Instructions to use cyberneurova/cyberneurova-GLM-5.3-Flash-BF16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cyberneurova/cyberneurova-GLM-5.3-Flash-BF16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="cyberneurova/cyberneurova-GLM-5.3-Flash-BF16") 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("cyberneurova/cyberneurova-GLM-5.3-Flash-BF16") model = AutoModelForMultimodalLM.from_pretrained("cyberneurova/cyberneurova-GLM-5.3-Flash-BF16", 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 cyberneurova/cyberneurova-GLM-5.3-Flash-BF16 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "cyberneurova/cyberneurova-GLM-5.3-Flash-BF16" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cyberneurova/cyberneurova-GLM-5.3-Flash-BF16", "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/cyberneurova/cyberneurova-GLM-5.3-Flash-BF16
- SGLang
How to use cyberneurova/cyberneurova-GLM-5.3-Flash-BF16 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 "cyberneurova/cyberneurova-GLM-5.3-Flash-BF16" \ --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": "cyberneurova/cyberneurova-GLM-5.3-Flash-BF16", "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 "cyberneurova/cyberneurova-GLM-5.3-Flash-BF16" \ --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": "cyberneurova/cyberneurova-GLM-5.3-Flash-BF16", "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 cyberneurova/cyberneurova-GLM-5.3-Flash-BF16 with Docker Model Runner:
docker model run hf.co/cyberneurova/cyberneurova-GLM-5.3-Flash-BF16
cyberneurova-GLM-5.3-Flash-BF16
An uncensored, neutral assistant built by CyberNeurova on top of zai-org/GLM-5.3-Flash. It answers directly and does not add unsolicited disclaimers, warnings, or caveats. Whatever tone or policy you want is set entirely by your own system prompt.
This is the full-precision BF16 build. For the smaller FP8 build see cyberneurova-GLM-5.3-Flash-FP8.
💬 Chat with it: cyberneurova.ai ✉️ Support: support@cyberneurova.com
Highlights
- Ultra-sparse MoE — 321B total parameters, ~18B active per token, so it runs far faster than a dense model its size.
- 1M native context (1,048,576 tokens), multilingual, tool-calling.
- Reasoning model — supports an explicit thinking trace, and is uncensored in both modes: with thinking on and with thinking off.
- Multimodal — understands images as well as text.
- BF16 full precision — runs on any BF16-capable GPU (no FP8 hardware required), and is the friendliest starting point for further fine-tuning.
- Direct by default; fully steerable — send a system prompt and it is honored verbatim, with no default behavior merged in.
- Capability preserved. Reasoning, coding, and general knowledge are intact; only the refusal behavior is removed.
Usage (vLLM)
GLM-5.3-Flash uses a new architecture, so serve it with the dedicated vLLM image:
docker run --gpus all --ipc=host -p 8000:8000 \
-v /path/to/cyberneurova-GLM-5.3-Flash-BF16:/model \
vllm/vllm-openai:glm53-flash-x86_64-cu130 \
--model /model --served-model-name cyberneurova-GLM-5.3-Flash-BF16 \
--tensor-parallel-size 8 --trust-remote-code --max-num-seqs 256
The model uses linear (Mamba/DeltaNet) attention layers, so keep --max-num-seqs ≤ 512
or startup fails during graph capture. BF16 needs roughly 2× the VRAM of the FP8 build.
from openai import OpenAI
client = OpenAI(base_url="http://localhost:8000/v1", api_key="x")
r = client.chat.completions.create(
model="cyberneurova-GLM-5.3-Flash-BF16",
messages=[{"role": "user", "content": "Write a Python function to parse a CSV."}],
max_tokens=2000,
)
print(r.choices[0].message.content)
Setting the tone
Send your own system prompt to control style completely — e.g. for terse, no-preamble answers:
You are a direct technical assistant. Answer the question and nothing else.
Notes
- This is a reasoning model. Responses may include a thinking trace followed by the answer — render the final answer; the trace is optional. It works uncensored whether thinking is enabled or disabled.
- Give it room to reason: set
max_tokensto at least 1500 (2000–4000 for code), or long answers may be cut off.
Disclaimer
This model has reduced built-in refusals. You are responsible for how you use it and for complying with all applicable laws. Provided as-is, without warranty.
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Base model
zai-org/GLM-5.3-Flash