Model will be ungated for open download soon! These models are undertrained, and are NOT meant to be finished. These are Research Artifacts only.

Mycel-LM is a 79.2M-parameter research language model whose channel-mixing block is not an MLP. It is a differentiable Neighbour-Sensing fungal-colony-growth model: each token is expanded into a colony of hyphal tips that grow in a bounded latent region, sense a shared density field, and steer their own growth — the "MLP" is replaced by a few differentiable steps of colony growth, read back out into the hidden state.

It is part of a family of models that ask a single question: can the generalizing ability of a transformer be carried by an unusual, self-organizing dynamical system in place of the feed-forward block? Mycel-LM keeps the family's tokenizer, traits, and data fixed and swaps only the mixer, so it is a controlled experiment against the sibling Quazimoto models (whose mixer is a bank of coupled Kuramoto oscillators).

⚠️ Research artifact, not a product. At ~79M parameters it is fluent but small: it models the shape of language well and generates coherent, grammatical text, but it is not factual and will confidently hallucinate. See Limitations.

Architecture graph for Quazim0t0/Mycel-LM-79M. Open in hfviewer

Highlights
Architecture
Repository layout
Install
Quickstart
Command-line usage
Live visualizer & Space
Training from scratch
Fine-tuning (SFT)
Checkpoints
Limitations
Citation / basis

Highlights

Novel mixer. The per-layer feed-forward block is replaced by a MycelBlock — a differentiable simulation of fungal colony growth (Neighbour-Sensing).
Self-describing checkpoints. Each .pt embeds a family_config recording the exact geometry, so generate.py / healthcheck.py / visualize.py rebuild the model with no external config.
KV cache. Incremental decoding is wired through the whole stack (attention presents are threaded per layer); generate() prefills the prompt once and decodes one token per forward.
Self-speculative decoding. Four MTP draft heads propose the next tokens and the main head verifies them in one parallel forward — bit-identical to greedy, just fewer forwards.
Live 3-D visualizer. Watch the colony grow token-by-token as a Three.js filament web.

Architecture

Standard causal Transformer backbone (token-mixing = attention, tied LM head), with the per-layer feed-forward network replaced by a MycelBlock.

The MycelBlock (the novel part)

Based on the Meškauskas / Fricker / Moore (2004) Neighbour-Sensing model of fungal colony growth:

The hidden state projects to N = 96 hyphal tips per token, each with a position in a bounded 3-D latent region and a growth vector.
A few differentiable growth steps run: each tip senses the local density field, steers away from the colony's own density (negative autotropism) with persistence, moves, and is re-clamped into the bounded region (the colony can't grow unbounded).
The final [position, growth-vector, sensed-density] of every tip is read out back into the hidden state, behind a family gate.

The density field is evaluated against 16 learnable field centres (a low-rank sample of the field) so cost is O(N·F) per step, not O(N²) — the same mean-field trick that keeps the sibling oscillator block cheap. Health-checking a trained checkpoint shows the tropism parameter converges strongly negative across layers, i.e. the model genuinely learns the grow-away-from-density behaviour rather than leaving it at init.

Trait stations (MycelStations): tiny memory specialists sit at fixed anchor positions in the colony. A tip interacts with a station by proximity — which is emergent from where the tip grew — so which tips use which trait "comes to be" during growth rather than being assigned to a fixed index. The stations hold test-time-writable input/output stores that act as an addressable context memory at inference.

Attention

Family attention ported from the Quazimoto v2 stack:

MLA low-rank Q/O projections
Partial RoPE (nope + rope split), QK-Norm, GQA (4 KV heads)
optional DERF (erf attention) and XSA (value-subspace removal) — off in this checkpoint
KV cache for incremental decoding (per-layer (k, v) presents threaded through the stack)

Opt-in family traits (all live in this checkpoint)

Trait	Role
HRM	iterative gated hidden-state refinement (random init state, gates open)
MoE	SwiGLU mixture (4 routed + 1 shared, top-2) refining the trunk
MTP (×4)	multi-token-prediction draft heads → enables self-speculative decoding
JEPA	representation-prediction aux loss (train-only; never runs at inference)
Ring Specialists (7/ring)	the trait stations described above
Fractal Phase Seed	seeds tip positions from each token's Mandelbrot orbit angles (gated)

Config (this checkpoint)


params	79.2M
layers	10
d_model	768
heads	12 (4 KV)
vocab	16512 (SpikeWhale byte-merge)
block size	2048
tips / token	96, in a 3-D bounded colony
field centres	16 · growth steps 3 · stations 16

The checkpoint is self-describing: family_config inside the .pt records the exact geometry so the model rebuilds itself on load.

Repository layout

model.py               QuazimotoLM + QuazimotoConfig — the transformer backbone, attention,
                       KV cache, traits (HRM/MoE/MTP/JEPA), generate() and forward_drafts()
mycel.py               MycelBlock (Neighbour-Sensing growth mixer) + MycelStations
family.py              shared family layers (MoE, HRM, specialists, norms, ...)
fractal.py             hierarchical Mandelbrot phase seeding (FractalSeed trait)
instrument.py          zero-cost capture hooks the visualizer reads from
special_tokens.py      ChatML / control-token definitions
spike_tokenizer.py     SpikeWhale byte-merge tokenizer (subclasses PreTrainedTokenizer)
tokenizer.json         the tokenizer vocab / merges (vocab 16,512)
fractal_phase.pt       precomputed hierarchical Mandelbrot phase table (regenerable)

generate.py            inference harness — KV cache + self-speculative decoding + sampling
healthcheck.py         per-layer weight / gate / PPL diagnostics for a checkpoint
visualize.py           builds the 3-D colony dashboard (viz.html) from a generation

train.py               pretraining entry point (streamed multi-corpus blend)
train_sft.py           supervised fine-tuning (ChatML, assistant-only loss masking)
chat_sft.py            chat-format rendering / loss masking helpers used by SFT
train_opd.py           OPD (on-policy distillation) training loop
distill_uld.py         universal-logit-distillation utilities
opd_teacher.py         teacher wrapper for distillation
build_fractal_table.py regenerates fractal_phase.pt
train.bat / train_sft.bat   Windows convenience launchers

chkpt/quazimoto.pt       pretraining checkpoint (step 149,000)
chkpt/quazimoto_sft.pt   SFT checkpoint (step 4,000, ChatML)

Note: the Modal cloud launchers (modal_train.py, modal_sft.py) are intentionally not part of this package. The scripts above run locally on CPU or a single GPU.

Install

pip install -r requirements.txt

Requirements are minimal: torch, numpy, transformers (the tokenizer subclasses PreTrainedTokenizer). Training additionally uses datasets and huggingface_hub. Everything below runs on CPU (slow but functional) or a single GPU.

Quickstart

python

import torch
from model import QuazimotoLM, QuazimotoConfig
from spike_tokenizer import SpikeTokenizer

ck  = torch.load("chkpt/quazimoto.pt", map_location="cpu", weights_only=False)
cfg = QuazimotoConfig(**ck["family_config"])          # self-describing
model = QuazimotoLM(cfg); model.load_state_dict(ck["model"], strict=False); model.eval()
tok = SpikeTokenizer(vocab_file="tokenizer.json")

ids = torch.tensor([tok.encode("The mycelium spreads through the soil", add_special_tokens=False)])
out = model.generate(ids, n_new=80, temperature=0.8, top_k=40)   # KV cache on by default
print(tok.decode(out[0].tolist(), skip_special_tokens=True))

For a chat turn, wrap the prompt in ChatML and stop on <|im_end|> (the SFT checkpoint was trained on this framing):

python

prompt = "<|im_start|><|user|>\nWhat is mycelium?<|im_end|>\n<|im_start|><|assistant|>\n"
ids = torch.tensor([tok.encode(prompt, add_special_tokens=False)])
out = model.generate(ids, n_new=120, temperature=0.7, top_k=40)

Command-line usage

bash

# plain completion (KV cache on by default)
python generate.py --ckpt chkpt/quazimoto.pt --prompt "In the beginning" --max_new_tokens 80

# chat turn (ChatML framing + stop on <|im_end|>)
python generate.py --ckpt chkpt/quazimoto_sft.pt --chat --prompt "Hello, who are you?"

# interactive REPL
python generate.py --ckpt chkpt/quazimoto_sft.pt --interactive

# self-speculative decoding (MTP heads draft, main head verifies; report acceptance)
python generate.py --ckpt chkpt/quazimoto.pt --speculative --spec_stats

# disable the KV cache (full recompute each step — for comparison)
python generate.py --ckpt chkpt/quazimoto.pt --no_cache

# per-layer diagnostics (weights / gates / PPL)
python healthcheck.py --ckpt chkpt/quazimoto.pt

Sampling knobs: --temperature, --top_k, --top_p, --repetition_penalty, --seed.

Live visualizer & Space

visualize.py renders the colony growing in 3-D as the model generates, token by token — hyphal tips linked into a filament web, coloured by local density, with the trait stations shown as orange wire-spheres. It writes a self-contained viz.html (Three.js from a CDN):

python visualize.py --ckpt chkpt/quazimoto_sft.pt --prompt "the mycelium spreads" --tokens 50

A companion Hugging Face Space (Mycel-LM v1) wraps the same architecture in an interactive chat — KV-cache decoding drives the reply while the 3-D colony visualizer animates the growth for the generated tokens.

Training from scratch

bash

python train.py --device cuda --steps 160000 --batch 12 --block 2048 --amp \
    --use-hrm --use-moe --use-mtp --use-jepa --use-ring-specialists --use-fractal-phase-seed \
    --stream --math-frac 0.25 --out chkpt/quazimoto.pt --ckpt-every 500 --resume

Tokenizer: SpikeWhale byte-merge, vocab 16,512. (Byte-merge perplexity is tokenizer-inflated; bits/byte is the honest metric.)
Pretraining blend: 35% Ultra-FineWeb-L3 / 25% FineWeb-Edu / 25% FineMath / 15% Quazim0t0/PretrainNew, streamed. Streamed datasets are pulled with datasets; gated corpora need huggingface-cli login.
--resume continues from the checkpoint at --out. The growth loop is activation-heavy, so keep the batch modest; --amp gives a bf16 speedup on GPU.

Pass --help to train.py for the full trait / optimiser / schedule surface.

Fine-tuning (SFT)

bash

python train_sft.py --init chkpt/quazimoto.pt --out chkpt/quazimoto_sft.pt \
    --steps 4000 --batch 8 --block 2048 --amp

Renders a chat mix in ChatML with assistant-only loss masking (chat_sft.py).
SFT blend: ultrachat_200k_sft + ultrafeedback-sft + UltraData-SFT-2605/Knowledge + OpenThoughts2-1M-ShortThink.
The bundled SFT checkpoint is only 4k steps — the chat format transferred but the model is still shallow.

The distributed checkpoints carry weights only (optimizer state stripped to keep the download small). Fine-tuning starts a fresh optimizer from them, which is the normal path; only exact resumption of the original pretraining run would need the optimizer state.

Checkpoints

chkpt/quazimoto.pt — pretraining checkpoint, step 149,000
chkpt/quazimoto_sft.pt — SFT checkpoint, step 4,000 (ChatML, early)

Both embed family_config (self-describing) and load with strict=False so future trait additions stay backward-compatible.

Limitations

Not factual. Small-model behaviour: fluent and grammatical, but it invents facts ("the capital of France is the largest and most important part of the world").
SFT is early (4k steps) — answers follow the chat format but hallucinate.
No safety tuning. No RLHF/guardrails; do not deploy in user-facing settings.
Custom architecture — cannot be loaded with AutoModel; use the bundled model.py.
This is an experiment in architecture, released to study whether a self-organizing growth process can carry a transformer's generalization. Treat outputs accordingly.

Citation / basis

Neighbour-Sensing model of hyphal growth: Meškauskas, Fricker & Moore (2004), Simulating colonial growth of fungi with the Neighbour-Sensing model of hyphal growth, Mycological Research 108(11).

License: Apache-2.0.

Citation

If you use this model, please cite:

bibtex

@misc{mycellm79m,
  title        = {Mycel-LM-79M: A ~79M-parameter Neighbour-Sensing fungal-colony language model},
  author       = {Dean Byrne (Quazim0t0)},
  year         = {2026},
  howpublished = {HuggingFace, \url{https://huggingface.co/Quazim0t0/Mycel-LM-79M}},
  note         = {Quazim0t0/Mycel-LM-79M}
}

Update: format-blended SFT

Short instruction/format SFT on a 60/25/15 blend of HuggingFaceTB/smoltalk, GSM8K-train ('#### N' reasoning), and MMLU-style ('Answer: ') examples, so chat fluency improves while the benchmark output-formats are preserved. Held-out (test-split) before->after:

held-out 2.04->1.18, MMLU acc 0.010->0.255, format 0.075->1.000

Note: fluency + output-format gains. Benchmark accuracy remains near the floor for a model this size -- the SFT does not add reasoning ability.

Quazim0t0/Mycel-LM-79M

Mycel-LM (79M)

Table of contents

Highlights

Architecture

The MycelBlock (the novel part)

Attention

Opt-in family traits (all live in this checkpoint)

Config (this checkpoint)

Repository layout

Install

Quickstart

Command-line usage

Live visualizer & Space

Training from scratch

Fine-tuning (SFT)

Checkpoints

Limitations

Citation / basis

Citation

Update: format-blended SFT

Quazim0t0/
Mycel-LM-79M