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@@ -16,7 +16,7 @@
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},
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"source": [
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"# **Getting to know Llama 3: Everything you need to start building**\n",
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- "Our goal in this session is to provide a guided tour of Llama 3, including understanding different Llama 3 models, how and where to access them, Generative AI and Chatbot architectures, prompt engineering, RAG (Retrieval Augmented Generation), Fine-tuning and more. All this is implemented with a starter code for you to take it and use it in your Llama 3 projects."
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+ "Our goal in this session is to provide a guided tour of Llama 3 with comparison with Llama 2, including understanding different Llama 3 models, how and where to access them, Generative AI and Chatbot architectures, prompt engineering, RAG (Retrieval Augmented Generation), Fine-tuning and more. All this is implemented with a starter code for you to take it and use it in your Llama 3 projects."
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]
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},
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{
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@@ -25,9 +25,8 @@
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"id": "ioVMNcTesSEk"
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},
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"source": [
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- "##**0 - Prerequisites**\n",
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+ "### **0 - Prerequisites**\n",
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"* Basic understanding of Large Language Models\n",
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- "\n",
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"* Basic understanding of Python"
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]
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},
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@@ -303,7 +302,9 @@
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"source": [
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"## **2 - Using and Comparing Llama 3 and Llama 2**\n",
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"\n",
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- "In this notebook, we will use the Llama 2 7b chat and Llama 3 8b instruct models hosted on [Groq](https://console.groq.com/). You'll need to first [sign in](https://console.groq.com/) with your github or gmail account, then get an [API token](https://console.groq.com/keys) to try Groq out for free.\n"
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+ "In this notebook, we will use the Llama 2 70b chat and Llama 3 8b and 70b instruct models hosted on [Groq](https://console.groq.com/). You'll need to first [sign in](https://console.groq.com/) with your github or gmail account, then get an [API token](https://console.groq.com/keys) to try Groq out for free. (Groq runs Llama models very fast and they only support one Llama 2 model: the Llama 2 70b chat).\n",
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+ "\n",
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+ "**Note: You can also use other Llama hosting providers such as [Replicate](https://replicate.com/blog/run-llama-3-with-an-api?input=python), [Togther](https://docs.together.ai/docs/quickstart). Simply click the links here to see how to run `pip install` and use their freel trial API key with example code to modify the following three cells in 2.1 and 2.2.**\n"
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]
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},
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{
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@@ -1552,46 +1553,11 @@
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"* Types (PEFT, LoRA, QLoRA)\n",
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"* Using PyTorch for Pre-Training & Fine-Tuning\n",
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"\n",
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- "* Evals + Quality\n"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 7,
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- "metadata": {
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- "colab": {
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- "base_uri": "https://localhost:8080/",
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- "height": 79
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- },
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- "executionInfo": {
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- "elapsed": 327,
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- "status": "ok",
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- "timestamp": 1695832272878,
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- "user": {
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- "displayName": "Amit Sangani",
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- "userId": "11552178012079240149"
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- },
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- "user_tz": 420
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- },
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- "id": "0a9CvJ8YcTzV",
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- "outputId": "56a6d573-a195-4e3c-834d-a3b23485186c"
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- },
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- "outputs": [
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- {
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- "data": {
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- "text/html": [
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- "<img src=\"https://mermaid.ink/img/CiAgZ3JhcGggTFI7CiAgICAgIEN1c3RvbV9EYXRhc2V0IC0tPiBQcmUtdHJhaW5lZF9MbGFtYQogICAgICBQcmUtdHJhaW5lZF9MbGFtYSAtLT4gRmluZS10dW5lZF9MbGFtYQogICAgICBGaW5lLXR1bmVkX0xsYW1hIC0tPiBSTEhGCiAgICAgIFJMSEYgLS0+IHxMb3NzOkNyb3NzLUVudHJvcHl8RmluZS10dW5lZF9MbGFtYQogICAgICBjbGFzc0RlZiBkZWZhdWx0IGZpbGw6I0NDRTZGRixzdHJva2U6Izg0QkNGNSx0ZXh0Q29sb3I6IzFDMkIzMyxmb250RmFtaWx5OnRyZWJ1Y2hldCBtczsKICA=\"/>"
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- ],
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- "text/plain": [
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- "<IPython.core.display.Image object>"
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- ]
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- },
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- "metadata": {},
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- "output_type": "display_data"
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- }
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- ],
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- "source": [
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- "fine_tuned_arch()"
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+ "* Evals + Quality\n",
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+ "\n",
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+ "Examples of Fine-Tuning:\n",
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+ "* [Meta Llama Recipes](https://github.com/meta-llama/llama-recipes/tree/main/recipes/finetuning)\n",
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+ "* [Hugging Face fine-tuning with Llama 3](https://huggingface.co/blog/llama3#fine-tuning-with-%F0%9F%A4%97-trl)\n"
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]
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},
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{
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