| --- |
| license: apache-2.0 |
| pipeline_tag: image-text-to-text |
| tags: |
| - prism |
| - neural-architecture-search |
| - multimodal |
| - under-development |
| --- |
| |
| <div align="center"> |
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| <h1 align="center">BASE-1</h1> |
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| <p align="center"><b>A multimodal foundation model whose architecture is discovered through decentralized neural architecture search</b></p> |
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| [](https://github.com/PlatformNetwork/prism) |
| []() |
| []() |
| [](https://www.apache.org/licenses/LICENSE-2.0) |
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| </div> |
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| --- |
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| ## Status: In Development |
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| BASE-1 is currently under active development. No weights are available yet. This repository will host the model checkpoints, configuration, and usage documentation once the architecture search and training phases are complete. |
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| ## Model Summary |
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| | **Developer** | [Cortex Foundation](https://cortex.foundation) in partnership with [Platform](https://platform.network) | |
| | **Architecture** | Determined by neural architecture search (in progress) | |
| | **Parameters** | To be announced after architecture search | |
| | **Input modalities** | Text, Image | |
| | **Output modality** | Text | |
| | **Architecture search** | [PRISM](https://github.com/PlatformNetwork/prism) — decentralized NAS on the Platform Network | |
| | **License** | Apache 2.0 | |
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| ## Overview |
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| BASE-1 is a foundation model being developed through [PRISM](https://github.com/PlatformNetwork/prism), a decentralized neural architecture search (NAS) challenge running on the Platform Network. Rather than committing to a hand-designed architecture upfront, BASE-1's design is discovered competitively: miners across the network submit novel architecture families and training recipes, which are evaluated in isolated benchmark environments for learning quality, training stability, and scaling behavior. |
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| The best-performing architecture that emerges from this search will be used to train BASE-1 at scale. |
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| ### How the architecture is discovered |
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| PRISM fixes the dataset and evaluation protocol, not the search space. Candidate submissions are scored on: |
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| - **Learning quality** — proxy loss performance under a shared, deterministic evaluation contract |
| - **Training stability** — smooth loss curves, stable gradients, and well-behaved activations |
| - **Scaling signals** — consistent improvements across model size, depth, sequence length, and batch scaling |
| - **Noise resistance** — dynamic thresholds prevent marginal random fluctuations from being rewarded as improvements |
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| Architecture discovery and training-recipe improvements (optimizer, loss computation, inference, train step) are attributed and rewarded independently, so both the model design and its training procedure are optimized by the network. |
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| ## Modalities |
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| BASE-1 will support **Text/Image to Text**: it will accept text and images as input and generate text as output. |
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| | Input | Output | |
| |-------|--------| |
| | Text | Text | |
| | Image | Text | |
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| ## Why is the model size not announced? |
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| The parameter count of BASE-1 is genuinely not decided yet — and this is by design. |
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| In a conventional training pipeline, the architecture and parameter budget are fixed first, then training begins. BASE-1 inverts this process: |
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| 1. **Architecture search comes first.** PRISM evaluates candidate architectures at compact proxy scales, measuring loss curves, gradient stability, activation behavior, and how performance evolves across model size, depth, sequence length, and batch size. |
| 2. **Scaling laws are derived from the winning architecture.** Each architecture family exhibits its own scaling behavior. The optimal parameter count depends on the scaling-law signals of the architecture that wins the search — a number that cannot be known before the search concludes. |
| 3. **The final size is chosen from evidence, not convention.** Once the winning architecture's scaling characteristics are measured, the parameter budget will be set where the compute/performance trade-off is optimal for that specific design. |
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| The final model size will be announced once the architecture search is complete. |
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| ## Roadmap |
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| | Phase | Description | Status | |
| |-------|-------------|--------| |
| | 1. PRISM challenge launch | Open the decentralized architecture search to miners on the Platform Network | In progress | |
| | 2. Architecture selection | Identify the best-performing architecture family from competitive evaluation and scaling analysis | Pending | |
| | 3. Dataset curation | Assemble and validate the large-scale multimodal training corpus | Pending | |
| | 4. Large-scale training | Train BASE-1 at the parameter budget derived from the winning architecture's scaling laws | Pending | |
| | 5. Model release | Publish weights, configuration, evaluation results, and usage documentation in this repository | Pending | |
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| ## Intended Use |
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| BASE-1 is intended as a general-purpose multimodal foundation model for text generation conditioned on text and image inputs. Detailed intended-use guidance, limitations, and evaluation results will be published with the model release. |
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| ## Evaluation |
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| Benchmark results will be published alongside the weights once training is complete. Architecture-search-stage evaluations follow the PRISM scoring protocol, documented in [Scoring and rewards](https://github.com/PlatformNetwork/prism/blob/main/docs/scoring.md) and [Scaling evaluation](https://github.com/PlatformNetwork/prism/blob/main/docs/scaling.md). |
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| ## Resources |
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| - PRISM (architecture search): [github.com/PlatformNetwork/prism](https://github.com/PlatformNetwork/prism) |
| - PRISM documentation: [Overview](https://github.com/PlatformNetwork/prism/blob/main/docs/overview.md) | [Architecture](https://github.com/PlatformNetwork/prism/blob/main/docs/architecture.md) | [Scoring](https://github.com/PlatformNetwork/prism/blob/main/docs/scoring.md) | [Scaling](https://github.com/PlatformNetwork/prism/blob/main/docs/scaling.md) |
| - Platform Network: [platform.network](https://platform.network) |
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| ## License |
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| This repository is released under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0). |
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