Instructions to use inference-optimization/GLM-5.3-Flash-0.1B-A0.1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use inference-optimization/GLM-5.3-Flash-0.1B-A0.1B with Transformers:

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

pipe = pipeline("image-text-to-text", model="inference-optimization/GLM-5.3-Flash-0.1B-A0.1B")
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("inference-optimization/GLM-5.3-Flash-0.1B-A0.1B")
model = AutoModelForMultimodalLM.from_pretrained("inference-optimization/GLM-5.3-Flash-0.1B-A0.1B", 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 inference-optimization/GLM-5.3-Flash-0.1B-A0.1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "inference-optimization/GLM-5.3-Flash-0.1B-A0.1B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "inference-optimization/GLM-5.3-Flash-0.1B-A0.1B",
		"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/inference-optimization/GLM-5.3-Flash-0.1B-A0.1B

SGLang

How to use inference-optimization/GLM-5.3-Flash-0.1B-A0.1B 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 "inference-optimization/GLM-5.3-Flash-0.1B-A0.1B" \
    --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": "inference-optimization/GLM-5.3-Flash-0.1B-A0.1B",
		"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 "inference-optimization/GLM-5.3-Flash-0.1B-A0.1B" \
        --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": "inference-optimization/GLM-5.3-Flash-0.1B-A0.1B",
		"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 inference-optimization/GLM-5.3-Flash-0.1B-A0.1B with Docker Model Runner:
```
docker model run hf.co/inference-optimization/GLM-5.3-Flash-0.1B-A0.1B
```

GLM-5.3-Flash-0.1B-A0.1B / README.md

kylesayrs

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metadata

license: mit
base_model:
  - zai-org/GLM-5.3-Flash
library_name: transformers

GLM-5.3-Flash-0.1B-A0.1B

This is a tiny version of zai-org/GLM-5.3-Flash created for testing and development.

Model Details

Base Model: zai-org/GLM-5.3-Flash
Architecture: glm5_next (Glm5NextForConditionalGeneration)
Total Parameters: 0.084B
Activated Parameters: 0.084B (MoE: 4 of 8 routed experts + 1 shared expert per sparse layer)

This tiny model preserves the full architecture of the base model:

Hybrid attention: KDA linear attention (linear_attention) layers and DeepSeek sparse attention / MLA (deepseek_sparse_attention) layers with the token indexer.
Mixed FFN schedule: dense MLP layers (first 3) and sparse MoE layers (routed experts + shared expert).
Manifold-Constrained Hyper-Connections (mHC) at every attention/FFN site.
The vision tower (Glm5NextVisionModel) and multimodal projector.

The model is a bf16 dense checkpoint (the base model's fp8 quantization_config was removed so the tiny model can be randomly initialized and fine-tuned).

Configuration Changes

The following parameters were reduced from the original model:

Parameter	Original	Tiny
text `hidden_size`	4096	256
text `num_hidden_layers`	45	5
text `intermediate_size` (dense)	12288	256
text `moe_intermediate_size`	2048	128
`n_routed_experts`	288	8
`num_experts_per_tok`	8	4
`n_shared_experts`	1	1
`num_attention_heads` / `num_key_value_heads`	64	4
`q_lora_rank`	1536	128
`kv_lora_rank`	512	64
`qk_nope_head_dim` / `v_head_dim`	256	64
`index_n_heads`	32	4
`index_head_dim`	128	64
`index_topk`	2048	64
`index_kpool`	4	4
linear attn `num_heads`	64	4
vision `depth`	24	2
vision `hidden_size`	1024	128
vision `out_hidden_size`	4096	256
`vocab_size`	154880	154880 (unchanged)
quantization	fp8 (block 128×128)	none (bf16)

Per-layer schedules were regenerated for the reduced depth:

layer_types: [linear, linear, linear, deepseek_sparse_attention, linear]
mlp_layer_types: [dense, dense, dense, sparse, sparse]
indexer_types: [full, full, full, full, full]

Checkpoint Structure

Single-file model.safetensors (223 tensors). The tensor naming is analogous to the original sharded checkpoint (model.language_model.layers.*, model.visual.*, lm_head.weight, hyper-connection params hc_attn_* / hc_ffn_*, MLA params q_a_proj/q_b_proj/kv_a_proj_with_mqa/kv_b_proj, indexer params, KDA linear-attention params, and packed MoE mlp.experts.*).

Two intentional differences vs. the original:

No weight_scale_inv tensors — the tiny model is bf16, not fp8.
No MTP layer (original layer index 45 with eh_proj/enorm/hnorm/shared_head.norm) — the transformers Glm5Next model does not build the multi-token-prediction layer (_keys_to_ignore_on_load_unexpected skips layers.45.*), so no converter is required.

Usage

from transformers import Glm5NextForConditionalGeneration, AutoTokenizer

model = Glm5NextForConditionalGeneration.from_pretrained(
    "inference-optimization/GLM-5.3-Flash-0.1B-A0.1B", device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("inference-optimization/GLM-5.3-Flash-0.1B-A0.1B")

input_ids = tokenizer("According to all known laws", return_tensors="pt").input_ids.to(model.device)
output = model.generate(input_ids, max_new_tokens=20)
print(tokenizer.decode(output[0]))

Creation Process

This model was created using the llm-compressor create-tiny-model claude skill.

Built a reduced Glm5NextConfig from the base config (removed quantization_config; shrank hidden/layer/expert/MLA/indexer/vision dims; regenerated per-layer schedules).
Randomly initialized weights (init_weights + non-finite/extreme-value fixup) with transformers 5.16.1.
Fine-tuned text-only on a small copypasta dataset until the training perplexity converged well below 3.0.
Verified the saved checkpoint structure matches the original naming convention (minus fp8 scales and the MTP layer).

Validation

perplexity = 1.05 (target <= 10)   PASS
GEN: According to all known laws of aviation, there is no way a bee should be able to fly. Its wings are too small
total params: 84,361,950

Notes

Requires transformers >= 5.16.0 (which registers the glm5_next model type).
This is a randomly-initialized, fine-tuned-on-toy-data model. It is intended solely for testing/development of tooling (quantization, serving, CI) and has no real language or vision capability.
Fine-tuning was text-only; the vision tower is randomly initialized.