Add files using upload-large-folder tool

.gitattributes

@@ -33,3 +33,6 @@ 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
+figures/Nex-N2-Benchmark-white.png filter=lfs diff=lfs merge=lfs -text
+model.safetensors.index.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,249 @@
+---
+license: apache-2.0
+pipeline_tag: text-generation
+library_name: transformers
+
+base_model: nex-agi/Nex-N2-mini
+quantized_by: palmfuture
+
+tags:
+  - gptq
+  - int4
+  - moe
+  - qwen3.5
+  - gptqmodel
+  - quantized
+---
+
+# Nex-N2-mini-GPTQ-Int4
+
+GPTQ Int4 quantization of Nex-N2-mini using GPTQModel.
+
+## Quantization Details
+
+- Method: GPTQ
+- Bits: 4
+- Group Size: 128
+- Symmetric: True
+- desc_act: False
+- Calibration Samples: 256
+- Calibration Tokens: ~124k
+- Quantizer: GPTQModel 7.1.0
+- Model Size: ~22 GB
+
+## Quantization Statistics
+
+| Metric | Value |
+|----------|----------|
+| Total Modules | 30,720 |
+| GPTQ Success Rate | 99.736% |
+| RTN Fallback Rate | 0.264% |
+| Mean Loss | 1.53e-04 |
+| P95 Loss | 5.18e-04 |
+| P99 Loss | 8.16e-04 |
+| Max Loss | 1.96e-03 |
+
+## Calibration
+
+Domain-mixed calibration set:
+
+| Source | Samples | Purpose |
+|---|---:|---|
+| `allenai/c4` | 102 | General English text |
+| `allenai/tulu-3-sft-mixture` | 77 | Instruction-following |
+| `codeparrot/codeparrot-clean-valid` | 51 | Code generation |
+| `HuggingFaceH4/MATH-500` | 26 | Mathematical reasoning |
+| **Total** | **256** | `seq_len=1024`, ~124k tokens |
+
+## Notes
+
+This is an unofficial community quantization by [palmfuture](https://huggingface.co/palmfuture).
+
+All credit for the original model goes to the Nex-AI team.
+
+---
+
+<div align="center">
+<img src="./figures/NEX_logo.svg" width="20%"/>
+</div>
+
+---
+
+<div align="center">
+🤗 <a href="https://hf.co/collections/nex-agi/nex-n2"><b>Model</b></a>&nbsp&nbsp | &nbsp&nbsp
+💻 <a href="https://github.com/nex-agi/Nex-N2"><b>Github</b></a>&nbsp&nbsp | &nbsp&nbsp
+🧭 <a href="https://www.modelscope.cn/collections/nex-agi/Nex-N2"><b>ModelScope</b></a>&nbsp&nbsp | &nbsp&nbsp
+🚀 <a href="https://nex-agi.com"><b>Nex-AGI</b></a>
+</div>
+
+# Nex-N2
+
+**An agentic model with Agentic Thinking.**
+
+Today, we are officially releasing and open-sourcing our next-generation model, **Nex-N2** — an agent model built for real-world productivity scenarios. With first-tier coding and agentic capabilities, Nex-N2 keeps driving complex, long-horizon tasks forward in real environments to deliver stable, end-to-end results.
+
+Over the past year, a paradigm shift led by Vibe Coding and Harness Engineering has been redefining the limits of LLM agents. From dialogue, to reasoning, to agents that execute long-horizon tasks with environmental feedback, the tasks models must handle keep growing harder, the contexts longer, and the environments more realistic. The core of next-generation model competition is no longer *whether a model can think*, but whether it can reliably and efficiently turn thinking into actions that are executable, verifiable, and iterable.
+
+Rather than treating reasoning, tool use, and environment execution as separate capabilities, Nex-N2 unifies them through an **Agentic Thinking** framework that connects requirement understanding, task planning, code implementation, environmental feedback, evaluation and debugging, and continuous iteration into a single closed loop. The framework has two parts:
+
+- **Adaptive Thinking** lets the model decide on its own when to think and how deeply — executing simple actions quickly while reasoning thoroughly on critical decisions.
+- **Coherent Thinking** carries one consistent reasoning paradigm across general reasoning and diverse agentic tasks, staying consistent across tasks and modalities to enable stable capability transfer.
+
+Across real agentic workflows — agentic coding, deep research, tool calling, and terminal execution — Nex-N2 reaches first-tier performance, with substantial gains over the previous-generation Nex-N1 on multiple authoritative benchmarks. In real productivity scenarios such as OpenClaw one-person-company workflows, end-to-end game development, and web and multimodal generation, it likewise demonstrates outstanding usability, robustness, and stability.
+
+## Open Source
+
+In keeping with our commitment to open source, we are releasing both **Nex-N2-Pro** and **Nex-N2-mini** as open-source models starting today.
+
+- **Nex-N2-Pro:** [Hugging Face](https://huggingface.co/nex-agi/Nex-N2-Pro) | [ModelScope](https://www.modelscope.cn/models/nex-agi/Nex-N2-Pro)
+- **Nex-N2-mini:** [Hugging Face](https://huggingface.co/nex-agi/Nex-N2-mini) | [ModelScope](https://www.modelscope.cn/models/nex-agi/Nex-N2-mini)
+- **Early Access:** [SiliconFlow](https://cloud.siliconflow.cn/me/models?target=nex-agi%2FNex-N2-Pro)
+
+We welcome developers and enterprises to integrate and try Nex-N2 and share their feedback.
+
+## Performance
+
+We evaluate Nex-N2 in real agentic workflows along three directions — agentic tasks, coding tasks, and general tasks — covering benchmarks across tool calling, search-based decision-making, software engineering, and terminal execution. Nex-N2-Pro delivers strong performance that keeps pace with top-tier models such as GPT-5.5 and Opus 4.7: it excels at coding (e.g., 75.3 on Terminal-Bench 2.1) and long-horizon tasks (1585 on GDPval), and shows especially strong generalization and competitiveness on newer benchmarks like SWE-Atlas and DeepSWE. On general capability and core reasoning, it stands on par with leading frontier models.
+
+![Nex-N2 Benchmark Overview](./figures/Nex-N2-Benchmark-white.png)
+
+Nex-N2 ships in two variants, both post-trained on the Qwen3.5 series: **Nex-N2-Pro** (built on `Qwen3.5-397B-A17B`) and **Nex-N2-mini** (built on `Qwen3.5-35B-A3B-Base`), covering different latency and quality trade-offs. The table below reports their scores alongside leading proprietary and open models across our full evaluation suite.
+
+| Benchmark | **Nex-N2-mini** | **Nex-N2-Pro** | GPT-5.5 | Opus 4.7 | Kimi-K2.6 | GLM-5.1 | MiniMax M3 | DeepSeek-V4-Pro |
+| --- | --- | --- | --- | --- | --- | --- | --- | --- |
+| **Agent** |  |  |  |  |  |  |  |  |
+| BrowseComp | 74.1 | 83.7 | 84.4 | 79.8 | 83.2 | 79.3 | 83.5 | 83.4 |
+| GDPval | 1402 | 1585 | 1769 | 1753 | 1481 | 1535 | - | 1554 |
+| Toolathlon | 33.3 | 51.9 | 55.6 | 52.8 | 50.0 | 40.7 | - | 51.8 |
+| WildClawBench | 47.7 | 53.5 | 58.2 | 62.2 | - | 48.2 | - | 43.7 |
+| WideSearch | 62.0 | 75.6 | - | - | 80.8 | - | - | - |
+| TAU3 | 65.9 | 71.1 | - | - | - | 70.6 | - | - |
+| **Coding & SWE** |  |  |  |  |  |  |  |  |
+| SWE-Bench Pro | 50.2 | 58.8 | 58.6 | 64.3 | 58.6 | 58.4 | 59.0 | 55.4 |
+| Terminal-Bench 2.1 | 60.7 | 75.3 | 83.4 | 69.7 | - | 58.7 | 66.0 | 72.0 |
+| DeepSWE | 8.0 | 33.6 | 70 | 54 | 24 | 18 | - | 8 |
+| SWE-Bench Verified | 74.4 | 80.8 | 82.9 | 87.6 | 80.2 | - | 80.5 | 80.6 |
+| SWE Atlas QnA | 31.5 | 37.9 | 45.4 | 45.2 | - | - | 37.9 | - |
+| SWE Atlas RF | 30.0 | 32.9 | 44.8 | 48.6 | - | - | - | - |
+| SWE Atlas TW | 23.3 | 40.0 | 42.6 | 38.2 | - | - | 30.8 | - |
+| **General & Reasoning** |  |  |  |  |  |  |  |  |
+| GPQA Diamond | 82.6 | 90.7 | 93.6 | 94.2 | 90.5 | 86.2 | - | 90.1 |
+| IFEval | 89.1 | 94.0 | - | - | 94.5 | 94.5 | - | 91.9 |
+| Apex | 9.4 | 36.5 | - | - | 24.0 | 11.5 | - | 38.3 |
+
+## Usage
+
+### Local Deployment
+
+> **Note:** For the best performance with Nex-series models, we recommend serving them with our customized `sglang` fork.
+
+First, install our `sglang` fork:
+
+```bash
+# Use the customized `sglang` fork
+git clone https://github.com/nex-agi/sglang.git
+cd sglang
+
+# Install the python packages
+pip install --upgrade pip
+pip install -e "python"
+```
+
+#### Nex-N2-Pro
+
+Launch the server (example on two 8× H100 servers with CUDA 13.0):
+
+```bash
+# Multi-node (2 nodes). Run the same command on every node with:
+#   <node-rank> = 0 on the head node, 1 on the other node
+#   <node0-ip>  = IP of the head node (reachable from all others)
+python -m sglang.launch_server \
+  --model-path /path/to/your/model  \
+  --tp 16 \
+  --nnodes 2 \
+  --node-rank <node-rank> \
+  --dist-init-addr <node0-ip>:20000 \
+  --reasoning-parser qwen3 \
+  --tool-call-parser qwen3_coder \
+  --mamba-scheduler-strategy extra_buffer
+```
+
+#### Nex-N2-mini
+
+Launch the server (example on one 2× H100 server with CUDA 13.0):
+
+```bash
+python -m sglang.launch_server \
+  --model-path /path/to/your/model  \
+  --tp 2 \
+  --reasoning-parser qwen3 \
+  --tool-call-parser qwen3_coder \
+  --mamba-scheduler-strategy extra_buffer
+```
+
+### Docker Deployment
+
+We also provide a prebuilt Docker image with our customized `sglang` fork preinstalled: **`nexagi/sglang:v0.5.12`**. The launch command is the same as above.
+
+#### Nex-N2-Pro
+
+```bash
+# Multi-node (2 nodes). Run the same command on every node with:
+#   <node-rank> = 0 on the head node, 1 on the other node
+#   <node0-ip>  = IP of the head node (reachable from all others)
+docker run --gpus all --shm-size 32g --network host \
+  -v /path/to/your/model:/model \
+  nexagi/sglang:v0.5.12 \
+  python3 -m sglang.launch_server \
+    --model-path /model \
+    --tp 16 \
+    --nnodes 2 \
+    --node-rank <node-rank> \
+    --dist-init-addr <node0-ip>:20000 \
+    --host 0.0.0.0 --port 30000 \
+    --reasoning-parser qwen3 \
+    --tool-call-parser qwen3_coder \
+    --mamba-scheduler-strategy extra_buffer
+```
+
+#### Nex-N2-mini
+
+Single node with 2× H100:
+
+```bash
+docker run --gpus all --shm-size 32g --ipc=host \
+  -p 30000:30000 \
+  -v /path/to/your/model:/model \
+  nexagi/sglang:v0.5.12 \
+  python3 -m sglang.launch_server \
+    --model-path /model \
+    --tp 2 \
+    --host 0.0.0.0 --port 30000 \
+    --reasoning-parser qwen3 \
+    --tool-call-parser qwen3_coder \
+    --mamba-scheduler-strategy extra_buffer
+```
+
+### Recommended Sampling Parameters
+
+For the best generation quality, we recommend the following sampling parameters:
+
+- `temperature`: 0.7
+- `top_p`: 0.95
+- `top_k`: 40
+
+### Function Calling
+
+Nex-series models support robust function-calling capabilities. To enable function calling, add the `--tool-call-parser qwen3_coder` flag when launching the server:
+
+```bash
+python -m sglang.launch_server --model-path /path/to/your/model --tool-call-parser qwen3_coder
+```
+
+### Reasoning Parser
+
+Nex-series models emit explicit reasoning traces. Add the `--reasoning-parser qwen3` flag to parse the reasoning content separately from the final response. It can be combined with the function-calling parser above:
+
+```bash
+python -m sglang.launch_server --model-path /path/to/your/model --tool-call-parser qwen3_coder --reasoning-parser qwen3
+```

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 %}
+            {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+        {%- 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 | 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>' }}
+    {%- endif %}
+{%- endif %}

config.json

@@ -0,0 +1,186 @@
+{
+  "architectures": [
+    "Qwen3_5MoeForConditionalGeneration"
+  ],
+  "dtype": "bfloat16",
+  "hidden_size": 2048,
+  "image_token_id": 248056,
+  "model_type": "qwen3_5_moe",
+  "quantization_config": {
+    "bits": 4,
+    "checkpoint_format": "gptq",
+    "desc_act": false,
+    "dynamic": {
+      "-:.*attn.*": {},
+      "-:.*mlp\\.gate$": {},
+      "-:.*mtp.*": {},
+      "-:.*shared_expert.*": {},
+      "-:.*vision.*": {},
+      "-:.*visual.*": {},
+      "lm_head": {},
+      "model.language_model.embed_tokens": {},
+      "model.lm_head": {}
+    },
+    "format": "gptq",
+    "group_size": 128,
+    "lm_head": false,
+    "meta": {
+      "act_group_aware": true,
+      "auto_forward_data_parallel": true,
+      "damp_auto_increment": 0.01,
+      "damp_percent": 0.05,
+      "dense_vram_strategy": "exclusive",
+      "dense_vram_strategy_devices": null,
+      "fallback": {
+        "smooth": null,
+        "strategy": "rtn",
+        "threshold": "0.5%"
+      },
+      "foem": null,
+      "gc_mode": "interval",
+      "gptaq": null,
+      "hessian": {
+        "chunk_bytes": null,
+        "chunk_size": null,
+        "staging_dtype": "float32"
+      },
+      "mock_quantization": false,
+      "moe_vram_strategy": "exclusive",
+      "moe_vram_strategy_devices": null,
+      "mse": 0.0,
+      "offload_to_disk": true,
+      "offload_to_disk_path": "/tmp/gptqmodel_7ct0h3d0",
+      "pack_impl": "cpu",
+      "quantizer": [
+        "gptqmodel:7.1.0"
+      ],
+      "static_groups": false,
+      "true_sequential": true,
+      "uri": "https://github.com/modelcloud/gptqmodel",
+      "wait_for_submodule_finalizers": false
+    },
+    "method": "gptq",
+    "pack_dtype": "int32",
+    "quant_method": "gptq",
+    "sym": true
+  },
+  "rope_parameters": {
+    "rope_theta": 10000.0,
+    "rope_type": "default"
+  },
+  "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,
+    "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_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_moe_text",
+    "moe_intermediate_size": 512,
+    "mtp_num_hidden_layers": 1,
+    "mtp_use_dedicated_embeddings": false,
+    "num_attention_heads": 16,
+    "num_experts": 256,
+    "num_experts_per_tok": 8,
+    "num_hidden_layers": 40,
+    "num_key_value_heads": 2,
+    "output_router_logits": false,
+    "pad_token_id": 248044,
+    "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"
+    },
+    "router_aux_loss_coef": 0.001,
+    "shared_expert_intermediate_size": 512,
+    "tie_word_embeddings": false,
+    "use_cache": true,
+    "vocab_size": 248320
+  },
+  "tie_word_embeddings": false,
+  "transformers_version": "5.10.2",
+  "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_moe_vision",
+    "num_heads": 16,
+    "num_position_embeddings": 2304,
+    "out_hidden_size": 2048,
+    "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 +1,9 @@
+{
+  "_from_model_config": true,
+  "do_sample": true,
+  "eos_token_id": 248044,
+  "output_attentions": false,
+  "output_hidden_states": false,
+  "transformers_version": "5.10.2",
+  "use_cache": true
+}

model-00001-of-00006.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:70437bfbe9e018bd9e69538033214041b6c1665c1f419cc434f17836914ee04e
+size 4291974936

model-00002-of-00006.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6463268cda07b1e9c6a2b35dd5081df6f81e1b9d57ae1ef56e148d37b71f0468
+size 4297721744

model-00003-of-00006.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f771678087a6cfe3b56b72c163a1b70cbdb8f258b8efaf0594c15ac13cd765c7
+size 4299157440

model-00004-of-00006.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4491c61b7890a95bb379a747b51f5a7245cfaea7c2b91266cac76e5d74e07d88
+size 4299149176

model-00005-of-00006.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5206f0f2359256923ccfc6e367ba118690f91742b6332e012e8b3a2ed9dbdcf4
+size 4299142712

model-00006-of-00006.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8de28582ce1a84127340f51b766e2370073ea53df978a0353e1c1988fd117c77
+size 1243499560

model.safetensors.index.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:483170bd31d6eebd8e322b1742dfe0435791105bff74846a8f158201037b1f56
+size 13071821

processor_config.json

@@ -0,0 +1,63 @@
+{
+  "image_processor": {
+    "data_format": "channels_first",
+    "do_convert_rgb": true,
+    "do_normalize": true,
+    "do_rescale": true,
+    "do_resize": true,
+    "image_mean": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "image_processor_type": "Qwen2VLImageProcessor",
+    "image_std": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "merge_size": 2,
+    "patch_size": 16,
+    "resample": 3,
+    "rescale_factor": 0.00392156862745098,
+    "size": {
+      "longest_edge": 16777216,
+      "shortest_edge": 65536
+    },
+    "temporal_patch_size": 2
+  },
+  "processor_class": "Qwen3VLProcessor",
+  "video_processor": {
+    "data_format": "channels_first",
+    "default_to_square": true,
+    "do_convert_rgb": true,
+    "do_normalize": true,
+    "do_rescale": true,
+    "do_resize": true,
+    "do_sample_frames": true,
+    "fps": 2,
+    "image_mean": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "image_std": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "max_frames": 768,
+    "merge_size": 2,
+    "min_frames": 4,
+    "patch_size": 16,
+    "resample": 3,
+    "rescale_factor": 0.00392156862745098,
+    "return_metadata": false,
+    "size": {
+      "longest_edge": 234881024,
+      "shortest_edge": 4096
+    },
+    "temporal_patch_size": 2,
+    "video_processor_type": "Qwen3VLVideoProcessor"
+  }
+}

quantize_config.json

@@ -0,0 +1,58 @@
+{
+  "bits": 4,
+  "dynamic": {
+    "-:.*visual.*": {},
+    "-:.*vision.*": {},
+    "-:.*attn.*": {},
+    "-:.*shared_expert.*": {},
+    "-:.*mtp.*": {},
+    "-:.*mlp\\.gate$": {},
+    "lm_head": {},
+    "model.lm_head": {},
+    "model.language_model.embed_tokens": {}
+  },
+  "group_size": 128,
+  "desc_act": false,
+  "lm_head": false,
+  "method": "gptq",
+  "quant_method": "gptq",
+  "format": "gptq",
+  "checkpoint_format": "gptq",
+  "pack_dtype": "int32",
+  "meta": {
+    "quantizer": [
+      "gptqmodel:7.1.0"
+    ],
+    "uri": "https://github.com/modelcloud/gptqmodel",
+    "damp_percent": 0.05,
+    "damp_auto_increment": 0.01,
+    "static_groups": false,
+    "true_sequential": true,
+    "mse": 0.0,
+    "gptaq": null,
+    "foem": null,
+    "act_group_aware": true,
+    "fallback": {
+      "strategy": "rtn",
+      "threshold": "0.5%",
+      "smooth": null
+    },
+    "offload_to_disk": true,
+    "offload_to_disk_path": "/tmp/gptqmodel_7ct0h3d0",
+    "pack_impl": "cpu",
+    "gc_mode": "interval",
+    "wait_for_submodule_finalizers": false,
+    "auto_forward_data_parallel": true,
+    "dense_vram_strategy": "exclusive",
+    "dense_vram_strategy_devices": null,
+    "moe_vram_strategy": "exclusive",
+    "moe_vram_strategy_devices": null,
+    "mock_quantization": false,
+    "hessian": {
+      "chunk_size": null,
+      "chunk_bytes": null,
+      "staging_dtype": "float32"
+    }
+  },
+  "sym": true
+}

tokenizer.json

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

tokenizer_config.json

@@ -0,0 +1,34 @@
+{
+  "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": "<|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": "TokenizersBackendFast",
+  "unk_token": null,
+  "video_token": "<|video_pad|>",
+  "vision_bos_token": "<|vision_start|>",
+  "vision_eos_token": "<|vision_end|>",
+  "_commit_hash": null
+}