ali_mem-split_s1 / config.yaml
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model: {architecture: ali, d_enc: 256, d_main: 512, n_enc: 2, n_dec: 2, n_main: 6,
n_heads_enc: 4, n_heads_main: 8, seq_len: 1024, dropout: 0.0, target_ratio: 6.0,
max_chunk_mult: 3.0, dechunk: ema, router_noise: 0.0, rope_base: 10000.0}
memory:
ali:
budget_B: 65536
staging_capacity: 262144
policy: frequency
value_mode: learned
candidate_types: [split]
write_rule: mean
eta_min: 0.0
memory_scope: persistent
consolidate_every: 50
max_span: 64
gate: vector
retrieval: true
read_dropout: 0.1
gate_hidden: 0
type_dim: 16
gate_bias_init: -2.0
probe_seqs: 2
probe_reps: 8
horizon: 32
predictor: mlp
lambda_util: 0.1
enabled: true
inventory_kwargs: {}
probe_mix: null
util_batch_slots: 1024
pkm: {n_keys: 256, topk: 8, heads: 1, d_key: null, value_lr_mult: 1.0}
objective: {name: ratio, schedule: null, alpha: 0.03, alpha_parity_cap: 0.06, global_anchor: 0.0,
premium_clip: 4.0, premium_kind: flores_per_sent, tau1: 0.4, tau2: 0.7, freeze_scope: router,
cpc_decay: 0.9, genreg_target_entropy: null, genreg_eta: 0.05}
data:
corpus: corpus
langs: [en, tr, fi]
mixture: null
doc_filters: null
byte_classes: scratch/data/byte_classes.json
training:
seed: 1
max_steps: 6000
batch_size: 32
grad_accum: 1
lr: 0.0003
min_lr_frac: 0.1
warmup: 300
weight_decay: 0.1
grad_clip: 1.0
betas: [0.9, 0.95]
router_lr_mult: 1.0
log_every: 50
amp: bf16
compile: false
device: cuda
checkpointing:
run_name: ali_mem-split_s1
runs_dir: runs
ckpt_every: 200
keep: false
milestones: [1000, 3000]
resume: auto
keep_schedule: log2
keep_extra_offsets: [2, 4, 6, 8]
hub_repo: AdaptiveChunking/{run_name}
hub_private: false
keep_local: false
evaluation:
eval_every: 200
eval_batches: 8
probe_sents: 128
probe_bpb_batches: 4
log_spaced_probe: true
dense_early_every: 0
dense_early_until: 0
dump_reps: true
dump_reps_thresholds: [0.3, 0.4, 0.5, 0.6, 0.7]