Platform · Training evidence
Define the training evidence before enabling execution.
Generic training runs only on approved immutable dataset versions and approved trainer sources; the registry enforces both at queue time, and unapproved input is rejected with the reason. This page preserves historical method evidence and specifies what an authoritative future integration must bind.
Adapter tuning
You rarely need a new model — you need the base to learn your domain
Full fine-tuning rewrites every weight, costs the most, and produces a fresh multi-gigabyte checkpoint you now have to govern. Adapter methods learn the delta instead: a small, swappable set of parameters that ride on a frozen base. Same behavior change, a fraction of the cost and the surface area to defend.
On the SprintLoop track, a full fine-tune of the 7B base scored 34/36 — no better than LoRA at the same data — so it was reverted. The adapter was the right tool, and the registry recorded the experiment either way.
LoRA
Freeze the base and train small low-rank matrices in each attention projection. Historical SprintLoop evidence records this method, but LeanLogix does not currently expose it as a runnable Studio job or an adapter-serving promise.
QLoRA
Hold the frozen base in 4-bit, train the LoRA adapters in full precision on top. It is what lets a 32B base fine-tune on a single Apple Silicon box — the 8-bit 32B candidate was trained this way. The tradeoff is a small precision cost on the frozen weights, not the learned ones.
DoRA
Weight-decomposed LoRA. It splits each weight into magnitude and direction and adapts them separately, recovering quality that plain LoRA leaves on the table — at roughly the same memory and adapter size. Reach for it when LoRA plateaus below where you need to be.
GaLore
Gradient low-rank projection. Instead of constraining the weights to low rank, it projects the gradients — so every parameter still moves, but the optimizer state stays small. It targets full fine-tune quality at memory closer to LoRA, for cases where adapter rank is the limiting factor.
At a glance
Reproducible recipe
A run you can re-run and get the same model
This historical recipe illustrates the provenance a future authoritative runner must produce: pinned base, method, hyperparameters, corpus snapshot, and observed validation evidence. It is not executable from this page or Studio.
The recorded SprintLoop-7B experiment used MLX on Apple Silicon against a 2,800-example corpus marked as containing no customer data. Its historical evidence records a val-loss minimum of 0.69 around iteration 20.
A recorded 8-bit 32B QLoRA experiment reports a lower val-loss of 0.49. That historical number is comparison evidence, not authorization to run, release, or serve the model.
recipe.yaml · representative
<span class="code-comment"># SprintLoop-7B · LoRA fine-tune (MLX)</span> <span class="code-keyword">base</span>: <span class="code-string">Qwen2.5-Coder-7B-Instruct</span> <span class="code-keyword">method</span>: <span class="code-string">lora</span> <span class="code-keyword">backend</span>: <span class="code-string">mlx</span> <span class="code-comment"># Apple Silicon</span> <span class="code-keyword">lora</span>: <span class="code-keyword">rank</span>: 16 <span class="code-keyword">alpha</span>: 32 <span class="code-keyword">dropout</span>: 0.05 <span class="code-keyword">target_modules</span>: <span class="code-string">["q_proj","k_proj","v_proj","o_proj"]</span> <span class="code-keyword">data</span>: <span class="code-keyword">corpus</span>: <span class="code-string">sprintloop-corpus</span> <span class="code-keyword">examples</span>: 2800 <span class="code-keyword">customer_data</span>: <span class="code-keyword">false</span> <span class="code-comment"># enforced at registry</span> <span class="code-keyword">train</span>: <span class="code-keyword">batch_size</span>: 4 <span class="code-keyword">learning_rate</span>: <span class="code-number">1e-4</span> <span class="code-keyword">select_on</span>: <span class="code-string">val_loss_min</span> <span class="code-comment"># 0.69 @ iter 20</span> <span class="code-function">register</span>(<span class="code-keyword">sign</span>=<span class="code-keyword">true</span>, <span class="code-keyword">channel</span>=<span class="code-string">"candidate"</span>)
Alignment
Sharpen behavior after the task is learned
Fine-tuning and alignment are documented design choices for a future governed runner. Their catalog status describes evidence or roadmap maturity, not executable Studio availability.
DPO / GRPO
preference + reasoning RL — sharpen refusal behavior
RLAIF / self-rewarding
AI feedback loop — scale alignment without labelers
What you get
An authoritative runner must end with defensible evidence
Before training can return as a supported lane, its output must bind exact weights, source authority, data, evaluation, and immutable provenance into a non-routable candidate.
A versioned adapter
A pinned artifact tagged to its exact base and recipe. Historical versions may remain inspectable, but no adapter is promised to be re-servable.
A tracked val-loss curve
The validation-loss minimum that selected the checkpoint, recorded against corpus size — the evidence that promotion was earned, not assumed.
A signed record
The run's lineage sealed into the registry and ready for the benchmark and release gates — no hand-off, no re-entry, no second source of truth.
SprintLoop-7B · v0 (POC) → 32B v1 · monitor: no regression · 14 cycles · next: harder 40–60 probe benchmark to separate 7B vs 32B
Review the training evidence contract
Inspect the roadmap and completed-run evidence requirements. Generic training remains unavailable until authoritative model, dataset, runner, and artifact identities are enforced.