Platform · Training evidence
Define the training evidence before enabling execution.
Generic training is unavailable until approved immutable dataset and trainer-source registries are enforced. 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.