| --- |
| language: |
| - en |
| tags: |
| - research |
| - quantization |
| - quantization-aware-training |
| - model-compression |
| - low-bit |
| - experimental |
| library_name: pytorch |
| --- |
| |
| # CECP — Co-Evolving Compositional Pre-Training |
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| ### Experimental Low Memory Language Model Training Architecture |
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| **CECP (Co-Evolving Compositional Pre-Training)** is an experimental AI training architecture focused on reducing the memory and storage requirements of large language models while preserving as much of their original performance as possible. |
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| Unlike conventional approaches that train a model at higher precision and compress it afterward, CECP explores integrating compression directly into the learning process. |
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| The central idea is simple: **rather than forcing an already trained model into a smaller representation, allow the model to adapt to a compact representation as it learns.** |
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| This approach aims to minimize performance degradation associated with aggressive model compression, potentially enabling capable AI models to operate on hardware with significantly lower memory requirements. |
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| CECP is currently under active research and development. |
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| --- |
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| ## Research Objectives |
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| CECP is designed around three primary goals: |
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| - **Extreme model compression:** Reduce model storage requirements without proportionally sacrificing language model performance. |
| - **Compression aware learning:** Allow neural networks to adapt to compact representations throughout training. |
| - **Accessible local AI:** Make increasingly capable language models practical on consumer hardware with limited RAM and VRAM. |
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| Rather than focusing exclusively on the smallest possible model, CECP investigates the relationship between model capacity, compression, and retained performance. |
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| ## Preliminary Experimental Results |
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| CECP has been evaluated on several experimental language model configurations, including models with approximately 21 million and 53 million parameters. |
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| The latest experiments compare CECP against a conventional FP32 baseline using the TinyStories dataset. |
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| ### 53M Parameter Language Model |
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| **Experimental configuration:** |
| - Model size: approximately 52.8 million parameters |
| - Dataset: TinyStories |
| - Training duration: 2,500 optimizer steps |
| - Evaluation: validation perplexity |
| - Independent training seeds: 3 |
| - Hardware: NVIDIA Tesla T4 |
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| | Model | Mean Validation Perplexity ↓ | |
| |---|---| |
| | Dense FP32 | 18.146 ± 0.298 | |
| | **CECP-256** | **17.986 ± 0.082** | |
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| In these experiments, CECP achieved validation perplexity comparable to the conventional FP32 baseline despite operating with aggressively compressed weight representations. |
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| The small difference between the two models should be interpreted as approximate performance parity rather than evidence of general superiority. |
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| CECP currently requires more training time than conventional dense training. Improving training efficiency remains an active development priority. |
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| ### Compression Efficiency |
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| CECP's weight representation is designed to support extremely low bit storage. |
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| Its compressed projection representation can achieve a nominal one bit per weight index payload, with additional storage required for supporting representation data. |
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| The effective compression ratio depends on model architecture, layer dimensions, and configuration. |
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| Early experiments have demonstrated substantial projection weight storage reductions. Large models may benefit from reduced relative representation overhead, although this remains an area of ongoing evaluation. |
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| --- |
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| ## Potential Scaling |
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| One of the central research questions surrounding CECP is how compression aware learning behaviors as model size increases. |
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| Early experiments suggest that CECP can maintain competitive language model performance across the small model sizes evaluated so far. |
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| Future research will investigate whether these properties extend to: |
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| - 100M–500M parameter language models |
| - Billon parameter architectures |
| - Larger and more diverse training datasets |
| - Longer context applications |
| - Local deployment on memory constrained consumer hardware |
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| As model capacity increases, CECP may offer opportunities to improve the balance between model size and retained capabilities. |
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| However, these scaling benefits remain hypotheses until validated through larger experiments. |
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| ## Research Status |
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| **Status: Experiment — Active Development** |
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| CECP has demonstrated functional compression aware language model training and promising early validation perplecity results. |
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| Further work is required to establish its performance against established low bit training and quantization methods at larger scales. |
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| Current research priorities include improving training efficiency, evaluating larger models, verifing end to end deployment memory requirements, and strengthening comparative benckmarks. |
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| The complete implementation and selected technical details are not publicly available at this stage. |
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| ## Project Vision |
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| CECP is being developed with a broader objective: making capable AI systems more accessible without requiring increasingly expensive hardware. |
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| The long term vision is to explore whether language models can achieve a better balance between intelligence, memory consumption, and deployment efficiency by adapting to compression throughout training. |
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| **The objective isn't simply to make AI models smaller. It's to make them learn effectively while being smaller.** |
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| --- |
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| *CECP — Co-Evolving Compositional Pre-Training* |
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| *Indepedent experimental AI architecture research.* |
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| *Preliminary results are not evidence of superiorty over production scale language models or established quantization methods.* |
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