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.gitattributes

@@ -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 +1,63 @@
+---
+license: mit
+language:
+- zh
+- en
+base_model:
+- zai-org/GLM-4.5-Air-Base
+pipeline_tag: image-text-to-text
+library_name: transformers
+---
+
+# GLM-4.5V
+
+<div align="center">
+<img src=https://raw.githubusercontent.com/zai-org/GLM-V/refs/heads/main/resources/logo.svg width="40%"/>
+</div>
+<p align="center">
+    👋 加入我们的<a href="https://github.com/zai-org/GLM-V/blob/main/resources/WECHAT.md" target="_blank"> 微信群 </a>。
+    <br>
+    📖 查看 <a href="https://arxiv.org/abs/2507.01006" target="_blank">论文</a> 。
+    <br>
+    📍 在 <a href="https://www.bigmodel.cn">智谱大模型开放平台</a> 使用 GLM-V 系列模型的API服务。
+</p>
+
+## 模型介绍
+
+视觉语言大模型（VLM）已经成为智能系统的关键基石。随着真实世界的智能任务越来越复杂，VLM模型也亟需在基本的多模态感知之外，逐渐增强复杂任务中的推理能力，提升自身的准确性、全面性和智能化程度，使得复杂问题解决、长上下文理解、多模态智能体等智能任务成为可能。
+
+我们希望通过我们的开源工作，与社区一起探索技术前沿，同时赋能更多开发者发挥创意做出更多好玩的应用。
+
+GLM-4.5V 基于智谱新一代旗舰文本基座模型 GLM-4.5-Air（106B参数，12B激活），延续 GLM-4.1V-Thinking 技术路线，在 42 个公开视觉多模态榜单中综合效果达到同级别开源模型 SOTA 性能，涵盖图像、视频、文档理解以及 GUI Agent 等常见任务。
+
+![bench_45](https://raw.githubusercontent.com/zai-org/GLM-V/refs/heads/main/resources/bench_45v.jpeg)
+
+在多模态榜单之外，我们更重视模型在真实场景下的表现与可用性。GLM-4.5V 通过高效混合训练，具备覆盖不同种视觉内容的处理能力，实现全场景视觉推理，包括：
+
+- 图像推理（场景理解、复杂多图分析、位置识别）
+- 视频理解（长视频分镜分析、事件识别）
+- GUI 任务（屏幕读取、图标识别、桌面操作辅助）
+- 复杂图表与长文档解析（研报分析、信息提取）
+- Grounding 能力（精准定位视觉元素）
+
+同时，模型新增 “思考模式” 开关，用户可灵活选择快速响应或深度推理，平衡效率与效果，该开关的使用方式与GLM-4.5 语言模型相同。
+
+## 快速开始
+
+请通过我们的 [Github](https://github.com/zai-org/GLM-V) 了解更多代码信息。
+
+## 引用论文
+
+如果您使用了本模型，请引用以下论文：
+
+```bibtex
+@misc{glmvteam2025glm41vthinkingversatilemultimodalreasoning,
+      title={GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning},
+      author={GLM-V Team and Wenyi Hong and Wenmeng Yu and Xiaotao Gu and Guo Wang and Guobing Gan and Haomiao Tang and Jiale Cheng and Ji Qi and Junhui Ji and Lihang Pan and Shuaiqi Duan and Weihan Wang and Yan Wang and Yean Cheng and Zehai He and Zhe Su and Zhen Yang and Ziyang Pan and Aohan Zeng and Baoxu Wang and Boyan Shi and Changyu Pang and Chenhui Zhang and Da Yin and Fan Yang and Guoqing Chen and Jiazheng Xu and Jiali Chen and Jing Chen and Jinhao Chen and Jinghao Lin and Jinjiang Wang and Junjie Chen and Leqi Lei and Letian Gong and Leyi Pan and Mingzhi Zhang and Qinkai Zheng and Sheng Yang and Shi Zhong and Shiyu Huang and Shuyuan Zhao and Siyan Xue and Shangqin Tu and Shengbiao Meng and Tianshu Zhang and Tianwei Luo and Tianxiang Hao and Wenkai Li and Wei Jia and Xin Lyu and Xuancheng Huang and Yanling Wang and Yadong Xue and Yanfeng Wang and Yifan An and Yifan Du and Yiming Shi and Yiheng Huang and Yilin Niu and Yuan Wang and Yuanchang Yue and Yuchen Li and Yutao Zhang and Yuxuan Zhang and Zhanxiao Du and Zhenyu Hou and Zhao Xue and Zhengxiao Du and Zihan Wang and Peng Zhang and Debing Liu and Bin Xu and Juanzi Li and Minlie Huang and Yuxiao Dong and Jie Tang},
+      year={2025},
+      eprint={2507.01006},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2507.01006},
+}
+```

amaas.yaml

@@ -0,0 +1,85 @@
+meta:
+  backend_type: "ftransformers"
+  categories: ""
+  dir_path: "GLM-4.5V-GPU-weight"
+  display_model_name: "GLM-4.5V-GPU-weight"
+  display_model_quanted_type: "BF16"
+  family: "ZhipuAI"
+  local_path: "GLM-4.5V-GPU-weight"
+  model_id: "ApproachingAI2024/GLM-4.5V-GPU-weight"
+  name: "GLM-4.5V-GPU-weight"
+  parameters:
+    cpuinfer: "80"
+    amx-weight-path: "ApproachingAI2024/GLM-4.5V-CPU-weight"
+    mem-fraction-static: "0.98"
+    amx-method: "AMXINT8"
+    attention-backend: "flashinfer"
+    subpool-count: ""
+    tool-call-parser: ""
+  quanted_type: "BF16"
+  required_disk: "216895848448"
+  required_dram: "107946704896"
+  required_vram: "121869697024"
+  subpool_count: "2"
+  type: "vlm"
+  weight_size: "106"
+
+integrity:
+  files:
+    "chat_template.jinja": "3858"
+    "config.json": "23130"
+    "configuration.json": "51"
+    "generation_config.json": "234"
+    "model-00001-of-00046.safetensors": "2957438824"
+    "model-00002-of-00046.safetensors": "2470328656"
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+    "preprocessor_config.json": "364"
+    "README.md": "4043"
+    "tokenizer_config.json": "7307"
+    "tokenizer.json": "19970699"
+    "video_preprocessor_config.json": "365"
+  sha256: "e903ac50faf8b68c7d469c20b309a8e2670066adc09cff02f65ca4ea2d17e444"

chat_template.jinja

@@ -0,0 +1,118 @@
+[gMASK]<sop>
+{%- if tools -%}
+<|system|>
+# Tools
+
+You may call one or more functions to assist with the user query.
+
+You are provided with function signatures within <tools></tools> XML tags:
+<tools>
+{% for tool in tools %}
+{{ tool | tojson(ensure_ascii=False) }}
+{% endfor %}
+</tools>
+
+For each function call, output the function name and arguments within the following XML format:
+<tool_call>{function-name}
+<arg_key>{arg-key-1}</arg_key>
+<arg_value>{arg-value-1}</arg_value>
+<arg_key>{arg-key-2}</arg_key>
+<arg_value>{arg-value-2}</arg_value>
+...
+</tool_call>{%- endif -%}
+{%- macro visible_text(content) -%}
+    {%- if content is string -%}
+        {{- content }}
+    {%- elif content is iterable and content is not mapping -%}
+        {%- for item in content -%}
+            {%- if item is mapping and item.type == 'text' -%}
+                {{- item.text }}
+            {%- elif item is mapping and (item.type == 'image' or 'image' in item) -%}
+                <|begin_of_image|><|image|><|end_of_image|>
+            {%- elif item is mapping and (item.type == 'video' or 'video' in item) -%}
+                <|begin_of_video|><|video|><|end_of_video|>
+            {%- elif item is string -%}
+                {{- item }}
+            {%- endif -%}
+        {%- endfor -%}
+    {%- else -%}
+        {{- content }}
+    {%- endif -%}
+{%- endmacro -%}
+{%- set ns = namespace(last_user_index=-1) %}
+{%- for m in messages %}
+    {%- if m.role == 'user' %}
+        {% set ns.last_user_index = loop.index0 -%}
+    {%- endif %}
+{%- endfor %}
+{% for m in messages %}
+{%- if m.role == 'user' -%}<|user|>
+{% if m.content is string %}
+{{ m.content }}
+{%- else %}
+{%- for item in m.content %}
+{% if item.type == 'video' or 'video' in item %}
+<|begin_of_video|><|video|><|end_of_video|>{% elif item.type == 'image' or 'image' in item %}
+<|begin_of_image|><|image|><|end_of_image|>{% elif item.type == 'text' %}
+{{ item.text }}
+{%- endif %}
+{%- endfor %}
+{%- endif %}
+{{- '/nothink' if (enable_thinking is defined and not enable_thinking and not visible_text(m.content).endswith("/nothink")) else '' -}}
+{%- elif m.role == 'assistant' -%}
+<|assistant|>
+{%- set reasoning_content = '' %}
+{%- set content = visible_text(m.content) %}
+{%- if m.reasoning_content is string %}
+    {%- set reasoning_content = m.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 %}
+{%- if loop.index0 > ns.last_user_index and reasoning_content -%}
+{{ '\n<think>' + reasoning_content.strip() +  '</think>'}}
+{%- else -%}
+{{ '\n<think></think>' }}
+{%- endif -%}
+{%- if content.strip() -%}
+{{ '\n' + content.strip() }}
+{%- endif -%}
+{% if m.tool_calls %}
+{% for tc in m.tool_calls %}
+{%- if tc.function %}
+    {%- set tc = tc.function %}
+{%- endif %}
+{{ '\n<tool_call>' + tc.name }}
+{% set _args = tc.arguments %}
+{% for k, v in _args.items() %}
+<arg_key>{{ k }}</arg_key>
+<arg_value>{{ v | tojson(ensure_ascii=False) if v is not string else v }}</arg_value>
+{% endfor %}
+</tool_call>{% endfor %}
+{% endif %}
+{%- elif m.role == 'tool' -%}
+{%- if m.content is string -%}
+{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+    {{- '<|observation|>' }}
+{%- endif %}
+{{- '\n<tool_response>\n' }}
+{{- m.content }}
+{{- '\n</tool_response>' }}
+{%- else -%}
+<|observation|>{% for tr in m.content %}
+
+<tool_response>
+{{ tr.output if tr.output is defined else tr }}
+</tool_response>{% endfor -%}
+{% endif -%}
+{%- elif m.role == 'system' -%}
+<|system|>
+{{ visible_text(m.content) }}
+{%- endif -%}
+{%- endfor -%}
+{%- if add_generation_prompt -%}
+<|assistant|>
+{{'<think></think>\n' if (enable_thinking is defined and not enable_thinking) else ''}}
+{%- endif -%}

config.json

@@ -0,0 +1,429 @@
+{
+  "architectures": [
+    "Glm4vMoeForConditionalGeneration"
+  ],
+  "model_type": "glm4v_moe",
+  "text_config": {
+    "pad_token_id": 151329,
+    "vocab_size": 151552,
+    "eos_token_id": [
+      151329,
+      151336,
+      151338
+    ],
+    "image_end_token_id": 151340,
+    "image_start_token_id": 151339,
+    "image_token_id": 151363,
+    "head_dim": 128,
+    "attention_bias": true,
+    "attention_dropout": 0.0,
+    "first_k_dense_replace": 1,
+    "hidden_act": "silu",
+    "hidden_size": 4096,
+    "initializer_range": 0.02,
+    "intermediate_size": 10944,
+    "max_position_embeddings": 65536,
+    "model_type": "glm4v_moe_text",
+    "moe_intermediate_size": 1408,
+    "n_group": 1,
+    "n_routed_experts": 128,
+    "n_shared_experts": 1,
+    "norm_topk_prob": true,
+    "num_attention_heads": 96,
+    "num_experts_per_tok": 8,
+    "num_hidden_layers": 46,
+    "num_key_value_heads": 8,
+    "partial_rotary_factor": 0.5,
+    "quantization_config": {
+      "config_groups": {
+        "group_0": {
+          "input_activations": null,
+          "output_activations": null,
+          "targets": [
+            "Linear"
+          ],
+          "weights": {
+            "actorder": null,
+            "block_structure": null,
+            "dynamic": false,
+            "group_size": null,
+            "num_bits": 8,
+            "observer": "minmax",
+            "observer_kwargs": {},
+            "strategy": "channel",
+            "symmetric": true,
+            "type": "int"
+          }
+        }
+      },
+      "format": "pack-quantized",
+      "global_compression_ratio": null,
+      "ignore": [
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+        "model.language_model.layers.10.mlp.shared_experts.down_proj",
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