This repo is the Unicron-assimilated Qwen3.5-9B prove (4 shards + unicron reports). Apache 2.0 from Qwen3.5-9B. It is not DeepSeek Flash and not Qwen 397B.

Job	State
This 9B assimilate upload	on the card (weights in-repo)
Qwen3.5-9B full assimilate (RTX 4090, budget 0.01)	DONE — 20.2% of fp16, ppl 29.8454 → 29.8400. See "Measured runs" below.
Qwen3.5-72B assimilate (RTX 3060, budget 0.05)	DONE — 145.4GB → 57.8GB (39.7% of fp16), wikitext-2 ppl 15.2443 → 15.2389 (−0.04%)
Moose 0.2.11 Qwen3.5-0.8B `--registers 128 --passages 200`	DONE — published at `staccs/Qwen3.5-0.8B-lecore-assimilated`
DeepSeek-V4-Flash in-weight HRR	`staccs/lecore-deepseek-v4-flash-hrr`
OpenRouter Provider	not applied

Measured runs (2026-08-16)

Both from operator hardware, not a datacentre. Numbers are read straight from the run artifacts (qwen9b_result.json, qwen72b_report.json, wikitext_ppl.log), not restated from memory.

	Qwen3.5-9B (RTX 4090)	Qwen3.5-72B (RTX 3060)
params	9.65B	72B (37 shards)
budget	0.01	0.05
mean bits/weight	3.234	5.038
size vs fp16	20.21%	39.75% (145.4GB → 57.8GB)
perplexity before → after	29.8454 → 29.8400	15.2443 → 15.2389
delta	−0.018%	−0.04%
wall clock	5h 10m	2h 21m
tensors quantized	369	560
left fp16 / skipped	0 / 3	— / 403

Both deltas are negative — perplexity came out marginally lower after assimilation. That is within noise and should be read as "no measurable regression", not as evidence that assimilation improves a model.

The 72B run is the more useful datapoint: a 145GB model reduced to 57.8GB on a 3060, with wikitext-2 perplexity measured over the full test split (sliding window, 297,193 tokens) rather than a sample.

Prove run, not a capability-beat of vanilla 9B.

A Qwen3.5-9B checkpoint that went through leCore’s Unicron gate — built to sit under holographic (HRR) memory & programs.
Apache 2.0 from the base. Prove run, not a “we beat GPT” claim.

For degens & non-ML folks (read this first)

Think of a normal model as a brain with a whiteboard (context window). When the board fills, old stuff gets erased.
leCore adds a second trick: holographic memory — like writing with light through the whole crystal. Scratch half the crystal and you still read the picture. That’s HRR.

What you care about	Normal chat model vibe	This stack (Qwen3.5-9B + leCore HRR)
“How much can it keep in one go?”	Many models choke at 4k–32k tokens. You paste a PDF, it forgets the start.	Base window here is 262,144 tokens native (~a whole book+). Upstream also ships YaRN ~1M extension. Not infinite — still a hard ceiling — but it’s “bring the whole thread / repo chunk / filing cabinet,” not “hope 8k fits.”
“What if storage / state gets wrecked?”	Normal memory is like a spreadsheet: delete 10% of cells and whole rows die.	HRR memory is damage-tolerant. Same lab test (16 facts in 1024 numbers): trash 40% of slots → holographic still 100% recall; normal packing is already ~0%. Your “notes” degrade gracefully instead of hard-crashing.
“Can it mix facts like Lego?”	Usually needs more training or brittle prompts.	Bind / unbind = compose roles & facts with invertible math (FHRR). Name ↔ attribute ↔ value without a mystery neural adapter.
“Can I run little programs on the memory?”	Tool calling / agents, heavy stacks.	HoloMachine: short holographic instruction streams on the same algebra — inspectable, deterministic, no black-box weights inside the HRR core.
“Did you shrink the model / make it smarter?”	Marketing often lies here.	This prove: Unicron looked at every fat matrix, found heavy-tail / well-trained regime → cut nothing. Sample perplexity 2.043 → 2.043 (Δ +0.00%). Honest win = safe gate + HRR surface, not a free size drop on 9B.
“What’s in it for me shipping / memeing?”	Weights alone.	Drop-in Qwen3.5-9B plus a path into leCore: long-context chat, holographic scratchpad that survives corruption, composable memory ops — MIT/Apache-friendly open stack. Next sell SKU we’re aiming at is DeepSeek V4-Flash (bigger iron).

Context window — say it loud

	Tokens (approx)	Human vibe
Many “chat” models	4k–32k	A long Discord scroll, then amnesia
This base (native)	262k	Novel / big codebase slice / fat research dump in one shot
This base (YaRN / ext, upstream)	~1M	Multi-book / serious long-doc — still not ∞
True infinite	❌	Transformer KV is not infinite; HRR external memory is big + sturdy, but has a measured capacity cliff

Side-by-side (tech receipt)

	Qwen3.5-9B original	This repo (Unicron-assimilated)	Factored sidecar	27B / 397B / V4-Flash refs
License	Apache 2.0	Apache 2.0	—	Apache / Apache / MIT
Context	262k native; ~1M YaRN (upstream)	same	—	see each card
On-disk	19G BF16	19G BF16	skipped (no pay)	larger → multi-hundred-G
Serve VRAM (rough)	~18–24GB BF16 + KV	same	—	Flash class ~170–175GB native
Unicron	—	0 filtered, heavy-tail passthrough	—	—
Sample ppl	2.043	2.043 (+0.00%)	—	—

How to load

python

from transformers import AutoModelForCausalLM, AutoTokenizer
m = AutoModelForCausalLM.from_pretrained(
    "staccs/lecore-qwen35-9b-assimilated",
    torch_dtype="auto",
    device_map="auto",
)
t = AutoTokenizer.from_pretrained("staccs/lecore-qwen35-9b-assimilated")

Same shapes as Qwen/Qwen3.5-9B. For HRR faculties (bind/unbind, HoloMachine, fingerprints), use leCore 0.2.11.

Provenance

Base: https://huggingface.co/Qwen/Qwen3.5-9B
Engine: leCore 0.2.11 / Unicron assimilate + sample eval
HRR degradation numbers: leCore README / tests/test_degradation_table.py
Operator: stacc (2026-08-12)

Not claims

Not frontier AGI. Not infinite context. Not “we compressed 9B for free.”
We measured retention, kept the weights honest, and wired them toward holographic memory you can actually explain to a human.

What this checkpoint is measured to be (2026-08-15)

Capability delta vs vanilla Qwen3.5-9B: 0.00%. That is disclosed, not hidden, and it is the correct expectation for a prove run: Unicron's gate certifies and installs, it does not retrain. Anyone benchmarking this against stock Qwen should expect a tie, and independent reproduction (ratimics v6) found the same: assimilation regression 0%, no quality or speed advantage from the HRR arm.

Where the value is claimed instead — memory, not accuracy. The requantize pass took this model from 18 GiB to ~4 GiB, with every sampled tensor landing at 3 bits. On a 16 GB laptop that is the difference between "resident" and "impossible", which is the whole point for zero-egress / local-first deployments.

What is NOT yet measured, stated plainly. There is no head-to-head against the obvious baselines — llama.cpp Q3_K_M and AWQ at matched quality — on the two numbers that decide it: resident RSS (init and steady state) and perplexity. Until that exists, "3-bit, 4.5x smaller" is a real measurement of this pipeline, not a demonstrated advantage over standard quantization. Treat the memory claim as promising and unproven rather than settled.