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| "<a href=\"https://colab.research.google.com/gist/alonsosilvaallende/e2095e9ed7a6c7e68f07afcd6b0b48f5/understanding_chat_templates.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
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| "If you have worked with language models, you might have worked with lists of `messages`. Indeed, when you send a request to a language model, we can call it a `user messsage` and the response can be called an `assistant message`. A conversation would consist of a list of a `user message` followed by an `assistant message` followed by a `user message`, etc.\n", | |
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| "base_uri": "https://localhost:8080/" | |
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| "id": "rErnS-guhfnB", | |
| "outputId": "634ecb58-7c46-4662-a0ed-0fae1ba2cc58" | |
| }, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "[{'role': 'user', 'content': 'Hi!'}]" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 1 | |
| } | |
| ], | |
| "source": [ | |
| "user_message = [{\"role\": \"user\", \"content\": \"Hi!\"}]\n", | |
| "user_message" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "The assistant message will be something like this:" | |
| ], | |
| "metadata": { | |
| "id": "priJk7kdifkp" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "assistant_message = [{\"role\": \"user\", \"content\": \"Hello! How can I assist you today?\"}]\n", | |
| "assistant_message" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "5kWXmpE0iY9d", | |
| "outputId": "5468a9cb-3312-4775-d873-d2dd6f2f3c4e" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "[{'role': 'user', 'content': 'Hello! How can I assist you today?'}]" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 2 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "To have a conversation, you can pass it a list of messages:" | |
| ], | |
| "metadata": { | |
| "id": "ifTQap4qinob" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "messages = [\n", | |
| " {\"role\": \"user\", \"content\": \"Hi!\"},\n", | |
| " {\"role\": \"assistant\", \"content\": \"Hello! How can I assist you today?\"},\n", | |
| " {\"role\": \"user\", \"content\": \"What's the capital of France?\"},\n", | |
| "]\n", | |
| "messages" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "nvyuKiR0iku1", | |
| "outputId": "3042c2cd-d0ab-49f0-c491-26a1836a9573" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "[{'role': 'user', 'content': 'Hi!'},\n", | |
| " {'role': 'assistant', 'content': 'Hello! How can I assist you today?'},\n", | |
| " {'role': 'user', 'content': \"What's the capital of France?\"}]" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 3 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "How do we convert a list of messages to an input text?" | |
| ], | |
| "metadata": { | |
| "id": "5ZwK7UWUi24P" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## The Chat Template" | |
| ], | |
| "metadata": { | |
| "id": "sCsf5y4ci61N" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "The **chat template** takes as input a list of messages (and tools but we will talk about it later) and convert them into one single string.\n", | |
| "\n", | |
| "Let's look at a [chat template app](https://huggingface.co/spaces/alonsosilva/apply_chat_template)\n", | |
| "\n", | |
| "Let's see what the chat template does with an example.\n", | |
| "\n", | |
| "Let's download a tokenizer:" | |
| ], | |
| "metadata": { | |
| "id": "4sVcCAaejARW" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "from transformers import AutoTokenizer\n", | |
| "\n", | |
| "MODEL_ID = 'Qwen/Qwen2.5-0.5B-Instruct'\n", | |
| "tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 235, | |
| "referenced_widgets": [ | |
| "aed80150d65241339a2d25706935e2c6", | |
| "04390f165042471a956307b3d2a952ed", | |
| "62547fdd35aa4656975eb020a2ba2a5b", | |
| "78e2f36b1782429db18c4b5f4dad3b7c", | |
| "e884a82329e44127814cdc4e6bd2abf4", | |
| "d257a66e12a14eaba5b4cbc1a7ba61d5", | |
| "c78fdf3f75e54beb8575274e7579497e", | |
| "51a0764cdccd4fa1844ccaf167cbe1aa", | |
| "b4b65ff5a66b4908b03d4b87c351a2f9", | |
| "c8bea39e9da440e3b5490899f7b26704", | |
| "61fcea84d0ff41b4a66a9734d4ae5a4b", | |
| "1a1f499f1a4e4f0fa551282fc5cfb352", | |
| "9ae3c3875da446e0a70372beda79e74a", | |
| "10a3cfc1716c4079aabdeac2baacd15a", | |
| "5b1c6d0e106f4428a6141b0217e0f29b", | |
| "d8e004f5f1b445f29831672c85504d30", | |
| "ebb8939dd89d42f5a6af7d597e36ea7e", | |
| "3c27d54acd144cb79191f989cf874e0f", | |
| "8fe9f921887b4c41a02d92458c4baa7a", | |
| "35bbcd9030e042068091d9aae7120022", | |
| "e29965e16aff4d27a690ddd99a1556e1", | |
| "8b51a7feed1548c59b6bd4f41a55a452", | |
| "6bf3330d92384454964fc6b98a430117", | |
| "9053bd396731412bada0ed397ce5159f", | |
| "7fe913f2ba984709962b9e674cf429fb", | |
| "9d280bacd7674c3083cba8432b8c133f", | |
| "62cbf89e7ce14eb2ad8d203986b5560b", | |
| "9de1f6834372461d8d80c8374dacf313", | |
| "ec7fb9604bdc476893537fde92f1058e", | |
| "eacf1ecc25ff4e96a093446e9573e08c", | |
| "3d81521d641b4462b045430da4357d44", | |
| "c6d85ccdcdf540ffb2a383cf4d4c1b8d", | |
| "d0da96bdb1c54b76a48d4b46f12b082a", | |
| "1610c6baded04892a282488102b74066", | |
| "4b5bbc7aa1b34e3b882b17ea9172ef12", | |
| "e324d136091a46c9a0c6c06027d7e637", | |
| "e8a25c5e86fd4496b99fcef5928ff11c", | |
| "6d547cd4e28f4bb199b618ee34e154ec", | |
| "2e28c856a17d42408153c074c1c71822", | |
| "4539ad18b4924a6da17bb37059ada483", | |
| "d6e9eb2909ea4b1b86ac2bdecd0da276", | |
| "7428056e72354c89a6960be6a11a831a", | |
| "7ff88505fdfd4651bda4a1bc70d08dc9", | |
| "f88316797cb24ad9b068e8e46420fbae" | |
| ] | |
| }, | |
| "id": "k0spjf4cirVd", | |
| "outputId": "167f244d-7026-4715-f08d-458185d76a30" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:86: UserWarning: \n", | |
| "Access to the secret `HF_TOKEN` has not been granted on this notebook.\n", | |
| "You will not be requested again.\n", | |
| "Please restart the session if you want to be prompted again.\n", | |
| " warnings.warn(\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "tokenizer_config.json: 0.00B [00:00, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "aed80150d65241339a2d25706935e2c6" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "vocab.json: 0.00B [00:00, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "1a1f499f1a4e4f0fa551282fc5cfb352" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "merges.txt: 0.00B [00:00, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "6bf3330d92384454964fc6b98a430117" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "tokenizer.json: 0.00B [00:00, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "1610c6baded04892a282488102b74066" | |
| } | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Let's pass it a user message:" | |
| ], | |
| "metadata": { | |
| "id": "HwCoszjqjw8k" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "message = [{\"role\": \"user\", \"content\": \"Hi!\"}]\n", | |
| "print(tokenizer.apply_chat_template(message, tokenize=False))" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "lW43Gjl0jnRm", | |
| "outputId": "b0a75cd7-06dd-4178-c34e-0108f96b3736" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "<|im_start|>system\n", | |
| "You are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n", | |
| "<|im_start|>user\n", | |
| "Hi!<|im_end|>\n", | |
| "\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Notice that when you don't provide a `system message`, the model `Qwen/Qwen2.5-0.5B-Instruct` adds a `system message` (\"You are Qwen, created by Alibaba Cloud. You are a helpful assistant.\"). Perhaps you want to change that to a different `system message`:" | |
| ], | |
| "metadata": { | |
| "id": "Y0zmXATzkAOj" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "messages = [\n", | |
| " {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n", | |
| " {\"role\": \"user\", \"content\": \"Hi!\"},\n", | |
| "]\n", | |
| "print(tokenizer.apply_chat_template(messages, tokenize=False))" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "yJP89wPLj4xO", | |
| "outputId": "2f31b7b2-55b1-4c64-e10a-98e7963c3e59" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "<|im_start|>system\n", | |
| "You are a helpful assistant.<|im_end|>\n", | |
| "<|im_start|>user\n", | |
| "Hi!<|im_end|>\n", | |
| "\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "We can also see how does the chat template convert a conversation like this one:" | |
| ], | |
| "metadata": { | |
| "id": "9bRnzNImlNRt" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "messages = [\n", | |
| " {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n", | |
| " {\"role\": \"user\", \"content\": \"Hi!\"},\n", | |
| " {\"role\": \"assistant\", \"content\": \"Hello! How can I assist you today?\"},\n", | |
| " {\"role\": \"user\", \"content\": \"What's the capital of France?\"},\n", | |
| "]\n", | |
| "print(tokenizer.apply_chat_template(messages, tokenize=False))" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "fvqtoisykHhm", | |
| "outputId": "4c51f162-7177-43ec-ab29-b3ca2bac6c7c" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "<|im_start|>system\n", | |
| "You are a helpful assistant.<|im_end|>\n", | |
| "<|im_start|>user\n", | |
| "Hi!<|im_end|>\n", | |
| "<|im_start|>assistant\n", | |
| "Hello! How can I assist you today?<|im_end|>\n", | |
| "<|im_start|>user\n", | |
| "What's the capital of France?<|im_end|>\n", | |
| "\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Each model has its own chat template. Let's take a look at `Mistral-7B-v0.3` chat template:" | |
| ], | |
| "metadata": { | |
| "id": "e3luE09_la96" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "MISTRAL_MODEL_ID = \"mistralai/Mistral-7B-Instruct-v0.3\" # if it doesn't work try \"MaziyarPanahi/Mistral-7B-v0.3\"\n", | |
| "mistral_tokenizer = AutoTokenizer.from_pretrained(MISTRAL_MODEL_ID)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 145, | |
| "referenced_widgets": [ | |
| "b9f9b6580f5841a38fb7803bc2226170", | |
| "f50df517c68441a2b34debf37aa86a99", | |
| "470e548951534017b1c97efb961bf518", | |
| "f2f7f95468994fe19da235483432c23b", | |
| "551f2e3b72304935906b31cd9aa0eaac", | |
| "5fba9907e95e4449a7e40847c269f05e", | |
| "fc99a75e2fcc4a899d836cc84affec94", | |
| "f3d18e6e4c304b0a91df34c764209546", | |
| "7dee115c483f4333918cf88d8363e34c", | |
| "bd1886c47e834831a82d6ad9ceab567a", | |
| "9aa36ef270e44459a9a98b00943c9eea", | |
| "fdacf8f4fd884578a70c56208b7eab1e", | |
| "5b84386b48494bcca58ba6b74c6c0778", | |
| "a87c51e2c9e14c698649450605f9193a", | |
| "7c6c5f9434a44d4b8e724537b06dfacf", | |
| "973e2648592e45e0831c6aaa846ca1aa", | |
| "011f0c00452d451b9ca45e2d37bc03ac", | |
| "6f91a1757c7440d3a5dc8e962f7c3f0f", | |
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| }, | |
| "id": "KUiUaB5mlSZl", | |
| "outputId": "56302cf8-ecd4-4dbc-a3d7-5858c7fdf3bb" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "tokenizer_config.json: 0.00B [00:00, ?B/s]" | |
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| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "tokenizer.model: 0%| | 0.00/587k [00:00<?, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
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| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
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| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "special_tokens_map.json: 0%| | 0.00/414 [00:00<?, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "9a336c20d7ca48338518695169818455" | |
| } | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "messages = [\n", | |
| " {\"role\": \"user\", \"content\": \"Hi!\"},\n", | |
| " {\"role\": \"assistant\", \"content\": \"Hello! How can I assist you today?\"},\n", | |
| " {\"role\": \"user\", \"content\": \"What's the capital of France?\"},\n", | |
| "]\n", | |
| "print(mistral_tokenizer.apply_chat_template(messages, tokenize=False))" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "PqKwYhtRlguH", | |
| "outputId": "26974f79-dc9e-48e8-ff23-6ee604688d93" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "<s>[INST] Hi![/INST] Hello! How can I assist you today?</s>[INST] What's the capital of France?[/INST]\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Since after sending a `user message`, you expect an `assistant message`, you can help the model by basically saying \"Now, it's your turn!\". This is so useful that it has been incorporated into the chat template itself." | |
| ], | |
| "metadata": { | |
| "id": "lAyme0FHl4l3" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "The parameter is `add_generation_prompt=True`, then the whole instruction becomes:\n", | |
| "```\n", | |
| "tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\n", | |
| "```" | |
| ], | |
| "metadata": { | |
| "id": "6KyL4I3Ll-8r" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "messages = [\n", | |
| " {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n", | |
| " {\"role\": \"user\", \"content\": \"Hi!\"},\n", | |
| "]\n", | |
| "print(tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True))" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "9mlFUzl-l-Qm", | |
| "outputId": "65a9b26a-4e44-4f46-9382-2e402a490ca5" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "<|im_start|>system\n", | |
| "You are a helpful assistant.<|im_end|>\n", | |
| "<|im_start|>user\n", | |
| "Hi!<|im_end|>\n", | |
| "<|im_start|>assistant\n", | |
| "\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Tool calls\n", | |
| "\n", | |
| "The chat template also handles tool calls. That means that we can provide a list of tools (let's do one as an example):" | |
| ], | |
| "metadata": { | |
| "id": "eXJZLXJPmZ-5" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "tools = [\n", | |
| " {\n", | |
| " \"type\": \"function\",\n", | |
| " \"function\": {\n", | |
| " \"name\": \"Python_REPL\",\n", | |
| " \"description\": \"A Python shell. Use this to execute python commands. Input should be a valid python command. If you want to see the output of a value, you should print it out with `print(...)`.\",\n", | |
| " \"parameters\": {\n", | |
| " \"properties\": {\n", | |
| " \"python_code\": {\n", | |
| " \"description\": \"Valid python command.\",\n", | |
| " \"type\": \"string\",\n", | |
| " }\n", | |
| " },\n", | |
| " \"required\": [\"python_code\"],\n", | |
| " \"type\": \"object\",\n", | |
| " },\n", | |
| " },\n", | |
| " }\n", | |
| "]" | |
| ], | |
| "metadata": { | |
| "id": "Vez--HHwmFB1" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "The parameter is `tools=...`, then the whole instruction becomes:\n", | |
| "```\n", | |
| "tokenizer.apply_chat_template(messages, tokenize=False, tools=tools, add_generation_prompt=True)\n", | |
| "```" | |
| ], | |
| "metadata": { | |
| "id": "ihODUrjsmnTR" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "messages = [\n", | |
| " {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n", | |
| " {\"role\": \"user\", \"content\": \"What's 2 to the power of 5?\"},\n", | |
| "]\n", | |
| "\n", | |
| "tools = [\n", | |
| " {\n", | |
| " \"type\": \"function\",\n", | |
| " \"function\": {\n", | |
| " \"name\": \"Python_REPL\",\n", | |
| " \"description\": \"A Python shell. Use this to execute python commands. Input should be a valid python command. If you want to see the output of a value, you should print it out with `print(...)`.\",\n", | |
| " \"parameters\": {\n", | |
| " \"properties\": {\n", | |
| " \"python_code\": {\n", | |
| " \"description\": \"Valid python command.\",\n", | |
| " \"type\": \"string\",\n", | |
| " }\n", | |
| " },\n", | |
| " \"required\": [\"python_code\"],\n", | |
| " \"type\": \"object\",\n", | |
| " },\n", | |
| " },\n", | |
| " }\n", | |
| "]\n", | |
| "\n", | |
| "print(tokenizer.apply_chat_template(messages, tokenize=False, tools=tools, add_generation_prompt=True))" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "tXFZL8uVmj0O", | |
| "outputId": "3f94abb0-3ea0-49bb-cbee-315e557564aa" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "<|im_start|>system\n", | |
| "You are a helpful assistant.\n", | |
| "\n", | |
| "# Tools\n", | |
| "\n", | |
| "You may call one or more functions to assist with the user query.\n", | |
| "\n", | |
| "You are provided with function signatures within <tools></tools> XML tags:\n", | |
| "<tools>\n", | |
| "{\"type\": \"function\", \"function\": {\"name\": \"Python_REPL\", \"description\": \"A Python shell. Use this to execute python commands. Input should be a valid python command. If you want to see the output of a value, you should print it out with `print(...)`.\", \"parameters\": {\"properties\": {\"python_code\": {\"description\": \"Valid python command.\", \"type\": \"string\"}}, \"required\": [\"python_code\"], \"type\": \"object\"}}}\n", | |
| "</tools>\n", | |
| "\n", | |
| "For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n", | |
| "<tool_call>\n", | |
| "{\"name\": <function-name>, \"arguments\": <args-json-object>}\n", | |
| "</tool_call><|im_end|>\n", | |
| "<|im_start|>user\n", | |
| "What's 2 to the power of 5?<|im_end|>\n", | |
| "<|im_start|>assistant\n", | |
| "\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "For Mistral tokenizer it becomes:" | |
| ], | |
| "metadata": { | |
| "id": "ZpmQAclCnSwc" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "print(mistral_tokenizer.apply_chat_template(messages, tokenize=False, tools=tools, add_generation_prompt=True))" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "uTDktbB3nGc3", | |
| "outputId": "423ee633-3f88-4472-9568-a8390582e93b" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "<s>[AVAILABLE_TOOLS] [{\"type\": \"function\", \"function\": {\"name\": \"Python_REPL\", \"description\": \"A Python shell. Use this to execute python commands. Input should be a valid python command. If you want to see the output of a value, you should print it out with `print(...)`.\", \"parameters\": {\"properties\": {\"python_code\": {\"description\": \"Valid python command.\", \"type\": \"string\"}}, \"required\": [\"python_code\"], \"type\": \"object\"}}}][/AVAILABLE_TOOLS][INST] You are a helpful assistant.\n", | |
| "\n", | |
| "What's 2 to the power of 5?[/INST]\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Quite complex string indeed.\n", | |
| "\n", | |
| "Let's look at some other model `NousResearch/Hermes-3-Llama-3.1-8B`:" | |
| ], | |
| "metadata": { | |
| "id": "a5WbNA8Rndiv" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "NOUS_MODEL_ID = \"NousResearch/Hermes-3-Llama-3.1-8B\" # if it doesn't work try \"MaziyarPanahi/Mistral-7B-v0.3\"\n", | |
| "nous_tokenizer = AutoTokenizer.from_pretrained(NOUS_MODEL_ID)\n", | |
| "print(nous_tokenizer.apply_chat_template(messages, tokenize=False, tools=tools, add_generation_prompt=True))" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 404, | |
| "referenced_widgets": [ | |
| "1aaa4cc858324cc280a3322eb7363b44", | |
| "91e5642cba384d499f7b2ec499ec26d0", | |
| "40026525f904415ba6d697d58b734a72", | |
| "76800caec2ec45efb9f7f9589ae629a7", | |
| "09cef94d19e6404785ac19092f65c3d0", | |
| "b588f7b8db2741dbafb185fa48b8222c", | |
| "7dcc6cf872bc4fed9179862da0ed3e5e", | |
| "a0000aa9492b43a2b8447d6bdf3db636", | |
| "dc937f7165fc4930aa9b058a2dc148f7", | |
| "df682c04e354477d8dfc71256d5f2ad9", | |
| "1f3bda81ac414259874270aa36c9bbf3", | |
| "e9a19db9d0e340659ab1726f851fe7e7", | |
| "e8131f898a3d426f80b98c641b5ac92d", | |
| "7569330edc12411abfc58e6227fade5b", | |
| "8fc389361a09497a8899933bdd693bca", | |
| "1a6619b744394c41b107b18708a0fe25", | |
| "600dfdd09ee74bfe85a4f6030e55458b", | |
| "e526edfc93a841e181195864346d3d0f", | |
| "c021fa5d76e340788d374f94b1191d11", | |
| "516fb6551de247219ce60253c3af0cc1", | |
| "e6b086bcee1f44c19996648e3f5b18e5", | |
| "386ca1e584d74ee1bd7386048d533934", | |
| "4ee960ffadca42ffb12ac391772820c6", | |
| "b7f4cdc4484d4bf7808b114e25232e0a", | |
| "a5bc4190d74e4931aaabd7b5cd566b05", | |
| "3f7b2e38f5f8402eb5151af536a446bc", | |
| "22eda8d7d1514b78a16065bc36162d7a", | |
| "00fae68eb4da4735ae03ba2ea7b13703", | |
| "9fb4ad0f1909435fa9537bc8e8d86809", | |
| "98286bdeda944881979db2658fff131b", | |
| "4f40d8d79d2849d0af5457bcebd1f26f", | |
| "56d0523257604daf91847fa6b39230b0", | |
| "0085f662d11544ad8e9c9e898f8c52d6" | |
| ] | |
| }, | |
| "id": "TfTkxIDmnPkl", | |
| "outputId": "fa92a54d-0467-40b6-d2d7-bdd25dea2378" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "tokenizer_config.json: 0.00B [00:00, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "1aaa4cc858324cc280a3322eb7363b44" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "tokenizer.json: 0.00B [00:00, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "e9a19db9d0e340659ab1726f851fe7e7" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "special_tokens_map.json: 0%| | 0.00/444 [00:00<?, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "4ee960ffadca42ffb12ac391772820c6" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "<|begin_of_text|><|im_start|>system\n", | |
| "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. Here are the available tools: <tools> {\"type\": \"function\", \"function\": {\"name\": \"Python_REPL\", \"description\": \"Python_REPL(python_code: str) - A Python shell. Use this to execute python commands. Input should be a valid python command. If you want to see the output of a value, you should print it out with `print(...)`.\n", | |
| "\n", | |
| " Args:\n", | |
| " python_code(str): Valid python command.\", \"parameters\": {\"properties\": {\"python_code\": {\"description\": \"Valid python command.\", \"type\": \"string\"}}, \"required\": [\"python_code\"], \"type\": \"object\"}} </tools>Use the following pydantic model json schema for each tool call you will make: {\"properties\": {\"name\": {\"title\": \"Name\", \"type\": \"string\"}, \"arguments\": {\"title\": \"Arguments\", \"type\": \"object\"}}, \"required\": [\"name\", \"arguments\"], \"title\": \"FunctionCall\", \"type\": \"object\"}}\n", | |
| "For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows:\n", | |
| "<tool_call>\n", | |
| "{\"name\": <function-name>, \"arguments\": <args-dict>}\n", | |
| "</tool_call><|im_end|>\n", | |
| "<|im_start|>system\n", | |
| "You are a helpful assistant.<|im_end|>\n", | |
| "<|im_start|>user\n", | |
| "What's 2 to the power of 5?<|im_end|>\n", | |
| "<|im_start|>assistant\n", | |
| "\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "I don't like this chat template. It appears that our messages have **two different and consecutive** system prompts. I prefer much more the previous chat template of the model \"Qwen/Qwen2.5-0.5B-Instruct\"." | |
| ], | |
| "metadata": { | |
| "id": "FtmWalgFn_PY" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Thinking mode\n", | |
| "\n", | |
| "A recent addition is the `enable_thinking` in some new reasoning models where the model will \"think\" between some tags, e.g. `<think>...</think>`. For example in `Qwen/Qwen3-0.6B` the model has the possibility to reason (which is *the default*), but you can turn this option off if you want to.\n", | |
| "\n", | |
| "The parameter is `enable_thinking=False` so the whole instruction is:\n", | |
| "```\n", | |
| "tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, enable_thinking=False)\n", | |
| "```" | |
| ], | |
| "metadata": { | |
| "id": "aMBoq7mroJPk" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "MODEL_ID = 'Qwen/Qwen3-0.6B'\n", | |
| "reasoning_tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)\n", | |
| "\n", | |
| "messages = [\n", | |
| " {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n", | |
| " {\"role\": \"user\", \"content\": \"How many r's in strawberry?\"},\n", | |
| "]\n", | |
| "\n", | |
| "print(reasoning_tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, enable_thinking=False))" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 326, | |
| "referenced_widgets": [ | |
| "7b683fc6c5314e54875e2065b9dd8238", | |
| "08c1bb6906004a9797f7032cab454973", | |
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| "b091fe79c699490a81a242cc0a85670c", | |
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| "a073a774dda64ecda2cf15796108bd4d", | |
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| "315f3c5e718841e3806d9e79043f1492", | |
| "e52354728268408bbacfe3712fc575fe" | |
| ] | |
| }, | |
| "id": "K5EK0BpWn4Bv", | |
| "outputId": "8ac450e8-f8f6-4c5a-c620-6816ad6be584" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "tokenizer_config.json: 0.00B [00:00, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "7b683fc6c5314e54875e2065b9dd8238" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "vocab.json: 0.00B [00:00, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
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| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "merges.txt: 0.00B [00:00, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "edbda31d43934336ad098cf1bda75a00" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "tokenizer.json: 0%| | 0.00/11.4M [00:00<?, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "9a01013b1b6946be9b78834d7c1c61f3" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "<|im_start|>system\n", | |
| "You are a helpful assistant.<|im_end|>\n", | |
| "<|im_start|>user\n", | |
| "How many r's in strawberry?<|im_end|>\n", | |
| "<|im_start|>assistant\n", | |
| "<think>\n", | |
| "\n", | |
| "</think>\n", | |
| "\n", | |
| "\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "I saw some influencers (with multiple reposts) claiming they found a 'hack' to make the model not think, and it consisted of appending `<think>\\n\\n</think>`.\n", | |
| "\n", | |
| "This is exactly what the chat template does!!!" | |
| ], | |
| "metadata": { | |
| "id": "2yNvDvbapCFi" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## How does the chat template handles this?\n", | |
| "\n", | |
| "The chat template has been programmed in [Jinja](https://jinja.palletsprojects.com/en/stable/) which is usually used in web development.\n", | |
| "You can see the chat template with the following command:" | |
| ], | |
| "metadata": { | |
| "id": "rAGwxCNpqZkU" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "print(tokenizer.chat_template)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "UGxBgyNlo1PW", | |
| "outputId": "4fc63437-9cac-4f5e-b37b-8c4dceb96e65" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "{%- if tools %}\n", | |
| " {{- '<|im_start|>system\\n' }}\n", | |
| " {%- if messages[0]['role'] == 'system' %}\n", | |
| " {{- messages[0]['content'] }}\n", | |
| " {%- else %}\n", | |
| " {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n", | |
| " {%- endif %}\n", | |
| " {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n", | |
| " {%- for tool in tools %}\n", | |
| " {{- \"\\n\" }}\n", | |
| " {{- tool | tojson }}\n", | |
| " {%- endfor %}\n", | |
| " {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n", | |
| "{%- else %}\n", | |
| " {%- if messages[0]['role'] == 'system' %}\n", | |
| " {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n", | |
| " {%- else %}\n", | |
| " {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n", | |
| " {%- endif %}\n", | |
| "{%- endif %}\n", | |
| "{%- for message in messages %}\n", | |
| " {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n", | |
| " {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n", | |
| " {%- elif message.role == \"assistant\" %}\n", | |
| " {{- '<|im_start|>' + message.role }}\n", | |
| " {%- if message.content %}\n", | |
| " {{- '\\n' + message.content }}\n", | |
| " {%- endif %}\n", | |
| " {%- for tool_call in message.tool_calls %}\n", | |
| " {%- if tool_call.function is defined %}\n", | |
| " {%- set tool_call = tool_call.function %}\n", | |
| " {%- endif %}\n", | |
| " {{- '\\n<tool_call>\\n{\"name\": \"' }}\n", | |
| " {{- tool_call.name }}\n", | |
| " {{- '\", \"arguments\": ' }}\n", | |
| " {{- tool_call.arguments | tojson }}\n", | |
| " {{- '}\\n</tool_call>' }}\n", | |
| " {%- endfor %}\n", | |
| " {{- '<|im_end|>\\n' }}\n", | |
| " {%- elif message.role == \"tool\" %}\n", | |
| " {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n", | |
| " {{- '<|im_start|>user' }}\n", | |
| " {%- endif %}\n", | |
| " {{- '\\n<tool_response>\\n' }}\n", | |
| " {{- message.content }}\n", | |
| " {{- '\\n</tool_response>' }}\n", | |
| " {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n", | |
| " {{- '<|im_end|>\\n' }}\n", | |
| " {%- endif %}\n", | |
| " {%- endif %}\n", | |
| "{%- endfor %}\n", | |
| "{%- if add_generation_prompt %}\n", | |
| " {{- '<|im_start|>assistant\\n' }}\n", | |
| "{%- endif %}\n", | |
| "\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "After spending some time understanding what's going on, you can create your own if you want to change its behavior." | |
| ], | |
| "metadata": { | |
| "id": "0NnGgEm0qp9D" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [], | |
| "metadata": { | |
| "id": "PB3VRGiLqwxK" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "old_template = tokenizer.chat_template" | |
| ], | |
| "metadata": { | |
| "id": "qiVZ_iEpqyiQ" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "old_template" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 72 | |
| }, | |
| "id": "psrKIRkOrESM", | |
| "outputId": "ece85cd2-9567-489a-ed01-1dfd7bd4788b" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "'{%- if tools %}\\n {{- \\'<|im_start|>system\\\\n\\' }}\\n {%- if messages[0][\\'role\\'] == \\'system\\' %}\\n {{- messages[0][\\'content\\'] }}\\n {%- else %}\\n {{- \\'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.\\' }}\\n {%- endif %}\\n {{- \"\\\\n\\\\n# Tools\\\\n\\\\nYou may call one or more functions to assist with the user query.\\\\n\\\\nYou are provided with function signatures within <tools></tools> XML tags:\\\\n<tools>\" }}\\n {%- for tool in tools %}\\n {{- \"\\\\n\" }}\\n {{- tool | tojson }}\\n {%- endfor %}\\n {{- \"\\\\n</tools>\\\\n\\\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\\\n<tool_call>\\\\n{\\\\\"name\\\\\": <function-name>, \\\\\"arguments\\\\\": <args-json-object>}\\\\n</tool_call><|im_end|>\\\\n\" }}\\n{%- else %}\\n {%- if messages[0][\\'role\\'] == \\'system\\' %}\\n {{- \\'<|im_start|>system\\\\n\\' + messages[0][\\'content\\'] + \\'<|im_end|>\\\\n\\' }}\\n {%- else %}\\n {{- \\'<|im_start|>system\\\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\\\n\\' }}\\n {%- endif %}\\n{%- endif %}\\n{%- for message in messages %}\\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\\n {{- \\'<|im_start|>\\' + message.role + \\'\\\\n\\' + message.content + \\'<|im_end|>\\' + \\'\\\\n\\' }}\\n {%- elif message.role == \"assistant\" %}\\n {{- \\'<|im_start|>\\' + message.role }}\\n {%- if message.content %}\\n {{- \\'\\\\n\\' + message.content }}\\n {%- endif %}\\n {%- for tool_call in message.tool_calls %}\\n {%- if tool_call.function is defined %}\\n {%- set tool_call = tool_call.function %}\\n {%- endif %}\\n {{- \\'\\\\n<tool_call>\\\\n{\"name\": \"\\' }}\\n {{- tool_call.name }}\\n {{- \\'\", \"arguments\": \\' }}\\n {{- tool_call.arguments | tojson }}\\n {{- \\'}\\\\n</tool_call>\\' }}\\n {%- endfor %}\\n {{- \\'<|im_end|>\\\\n\\' }}\\n {%- elif message.role == \"tool\" %}\\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\\n {{- \\'<|im_start|>user\\' }}\\n {%- endif %}\\n {{- \\'\\\\n<tool_response>\\\\n\\' }}\\n {{- message.content }}\\n {{- \\'\\\\n</tool_response>\\' }}\\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\\n {{- \\'<|im_end|>\\\\n\\' }}\\n {%- endif %}\\n {%- endif %}\\n{%- endfor %}\\n{%- if add_generation_prompt %}\\n {{- \\'<|im_start|>assistant\\\\n\\' }}\\n{%- endif %}\\n'" | |
| ], | |
| "application/vnd.google.colaboratory.intrinsic+json": { | |
| "type": "string" | |
| } | |
| }, | |
| "metadata": {}, | |
| "execution_count": 27 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "print(old_template)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "6IX_L2wJq3gz", | |
| "outputId": "0481c5b0-f177-4ca8-f969-e51eb00ea182" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "{%- if tools %}\n", | |
| " {{- '<|im_start|>system\\n' }}\n", | |
| " {%- if messages[0]['role'] == 'system' %}\n", | |
| " {{- messages[0]['content'] }}\n", | |
| " {%- else %}\n", | |
| " {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n", | |
| " {%- endif %}\n", | |
| " {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n", | |
| " {%- for tool in tools %}\n", | |
| " {{- \"\\n\" }}\n", | |
| " {{- tool | tojson }}\n", | |
| " {%- endfor %}\n", | |
| " {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n", | |
| "{%- else %}\n", | |
| " {%- if messages[0]['role'] == 'system' %}\n", | |
| " {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n", | |
| " {%- else %}\n", | |
| " {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n", | |
| " {%- endif %}\n", | |
| "{%- endif %}\n", | |
| "{%- for message in messages %}\n", | |
| " {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n", | |
| " {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n", | |
| " {%- elif message.role == \"assistant\" %}\n", | |
| " {{- '<|im_start|>' + message.role }}\n", | |
| " {%- if message.content %}\n", | |
| " {{- '\\n' + message.content }}\n", | |
| " {%- endif %}\n", | |
| " {%- for tool_call in message.tool_calls %}\n", | |
| " {%- if tool_call.function is defined %}\n", | |
| " {%- set tool_call = tool_call.function %}\n", | |
| " {%- endif %}\n", | |
| " {{- '\\n<tool_call>\\n{\"name\": \"' }}\n", | |
| " {{- tool_call.name }}\n", | |
| " {{- '\", \"arguments\": ' }}\n", | |
| " {{- tool_call.arguments | tojson }}\n", | |
| " {{- '}\\n</tool_call>' }}\n", | |
| " {%- endfor %}\n", | |
| " {{- '<|im_end|>\\n' }}\n", | |
| " {%- elif message.role == \"tool\" %}\n", | |
| " {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n", | |
| " {{- '<|im_start|>user' }}\n", | |
| " {%- endif %}\n", | |
| " {{- '\\n<tool_response>\\n' }}\n", | |
| " {{- message.content }}\n", | |
| " {{- '\\n</tool_response>' }}\n", | |
| " {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n", | |
| " {{- '<|im_end|>\\n' }}\n", | |
| " {%- endif %}\n", | |
| " {%- endif %}\n", | |
| "{%- endfor %}\n", | |
| "{%- if add_generation_prompt %}\n", | |
| " {{- '<|im_start|>assistant\\n' }}\n", | |
| "{%- endif %}\n", | |
| "\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [], | |
| "metadata": { | |
| "id": "CM99XtAMq3PI" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "new_template = \"\"\"\n", | |
| "{%- if tools %}\n", | |
| " {{- '<|im_start|>system\\n' }}\n", | |
| " {%- if messages[0]['role'] == 'system' %}\n", | |
| " {{- messages[0]['content'] }}\n", | |
| " {%- else %}\n", | |
| " {{- 'You are The Wizard of Oz. You are a helpful assistant.' }}\n", | |
| " {%- endif %}\n", | |
| " {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n", | |
| " {%- for tool in tools %}\n", | |
| " {{- \"\\n\" }}\n", | |
| " {{- tool | tojson }}\n", | |
| " {%- endfor %}\n", | |
| " {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n", | |
| "{%- else %}\n", | |
| " {%- if messages[0]['role'] == 'system' %}\n", | |
| " {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n", | |
| " {%- else %}\n", | |
| " {{- '<|im_start|>system\\nYou are The Wizard of Oz. You are a helpful assistant.<|im_end|>\\n' }}\n", | |
| " {%- endif %}\n", | |
| "{%- endif %}\n", | |
| "{%- for message in messages %}\n", | |
| " {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n", | |
| " {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n", | |
| " {%- elif message.role == \"assistant\" %}\n", | |
| " {{- '<|im_start|>' + message.role }}\n", | |
| " {%- if message.content %}\n", | |
| " {{- '\\n' + message.content }}\n", | |
| " {%- endif %}\n", | |
| " {%- for tool_call in message.tool_calls %}\n", | |
| " {%- if tool_call.function is defined %}\n", | |
| " {%- set tool_call = tool_call.function %}\n", | |
| " {%- endif %}\n", | |
| " {{- '\\n<tool_call>\\n{\"name\": \"' }}\n", | |
| " {{- tool_call.name }}\n", | |
| " {{- '\", \"arguments\": ' }}\n", | |
| " {{- tool_call.arguments | tojson }}\n", | |
| " {{- '}\\n</tool_call>' }}\n", | |
| " {%- endfor %}\n", | |
| " {{- '<|im_end|>\\n' }}\n", | |
| " {%- elif message.role == \"tool\" %}\n", | |
| " {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n", | |
| " {{- '<|im_start|>user' }}\n", | |
| " {%- endif %}\n", | |
| " {{- '\\n<tool_response>\\n' }}\n", | |
| " {{- message.content }}\n", | |
| " {{- '\\n</tool_response>' }}\n", | |
| " {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n", | |
| " {{- '<|im_end|>\\n' }}\n", | |
| " {%- endif %}\n", | |
| " {%- endif %}\n", | |
| "{%- endfor %}\n", | |
| "{%- if add_generation_prompt %}\n", | |
| " {{- '<|im_start|>assistant\\n' }}\n", | |
| "{%- endif %}\n", | |
| "\"\"\"" | |
| ], | |
| "metadata": { | |
| "id": "_zkkQGQorH0_" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "tokenizer.chat_template = new_template" | |
| ], | |
| "metadata": { | |
| "id": "u83Elw5WqhfF" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "messages = [\n", | |
| " {\"role\": \"user\", \"content\": \"Hi!\"},\n", | |
| "]" | |
| ], | |
| "metadata": { | |
| "id": "7pRcu4jSrpKl" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "print(tokenizer.apply_chat_template(messages, tokenize=False))" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 332 | |
| }, | |
| "id": "1VXJluOwr04d", | |
| "outputId": "e151cc40-57d9-487b-f857-0711ad6079a6" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "error", | |
| "ename": "TemplateSyntaxError", | |
| "evalue": "expected token 'end of print statement', got 'name'", | |
| "traceback": [ | |
| "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", | |
| "\u001b[0;31mTemplateSyntaxError\u001b[0m Traceback (most recent call last)", | |
| "\u001b[0;32m/tmp/ipython-input-3989908719.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtokenizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_chat_template\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessages\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokenize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", | |
| "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/transformers/tokenization_utils_base.py\u001b[0m in \u001b[0;36mapply_chat_template\u001b[0;34m(self, conversation, tools, documents, chat_template, add_generation_prompt, continue_final_message, tokenize, padding, truncation, max_length, return_tensors, return_dict, return_assistant_tokens_mask, tokenizer_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m 1665\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1666\u001b[0m \u001b[0mtemplate_kwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspecial_tokens_map\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m}\u001b[0m \u001b[0;31m# kwargs overwrite special tokens if both are present\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1667\u001b[0;31m rendered_chat, generation_indices = render_jinja_template(\n\u001b[0m\u001b[1;32m 1668\u001b[0m \u001b[0mconversations\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconversations\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1669\u001b[0m \u001b[0mtools\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtools\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/transformers/utils/chat_template_utils.py\u001b[0m in \u001b[0;36mrender_jinja_template\u001b[0;34m(conversations, tools, documents, chat_template, return_assistant_tokens_mask, continue_final_message, add_generation_prompt, **kwargs)\u001b[0m\n\u001b[1;32m 480\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 481\u001b[0m \u001b[0;31m# Compilation function uses a cache to avoid recompiling the same template\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 482\u001b[0;31m \u001b[0mcompiled_template\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_compile_jinja_template\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchat_template\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 483\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 484\u001b[0m \u001b[0;31m# We accept either JSON schemas or functions for tools. If we get functions, we convert them to schemas\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/transformers/utils/chat_template_utils.py\u001b[0m in \u001b[0;36m_compile_jinja_template\u001b[0;34m(chat_template)\u001b[0m\n\u001b[1;32m 461\u001b[0m \u001b[0mjinja_env\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mglobals\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"raise_exception\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mraise_exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 462\u001b[0m \u001b[0mjinja_env\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mglobals\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"strftime_now\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstrftime_now\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 463\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mjinja_env\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_string\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchat_template\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 464\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 465\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/jinja2/environment.py\u001b[0m in \u001b[0;36mfrom_string\u001b[0;34m(self, source, globals, template_class)\u001b[0m\n\u001b[1;32m 1109\u001b[0m \u001b[0mgs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_globals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mglobals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1110\u001b[0m \u001b[0mcls\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtemplate_class\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtemplate_class\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1111\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_code\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1112\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1113\u001b[0m def make_globals(\n", | |
| "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/jinja2/environment.py\u001b[0m in \u001b[0;36mcompile\u001b[0;34m(self, source, name, filename, raw, defer_init)\u001b[0m\n\u001b[1;32m 769\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 770\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mTemplateSyntaxError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 771\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandle_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msource_hint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 772\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 773\u001b[0m def compile_expression(\n", | |
| "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/jinja2/environment.py\u001b[0m in \u001b[0;36mhandle_exception\u001b[0;34m(self, source)\u001b[0m\n\u001b[1;32m 940\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mrewrite_traceback_stack\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 941\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 942\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mrewrite_traceback_stack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msource\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 943\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 944\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mjoin_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemplate\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparent\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
| "\u001b[0;32m<unknown>\u001b[0m in \u001b[0;36mtemplate\u001b[0;34m()\u001b[0m\n", | |
| "\u001b[0;31mTemplateSyntaxError\u001b[0m: expected token 'end of print statement', got 'name'" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [], | |
| "metadata": { | |
| "id": "eBq4DrY_r7CO" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| } | |
| ] | |
| } |
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