ChatGPT 3.5 Turbo Fine Tuning For Specific Tasks - Tutorial with Synthetic Data

ChatGPT 3.5 Turbo Fine Tuning For Specific Tasks - Tutorial with Synthetic Data

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I created a tutorial on how you can fine tune chatgpt 3.5 turbo for a specific task or job with synthetic data. This is a step by step guide to fine tune OpenAIs ChatGPT 3.5 Turbo with Syntethic Data

00:00 ChatGPT 3.5 Turbo Fine Tuning Intro
00:18 When to Fine Tune a model?
01:42 Why do Fine-tuning?
02:42 Todays Task
04:24 Creating Synthetic Data Python
09:13 Cleaning the Dataset
11:04 Fine Tuning ChatGPT 3.5 Turbo
14:32 Testing The Fine Tuned Model

[AllAboutAI]

The Price ended up on: $1.78. Thanks for tuning in 😀

[cxoyooq]

That was very valuable for me. Thanks!

[DJPapzin]

🎯 Key Takeaways for quick navigation:

00:00 🎬 *The video explores fine-tuning ChatGPT 3.5 Turbo for specific tasks using synthetic data.*
01:52 🛠️ *Fine-tuning is recommended for specific tasks with a desired output format, requiring datasets and offering advantages like token savings.*
04:52 🔄 *Synthetic datasets are created using GPT-4, ensuring examples with forced responses. A script for synthetic dataset creation is provided.*
08:29 🧹 *Data cleaning involves reviewing examples and removing inaccuracies to enhance the dataset.*
11:12 📤 *Uploading the dataset and initiating fine-tuning with specific model selection is demonstrated.*
13:45 📊 *The fine-tuning interface shows metrics like training loss, indicating the model's performance and learning progress.*
16:33 🚀 *Testing the fine-tuned model in the playground with different inputs and examining the responses.*
17:57 🌐 *The video concludes that fine-tuning works well for specific tasks, and the presenter expresses excitement about exploring fine-tuning with other models.*

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[youwang]

thank you for ur video !!! just wonder what kind of finetuning is this? is this SPF? Lora, Qlora for chatgpt 3.5 ?

[andrewandreas]

Very nice video!! Could you say how much did it cost you to implement the whole process? Not only the fine-tuning but also the dataset generation.

[devtest]

thank you!!! Excellent material, I'm going to subscribe, I ask you a question, if you have, for example, 10 different prompt models that handle different labels, would you recommend training them all on the same model or for each type of propomt training a separate model? If we train everything on the same model, would the way to differentiate them be by keys on the label? Thank you

[yizhouqian]

Great video, thank you so much for doing the tutorial. I’ve completed fine-tuning a model with a training dataset. However, upon testing in the playground, the model did not perform as expected. The outputs were inconsistent with the instructions in the training dataset, almost as if the fine-tuning had no effect.

When testing, I didn’t include a system prompt like what's shown in your video. Yet, when the original system prompt was added, it worked just fine. Is it necessary to use the system prompt in actual use as well to align with the training setup?

[mrd]

Question:Could you get this model to tune itself by breaking down the task and having it ask you follow up questions as to be clear on what you want exactly....(I do the same thing at my job, when i dont understand something)
Or does the data set answer the questions ahead of time....

[dorisz]

Thanks for your video. May I know any ideas about how's it emplemented underlying? It must use some PEFT method. But still it's amzing by tuing the model with 10+ examples.

[frazuppi]

would it be possible to share the train data?

[AIhackers]

Difficult to see here if you can get better results with 4 turbo but with better instructions or 3 turbo fine tuned. You could have requested a csv response on each field avoid commas, would have done the job. I’d like to see an example that gpt4 tuebo really cant handle

[yoagcur]

Thanks for this. Did you try re-entering the ones that produced multiple genres e, g the Margaret Attwood one that you discarded from the training data. Unless it handles these differently e.g with "/" rather than ", " then have you really proved that this fine-tuning has worked?

[uwegenosdude]

Thanks for your interesting video. I have question concerning the development of a chatbot for a library. I've downloaded 3 websites (library, computing center and another partner institute) using gpt-crawler. So I got the content per website as a huge JSON file. All 3 files have been uploaded to a custom GPT as "my" knowledge base. The answers to questions from our users are almost unpredictable. Most of the time ChatGPT starts to halluzinate. Can I fix this scenario by doing some kind of finetuning? If you think yes, how should a set of finetuning records look like?
I hope that you are willing to share your ideas. Thanks a lot in advance.

[flethacker]

can you do a video on doing this with a Local LLM? paying openai is not a sustainable option for this hobby

[AIEntusiast_]

how would sort legal data for thousands off pdf``s ?

[DangRenBo]

The foundation 3.5 example also had the comma inside the quotes and inconsistent use of quotes. That makes the output useless.

[bretthuang]

Why does the "RESPONSE:" keyword matter? It's just used to generate sample data but not actually in the training json file if I am not mistaken. 😵‍💫🧐

[nipqufe]

Very nice video!! Can you share please your github profile?

[medoeldin]

Why do you set temp to zero? Is this best practice with finetuned model?

[Civasen]

🎯 Key Takeaways for quick navigation:

00:00 🚀 *Uso de fine tuning en ChatGPT 3.5 Turbo.*
- Se explica cuándo se debe usar el fine tuning en ChatGPT 3.5 Turbo.
- La importancia de tener un conjunto de datos de calidad para el fine tuning.
- Ejemplo de cómo se utiliza el fine tuning para obtener respuestas específicas en formato CSV.
04:24 💼 *Creación de conjuntos de datos sintéticos para fine tuning.*
- Se presenta un script para crear conjuntos de datos sintéticos para fine tuning.
- Ejemplos de cómo se generan los datos de entrada y salida para el fine tuning.
- Importancia de tener ejemplos de respuesta deseada para el fine tuning.
08:14 🤖 *Proceso de fine tuning y análisis de resultados.*
- Se muestra cómo se carga el conjunto de datos creado en el proceso de fine tuning.
- Se discuten métricas como la pérdida de entrenamiento para evaluar el rendimiento del modelo.
- Se realiza una prueba con el modelo fine tuneado y se analizan los resultados obtenidos.

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