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README.md
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---
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license: mit
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language:
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- en
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- zh
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pipeline_tag: text-generation
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library_name: transformers
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base_model:
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- zai-org/GLM-4.5
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---
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# GLM-4.5-AWQ
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## Method
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Quantised using [vllm-project/llm-compressor](https://github.com/vllm-project/llm-compressor.git), [nvidia/Llama-Nemotron-Post-Training-Dataset](https://huggingface.co/datasets/nvidia/Llama-Nemotron-Post-Training-Dataset) and the following configs:
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```
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config_groups = {
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"group_0": {
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"targets": ["Linear"],
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"input_activations": None,
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"output_activations": None,
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"weights": {
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"num_bits": 4,
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"type": "int",
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"symmetric": True,
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"strategy": "group",
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"group_size": 32,
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}
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}
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}
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recipe = [
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AWQModifier(
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ignore=["lm_head", "re:.*mlp.gate$"],
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config_groups=config_groups,
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),
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]
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```
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Note: the last layer, i.e., the MTP layer index 46 is ignored due to transformers not having MTP implementations.
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## Inference
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### Prerequisite
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Install the latest vllm version:
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```
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pip install -U vllm \
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--pre \
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--extra-index-url https://wheels.vllm.ai/nightly
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```
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### vllm
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Please load the model into vllm and sglang as float16 data type for AWQ support and use `tensor_parallel_size <= 2` i.e.,
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```
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vllm serve cpatonn/GLM-4.5-AWQ --dtype float16 --tensor-parallel-size 2 --pipeline-parallel-size 2
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```
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# GLM-4.5
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<div align="center">
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<img src=https://raw.githubusercontent.com/zai-org/GLM-4.5/refs/heads/main/resources/logo.svg width="15%"/>
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</div>
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<p align="center">
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π Join our <a href="https://discord.gg/QR7SARHRxK" target="_blank">Discord</a> community.
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<br>
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π Check out the GLM-4.5 <a href="https://z.ai/blog/glm-4.5" target="_blank">technical blog</a>.
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<br>
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π 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>.
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<br>
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π One click to <a href="https://chat.z.ai">GLM-4.5</a>.
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</p>
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## Model Introduction
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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.
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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.
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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.
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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.
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For more eval results, show cases, and technical details, please visit
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our [technical blog](https://z.ai/blog/glm-4.5). The technical report will be released soon.
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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).
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## Quick Start
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Please refer our [github page](https://github.com/zai-org/GLM-4.5) for more detail.
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