
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eval_results/Vidore2ESGReportsHLRetrieval.json

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eval_results/Vidore2ESGReportsRetrieval.json

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eval_results/Vidore2EconomicsReportsRetrieval.json

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+}

eval_results/Vidore3ComputerScienceRetrieval.json

@@ -0,0 +1,942 @@
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eval_results/Vidore3EnergyRetrieval.json

@@ -0,0 +1,942 @@
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eval_results/Vidore3FinanceEnRetrieval.json

@@ -0,0 +1,942 @@
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+}

eval_results/Vidore3FinanceFrRetrieval.json

@@ -0,0 +1,942 @@
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+  "date": 1781787790.391208
+}

eval_results/Vidore3HrRetrieval.json

@@ -0,0 +1,942 @@
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+}

eval_results/Vidore3IndustrialRetrieval.json

@@ -0,0 +1,942 @@
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+}

eval_results/Vidore3PharmaceuticalsRetrieval.json

@@ -0,0 +1,942 @@
+{
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+  "task_name": "Vidore3PharmaceuticalsRetrieval",
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eval_results/Vidore3PhysicsRetrieval.json

@@ -0,0 +1,942 @@
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eval_results/VidoreArxivQARetrieval.json

@@ -0,0 +1,167 @@
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eval_results/VidoreDocVQARetrieval.json

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eval_results/VidoreInfoVQARetrieval.json

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+}

eval_results/VidoreShiftProjectRetrieval.json

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eval_results/VidoreSyntheticDocQAAIRetrieval.json

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eval_results/VidoreSyntheticDocQAEnergyRetrieval.json

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eval_results/VidoreSyntheticDocQAGovernmentReportsRetrieval.json

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eval_results/VidoreSyntheticDocQAHealthcareIndustryRetrieval.json

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eval_results/VidoreTabfquadRetrieval.json

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