You can deploy this model and start earning money today!

Explyt/
GLM-5-AWQ-pruned-20

Name: GLM-5-AWQ-pruned-20
Availability: InStock

Views

No views yet

GLM-5-AWQ

Base model: zai-org/GLM-5

This repo quantizes the model using data-free quantization (no calibration dataset required).

【Dependencies / Installation】

python

# NOTE: 
# vllm==0.16.0rc2 absolutely would NOT work! 
# Must upgrade to >=0.16.1rc1
vllm>=0.16.1rc1.dev7    
transformers>=5.3.0.dev0

As of 2026-02-26, make sure your system has cuda12.8 installed.

Then, create a fresh Python environment (e.g. python3.12 venv) and run:

bash

pip install -U vllm --pre --index-url https://pypi.org/simple --extra-index-url https://wheels.vllm.ai/nightly
pip install git+https://github.com/huggingface/transformers.git
pip install git+https://github.com/deepseek-ai/DeepGEMM.git@v2.1.1.post3 --no-build-isolation

vLLM Official Guide

【vLLM Startup Command】

Note: When launching with TP=8, include --enable-expert-parallel; otherwise the expert tensors wouldn’t be evenly sharded across GPU devices.

export VLLM_USE_DEEP_GEMM=0
export VLLM_USE_FLASHINFER_MOE_FP16=1
export VLLM_USE_FLASHINFER_SAMPLER=0
export OMP_NUM_THREADS=4

vllm serve \
    __YOUR_PATH__/QuantTrio/GLM-5-AWQ \
    --served-model-name MY_MODEL \
    --swap-space 16 \
    --max-num-seqs 32 \
    --max-model-len 32768  \
    --gpu-memory-utilization 0.9 \
    --tensor-parallel-size 8 \
    --enable-expert-parallel \ 
    --enable-auto-tool-choice \    
    --tool-call-parser glm47 \
    --reasoning-parser glm45 \
    --speculative-config '{"method":"mtp","num_speculative_tokens":1}' \
    --trust-remote-code \
    --host 0.0.0.0 \
    --port 8000

【Logs】

2026-02-26
1. Initial commit

【Model Files】

File Size	Last Updated
`392 GiB`	`2026-02-26`

【Model Download】

python

from huggingface_hub import snapshot_download
snapshot_download('QuantTrio/GLM-5-AWQ', cache_dir="your_local_path")

【Overview】

GLM-5

👋 Join our WeChat or Discord community.
📖 Check out the GLM-5 technical blog.
📍 Use GLM-5 API services on Z.ai API Platform.
👉 One click to GLM-5.

Introduction

We are launching GLM-5, targeting complex systems engineering and long-horizon agentic tasks. Scaling is still one of the most important ways to improve the intelligence efficiency of Artificial General Intelligence (AGI). Compared to GLM-4.5, GLM-5 scales from 355B parameters (32B active) to 744B parameters (40B active), and increases pre-training data from 23T to 28.5T tokens. GLM-5 also integrates DeepSeek Sparse Attention (DSA), largely reducing deployment cost while preserving long-context capacity.

Reinforcement learning aims to bridge the gap between competence and excellence in pre-trained models. However, deploying it at scale for LLMs is a challenge due to the RL training inefficiency. To this end, we developed slime, a novel asynchronous RL infrastructure that substantially improves training throughput and efficiency, enabling more fine-grained post-training iterations. With advances in both pre-training and post-training, GLM-5 delivers significant improvement compared to GLM-4.7 across a wide range of academic benchmarks and achieves best-in-class performance among all open-source models in the world on reasoning, coding, and agentic tasks, closing the gap with frontier models.

Benchmark

	GLM-5	GLM-4.7	DeepSeek-V3.2	Kimi K2.5	Claude Opus 4.5	Gemini 3 Pro	GPT-5.2 (xhigh)
HLE	30.5	24.8	25.1	31.5	28.4	37.2	35.4
HLE (w/ Tools)	50.4	42.8	40.8	51.8	43.4*	45.8*	45.5*
AIME 2026 I	92.7	92.9	92.7	92.5	93.3	90.6	-
HMMT Nov. 2025	96.9	93.5	90.2	91.1	91.7	93.0	97.1
IMOAnswerBench	82.5	82.0	78.3	81.8	78.5	83.3	86.3
GPQA-Diamond	86.0	85.7	82.4	87.6	87.0	91.9	92.4
SWE-bench Verified	77.8	73.8	73.1	76.8	80.9	76.2	80.0
SWE-bench Multilingual	73.3	66.7	70.2	73.0	77.5	65.0	72.0
Terminal-Bench 2.0 (Terminus 2)	56.2 / 60.7 †	41.0	39.3	50.8	59.3	54.2	54.0
Terminal-Bench 2.0 (Claude Code)	56.2 / 61.1 †	32.8	46.4	-	57.9	-	-
CyberGym	43.2	23.5	17.3	41.3	50.6	39.9	-
BrowseComp	62.0	52.0	51.4	60.6	37.0	37.8	-
BrowseComp (w/ Context Manage)	75.9	67.5	67.6	74.9	67.8	59.2	65.8
BrowseComp-Zh	72.7	66.6	65.0	62.3	62.4	66.8	76.1
τ²-Bench	89.7	87.4	85.3	80.2	91.6	90.7	85.5
MCP-Atlas (Public Set)	67.8	52.0	62.2	63.8	65.2	66.6	68.0
Tool-Decathlon	38.0	23.8	35.2	27.8	43.5	36.4	46.3
Vending Bench 2	$4,432.12	$2,376.82	$1,034.00	$1,198.46	$4,967.06	$5,478.16	$3,591.33

*: refers to their scores of full set.

†: A verified version of Terminal-Bench 2.0 that fixes some ambiguous instructions. See footnote for more evaluation details.

Footnote

Humanity’s Last Exam (HLE) & other reasoning tasks: We evaluate with a maximum generation length of 131,072 tokens (temperature=1.0, top_p=0.95, max_new_tokens=131072). By default, we report the text-only subset; results marked with * are from the full set. We use GPT-5.2 (medium) as the judge model. For HLE-with-tools, we use a maximum context length of 202,752 tokens.
SWE-bench & SWE-bench Multilingual: We run the SWE-bench suite with OpenHands using a tailored instruction prompt. Settings: temperature=0.7, top_p=0.95, max_new_tokens=16384, with a 200K context window.
BrowserComp: Without context management, we retain details from the most recent 5 turns. With context management, we use the same discard-all strategy as DeepSeek-v3.2 and Kimi K2.5.
Terminal-Bench 2.0 (Terminus 2): We evaluate with the Terminus framework using timeout=2h, temperature=0.7, top_p=1.0, max_new_tokens=8192, with a 128K context window. Resource limits are capped at 16 CPUs and 32 GB RAM.
Terminal-Bench 2.0 (Claude Code): We evaluate in Claude Code 2.1.14 (think mode, default effort) with temperature=1.0, top_p=0.95, max_new_tokens=65536. We remove wall-clock time limits due to generation speed, while preserving per-task CPU and memory constraints. Scores are averaged over 5 runs. We fix environment issues introduced by Claude Code and also report results on a verified Terminal-Bench 2.0 dataset that resolves ambiguous instructions (see: https://huggingface.co/datasets/zai-org/terminal-bench-2-verified).
CyberGym: We evaluate in Claude Code 2.1.18 (think mode, no web tools) with (temperature=1.0, top_p=1.0, max_new_tokens=32000) and a 250-minute timeout per task. Results are single-run Pass@1 over 1,507 tasks.
MCP-Atlas: All models are evaluated in think mode on the 500-task public subset with a 10-minute timeout per task. We use Gemini 3 Pro as the judge model.
τ²-bench: We add a small prompt adjustment in Retail and Telecom to avoid failures caused by premature user termination. For Airline, we apply the domain fixes proposed in the Claude Opus 4.5 system card.
Vending Bench 2: Runs are conducted independently by Andon Labs.

Serve GLM-5 Locally

Prepare environment

vLLM, SGLang, and xLLM all support local deployment of GLM-5. A simple deployment guide is provided here.

vLLM

Using Docker as:

docker pull vllm/vllm-openai:nightly

or using pip:

pip install -U vllm --pre --index-url https://pypi.org/simple --extra-index-url https://wheels.vllm.ai/nightly

then upgrade transformers:

pip install git+https://github.com/huggingface/transformers.git
SGLang

Using Docker as:

bash
1docker pull lmsysorg/sglang:glm5-hopper # For Hopper GPU 2docker pull lmsysorg/sglang:glm5-blackwell # For Blackwell GPU

Deploy

vLLM

shell
1vllm serve zai-org/GLM-5-FP8 \ 2 --tensor-parallel-size 8 \ 3 --gpu-memory-utilization 0.85 \ 4 --speculative-config.method mtp \ 5 --speculative-config.num_speculative_tokens 1 \ 6 --tool-call-parser glm47 \ 7 --reasoning-parser glm45 \ 8 --enable-auto-tool-choice \ 9 --served-model-name glm-5-fp8

Check the recipes for more details.
SGLang

shell
1python3 -m sglang.launch_server \ 2 --model-path zai-org/GLM-5-FP8 \ 3 --tp-size 8 \ 4 --tool-call-parser glm47 \ 5 --reasoning-parser glm45 \ 6 --speculative-algorithm EAGLE \ 7 --speculative-num-steps 3 \ 8 --speculative-eagle-topk 1 \ 9 --speculative-num-draft-tokens 4 \ 10 --mem-fraction-static 0.85 \ 11 --served-model-name glm-5-fp8

Check the sglang cookbook for more details.
xLLM and other Ascend NPU

Please check the deployment guide here.

Citation

Our technical report is coming soon.

Explyt/GLM-5-AWQ-pruned-20