GLM-5.3-Flash-9B-Surgery-Dummy-etheroi

Instructions to use developerjeremylive/GLM-5.3-Flash-9B-Surgery-Dummy-etheroi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use developerjeremylive/GLM-5.3-Flash-9B-Surgery-Dummy-etheroi with Transformers:

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

pipe = pipeline("text-generation", model="developerjeremylive/GLM-5.3-Flash-9B-Surgery-Dummy-etheroi")
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("developerjeremylive/GLM-5.3-Flash-9B-Surgery-Dummy-etheroi")
model = AutoModelForMultimodalLM.from_pretrained("developerjeremylive/GLM-5.3-Flash-9B-Surgery-Dummy-etheroi", 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use developerjeremylive/GLM-5.3-Flash-9B-Surgery-Dummy-etheroi with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "developerjeremylive/GLM-5.3-Flash-9B-Surgery-Dummy-etheroi"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "developerjeremylive/GLM-5.3-Flash-9B-Surgery-Dummy-etheroi",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/developerjeremylive/GLM-5.3-Flash-9B-Surgery-Dummy-etheroi

SGLang

How to use developerjeremylive/GLM-5.3-Flash-9B-Surgery-Dummy-etheroi 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 "developerjeremylive/GLM-5.3-Flash-9B-Surgery-Dummy-etheroi" \
    --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": "developerjeremylive/GLM-5.3-Flash-9B-Surgery-Dummy-etheroi",
		"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 "developerjeremylive/GLM-5.3-Flash-9B-Surgery-Dummy-etheroi" \
        --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": "developerjeremylive/GLM-5.3-Flash-9B-Surgery-Dummy-etheroi",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use developerjeremylive/GLM-5.3-Flash-9B-Surgery-Dummy-etheroi with Docker Model Runner:
```
docker model run hf.co/developerjeremylive/GLM-5.3-Flash-9B-Surgery-Dummy-etheroi
```

GLM-5.3-Flash 9B Surgery Dummy

Experimental text-only test model. Not an official Z.ai release and not yet quality-recovered. Do not use it as a production or benchmark model.

This checkpoint preserves GLM-5.3-Flash width/kernel geometry while reducing the decoder from 45 to 10 layers and each routed MoE from 288 to 32 experts. It has 8,895,622,684 text parameters. Source layers are [0, 1, 2, 3, 8, 18, 25, 31, 38, 44]. Each target routed expert is a four-donor functional mosaic: 512 individually selected, coupled SwiGLU units come from each donor (gate/up rows plus matching down columns), a closed-form down-projection scale matches synthetic output variance, and all three expert matrices receive fresh per-128x128 FP8 E4M3 scales. Router rows use balanced router-space clusters. No donor forward pass, activation cache, distillation, or parameter training is used. Student-only evaluation remains required before calling the model functionally useful.

Vision is intentionally disabled. A zeroed 49,056-parameter visual compatibility stub exists only because the stock Transformers wrapper currently constructs a visual submodule. It is not a vision model.

The exact source revision, tensor map, expert clusters, and provenance hashes are stored in surgery_plan.json; output shard hashes are in surgery_manifest.json.

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Safetensors

Model size

9B params

Tensor type

BF16

F32

F8_E4M3

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