Instructions to use Tech2wild/GLM-5.3-Int4-Int8Mix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Tech2wild/GLM-5.3-Int4-Int8Mix with Transformers:

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

pipe = pipeline("text-generation", model="Tech2wild/GLM-5.3-Int4-Int8Mix")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Tech2wild/GLM-5.3-Int4-Int8Mix")
model = AutoModelForCausalLM.from_pretrained("Tech2wild/GLM-5.3-Int4-Int8Mix", device_map="auto")
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 Tech2wild/GLM-5.3-Int4-Int8Mix with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Tech2wild/GLM-5.3-Int4-Int8Mix"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Tech2wild/GLM-5.3-Int4-Int8Mix",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Tech2wild/GLM-5.3-Int4-Int8Mix

SGLang

How to use Tech2wild/GLM-5.3-Int4-Int8Mix 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 "Tech2wild/GLM-5.3-Int4-Int8Mix" \
    --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": "Tech2wild/GLM-5.3-Int4-Int8Mix",
		"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 "Tech2wild/GLM-5.3-Int4-Int8Mix" \
        --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": "Tech2wild/GLM-5.3-Int4-Int8Mix",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Tech2wild/GLM-5.3-Int4-Int8Mix with Docker Model Runner:
```
docker model run hf.co/Tech2wild/GLM-5.3-Int4-Int8Mix
```

Tony DeAngelo commited on 1 day ago

Commit

02ba89d

verified ·

1 Parent(s): a101464

Tighten credits: Neural Magic (compressed-tensors/llm-compressor), QuantTrio (GLM recipe), IncoAI (DFlash2)

Browse files

Files changed (1) hide show

README.md +8 -5

README.md CHANGED Viewed

@@ -73,12 +73,15 @@ Measured on 4x DGX Spark (GB10, sm121, aarch64) over a RoCE fabric. **TBD — fi
 | + DFlash2 | TBD | TBD | TBD | TBD |
 | + DCP4 | TBD | ~600K | TBD | TBD |
-## Credits
-- **Base model:** [zai-org/GLM-5.3](https://huggingface.co/zai-org/GLM-5.3) (743B / ~40B active MoE).
-- **Quantization recipe:** the data-free Int4-Int8Mix compressed-tensors method popularized by [QuantTrio](https://huggingface.co/QuantTrio) (e.g. GLM-5.2-Int4-Int8Mix); reproduced here for GLM-5.3 with a verified layer-map coverage check.
-- **Speculative drafter:** [IncoAI](https://huggingface.co/incoai) (DFlash2).
-- **4x DGX Spark deployment + this quant:** [@tonyd2wild](https://github.com/tonyd2wild).
 ## License

 | + DFlash2 | TBD | TBD | TBD | TBD |
 | + DCP4 | TBD | ~600K | TBD | TBD |
+## Credits (what's whose)
+We stand on a lot of other people's work here, so to be precise about who did what:
+- **Base model:** [zai-org / Z.ai](https://huggingface.co/zai-org/GLM-5.3) — GLM-5.3 (743B / ~40B active MoE). Everything starts here.
+- **Quantization format + tooling:** the **`compressed-tensors`** format and **`llm-compressor`** are by **[Neural Magic](https://github.com/neuralmagic) (now Red Hat)**. Mixed-precision weight quantization (some layers 4-bit, some 8-bit) is a general, well-established technique, not any one group's invention.
+- **The GLM recipe (the layer map):** curated by **[QuantTrio](https://huggingface.co/QuantTrio)** (e.g. GLM-5.2-Int4-Int8Mix) — specifically the choice of which layers stay Int4 vs Int8 vs full-precision so GLM MoE routing and accuracy hold up. We reproduced that map for GLM-5.3 and verified its coverage before quantizing.
+- **Speculative drafter:** **[IncoAI](https://huggingface.co/incoai)** — the DFlash2 drafter used in the serving stack.
+- **This artifact** — the first Int4-Int8Mix of the big GLM-5.3, and the first one that serves on **4x NVIDIA DGX Spark** with the DFlash2 + DCP4 stack: **[@tonyd2wild](https://github.com/tonyd2wild)**.
 ## License