Body: <p>I tried to fine tuning using this dataset: <a href="https://huggingface.co/datasets/celsowm/enunciados_pge_rj_orpo" rel="nofollow noreferrer">https://huggingface.co/datasets/celsowm/enunciados_pge_rj_orpo</a> created using some techniques of data augumentation using as source this simples page: <a href="https://pge.rj.gov.br/entendimentos/enunciados" rel="nofollow noreferrer">https://pge.rj.gov.br/entendimentos/enunciados</a></p>
  <p>I tried llama3.2 1B using SFT + lora and even ORPO, all with low loss.</p>
  <p>But when I prompt something present in the dataset like: &quot;Qual é o conteúdo do Enunciado 47 da PGE-RJ?.&quot; (What is the content of Statement 47 of the PGE-RJ?)</p>
  <p>The response is a Hallucination from another Enunciado (Statement);</p>
  <p>Unfortunately my 3060 ti 12gb is not enough to full fine tuning so I did not try yet.</p>
  <p>Any hints???</p>
  <p>Thanks in advance !</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-11-11T07:06:00.797Z
Comments.Text: Why fine tuning here instead of RAG for your example use case? Your specialized legal statements dataset in Spanish doesn't seem to have much contextual information where fine tuning is better. With GPU memory-efficient RAG when prompted the model can draw from exact statements stored in the database rather than relying on memory which dramatically reduces hallucinations for fact-based questions.
Comments.UserDisplayName: cinch
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-11-11T22:32:23.763Z
Comments.Text: It is portuguese not spanish. I really would like to fine tuning
Comments.UserDisplayName: celsowm
Tags: <fine-tuning><training-datasets>
Title: Hallucinations after fine tuning data augmentated dataset even with low loss
UserDisplayName: celsowm
ThreadText: # Hallucinations after fine tuning data augmentated dataset even with low loss
  
  (Asked by: username_0 on 2024-11-10)
  
  I tried to fine tuning using this dataset: <https://huggingface.co/datasets/celsowm/enunciados_pge_rj_orpo> created using some techniques of data augumentation using as source this simples page: <https://pge.rj.gov.br/entendimentos/enunciados>
  
  I tried llama3.2 1B using SFT + lora and even ORPO, all with low loss.
  
  But when I prompt something present in the dataset like: "Qual é o conteúdo do Enunciado 47 da PGE-RJ?." (What is the content of Statement 47 of the PGE-RJ?)
  
  The response is a Hallucination from another Enunciado (Statement);
  
  Unfortunately my 3060 ti 12gb is not enough to full fine tuning so I did not try yet.
  
  Any hints???
  
  Thanks in advance !
  
  **Tags:** fine-tuning, training-datasets
  > **Comments:**
  > **username_1**: Why fine tuning here instead of RAG for your example use case? Your specialized legal statements dataset in Spanish doesn't seem to have much contextual information where fine tuning is better. With GPU memory-efficient RAG when prompted the model can draw from exact statements stored in the database rather than relying on memory which dramatically reduces hallucinations for fact-based questions.  
  > **username_0**: It is portuguese not spanish. I really would like to fine tuning  
  
  ---
  
  ## 0 answers
  
  