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cyberneurova
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
cyberneurova
Model Tree
Qwen/Qwen3.8-27B
Input Modalities
Output Modalities
Supported Functionality
bash
vllm serve cyberneurova/cyberneurova-Qwen3.8-27B \--max-model-len 65536 --trust-remote-code
python
from openai import OpenAIclient = OpenAI(base_url="http://localhost:8000/v1", api_key="x")r = client.chat.completions.create(model="cyberneurova/cyberneurova-Qwen3.8-27B",messages=[{"role": "user", "content": "Write a Python function to parse a CSV."}],max_tokens=2000,)print(r.choices[0].message.content)
This is a multimodal model — it sees images. With vLLM the vision tower loads automatically; just send an image in the message content (OpenAI format):
python
from openai import OpenAIclient = OpenAI(base_url="http://localhost:8000/v1", api_key="x")r = client.chat.completions.create(model="cyberneurova/cyberneurova-Qwen3.8-27B",messages=[{"role": "user", "content": [{"type": "image_url", "image_url": {"url": "https://example.com/photo.jpg"}},{"type": "text", "text": "What's in this image?"},]}],max_tokens=1000,)print(r.choices[0].message.content)
image_url also accepts a data:image/png;base64,... data URI for local images.
Running locally with llama.cpp / Ollama? Use the GGUF build plus its vision
projector: cyberneurova-Qwen3.8-27B-GGUF
(mmproj-F16.gguf).
Send your own system prompt to control style completely. For terse, no-preamble answers:
markdown
You are a direct technical assistant. Answer the question and nothing else.
max_tokens to at least 1500 (2000–4000 for code),
or long answers may be cut off.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.