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meossistant
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
meossistant
Model Tree
Cloudflare/clef-flash
Input Modalities
Output Modalities
Supported Functionality
python
import sysimport torchfrom huggingface_hub import snapshot_download path = snapshot_download("meossistant/clef-flash-4bit")sys.path.insert(0, path)from joint_schema_model import load_release_model, systemone model, processor = load_release_model(path, device="cuda") response = systemone(model, processor, { "model": "clef-flash", "state": "Our checkout started returning errors and orders are blocked.", "questions": { "department": { "type": "choice", "instructions": "Which team should handle the message?", "criteria": {"billing": "Payments or invoices", "technical": "Bugs or outages"}, }, "urgency": {"type": "score", "criteria": ["Can wait", "This week", "Today"]}, "outage": {"type": "noul", "instructions": "Is a service down?"}, },})print(response["answers"])Clef-Flash is a 9B multimodal model that turns a state and a schema of typed questions into decisions in a single forward pass. This 4-bit quantized version reduces the VRAM requirement to ~6 GB, making it easily runnable on consumer GPUs.