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"_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } } } } }, "cells": [ { "cell_type": "code", "source": [ "try:\n", " !pip uninstall -qy geometricvocab geofractal\n", "except:\n", " pass\n", "\n", "!pip install -q git+https://github.com/AbstractEyes/geofractal.git" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "LhqMG1Ayd6W6", "outputId": "77755fa5-ee9f-4908-ed49-3a36836be070" }, "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[33mWARNING: Skipping geometricvocab as it is not installed.\u001b[0m\u001b[33m\n", "\u001b[0m\u001b[33mWARNING: Skipping geofractal as it is not installed.\u001b[0m\u001b[33m\n", "\u001b[0m Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", " Building wheel for geofractal (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", " Building wheel for geometricvocab (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n" ] } ] }, { "cell_type": "markdown", "source": [], "metadata": { "id": "HziRthapehOa" } }, { "cell_type": "code", "source": [ "# Cell: The Galaxy Brain Collective - Complete Setup\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "from transformers import BertTokenizer, BertModel, T5Tokenizer, T5EncoderModel\n", "from datasets import load_dataset\n", "from torch.utils.data import DataLoader\n", "from geofractal.router.head import build_standard_head, HeadConfig\n", "from tqdm.auto import tqdm\n", "import re\n", "import sympy\n", "from sympy.parsing.sympy_parser import parse_expr\n", "\n", "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", "print(f\"Device: {device}\")\n", "\n", "# ============================================================================\n", "# FROZEN SEMANTIC BACKBONES\n", "# ============================================================================\n", "\n", "# MathBERT - understands mathematical notation\n", "mathbert_tokenizer = BertTokenizer.from_pretrained(\"tbs17/MathBERT\")\n", "mathbert = BertModel.from_pretrained(\"tbs17/MathBERT\").to(device)\n", "mathbert.eval()\n", "for p in mathbert.parameters():\n", " p.requires_grad = False\n", "\n", "# T5-base - general language understanding\n", "t5_tokenizer = T5Tokenizer.from_pretrained(\"t5-base\")\n", "t5 = T5EncoderModel.from_pretrained(\"t5-base\").to(device)\n", "t5.eval()\n", "for p in t5.parameters():\n", " p.requires_grad = False\n", "\n", "print(f\"MathBERT hidden: {mathbert.config.hidden_size}\")\n", "print(f\"T5 hidden: {t5.config.d_model}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 426, "referenced_widgets": [ "4a7675298b354c82b369a728eb707456", "493ac6f6260c4bc08705ebd3970edab8", "764d5d6529de4fb2ab4022bf436638af", "bea62837b6484e22baba12fa07542d25", "8461c61fd06a431eb090a24ff6fdec24", "b1ce8473b3274ba39ad611bda446db8b", "234f185572b74b39b9fd18a4fc8be245", "7ce7886ce5ef452f89745818e429b1fd", "b9995f64f567411cb5b9f865f334c937", "05fea5a1e49946d4b15eb94850ac53c3", "fbd50ee1e0584c8b8a9c691ae56db0d6", "f754a256ba6a4e97b0ba1df610876d9b", "229249f86f9c41e99e8f77468d12af36", "1efacfd8ad37407f85fb1fe36b1ffb76", "0b3e55dd0ee24ba9a64b5be42529ad7b", "11e39c3486d847fd9761d72d89c3337e", "4a84bf2b4b6f48d3ba945f5f9dd0310e", "afcf3caee8ca46af9232b94299ed285a", "bb573b05244641e59a80165aab5bc61d", "320a508bbf1b4af8a3357da2bc1640e4", "b390b273113d4d269b147daeddd4588c", "9b23472a0b014eed9914aa0268960f02", "c8bd7a3de96d4402adf5cec8645f7bc5", "967459450385433ab154c3ccd386ecc9", "0ec19cd334944a8787edc113d32e2b65", "39d2e7f5cf524851b291e5ea1f66c454", "ff10a617ccf0445da71e5e813a73ca25", "f053a19f85724c06b4d3b139ee946e4b", "2bb6cdc617f34e0fbc4c7ee7229efd6c", "6ad71323f72b4d3185138f56fb321f4f", "cdbdbbd2941c4e5f8deecbf77f567de8", "cf763b26a8384fc7afd75342168decb7", "9f4ecb73fd58430b95118b4bb46678ff", "933846ff883a415fac94f2bf82c39d76", "8e5b12b1c08240279f323ef98acdbff1", "ed382aee3e6746e080a0f3fb4b66c7f1", "8fff6753ec2e4f518f0a42e119d3771c", "a38e6a374ee54eb88614d0745cd0acc7", "439740082b294bc0808f421b90a6cdfe", "81af3ae35e9e44c0b78d2eb403d35fa3", "2d307cd421a44f79b4f14a343c65efb8", "1e4b3f75aa214814bfdf6daeee4de4d0", "b2bdd47b51644a35afc9f4991768d5a6", "f26cbd0029c14556a977431a8a11403a", "b328f58866cc495e90acadbd02879663", "76e1313a6e3043a1bdef3fab5da69d47", "f32ff73bf680401fb3fee8ea93b2194f", "2ca54082268a4c6d83ed02234b5b32c8", "8fa79f86964a4c7ab9aa44777709dc81", "399dc3d3f1c7483ab51ee1adbe4a827b", "b00e5955b5e249cb9857e6c18c682499", "a6e822ced4564e36987195f2fb5314cf", "0b5bfd7893e045409ffe2d25fd8947f5", "d5f9c049c1dc40918ead5efe5453580d", "a4abeb04fcb84def97c2f7d9bb1fc99f", "f076982c14c2418da0bd7d45cc97797d", "c43c9280c4ff40fd8c294c09ef689a33", "06bdd79cbad0487d85f8acab27c82bb7", "4b0b06638ebb45008bc1678094584efe", "4455d980cd964ea292c76fef983596ca", "5688d7e1376844eeb2b21afb5165ca64", "c1e7e95881ec4785a5d5fd8396afefc4", "7b5b78c68041454b956fa4dcbb75ad30", "a4938f5d9590473bbe6faad5f0d6fe7b", "bbf3396ea3cd43fa85c53e3e8fcdfd9e", "bce80349982a4915a06089b78c61b663", "35cc34cab6904b978dfa6663cf4ea1b7", "6078be45cc954a8cb705b65c650641ab", "64cd307b8ba14ddea4cd45dcc213b148", "162d9095994d45f88325c7648030e330", "983bd75cc68d43e6940b5be1212ce638", "0deeadf1d39640b3b88d6a2db2a6723e", "8a22c81242834739861110a40b06c651", "6056d2b46c464d7cba6f3c5223252461", "239b58f4573d4c25bb48222f73946622", "082154fb8a1847179364b33fd6805df2", "7de15d44d1104f3e92a474d393dd9a19", "a3b5aadb6df14dedb65f612834845659", "926095284dc54ff6ae418bbed1451787", "63578edaadff4c5f91e25d28252ed115", "ca5b2976e1f646508c2238ff57c2dc54", "b34dffa98ee641e2906d26361b4db854", "5b52b22749c34e8498d222c68ba5f8ea", "b9f87de29f794d75bfdf76a4d5727ce0", "d9e25177ddf94b6da47b43e0a2dc0a89", "7679c7208c9545a0b5f6b5b97f78dd76", "51b0848d8db9488da2399c870bb8bfc8", "508d114d655641af908db96f605aa71c", "a1d8b16c8f67427db121d5ea3d34da1b", "4a413464418c49ca9647d862595f8910", "bdea4d1b08c54e748b29f2c0455cb952", "f872caea91a64c62af23b60571c28493", "894a6f947fff4f2e8567deb220cdfbc5", "b0790dbcf7db4f10b06d8b09f2bc4ab5", "88d5087598d9427aba65fcffb674e75e", "41f6d4c636594d219311a32760a3ddb0", "a58aeded57e84a0c819154d727311d3d", "fdc8898bc6f34f0eb3d4d7d4e9ac2ced", "18200c5e730346469433dcce598a0853", "e38b7c6ddfcb4cd98bb948503b3a6ff9", "caf14099991a4e56847b38ccacca8a11", "21d7d65183284b1aad262502855cddaa", "68dd457325a74bec983aefee3d045d37", "9c86634178514469b78fd6ff1d1d54c7", "a23a917823ea4e92a098ff4daa977285", "a5079052a44f43fc8c60c5ab7fb1fc9b", "5a5df5f3c0e044aebffb56d154006482", "2d036df8953e4b798d27915d510c7a66", "3b5c529349d44d1e8ada70f113a2b2e5", "b9fca78f001a44aaa630cff29db25abd" ] }, "id": "nec0CjX_efa2", "outputId": "421b84d3-f4da-47f5-ebd0-586ec80efe1d" }, "execution_count": 2, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Device: cuda\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "tokenizer_config.json: 0%| | 0.00/28.0 [00:00. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "model.safetensors: 0%| | 0.00/892M [00:00= self.max_features:\n", " eigen_features = eigenvalues[:, -self.max_features:]\n", " else:\n", " padding = torch.zeros(B, self.max_features - D, device=embeddings.device)\n", " eigen_features = torch.cat([padding, eigenvalues], dim=-1)\n", "\n", " # Expand to sequence and project\n", " eigen_seq = eigen_features.unsqueeze(1).expand(-1, S, -1)\n", " return self.projection(eigen_seq)\n", "\n", "\n", "class CayleyMengerStream(nn.Module):\n", " \"\"\"\n", " Computes Cayley-Menger determinants for simplex volumes.\n", " DETERMINISTIC - captures distance geometry.\n", " \"\"\"\n", " def __init__(self, output_dim, num_features=64):\n", " super().__init__()\n", " self.num_features = num_features\n", " self.projection = nn.Linear(num_features, output_dim)\n", "\n", " def forward(self, embeddings):\n", " B, S, D = embeddings.shape\n", "\n", " # Sample points for simplex computation\n", " k = min(self.num_features, S)\n", " indices = torch.linspace(0, S-1, k).long().to(embeddings.device)\n", " sampled = embeddings[:, indices, :] # [B, k, D]\n", "\n", " # Pairwise distances\n", " dists = torch.cdist(sampled, sampled) # [B, k, k]\n", "\n", " # Distance matrix eigenspectrum as proxy for CM geometry\n", " dist_sq = dists ** 2\n", " eigenvalues = torch.linalg.eigvalsh(dist_sq) # [B, k]\n", "\n", " # Normalize\n", " cm_features = eigenvalues / (eigenvalues.abs().sum(dim=-1, keepdim=True) + 1e-8)\n", "\n", " # Pad to fixed size\n", " if k < self.num_features:\n", " padding = torch.zeros(B, self.num_features - k, device=embeddings.device)\n", " cm_features = torch.cat([cm_features, padding], dim=-1)\n", "\n", " # Expand and project\n", " cm_seq = cm_features.unsqueeze(1).expand(-1, S, -1)\n", " return self.projection(cm_seq)\n", "\n", "\n", "class SymbolicCalculatorStream(nn.Module):\n", " \"\"\"\n", " Extracts numbers from text and computes features.\n", " DETERMINISTIC - actual arithmetic.\n", " \"\"\"\n", " def __init__(self, output_dim, max_numbers=10):\n", " super().__init__()\n", " self.max_numbers = max_numbers\n", " self.num_features = 12\n", " self.projection = nn.Linear(self.num_features, output_dim)\n", "\n", " def extract_numbers(self, text):\n", " pattern = r'-?\\d+\\.?\\d*'\n", " matches = re.findall(pattern, text)\n", " numbers = [float(m) for m in matches if m not in ['-', '.']]\n", " return numbers[:self.max_numbers]\n", "\n", " def compute_features(self, numbers):\n", " if len(numbers) == 0:\n", " return torch.zeros(self.num_features)\n", "\n", " t = torch.tensor(numbers, dtype=torch.float32)\n", "\n", " features = [\n", " len(numbers),\n", " t.sum().item(),\n", " t.prod().item() if len(t) < 10 else 0,\n", " t.mean().item(),\n", " t.std().item() if len(t) > 1 else 0,\n", " t.min().item(),\n", " t.max().item(),\n", " (t.max() - t.min()).item(),\n", " (t > 0).sum().item(),\n", " (t < 0).sum().item(),\n", " (t == 0).sum().item(),\n", " t.abs().mean().item(),\n", " ]\n", "\n", " return torch.tensor(features, dtype=torch.float32)\n", "\n", " def forward(self, texts, seq_len):\n", " batch_features = []\n", " for text in texts:\n", " numbers = self.extract_numbers(text)\n", " features = self.compute_features(numbers)\n", " batch_features.append(features)\n", "\n", " features = torch.stack(batch_features).to(next(self.parameters()).device)\n", " features_seq = features.unsqueeze(1).expand(-1, seq_len, -1)\n", " return self.projection(features_seq)\n", "\n", "\n", "class FractalDimensionStream(nn.Module):\n", " \"\"\"\n", " Estimates fractal/correlation dimension of embeddings.\n", " DETERMINISTIC - geometric complexity measure.\n", " \"\"\"\n", " def __init__(self, output_dim, num_scales=8):\n", " super().__init__()\n", " self.num_scales = num_scales\n", " self.projection = nn.Linear(num_scales * 2, output_dim)\n", "\n", " def forward(self, embeddings):\n", " B, S, D = embeddings.shape\n", "\n", " # Compute pairwise distances\n", " dists = torch.cdist(embeddings, embeddings) # [B, S, S]\n", "\n", " # Correlation dimension at multiple scales\n", " scales = torch.logspace(-2, 1, self.num_scales).to(embeddings.device)\n", "\n", " features = []\n", " for scale in scales:\n", " count = (dists < scale).float().sum(dim=(1, 2)) / (S * S)\n", " features.append(count)\n", " features.append(torch.log(count + 1e-8))\n", "\n", " fractal_features = torch.stack(features, dim=-1) # [B, num_scales*2]\n", " fractal_seq = fractal_features.unsqueeze(1).expand(-1, S, -1)\n", " return self.projection(fractal_seq)\n", "\n", "\n", "print(\"✓ Fixed deterministic streams defined\")\n", "\n", "print(\"Deterministic streams defined:\")\n", "print(\" - EigenspectrumStream: covariance geometry\")\n", "print(\" - CayleyMengerStream: distance geometry\")\n", "print(\" - SymbolicCalculatorStream: actual arithmetic\")\n", "print(\" - FractalDimensionStream: complexity measure\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "m3STWcCxemhm", "outputId": "1ae0045d-7296-461e-ce98-61cfee96bf3e" }, "execution_count": 11, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "✓ Fixed deterministic streams defined\n", "Deterministic streams defined:\n", " - EigenspectrumStream: covariance geometry\n", " - CayleyMengerStream: distance geometry\n", " - SymbolicCalculatorStream: actual arithmetic\n", " - FractalDimensionStream: complexity measure\n" ] } ] }, { "cell_type": "code", "source": [ "# Cell: Patch the combiner issue\n", "from geofractal.router.head.components import LearnableWeightCombiner, GatedCombiner, HeadConfig\n", "from geofractal.router.head.builder import HeadBuilder, ComposedHead\n", "from geofractal.router.head.components import (\n", " CantorAttention, TopKRouter, ConstitutiveAnchorBank,\n", " FingerprintGate, FFNRefinement\n", ")\n", "\n", "def fixed_build(self) -> ComposedHead:\n", " \"\"\"Build the composed head - FIXED.\"\"\"\n", " attention = self._build_component(\n", " self._attention_cls, self._attention_instance,\n", " self._attention_kwargs, CantorAttention\n", " )\n", " router = self._build_component(\n", " self._router_cls, self._router_instance,\n", " self._router_kwargs, TopKRouter\n", " )\n", " anchors = self._build_component(\n", " self._anchor_cls, self._anchor_instance,\n", " self._anchor_kwargs, ConstitutiveAnchorBank\n", " )\n", " gate = self._build_component(\n", " self._gate_cls, self._gate_instance,\n", " self._gate_kwargs, FingerprintGate\n", " )\n", "\n", " # FIX: Pass only config to combiner, ignore signal_names\n", " combiner_cls = self._combiner_cls or LearnableWeightCombiner\n", " if self._combiner_instance is not None:\n", " combiner = self._combiner_instance\n", " else:\n", " combiner = combiner_cls(self.config)\n", "\n", " refinement = self._build_component(\n", " self._refinement_cls, self._refinement_instance,\n", " self._refinement_kwargs, FFNRefinement\n", " )\n", "\n", " return ComposedHead(\n", " config=self.config,\n", " attention=attention,\n", " router=router,\n", " anchors=anchors,\n", " gate=gate,\n", " combiner=combiner,\n", " refinement=refinement,\n", " )\n", "\n", "HeadBuilder.build = fixed_build\n", "print(\"✓ HeadBuilder.build patched\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Mh2iyuLBf5co", "outputId": "27b3caab-66df-4954-8162-1fae29399487" }, "execution_count": 7, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "✓ HeadBuilder.build patched\n" ] } ] }, { "cell_type": "code", "source": [ "# Cell: Build the Galaxy Brain Collective\n", "# ============================================================================\n", "# ROUTING HEADS & FUSION\n", "# ============================================================================\n", "# Cell: Rebuild with fixed streams\n", "ROUTE_DIM = 512\n", "\n", "# Projections\n", "proj_mathbert = nn.Linear(768, ROUTE_DIM).to(device)\n", "proj_t5 = nn.Linear(768, ROUTE_DIM).to(device)\n", "\n", "# Fixed deterministic streams\n", "eigen_stream = EigenspectrumStream(output_dim=ROUTE_DIM, max_features=64).to(device)\n", "cayley_stream = CayleyMengerStream(output_dim=ROUTE_DIM, num_features=64).to(device)\n", "symbolic_stream = SymbolicCalculatorStream(output_dim=ROUTE_DIM).to(device)\n", "fractal_stream = FractalDimensionStream(output_dim=ROUTE_DIM, num_scales=8).to(device)\n", "\n", "# Routing heads\n", "head_config = HeadConfig(feature_dim=ROUTE_DIM, fingerprint_dim=64, num_anchors=16, num_routes=4)\n", "head_mathbert = build_standard_head(head_config).to(device)\n", "head_t5 = build_standard_head(head_config).to(device)\n", "head_eigen = build_standard_head(head_config).to(device)\n", "head_cayley = build_standard_head(head_config).to(device)\n", "head_symbolic = build_standard_head(head_config).to(device)\n", "head_fractal = build_standard_head(head_config).to(device)\n", "\n", "# 6-way fusion\n", "fusion = nn.Sequential(\n", " nn.Linear(ROUTE_DIM * 6, ROUTE_DIM * 2),\n", " nn.LayerNorm(ROUTE_DIM * 2),\n", " nn.GELU(),\n", " nn.Dropout(0.1),\n", " nn.Linear(ROUTE_DIM * 2, ROUTE_DIM),\n", " nn.LayerNorm(ROUTE_DIM),\n", " nn.GELU(),\n", ").to(device)\n", "\n", "# Classifiers\n", "NUM_BUCKETS = 20\n", "classifier = nn.Sequential(\n", " nn.Linear(ROUTE_DIM, 256),\n", " nn.GELU(),\n", " nn.Dropout(0.1),\n", " nn.Linear(256, NUM_BUCKETS),\n", ").to(device)\n", "\n", "classifier_mathbert = nn.Linear(ROUTE_DIM, NUM_BUCKETS).to(device)\n", "classifier_t5 = nn.Linear(ROUTE_DIM, NUM_BUCKETS).to(device)\n", "classifier_eigen = nn.Linear(ROUTE_DIM, NUM_BUCKETS).to(device)\n", "classifier_cayley = nn.Linear(ROUTE_DIM, NUM_BUCKETS).to(device)\n", "classifier_symbolic = nn.Linear(ROUTE_DIM, NUM_BUCKETS).to(device)\n", "classifier_fractal = nn.Linear(ROUTE_DIM, NUM_BUCKETS).to(device)\n", "\n", "# Optimizer\n", "trainable = (\n", " list(proj_mathbert.parameters()) + list(proj_t5.parameters()) +\n", " list(eigen_stream.parameters()) + list(cayley_stream.parameters()) +\n", " list(symbolic_stream.parameters()) + list(fractal_stream.parameters()) +\n", " list(head_mathbert.parameters()) + list(head_t5.parameters()) +\n", " list(head_eigen.parameters()) + list(head_cayley.parameters()) +\n", " list(head_symbolic.parameters()) + list(head_fractal.parameters()) +\n", " list(fusion.parameters()) + list(classifier.parameters()) +\n", " list(classifier_mathbert.parameters()) + list(classifier_t5.parameters()) +\n", " list(classifier_eigen.parameters()) + list(classifier_cayley.parameters()) +\n", " list(classifier_symbolic.parameters()) + list(classifier_fractal.parameters())\n", ")\n", "\n", "optimizer = torch.optim.AdamW(trainable, lr=2e-4, weight_decay=0.01)\n", "criterion = nn.CrossEntropyLoss()\n", "\n", "print(f\"✓ Rebuilt Galaxy Brain Collective\")\n", "print(f\" Trainable params: {sum(p.numel() for p in trainable):,}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "R5ZTLUREfrJt", "outputId": "652ecea2-b060-4e4d-e67b-ac818b16b678" }, "execution_count": 13, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "✓ Rebuilt Galaxy Brain Collective\n", " Trainable params: 27,328,884\n" ] } ] }, { "cell_type": "code", "source": [ "# Cell: Load and preprocess GSM8K\n", "dataset = load_dataset(\"openai/gsm8k\", \"main\")\n", "\n", "print(f\"Train: {len(dataset['train'])}\")\n", "print(f\"Test: {len(dataset['test'])}\")\n", "\n", "# Sample\n", "sample = dataset['train'][0]\n", "print(f\"\\nQuestion: {sample['question'][:200]}...\")\n", "print(f\"Answer: {sample['answer'][-100:]}\")\n", "\n", "def extract_final_answer(answer_text):\n", " \"\"\"Extract the numeric answer from GSM8K format.\"\"\"\n", " # GSM8K answers end with #### followed by the number\n", " match = re.search(r'####\\s*(-?\\d+\\.?\\d*)', answer_text)\n", " if match:\n", " return float(match.group(1))\n", " return None\n", "\n", "# Compute answer buckets\n", "answers = [extract_final_answer(ex['answer']) for ex in dataset['train']]\n", "answers = [a for a in answers if a is not None]\n", "\n", "import numpy as np\n", "percentiles = np.percentile(answers, np.linspace(0, 100, NUM_BUCKETS + 1))\n", "print(f\"\\nAnswer range: {min(answers)} to {max(answers)}\")\n", "print(f\"Bucket boundaries: {percentiles[:5]}... {percentiles[-5:]}\")\n", "\n", "def answer_to_bucket(answer):\n", " \"\"\"Convert numeric answer to bucket index.\"\"\"\n", " for i, (low, high) in enumerate(zip(percentiles[:-1], percentiles[1:])):\n", " if answer <= high:\n", " return i\n", " return NUM_BUCKETS - 1" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "KvQlMTwwerGK", "outputId": "8cdddad8-c78b-44c6-b2d1-7c75285d3b0d" }, "execution_count": 14, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Train: 7473\n", "Test: 1319\n", "\n", "Question: Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether in April and May?...\n", "Answer: =24>>24 clips in May.\n", "Natalia sold 48+24 = <<48+24=72>>72 clips altogether in April and May.\n", "#### 72\n", "\n", "Answer range: -47.0 to 192000000.0\n", "Bucket boundaries: [-47. 3. 5. 7. 10.]... [2.5000e+02 4.2000e+02 9.0000e+02 2.9304e+03 1.9200e+08]\n" ] } ] }, { "cell_type": "code", "source": [ "# Cell: Forward pass\n", "def forward_galaxy_brain(questions, answers_text):\n", " \"\"\"Full forward pass through all 6 streams.\"\"\"\n", " B = len(questions)\n", "\n", " # === ENCODE WITH SEMANTIC BACKBONES ===\n", "\n", " # MathBERT\n", " enc_mathbert = mathbert_tokenizer(questions, return_tensors=\"pt\",\n", " padding=True, truncation=True, max_length=256)\n", " with torch.no_grad():\n", " hidden_mathbert = mathbert(\n", " enc_mathbert.input_ids.to(device),\n", " attention_mask=enc_mathbert.attention_mask.to(device)\n", " ).last_hidden_state\n", "\n", " # T5\n", " enc_t5 = t5_tokenizer(questions, return_tensors=\"pt\",\n", " padding=True, truncation=True, max_length=256)\n", " with torch.no_grad():\n", " hidden_t5 = t5(\n", " enc_t5.input_ids.to(device),\n", " attention_mask=enc_t5.attention_mask.to(device)\n", " ).last_hidden_state\n", "\n", " S = hidden_mathbert.shape[1] # sequence length\n", "\n", " # === PROJECT TO COMMON SPACE ===\n", " proj_mb = proj_mathbert(hidden_mathbert) # [B, S, 512]\n", " proj_t = proj_t5(hidden_t5) # [B, S', 512]\n", "\n", " # Align sequence lengths\n", " S_min = min(proj_mb.shape[1], proj_t.shape[1])\n", " proj_mb = proj_mb[:, :S_min, :]\n", " proj_t = proj_t[:, :S_min, :]\n", "\n", " # === DETERMINISTIC STREAMS ===\n", " # These compute geometric features from the embeddings\n", " eigen_feat = eigen_stream(proj_mb) # [B, S, 512]\n", " cayley_feat = cayley_stream(proj_mb) # [B, S, 512]\n", " symbolic_feat = symbolic_stream(questions, S_min) # [B, S, 512]\n", " fractal_feat = fractal_stream(proj_mb) # [B, S, 512]\n", "\n", " # === ROUTE THROUGH HEADS ===\n", " routed_mathbert = head_mathbert(proj_mb)\n", " routed_t5 = head_t5(proj_t)\n", " routed_eigen = head_eigen(eigen_feat)\n", " routed_cayley = head_cayley(cayley_feat)\n", " routed_symbolic = head_symbolic(symbolic_feat)\n", " routed_fractal = head_fractal(fractal_feat)\n", "\n", " # === POOL (CLS token) ===\n", " pooled_mathbert = routed_mathbert[:, 0]\n", " pooled_t5 = routed_t5[:, 0]\n", " pooled_eigen = routed_eigen[:, 0]\n", " pooled_cayley = routed_cayley[:, 0]\n", " pooled_symbolic = routed_symbolic[:, 0]\n", " pooled_fractal = routed_fractal[:, 0]\n", "\n", " # === FUSE ===\n", " fused = fusion(torch.cat([\n", " pooled_mathbert, pooled_t5, pooled_eigen,\n", " pooled_cayley, pooled_symbolic, pooled_fractal\n", " ], dim=-1))\n", "\n", " # === CLASSIFY ===\n", " logits = classifier(fused)\n", "\n", " # Individual predictions\n", " ind_logits = {\n", " 'mathbert': classifier_mathbert(pooled_mathbert),\n", " 't5': classifier_t5(pooled_t5),\n", " 'eigen': classifier_eigen(pooled_eigen),\n", " 'cayley': classifier_cayley(pooled_cayley),\n", " 'symbolic': classifier_symbolic(pooled_symbolic),\n", " 'fractal': classifier_fractal(pooled_fractal),\n", " }\n", "\n", " return logits, ind_logits\n", "\n", "# Test forward\n", "test_q = [\"John has 5 apples and buys 3 more. How many apples does John have?\"]\n", "test_a = [\"John starts with 5 apples. He buys 3 more. 5 + 3 = 8. #### 8\"]\n", "logits, ind_logits = forward_galaxy_brain(test_q, test_a)\n", "print(f\"Collective output: {logits.shape}\")\n", "print(f\"Individual outputs: {[k for k in ind_logits.keys()]}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "DCgSJ1OJeuFB", "outputId": "7c784456-819b-4cdb-cc38-ce10b11cd446" }, "execution_count": 15, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Collective output: torch.Size([1, 20])\n", "Individual outputs: ['mathbert', 't5', 'eigen', 'cayley', 'symbolic', 'fractal']\n" ] } ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "1blxYR4MdxJU", "outputId": "70c98ad8-5709-4cad-bb8b-12943c1adaef" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "✓ Created repo: AbstractPhil/math_collective\n" ] } ], "source": [ "# Cell: Create HuggingFace repo and setup checkpointing\n", "from huggingface_hub import HfApi, create_repo, upload_file, upload_folder\n", "import json\n", "import os\n", "\n", "# Create the repo\n", "api = HfApi()\n", "\n", "try:\n", " create_repo(\n", " repo_id=\"AbstractPhil/math_collective\",\n", " repo_type=\"model\",\n", " exist_ok=True,\n", " private=False,\n", " )\n", " print(\"✓ Created repo: AbstractPhil/math_collective\")\n", "except Exception as e:\n", " print(f\"Repo exists or error: {e}\")\n", "\n", "# Create local checkpoint directory\n", "os.makedirs(\"/content/math_collective_checkpoints\", exist_ok=True)" ] }, { "cell_type": "code", "source": [ "# Cell: Model saving/loading utilities\n", "import torch\n", "from datetime import datetime\n", "\n", "def save_checkpoint(\n", " epoch,\n", " metrics,\n", " path=\"/content/math_collective_checkpoints\",\n", " push_to_hub=True\n", "):\n", " \"\"\"Save all trainable components.\"\"\"\n", "\n", " checkpoint = {\n", " 'epoch': epoch,\n", " 'metrics': metrics,\n", " 'timestamp': datetime.now().isoformat(),\n", "\n", " # Projections\n", " 'proj_mathbert': proj_mathbert.state_dict(),\n", " 'proj_t5': proj_t5.state_dict(),\n", "\n", " # Deterministic streams (they have learned projections)\n", " 'eigen_stream': eigen_stream.state_dict(),\n", " 'cayley_stream': cayley_stream.state_dict(),\n", " 'symbolic_stream': symbolic_stream.state_dict(),\n", " 'fractal_stream': fractal_stream.state_dict(),\n", "\n", " # Routing heads\n", " 'head_mathbert': head_mathbert.state_dict(),\n", " 'head_t5': head_t5.state_dict(),\n", " 'head_eigen': head_eigen.state_dict(),\n", " 'head_cayley': head_cayley.state_dict(),\n", " 'head_symbolic': head_symbolic.state_dict(),\n", " 'head_fractal': head_fractal.state_dict(),\n", "\n", " # Fusion and classifiers\n", " 'fusion': fusion.state_dict(),\n", " 'classifier': classifier.state_dict(),\n", " 'classifier_mathbert': classifier_mathbert.state_dict(),\n", " 'classifier_t5': classifier_t5.state_dict(),\n", " 'classifier_eigen': classifier_eigen.state_dict(),\n", " 'classifier_cayley': classifier_cayley.state_dict(),\n", " 'classifier_symbolic': classifier_symbolic.state_dict(),\n", " 'classifier_fractal': classifier_fractal.state_dict(),\n", "\n", " # Optimizer\n", " 'optimizer': optimizer.state_dict(),\n", "\n", " # Config\n", " 'config': {\n", " 'route_dim': ROUTE_DIM,\n", " 'num_buckets': NUM_BUCKETS,\n", " 'head_config': {\n", " 'feature_dim': head_config.feature_dim,\n", " 'fingerprint_dim': head_config.fingerprint_dim,\n", " 'num_anchors': head_config.num_anchors,\n", " 'num_routes': head_config.num_routes,\n", " },\n", " 'percentiles': percentiles.tolist(),\n", " }\n", " }\n", "\n", " # Save locally\n", " filename = f\"checkpoint_epoch_{epoch}.pt\"\n", " local_path = os.path.join(path, filename)\n", " torch.save(checkpoint, local_path)\n", " print(f\"✓ Saved checkpoint: {local_path}\")\n", "\n", " # Push to hub\n", " if push_to_hub:\n", " api.upload_file(\n", " path_or_fileobj=local_path,\n", " path_in_repo=f\"checkpoints/{filename}\",\n", " repo_id=\"AbstractPhil/math_collective\",\n", " repo_type=\"model\",\n", " )\n", " print(f\"✓ Pushed to hub: checkpoints/{filename}\")\n", "\n", " return local_path\n", "\n", "\n", "def load_checkpoint(path_or_epoch, from_hub=False):\n", " \"\"\"Load checkpoint from local path or hub.\"\"\"\n", "\n", " if from_hub:\n", " from huggingface_hub import hf_hub_download\n", " local_path = hf_hub_download(\n", " repo_id=\"AbstractPhil/math_collective\",\n", " filename=f\"checkpoints/checkpoint_epoch_{path_or_epoch}.pt\",\n", " repo_type=\"model\",\n", " )\n", " else:\n", " local_path = path_or_epoch\n", "\n", " checkpoint = torch.load(local_path, map_location=device)\n", "\n", " # Load all components\n", " proj_mathbert.load_state_dict(checkpoint['proj_mathbert'])\n", " proj_t5.load_state_dict(checkpoint['proj_t5'])\n", "\n", " eigen_stream.load_state_dict(checkpoint['eigen_stream'])\n", " cayley_stream.load_state_dict(checkpoint['cayley_stream'])\n", " symbolic_stream.load_state_dict(checkpoint['symbolic_stream'])\n", " fractal_stream.load_state_dict(checkpoint['fractal_stream'])\n", "\n", " head_mathbert.load_state_dict(checkpoint['head_mathbert'])\n", " head_t5.load_state_dict(checkpoint['head_t5'])\n", " head_eigen.load_state_dict(checkpoint['head_eigen'])\n", " head_cayley.load_state_dict(checkpoint['head_cayley'])\n", " head_symbolic.load_state_dict(checkpoint['head_symbolic'])\n", " head_fractal.load_state_dict(checkpoint['head_fractal'])\n", "\n", " fusion.load_state_dict(checkpoint['fusion'])\n", " classifier.load_state_dict(checkpoint['classifier'])\n", " classifier_mathbert.load_state_dict(checkpoint['classifier_mathbert'])\n", " classifier_t5.load_state_dict(checkpoint['classifier_t5'])\n", " classifier_eigen.load_state_dict(checkpoint['classifier_eigen'])\n", " classifier_cayley.load_state_dict(checkpoint['classifier_cayley'])\n", " classifier_symbolic.load_state_dict(checkpoint['classifier_symbolic'])\n", " classifier_fractal.load_state_dict(checkpoint['classifier_fractal'])\n", "\n", " optimizer.load_state_dict(checkpoint['optimizer'])\n", "\n", " print(f\"✓ Loaded checkpoint from epoch {checkpoint['epoch']}\")\n", " print(f\" Metrics: {checkpoint['metrics']}\")\n", "\n", " return checkpoint\n", "\n", "print(\"✓ Checkpoint utilities defined\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "HG3NLzo9d_im", "outputId": "d5845183-8253-4f90-98d5-64a7abacca7c" }, "execution_count": 17, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "✓ Checkpoint utilities defined\n" ] } ] }, { "cell_type": "code", "source": [ "# Cell: Create model card and push\n", "model_card = \"\"\"---\n", "license: apache-2.0\n", "tags:\n", " - geofractal-router\n", " - collective-intelligence\n", " - math-reasoning\n", " - multi-stream\n", " - emergence\n", " - routing\n", "datasets:\n", " - openai/gsm8k\n", "base_model:\n", " - tbs17/MathBERT\n", " - google-t5/t5-base\n", "pipeline_tag: text-classification\n", "---\n", "\n", "# Math Collective - Galaxy Brain Router\n", "\n", "**6-stream collective intelligence system for mathematical reasoning.**\n", "\n", "## Architecture\n", "```\n", "┌─────────────────────────────────────────────────────────────┐\n", "│ GALAXY BRAIN COLLECTIVE │\n", "├─────────────────────────────────────────────────────────────┤\n", "│ FUZZY STREAMS (learned representations) │\n", "│ ├── MathBERT (frozen) → Head A → \"symbolic understanding\" │\n", "│ └── T5-base (frozen) → Head B → \"linguistic reasoning\" │\n", "│ │\n", "│ DETERMINISTIC STREAMS (pure computation) │\n", "│ ├── Eigenspectrum → Head C → \"covariance geometry\" │\n", "│ ├── Cayley-Menger → Head D → \"distance geometry\" │\n", "│ ├── Symbolic Calc → Head E → \"actual arithmetic\" │\n", "│ └── Fractal Dim → Head F → \"complexity measure\" │\n", "│ │\n", "│ All 6 streams → Fusion → Classifier │\n", "└─────────────────────────────────────────────────────────────┘\n", "```\n", "\n", "## Key Innovation\n", "\n", "**Fuzzy + Deterministic Routing**\n", "\n", "The router learns WHEN to trust each stream:\n", "- Simple arithmetic? Trust the Symbolic Calculator (deterministic)\n", "- Complex word problem? Trust MathBERT/T5 (semantic)\n", "- Ambiguous? Triangulate across all 6 perspectives\n", "\n", "## Streams\n", "\n", "| Stream | Type | Source | Purpose |\n", "|--------|------|--------|---------|\n", "| MathBERT | Fuzzy | tbs17/MathBERT (frozen) | Mathematical notation understanding |\n", "| T5-base | Fuzzy | t5-base (frozen) | General language reasoning |\n", "| Eigenspectrum | Deterministic | Covariance eigenvalues | Geometric structure of embeddings |\n", "| Cayley-Menger | Deterministic | Distance matrix geometry | Simplex volume features |\n", "| Symbolic | Deterministic | Regex + arithmetic | Actual number extraction & computation |\n", "| Fractal | Deterministic | Correlation dimension | Problem complexity measure |\n", "\n", "## Training\n", "\n", "- **Dataset**: GSM8K (Grade School Math 8K)\n", "- **Task**: Answer magnitude bucket prediction (20 buckets)\n", "- **Frozen params**: ~330M (MathBERT + T5)\n", "- **Trainable params**: ~15M (routing heads, fusion, projections)\n", "\n", "## Emergence Metric (ρ)\n", "```\n", "ρ = collective_accuracy / max(individual_accuracies)\n", "\n", "ρ > 1.0 = emergence (collective outperforms best individual)\n", "```\n", "\n", "## Usage\n", "```python\n", "from huggingface_hub import hf_hub_download\n", "import torch\n", "\n", "# Download checkpoint\n", "checkpoint_path = hf_hub_download(\n", " repo_id=\"AbstractPhil/math_collective\",\n", " filename=\"checkpoints/checkpoint_epoch_5.pt\"\n", ")\n", "\n", "# Load and use (see geofractal-router for full implementation)\n", "checkpoint = torch.load(checkpoint_path)\n", "print(f\"Epoch: {checkpoint['epoch']}\")\n", "print(f\"Metrics: {checkpoint['metrics']}\")\n", "```\n", "\n", "## Related\n", "\n", "- **Framework**: [AbstractPhil/geofractal_router](https://huggingface.co/AbstractPhil/geofractal_router)\n", "- **Paper**: Coming soon\n", "- **Code**: [GitHub - geofractal](https://github.com/AbstractEyes/geofractal)\n", "\n", "## Citation\n", "```bibtex\n", "@misc{abstractphil2025mathcollective,\n", " title={Math Collective: Galaxy Brain Routing for Mathematical Reasoning},\n", " author={AbstractPhil},\n", " year={2025},\n", " publisher={Hugging Face},\n", " url={https://huggingface.co/AbstractPhil/math_collective}\n", "}\n", "```\n", "\n", "## License\n", "\n", "Apache 2.0\n", "\"\"\"\n", "\n", "# Save and push model card\n", "with open(\"/content/math_collective_checkpoints/README.md\", \"w\") as f:\n", " f.write(model_card)\n", "\n", "api.upload_file(\n", " path_or_fileobj=\"/content/math_collective_checkpoints/README.md\",\n", " path_in_repo=\"README.md\",\n", " repo_id=\"AbstractPhil/math_collective\",\n", " repo_type=\"model\",\n", ")\n", "print(\"✓ Model card pushed to hub\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "9TP-FhideBsS", "outputId": "61be7dcf-f0bd-4c8a-fd40-27863d653462" }, "execution_count": 18, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "✓ Model card pushed to hub\n" ] } ] }, { "cell_type": "code", "source": [ "# Cell: Training loop with checkpointing\n", "def collate_fn(examples):\n", " return {\n", " 'question': [ex['question'] for ex in examples],\n", " 'answer': [ex['answer'] for ex in examples],\n", " }\n", "\n", "train_loader = DataLoader(dataset['train'], batch_size=16, shuffle=True, collate_fn=collate_fn)\n", "test_loader = DataLoader(dataset['test'], batch_size=16, shuffle=False, collate_fn=collate_fn)\n", "\n", "EPOCHS = 5\n", "history = []\n", "\n", "for epoch in range(EPOCHS):\n", " # Train\n", " for h in [head_mathbert, head_t5, head_eigen, head_cayley, head_symbolic, head_fractal]:\n", " h.train()\n", " fusion.train(); classifier.train()\n", "\n", " correct, total = 0, 0\n", " pbar = tqdm(train_loader, desc=f\"Epoch {epoch+1}\")\n", "\n", " for batch in pbar:\n", " questions = batch['question']\n", " answers_text = batch['answer']\n", "\n", " # Extract labels\n", " labels = []\n", " for ans in answers_text:\n", " num = extract_final_answer(ans)\n", " if num is not None:\n", " labels.append(answer_to_bucket(num))\n", " else:\n", " labels.append(0)\n", " labels = torch.tensor(labels).to(device)\n", "\n", " optimizer.zero_grad()\n", " logits, ind_logits = forward_galaxy_brain(questions, answers_text)\n", "\n", " # Collective loss\n", " loss = criterion(logits, labels)\n", "\n", " # Individual supervision (light)\n", " for name, ind_log in ind_logits.items():\n", " loss += 0.05 * criterion(ind_log, labels)\n", "\n", " loss.backward()\n", " optimizer.step()\n", "\n", " correct += (logits.argmax(-1) == labels).sum().item()\n", " total += labels.size(0)\n", " pbar.set_postfix({'acc': f'{correct/total:.1%}'})\n", "\n", " # Eval\n", " for h in [head_mathbert, head_t5, head_eigen, head_cayley, head_symbolic, head_fractal]:\n", " h.eval()\n", " fusion.eval(); classifier.eval()\n", "\n", " metrics = {name: 0 for name in ['collective', 'mathbert', 't5', 'eigen', 'cayley', 'symbolic', 'fractal']}\n", " val_total = 0\n", "\n", " with torch.no_grad():\n", " for batch in tqdm(test_loader, desc=\"Validating\"):\n", " questions = batch['question']\n", " answers_text = batch['answer']\n", "\n", " labels = []\n", " for ans in answers_text:\n", " num = extract_final_answer(ans)\n", " if num is not None:\n", " labels.append(answer_to_bucket(num))\n", " else:\n", " labels.append(0)\n", " labels = torch.tensor(labels).to(device)\n", "\n", " logits, ind_logits = forward_galaxy_brain(questions, answers_text)\n", "\n", " metrics['collective'] += (logits.argmax(-1) == labels).sum().item()\n", " for name, ind_log in ind_logits.items():\n", " metrics[name] += (ind_log.argmax(-1) == labels).sum().item()\n", " val_total += labels.size(0)\n", "\n", " # Compute accuracies\n", " accs = {k: v / val_total for k, v in metrics.items()}\n", " max_ind = max(v for k, v in accs.items() if k != 'collective')\n", " rho = accs['collective'] / max_ind if max_ind > 0 else 0\n", "\n", " epoch_metrics = {**accs, 'rho': rho, 'epoch': epoch + 1}\n", " history.append(epoch_metrics)\n", "\n", " print(f\"\\nEpoch {epoch+1} Results:\")\n", " print(f\" Collective: {accs['collective']:.1%}\")\n", " print(f\" MathBERT: {accs['mathbert']:.1%}, T5: {accs['t5']:.1%}\")\n", " print(f\" Eigen: {accs['eigen']:.1%}, Cayley: {accs['cayley']:.1%}\")\n", " print(f\" Symbolic: {accs['symbolic']:.1%}, Fractal: {accs['fractal']:.1%}\")\n", " print(f\" ρ = {rho:.3f}\")\n", "\n", " # Save checkpoint after each epoch\n", " save_checkpoint(epoch + 1, epoch_metrics, push_to_hub=True)\n", "\n", "# Save training history\n", "history_path = \"/content/math_collective_checkpoints/training_history.json\"\n", "with open(history_path, \"w\") as f:\n", " json.dump(history, f, indent=2)\n", "\n", "api.upload_file(\n", " path_or_fileobj=history_path,\n", " path_in_repo=\"training_history.json\",\n", " repo_id=\"AbstractPhil/math_collective\",\n", " repo_type=\"model\",\n", ")\n", "print(\"✓ Training history pushed to hub\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 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