Training Parameters
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)
Training Parameters has 20 facts recorded in Dontopedia across 5 references, with 3 live disagreements.
Mostly:includes(4), rdf:type(3), total steps(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (1)
Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.
belongsToListBelongs to List(1)
- Optimizer
ex:optimizer
Other facts (18)
The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.
| Predicate | Value | Ref |
|---|---|---|
| Includes | X_train | [4] |
| Includes | y_train | [4] |
| Includes | Learning Rate 0.01 | [5] |
| Includes | Epoch Count 100 | [5] |
| Rdf:type | Configuration | [1] |
| Rdf:type | Hyperparameter Category | [3] |
| Rdf:type | Function Arguments | [4] |
| Total Steps | 622 | [1] |
| Batch Size | 64 | [1] |
| Sequence Length | 2048 | [1] |
| Learning Rate | 0.0001 | [1] |
| Warmup Steps | 50 | [1] |
| Save Interval | 1000 | [1] |
| Validation Interval | 1000 | [1] |
| Logging Interval | 25 | [1] |
| Phase Interval | 1 | [1] |
| Lite Phase Setting | false | [1] |
| Grouped Together | true | [2] |
Timeline
Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.
References (5)
ctx:discord/blah/watt-activation/264- full textwatt-activation-264text/plain2 KB
doc:agent/watt-activation-264/555cd9a1-321c-4f18-8f17-7bef422894a1Show excerpt
[2026-03-13 05:30] xenonfun: ``` I wrote the full plan in docs/claude/plans/tokenizerless_phase_stream_plan.md. Core recommendation from the plan: - do not do pure one-byte-per-step modeling first - build a tokenizerless byte_patch…
ctx:claims/beam/6a89aa37-552f-4aee-a292-66e6244045bc- full textbeam-chunktext/plain1 KB
doc:beam/6a89aa37-552f-4aee-a292-66e6244045bcShow excerpt
self.fc2 = nn.Linear(64, 1) def forward(self, x): x = torch.relu(self.bn1(self.fc1(x))) x = self.fc2(x) return x model = RankingModel() ``` #### 3. Training Loop Improve the training loop to include va…
ctx:claims/beam/1a9575d4-0f05-41b2-a8bf-3a9f1dd9dcb9- full textbeam-chunktext/plain1 KB
doc:beam/1a9575d4-0f05-41b2-a8bf-3a9f1dd9dcb9Show excerpt
- **Description**: Coefficient for L2 norm of the weights. - **Range**: Typically between \(10^{-6}\) and \(10^{-2}\). - **Example Values**: \(1e-6\), \(1e-5\), \(1e-4\), \(1e-3\), \(1e-2\). - **Dropout Rate** - **De…
ctx:claims/beam/9fbd5d54-37d5-44fc-b34f-86313fb7e94a- full textbeam-chunktext/plain1 KB
doc:beam/9fbd5d54-37d5-44fc-b34f-86313fb7e94aShow excerpt
logging.info(f"Iteration {iteration}: Model accuracy = {accuracy:.4f}") # Example usage: model = RandomForestClassifier(n_estimators=100) for i in range(5): # Example: Fine-tune and evaluate the model 5 times fine_tuned_model = fi…
ctx:claims/beam/ffb8ee8e-17cf-4b81-bea0-320e8177cbdf- full textbeam-chunktext/plain1 KB
doc:beam/ffb8ee8e-17cf-4b81-bea0-320e8177cbdfShow excerpt
Would you like to explore any specific aspect further, such as mixed precision training or gradient accumulation? [Turn 9464] User: I'm using PyTorch 2.1.8 for secure training, and I've noticed its 99.9% stability in 9,000 runs. However, I…
See also
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