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description: Learn about the core features of LLM Studio. |
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# Core features |
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## No-code fine-tuning |
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NLP practioners can easily fine-tune models without the need for code expertise. The user interface, which is specifically designed for LLMs, allows users to upload large datasets easily and configure [hyperparameters](../concepts#parameters-and-hyperparameters) to fine-tune the model. |
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## Highly customizable (wide range of hyperparameters) |
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H2O LLM Studio supports a wide variety of hyperparameters that can be used to fine-tune the model and supports the following fine-tuning techniques to enable advanced customization: |
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- [Low-Rank Adaptation (LoRA)](../concepts#lora-low-rank-adaptation) |
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- [8-bit model training with a low memory footprint](../concepts#8-bit-model-training-with-a-low-memory-footprint) |
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## Advanced evaluation metrics and experiment comparison |
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Advanced evaluation metrics in H2O LLM Studio can be used to validate the answers generated by the LLM. This helps to make data-driven decisions about the model. It also offers visual tracking and comparison of experiment performance, making it easy to analyze and compare different fine-tuned models.You can also visualize how different parameters affect the model performance, and optionally use the [Neptune](https://neptune.ai/) integraton to track and log your experiments. |
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## Instant publishing models |
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H2O LLM Studio enables easy model sharing with the community by allowing you to export the model to the [Hugging Face Hub](https://huggingface.co/h2oai) with a single click. |
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## Instant feedback on model performance |
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Additionally, H2O LLM Studio lets you chat with the fine-tuned model and recieve instant feedback about model performance. |
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