Merge pull request #40 from linsong8208/main

correct spelling mistakes

Common_use_cases.ipynb

@@ -189,7 +189,7 @@
         "* Token to indicate the beginning of dialogue turn: `<start_of_turn>`\n",
         "* Token to indicate the end of dialogue turn: `<end_of_turn>`\n",
         "\n",
-        "Here's the [official documentation](https://ai.google.dev/gemma/docs/formatting) regarding promping instruction-tuned models."
+        "Here's the [official documentation](https://ai.google.dev/gemma/docs/formatting) regarding prompting instruction-tuned models."
       ]
     },
     {

Gemma/Advanced_Prompting_Techniques.ipynb

@@ -181,7 +181,7 @@
         "* Token to indicate the beginning of dialogue turn: `<start_of_turn>`\n",
         "* Token to indicate the end of dialogue turn: `<end_of_turn>`\n",
         "\n",
-        "Here's the [official documentation](https://ai.google.dev/gemma/docs/formatting) regarding promping instruction-tuned models."
+        "Here's the [official documentation](https://ai.google.dev/gemma/docs/formatting) regarding prompting instruction-tuned models."
       ]
     },
     {

Gemma/Prompt_chaining.ipynb

@@ -189,7 +189,7 @@
         "\n",
         "Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. They are text-to-text, decoder-only large language models, available in English, with open weights, pre-trained variants, and instruction-tuned variants. Gemma models are well-suited for a variety of text generation tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as a laptop, desktop or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone.\n",
         "\n",
-        "Here's the [official documentation](https://ai.google.dev/gemma/docs/formatting) regarding promping instruction-tuned models."
+        "Here's the [official documentation](https://ai.google.dev/gemma/docs/formatting) regarding prompting instruction-tuned models."
       ]
     },
     {

Gemma/Using_Gemma_with_LangChain.ipynb

@@ -162,7 +162,7 @@
         "* Token to indicate the beginning of dialogue turn: `<start_of_turn>`\n",
         "* Token to indicate the end of dialogue turn: `<end_of_turn>`\n",
         "\n",
-        "Here's the [official documentation](https://ai.google.dev/gemma/docs/formatting) regarding promping instruction-tuned models."
+        "Here's the [official documentation](https://ai.google.dev/gemma/docs/formatting) regarding prompting instruction-tuned models."
       ]
     },
     {
@@ -621,7 +621,7 @@
         "# Create an actual chain\n",
         "\n",
         "rag_chain = (\n",
-        "    # First you need retrieve documnets that are relevant to the\n",
+        "    # First you need retrieve documents that are relevant to the\n",
         "    # given query\n",
         "    {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
         "    # The output is passed the prompt and fills fields like `{question}`\n",