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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n36Uicw5axSb"
      },
      "source": [
        "# SmartCore V1 — Faz 2 Colab Smoke (GPU)\n",
        "\n",
        "180M Mamba-3 (SISO) + GQA 5:1 hibrit modelin **smoke testi**: HF'deki pre-tokenize veriden\n",
        "(`kdirgul/smartcore-v1-data`) streaming + model + WSD eğitim döngüsü, GPU'da.\n",
        "\n",
        "**Önkoşul:** Colab **Pro+ / A100** (Runtime > Change runtime type > A100). HF token (private repo erişimi).\n",
        "\n",
        "**Smoke amacı:** veri akışı + model + loop + GPU uçtan uca çalışıyor mu (loss ~10.8'den düşüyor, NaN/OOM yok).\n",
        "**NOT:** Bu smoke saf-PyTorch minimal modeli kullanır (her yerde çalışır, yavaş). **Tam-hız Faz 3** için\n",
        "`mamba-og` fork (Triton SISO kernel) entegre edilecek — ayrı adım."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4iMibUmpaxSd",
        "outputId": "e2bf59b8-9ca5-4ea5-d380-07f171ae5288",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GPU 0: NVIDIA A100-SXM4-40GB (UUID: GPU-14abd7b2-d5a9-2d64-6756-bdd5ffc37da3)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m555.1/555.1 kB\u001b[0m \u001b[31m43.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m684.4/684.4 kB\u001b[0m \u001b[31m54.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m48.9/48.9 MB\u001b[0m \u001b[31m57.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!nvidia-smi -L\n",
        "!pip -q install -U einops sentencepiece \"datasets>=2.18\" \"huggingface_hub>=0.23\" pyarrow"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9xL15dAmaxSe"
      },
      "source": [
        "## 1) HF login (private repo erişimi)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r0vrDO9kaxSe"
      },
      "execution_count": null,
      "outputs": [],
      "source": [
        "from huggingface_hub import login, get_token\n",
        "login()\n",
        "assert get_token(), 'Token yok - once login'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bjMvH7IAaxSf"
      },
      "source": [
        "## 2) Kodu HF'den indir"
      ]
    },
    {
      "cell_type": "code",
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        "id": "jm_PiGuqaxSf",
        "outputId": "f3651ee1-e690-44db-8801-f6500b28da63",
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          "height": 98,
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            "0ef2dcd762eb4a29b842d1c375659359"
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        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (incomplete total...): 0.00B [00:00, ?B/s]"
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Fetching 24 files:   0%|          | 0/24 [00:00<?, ?it/s]"
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "kod dizini: /root/.cache/huggingface/hub/models--kdirgul--smartcore-v1/snapshots/87cde766b3c6416b400c906b6aab1a020bee07db/code\n"
          ]
        }
      ],
      "source": [
        "import os, sys\n",
        "from huggingface_hub import snapshot_download, get_token\n",
        "tok = get_token(); os.environ['HF_TOKEN'] = tok\n",
        "p = snapshot_download('kdirgul/smartcore-v1', allow_patterns=['code/**'], repo_type='model', token=tok)\n",
        "CODE = os.path.join(p, 'code')\n",
        "print('kod dizini:', CODE)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7_MMSpBdaxSf"
      },
      "source": [
        "## 3) Smoke (küçük config — hızlı doğrulama)\n",
        "Pipeline'ı kanıtlar. Tam 180M (d_model=768 n_layer=20 seq=2048) da çalışır ama minimal modelle yavaştır."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WWQK1aZIaxSf",
        "outputId": "c575989d-bc71-4a4c-b8df-79e02453ac0f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "model: d=512 L=12 (11 Mamba + 1 GQA) | params=63.5M | dev=cuda\n",
            "veri: kdirgul/smartcore-v1-data (HF stream) | mixture {'en_fineweb_edu': 0.55, 'tr_fineweb2_hq': 0.22, 'code_codeparrot': 0.13, 'math_openwebmath': 0.1}\n",
            "smoke | 40 adım | bf16=False | başlangıç loss ~ln(48000)=10.78\n",
            "Resolving data files: 100% 25/25 [00:00<00:00, 21338.54it/s]\n",
            "Resolving data files: 100% 41/41 [00:00<00:00, 22301.45it/s]\n",
            "Resolving data files: 100% 100/100 [00:00<00:00, 23675.23it/s]\n",
            "  step   0 | loss 10.875 | grad_norm 225.17 | lr 5.00e-04 | 0.5k tok/s\n",
            "Resolving data files: 100% 19/19 [00:00<00:00, 20834.45it/s]\n",
            "  step   1 | loss 10.877 | grad_norm 48.50 | lr 5.00e-04 | 0.8k tok/s\n",
            "  step   2 | loss 10.875 | grad_norm 103.57 | lr 5.00e-04 | 1.2k tok/s\n",
            "  step   3 | loss 10.856 | grad_norm 52.10 | lr 5.00e-04 | 1.5k tok/s\n",
            "  step   4 | loss 10.791 | grad_norm 48.26 | lr 5.00e-04 | 1.9k tok/s\n",
            "  step   5 | loss 10.681 | grad_norm 47.06 | lr 5.00e-04 | 2.2k tok/s\n",
            "  step   6 | loss 10.751 | grad_norm 52.34 | lr 5.00e-04 | 2.5k tok/s\n",
            "  step   7 | loss 10.695 | grad_norm 47.03 | lr 5.00e-04 | 2.8k tok/s\n",
            "  step   8 | loss 10.867 | grad_norm 47.15 | lr 5.00e-04 | 3.0k tok/s\n",
            "  step   9 | loss 10.392 | grad_norm 46.40 | lr 5.00e-04 | 3.3k tok/s\n",
            "  step  10 | loss 10.015 | grad_norm 46.45 | lr 5.00e-04 | 3.5k tok/s\n",
            "  step  11 | loss 10.603 | grad_norm 47.08 | lr 5.00e-04 | 3.8k tok/s\n",
            "  step  12 | loss 10.407 | grad_norm 46.09 | lr 5.00e-04 | 4.0k tok/s\n",
            "  step  13 | loss 10.341 | grad_norm 44.39 | lr 5.00e-04 | 4.2k tok/s\n",
            "  step  14 | loss 10.830 | grad_norm 45.62 | lr 5.00e-04 | 4.4k tok/s\n",
            "  step  15 | loss 10.823 | grad_norm 46.27 | lr 5.00e-04 | 4.6k tok/s\n",
            "  step  16 | loss 10.810 | grad_norm 60.42 | lr 5.00e-04 | 4.8k tok/s\n",
            "  step  17 | loss 10.776 | grad_norm 45.28 | lr 5.00e-04 | 5.0k tok/s\n",
            "  step  18 | loss 10.723 | grad_norm 49.04 | lr 5.00e-04 | 5.1k tok/s\n",
            "  step  19 | loss 10.536 | grad_norm 46.77 | lr 5.00e-04 | 5.3k tok/s\n",
            "  step  20 | loss 10.481 | grad_norm 45.51 | lr 5.00e-04 | 5.4k tok/s\n",
            "  step  21 | loss 10.694 | grad_norm 48.19 | lr 5.00e-04 | 5.6k tok/s\n",
            "  step  22 | loss 10.678 | grad_norm 45.26 | lr 5.00e-04 | 5.7k tok/s\n",
            "  step  23 | loss 10.613 | grad_norm 47.08 | lr 5.00e-04 | 5.9k tok/s\n",
            "  step  24 | loss 10.392 | grad_norm 52.67 | lr 5.00e-04 | 6.0k tok/s\n",
            "  step  25 | loss 10.248 | grad_norm 60.87 | lr 5.00e-04 | 6.2k tok/s\n",
            "  step  26 | loss  9.683 | grad_norm 73.81 | lr 5.00e-04 | 6.3k tok/s\n",
            "  step  27 | loss 10.066 | grad_norm 88.77 | lr 5.00e-04 | 6.4k tok/s\n",
            "  step  28 | loss  9.398 | grad_norm 109.29 | lr 5.00e-04 | 6.5k tok/s\n",
            "  step  29 | loss  9.379 | grad_norm 114.56 | lr 5.00e-04 | 6.6k tok/s\n",
            "  step  30 | loss  8.853 | grad_norm 120.78 | lr 5.00e-04 | 6.7k tok/s\n",
            "  step  31 | loss  9.620 | grad_norm 129.50 | lr 4.55e-04 | 6.8k tok/s\n",
            "  step  32 | loss  9.784 | grad_norm 137.93 | lr 4.10e-04 | 7.0k tok/s\n",
            "  step  33 | loss  9.348 | grad_norm 245.60 | lr 3.65e-04 | 7.1k tok/s\n",
            "  step  34 | loss  9.490 | grad_norm 158.82 | lr 3.20e-04 | 7.1k tok/s\n",
            "  step  35 | loss  9.447 | grad_norm 111.69 | lr 2.75e-04 | 7.2k tok/s\n",
            "  step  36 | loss  9.160 | grad_norm 326.22 | lr 2.30e-04 | 7.3k tok/s\n",
            "  step  37 | loss  9.356 | grad_norm 192.43 | lr 1.85e-04 | 7.4k tok/s\n",
            "  step  38 | loss  9.387 | grad_norm 131.03 | lr 1.40e-04 | 7.5k tok/s\n",
            "  step  39 | loss  9.428 | grad_norm 144.60 | lr 9.50e-05 | 7.6k tok/s\n",
            "\n",
            "smoke BİTTİ. NaN yok: True | 7.6k tok/s\n"
          ]
        }
      ],
      "source": [
        "import os, sys\n",
        "os.chdir(CODE)\n",
        "!{sys.executable} -u kod/faz2_train_shards.py --data kdirgul/smartcore-v1-data --hf --d_model 512 --n_layer 12 --seq_len 1024 --micro_batch 8 --steps 40 --device cuda"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1OTalOixaxSg"
      },
      "source": [
        "## Başarı kriterleri\n",
        "- loss ~10.8'den (=ln 48000) düşüyor (40 adımda hafif düşüş normal — gerçek veri).\n",
        "- grad_norm stabil (clip=1.0 ile sıçramalar kontrol altında).\n",
        "- NaN/OOM yok.\n",
        "- A100'de throughput makul.\n",
        "\n",
        "## Sıradaki: Faz 3 (tam pretraining)\n",
        "1. **Fork/Triton entegrasyonu:** minimal model yerine `mamba-og` (Triton SISO kernel) → ~10-50× hız.\n",
        "   `referans_kod/model.py` (Mamba3LM) + GQA katmanları (`mamba_ssm.modules.mha`) + bf16.\n",
        "2. Tam config: d_model=768, n_layer=20, seq=2048, batch ~0.5M token (grad accum), WSD, 12B token.\n",
        "3. Checkpoint + async HF push + `--resume` (Colab session). `referans_kod/train_180m.py` harness temel alınır.\n",
        "4. Faz 4 decay + Faz 5 eval (TR panel + EN panel)."
      ]
    }
  ],
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