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Production-ready GGUF quantization of Qwen/Qwen2.5-VL-3B-Instruct for distributed visual understanding and reasoning — powered by the Aether edge inference runtime on Edgework.ai.
| Property | Value |
|---|---|
| Base model | Qwen/Qwen2.5-VL-3B-Instruct |
| Parameters | 3B |
| Architecture | Qwen2 |
| Quantization | Q4_K_M |
| Format | GGUF |
| Size | ~1.8 GB |
| License | apache-2.0 |
./llama-cli -m Qwen2.5-VL-3B-Instruct-Q4_K_M.gguf -p "Your prompt here" -n 256.knot (sovereign format)qwen2.5-vl-3b.knot — the model weights in the KNOT container that the Aether distributed-inference runtime loads natively (the GGUF, when present, sits right beside it). A KNOT is a single self-describing file with a JSON table-of-contents, so any single tensor is one HTTP Range request — ideal for streaming weights to edge nodes.| GGUF | KNOT | |
|---|---|---|
| Container | format-specific header | single file, JSON table-of-contents |
| Per-tensor fetch | whole-file oriented | one tensor = one Range request |
| Ecosystem | broad (llama.cpp, …) | Aether / Gnosis runtime |
huggingface-cli download forkjoin-ai/qwen2.5-vl-3b-instruct-gguf qwen2.5-vl-3b.knot --local-dir ./knotsbun run open-source/bitwise/scripts/dump-knot.ts qwen2.5-vl-3b.knot.