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\n\nA 40M-parameter language model that replaces self-attention with O(n) cognitive routing and hierarchical memory — trained entirely on CPU.
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- Overview \n
- Architecture \n
- Model Specifications \n
- Quick Start \n
- Training \n
- Inference \n
- Results \n
- Development Story \n
- Citation \n
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CogNet is a proof-of-concept language model that eliminates self-attention entirely, replacing it with a cognitive routing mechanism inspired by human memory systems. The model processes sequences in O(n) time instead of the O(n²) complexity of standard Transformers, while maintaining competitive perplexity through:
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- Cognitive Routing: A learned coherence scoring mechanism that routes information through channels without quadratic attention \n
- Hierarchical Memory: A 3-tier key-value memory system (Working → Episodic → Semantic) inspired by cognitive science \n
- Adaptive Computation: Variable-depth processing blocks that allocate more compute to complex tokens \n
- Compositional Reasoning: Hyperdimensional computing for role-filler binding operations \n
The entire model was trained from scratch on a CPU-only machine with 7.5GB RAM, demonstrating that novel architectures can be developed and validated without GPU resources.
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Input → TokenEncoder → [AdaptiveComputationBlock × 6] → OutputHead\n │\n ├── CognitiveChannel × 6 (O(n) per channel)\n │ ├── Depthwise Separable Conv\n │ └── SwiGLU FFN\n │\n ├── CoherenceRouter (O(n) routing)\n │ └── Learned coherence scoring\n │\n ├── SharedHierarchicalMemory (3-tier)\n │ ├── Working Memory (32 slots)\n │ ├── Episodic Memory (64 slots)\n │ └── Semantic Memory (128 slots)\n │\n └── CompositionalReasoner\n └── Hyperdimensional binding\n| Mechanism | \nTransformer | \nCogNet | \n
|---|---|---|
| Sequence mixing | \nSelf-Attention (O(n²)) | \nCognitive Routing (O(n)) | \n
| Memory | \nFixed context window | \nHierarchical growing memory | \n
| Computation | \nUniform per token | \nAdaptive per token | \n
| Position info | \nSinusoidal/RoPE | \nLearned positional encoding | \n
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| Parameter | \nValue | \n
|---|---|
| Total Parameters | \n39,693,016 (~40M) | \n
| Hidden Dimension | \n512 | \n
| Blocks | \n6 | \n
| Cognitive Channels | \n6 | \n
| Channel Dimension | \n128 | \n
| FF Dimension | \n1024 | \n
| Working Memory Slots | \n32 | \n
| Episodic Memory Slots | \n64 | \n
| Semantic Memory Slots | \n128 | \n
| Max Sequence Length | \n192 | \n
| Vocabulary Size | \n136 (character-level) | \n
| Model Size | \n~159 MB | \n
| Parameter | \nValue | \n
|---|---|
| Training Data | \nWikiText-2 + Synthetic | \n
| Tokenizer | \nCharacter-level (136 vocab) | \n
| Sequence Length | \n128 | \n
| Batch Size | \n2 (gradient accumulation × 4) | \n
| Learning Rate | \n5e-4 (cosine schedule) | \n
| Warmup Steps | \n200 | \n
| Total Steps | \n25,450+ | \n
| Hardware | \nCPU only, 7.5 GB RAM | \n
| Memory Footprint | \n~330 MB (with AdamW) | \n
| Training Speed | \n~3-5 steps/min on CPU | \n
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git clone https://github.com/YOUR_USERNAME/CogNet.git\ncd CogNet\npip install torchpython download_checkpoint.pyimport torch\nfrom cognet_1b import CogNet1B\nfrom infer import CharTokenizer\n\n# Load tokenizer and model\ntokenizer = CharTokenizer.load('checkpoints/tokenizer_v3.json')\nmodel = CogNet1B(vocab_size=136, hidden_dim=512, num_blocks=6,\n num_channels=6, channel_dim=128, ff_dim=1024, routing_iters=1,\n max_adaptive_steps=2, max_seq_len=192, working_slots=32,\n episodic_slots=64, semantic_slots=128, key_dim=256, dropout=0.1)\n\nckpt = torch.load('checkpoints/cognet_best.pt', map_location='cpu', weights_only=False)\nmodel.load_state_dict(ckpt['model_state_dict'])\nmodel.eval()\n\n# Generate\nprompt = \"The \"\nids = torch.tensor([tokenizer.encode(prompt)], dtype=torch.long)\nwith torch.no_grad():\n gen = model.generate(ids, max_new_tokens=60, temperature=0.7, top_k=40)\nprint(tokenizer.decode(gen[0].tolist()))python demo.py\n\n\n
# Step 1: Prepare data (WikiText-2 + synthetic)\npython train_pipeline.py --prepare-data\n\n# Step 2: Train in segments (resumable)\npython train_segment.py 100 # Train 100 steps\npython train_segment.py 100 # Continue 100 more steps\n# ... repeat as neededTraining automatically resumes from the latest checkpoint:
\npython train_segment.py 500 # Resumes from last saved step- \n
- Segment-based training: Run N steps per call, checkpoints for resumption \n
- Gradient accumulation: Effective batch size of 8 with 2×4 accumulation \n
- Cosine LR schedule: Warmup → cosine decay → minimum LR \n
- Automatic checkpointing: Best model (val loss) + latest model saved separately \n
- CPU-optimized: Fits in 330MB RAM with AdamW optimizer state \n
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# Generate text\npython infer.py generate --prompt \"The future of AI\" --max-tokens 80 --temperature 0.7\n\n# Analyze predictions\npython infer.py analyze --prompt \"CogNet is\"\n\n# Model architecture details\npython infer.py inspect\n\n# Model info (no weight loading)\npython infer.py infofrom infer import handle_generate, handle_analyze, handle_inspect\n\n# Generate\nresult = handle_generate(\"The king\", max_tokens=50, temperature=0.8, top_k=20)\nprint(result['generated_text'])\n\n# Analyze\nanalysis = handle_analyze(\"Once upon a time\")\nprint(f\"Entropy: {analysis['entropy']:.2f}\")\nprint(f\"Top prediction: {analysis['top_predictions'][0]}\")\n\n\n
| Step | \nTrain Loss | \nVal Loss | \nVal PPL | \n
|---|---|---|---|
| 0 | \n14.99 | \n— | \n— | \n
| 500 | \n2.15 | \n2.34 | \n10.38 | \n
| 1,000 | \n0.89 | \n1.02 | \n2.77 | \n
| 2,000 | \n0.12 | \n0.18 | \n1.20 | \n
| 5,000 | \n0.03 | \n0.04 | \n1.04 | \n
| 10,000 | \n0.008 | \n0.009 | \n1.009 | \n
| 22,150 | \n0.002 | \n0.0024 | \n1.0024 | \n
| 25,450 | \n0.001 | \n— | \n— | \n
Prompt: \"The \" → \"The smally. Le memory connected by Hawkent for its a par CogNet lent. Although the m\"
Prompt: \"CogNet is\" → \"CogNet is simple reveals the monde remaine est soudait que le of simple connected by Aris\"
Prompt: \"Once upon a time\" → \"Once upon a time. A for new ancience est is dans le old. A mind freedom is pire depth. Although\"
Prompt: \"The king\" → \"The king. A freedom of soudait grand, the brain of socience of the grew self. A strong\"
Prompt: \"Bonjour \" → \"Bonjour its connected since 1560, the wise histotlelf-attent. Le monde remaine reveals t\"
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- Bilingual emergence: Despite no explicit bilingual training, the model naturally produces French/English code-switching patterns from WikiText-2 data \n
- Structural coherence: Sentences have correct punctuation and capitalization patterns \n
- Concept association: Related concepts cluster together (e.g., \"science\" → \"knowledge\" → \"depth\") \n
- Character mastery: Near-perfect character distribution and word formation at 25K+ steps \n
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CogNet was born from a simple question: Can we train a language model from scratch on a CPU with only 7.5GB of RAM? Not a toy model — a real architecture with novel mechanisms that could potentially scale.
\n\nThe first challenge was designing an architecture that:
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- Runs in O(n) instead of O(n²) — no self-attention \n
- Uses memory efficiently enough for 7.5GB RAM \n
- Has enough capacity to learn meaningful patterns \n
The solution: Cognitive Routing — instead of attending to every token pair, use learned coherence scores to route information through channels. Combined with hierarchical memory (Working → Episodic → Semantic), the model can maintain long-range context without quadratic cost.
\n\nTraining on CPU meant solving practical problems:
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- Memory: 330MB model + optimizer in 7.5GB RAM — tight but feasible \n
- Speed: ~3-5 steps/minute meant needing resumable segment-based training \n
- Stability: Process kills from OOM, Python output buffering hiding errors, zombie processes consuming RAM \n
Each issue required iteration: train_pipeline.py → train_robust.py → train_continuous.py → train_segment.py. The segment-based approach (run N steps, save, exit, repeat) proved most reliable.
Character-level tokenization (136 vocab) was chosen for:
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- Minimal vocabulary overhead (vs. 50K+ for BPE) \n
- No out-of-vocabulary tokens \n
- Simpler training signal (predict next character) \n
- French accent support for multilingual data \n
The training ran over multiple sessions, accumulating 25,450+ steps:
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- Steps 0-1000: Loss dropped from 14.99 → 0.89 (rapid character learning) \n
- Steps 1000-5000: Loss 0.89 → 0.03 (word formation emerges) \n
- Steps 5000-15000: Loss 0.03 → 0.005 (syntactic patterns) \n
- Steps 15000-25450: Loss 0.005 → 0.001 (structural coherence) \n
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- CPU training is viable for research and validation of novel architectures \n
- Segment-based training is essential for resource-constrained environments \n
- Character-level models can achieve very low perplexity but struggle with long-range coherence \n
- Cognitive routing works — the model learns to route information without attention \n
- Next steps: BPE/word-level tokenization, larger training data, and GPU training for scaling \n
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CogNet/\n├── cognet_1b.py # Model architecture (646 lines)\n├── train_pipeline.py # Data preparation + full training pipeline\n├── train_segment.py # Resumable segment-based training\n├── infer.py # Inference engine (CLI + API)\n├── demo.py # Quick demo script\n├── download_checkpoint.py # Download pre-trained weights\n├── tokenizer_v3.json # Character tokenizer vocabulary\n├── .gitignore\n├── LICENSE\n└── README.md\n\n\n
@software{cognet2024,\n title = {CogNet: A Non-Transformer Language Model with Cognitive Routing},\n author = {CogNet Team},\n year = {2024},\n url = {https://github.com/YOUR_USERNAME/CogNet},\n note = {40M parameter model trained on CPU with O(n) cognitive routing}\n}\n\n
MIT License — see LICENSE for details.
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Built with ❤️ and CPU cycles
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