Instructions to use imvladikon/Ling-3.0-tiny-GLM-5.3-Flash-surgery with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use imvladikon/Ling-3.0-tiny-GLM-5.3-Flash-surgery with Transformers:

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

pipe = pipeline("text-generation", model="imvladikon/Ling-3.0-tiny-GLM-5.3-Flash-surgery")
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("imvladikon/Ling-3.0-tiny-GLM-5.3-Flash-surgery")
model = AutoModelForMultimodalLM.from_pretrained("imvladikon/Ling-3.0-tiny-GLM-5.3-Flash-surgery", 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 imvladikon/Ling-3.0-tiny-GLM-5.3-Flash-surgery with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "imvladikon/Ling-3.0-tiny-GLM-5.3-Flash-surgery"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "imvladikon/Ling-3.0-tiny-GLM-5.3-Flash-surgery",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/imvladikon/Ling-3.0-tiny-GLM-5.3-Flash-surgery

SGLang

How to use imvladikon/Ling-3.0-tiny-GLM-5.3-Flash-surgery 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 "imvladikon/Ling-3.0-tiny-GLM-5.3-Flash-surgery" \
    --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": "imvladikon/Ling-3.0-tiny-GLM-5.3-Flash-surgery",
		"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 "imvladikon/Ling-3.0-tiny-GLM-5.3-Flash-surgery" \
        --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": "imvladikon/Ling-3.0-tiny-GLM-5.3-Flash-surgery",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use imvladikon/Ling-3.0-tiny-GLM-5.3-Flash-surgery with Docker Model Runner:
```
docker model run hf.co/imvladikon/Ling-3.0-tiny-GLM-5.3-Flash-surgery
```

Ling → GLM-5.3-Flash surgery

Experimental 7.80B text checkpoint, saved with stock Transformers save_pretrained(). Weight and tokenizer donor: inclusionAI/Ling-3.0-tiny, transferred into the native GLM-5.3-Flash architecture through weight surgery. Full donor tokenizer and untied embedding/output tables retained.

This model is intended for testing, continued training, and post-training experiments. Generation quality is unstable. Known limitations include:

Repetition loops: generation can get stuck repeating text.
Morphological errors: malformed word forms and incorrect inflections.
Unintended language switching / language mixing: Chinese characters can appear unexpectedly in otherwise English output.
Arithmetic errors, including an observed 6 × 7 → 49 response.

from transformers import AutoTokenizer, Glm5NextForConditionalGeneration

repo = "imvladikon/Ling-3.0-tiny-GLM-5.3-Flash-surgery"
tokenizer = AutoTokenizer.from_pretrained(repo)
model = Glm5NextForConditionalGeneration.from_pretrained(repo)

Tested with Transformers 5.16.1, including ten English generations using these plain loading calls. No custom model code or auto_map. Only text weights are supplied. The stock loader reports missing model.visual.* weights for a tiny unused vision scaffold (depth 0, width 32). Image/video inference is unsupported. Context configured to 2048; sparse indexer selection remains untrained.

The following ten examples demonstrate basic English generation. Questions are abbreviated; answers are verbatim. Generation used do_sample=False, max_new_tokens=40, and enable_thinking=False in the chat template. These examples provide limited evidence of overall quality and generation stability.

Question (abbreviated)	Verbatim answer
Capital of Japan?	The capital of Japan is Tokyo.
Largest planet?	Jupiter
Spider's legs?	8
Water freezing temperature, °C?	0°C
Chemical symbol for oxygen?	O
Largest ocean?	The Pacific Ocean is the largest ocean on Earth.
Triangle's sides?	3
Opposite of “hot”?	Cold
15 minus 6?	9
Animal known for barking?	Dog

Downloads last month: 855

Safetensors

Model size

8B params

Tensor type

BF16

F32

Model tree for imvladikon/Ling-3.0-tiny-GLM-5.3-Flash-surgery

Base model

zai-org/GLM-5.3-Flash

Finetuned

(16)

this model

Finetunes

1 model