# Evaluation of Fine-Tuned Models

Evaluating fine-tuned models is an important step in the development process, as it ensures the model performs well on the target task and generalizes effectively to unseen data. Without thorough evaluation, it’s impossible to determine whether the fine-tuning process has succeeded or if adjustments are needed to improve performance. This step also helps identify potential biases, weaknesses, or overfitting issues that might hinder the model's real-world applicability. In this section, we will explore key evaluation strategies, metrics, and best practices to assess the effectiveness of your fine-tuned model comprehensively.

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[Evaluation Techniques](/llm-guide/evaluation-of-fine-tuned-models/evaluation-techniques.md)
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[Task specific Evaluation Metrics](/llm-guide/evaluation-of-fine-tuned-models/task-specific-evaluation-metrics.md)
{% endcontent-ref %}

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[Popular Benchmarks](/llm-guide/evaluation-of-fine-tuned-models/popular-benchmarks.md)
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[Best Practices for Model Evaluation](/llm-guide/evaluation-of-fine-tuned-models/best-practices-for-model-evaluation.md)
{% endcontent-ref %}


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