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# mini-AGI — the settings worth changing.

# Read when a model is created and every time it reads. Anything not here is

# either derived or was not worth a knob.

model:

# THIS BLOCK BUILDS A MODEL; IT DOES NOT CHANGE ONE. Down as far as the

# depth rule, these are fixed in the manifest when the weights are created.

# Editing them later does nothing - the shapes would no longer match.

d_model: 512 # width of the trunk

n_head: 8 # attention heads

d_ff: 1408 # the trunk's own feed-forward

n_prelude: 2 # dense blocks before the recurrent core; run once

n_recur: 1 # blocks per row

n_coda: 0 # readout blocks after the last row

# ------------------------------------------------------------------ DEPTH

# The model runs one row at a time and decides after each whether to stop.

# A row is n_recur + n_coda block applications.

# RESCALING RULE: max_steps, halt_prior, bptt_window and train_steps_mean

# are all counted in ROWS, so all four move together if n_recur or n_coda

# changes what a row is. Missing one is silent.

max_steps: 24 # most rows a character may take

min_steps: 1 # rows it must run before halting may stop it

halt_prior: 0.072 # halting is held to a mean of 1/this rows

ha 8000 lt_thresh: 0.9 # at inference, stop at the first row past this

halt_freeze: true # a halted character stops being computed, in

# training and writing alike; later characters read

# its final state at the deeper rows

ponder_beta: 0.01 # weight on the halting KL

bptt_window: 24 # rows gradient reaches back through. Also the

# activation memory, so it sets peak VRAM

train_steps_mean: 12.8 # depth sampled while training: poisson(this) + 1,

# clipped. 0 runs the ceiling every step

# The window attention reaches back over. Reading starts at context_start

# and grows only when the model is still gaining from the far end of what

# it has; context_end is the ceiling and binds at load and at every growth.

context_start: 2048 # where a fresh model begins

context_end: 4096 # and the furthest it may ever reach

context_step: 1 # characters added per growth

context_grow_every_chars: 100_000 # characters between growths

context_gain_min: 0.015 # stop growing below this gain from depth

context_every_chars: 65536 # characters between asking

pool:

# experts, width, depth and top_k are fixed in the manifest, like the model

# block. resident, ram_cache and capacity_factor are run-time and can be

# changed freely.

experts: 64 # how many exist at the start; it grows from here

width: 2048 # hidden units in one expert

depth: 1 # SwiGLU blocks stacked inside one expert

top_k: 8 # how many one character routes through

# HOW EXPERTS ARE CHOSEN - one rule: the text chooses. Every character

# ranks the whole pool with the router and asks for its top_k, and every

# forward's first pass adds its characters' requests to the vote of its

# text - everything read since position 0. A forward - a training window, a

# chunk of held-out file, a prompt, each character of a reply - may use at

# most `resident` different experts: the ones its text has voted for most,

# and a character whose request was not admitted takes its best admitted

# expert instead. A character being written routes among what its prompt

# and the reply so far asked for, as it would inside a training window.

# Three tiers: disk holds every expert, RAM caches the recently wanted,

# VRAM holds what the current forward admitted. Only VRAM is scarce.

resident: 32 # experts one forward may use - the card's slots, each

# holding weights, gradient and Adam's moments

ram_cache: 96 # RAM - experts kept in system memory

capacity_factor: 1.5 # most of a batch one expert may take, as a multiple

# of its fair share; the rest is dropped. 0 removes

# THE BALANCE TERM - how the whole pool gets used. Left alone the router

# keeps choosing the same experts: reading held-out on its own it put 59 of

# 118 on a card across every subject. This term in the loss charges the

# router, in every forward that trains, for the probability it puts on each

# expert over the whole pool, in proportion to that expert's share of recent

# admissions (the last ~1,000 training forwards). Busy experts get dearer and

# idle ones cheaper, so the router's own rows move toward the idle - and

# reading and writing use them too, not only training. A text is still free

# to want few experts; what gets evened out is use across texts.

# The weight sets how hard it leans against learning the text on the experts

# in use. An expert no text admits gets no other gradient, so Adam moves its

# row at the usual pace whatever the weight. 0 turns it off. In a 15-minute

# test 0.01 took the experts in use on held-out from 59 to 114, at a cost of

# 0.023 in held-out loss over those minutes.

balance: 0.001

# THE TEMPERATURE OF EXPERT SELECTION - which experts compute a character,

# NOT which character comes out. Characters are always chosen greedily; any

# randomness in what the model writes comes from here.

# 0 threshold selection, the rule as it has run: the card holds the

# 32 experts the text's first row asks for most, and every character

# takes its own top 8 of those

# > 0 drawn: at each row, 8 experts are drawn from the text's

# probabilities (the router's full distribution, summed over every

# character in the context window) in proportion to p^(1/T), until

# the card's 32 are full, and every character draws its own 8 from

# its own probabilities over the card, the same way

# 1 draws in proportion to the router's probabilities, 0.5 to their

# squares, 0.2 to their fifth powers. Close to 0 the draws approach each

# row's top 8 - which is not quite 0 itself: a drawn card fills 8 a row, so

# the first row has only 8 experts where 0 gives it all 32.

select_temperature: 0

training:

save_every: 5 # minutes between checkpoints

precision: bf16 # what the forward computes in. Weights, gradients

# and the expert files stay fp32

# THE LEARNING RATE IS NOT SCHEDULED. minagi/plasticity.py watches held-out

# and moves the rate in both directions: easing it down without evidence of

# improvement, stepping it back up when held-out jumps and stays there.

chunk: 2048 # characters between steps within the window

lr: 3.0e-4

trunk_lr_mult: 0.1 # the trunk is shared by every domain and is where

# forgetting happens; it learns slower on purpose

weight_decay: 0.1

clip: 1.0

growth:

# Every `every_chars`, add `k` experts if all five brakes are off:

# ROOM there is disk and VRAM for it

# USED the capacity it already has is being asked for

# EARNING the experts added recently are being asked for

# FITS not too many are already inside their trial

# HONEST train and held-out have not separated

every_chars: 2_000_000 # characters between growth decisions

k: 1 # experts added per decision

max_gap: 0.4 # HONEST: train minus held-out. 0 disables

# USED. Staleness is a share of prune.survival_chars: 0.0 when a forward

# admits an expert, 1.0 when it has gone long enough unadmitted to be

# deleted. Past prune.dying_at it counts as dying.

dying_frac_max: 0.35 # stop growing once this share of the pool is dying

# FITS.

max_in_flight: 50 # experts allowed inside their trial at once

# EARNING. Of the experts past their trial and still young, this share must

# have earned a gate.

keep_ratio_min: 0.35

recent_mult: 4.0 # "still young" is age < this x the trial

# ROOM. An expert is 25.2 MB, so 10 GB is about 397 of them.

max_disk_gb: 10.0 # ceiling on the pool on disk. 0 for no limit

mem_frac: 0.95 # refuse to grow past this share of the card

birth_gate: 0.001 # gate a new expert starts at - a scale, not a

# verdict. Small so it cannot shout before it earns

prune:

# USE IT OR LOSE IT. An expert is deleted when nothing has admitted it for

# this long, and it is never deleted inside its first survival_chars.

survival_chars: 100_000_000

dying_at: 0.65 # how far through that window an expert must get,

# unaddressed, to count as dying for growth's brake

data:

train: data/train

held_out: data/val

weights: weights # created on the first run if it is not there

shuffle_seed: 0 # which file and which offset each window comes from

# Each sub-folder of `train` is a subject, and reading rotates between them.

# A window never spans two subjects, but a subject may take several in a row.

passage: 32768 # characters read from one file before moving on

decoding:

# Generation is greedy - no random numbers - so two samples taken at the

# same point in training are the same text. What holds off the loops greedy

# decoding usually falls into is a per-character trace that rises when a

# character is used and decays after.

adapt_strength: 2.5 # logits of suppression for a character just used

adapt_decay: 0.88 # half-life of 5.4 characters; 0 disables it

rep_penalty: 1.0 # the weaker alternative, off at 1.0

plots:

enabled: true # redraw the graphs whenever the sample log grows

last: 100 # millions of characters the recent-progress graph

# covers, counted back from wherever reading has got

hardware:

# What the machine is, where the code cannot tell for itself - read by

# minagi/device.py. Every key is optional; left out, a discrete NVIDIA card

# behaves exactly as it always has. See configs/strix_halo.yaml for an APU.

# gpu_mem_gb: 8 # the budget growth's ROOM brake measures against.

# # Unset, the card's reported size. Set it on an APU,

# # where the reported size is the firmware carve-out

# unified: false # CPU and GPU share memory. Detected for gfx1150/1/2

# ram_tier_on_gpu: false # hold the expert RAM tier on the device. Defaults

# # to `unified`

# expandable_segments: true # PYTORCH_{CUDA,HIP}_ALLOC_CONF

# rocm_aotriton: false # TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1

# tunableop: false # PYTORCH_TUNABLEOP_ENABLED=1, results in runs/

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