Warmup Steps

as donto believed on

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)

Warmup Steps has 23 facts recorded in Dontopedia across 8 references, with 2 live disagreements.

23 facts·16 predicates·8 sources·2 in dispute

Mostly:rdf:type(5), has value(2), value(1)

Inbound mentions (6)

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.

`hasParameter`Has Parameter(2)

Training Argsex:training-args
Training Argumentsex:training-arguments

`controls-training-behavior`Controls Training Behavior(1)

Training Argumentsex:training-arguments

`has-parameter`Has Parameter(1)

Training Argumentsex:training-arguments

`includes`Includes(1)

Hyperparameter Tuningex:hyperparameter-tuning

`is-controlled-by`Is Controlled by(1)

Learning Rate Progressionex:learning-rate-progression

Other facts (21)

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.

21 facts

Predicate	Value	Ref
Rdf:type	Training Parameter	[1]
Rdf:type	Training Parameter	[4]
Rdf:type	Warmup Parameter	[5]
Rdf:type	Hyperparameter	[6]
Rdf:type	Hyperparameter	[7]
Has Value	500	[5]
Has Value	500	[8]
Value	500	[1]
Parameter Value	500	[2]
Controls Learning Rate	Learning Rate Schedule	[2]
Quantity	500	[3]
Has Parameter Name	warmup_steps	[6]
Has Suggested Value Range	500 to 1000	[6]
Has Function	help the model gradually reach the full learning rate	[6]
Has Benefit	improve convergence	[6]
Controls	Learning Rate Progression	[6]
Has Type	Training Hyperparameter	[6]
Has Lower Bound	500	[6]
Has Upper Bound	1000	[6]
Has Effect	Gradual Learning Rate Reach	[6]
Has Effect	Convergence Improvement	[6]

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.

23 undated

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typebeam/75f58362-300a-4d5c-94a5-4285b391366e

ex:TrainingParameter

—

valuebeam/75f58362-300a-4d5c-94a5-4285b391366e

500

—

parameter-valuebeam/9500e1c6-ed0c-41a2-ace0-794604c62109

500

—

controls-learning-ratebeam/9500e1c6-ed0c-41a2-ace0-794604c62109

ex:learning-rate-schedule

—

quantityblah/watt-activation/263

500

—

typebeam/b4e1fa92-87bc-4489-ba1e-895a84d083b0

ex:TrainingParameter

—

typebeam/018e6829-a4ce-4a26-9be8-6d8ad3231779

ex:WarmupParameter

—

hasValuebeam/018e6829-a4ce-4a26-9be8-6d8ad3231779

500

—

typebeam/1714914a-4272-4b7c-91df-6c89df9429f8

ex:Hyperparameter

—

hasParameterNamebeam/1714914a-4272-4b7c-91df-6c89df9429f8

warmup_steps

—

hasSuggestedValueRangebeam/1714914a-4272-4b7c-91df-6c89df9429f8

500 to 1000

—

hasFunctionbeam/1714914a-4272-4b7c-91df-6c89df9429f8

help the model gradually reach the full learning rate

—

hasBenefitbeam/1714914a-4272-4b7c-91df-6c89df9429f8

improve convergence

—

labelbeam/1714914a-4272-4b7c-91df-6c89df9429f8

Warmup Steps

—

controlsbeam/1714914a-4272-4b7c-91df-6c89df9429f8

ex:learning-rate- progression

—

has-typebeam/1714914a-4272-4b7c-91df-6c89df9429f8

ex:Training-Hyperparameter

—

hasLowerBoundbeam/1714914a-4272-4b7c-91df-6c89df9429f8

500

—

hasUpperBoundbeam/1714914a-4272-4b7c-91df-6c89df9429f8

1000

—

has-effectbeam/1714914a-4272-4b7c-91df-6c89df9429f8

ex:gradual-learning-rate-reach

—

hasEffectbeam/1714914a-4272-4b7c-91df-6c89df9429f8

ex:convergence-improvement

—

typebeam/8f504244-e3b7-477b-ba46-cb8bb984f219

ex:Hyperparameter

—

labelbeam/8f504244-e3b7-477b-ba46-cb8bb984f219

Warmup Steps

—

hasValuebeam/08d01dee-8025-41e7-bdd4-fa05629b996c

500

References (8)

8 references

[1]75f58362 300a 4d5c 94a5 4285b391366e2 facts
ctx:claims/beam/75f58362-300a-4d5c-94a5-4285b391366e
- full textbeam-chunk
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  #### 3. Define Training Arguments ```python # Define training arguments training_args = TrainingArguments( output_dir='./results', num_train_epochs=3, per_device_train_batch_size=2, # Smaller batch size for CPU per_device_…
[2]9500e1c6 Ed0c 41a2 Ace0 794604c621092 facts
ctx:claims/beam/9500e1c6-ed0c-41a2-ace0-794604c62109
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  - **Strategy**: Use `True` if your hardware supports it (e.g., NVIDIA GPUs with Tensor Cores). ### Example Configuration Here's an example configuration for fine-tuning Llama 2 13B: ```python from transformers import LlamaForCausalLM…
[3]2631 fact
ctx:discord/blah/watt-activation/263
- full textwatt-activation-263
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  [2026-03-13 04:59] xenonfun: ``` • Yes. Several plausible reasons, even without blaming the architecture. Most important ones: - Warmup/schedule mismatch - the 2K run had 12,432 steps/epoch - the 128K run had only 1,554 …
[4]B4e1fa92 87bc 4489 Ba1e 895a84d083b01 fact
ctx:claims/beam/b4e1fa92-87bc-4489-ba1e-895a84d083b0
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  6. **Ensemble Methods**: Combine multiple models to improve overall accuracy. ### Enhanced Code Example Here's an enhanced version of your code that incorporates these strategies: ```python import torch from transformers import AutoModel…
[5]018e6829 A4ce 4a26 9be8 6d8ad32317792 facts
ctx:claims/beam/018e6829-a4ce-4a26-9be8-6d8ad3231779
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  # Define training arguments training_args = TrainingArguments( output_dir='./results', num_train_epochs=3, per_device_train_batch_size=16, per_device_eval_batch_size=16, warmup_steps=500, weight_decay=0.01, loggi…
[6]1714914a 4272 4b7c 91df 6c89df9429f812 facts
ctx:claims/beam/1714914a-4272-4b7c-91df-6c89df9429f8
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  - **Reason**: More epochs can lead to overfitting, but fewer epochs might not be enough for the model to learn the data well. 2. **Batch Size (`per_device_train_batch_size` and `per_device_eval_batch_size`)**: - **Suggested Value**: …
[7]8f504244 E3b7 477b Ba46 Cb8bb984f2192 facts
ctx:claims/beam/8f504244-e3b7-477b-ba46-cb8bb984f219
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  After generating the reformulated query, you can apply post-processing steps such as removing unnecessary words, correcting grammar, or ensuring the reformulated query adheres to certain constraints (e.g., length, structure). ### Example o…
[8]08d01dee 8025 41e7 Bdd4 Fa05629b996c1 fact
ctx:claims/beam/08d01dee-8025-41e7-bdd4-fa05629b996c
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  - The `reformulate` function takes an input query, encodes it with the tokenizer, and generates a reformulated query using the model. 3. **Prefix for Task Guidance**: - The prefix `"reformulate: "` guides the model on the task at han…

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Warmup Steps

Inbound mentions (6)

hasParameterHas Parameter(2)

controls-training-behaviorControls Training Behavior(1)

has-parameterHas Parameter(1)

includesIncludes(1)

is-controlled-byIs Controlled by(1)

Other facts (21)

Timeline

References (8)

See also

Keep researching

`hasParameter`Has Parameter(2)

`controls-training-behavior`Controls Training Behavior(1)

`has-parameter`Has Parameter(1)

`includes`Includes(1)

`is-controlled-by`Is Controlled by(1)