license: mit

language:
  - en

library_name: pytorch

pipeline_tag: text-generation

tags:
  - text-generation
  - causal-lm
  - tiny-transformer
  - edge-ai
  - onnx
  - int8
  - quantization
  - knowledge-distillation
  - tinybert-style
  - efficient-ml
  - mobile-ai

model_name: Krull-Micro

base_model: Krull-Micro (distilled from causal LM teacher)

datasets:
  - custom

metrics:
  - perplexity

model-index:
  - name: Krull-Micro
    results:
      - task:
          type: text-generation
        dataset:
          name: Custom corpus
          type: custom
        metrics:
          - name: Perplexity
            type: perplexity
            value: null

config:
  architecture: decoder-only transformer
  hidden_size: 192
  num_hidden_layers: 2
  num_attention_heads: 4
  intermediate_size: 512
  max_position_embeddings: 256
  vocab_size: 8000
  attention_type: causal
  distillation: full (embedding + hidden + attention + logits)

inference:
  parameters:
    max_new_tokens: 64
    temperature: 0.8
    top_k: 50
    top_p: 0.95

deployment:
  framework: onnxruntime
  quantization: int8
  edge_ready: true
  memory_footprint: ~8-20MB

training:
  method: knowledge distillation
  teacher_model: causal language model (e.g., GPT-style)
  losses:
    - embedding_mse
    - hidden_state_mse
    - attention_mse
    - kl_divergence
    - cross_entropy

limitations:
  - Small model capacity may reduce long-range coherence
  - Sensitive to training corpus quality
  - Not suitable for high-stakes or safety-critical use

intended_use:
  - Edge text generation
  - Embedded AI systems
  - Mobile applications
  - Low-latency inference

authors:
  - name: Igor Machado

version: 0.1.0