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 / config.json

kylesayrs

Upload folder using huggingface_hub

7c3a6d3 verified 1 day ago

Raw

History Blame Contribute Delete

3.2 kB

	{
	"architectures": [
	"Glm5NextForConditionalGeneration"
	],
	"dtype": "bfloat16",
	"image_end_token_id": 154831,
	"image_start_token_id": 154830,
	"image_token_id": 154854,
	"model_type": "glm5_next",
	"text_config": {
	"attention_bias": false,
	"attention_dropout": 0.0,
	"bos_token_id": null,
	"dtype": "bfloat16",
	"eos_token_id": [
	154820,
	154827,
	154829
	],
	"first_k_dense_replace": 3,
	"hc_eps": 1e-06,
	"hc_mult": 4,
	"hc_sinkhorn_iters": 20,
	"head_dim": 0,
	"hidden_act": "silu",
	"hidden_size": 256,
	"index_head_dim": 64,
	"index_kpool": 4,
	"index_kpool_always_select_tail": true,
	"index_kpool_compress": true,
	"index_n_heads": 4,
	"index_share_for_mtp_iteration": true,
	"index_topk": 64,
	"indexer_rope_interleave": true,
	"indexer_types": [
	"full",
	"full",
	"full",
	"full",
	"full"
	],
	"initializer_range": 0.02,
	"intermediate_size": 256,
	"kv_lora_rank": 64,
	"layer_types": [
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"deepseek_sparse_attention",
	"linear_attention"
	],
	"linear_attn_config": {
	"gate_lower_bound": -5.0,
	"head_dim": 64,
	"num_heads": 4,
	"short_conv_kernel_size": 4
	},
	"linear_conv_kernel_dim": 4,
	"linear_head_dim": 64,
	"linear_lower_bound": -5.0,
	"linear_num_heads": 4,
	"max_position_embeddings": 1048576,
	"mhc": true,
	"mla_use_nope": true,
	"mlp_layer_types": [
	"dense",
	"dense",
	"dense",
	"sparse",
	"sparse"
	],
	"model_type": "glm5_next_text",
	"moe_intermediate_size": 128,
	"moe_router_dtype": "float32",
	"n_group": 1,
	"n_routed_experts": 8,
	"n_shared_experts": 1,
	"norm_topk_prob": true,
	"num_attention_heads": 4,
	"num_experts_per_tok": 4,
	"num_hidden_layers": 5,
	"num_key_value_heads": 4,
	"num_nextn_predict_layers": 1,
	"output_router_logits": false,
	"pad_token_id": 154820,
	"q_lora_rank": 128,
	"qk_head_dim": 256,
	"qk_nope_head_dim": 64,
	"qk_rope_head_dim": 0,
	"rms_norm_eps": 1e-05,
	"routed_scaling_factor": 2.5,
	"router_aux_loss_coef": 0.001,
	"scoring_func": "sigmoid",
	"swiglu_limit": 10.0,
	"tie_word_embeddings": false,
	"topk_group": 1,
	"topk_method": "noaux_tc",
	"use_cache": true,
	"v_head_dim": 64,
	"vocab_size": 154880
	},
	"tie_word_embeddings": false,
	"transformers_version": "5.16.1",
	"use_cache": false,
	"video_end_token_id": 154833,
	"video_start_token_id": 154832,
	"video_token_id": 154855,
	"vision_config": {
	"attention_bias": true,
	"attention_dropout": 0.0,
	"depth": 2,
	"dtype": "bfloat16",
	"hidden_act": "silu",
	"hidden_size": 128,
	"image_size": 448,
	"in_channels": 3,
	"initializer_range": 0.02,
	"intermediate_size": 256,
	"model_type": "glm5_next_vision",
	"num_heads": 4,
	"out_hidden_size": 256,
	"patch_size": 14,
	"projection_intermediate_size": 256,
	"rms_norm_eps": 1e-05,
	"spatial_merge_size": 2,
	"swiglu_limit": 10.0,
	"temporal_patch_size": 2
	}
	}