| license: apache-2.0 | |
| base_model: Qwen/Qwen3-Coder-Next | |
| tags: | |
| - knot | |
| - fat-station | |
| - distributed-inference | |
| - qwen3next | |
| - moe | |
| # qwen3-coder-next.knot | |
| Sovereign `.knot` encoding of **Qwen3-Coder-Next** (`arch=qwen3next`) for the | |
| Gnosis fat-station runtime. Converted from GGUF with `gguf-to-knot.py` | |
| (K-quant block passthrough, arch-aware config). | |
| ## Why a knot | |
| `.knot` is a range-addressable weight container: a station fetches only the | |
| tensors for the layers it owns, over HTTP, instead of loading a whole file. | |
| That is what lets an A3B MoE this size run **sharded across Cloudflare | |
| Containers** (12 GiB each) rather than needing one large host. | |
| ## Architecture | |
| Hybrid Mamba-2 gated-DeltaNet SSM with periodic full attention, and a | |
| sparse MoE FFN on every layer. Roughly 3B parameters are active per token. | |
| Long context is cheap because only the periodic attention layers carry a | |
| KV cache. | |
| ## Serving | |
| Pipeline-parallel across N stations, each owning a contiguous layer range: | |
| ``` | |
| FAT_STATION_KNOT=<this knot url> | |
| FAT_STATION_SHARDED=1 | |
| FAT_STATION_ROLE=entry|mid|exit | |
| FAT_STATION_LAYERS=0..6 | |
| ``` | |
| MoE shards cleanly along layers: a layer owns its entire expert set, so | |
| top-k routing never needs a tensor held by another station. | |
| Published by AFFECTIVELY. https://huggingface.co/forkjoin-ai | |