Instructions to use malaiwah/GLM-5.3-Flash-TR3-8bpw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use malaiwah/GLM-5.3-Flash-TR3-8bpw with Transformers:

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

pipe = pipeline("image-text-to-text", model="malaiwah/GLM-5.3-Flash-TR3-8bpw")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("malaiwah/GLM-5.3-Flash-TR3-8bpw")
model = AutoModelForMultimodalLM.from_pretrained("malaiwah/GLM-5.3-Flash-TR3-8bpw", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Trellis

How to use malaiwah/GLM-5.3-Flash-TR3-8bpw with Trellis:

# No code snippets available yet for this library.

# To use this model, check the repository files and the library's documentation.

# Want to help? PRs adding snippets are welcome at:
# https://github.com/huggingface/huggingface.js

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use malaiwah/GLM-5.3-Flash-TR3-8bpw with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "malaiwah/GLM-5.3-Flash-TR3-8bpw"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "malaiwah/GLM-5.3-Flash-TR3-8bpw",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/malaiwah/GLM-5.3-Flash-TR3-8bpw

SGLang

How to use malaiwah/GLM-5.3-Flash-TR3-8bpw 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 "malaiwah/GLM-5.3-Flash-TR3-8bpw" \
    --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": "malaiwah/GLM-5.3-Flash-TR3-8bpw",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "malaiwah/GLM-5.3-Flash-TR3-8bpw" \
        --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": "malaiwah/GLM-5.3-Flash-TR3-8bpw",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use malaiwah/GLM-5.3-Flash-TR3-8bpw with Docker Model Runner:
```
docker model run hf.co/malaiwah/GLM-5.3-Flash-TR3-8bpw
```

malaiwah commited on 7 days ago

Commit

1e5c97a

verified ·

1 Parent(s): 6233eeb

card: explicit Hub lineage note (two sibling roots), cross-links to the measured family, discovery tags

Browse files

Files changed (1) hide show

README.md +23 -0

README.md CHANGED Viewed

@@ -15,6 +15,10 @@ tags:
   - 8-bit
   - moe
   - reasoning
 ---
 # GLM-5.3-Flash-TR3-8bpw (K8)
@@ -102,6 +106,25 @@ combine order. Materialization receipt: bits 8, `complete`,
 `main_and_mtp_complete`, `nonrouted_native_exact`, 331,449,761,784 logical
 bytes, 37,152 routed choices, 1,618 native tensors.
 ## Credits
 Base model by [Z.ai](https://huggingface.co/zai-org). Quantization pipeline,

   - 8-bit
   - moe
   - reasoning
+  - text-generation
+  - fidelity
+  - kl-divergence
+  - exllamav3
 ---
 # GLM-5.3-Flash-TR3-8bpw (K8)
 `main_and_mtp_complete`, `nonrouted_native_exact`, 331,449,761,784 logical
 bytes, 37,152 routed choices, 1,618 native tensors.
+## Lineage on the Hub
+Z.ai published two sibling roots for this model and neither declares the other:
+[`zai-org/GLM-5.3-Flash`](https://huggingface.co/zai-org/GLM-5.3-Flash) (the
+**FP8** release, where most traffic lands) and
+[`zai-org/GLM-5.3-Flash-BF16`](https://huggingface.co/zai-org/GLM-5.3-Flash-BF16)
+(the **BF16** weights). This quant declares BF16 as its `base_model` because
+that is what it was actually quantized from — the FP8 release is a *sibling*
+quantization of the same model, not our source, and it is the baseline we
+measure against rather than build on. Quants that list FP8 as their base were
+genuinely made from the FP8 weights; the trees differ for real reasons.
+Related work on the same model, all measured on one panel in the
+[quant-fidelity registry](https://huggingface.co/datasets/malaiwah/quant-fidelity-registry):
+[brandonmusic 4bpw](https://huggingface.co/brandonmusic/GLM-5.3-Flash-tr3-4bpw),
+[0xSero Dione Q4](https://huggingface.co/0xSero/GLM-5.3-Flash-EXL3-Q4),
+[orcarouter MLX](https://huggingface.co/orcarouter/GLM-5.3-Flash-MLX).
+Collection: [GLM-5.3-Flash — measured quants & fidelity](https://huggingface.co/collections/malaiwah/glm-53-flash-measured-quants-and-fidelity-6a91f253e7107818359f37c8).
 ## Credits
 Base model by [Z.ai](https://huggingface.co/zai-org). Quantization pipeline,