Instructions to use QuantTrio/GLM-4.6-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantTrio/GLM-4.6-AWQ with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="QuantTrio/GLM-4.6-AWQ")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("QuantTrio/GLM-4.6-AWQ")
model = AutoModelForCausalLM.from_pretrained("QuantTrio/GLM-4.6-AWQ")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use QuantTrio/GLM-4.6-AWQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantTrio/GLM-4.6-AWQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantTrio/GLM-4.6-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantTrio/GLM-4.6-AWQ

SGLang

How to use QuantTrio/GLM-4.6-AWQ with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "QuantTrio/GLM-4.6-AWQ" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantTrio/GLM-4.6-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "QuantTrio/GLM-4.6-AWQ" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantTrio/GLM-4.6-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use QuantTrio/GLM-4.6-AWQ with Docker Model Runner:
```
docker model run hf.co/QuantTrio/GLM-4.6-AWQ
```

GLM-4.6-AWQ / README.md

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	---
	library_name: transformers
	license: mit
	pipeline_tag: text-generation
	tags:
	- AWQ
	- vLLM
	base_model:
	- zai-org/GLM-4.6
	base_model_relation: quantized
	---
	# GLM-4.6-AWQ
	Base Model: [zai-org/GLM-4.6](https://huggingface.co/zai-org/GLM-4.6)

	### 【Dependencies / Installation】
	As of 2025-10-01, create a fresh Python environment and run:
	```bash
	pip install -U pip
	pip install vllm==0.10.2
	```

	### 【vLLM Startup Command】
	<i>Note: When launching with TP=8, include `--enable-expert-parallel`;
	otherwise the expert tensors couldn’t be evenly sharded across GPU devices.</i>

	```
	CONTEXT_LENGTH=32768
	vllm serve \
	QuantTrio/GLM-4.6-AWQ \
	--served-model-name My_Model \
	--enable-auto-tool-choice \
	--tool-call-parser glm45 \
	--reasoning-parser glm45 \
	--swap-space 16 \
	--max-num-seqs 64 \
	--max-model-len $CONTEXT_LENGTH \
	--gpu-memory-utilization 0.9 \
	--tensor-parallel-size 8 \
	--enable-expert-parallel \
	--trust-remote-code \
	--disable-log-requests \
	--host 0.0.0.0 \
	--port 8000
	```

	### 【Logs】
	```
	2025-10-01
	1. Initial commit
	```

	### 【Model Files】
	\| File Size \| Last Updated \|
	\|-----------\|--------------\|
	\| `184GB` \| `2025-10-01` \|

	### 【Model Download】
	```python
	from modelscope import snapshot_download
	snapshot_download('QuantTrio/GLM-4.6-AWQ', cache_dir="your_local_path")
	```

	### 【Overview】
	# GLM-4.6

	<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-4.6 <a href="https://z.ai/blog/glm-4.6" target="_blank">technical blog</a>, <a href="https://arxiv.org/abs/2508.06471" target="_blank">technical report(GLM-4.5)</a>, and <a href="https://zhipu-ai.feishu.cn/wiki/Gv3swM0Yci7w7Zke9E0crhU7n7D" target="_blank">Zhipu AI technical documentation</a>.
	<br>
	📍 Use GLM-4.6 API services on <a href="https://docs.z.ai/guides/llm/glm-4.6">Z.ai API Platform. </a>
	<br>
	👉 One click to <a href="https://chat.z.ai">GLM-4.6</a>.
	</p>

	## Model Introduction

	Compared with GLM-4.5, GLM-4.6 brings several key improvements:

	* Longer context window: The context window has been expanded from 128K to 200K tokens, enabling the model to handle more complex agentic tasks.
	* Superior coding performance: The model achieves higher scores on code benchmarks and demonstrates better real-world performance in applications such as Claude Code、Cline、Roo Code and Kilo Code, including improvements in generating visually polished front-end pages.
	* Advanced reasoning: GLM-4.6 shows a clear improvement in reasoning performance and supports tool use during inference, leading to stronger overall capability.
	* More capable agents: GLM-4.6 exhibits stronger performance in tool using and search-based agents, and integrates more effectively within agent frameworks.
	* Refined writing: Better aligns with human preferences in style and readability, and performs more naturally in role-playing scenarios.

	We evaluated GLM-4.6 across eight public benchmarks covering agents, reasoning, and coding. Results show clear gains over GLM-4.5, with GLM-4.6 also holding competitive advantages over leading domestic and international models such as DeepSeek-V3.1-Terminus and Claude Sonnet 4.

	![bench](https://raw.githubusercontent.com/zai-org/GLM-4.5/refs/heads/main/resources/bench_glm46.png)

	## Inference

	Both GLM-4.5 and GLM-4.6 use the same inference method.

	you can check our [github](https://github.com/zai-org/GLM-4.5) for more detail.

	## Recommended Evaluation Parameters

	For general evaluations, we recommend using a sampling temperature of 1.0.

	For code-related evaluation tasks (such as LCB), it is further recommended to set:

	- `top_p = 0.95`
	- `top_k = 40`


	## Evaluation

	- For tool-integrated reasoning, please refer to [this doc](https://github.com/zai-org/GLM-4.5/blob/main/resources/glm_4.6_tir_guide.md).
	- For search benchmark, we design a specific format for searching toolcall in thinking mode to support search agent, please refer to [this](https://github.com/zai-org/GLM-4.5/blob/main/resources/trajectory_search.json). for the detailed template.