NumericalLM — Model Card
Overview
NumericalLM is a lightweight autoregressive transformer model designed to operate directly on numerical token sequences. The model focuses on efficient reasoning over tokenized numerical representations rather than natural language tokens.
Unlike traditional language models, NumericalLM is intended to work with pre-tokenized inputs, allowing external systems to control tokenization and interpretation layers.
Architecture
NumericalLM uses a standard transformer decoder architecture:
- Autoregressive causal transformer
- Multi-head self-attention
- Feed-forward MLP blocks
- Learned positional embeddings
- Token embedding layer
The model predicts the next token in a numerical token sequence.
Core Components
| Component | Description |
|---|---|
| Token Embedding | Maps token IDs to embeddings |
| Positional Embedding | Encodes token position |
| Transformer Blocks | Attention + feed-forward layers |
| LayerNorm | Stabilizes training |
| Output Head | Projects embeddings to token logits |
Input Format
The model expects integer token IDs as input.
Example:
[12, 45, 8, 91]
The model generates continuations of the token sequence.
Example output:
[12, 45, 8, 91, 34, 22, 17]
Tokenization and interpretation are handled outside the model.
Intended Use
NumericalLM can be used for:
- Token-based reasoning experiments
- Lightweight inference pipelines
- Tool routing experiments
- Agent architectures
- Embedded AI systems
- Research on numerical token models
Limitations
- NumericalLM does not include a tokenizer
- The model operates purely on token IDs
- Output tokens must be interpreted externally
- Reasoning quality depends on training data
Safety Considerations
The model produces token continuations and does not contain built-in safety layers.
Applications using the model should implement their own safety mechanisms where appropriate.
License
This model is released under the BRSX Open License.
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