license: mit
language:
- en
- zh
pipeline_tag: text-generation
library_name: transformers
base_model:
- zai-org/GLM-4.5-Air
GLM-4.5-Air-AWQ
Method
Quantised using vllm-project/llm-compressor, nvidia/Llama-Nemotron-Post-Training-Dataset and the following configs:
config_groups = {
"group_0": {
"targets": ["Linear"],
"input_activations": None,
"output_activations": None,
"weights": {
"num_bits": 4,
"type": "int",
"symmetric": True,
"strategy": "group",
"group_size": 32,
}
}
}
recipe = [
AWQModifier(
ignore=["lm_head", "re:.*mlp.gate$"],
config_groups=config_groups,
),
]
Note: the last layer, i.e., the MTP layer index 46 is ignored due to transformers not having MTP implementations.
Inference
Prerequisite
Install the latest vllm version:
pip install -U vllm \
--pre \
--extra-index-url https://wheels.vllm.ai/nightly
vllm
Please load the model into vllm and sglang as float16 data type for AWQ support and use tensor_parallel_size <= 2 i.e.,
vllm serve cpatonn/GLM-4.5-Air-AWQ --dtype float16 --tensor-parallel-size 2 --pipeline-parallel-size 2
GLM-4.5-Air
👋 Join our Discord community.
📖 Check out the GLM-4.5 technical blog.
📍 Use GLM-4.5 API services on Z.ai API Platform (Global) or
Zhipu AI Open Platform (Mainland China).
👉 One click to GLM-4.5.
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.
For more eval results, show cases, and technical details, please visit our technical blog. The technical report will be released soon.
The model code, tool parser and reasoning parser can be found in the implementation of transformers, vLLM and SGLang.
Quick Start
Please refer our github page for more detail.