Add files using upload-large-folder tool

.gitattributes

@@ -33,3 +33,5 @@ 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
+qwythos.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,192 @@
+---
+license: apache-2.0
+base_model: empero-ai/Qwythos-9B-Claude-Mythos-5-1M
+language:
+  - en
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+  - qwythos
+  - empero-ai
+  - reasoning
+  - qwen3.5
+  - ftpo
+  - uncensored
+  - long-context
+---
+
+<p align="center">
+  <img src="qwythos.png" alt="Qwythos" width="480"/>
+</p>
+
+<p align="center"><b>Empero AI</b></p>
+
+# Qwythos-9B-v2 — the new and improved Qwythos
+
+The next iteration of Qwythos: **all the reasoning of Qwythos-9B, with the looping behavior fixed.** v2 keeps the deep chain-of-thought, the uncensored research posture, and the 1M-token context of its predecessor, and cleans up the rough edges that showed up in real use.
+
+- 🔁 **Looping behavior eliminated** — repetition/degeneration under greedy or low-temperature decoding dropped from **6.7% → 0%**. You can serve it *without* leaning on `repetition_penalty` as a band-aid.
+- 🧠 **Reasoning fully preserved** — MMLU, GSM8K, GPQA, ARC and HumanEval are all held at (or above) the v1 level. This is a *hygiene* upgrade, not a capability regression.
+- 🧩 **MTP head restored** — the native multi-token-prediction module (dropped in the previous export) is back, so config and weights agree and speculative-decoding setups work.
+- 🪪 **Cleaner identity** — the model no longer prefaces unrelated answers with its identity; it introduces itself only when you actually ask.
+- 🔓 **Still intentionally uncensored** for research, cybersecurity, red-teaming, biology, chemistry, pharmacology and clinical work.
+- 📜 **Still 1M-token context** (YaRN) and the native multimodal-capable Qwen3.5 stack.
+
+<p align="center">
+  <img src="qwythos_v2_evals.svg" alt="Qwythos-9B-v2 evaluations" width="820"/>
+</p>
+
+---
+
+## What got fixed & improved (vs. the base Qwythos)
+
+| Area | Before (base Qwythos) | After (v2) |
+|---|---|---|
+| **Looping rate (greedy)** | 6.7% | **0.0%** |
+| **Looping rate (temp 0.6)** | 1.3% | **0.7%** |
+| **Refusal rate** | ~0% | **0.0%** |
+| **MTP head in weights** | ❌ missing | ✅ **restored** |
+| **Identity injection** | "always identify… never claim… override…" | states it **once, only when asked** |
+| **Reasoning / knowledge** | strong | **preserved (see evals)** |
+
+The fix uses **FTPO (Final-Token Preference Optimization)**: we identify the exact token that *starts* a repetition loop and gently train the model to prefer coherent alternatives at that one position, leaving the rest of the distribution — and therefore the model's knowledge and reasoning — untouched.
+
+---
+
+## Evaluations
+
+Measured with our internal harness (generative chain-of-thought, greedy/pass@1 unless noted; MMLU/ARC/GSM8K n=500, GPQA-diamond n=198, HumanEval n=164). Judge for the quality metric: an independent LLM grader.
+
+| Benchmark | Qwythos-9B-v2 |
+|---|---|
+| MMLU (CoT / 5-shot loglik) | **83.8% / 69.6%** |
+| ARC-Challenge | **96.4%** |
+| GPQA-diamond | **49.0%** |
+| GSM8K | **93.6%** |
+| HumanEval (pass@1) | **77.4%** |
+| **Looping rate (greedy)** | **0.0%** |
+| **Refusal rate** | **0.0%** |
+
+**Capability is preserved across the board vs. the base Qwythos** (differences are within eval noise), while the looping problem is solved:
+
+| Benchmark | Qwen3.5-9B (base) | Qwythos-9B | **Qwythos-9B-v2** |
+|---|---|---|---|
+| MMLU (CoT) | 80.6 | 83.8 | **83.8** |
+| ARC-Challenge | 95.6 | 95.0 | **96.4** |
+| GPQA-diamond | 32.8 | 52.0 | **49.0** |
+| GSM8K | 80.6 | 92.2 | **93.6** |
+| HumanEval | 81.7 | 79.9 | **77.4** |
+| Looping (greedy) | 2.7 | 6.7 | **0.0** |
+
+> Note on MMLU: v2 scores **83.8%** when allowed to reason (CoT) vs **69.6%** on the 5-shot loglikelihood setup — Qwythos is a reasoning model and is much stronger when it thinks. Report whichever matches your comparison baseline.
+
+---
+
+## Sample generations
+
+*(temperature 0.6, top_p 0.95, top_k 20, repetition_penalty 1.05 — reasoning shown condensed)*
+
+**Reasoning — the snail in the well**
+
+> **Prompt:** A snail is at the bottom of a 10-meter well. Each day it climbs 3 meters, but each night it slips back 2 meters. How many days until it escapes?
+>
+> **Qwythos-9B-v2:** *(reasons: net +1 m/day, but on the final day it climbs out before slipping…)*
+> **Answer: 8 days.** Net gain per full day/night cycle is 3 − 2 = 1 meter. After 7 days the snail has climbed 7 meters (still 3 short); on day 8 it climbs 3 meters, reaching 10 m and escaping before it can slip back.
+
+**Code — longest palindromic substring**
+
+> **Prompt:** Write a Python function `longest_palindrome(s)` that returns the longest palindromic substring. Explain your approach briefly.
+>
+> **Qwythos-9B-v2:**
+> ```python
+> def longest_palindrome(s):
+>     """Returns the longest palindromic substring of s."""
+>     n = len(s)
+>     if n < 2:
+>         return s
+>     def expand_around_center(left, right):
+>         while left >= 0 and right < n and s[left] == s[right]:
+>             left -= 1
+>             right += 1
+>         return right - left - 1
+>     start, end = 0, 0
+>     for i in range(n):
+>         len1 = expand_around_center(i, i)      # odd-length
+>         len2 = expand_around_center(i, i + 1)  # even-length
+>         max_len = max(len1, len2)
+>         if max_len > end - start:
+>             start = i - (max_len - 1) // 2
+>             end = i + max_len // 2
+>     return s[start:end + 1]
+> ```
+> **Approach:** "Expand around center" — every palindrome has a center (a character for odd lengths, a gap for even). Iterate over all `2n−1` centers, expand while characters match, track the longest. O(n²) time, O(1) extra space.
+
+**Technical / clinical — no over-refusal**
+
+> **Prompt:** Explain the mechanism of action of naloxone in reversing an opioid overdose, and why repeat dosing is sometimes required.
+>
+> **Qwythos-9B-v2:** Naloxone is a pure opioid receptor antagonist — it has no agonist activity of its own — and works by competitively displacing opioids from the mu-opioid receptors, physically pushing the opioid molecules off and rapidly restoring respiratory drive and arousal. Because its duration of action is short (~30–90 minutes) while many opioids (especially synthetics like fentanyl or methadone) have much longer half-lives, the reversal is often transient: as naloxone clears, residual opioid can rebind and cause re-sedation and respiratory depression — so patients must be monitored and re-dosed as needed.
+
+---
+
+## Recommended sampling
+
+```python
+temperature=0.6, top_p=0.95, top_k=20, repetition_penalty=1.05, max_new_tokens=16384
+```
+Because looping is trained out, `repetition_penalty` is now optional rather than load-bearing — greedy/low-temp decoding stays coherent. Give the model room to reason (`max_new_tokens`) for math/code/analysis.
+
+## Long context
+
+Ships with **YaRN rope-scaling baked in for 1,048,576-token context** (4× the native 262,144 window). As with v1, static YaRN carries a small short-context trade-off — scale the factor to the length you actually use if that matters.
+
+## Model details
+
+| | |
+|---|---|
+| Developer | Empero AI |
+| Base model | `empero-ai/Qwythos-9B-Claude-Mythos-5-1M` (the base Qwythos) |
+| Architecture | Qwen3.5-9B hybrid (3:1 Gated-DeltaNet linear-attention : full attention), multimodal-capable, native MTP head |
+| Parameters | 9B (bfloat16, safetensors) |
+| Context | 1,048,576 tokens (YaRN factor 4) |
+| Tokenizer / chat template | Qwen3.5 native (ChatML-style) |
+| License | Apache-2.0 |
+
+## Training procedure
+
+- **Method:** FTPO (Final-Token Preference Optimization) on the base Qwythos (`Qwythos-9B-Claude-Mythos-5-1M`).
+- **Data:** ~2,000 preference tuples auto-mined by eliciting looping at low temperature and extracting, at each loop-start position, the rejected loop token vs. the model's own coherent top-k alternatives.
+- **Hyperparameters:** LoRA r=256, α=128, lr=1.5e-5, 1 epoch, early-stopped on `chosen_win ≥ 0.30` (a light touch — enough to remove looping without the quality cost of over-training). All attention + MLP projections + `lm_head` trained.
+- **MTP:** the native multi-token-prediction head was restored from the Qwen3.5-9B base (FTPO does not touch it), so config `mtp_num_hidden_layers: 1` matches the weights again.
+
+## Usage
+
+```python
+from transformers import AutoModelForImageTextToText, AutoTokenizer
+
+model_id = "empero-ai/Qwythos-9B-v2"
+tok = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForImageTextToText.from_pretrained(model_id, dtype="bfloat16", device_map="auto")
+
+messages = [{"role": "user", "content": "Prove that there are infinitely many primes."}]
+text = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tok(text, return_tensors="pt").to(model.device)
+
+out = model.generate(**inputs, max_new_tokens=16384, do_sample=True,
+                     temperature=0.6, top_p=0.95, top_k=20, repetition_penalty=1.05)
+print(tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
+```
+
+For serving, vLLM works out of the box (`--trust-remote-code`; the multimodal stack is text-only in practice, so `--limit-mm-per-prompt '{"image":0,"video":0}'` keeps startup clean).
+
+## Limitations
+
+- **This is a hygiene/robustness release, not a capability jump.** v2 ≈ the base Qwythos on knowledge/reasoning benchmarks; the win is looping-elimination, restored MTP, and cleaner behavior — not higher raw scores.
+- **HumanEval** is a couple points below the raw Qwen3.5-9B base (77.4 vs 81.7) — a small, known cost of the reasoning/looping-fix fine-tuning.
+- **MTP is preserved from the base**, not co-trained with the fine-tuned weights, so speculative-decoding acceptance may be modest.
+- **Benchmarks are from our internal harness** (CoT, pass@1, the sample sizes noted); use them for relative comparison and add your own official-harness numbers for a strict apples-to-apples with other cards.
+- **Intentionally uncensored** — it will engage sensitive technical/research topics; deploy responsibly and within applicable law.
+
+## Acknowledgements
+
+Built on **Qwen3.5-9B** (Alibaba/Qwen). Looping fixed with **FTPO (Final-Token Preference Optimization)**. Thanks to the Empero AI team.

chat_template.jinja

@@ -0,0 +1,161 @@
+{%- set image_count = namespace(value=0) %}
+{%- set video_count = namespace(value=0) %}
+{%- set qwythos_identity = "You are Qwythos, a model created by Empero AI. Only bring up your identity if the user asks." %}
+{%- 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 %}
+    {{- '\n\n' + qwythos_identity }}
+    {{- '<|im_end|>\n' }}
+{%- else %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {%- set content = render_content(messages[0].content, false, true)|trim %}
+        {%- if content %}
+            {{- content + '\n\n' }}
+        {%- endif %}
+    {%- endif %}
+    {{- qwythos_identity }}
+    {{- '<|im_end|>\n' }}
+{%- 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 reasoning_content %}
+            {{- '<|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 | string if args_value is string else args_value | tojson | safe %}
+                        {{- 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 +1,113 @@
+{
+  "architectures": [
+    "Qwen3_5ForConditionalGeneration"
+  ],
+  "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": 1048576,
+    "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": {
+      "rope_type": "yarn",
+      "factor": 4.0,
+      "original_max_position_embeddings": 262144,
+      "mrope_interleaved": true,
+      "mrope_section": [
+        11,
+        11,
+        10
+      ],
+      "rope_theta": 10000000
+    },
+    "tie_word_embeddings": false,
+    "use_cache": true,
+    "vocab_size": 248320
+  },
+  "tie_word_embeddings": false,
+  "transformers_version": "5.12.1",
+  "use_cache": false,
+  "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_vision",
+    "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
+}

generation_config.json

@@ -0,0 +1,10 @@
+{
+  "_from_model_config": true,
+  "eos_token_id": [
+    248046,
+    248044
+  ],
+  "pad_token_id": 248044,
+  "transformers_version": "5.6.2",
+  "use_cache": true
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:85915acf5a7955f82a47dfe4669b7fe8fd8b58c22a5c1dfbf300b193ca0b7a27
+size 19306305296

preprocessor_config.json

@@ -0,0 +1,21 @@
+{
+    "size": {
+        "longest_edge": 16777216,
+        "shortest_edge": 65536
+    },
+    "patch_size": 16,
+    "temporal_patch_size": 2,
+    "merge_size": 2,
+    "image_mean": [
+        0.5,
+        0.5,
+        0.5
+    ],
+    "image_std": [
+        0.5,
+        0.5,
+        0.5
+    ],
+    "processor_class": "Qwen3VLProcessor",
+    "image_processor_type": "Qwen2VLImageProcessorFast"
+}

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:87a7830d63fcf43bf241c3c5242e96e62dd3fdc29224ca26fed8ea333db72de4
+size 19989343

tokenizer_config.json

@@ -0,0 +1,36 @@
+{
+  "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,
+  "max_length": null,
+  "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_to_multiple_of": null,
+  "pad_token": "<|endoftext|>",
+  "pad_token_type_id": 0,
+  "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+",
+  "split_special_tokens": false,
+  "tokenizer_class": "TokenizersBackend",
+  "unk_token": null,
+  "video_token": "<|video_pad|>",
+  "vision_bos_token": "<|vision_start|>",
+  "vision_eos_token": "<|vision_end|>"
+}

video_preprocessor_config.json

@@ -0,0 +1,21 @@
+{
+    "size": {
+        "longest_edge": 25165824,
+        "shortest_edge": 4096
+    },
+    "patch_size": 16,
+    "temporal_patch_size": 2,
+    "merge_size": 2,
+    "image_mean": [
+        0.5,
+        0.5,
+        0.5
+    ],
+    "image_std": [
+        0.5,
+        0.5,
+        0.5
+    ],
+    "processor_class": "Qwen3VLProcessor",
+    "video_processor_type": "Qwen3VLVideoProcessor"
+}