Instructions to use malaiwah/GLM-5.3-Flash-TR3-6bpw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use malaiwah/GLM-5.3-Flash-TR3-6bpw 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-TR3-6bpw")
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-TR3-6bpw")
model = AutoModelForMultimodalLM.from_pretrained("malaiwah/GLM-5.3-Flash-TR3-6bpw", 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-TR3-6bpw 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-TR3-6bpw 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-TR3-6bpw"
# 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-TR3-6bpw",
		"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-TR3-6bpw

SGLang

How to use malaiwah/GLM-5.3-Flash-TR3-6bpw 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-TR3-6bpw" \
    --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-TR3-6bpw",
		"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-TR3-6bpw" \
        --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-TR3-6bpw",
		"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-TR3-6bpw with Docker Model Runner:
```
docker model run hf.co/malaiwah/GLM-5.3-Flash-TR3-6bpw
```

GLM-5.3-Flash-TR3-6bpw / receipts /k6-packed-kld.json

malaiwah

SEALED: 0.013723 nats, five bitwise-identical cold runs, full panel — 1.5x better than FP8 at 77% size

ae3748c verified 2 days ago

Raw

History Blame Contribute Delete

2.16 kB

	{
	"candidate_rate_grid_invoked": false,
	"checkpoint_receipt_sha256": "3cb08d4d526322694aa2c9c238ebc728c2a35a76745b70407c523c17ced5f417",
	"codec_family": "exl3-mcg",
	"evidence_artifacts": {
	"final_student_logits": "7511f3b1bd1357e8801eca3326b21603de2b15e4c07b3a9c5a13f9b33dc5713b",
	"teacher_logits": "003f5ecd7a6a8d59de2da9ec0121f92a5975f8f87091845a4069f1c43701a6da",
	"tokenwise_kl": "52e35723dacd0314acb85bcee86d2faefd5c12ff9d82c6e026e05d35ee15db4b"
	},
	"global_allocator_invoked": false,
	"kld_direction": "teacher_to_student",
	"kld_report_file_sha256": "de75fabab8747dd475b616c3c125131c35801ffb8d186303c09f297a5242cb49",
	"kld_report_sha256": "0da6271911f5a5907ea3792bc3c731db002cb412d5f040657157d3fd2ee4dc5f",
	"measured_mean_kld": 0.013723384665701147,
	"native_copy_receipt_sha256": "b3dc3992a01b29cb5984e99f22ad0d2b91b4b6b24bea813443a22a08dc45ee90",
	"packed_reader_abi_sha256": "3d659542e5acbf1e3436b4b01d04f7f4edbe8def1c3029fbd3a6a1976b573dee",
	"profile": "k6-tp4",
	"qualified": true,
	"quality_gate": {
	"metric": "mean_tokenwise_kld",
	"threshold_lt": 0.06
	},
	"quality_gate_passed": true,
	"reader_audit_qualified": true,
	"reader_audit_receipt_sha256": "c986a0a98d6c34d8a311401f90be24ee87e01d20602583fef5bb37d1ff504cc7",
	"receipt_sha256": "25eea6496a5c0e982cd16168f80714f4ea33c790ffd363ddf07018eb0afbcd31",
	"runtime_reader_sha256": "1ccce44602d4ccf41abe594ede448bf726516ac44f67a54dcd65cc0b5bf9dd14",
	"same_token_panel": true,
	"schema": "quant-pipeline.glm53-packed-kld-receipt.v1",
	"source_revision": "a6c167b62691b2bac901344b65cb651a70f53e43",
	"student_backend_identity_sha256": "d19c049f0ce497d1cf23c80f2f54f372c452f94f9af7b1c3ff47b1017ffb0e6a",
	"student_capture_receipt_sha256": "524f017f68664ffc5f251298739fae6c0144eda5ccb1b308c57dcd4932e8f535",
	"student_checkpoint_identity_sha256": "a8668be3592493035e98a52994e0e3c43548a9757eadb79f7ae939f2f32de1c1",
	"target_bits": 6,
	"teacher_capture_receipt_sha256": "2ae08117c3d4247f747b2a9a889b68e1a06387b788d56a0bf23bb950c77bc5a5",
	"token_panel_receipt_sha256": "0beec5770e5107547731b084f1bc5f9fb8ba79d67af56ddb70d919da367737d5"
	}