decontam 13-gram index (eval suite, v1.5b tokenize icin)

faz1_01: --n_shards/--shard_index paralelize (ds.shard, ~8x)

faz1_01: --target_tokens bilimsel gosterim kabul (5e7/7.5e9)

faz3_train: fix print satiri args.n_layers->cfg[n_layers] (preset ile None oluyordu)

faz3_train: --preset 350m (cfg parametrize; 177m=177.1M v1-birebir, 350m=348.6M dogrulandi); --attn_every/--d_model vb override

faz1_01: tr_tc100b kaynagi (TC-100B ~103B TR, data_dir=pretrain) — v1.5b ana TR; 4-tuple data_dir destegi

faz3_train: Muon optimizer entegrasyonu (--muon: 2D-Linear Muon + embed/norm AdamW; cklu-opt state + WSD-carpan; geriye uyumlu)

faz9_recur: gen_khop ZINCIR gorevi (perm dongu-kisayolu kaldirildi; gercek k-hop)

lamba_cpu: cuda-guvenli (GQACPU._rope arange + Mamba3CPU.forward zeros device=)

faz9_recur: sys.path fix (HF symlink-cache yan-klasor import)

faz9_recur: --sweep (k zorluk taramasi, GAP-vs-k trendi = asil GO/NO-GO kaniti)

faz9_recur: --fast (hep-attention, Mamba token-loop yok, k-hop GO/NO-GO ~3-4dk GPU)

faz9_recur: Faz B harness (k-hop pointer-chase, fixed-vs-recurrent esit-param + test-zamani R olcekleme, device-esnek)

faz9_recur: recurrent-depth Faz A smoke (post-norm recursion kararliligi BIZIM bloklarla dogrulandi; gnorm patlamasi notu)

faz7_rag.py: --int8 dynamic quant (~2x kucuk bellek 708->325MB, ~1.2x hiz, ciktilar birebir)

lamba_cpu.py: --int8 dynamic quant (~2x kucuk bellek 708->325MB, ~1.2x hiz, ciktilar birebir)

faz7_rag.py: decode-cache (step/KV-cache) — CPU uretim O(L), ~6x hizli, full-recompute ile birebir

lamba_cpu.py: decode-cache (step/KV-cache) — CPU uretim O(L), ~6x hizli, full-recompute ile birebir

faz7_rag: --device cpu (LambaCPU saf-PyTorch yolu; fork import soft, CPU RAG yerelde calisiyor)

lamba_cpu: fix GatedMLP gate order (silu(a)*b -> y*silu(gate)) — CPU port now matches fork to 0.06-0.09 logit

lamba_cpu: GQA _rope dtype fix (bf16 uyumu)

lamba_cpu: EXP_E=True default (Mamba mixer KALIBRE, maxdiff 0.14)

lamba_cpu: EXP_E bayragi (alpha base-e vs base-2 decay)

lamba_cpu: BETA_SHIFT bayragi (fwd trap_shifted) + 4-grid

lamba_cpu: beta alpha-cikar bayragi (fwd kernel beta=DT(1-trap))

lamba_cpu RoPE: angle=tanh(ang)*DT*pi + interleaved (fork referans)

lamba_cpu: kalibre bayraklari (ANG_SIGN/ANG_DT/ROPE_INTER) grid-search icin

lamba_cpu kalibre 1: angle birikim isareti negatif

v1.1 lamba_cpu.py: saf-PyTorch CPU iskelet (load+forward calisiyor, matematik kalibrasyon bekliyor)

model card: Related Mamba-3 models + hrsvrn acknowledgement

LAMBA release stage: LAMBA_Inference.ipynb

LAMBA release stage: LAMBA_V1_MODEL_CARD.md

LAMBA release stage: faz7_rag.py

faz8_dpo: --length_norm (uzunluk yanliligi cozumu)

Faz 8 DPO: faz8_dpo.py

Faz 8 DPO: faz8_prep_dpo.py

faz5_eval: xnli-tr + turkishmmlu gorevleri (tam TR panel)

faz7_prep_rag_sft: --answer_mode sentence (tam-cumle cevap)

faz6_sft: base HF sft/ yolu (devam SFT) + --save_subdir (cakisma onleme)

RAG-SFT: faz7_rag.py (extractive QA converter + prompt hizalama)

RAG-SFT: faz7_prep_rag_sft.py (extractive QA converter + prompt hizalama)

faz7_rag: kisa/no-think talimati + <think> strip + --max_new 96

faz7_rag: RAG prompt SFT formatina hizalandi + dil-duyarli yonerge

faz7_rag: --ab modu (RAG vs ham, tek kosu) + DEMO_QUERIES

faz7_rag: RAG pipeline (retrieve+compress+inject, demo corpus)

test_smartcore: --ckpt (HF) + --chat (SFT template) + continuation-only

faz5_eval: --ckpt (SFT checkpoint eval)

Upload code/kod/faz5_eval.py with huggingface_hub

Upload code/kod/faz6_sft.py with huggingface_hub

Upload code/kod/faz6_prep_data.py with huggingface_hub