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AMAImedia
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AMAImedia
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Released as part of the NOESIS Professional Multilingual Dubbing Automation Platform (framework: DHCF-FNO — Deterministic Hybrid Control Framework for Frozen Neural Operators).
Released and maintained by AMAImedia.
We introduce GLM-5.3-Flash, the first natively multimodal model in the GLM-5 series. With 320B total parameters and just 18B active parameters, it is designed for efficient long-context, coding, agentic, and multimodal workloads.
GLM-5.3-Flash starts from a newly trained base model, with its architecture and training recipe redesigned around capability and efficiency. It combines sparse and linear attention with Manifold-Constrained Hyper-Connections (mHC) and a multimodal pre-training corpus.
GLM-5.3-Flash supports deployment with the following frameworks:
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizermodel_id = "AMAImedia/GLM-5.3-Flash"tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained(model_id,torch_dtype=torch.bfloat16,device_map="auto",trust_remote_code=True,)messages = [{"role": "user", "content": "Explain the main benefits of multimodal agents."}]inputs = tokenizer.apply_chat_template(messages,add_generation_prompt=True,return_tensors="pt",).to(model.device)with torch.inference_mode():output = model.generate(inputs,max_new_tokens=512,temperature=0.7,top_p=0.9,do_sample=True,)print(tokenizer.decode(output[0][inputs.shape[-1]:], skip_special_tokens=True))
For image questions, use the official GLM processor and multimodal message format documented by the model implementation. Keep image inputs in the message content and use the processor to build model inputs; verify the exact API against the installed Transformers version.
python
from transformers import AutoProcessor, AutoModelForMultimodalLMmodel_id = "AMAImedia/GLM-5.3-Flash"processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)model = AutoModelForMultimodalLM.from_pretrained(model_id,device_map="auto",torch_dtype="auto",trust_remote_code=True,)messages = [{"role": "user","content": [{"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},{"type": "text", "text": "What is shown in this image?"},],}]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=128)print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))
bash
pip install -U vllmvllm serve AMAImedia/GLM-5.3-Flash \--trust-remote-code \--tensor-parallel-size 8 \--max-model-len 131072
The server exposes an OpenAI-compatible endpoint:
python
from openai import OpenAIclient = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")response = client.chat.completions.create(model="AMAImedia/GLM-5.3-Flash",messages=[{"role": "user", "content": "Write a concise summary of this document."}],temperature=0.7,max_tokens=512,)print(response.choices[0].message.content)
Use bfloat16 where supported, or a framework-supported quantized checkpoint when GPU memory is limited. For multimodal serving, follow the selected framework's documented image-input format. Confirm the installed framework version, GPU memory requirements, tensor-parallel configuration, and supported context length before production deployment.
If you find GLM-5.3-Flash useful in your research, please cite the official technical report:
bibtex
@misc{glm5team2026glm5vibecodingagentic,title={GLM-5: from Vibe Coding to Agentic Engineering},author={GLM-5-Team},year={2026},eprint={2602.15763},archivePrefix={arXiv},primaryClass={cs.LG},url={https://arxiv.org/abs/2602.15763},}