# Multiple tasks for one fine-tuned LLM

**URL:** <https://discuss.huggingface.co/t/multiple-tasks-for-one-fine-tuned-llm/31262>\
**Category:** Beginners\
**Created:** [February 7, 2023, 12:58pm UTC](https://discuss.huggingface.co/t/multiple-tasks-for-one-fine-tuned-llm/31262 "2023-02-07T12:58:23Z")\
**Posts on this page:** 3\
**Page:** 1

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**Author:** ![mbauwens](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/mbauwens/32/14018_2.png) [@mbauwens](https://discuss.huggingface.co/u/mbauwens)\
**Post date:** [February 7, 2023, 12:58pm UTC](https://discuss.huggingface.co/t/multiple-tasks-for-one-fine-tuned-llm/31262/1 "2023-02-07T12:58:24Z")

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Hi there! If you want to fine-tune an LLM on your own dataset but want it to perform multiple tasks, what would the best procedure be?

1. Fine-tune a pre-trained model on your own dataset for one of the specific tasks.
2. Fine-tune your `model.base_model` of your own fine-tuned model on another specific task (and repeat) (see reference [question](https://discuss.huggingface.co/t/load-fine-tuned-lm-without-the-head/14997)).

OR

1. Fine-tune a pre-trained model on your own dataset for one of the specific tasks.
2. Fine-tune that same pre-trained model on your own dataset with another one of the specific tasks (and repeat)

Or would it be better to create one model with multiple task heads (see this [Medium blog](https://towardsdatascience.com/how-to-create-and-train-a-multi-task-transformer-model-18c54a146240)) instead of fine-tuning the model for each task?

Thanks!

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**Author:** ![peterwu00](https://avatars.discourse-cdn.com/v4/letter/p/ea666f/32.png) [@peterwu00](https://discuss.huggingface.co/u/peterwu00)\
**Post date:** [September 18, 2023, 11:52pm UTC](https://discuss.huggingface.co/t/multiple-tasks-for-one-fine-tuned-llm/31262/2 "2023-09-18T23:52:04Z")

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I have same question. Is it better to combine datasets from multiple tasks train them once? Of better to train each dataset separately and get lora weights and then merge with base weights one-by-one afterward? Thanks.

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**Author:** ![peterwu00](https://avatars.discourse-cdn.com/v4/letter/p/ea666f/32.png) [@peterwu00](https://discuss.huggingface.co/u/peterwu00)\
**Post date:** [September 18, 2023, 11:57pm UTC](https://discuss.huggingface.co/t/multiple-tasks-for-one-fine-tuned-llm/31262/3 "2023-09-18T23:57:10Z")

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Found answer myself:

The approach to fine-tuning a Language Model (LLM) for multiple tasks depends on various factors, including the size of your datasets, the similarity of the tasks, and your available computational resources. Here are two common approaches:

1. **Multi-Task Training (Combined Datasets):**

2. **Task-Specific Fine-Tuning (Sequential Training):**

Consider these factors when deciding which approach to take:

- **Data Size:** If you have a large amount of data for each task, multi-task training on combined datasets can be effective. If data is limited, task-specific fine-tuning may be better.

- **Task Similarity:** If tasks are closely related, multi-task training can benefit from shared representations. If tasks are dissimilar, task-specific fine-tuning might be more appropriate.

- **Computational Resources:** Multi-task training can be computationally intensive, so consider your hardware limitations.

- **Evaluation Metrics:** Evaluate both approaches on your specific tasks using appropriate evaluation metrics to determine which works better in practice.

- **Experiment:** It’s often beneficial to experiment with both approaches to see which one yields better results for your specific use case.

The choice between these approaches can vary based on your specific requirements and constraints.