README.md

metadata

license: mit
language:
  - ru
  - en
pipeline_tag: text-generation
tags:
  - eblan
model-index:
  - name: eblan-2.0-flash-lite
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: Synthetic Benchmark
          type: synthetic
        metrics:
          - name: Accuracy
            type: accuracy
            value: 314

🚀 Eblan 2.0 Flash Lite (EblanForCausalLM)

Eblan 2.0 Flash Lite is a next-generation lightweight language model built on the custom EblanForCausalLM architecture.

By leveraging an innovative O(1) polynomial recurrent state kernel, the model delivers ultra-fast inference with virtually zero memory overhead, running natively via NumPy and Tiktoken.

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🏗️ Architecture (EblanForCausalLM)

Unlike traditional Transformer-based models that rely on heavy Attention blocks (Q, K, V), EblanForCausalLM utilizes a single scalar weight vector W (in R¹) with a direct scalar projection layer.

Forward Pass Formulation:

$$y=W\cdot (x^2+x)\cdot \text{vocab\_size}y\; =\; W\; \backslash cdot\; (x^2\; +\; x)\; \backslash cdot\; \backslash text\{vocab\backslash \_size\}$$

Where:

• x = current_id / vocab_size — normalized input token index.
• W — trained scalar weight (eblan-2.0-flash-lite.npy).
• Logits formula for distance-based logit calculation prior to Softmax sampling:

$${\text{Logits}}_i=-\frac{\mathrm{\mid }i-y\mathrm{\mid }}{\tau }\backslash text\{Logits\}\_i\; =\; -\backslash frac\{|i\; -\; y|\}\{\backslash tau\}$$

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📜 License

This project is licensed under the MIT License.