forkjoin-ai/mamba-2.8b-safetensors

Mamba 2.8b (KNOT)

Production-ready KNOT (sovereign-format) mirror of state-spaces/mamba-2.8b-hf for distributed text generation and conversation — powered by the Aether edge inference runtime on Edgework.ai.

Model Details

Property	Value
Base model	state-spaces/mamba-2.8b-hf
Parameters	2.8B
Architecture	Mamba
Quantization	— (lossless container)
Format	KNOT
Size	~11.2 GB
License	apache-2.0

Also available: .knot (sovereign format)

This repo ships mamba-2.8b.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/mamba-2.8b-safetensors mamba-2.8b.knot --local-dir ./knots

Full format spec: KNOT_FORMAT.md. Inspect the header with bun run open-source/bitwise/scripts/dump-knot.ts mamba-2.8b.knot.

Deployment Architecture

This model runs on the Aether distributed inference runtime — a custom engine that shards model layers across multiple nodes for parallel execution:

1. Coordinator receives requests and manages token generation
2. Layer nodes each hold a subset of model layers (2 nodes for this model)
3. Hidden states flow between nodes via gRPC
4. Zero cold start via warm pool scheduling

Deployed via Edgework.ai — bringing fast, cheap, and private inference as close to the user as possible.

About

Published by AFFECTIVELY · Managed by @buley

We quantize and publish production-ready models for distributed edge inference via the Aether runtime. Every release is tested for correctness and stability before publication.

• All models · GitHub · Edgework.ai