Image-Text-to-Text
Transformers
Safetensors
Chinese
English
glm4v_moe
AWQ
vLLM
conversational
4-bit precision
awq_marlin
Instructions to use QuantTrio/GLM-4.5V-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use QuantTrio/GLM-4.5V-AWQ with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="QuantTrio/GLM-4.5V-AWQ") 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("QuantTrio/GLM-4.5V-AWQ") model = AutoModelForMultimodalLM.from_pretrained("QuantTrio/GLM-4.5V-AWQ", 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 QuantTrio/GLM-4.5V-AWQ with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "QuantTrio/GLM-4.5V-AWQ" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "QuantTrio/GLM-4.5V-AWQ", "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/QuantTrio/GLM-4.5V-AWQ
- SGLang
How to use QuantTrio/GLM-4.5V-AWQ 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 "QuantTrio/GLM-4.5V-AWQ" \ --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": "QuantTrio/GLM-4.5V-AWQ", "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 "QuantTrio/GLM-4.5V-AWQ" \ --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": "QuantTrio/GLM-4.5V-AWQ", "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 QuantTrio/GLM-4.5V-AWQ with Docker Model Runner:
docker model run hf.co/QuantTrio/GLM-4.5V-AWQ
File size: 3,911 Bytes
ad5c24d 57b42d7 ad5c24d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 | {
"name_or_path": "tclf90/GLM-4.5V-AWQ",
"architectures": [
"Glm4vMoeForConditionalGeneration"
],
"model_type": "glm4v_moe",
"text_config": {
"pad_token_id": 151329,
"vocab_size": 151552,
"eos_token_id": [
151329,
151336,
151338
],
"image_end_token_id": 151340,
"image_start_token_id": 151339,
"image_token_id": 151363,
"head_dim": 128,
"attention_bias": true,
"attention_dropout": 0.0,
"first_k_dense_replace": 1,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 11264,
"max_position_embeddings": 65536,
"model_type": "glm4v_moe_text",
"moe_intermediate_size": 1408,
"n_group": 1,
"n_routed_experts": 128,
"n_shared_experts": 1,
"norm_topk_prob": true,
"num_attention_heads": 96,
"num_experts_per_tok": 8,
"num_hidden_layers": 46,
"num_key_value_heads": 8,
"partial_rotary_factor": 0.5,
"rms_norm_eps": 1e-05,
"torch_dtype": "bfloat16",
"rope_scaling": {
"rope_type": "default",
"mrope_section": [
8,
12,
12
]
},
"rope_theta": 10000.0,
"routed_scaling_factor": 1.0,
"topk_group": 1,
"use_cache": true,
"use_qk_norm": false
},
"torch_dtype": "float16",
"transformers_version": "4.55.0.dev0",
"video_end_token_id": 151342,
"video_start_token_id": 151341,
"video_token_id": 151364,
"vision_config": {
"attention_bias": false,
"attention_dropout": 0.0,
"depth": 24,
"hidden_act": "silu",
"hidden_size": 1536,
"image_size": 336,
"in_channels": 3,
"initializer_range": 0.02,
"intermediate_size": 11264,
"model_type": "glm4v_moe",
"num_heads": 12,
"out_hidden_size": 4096,
"patch_size": 14,
"rms_norm_eps": 1e-05,
"spatial_merge_size": 2,
"temporal_patch_size": 2
},
"quantization_config": {
"quant_method": "awq_marlin",
"bits": 4,
"group_size": 128,
"version": "gemm",
"zero_point": true,
"modules_to_not_convert": ["visual.", "model.embed_tokens", "model.layers.0.mlp.shared_experts.", "model.layers.1.mlp.shared_experts.", "model.layers.2.mlp.shared_experts.", "model.layers.3.mlp.shared_experts.", "model.layers.4.mlp.shared_experts.", "model.layers.5.mlp.shared_experts.", "model.layers.6.mlp.shared_experts.", "model.layers.7.mlp.shared_experts.", "model.layers.8.mlp.shared_experts.", "model.layers.9.mlp.shared_experts.", "model.layers.10.mlp.shared_experts.", "model.layers.11.mlp.shared_experts.", "model.layers.12.mlp.shared_experts.", "model.layers.13.mlp.shared_experts.", "model.layers.14.mlp.shared_experts.", "model.layers.15.mlp.shared_experts.", "model.layers.16.mlp.shared_experts.", "model.layers.17.mlp.shared_experts.", "model.layers.18.mlp.shared_experts.", "model.layers.19.mlp.shared_experts.", "model.layers.20.mlp.shared_experts.", "model.layers.21.mlp.shared_experts.", "model.layers.22.mlp.shared_experts.", "model.layers.23.mlp.shared_experts.", "model.layers.24.mlp.shared_experts.", "model.layers.25.mlp.shared_experts.", "model.layers.26.mlp.shared_experts.", "model.layers.27.mlp.shared_experts.", "model.layers.28.mlp.shared_experts.", "model.layers.29.mlp.shared_experts.", "model.layers.30.mlp.shared_experts.", "model.layers.31.mlp.shared_experts.", "model.layers.32.mlp.shared_experts.", "model.layers.33.mlp.shared_experts.", "model.layers.34.mlp.shared_experts.", "model.layers.35.mlp.shared_experts.", "model.layers.36.mlp.shared_experts.", "model.layers.37.mlp.shared_experts.", "model.layers.38.mlp.shared_experts.", "model.layers.39.mlp.shared_experts.", "model.layers.40.mlp.shared_experts.", "model.layers.41.mlp.shared_experts.", "model.layers.42.mlp.shared_experts.", "model.layers.43.mlp.shared_experts.", "model.layers.44.mlp.shared_experts.", "model.layers.45.mlp.shared_experts.", "lm_head"]
}
} |