Duplicate from zai-org/GLM-4.5

Co-authored-by: zR <ZAHNGYUXUAN@users.noreply.huggingface.co>

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README.md

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+---
+language:
+- en
+- zh
+library_name: transformers
+license: mit
+pipeline_tag: text-generation
+---
+
+# GLM-4.5
+
+<div align="center">
+<img src=https://raw.githubusercontent.com/zai-org/GLM-4.5/refs/heads/main/resources/logo.svg width="15%"/>
+</div>
+<p align="center">
+    👋 Join our <a href="https://discord.gg/QR7SARHRxK" target="_blank">Discord</a> community.
+    <br>
+    📖 Check out the GLM-4.5 <a href="https://z.ai/blog/glm-4.5" target="_blank">technical blog</a>, <a href="https://arxiv.org/abs/2508.06471" target="_blank">technical report</a>, and <a href="https://zhipu-ai.feishu.cn/wiki/Gv3swM0Yci7w7Zke9E0crhU7n7D" target="_blank">Zhipu AI technical documentation</a>.
+    <br>
+    📍 Use GLM-4.5 API services on <a href="https://docs.z.ai/guides/llm/glm-4.5">Z.ai API Platform (Global)</a> or <br> <a href="https://docs.bigmodel.cn/cn/guide/models/text/glm-4.5">Zhipu AI Open Platform (Mainland China)</a>.
+    <br>
+    👉 One click to <a href="https://chat.z.ai">GLM-4.5</a>.
+</p>
+  
+## Model Introduction
+
+The **GLM-4.5** series models are foundation models designed for intelligent agents. GLM-4.5 has **355** billion total parameters with **32** billion active parameters, while GLM-4.5-Air adopts a more compact design with **106** billion total parameters and **12** billion active parameters. GLM-4.5 models unify reasoning, coding, and intelligent agent capabilities to meet the complex demands of intelligent agent applications.
+
+Both GLM-4.5 and GLM-4.5-Air are hybrid reasoning models that provide two modes: thinking mode for complex reasoning and tool usage, and non-thinking mode for immediate responses.
+
+We have open-sourced the base models, hybrid reasoning models, and FP8 versions of the hybrid reasoning models for both GLM-4.5 and GLM-4.5-Air. They are released under the MIT open-source license and can be used commercially and for secondary development.
+
+As demonstrated in our comprehensive evaluation across 12 industry-standard benchmarks, GLM-4.5 achieves exceptional performance with a score of **63.2**, in the **3rd** place among all the proprietary and open-source models. Notably, GLM-4.5-Air delivers competitive results at **59.8** while maintaining superior efficiency.
+
+![bench](https://raw.githubusercontent.com/zai-org/GLM-4.5/refs/heads/main/resources/bench.png)
+
+For more eval results, show cases, and technical details, please visit
+our [technical blog](https://z.ai/blog/glm-4.5) or [technical report](https://arxiv.org/abs/2508.06471).
+
+The model code, tool parser and reasoning parser can be found in the implementation of [transformers](https://github.com/huggingface/transformers/tree/main/src/transformers/models/glm4_moe), [vLLM](https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/glm4_moe_mtp.py) and [SGLang](https://github.com/sgl-project/sglang/blob/main/python/sglang/srt/models/glm4_moe.py).
+
+## Model Downloads
+
+You can directly experience the model on [Hugging Face](https://huggingface.co/spaces/zai-org/GLM-4.5-Space)
+or [ModelScope](https://modelscope.cn/studios/ZhipuAI/GLM-4.5-Demo) or download the model by following the links below.
+
+| Model            | Download Links                                                                                                                                | Model Size | Precision |
+|------------------|-----------------------------------------------------------------------------------------------------------------------------------------------|------------|-----------|
+| GLM-4.5          | [🤗 Hugging Face](https://huggingface.co/zai-org/GLM-4.5)<br> [🤖 ModelScope](https://modelscope.cn/models/ZhipuAI/GLM-4.5)                   | 355B-A32B  | BF16      |
+| GLM-4.5-Air      | [🤗 Hugging Face](https://huggingface.co/zai-org/GLM-4.5-Air)<br> [🤖 ModelScope](https://modelscope.cn/models/ZhipuAI/GLM-4.5-Air)           | 106B-A12B  | BF16      |
+| GLM-4.5-FP8      | [🤗 Hugging Face](https://huggingface.co/zai-org/GLM-4.5-FP8)<br> [🤖 ModelScope](https://modelscope.cn/models/ZhipuAI/GLM-4.5-FP8)           | 355B-A32B  | FP8       |
+| GLM-4.5-Air-FP8  | [🤗 Hugging Face](https://huggingface.co/zai-org/GLM-4.5-Air-FP8)<br> [🤖 ModelScope](https://modelscope.cn/models/ZhipuAI/GLM-4.5-Air-FP8)   | 106B-A12B  | FP8       |
+| GLM-4.5-Base     | [🤗 Hugging Face](https://huggingface.co/zai-org/GLM-4.5-Base)<br> [🤖 ModelScope](https://modelscope.cn/models/ZhipuAI/GLM-4.5-Base)         | 355B-A32B  | BF16      |
+| GLM-4.5-Air-Base | [🤗 Hugging Face](https://huggingface.co/zai-org/GLM-4.5-Air-Base)<br> [🤖 ModelScope](https://modelscope.cn/models/ZhipuAI/GLM-4.5-Air-Base) | 106B-A12B  | BF16      |
+
+## System Requirements
+
+### Inference
+
+We provide minimum and recommended configurations for "full-featured" model inference. The data in the table below is
+based on the following conditions:
+
+1. All models use MTP layers and specify
+   `--speculative-num-steps 3 --speculative-eagle-topk 1 --speculative-num-draft-tokens 4` to ensure competitive
+   inference speed.
+2. The `cpu-offload` parameter is not used.
+3. Inference batch size does not exceed `8`.
+4. All are executed on devices that natively support FP8 inference, ensuring both weights and cache are in FP8 format.
+5. Server memory must exceed `1T` to ensure normal model loading and operation.
+
+The models can run under the configurations in the table below:
+
+| Model       | Precision | GPU Type and Count   | Test Framework |
+|-------------|-----------|----------------------|----------------|
+| GLM-4.5     | BF16      | H100 x 16 / H200 x 8 | sglang         |
+| GLM-4.5     | FP8       | H100 x 8 / H200 x 4  | sglang         |
+| GLM-4.5-Air | BF16      | H100 x 4 / H200 x 2  | sglang         |
+| GLM-4.5-Air | FP8       | H100 x 2 / H200 x 1  | sglang         |
+
+Under the configurations in the table below, the models can utilize their full 128K context length:
+
+| Model       | Precision | GPU Type and Count    | Test Framework |
+|-------------|-----------|-----------------------|----------------|
+| GLM-4.5     | BF16      | H100 x 32 / H200 x 16 | sglang         |
+| GLM-4.5     | FP8       | H100 x 16 / H200 x 8  | sglang         |
+| GLM-4.5-Air | BF16      | H100 x 8 / H200 x 4   | sglang         |
+| GLM-4.5-Air | FP8       | H100 x 4 / H200 x 2   | sglang         |
+
+### Fine-tuning
+
+The code can run under the configurations in the table below
+using [Llama Factory](https://github.com/hiyouga/LLaMA-Factory):
+
+| Model       | GPU Type and Count | Strategy | Batch Size (per GPU) |
+|-------------|--------------------|----------|----------------------|
+| GLM-4.5     | H100 x 16          | Lora     | 1                    |
+| GLM-4.5-Air | H100 x 4           | Lora     | 1                    |
+
+The code can run under the configurations in the table below using [Swift](https://github.com/modelscope/ms-swift):
+
+| Model       | GPU Type and Count | Strategy | Batch Size (per GPU) |
+|-------------|--------------------|----------|----------------------|
+| GLM-4.5     | H20 (96GiB) x 16   | Lora     | 1                    |
+| GLM-4.5-Air | H20 (96GiB) x 4    | Lora     | 1                    |
+| GLM-4.5     | H20 (96GiB) x 128  | SFT      | 1                    |
+| GLM-4.5-Air | H20 (96GiB) x 32   | SFT      | 1                    |
+| GLM-4.5     | H20 (96GiB) x 128  | RL       | 1                    |
+| GLM-4.5-Air | H20 (96GiB) x 32   | RL       | 1                    |
+
+## Quick Start
+
+Please install the required packages according to `requirements.txt`.
+
+```shell
+pip install -r requirements.txt
+```
+
+### transformers
+
+Please refer to the `trans_infer_cli.py` code in the `inference` folder.
+
+### vLLM
+
++ Both BF16 and FP8 can be started with the following code:
+
+```shell
+vllm serve zai-org/GLM-4.5-Air \
+    --tensor-parallel-size 8 \
+    --tool-call-parser glm45 \
+    --reasoning-parser glm45 \
+    --enable-auto-tool-choice \
+    --served-model-name glm-4.5-air
+```
+
+If you're using 8x H100 GPUs and encounter insufficient memory when running the GLM-4.5 model, you'll need
+`--cpu-offload-gb 16` (only applicable to vLLM).
+
+If you encounter `flash infer` issues, use `VLLM_ATTENTION_BACKEND=XFORMERS` as a temporary replacement. You can also
+specify `TORCH_CUDA_ARCH_LIST='9.0+PTX'` to use `flash infer` (different GPUs have different TORCH_CUDA_ARCH_LIST
+values, please check accordingly).
+
+### SGLang
+
++ BF16
+
+```shell
+python3 -m sglang.launch_server \
+  --model-path zai-org/GLM-4.5-Air \
+  --tp-size 8 \
+  --tool-call-parser glm45  \
+  --reasoning-parser glm45 \
+  --speculative-algorithm EAGLE \
+  --speculative-num-steps 3 \
+  --speculative-eagle-topk 1 \
+  --speculative-num-draft-tokens 4 \
+  --mem-fraction-static 0.7 \
+  --served-model-name glm-4.5-air \
+  --host 0.0.0.0 \
+  --port 8000
+```
+
++ FP8
+
+```shell
+python3 -m sglang.launch_server \
+  --model-path zai-org/GLM-4.5-Air-FP8 \
+  --tp-size 4 \
+  --tool-call-parser glm45  \
+  --reasoning-parser glm45  \
+  --speculative-algorithm EAGLE \
+  --speculative-num-steps 3  \
+  --speculative-eagle-topk 1  \
+  --speculative-num-draft-tokens 4 \
+  --mem-fraction-static 0.7 \
+  --disable-shared-experts-fusion \
+  --served-model-name glm-4.5-air-fp8 \
+  --host 0.0.0.0 \
+  --port 8000
+```
+
+### Request Parameter Instructions
+
++ When using `vLLM` and `SGLang`, thinking mode is enabled by default when sending requests. If you want to disable the
+  thinking switch, you need to add the `extra_body={"chat_template_kwargs": {"enable_thinking": False}}` parameter.
++ Both support tool calling. Please use OpenAI-style tool description format for calls.
++ For specific code, please refer to `api_request.py` in the `inference` folder.

chat_template.jinja

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+[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 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|>
+{{ visible_text(m.content) }}
+{{- '/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|>{{- '\n<think></think>' if (enable_thinking is defined and not enable_thinking) else '' -}}
+{%- endif -%}

config.json

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+{
+  "architectures": [
+    "Glm4MoeForCausalLM"
+  ],
+  "attention_bias": true,
+  "attention_dropout": 0.0,
+  "pad_token_id": 151329,
+  "eos_token_id": [
+    151329,
+    151336,
+    151338
+  ],
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "partial_rotary_factor": 0.5,
+  "initializer_range": 0.02,
+  "intermediate_size": 12288,
+  "max_position_embeddings": 131072,
+  "model_type": "glm4_moe",
+  "moe_intermediate_size": 1536,
+  "norm_topk_prob": true,
+  "num_attention_heads": 96,
+  "n_group": 1,
+  "topk_group": 1,
+  "n_routed_experts": 160,
+  "n_shared_experts": 1,
+  "routed_scaling_factor": 2.5,
+  "num_experts_per_tok": 8,
+  "first_k_dense_replace": 3,
+  "num_hidden_layers": 92,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "num_nextn_predict_layers": 1,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.54.0",
+  "use_cache": true,
+  "use_qk_norm": true,
+  "vocab_size": 151552
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "eos_token_id": [
+    151329,
+    151336,
+    151338
+  ],
+  "pad_token_id": 151329,
+  "transformers_version": "4.54.0"
+}

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