Its All Llama | FMN-GPT

I am stripping the architecture down to the foundation. The custom modules are gone. The experimental pipelines are archived. The recursive refinement loops are disabled. I am returning to a basic Llama-style transformer. Standard attention. Standard feed-forward networks. Standard tokenization. Just the baseline.

Complexity feels productive until you measure the loss curve. Simplicity feels like regression until you see the validation metrics stabilize. I am choosing stabilization.

The Baseline First

The Llama architecture works because it is predictable. It scales linearly with data and compute. It provides a reliable signal during training. It does not hide failures behind clever engineering. It exposes them clearly. That transparency is necessary right now.

I will train this baseline on the curated dataset. I will establish a performance floor. I will measure inference speed, memory footprint, and coherence scores. I will document everything. The baseline becomes the reference point for every future decision.

                        # New development workflow

                        Step 1: Train vanilla transformer baseline

                        Step 2: Record loss, PPL, and latency metrics

                        Step 3: Identify the largest bottleneck

                        Step 4: Add one feature that targets that bottleneck

                        Step 5: Retrain and compare against baseline

                        Step 6: Keep the feature if it improves metrics. Remove it if it does not.

                        # Measure twice. Cut once. Iterate slowly.

Removing The Noise

SleepGate is disabled. Latent reasoning is disabled. The custom decay schedules are archived. The recursive memory gates are paused. Each of these components introduced variables I could not isolate. Each one made debugging harder. Each one added training overhead without measurable gains.

Removing them is not a failure. It is a correction. The codebase shrinks. The training loop simplifies. The error messages become readable. I can finally see what the data is telling me instead of what my architecture is forcing it to say.

Adding What Actually Helps

Future features will follow a strict rule. They must improve a measurable metric. They must not increase training instability. They must justify their compute cost. Novelty is no longer a valid reason for inclusion. Only utility matters.

I will test rotary positional embeddings against absolute embeddings. I will test SwiGLU against standard activations. I will test different learning rate schedules. Each change will run against the baseline. The data will decide what stays. The data will decide what goes.

Engineering is the discipline of restraint. Innovation happens when constraints force clarity. I am applying that discipline now. The path forward is narrower. The footing is solid.

What Comes Next

The baseline training starts today. Glint variants will wait. Chroma TTS will wait. cAI-Grid will wait. All of them benefit from a stable foundation. All of them will integrate with the baseline once it proves itself. The timeline shifts. The quality standard rises.

I will publish the baseline metrics when they stabilize. I will publish the feature comparisons when they complete. The updates will be slower. They will also be accurate. That trade-off is intentional. That trade-off is necessary.

Final Thoughts

Its all llama now. The architecture is standard. The training loop is clean. The metrics are visible. I am stepping back from experimental complexity. I am stepping into measured iteration. The work continues. The standards remain. The progress becomes verifiable.

Thank you for following the adjustments. Thank you for patience during the transition. The foundation is being laid. The next phase begins when the baseline holds. Until then I will train. I will measure. I will refine.