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mamba-2.8b-safetensors
forkjoin-ai/mamba-2.8b-safetensors
mamba-2.8b-safetensors is a text generation model from forkjoin-ai. Use it when you need the model to write or continue text. The card lists the license as apache-2.0.
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.
Access
Public
Updated Jun 9, 2026
Repo size
3.1 GB
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0
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Repo makeup
Click a slice to open those files.
.knot3.1 GB · 100%
At a glance
- Task
- Text Generation
- License
- apache-2.0
- Access
- Public
- Created
- Jun 7, 2026
- Updated
- Jun 9, 2026
- SHA
- a65cc4c9
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Base models
Model card
From the Hugging Face model README
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:
- Coordinator receives requests and manages token generation
- Layer nodes each hold a subset of model layers (2 nodes for this model)
- Hidden states flow between nodes via gRPC
- 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.
- Task
- Text Generation
- License
- apache-2.0
- Languages
- en
- Created
- Jun 7, 2026
- Updated
- Jun 9, 2026