Quizzes details (#254)

sergiopaniego · web-flow · commit 39a647d51fff · 2025-09-24T19:17:51.000+02:00
diff --git a/units/en/unit1/1.md b/units/en/unit1/1.md
@@ -6,6 +6,8 @@ Welcome to the smollest course of fine-tuning! This module will guide you throug
 
 By the end of this course you will be fine tuning an LLM with SFT. This course is smol but fast! If you're like for a smoother gradient, check out the [The LLM Course](https://huggingface.co/learn/llm-course/chapter1/1).
 
+After completing this unit (and the assignment), don’t forget to test your knowledge with the [quiz](https://huggingface.co/spaces/smol-course/unit_1_quiz)!
+
 </Tip>
 
 ## What is Instruction Tuning?
diff --git a/units/en/unit1/6.md b/units/en/unit1/6.md
@@ -89,4 +89,9 @@ This will help us to reproduce your model evaluation before we add it to the lea
 
 ### Wait for the PR to be merged
 
-Once the PR is merged, your model will be added to the leaderboard! You can check the leaderboard [here](https://huggingface.co/spaces/smol-course/leaderboard).
+Once the PR is merged, your model will be added to the leaderboard! You can check the leaderboard [here](https://huggingface.co/spaces/smol-course/leaderboard).
+
+## Test your knowledge
+
+You’ve completed the unit — great work!
+Now put your learning to the test by taking the [quiz](https://huggingface.co/spaces/smol-course/unit_1_quiz).
diff --git a/units/en/unit2/1.md b/units/en/unit2/1.md
@@ -6,6 +6,8 @@ Evaluation is a critical step in developing and deploying language models. It he
 
 We are already using evaluation to submit models to the course leaderboard. In this unit we will explore evaluation in more detail and use what we learn to evaluate our models and submit them to the leaderboard.
 
+After completing this unit (and the assignment), don’t forget to test your knowledge with the [quiz](https://huggingface.co/spaces/smol-course/unit_2_quiz)!
+
 </Tip>
 
 ## Why Evaluation Matters
diff --git a/units/en/unit2/5.md b/units/en/unit2/5.md
@@ -90,6 +90,11 @@ This will help us reproduce your model evaluation before we add it to the leader
 
 Once the PR is merged, your model will be added to the leaderboard! You can check the leaderboard [here](https://huggingface.co/spaces/smol-course/leaderboard).
 
+## Test your knowledge
+
+You’ve completed the unit — great work!
+Now put your learning to the test by taking the [quiz](https://huggingface.co/spaces/smol-course/unit_2_quiz).
+
 ## What You've Learned
 
 By completing this unit, you've learned how to:
diff --git a/units/en/unit3/1.md b/units/en/unit3/1.md
@@ -6,6 +6,8 @@ Welcome to Unit 3 of the smollest course on fine-tuning! This module will guide
 
 By the end of this unit you will be aligning an LLM with human preferences using Direct Preference Optimization (DPO). This course is smol but fast! If you're looking for a smoother gradient, check out the [The LLM Course](https://huggingface.co/learn/llm-course/chapter1/1).
 
+After completing this unit (and the assignment), don’t forget to test your knowledge with the [quiz](https://huggingface.co/spaces/smol-course/unit_3_quiz)!
+
 </Tip>
 
 ## What is Preference Alignment?
diff --git a/units/en/unit3/4.md b/units/en/unit3/4.md
@@ -110,6 +110,11 @@ Within the PR text, share both your training and evaluation commands.
 
 Once the PR is merged, your DPO-aligned model will be added to the leaderboard! You can check the leaderboard [here](https://huggingface.co/spaces/smol-course/leaderboard).
 
+## Test your knowledge
+
+You’ve completed the unit — great work!
+Now put your learning to the test by taking the [quiz](https://huggingface.co/spaces/smol-course/unit_3_quiz).
+
 ## Resources and Further Reading
 
 - [Unit 3 DPO Exercise](./4) - Complete DPO training guide
diff --git a/units/en/unit4/1.md b/units/en/unit4/1.md
@@ -3,12 +3,13 @@
 Vision Language Models (VLMs) can understand both images and text simultaneously, enabling tasks like image captioning, visual question answering, and multimodal reasoning. Just like LLMs, VLMs are trained to predict the next token — but with the added ability to process visual information. For example, [`HuggingFaceTB/SmolVLM2-2.2B-Base`](https://huggingface.co/HuggingFaceTB/SmolVLM2-2.2B-Base) is a base VLM model, while [`HuggingFaceTB/SmolVLM2-2.2B-Instruct`](https://huggingface.co/HuggingFaceTB/SmolVLM2-2.2B-Instruct) is instruction-tuned for chat-like interactions with users.
 
 In this unit, we will explore how these models are built, how they work, and, most importantly, how you can use and adapt them for your own projects.
-
 <Tip>
 
 By the end of this unit, you’ll fine-tune a VLM using the same techniques you’ve already learned in previous units (like SFT). As ever, this unit is *smol but fast*!
 
-If you’re looking for a deeper dive into computer vision, check out [The Community Computer Vision Course](https://huggingface.co/learn/computer-vision-course).  
+If you’re looking for a deeper dive into computer vision, check out [The Community Computer Vision Course](https://huggingface.co/learn/computer-vision-course).
+
+After completing this unit (and the assignment), don’t forget to test your knowledge with the [quiz](https://huggingface.co/spaces/smol-course/unit_4_quiz)!
 
 </Tip>
 
diff --git a/units/en/unit4/4.md b/units/en/unit4/4.md
@@ -448,6 +448,11 @@ Once the job completes:
 
 This workflow removes the overhead of managing local resources, allowing you to focus on **model experimentation and evaluation**.
 
+## Test your knowledge
+
+You’ve completed the unit — great work!
+Now put your learning to the test by taking the [quiz](https://huggingface.co/spaces/smol-course/unit_4_quiz).
+
 ## Resources for Further Learning
 
 Here are some helpful resources to deepen your understanding and continue experimenting with vision language models and TRL workflows:
