Talos DDM

Talos Mini — Training Pipeline Milestone

Overview

Talos Mini has successfully completed its first end-to-end training smoke test using the canonical dense tiny configuration.

This milestone verifies that the core model architecture and training pipeline can initialize, execute forward and backward passes, update model parameters, and demonstrably learn from a fixed synthetic corpus.

Results

Model: Talos Mini — Dense Tiny
Parameters: 254,272
Vocabulary: 1,024 tokens
Layers: 2
Hidden Size: 64
Attention Heads: 4
KV Heads: 2
Training Steps: 200
Optimizer: AdamW

Loss Trajectory

Step | Loss -- | -- 0 | 6.9177 25 | 5.3568 50 | 3.9134 75 | 2.6434 100 | 1.6195 125 | 1.0475 150 | 0.5186 175 | 0.2652 200 | 0.2117

The model reduced loss from 6.9177 to 0.2117 over 200 training steps, demonstrating a substantial learning signal and successful parameter updates.

Verified Components

• Model initialization

• Talos dense architecture

• Forward pass

• Causal language-model loss

• Backpropagation

• AdamW optimization

• GPU execution through PyTorch

• Synthetic corpus generation

• End-to-end training loop

• Measurable learning/overfitting on the fixed corpus

Dataset Pipeline

A small 574-character text corpus has also been created and verified successfully in the Colab environment. This corpus will be used for the next training validation stage.

The current smoke test intentionally uses Talos's canonical synthetic corpus rather than the custom text dataset. This keeps the initial result isolated to the core model and optimization pipeline.

Next Steps

1. Integrate the verified text dataset with Talos's existing tokenizer and data pipeline.

2. Run an overfitting test against the fixed text corpus.

3. Verify that the tokenizer → dataset → model → loss pipeline works end-to-end.

4. Add or finalize automated training tests around loss reduction.

5. Expand the dataset and training configuration after the small-scale text test passes.

Status

Core Training Pipeline: VERIFIED

Talos Mini has successfully demonstrated that the current 254K-parameter prototype can learn through the complete training pipeline. The project can now proceed to validation using real text data before moving toward larger-scale training.