delete real questions from example

add uv dependency management
add unit 1 quiz as json with upload scripts
2025-01-29 09:54:35 +01:00 · 2025-01-28 11:09:53 +01:00 · 2025-01-28 11:09:36 +01:00 · 2025-01-28 11:09:14 +01:00
5 changed files with 634 additions and 115 deletions
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -5,6 +5,7 @@ description = "Add your description here"
 readme = "README.md"
 requires-python = ">=3.11"
 dependencies = [
+    "datasets>=3.2.0",
    "huggingface-hub>=0.27.1",
    "ipykernel>=6.29.5",
    "requests>=2.32.3",
--- a/quiz/data/unit_1.json
+++ b/quiz/data/unit_1.json
@@ -0,0 +1,10 @@
+[
+    {
+        "question": "Which of the following best describes a Large Language Model (LLM)?",
+        "answer_a": "A model specializing in language recognition",
+        "answer_b": "A massive neural network that understands and generates human language",
+        "answer_c": "A model exclusively used for language data tasks like summarization or classification",
+        "answer_d": "A rule-based chatbot used for conversations",
+        "correct_answer": "B"
+    }
+]
--- a/quiz/push_questions.py
+++ b/quiz/push_questions.py
@@ -0,0 +1,33 @@
+import json
+from pathlib import Path
+from datasets import Dataset
+from huggingface_hub import HfApi
+
+
+ORG_NAME = "agents-course"
+
+
+def main():
+    """Push quiz questions to the Hugging Face Hub"""
+
+    for file in Path("data").glob("*.json"):
+        print(f"Processing {file}")
+
+        with open(file, "r") as f:
+            quiz_data = json.load(f)
+
+        repo_id = f"{ORG_NAME}/{file.stem}_quiz"
+
+        dataset = Dataset.from_list(quiz_data)
+
+        print(f"Pushing {repo_id} to the Hugging Face Hub")
+
+        dataset.push_to_hub(
+            repo_id,
+            private=True,
+            commit_message=f"Update quiz questions for {file.stem}",
+        )
+
+
+if __name__ == "__main__":
+    main()
--- a/units/1_fundemantals/10_quizz.md
+++ b/units/1_fundemantals/10_quizz.md
@@ -1,116 +1,16 @@
-# Unit 1 Quiz: Fundamentals of AI Agents
+# Unit 1 Quiz

+Well done on working through the first unit!

-## Quiz on Large Language Models (LLMs)
+Let's test your understanding of the key concepts covered in this unit.

-1. Which of the following best describes a Large Language Model (LLM)?
-   - A. A model specializing in language recognition  
-   - B. A massive neural network that understands and generates human language  
-   - C. A model exclusively used for language data tasks like summarization or classification  
-   - D. A rule-based chatbot used for conversations  
+## Quiz

-2. LLMs are typically:  
-   - A. Pre-trained on small, curated datasets  
-   - B. Trained on large text corpora to capture linguistic patterns  
-   - C. Trained purely on translation tasks  
-   - D. Designed to function solely with GPU resources  
+Here is the interactive quiz. The quiz is hosted on the Hugging Face Hub in a space. It will take you through a set of multiple choice questions to test your understanding of the key concepts covered in this unit. Once you've completed the quiz, you'll be able to see your score and a breakdown of the correct answers.

-3. Which of the following is a common architecture for LLMs?  
-   - A. Convolutional Neural Networks (CNNs)  
-   - B. Transformer  
-   - C. Recurrent Neural Networks (RNNs) with LSTM  
-   - D. Support Vector Machines  
-
-4. What does it mean when we say LLMs are "autoregressive"?  
-   - A. They regress to the mean to reduce variance  
-   - B. They generate text by predicting the next token based on previous tokens  
-   - C. They can only handle labeled data  
-   - D. They can output text only after the entire input is known at once  
-
-5. Which of these is NOT a common use of LLMs?  
-   - A. Summarizing content  
-   - B. Generating code  
-   - C. Playing strategy games like chess or Go  
-   - D. Conversational AI  
-
-## Quiz on Messages and Special Tokens
-
-1. Which of the following best describes a "special token"?
-   - A. A token that makes the model forget all context  
-   - B. A model signature required for API calls  
-   - C. A token that helps segment or structure the conversation in the model  
-   - D. A token that always represents the end of text  
-
-2. What is the primary goal of a "chat template"?  
-   - A. To force the model into a single-turn conversation  
-   - B. To structure interactions and define roles in a conversation  
-   - C. To replace the need for system messages  
-   - D. To store prompts into the model's weights permanently  
-
-3. How do tokenizers handle text for modern NLP models?  
-   - A. By splitting text into individual words only  
-   - B. By breaking words into subword units and assigning numerical IDs  
-   - C. By storing text directly without transformation  
-   - D. By removing all punctuation automatically  
-
-4. Which role in a conversation sets the overall behavior for a model?
-   - A. user  
-   - B. system  
-   - C. assistant  
-   - D. developer  
-
-5. Which statement is TRUE about tool usage in chat templates?  
-   - A. Tools cannot be used within the conversation context.  
-   - B. Tools are used only for logging messages.  
-   - C. Tools allow the assistant to offload tasks like web search or calculations.  
-   - D. Tools are unsupported in all modern LLMs.  
-
-
-<!-- TODO: @burtenshaw add more questions on completion -->
-
-<details>
-<summary>solution</summary>
-
-### LLMs Solutions (Updated)
-1. **Question**: Which of the following best describes a Large Language Model (LLM)?  
-   **Answer**: B. A massive neural network that understands and generates human language  
-   **Explanation**: LLMs have millions to billions of parameters and can capture complex patterns in large text corpora.
-
-2. **Question**: LLMs are typically:  
-   **Answer**: B. Trained on large text corpora to capture linguistic patterns  
-   **Explanation**: Training on large datasets helps them learn grammar, context, and various linguistic structures.
-
-3. **Question**: Which of the following is a common architecture for LLMs?  
-   **Answer**: B. Transformer  
-   **Explanation**: The Transformer architecture is widely used by modern LLMs like Llama, GPT and BERT.
-
-4. **Question**: What does it mean when we say LLMs are "autoregressive"?  
-   **Answer**: B. They generate text by predicting the next token based on previous tokens  
-   **Explanation**: Autoregressive generation means output tokens are predicted sequentially, one after another.
-
-5. **Question**: Which of these is NOT a common use of LLMs?  
-   **Answer**: C. Playing strategy games like chess or Go  
-   **Explanation**: While LLMs excel at language tasks, they are not typically used for strategy board games.
-
-### Messages and Special Tokens Solutions (Unchanged)
-1. **Question**: Which of the following best describes a "special token"?  
-   **Answer**: C. A token that helps segment or structure the conversation in the model  
-   **Explanation**: Special tokens often indicate the start/end of roles or separate different parts of the conversation.
-
-2. **Question**: What is the primary goal of a "chat template"?  
-   **Answer**: B. To structure interactions and define roles in a conversation  
-   **Explanation**: Chat templates maintain format consistency, ensuring the model knows who is speaking and why.
-
-3. **Question**: How do tokenizers handle text for modern NLP models?  
-   **Answer**: B. By breaking words into subword units and assigning numerical IDs  
-   **Explanation**: Subword tokenization lets models handle rare or unknown words by splitting them into smaller chunks.
-
-4. **Question**: Which role in a conversation sets the overall behavior for a model?  
-   **Answer**: B. system  
-   **Explanation**: System messages set global instructions that the model follows during the entire conversation.
-
-5. **Question**: Which statement is TRUE about tool usage in chat templates?  
-   **Answer**: C. Tools allow the assistant to offload tasks like web search or calculations.  
-   **Explanation**: Tools extend model functionality by allowing external function calls within a conversation context.
-
-</details>
+<iframe
+    src="https://agents-course-unit_1_quiz.hf.space"
+    frameborder="0"
+    width="850"
+    height="450"
+></iframe>
--- a/uv.lock
+++ b/uv.lock
@@ -1,11 +1,16 @@
 version = 1
 requires-python = ">=3.11"
+resolution-markers = [
+    "python_full_version < '3.12'",
+    "python_full_version >= '3.12'",
+]

 [[package]]
 name = "agents-course"
 version = "0.1.0"
 source = { virtual = "." }
 dependencies = [
+    { name = "datasets" },
    { name = "huggingface-hub" },
    { name = "ipykernel" },
    { name = "requests" },
@@ -13,11 +18,95 @@ dependencies = [

 [package.metadata]
 requires-dist = [
+    { name = "datasets", specifier = ">=3.2.0" },
    { name = "huggingface-hub", specifier = ">=0.27.1" },
    { name = "ipykernel", specifier = ">=6.29.5" },
    { name = "requests", specifier = ">=2.32.3" },
 ]

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Author	SHA1	Message	Date
burtenshaw	68bde9fe8b	delete real questions from example	2025-01-29 09:54:35 +01:00
burtenshaw	f326f117fb	add uv dependency management	2025-01-28 11:09:53 +01:00
burtenshaw	8ee1e96ccd	add unit 1 quiz as json with upload scripts	2025-01-28 11:09:36 +01:00
burtenshaw	3b38de6a3a	set quiz page to use space iframe	2025-01-28 11:09:14 +01:00