fine-tuned model

as donto believed on

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fine-tuned model has 24 facts recorded in Dontopedia across 8 references, with 2 live disagreements.

24 facts·17 predicates·8 sources·2 in dispute

Mostly:rdf:type(6), trains on(2), generated(1)

Inbound mentions (18)

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.

`uses`Uses(3)

Reformulation Functionex:reformulation-function
Step 4ex:step-4
Tokenization Processex:tokenization-process

`analyzed`Analyzed(1)

Omega Botex:omega-bot

`areNonsensical`Are Nonsensical(1)

Generationsex:generations

`characterizesQualityOf`Characterizes Quality of(1)

Markov Chainex:markov-chain

`comparedTo`Compared to(1)

Lisamegawattsex:lisamegawatts

`coSavedWith`Co Saved With(1)

Tokenizerex:tokenizer

`discussModelFailure`Discuss Model Failure(1)

Chat Participantsex:chat-participants

`exhibitGibberish`Exhibit Gibberish(1)

Generationsex:generations

`hasOutput`Has Output(1)

Step5ex:step5

`instantiatedWith`Instantiated With(1)

Search Systemex:search-system

`isApplicationOf`Is Application of(1)

Reformulation Functionex:reformulation-function

`isMarkovian`Is Markovian(1)

Current Stateex:current-state

`possessesModel`Possesses Model(1)

Lisamegawattsex:lisamegawatts

`produces`Produces(1)

Step5ex:step5

`remainShallow`Remain Shallow(1)

Generation Samplesex:generation-samples

`savedTogetherWith`Saved Together With(1)

Tokenizerex:tokenizer

Other facts (23)

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.

23 facts

Predicate	Value	Ref
Rdf:type	AI Approach	[3]
Rdf:type	Trained Model	[4]
Rdf:type	Model	[5]
Rdf:type	Model	[6]
Rdf:type	Fine Tuned Model	[7]
Rdf:type	Machine Learning Model	[8]
Trains on	Fineweb Dataset	[2]
Trains on	Tinystories Dataset	[2]
Generated	Cute Kids Story	[1]
Believed by Omega to Lack	Nuance	[1]
Lacks Nuance	Kant Related Insults	[1]
Lacks Semantic Coherence	Generation God Said	[2]
Strings Words Without	semantic coherence or real understanding	[2]
Achieves Fluent Gibberish	Generation Samples	[2]
Has No Real Understanding	Semantic Coherence	[2]
Generates	shallow, associative but nonsensical fragments	[2]
Used in	Step6	[4]
Saved by	Model Saving	[6]
Directory Name	fine_tuned_model	[7]
Is Used in	Reformulation Function	[8]
Is Result of	Model Training	[8]
Is Specialized Version of	Pre Trained Model	[8]
Retains	Pre Trained Knowledge	[8]

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.

24 undated

—

generatedblah/watt-activation/part-146

ex:cute-kids-story

—

believedByOmegaToLackblah/watt-activation/part-146

ex:nuance

—

lacksNuanceblah/watt-activation/part-146

ex:kant-related-insults

—

lacksSemanticCoherenceblah/watt-activation/part-166

ex:generation-god-said

—

trainsOnblah/watt-activation/part-166

ex:fineweb-dataset

—

stringsWordsWithoutblah/watt-activation/part-166

semantic coherence or real understanding

—

achievesFluentGibberishblah/watt-activation/part-166

ex:generation-samples

—

hasNoRealUnderstandingblah/watt-activation/part-166

ex:semantic-coherence

—

trainsOnblah/watt-activation/part-166

ex:tinystories-dataset

—

generatesblah/watt-activation/part-166

shallow, associative but nonsensical fragments

—

typeblah/models/14

ex:AIApproach

—

typebeam/2155073f-6f86-4661-a2c4-49d7e078edee

ex:TrainedModel

—

usedInbeam/2155073f-6f86-4661-a2c4-49d7e078edee

ex:step6

—

typebeam/71b02d54-2e3e-4209-bc15-830d649e8e90

ex:Model

—

labelbeam/71b02d54-2e3e-4209-bc15-830d649e8e90

fine-tuned model

—

typebeam/295f009a-a391-49c7-a121-c659e587425e

ex:Model

—

savedBybeam/295f009a-a391-49c7-a121-c659e587425e

ex:model-saving

—

directory-namebeam/eb869acc-2b0a-4006-98fb-a7f182c6bf42

fine_tuned_model

—

typebeam/eb869acc-2b0a-4006-98fb-a7f182c6bf42

ex:Fine-tuned-model

—

typebeam/0e4dede6-52a5-49ce-a450-4813d1738359

ex:MachineLearningModel

—

isUsedInbeam/0e4dede6-52a5-49ce-a450-4813d1738359

ex:reformulation-function

—

isResultOfbeam/0e4dede6-52a5-49ce-a450-4813d1738359

ex:model-training

—

isSpecializedVersionOfbeam/0e4dede6-52a5-49ce-a450-4813d1738359

ex:pre-trained-model

—

retainsbeam/0e4dede6-52a5-49ce-a450-4813d1738359

ex:pre-trained-knowledge

References (8)

8 references

[1]Part 1463 facts
ctx:discord/blah/watt-activation/part-146
[2]Part 1667 facts
ctx:discord/blah/watt-activation/part-166
[3]141 fact
ctx:discord/blah/models/14
- full textmodels-14
  text/plain3 KBdoc:agent/models-14/9be084b3-fb97-4484-99b7-229d08b10598
  Show excerpt
  [2025-12-23 04:36] lisamegawatts: <@164501800613969920> i think you mentioned you were learning category theory so you might like this https://youtu.be/AWqvBdqCAAE?si=amQ_LmqwW_AgzhY8 [2025-12-24 13:38] lisamegawatts: Ok so round 2 of small…
[4]2155073f 6f86 4661 A2c4 49d7e078edee2 facts
ctx:claims/beam/2155073f-6f86-4661-a2c4-49d7e078edee
- full textbeam-chunk
  text/plain1 KBdoc:beam/2155073f-6f86-4661-a2c4-49d7e078edee
  Show excerpt
  - Define training arguments for the `Trainer` to control the training process. 5. **Trainer**: - Use the `Trainer` from the `transformers` library to fine-tune the model. 6. **Fine-Tuning and Evaluation**: - Fine-tune the model o…
[5]71b02d54 2e3e 4209 Bc15 830d649e8e902 facts
ctx:claims/beam/71b02d54-2e3e-4209-bc15-830d649e8e90
- full textbeam-chunk
  text/plain1 KBdoc:beam/71b02d54-2e3e-4209-bc15-830d649e8e90
  Show excerpt
  tokens = self.tokenizer.convert_ids_to_tokens(inputs['input_ids'][0]) return tokens def search(self, query): tokens = self.tokenize(query) # Perform search using the tokens return tokens # I…
[6]295f009a A391 49c7 A121 C659e587425e2 facts
ctx:claims/beam/295f009a-a391-49c7-a121-c659e587425e
- full textbeam-chunk
  text/plain1 KBdoc:beam/295f009a-a391-49c7-a121-c659e587425e
  Show excerpt
  - The model is trained on the GPU if available. 5. **Saving the Model**: - After training, the fine-tuned model and tokenizer are saved to disk. ### Next Steps - **Evaluate the Model**: After training, evaluate the model on a valid…
[7]Eb869acc 2b0a 4006 98fb A7f182c6bf422 facts
ctx:claims/beam/eb869acc-2b0a-4006-98fb-a7f182c6bf42
- full textbeam-chunk
  text/plain1 KBdoc:beam/eb869acc-2b0a-4006-98fb-a7f182c6bf42
  Show excerpt
  reformulated_queries = [model.generate(tokenizer(f"reformulate: {q}", return_tensors="pt", max_length=512, truncation=True)['input_ids'], max_length=512)[0] for q in original_queries] reformulated_texts = [tokenizer.decode(output, skip_spec…
[8]0e4dede6 52a5 49ce A450 4813d17383595 facts
ctx:claims/beam/0e4dede6-52a5-49ce-a450-4813d1738359
- full textbeam-chunk
  text/plain990 Bdoc:beam/0e4dede6-52a5-49ce-a450-4813d1738359
  Show excerpt
  - Load and split the dataset into training and testing sets. - Tokenize the data using the tokenizer. 2. **Model Fine-Tuning**: - Define a custom dataset class to handle the tokenized data. - Set up training arguments and defin…

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fine-tuned model

Inbound mentions (18)

usesUses(3)

analyzedAnalyzed(1)

areNonsensicalAre Nonsensical(1)

characterizesQualityOfCharacterizes Quality of(1)

comparedToCompared to(1)

coSavedWithCo Saved With(1)

discussModelFailureDiscuss Model Failure(1)

exhibitGibberishExhibit Gibberish(1)

hasOutputHas Output(1)

instantiatedWithInstantiated With(1)

isApplicationOfIs Application of(1)

isMarkovianIs Markovian(1)

possessesModelPossesses Model(1)

producesProduces(1)

remainShallowRemain Shallow(1)

savedTogetherWithSaved Together With(1)

Other facts (23)

Timeline

References (8)

See also

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`uses`Uses(3)

`analyzed`Analyzed(1)

`areNonsensical`Are Nonsensical(1)

`characterizesQualityOf`Characterizes Quality of(1)

`comparedTo`Compared to(1)

`coSavedWith`Co Saved With(1)

`discussModelFailure`Discuss Model Failure(1)

`exhibitGibberish`Exhibit Gibberish(1)

`hasOutput`Has Output(1)

`instantiatedWith`Instantiated With(1)

`isApplicationOf`Is Application of(1)

`isMarkovian`Is Markovian(1)

`possessesModel`Possesses Model(1)

`produces`Produces(1)

`remainShallow`Remain Shallow(1)

`savedTogetherWith`Saved Together With(1)