I’ve been pushing TinyStories-style models downward in size, and this is the smallest one so far:

MacroStories — 19,969 parameters, 81 KB FP32

https://huggingface.co/raincandy-u/MacroStories

For scale:

→ ~50× smaller than the 1M TinyStories model

→ ~3,000× smaller than AlexNet

→ 32-dim hidden state

→ 378-token vocabulary

→ one decoder block, recurrently applied 4 times with shared weights

It’s obviously not a general-purpose LM, but within its constrained story distribution it can maintain a 100–300 word narrative with a goal, problem, relevant actions, and resolution.

It also runs extremely fast on CPU and needs no GPU.

I’m mostly interested in how far the lower bound for coherent narrative generation can be pushed.

Would be curious to see how people manage to break it.☺️

Follow-up to my last thread.

Unfortunately GitHub closed many PRs after cleanup of repo's commit history. Niko's reply here for all PRs including this PR. So now we need to wait for new PR from closed PR's creator. Meanwhile I reminded Niko about this PR. His reply below.

>I will think about it!

So please just reply him there with your message, let him know that this has huge demand from many of us. I really want Strata to support many models(Qwen3.6-35B-A3B, Gemma-4-26B-A4B, Nemotron-3.5-Lightning-30B-A3B, Laguna-XS-2.1, North-Mini-Code-1.0, Kat-Coder-V2.5-Dev, etc.,) as much as possible, even experimental fine for me.

Expecting around 20 replies there. Consider it's a last chance to get it.

EDIT : I see new PR for same. Lets see

https://github.com/Niko1221/Strata/pull/1402

Repo : https://github.com/aflin/Strata

I have been trying a bunch of harness models and frameworks for the last month, and I realize most agent frameworks want you to rebuild your runtime around their harness.

We went the other way with AURA Harness: a thin membrane around loops you already run, plain Python, Ollama, LangGraph, whatever. You keep the body/runtime, while AURA records and optionally gates what crosses the boundary. Shouldn't this be what harness is about??

Local-first by default:

• Works offline with agent_ref + session IDs, no cloud identity required
• integrations/ollama/llama_loop.py, stdlib HTTP against Ollama (llama3.2:1b is our dev default)
• Optional verified operator identity if you need it later, not required for OSS/dev

What you get on close: JSONL spine, audit report, hash chain, aura verify chain for CI.

Loose coat = audit-only logging. Tight coat = rules/gates at egress when you wire tool paths.

Open source (Python): github.com/ARPAHLS/aura

Happy to answer setup questions, especially Ollama related + “wrap my script, don’t replace it.”

Contributors more than just welcome, bunch of good first issues open <3

Disclosure since this is my own project: I built Hillock specifically because I was tired of local RAG setups requiring massive amounts of VRAM. Most tutorials expect you to run Chroma alongside an 8B model just to parse text chunks. On an entry level GPU or a laptop with 6GB to 8GB of memory, that leaves almost zero room for your actual model to generate tokens.

Instead of storing messy text chunks, it acts like an index at the back of a book. It uses small classification models under 300MB to extract subject predicate object facts in about five seconds without using an LLM. Everything goes into SQLite, and it checks queries using hyperdimensional vector math before calling your local model. If your notes do not actually have the answer, the mathematical gate stops the model before it can guess or hallucinate.

Total memory footprint stays under 1.2 GB of VRAM, and the entire pipeline runs fine on pure CPU. We just shipped bit packed SIMD math so the CPU gating checks take under 0.01 milliseconds, added true streaming responses, and resolved Windows socket delays. It is also available on PyPI now via pip install hillock. Tested it on budget 6GB cards, Apple Silicon laptops, and pure CPU setups alongside local Ollama models.

Code is open source on GitHub: https://github.com/roandejager/Hillock
Docs are up at https://hillock.mintlify.site/ and we have a text only Discord at https://discord.com/invite/BGUPNBcVdp