}\n",
- " # To do this, we need to specify the judge to stop generating after it \n",
- " # closes the JSON dictionary (i.e., when it outputs \"}\")\n",
- " # Hint: Use the stop=[\"}\"] argument within the judge.ask() method to specify this.\n",
- " stop = \"}\"\n",
- "\n",
- " # TODO: Call the judge LLM with the system prompt and the prompt template. \n",
- " # Remember to stop the generation when the judge LLM outputs \"}\".\n",
+ " # TODO: Call the judge LLM with the system prompt and the prompt template.\n",
" res = self.judge.ask(\n",
- " system='''TODO''', \n",
- " user='''TODO''', \n",
+ " system='''TODO''',\n",
+ " user='''TODO''',\n",
" max_tokens='''TODO'''\n",
- " stop='''TODO'''\n",
" ) # TODO\n",
"\n",
" # Extract the assistant's content from the API response\n",
- " # Remember to add the stop character back to the end of the response to be a \n",
- " # valid JSON dictionary (its not there the judge LLM stoped once it saw it)\n",
- " res = res.choices[0].message.content + stop\n",
+ " res = res.choices[0].message.content\n",
" res_dict = json.loads(res)\n",
"\n",
" max_score = 10 # The maximum score that the LLM should output\n",
@@ -1015,15 +851,19 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "id": "xiW6rr7Lfnyz"
+ },
"source": [
- "Instaniate your Comet Opik judge using the LLMJudgeEvaluator class and system prompt."
+ "Instantiate your Comet Opik judge using the `LLMJudgeEvaluator` class and system prompt."
]
},
{
"cell_type": "code",
"execution_count": null,
- "metadata": {},
+ "metadata": {
+ "id": "m2wCbTn-fnyz"
+ },
"outputs": [],
"source": [
"judge = LLMJudgeEvaluator(llm, system_prompt=system_prompt)"
@@ -1031,11 +871,13 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "id": "tfphmTD2fny0"
+ },
"source": [
"## 2.4: Evaluating the model by scoring with your judge LLM\n",
"\n",
- "Now we can use the judge LLM to score the outputs of the model. We will use the `scoring_function` to score text using the judge LLM. \n",
+ "Now we can use the judge LLM to score the outputs of the model. We will use the `scoring_function` to score text using the judge LLM.\n",
"\n",
"Feed in a few probe sentences to get a vibe check on the judge LLM."
]
@@ -1044,11 +886,7 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "D_rvQDrvBvnJ",
- "outputId": "5d460cfb-4237-4a5b-e5d2-4974ea984805"
+ "id": "D_rvQDrvBvnJ"
},
"outputs": [],
"source": [
@@ -1068,7 +906,9 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "id": "iBjWAXfTfny0"
+ },
"source": [
"We will evaluate how well our fine-tuned model is doing by scoring the outputs of the model, as well as our base-style text (negative control) and the training-set text in Yoda-speak style (positive control).\n",
"\n",
@@ -1101,7 +941,9 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "id": "7Kz6t364fny0"
+ },
"source": [
"Let's also collect some base-style text (`base_samples`) and the training-set text in Yoda-speak style (`style_samples`). For these, we won't need to generate text, since we already have the text in the dataset."
]
@@ -1110,11 +952,7 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "ZEpUWV2EBvnK",
- "outputId": "ff1192d3-ca28-4429-d110-47736fbaf90c"
+ "id": "ZEpUWV2EBvnK"
},
"outputs": [],
"source": [
@@ -1124,7 +962,9 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "id": "drqXTryEfny0"
+ },
"source": [
"Now that we have our samples, we can score them using the judge LLM. We will use a multiprocessed scoring function to score the samples in parallel, because each sample is independent and we can submit them all as simultaneous requests to the judge LLM."
]
@@ -1160,9 +1000,11 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "id": "jQ5PKB4jfny0"
+ },
"source": [
- "Look at the average scores for each of the three types of text -- what do you observe? \n",
+ "Look at the average scores for each of the three types of text -- what do you observe?\n",
"\n",
"We can also plot the distribution of scores for each of the three types of text.\n"
]
@@ -1171,12 +1013,7 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 472
- },
- "id": "V4-g0Z3_BvnK",
- "outputId": "5497bdad-7878-4df5-b2b0-015b896ea072"
+ "id": "V4-g0Z3_BvnK"
},
"outputs": [],
"source": [
@@ -1198,16 +1035,130 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "id": "qtKce8CYfny0"
+ },
"source": [
"Use these observations to improve your model. Remember that the judge LLM is not perfect, and you can try to improve the judge LLM to better evaluate the model's outputs. A better judge LLM will give you a better evaluation of how well your Yoda model is doing, and that better evaluation will help you improve your Yoda model."
]
},
{
"cell_type": "markdown",
- "metadata": {},
"source": [
- "## 2.5: Conclusion\n",
+ "## 2.5: Monitoring with evals\n",
+ "\n",
+ "Just as we used Opik for evaluation metrics during fine-tuning and testing, we can also use Opik to monitor our LLM once it is live in deployment. This makes it easy to track the same metrics consistently across both development and deployment.\n",
+ "\n",
+ "In prior labs, we used Comet for experiment tracking, so you should have an account and API key. If not, you can sign up for a Comet account [here](https://www.comet.com/signup?from=llm&utm_source=mit_dl&utm_medium=notebook&utm_campaign=opik) if you have not done so already. We will configure Opik by setting the API key and naming our Opik project."
+ ],
+ "metadata": {
+ "id": "BeMLjanimqDr"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "os.environ[\"OPIK_API_KEY\"] = \"\" # TODO: add your OPIK or Comet API key here\n",
+ "assert OPIK_API_KEY != \"\", \"You must set your OPIK or Comet API key before running this cell!\"\n",
+ "\n",
+ "# Set the project name for Opik\n",
+ "os.environ[\"OPIK_PROJECT_NAME\"] = \"6S191_Lab3\"\n",
+ "\n",
+ "opik.configure()"
+ ],
+ "metadata": {
+ "id": "uA2nuV8n6STH"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "[Tracing](https://www.comet.com/docs/opik/tracing/concepts) helps you understand the end-to-end flow of your LLM application and pinpoint specific steps that may be causing issues.\n",
+ "\n",
+ "In the example below, we make a sample call to the chatbot and use Opik’s `@track` decorator to log data to the Opik UI, creating a record of live calls to the application. You can add the `@track` decorator to any function to trace not only LLM calls, but also other steps in your application pipeline."
+ ],
+ "metadata": {
+ "id": "VcwD9NcM6SnJ"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "@opik.track\n",
+ "def inference_chat(question, max_new_tokens=32, temperature=0.7, only_answer=False):\n",
+ "\n",
+ " # 1. Construct the prompt using the template\n",
+ " prompt = template_without_answer.format(question=question)\n",
+ "\n",
+ " # 2. Tokenize the text\n",
+ " input_ids = tokenizer(prompt, return_tensors=\"pt\").to(model.device)\n",
+ "\n",
+ " # 3. Feed through the model to predict the next token probabilities\n",
+ " with torch.no_grad():\n",
+ " outputs = model.generate(**input_ids, do_sample=True, max_new_tokens=max_new_tokens, temperature=temperature)\n",
+ "\n",
+ " # 4. Only return the answer if only_answer is True\n",
+ " output_tokens = outputs[0]\n",
+ " if only_answer:\n",
+ " output_tokens = output_tokens[input_ids['input_ids'].shape[1]:]\n",
+ "\n",
+ " # 5. Decode the tokens\n",
+ " result = tokenizer.decode(output_tokens, skip_special_tokens=True)\n",
+ "\n",
+ " # Update the current trace with evaluation scores\n",
+ " opik_context.update_current_trace(\n",
+ " feedback_scores=[\n",
+ " {\n",
+ " \"name\": \"Yoda style eval\",\n",
+ " \"value\": scoring_function(result)\n",
+ " }\n",
+ " ]\n",
+ " )\n",
+ "\n",
+ " return result"
+ ],
+ "metadata": {
+ "id": "VqQdGAKfm-8H"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Now you can make an example call to your model to see the trace logged to Opik. Once you run the cell below you should see a link to your Opik UI where your traces are logged to your project. Follow that link to see your traces in the Opik platform."
+ ],
+ "metadata": {
+ "id": "uYXA6wOPnAhb"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Let's try chatting with the model now to see the traces produced with the score\n",
+ "answer = inference_chat(\n",
+ " \"Who was the only non-Jedi to wield a lightsaber in the original Star Wars trilogy?\",\n",
+ " only_answer=True,\n",
+ " max_new_tokens=32,\n",
+ ")\n",
+ "\n",
+ "print(answer)"
+ ],
+ "metadata": {
+ "id": "uLYflR9DnCfM"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "pRhbKTy2fny0"
+ },
+ "source": [
+ "## 2.6: Conclusion\n",
"\n",
"Experiment with both your chat model and your judge LLM to try to improve the quality of the Yoda-speak. The competition for this lab will be based on the following criteria:\n",
"* **Likelihood of true Yoda-speak under your chat model**: the better your chat model does at understanding Yoda-speak, it will estimate a lower cross entropy loss for language that is true Yoda-speak. At the end of this lab, you will evaluate the likelihood of a held-out test-sample of true Yoda-speak under your chat model. Include this likelihood in your report. This gives us a quantitative measure to compare different chat models (which may have interacted with different judge LLMs).\n",
@@ -1220,11 +1171,7 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "MqnrG24FBvnK",
- "outputId": "b93c17db-6968-40f3-a012-b3b202185bb6"
+ "id": "MqnrG24FBvnK"
},
"outputs": [],
"source": [
@@ -1247,11 +1194,13 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "id": "XmlmIGJyfny0"
+ },
"source": [
"# Submission information\n",
"\n",
- "To enter the competition, please upload the following to the lab [submission site for the Large Language Models Lab](https://www.dropbox.com/request/vrDrNCkj4yDxgsi2O5Sw)):\n",
+ "To enter the competition, please upload the following to the lab [submission site for the Large Language Models Lab](https://www.dropbox.com/request/l2JH7UlrayUl1Ps5ZVZm):\n",
"\n",
"* Jupyter notebook with the code you used to generate your results;\n",
"* copy of the bar plot showing the judge LLM's scores of text in base style, generated text, and text in true Yoda-speak style;\n",
@@ -1265,11 +1214,6 @@
"\n",
"![](\"https://media4.giphy.com/media/v1.Y2lkPTc5MGI3NjExdDZsczFmcjcxeWZjbTA2djh5bDN1bzl5eHJpeHFhdHM0dmczcjkxMyZlcD12MV9pbnRlcm5hbF9naWZfYnlfaWQmY3Q9Zw/ArrVyXcjSzzxe/giphy.webp\")
"
]
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- "metadata": {},
- "source": []
}
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diff --git a/lab3/solutions/LLM_Finetuning_Solution.ipynb b/lab3/solutions/LLM_Finetuning_Solution.ipynb
index 04548c20..df20838d 100644
--- a/lab3/solutions/LLM_Finetuning_Solution.ipynb
+++ b/lab3/solutions/LLM_Finetuning_Solution.ipynb
@@ -2,7 +2,9 @@
"cells": [
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "id": "yh8WeSsQfnyw"
+ },
"source": [
"\n",
" | \n",
@@ -20,10 +22,12 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {},
+ "metadata": {
+ "id": "O-pRdpMbfnyw"
+ },
"outputs": [],
"source": [
- "# Copyright 2025 MIT Introduction to Deep Learning. All Rights Reserved.\n",
+ "# Copyright 2026 MIT Introduction to Deep Learning. All Rights Reserved.\n",
"#\n",
"# Licensed under the MIT License. You may not use this file except in compliance\n",
"# with the License. Use and/or modification of this code outside of MIT Introduction\n",
@@ -36,13 +40,15 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "id": "StmM5Grmfnyx"
+ },
"source": [
"# Laboratory 3: Large Language Model (LLM) Fine-tuning\n",
"\n",
"In this lab, you will fine-tune a multi-billion parameter large language model (LLM). We will go through several fundamental concepts of LLMs, including tokenization, templates, and fine-tuning. This lab provides a complete pipeline for fine-tuning a language model to generate responses in a specific style, and you will explore not only language model fine-tuning, but also ways to evaluate the performance of a language model.\n",
"\n",
- "You will use Google's [Gemma 2B](https://huggingface.co/google/gemma-2b-it) model as the base language model to fine-tune; [Liquid AI's](https://www.liquid.ai/) [LFM-40B](https://www.liquid.ai/liquid-foundation-models) as an evaluation \"judge\" model; and Comet ML's [Opik](https://www.comet.com/site/products/opik/) as a framework for streamlined LLM evaluation.\n",
+ "You will use [Liquid AI's](https://www.liquid.ai/) [LFM2-1.2B](https://huggingface.co/LiquidAI/LFM2-1.2B) as the base language model to fine-tune; Google's [Gemini 2.5](https://blog.google/technology/google-deepmind/gemini-model-thinking-updates-march-2025/) model as an evaluation \"judge\" model; and Comet ML's [Opik](https://www.comet.com/site/products/opik/) as a framework for streamlined LLM evaluation.\n",
"\n",
"First, let's download the MIT deep learning package, install dependencies, and import the relevant packages we'll need for this lab."
]
@@ -86,16 +92,20 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "id": "j-qsDChnfnyx"
+ },
"source": [
"# Part 1: Fine-tuning an LLM for style\n",
"\n",
- "In the first part of this lab, we will fine-tune an LLM as a chatbot that can generate responses in a specific style. We will use the [Gemma 2B model](https://huggingface.co/google/gemma-2b-it) as the base language model to finetune."
+ "In the first part of this lab, we will fine-tune an LLM as a chatbot that can generate responses in a specific style. We will use the [Liquid AI LFM2-1.2B model](https://huggingface.co/LiquidAI/LFM2-1.2B) as the base language model to finetune."
]
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "id": "VNE6ArjFfnyx"
+ },
"source": [
"## 1.1: Templating and tokenization\n",
"\n",
@@ -105,32 +115,30 @@
"\n",
"[Templating](https://huggingface.co/docs/transformers/main/chat_templating) is a way to format inputs and outputs in a consistent structure that a language model can understand. It involves adding special tokens or markers to indicate different parts of the conversation, like who is speaking and where turns begin and end. This structure helps the model learn the proper format for generating responses and maintain a coherent conversation flow. Without templates, the model may not know how to properly format its outputs or distinguish between different speakers in a conversation.\n",
"\n",
- "Let's start by defining some basic templates for the chatbot, for turns where the user asks a question and the model responds with an answer."
+ "Let's start by defining some basic templates for the LFM2-based chatbot, for turns where the user asks a question and the model responds with an answer."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "TN2zHVhfBvnE",
- "outputId": "abddea82-12cf-4a16-868b-2e41f85fd7f1"
+ "id": "TN2zHVhfBvnE"
},
"outputs": [],
"source": [
"# Basic question-answer template\n",
- "template_without_answer = \"user\\n{question}\\nmodel\\n\"\n",
- "template_with_answer = template_without_answer + \"{answer}\\n\"\n",
+ "template_without_answer = \"<|startoftext|><|im_start|>user\\n{question}<|im_end|>\\n<|im_start|>assistant\\n\"\n",
+ "template_with_answer = template_without_answer + \"{answer}<|im_end|>\\n\"\n",
"\n",
"# Let's try to put something into the template to see how it looks\n",
- "print(template_with_answer.format(question=\"What is your name?\", answer=\"My name is Gemma!\"))"
+ "print(template_with_answer.format(question=\"What is your name?\", answer=\"My name is Lili!\"))"
]
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "id": "keh0rVN-fnyx"
+ },
"source": [
"### 1.1.2: Tokenization\n",
"\n",
@@ -144,70 +152,19 @@
"\n",
"3. **Subword tokenization**: breaks words into smaller units (subwords) based on their frequency. The most popular and commonly used approach is [byte-pair encoding (BPE)](https://en.wikipedia.org/wiki/Byte_pair_encoding), which iteratively merges the most frequent character pairs. Modern language models typically use subword tokenization as it balances vocabulary size and sequence length while handling unknown words effectively by breaking them into known subword units.\n",
"\n",
- "In this lab we will use the tokenizer from the Gemma 2B model, which uses BPE. Let's load it and inspect it."
+ "In this lab we will use the tokenizer from the LFM2 model, which uses BPE. Let's load it and inspect it."
]
},
{
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"execution_count": null,
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- "64d0bc7735bf42ce800f56ebcce3cdce",
- "01b7fbea9de54e338e3862e09d7e353d",
- "9bead4274c0c4fc6acf12bf6b9dec75a",
- "34ff40c5c4cf405d8ef59a12171b03a5",
- "9d8d908e12b846d58aea8b0e48dd6b92",
- "e9c00880fa4b47c7bf645c3f91a950a9",
- "7d93f09ca25a498fbd4776daa0fc4c53",
- "1c35e9b4250f4fca9e65ecfe4dcb4006",
- "1eacc88f8b754c7e93582ce65f99b5db",
- "6311ea720e344309b1d6fa1445f347e3",
- "ba866548b5544345b37e29f6d8e92652",
- "d5f566c5de7d4dd1808975839ab8b973",
- "0e17dd9f94714fb38ecbe3bd68873c1c"
- ]
- },
- "id": "EeDF1JI-BvnF",
- "outputId": "6c9d3a2b-0b6b-4fa1-de66-dc7879ab4d15"
+ "id": "EeDF1JI-BvnF"
},
"outputs": [],
"source": [
- "# Load the tokenizer for Gemma 2B\n",
- "model_id = \"unsloth/gemma-2-2b-it\" #\"google/gemma-2-2b-it\"\n",
+ "# Load the tokenizer for Liquid AI LFM2-1.2B\n",
+ "model_id = \"LiquidAI/LFM2-1.2B\"\n",
"tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
"\n",
"# How big is the tokenizer?\n",
@@ -216,10 +173,12 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "id": "sSJxCx6Nfnyx"
+ },
"source": [
"We not only need to be able to tokenize the text into tokens (encode), but also de-tokenize the tokens back into text (decode). Our tokenizer will have:\n",
- "1. an `encode` function to tokenize the text into tokens, and \n",
+ "1. an `encode` function to tokenize the text into tokens, and\n",
"2. a `decode` function to de-tokenize back to text so that we can read out the model's outputs.\n",
"\n",
"Let's test out both steps and inspect to get a better understanding of how this works."
@@ -229,11 +188,7 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "JH1XzPkiBvnF",
- "outputId": "25e68cce-5aa0-432c-ab8c-246910d6c6b0"
+ "id": "JH1XzPkiBvnF"
},
"outputs": [],
"source": [
@@ -252,22 +207,20 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "id": "v_0H2XZUfnyx"
+ },
"source": [
"This is really cool. Now we have a way to move in and out of the token space.\n",
"\n",
- "To \"chat\" with our LLM chatbot, we need to use the tokenizer and the chat template together, in order for the model to respond to the user's question. We can use the templates defined earlier to construct a prompt for the model, without the answer. "
+ "To \"chat\" with our LLM chatbot, we need to use the tokenizer and the chat template together, in order for the model to respond to the user's question. We can use the templates defined earlier to construct a prompt for the model, without the answer."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "jyBxl6NIBvnF",
- "outputId": "06e54226-c434-4a84-868f-a8b5b5085bbd"
+ "id": "jyBxl6NIBvnF"
},
"outputs": [],
"source": [
@@ -277,69 +230,33 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "id": "nIw5Qzf2fnyy"
+ },
"source": [
- "If we were to feed this to the model, it would see that it is now the start of the model's turn, and it would generate the answer to this question. "
+ "If we were to feed this to the model, it would see that it is now the start of the model's turn, and it would generate the answer to this question."
]
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "id": "Lgp0JVnifnyy"
+ },
"source": [
"## 1.2: Getting started with the LLM\n",
"\n",
"Now that we have a way to prepare our data, we're ready to work with our LLM!\n",
"\n",
- "LLMs like Gemma 2B are trained on a large corpus of text, on the task of predicting the next token in a sequence, given the previous tokens. We call this training task \"next token prediction\"; you may also see it called \"causal language modeling\" or \"autoregressive language modeling\". We can leverage models trained in this way to generate new text by sampling from the predicted probability distribution over the next token.\n",
+ "LLMs like LFM2 are trained on a large corpus of text, on the task of predicting the next token in a sequence, given the previous tokens. We call this training task \"next token prediction\"; you may also see it called \"causal language modeling\" or \"autoregressive language modeling\". We can leverage models trained in this way to generate new text by sampling from the predicted probability distribution over the next token.\n",
"\n",
- "Let's load the Gemma 2B model and start working with it. We will construct a prompt in chat template form and tokenize it. Then, we will feed it to the model to predict next token probabilities. Finally, we will get the next token (which is still numerical) and decode it to text."
+ "Let's load the LFM2 model and start working with it. We will construct a prompt in chat template form and tokenize it. Then, we will feed it to the model to predict next token probabilities. Finally, we will get the next token (which is still numerical) and decode it to text."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 113,
- "referenced_widgets": [
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- "34ddb97a59d940879eb53d3e4dbe177e",
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- "d26f0017695b4e42b1c2736c07575775",
- "73aa48a573e349b1a05ba0bb5526bc2a",
- "a239a415866d47238ffa50a5c9c0a580",
- "00dffcff57a14ad28d665cd2c2a11960"
- ]
- },
- "id": "mWtWvgiuBvnG",
- "outputId": "b06295c8-b7b7-4d95-e0d6-31f65ac595ef"
+ "id": "mWtWvgiuBvnG"
},
"outputs": [],
"source": [
@@ -351,18 +268,14 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "2SMDd5dpBvnG",
- "outputId": "b5e63295-683a-4daa-9526-1ef93ed9e95a"
+ "id": "2SMDd5dpBvnG"
},
"outputs": [],
"source": [
"### Putting it together to prompt the model and generate a response ###\n",
"\n",
"# 1. Construct the prompt in chat template form\n",
- "question = \"What is the capital of France? Use one word.\" \n",
+ "question = \"What is the capital of France? Use one word.\"\n",
"prompt = template_without_answer.format(question=question)\n",
"# prompt = template_without_answer.format('''TODO''') # TODO\n",
"\n",
@@ -389,7 +302,9 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "id": "CJF74Cayfnyy"
+ },
"source": [
"Note that the model is not able to predict the answer to the question, it is only able to predict the next token in the sequence! For more complex questions, we can't just generate one token, but rather we need to generate a sequence of tokens.\n",
"\n",
@@ -402,11 +317,7 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "XnWMUQVbBvnG",
- "outputId": "d0c110d0-d740-427e-abf9-312fe2dd9f5e"
+ "id": "XnWMUQVbBvnG"
},
"outputs": [],
"source": [
@@ -418,24 +329,28 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "id": "B3zKg1qFfnyy"
+ },
"source": [
"Now we have the basic pipeline for generating text with an LLM!"
]
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "id": "lEW-YdEyfnyy"
+ },
"source": [
"## 1.3: Fine-tuning\n",
"\n",
"Fine-tuning is a technique that allows us to adapt a pre-trained neural network to better suit a downstream task, domain, or style, by training the model further on new data. By training the model further on a carefully curated dataset, we can modify its behavior, style, or capabilities. Fine-tuning is used in a variety of applications, not just language modeling. But in language modeling, fine-tuning can be used to:\n",
- "- Adapt the model's writing style \n",
+ "- Adapt the model's writing style\n",
"- Improve performance on specific tasks or domains\n",
"- Teach the model new capabilities or knowledge\n",
"- Reduce unwanted behaviors or biases\n",
"\n",
- "In this lab, you will fine-tune the Gemma LLM to adapt the model's writing style. Recall that in Lab 1 you built out a RNN-based sequence model to generate Irish folk songs. Continuing with our Irish theme, we will first fine-tune the LLM to chat in the style of a leprechaun.\n",
+ "In this lab, you will fine-tune the LFM2 LLM to adapt the model's writing style. Recall that in Lab 1 you built out a RNN-based sequence model to generate Irish folk songs. Continuing with our Irish theme, we will first fine-tune the LLM to chat in the style of a leprechaun.\n",
"\n",
"\n",
"\n",
@@ -446,58 +361,7 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 252,
- "referenced_widgets": [
- "453101669bb84ec784d30fdecf9e1052",
- "1acb7981a03c4d8491072db5b0f80b91",
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- "1caca54176f24a68841321407d5cb92c",
- "4bf984821d194c64945609ccf5d08ab0",
- "20f4fd378f6b44f386a6bdd9f0e787f7",
- "72693249b56e4995815d950d33ebbbba",
- "5dd29f36fb5745618d95abda81e869bb",
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- "1a1e342e7aa943cd82c91b224ea01932",
- "6a550e5a66704b7b819286707bd3a918",
- "3d6d0fa2af094773b593a85d6c51cf48",
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- "97f1a984a0a149bc9f305f18eb109b67",
- "4ec2221b24b94685887b091b45f3f746",
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- "389c5f0e14a24cf08aa175f1f21b22fc"
- ]
- },
- "id": "kN0pHHS8BvnH",
- "outputId": "a8422640-ba32-4d64-9379-1761062fd02e"
+ "id": "kN0pHHS8BvnH"
},
"outputs": [],
"source": [
@@ -515,7 +379,9 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "id": "eoAfOuCrfnyy"
+ },
"source": [
"### 1.3.1: Chat function\n",
"\n",
@@ -541,7 +407,7 @@
"def chat(question, max_new_tokens=32, temperature=0.7, only_answer=False):\n",
" # 1. Construct the prompt using the template\n",
" prompt = template_without_answer.format(question=question)\n",
- " # prompt = template_without_answer.format('''TODO''') # TODO \n",
+ " # prompt = template_without_answer.format('''TODO''') # TODO\n",
"\n",
" # 2. Tokenize the text\n",
" input_ids = tokenizer(prompt, return_tensors=\"pt\").to(model.device)\n",
@@ -566,7 +432,9 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "id": "rPAnt_Swfnyy"
+ },
"source": [
"Let's try chatting with the model now to test if it works! We have a sample question here (continuing with the Irish theme); feel free to try out other questions!"
]
@@ -575,11 +443,7 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "FDr5f2djBvnH",
- "outputId": "c42789d4-fbbf-438b-fd9d-57b3e037daa5"
+ "id": "FDr5f2djBvnH"
},
"outputs": [],
"source": [
@@ -592,29 +456,27 @@
"\n",
"print(answer)\n",
"\n",
- "'''TODO: Experiment with asking the model different questions and temperature values, and see how it responds!'''"
+ "### TODO: Experiment with asking the model different questions and temperature values, and see how it responds!"
]
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "id": "s7xpiTuCfnyy"
+ },
"source": [
"### 1.3.2: Parameter-efficient fine-tuning\n",
"\n",
- "In fine-tuning, the weights of the model are updated to better fit the fine-tuning dataset and/or task. Updating all the weights in a language model like Gemma 2B -- which has ~2 billion parameters -- is computationally expensive. There are many techniques to make fine-tuning more efficient.\n",
+ "In fine-tuning, the weights of the model are updated to better fit the fine-tuning dataset and/or task. Updating all the weights in a language model like LFM2-1.2B -- which has ~1 billion parameters -- is computationally expensive. There are many techniques to make fine-tuning more efficient.\n",
"\n",
- "We will use a technique called [LoRA](https://arxiv.org/abs/2106.09685) -- low-rank adaptation -- to make the fine-tuning process more efficient. LoRA is a way to fine-tune LLMs very efficiently by only updating a small subset of the model's parameters, and it works by adding trainable low-rank matrices to the model. While we will not go into the details of LoRA here, you can read more about it in the [LoRA paper](https://arxiv.org/abs/2106.09685). We will use the [`peft`](https://pypi.org/project/peft/) library to apply LoRA to the Gemma model."
+ "We will use a technique called [LoRA](https://arxiv.org/abs/2106.09685) -- low-rank adaptation -- to make the fine-tuning process more efficient. LoRA is a way to fine-tune LLMs very efficiently by only updating a small subset of the model's parameters, and it works by adding trainable low-rank matrices to the model. While we will not go into the details of LoRA here, you can read more about it in the [LoRA paper](https://arxiv.org/abs/2106.09685). We will use the [`peft`](https://pypi.org/project/peft/) library to apply LoRA to the LFM model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "Fb6Y679hBvnI",
- "outputId": "8070d39e-0fd9-44cd-9c35-d86afcd99caf"
+ "id": "Fb6Y679hBvnI"
},
"outputs": [],
"source": [
@@ -646,7 +508,9 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "id": "ze3rM7qmfnyy"
+ },
"source": [
"### 1.3.3: Forward pass and loss computation\n",
"\n",
@@ -689,7 +553,9 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "id": "09btP87tfnyz"
+ },
"source": [
"### 1.3.4: Training loop for fine-tuning\n",
"\n",
@@ -771,22 +637,20 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "blFoO-PhBvnI",
- "outputId": "d23f2002-6e4a-41b0-9710-13d394290f34"
+ "id": "blFoO-PhBvnI"
},
"outputs": [],
"source": [
- "# Call the train function to fine-tune the model! Hint: you'll start to see results after a few dozen steps.\n",
- "model = train(model, train_loader, tokenizer, max_steps=50)\n",
+ "# Call the train function to fine-tune the model! Hint: you'll start to see results after at least 100 steps.\n",
+ "model = train(model, train_loader, tokenizer, max_steps=200)\n",
"# model = train('''TODO''') # TODO"
]
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "id": "EKJOH7Ihfnyz"
+ },
"source": [
"Let's try chatting with the model again to see how it has changed!"
]
@@ -795,11 +659,7 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "su4ZAG3eBvnI",
- "outputId": "b21ce134-1763-4872-8b58-19328d98b76a"
+ "id": "su4ZAG3eBvnI"
},
"outputs": [],
"source": [
@@ -814,7 +674,7 @@
"source": [
"# Part 2: Evaluating a style-tuned LLM\n",
"\n",
- "How do we know if the model is doing well? How closely does the model's style match the style of a leprechaun? As you can see from the example above, determining whether a generated response is good or not is can seem qualitative, and it can be hard to measure how well the model is doing. \n",
+ "How do we know if the model is doing well? How closely does the model's style match the style of a leprechaun? As you can see from the example above, determining whether a generated response is good or not is can seem qualitative, and it can be hard to measure how well the model is doing.\n",
"\n",
"While benchmarks have been developed to evaluate the performance of language models on a variety of tasks, these benchmarks are not always representative of the real-world performance of the model. For example, a model may perform well on a benchmark but poorly on a more realistic task. Benchmarks are also limited in the scope of tasks they can cover and capabilities they can reflect, and there can be concerns about whether the data in the benchmark was used to train the model. Synthetic data generation and synthetic tasks are a way to address these limitations, and this is an active area of research.\n",
"\n",
@@ -823,7 +683,9 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "id": "fLvX1miFfnyz"
+ },
"source": [
"### 2.1: Fine-tune well, you must!\n",
"\n",
@@ -840,37 +702,21 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 292,
- "referenced_widgets": [
- "fe486852cda849d5b2cf2dda69c46feb",
- "9af5e516b8594e7da181917ff351e019",
- "c6487dbfe53345b9822b372069f34922",
- "baace428cd5545718ddc6d0749e53562",
- "b12294da6032493e9ac7783b8e3ddaff",
- "43e58008991640f1a96e123f545ca52d",
- "5c780ea0aeee467da497547d78453492",
- "bf06c4115ae54e7b9da2838c9b6069a0",
- "b73ef786040243589d43806a965f0eea",
- "0d4c7d8c22dc49b4be6d4948a3224852",
- "d95ba2612d5e409da8899e679e39c4ee"
- ]
- },
- "id": "-gLgE41YBvnJ",
- "outputId": "174004bd-f5f1-42e0-96ff-41d480254c87"
+ "id": "-gLgE41YBvnJ"
},
"outputs": [],
"source": [
"# Load the Yoda-speak dataset and fine-tune the model using your training function\n",
"train_loader, test_loader = mdl.lab3.create_dataloader(style=\"yoda\")\n",
- "model = train(model, train_loader, tokenizer, max_steps=50)\n",
+ "model = train(model, train_loader, tokenizer, max_steps=200)\n",
"# model = train('''TODO''') # TODO"
]
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "id": "nA8h2DcCfnyz"
+ },
"source": [
"Start by defining a system prompt for the judge LLM, setting the context that it will evaluate how well the outputs of your chat model follow Yoda speak. Experiment with different system prompts to see how they affect the judge LLM's evaluation! Keep in mind that a better judge LLM will give you a better evaluation of how well your Yoda model is doing, and that a better evaluation will help you improve your Yoda model."
]
@@ -879,11 +725,7 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "REkrJ1SCBvnJ",
- "outputId": "a5630cfb-5a6c-4874-9007-fe519de32220"
+ "id": "REkrJ1SCBvnJ"
},
"outputs": [],
"source": [
@@ -891,7 +733,7 @@
"\n",
"'''TODO: Experiment with different system prompts to see how they affect the judge LLM's evaluation!\n",
" Come back to this cell after you've generated some text from your model.'''\n",
- " \n",
+ "\n",
"system_prompt = \"\"\"\n",
"You are an impartial judge that evaluates if text was written by {style}.\n",
"\n",
@@ -904,12 +746,12 @@
"is written exactly in the style of {style}, 5 if mixed faithfulness to the\n",
"style, or 0 if the text is not at all written in the style of {style}.\n",
"\n",
- "The format of the your response should be a JSON dictionary and nothing else:\n",
+ "Directly answer with the score formatted in a dictionary.\n",
+ "The format of your response should only be the dictionary and nothing else:\n",
"{{\"score\": }}\n",
"\"\"\"\n",
"\n",
"style = \"Yoda\"\n",
- "# example = \"\"\"The very Republic is threatened, if involved the Sith are. Hard to see, the dark side is. \"\"\"\n",
"example = \"The very Republic is threatened, if involved the Sith are. Hard to see, the dark side is. Discover who this assassin is, we must. With this Naboo queen you must stay, Qui-Gon. Protect her. May the Force be with you. A vergence, you say? But you do! Revealed your opinion is. Trained as a Jedi, you request for him? Good, good, young one.\"\n",
"\n",
"system_prompt = system_prompt.format(style=style, example=example)\n",
@@ -919,15 +761,17 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "id": "gmdg3FNsfnyz"
+ },
"source": [
"### 2.2: Setting up the judge LLM\n",
"\n",
- "In LLM as a judge, we need to use a model that is larger (and therefore more capable) than our \"performer\" model, in our case the style fine-tuned Gemma 2B. Since it is infeasible to load larger models locally into notebooks, you will gain experience interfacing with these larger LLMs through an API served on [OpenRouter](https://openrouter.ai/). \n",
+ "In LLM as a judge, we need to use a model that is larger (and therefore more capable) than our \"performer\" model, in our case the style fine-tuned LFM2 1.2B. Since it is infeasible to load larger models locally into notebooks, you will gain experience interfacing with these larger LLMs through an API served on [OpenRouter](https://openrouter.ai/).\n",
"\n",
- "You will need to sign up for an [OpenRouter account](https://openrouter.ai/sign-up) and then [generate an API key](https://openrouter.ai/keys). Running powerful LLMs of this scale costs money -- for students in the in-person course, we can provide a credit to your OpenRouter account to allow you to run this lab. Come to office hours to receive your credit. \n",
+ "You will need to sign up for an [OpenRouter account](https://openrouter.ai/sign-up) and then [generate an API key](https://openrouter.ai/keys). Running powerful LLMs of this scale costs money -- for students in the in-person course, we can provide a credit to your OpenRouter account to allow you to run this lab. Come to office hours to receive your credit.\n",
"\n",
- "Through the OpenRouter interface, you will be able to experiment with different judge LLMs -- here we have suggested two possible larger LLMs to get you started: [Liquid AI's](https://www.liquid.ai/) [LFM-40B](https://openrouter.ai/models/liquid-ai/lfm-40b) andGoogle's [Gemma 9B](https://openrouter.ai/models/google/gemma-9b). Note there are also free models available on OpenRouter (e.g., [gemma-2-9b-it:free](https://openrouter.ai/google/gemma-2-9b-it:free)), but these will run into rate limitations if you run them too much.\n",
+ "Through the OpenRouter interface, you will be able to experiment with different judge LLMs -- here we have suggested one possible larger LLM to get you started: Google's [Gemini 2.5](https://openrouter.ai/google/gemini-2.5-flash/providers). Note there are also free models available on OpenRouter (e.g., [gemma-2-9b-it:free](https://openrouter.ai/google/gemma-2-9b-it:free)), but these will run into rate limitations if you run them too much.\n",
"\n",
"We have defined a simple class, `LLMClient`, to interact with the OpenRouter API. This class has a method `ask` that takes a user prompt and returns the model's response. Keep in mind that the judge LLM's response will be conditioned on the system prompt you provide -- the system prompt is critical to set the criteria for the evaluation!"
]
@@ -936,46 +780,49 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "9S7DtGZ5BvnJ",
- "outputId": "4ac889ba-43a8-4636-9341-f0b1f260faef"
+ "id": "9S7DtGZ5BvnJ"
},
"outputs": [],
"source": [
"OPENROUTER_API_KEY = \"\" # TODO: add your OpenRouter API key here\n",
"assert OPENROUTER_API_KEY != \"\", \"You must set your OpenRouter API key before running this cell!\"\n",
"\n",
- "model_name = \"liquid/lfm-40b\"\n",
- "# model_name = \"google/gemma-2-9b-it\"\n",
+ "model_name = \"google/gemini-2.5-flash\"\n",
"llm = mdl.lab3.LLMClient(model=model_name, api_key=OPENROUTER_API_KEY)"
]
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "id": "Hp9DnayMfnyz"
+ },
"source": [
"### 2.3: Defining the evaluation metric\n",
"\n",
"Great! We have set up our judge LLM, but we still need to make this quantitative. We can do this by defining a metric that uses the judge LLM to score the outputs of the model. Doing this is streamlined with Comet ML's [Opik library](https://www.comet.com/docs/opik/python-sdk-reference/), a platform for LLM evaluation and benchmarking.\n",
"\n",
"In prior labs, we used Comet for experiment tracking, so you should have an account and API key. If not, you can sign up for a Comet account [here](https://www.comet.com/signup?from=llm&utm_source=mit_dl&utm_medium=notebook&utm_campaign=opik) if you have not done so already. Now we will use the Comet Opik library to define a metric that uses the judge LLM to score the outputs of the model.\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Opik provides a framework for creating custom judge metrcs as well as a varity of pre-built metrics for common evaluation tasks. These metrics are designed to help you quickly and effectively gauge the performance of your LLM outputs and include metrics such as Hallucination, Answer Relevance, Context Precision/Recall and more. You can learn more about the available metrics in the [`Metrics Overview section`](https://www.comet.com/docs/opik/evaluation/metrics/overview) of the Opik documentation.\n",
"\n",
- "Opik has a base class for defining metrics, [`base_metric.BaseMetric`](https://www.comet.com/docs/opik/python-sdk-reference/evaluation/metrics/BaseMetric.html). You will use this to define a custom metric that uses the judge LLM to evaluate text for how well it adheres to Yoda speak. Note that the judge LLM and the metric can be applied to any text, not just the outputs of the model. This is important to keep in mind, since we need both a negative control -- text in the \"base\" standard English style -- and a positive control -- training-set text in Yoda-speak style -- against which to compare the model's generations.\n",
+ "The Opik python SDK has a base class for defining metrics, [`base_metric.BaseMetric`](https://www.comet.com/docs/opik/python-sdk-reference/evaluation/metrics/BaseMetric.html). You will use this to define a custom metric that uses the judge LLM to evaluate text for how well it adheres to Yoda speak. Note that the judge LLM and the metric can be applied to any text, not just the outputs of the model. This is important to keep in mind, since we need both a negative control -- text in the \"base\" standard English style -- and a positive control -- training-set text in Yoda-speak style -- against which to compare the model's generations.\n",
"\n",
"Set the judging criteria in the system prompt, and define the `score` function to evaluate text by querying the judge LLM."
- ]
+ ],
+ "metadata": {
+ "id": "1aTAb4JHlRQm"
+ }
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "llB3FgiwBvnJ",
- "outputId": "6c1dbf01-298c-4097-d2fa-8a212ca69822"
+ "id": "llB3FgiwBvnJ"
},
"outputs": [],
"source": [
@@ -996,32 +843,20 @@
" prompt = self.prompt_template.format(text=text)\n",
" # prompt = self.prompt_template.format('''TODO''') # TODO\n",
"\n",
- " # The system prompt asks the judge to output a JSON dictionary of the form: \n",
- " # {\"score\": }\n",
- " # To do this, we need to specify the judge to stop generating after it \n",
- " # closes the JSON dictionary (i.e., when it outputs \"}\")\n",
- " # Hint: Use the stop=[\"}\"] argument within the judge.ask() method to specify this.\n",
- " stop = \"}\"\n",
- "\n",
- " # TODO: Call the judge LLM with the system prompt and the prompt template. \n",
- " # Remember to stop the generation when the judge LLM outputs \"}\".\n",
+ " # TODO: Call the judge LLM with the system prompt and the prompt template.\n",
" res = self.judge.ask(\n",
" system=self.system_prompt,\n",
" user=prompt,\n",
- " max_tokens=10,\n",
- " stop=[stop]\n",
+ " max_tokens=20,\n",
" )\n",
" # res = self.judge.ask(\n",
- " # system='''TODO''', \n",
- " # user='''TODO''', \n",
+ " # system='''TODO''',\n",
+ " # user='''TODO''',\n",
" # max_tokens='''TODO'''\n",
- " # stop='''TODO'''\n",
" # ) # TODO\n",
"\n",
" # Extract the assistant's content from the API response\n",
- " # Remember to add the stop character back to the end of the response to be a \n",
- " # valid JSON dictionary (its not there the judge LLM stoped once it saw it)\n",
- " res = res.choices[0].message.content + stop\n",
+ " res = res.choices[0].message.content\n",
" res_dict = json.loads(res)\n",
"\n",
" max_score = 10 # The maximum score that the LLM should output\n",
@@ -1039,15 +874,19 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "id": "xiW6rr7Lfnyz"
+ },
"source": [
- "Instaniate your Comet Opik judge using the LLMJudgeEvaluator class and system prompt."
+ "Instantiate your Comet Opik judge using the `LLMJudgeEvaluator` class and system prompt."
]
},
{
"cell_type": "code",
"execution_count": null,
- "metadata": {},
+ "metadata": {
+ "id": "m2wCbTn-fnyz"
+ },
"outputs": [],
"source": [
"judge = LLMJudgeEvaluator(llm, system_prompt=system_prompt)"
@@ -1055,11 +894,13 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "id": "tfphmTD2fny0"
+ },
"source": [
"## 2.4: Evaluating the model by scoring with your judge LLM\n",
"\n",
- "Now we can use the judge LLM to score the outputs of the model. We will use the `scoring_function` to score text using the judge LLM. \n",
+ "Now we can use the judge LLM to score the outputs of the model. We will use the `scoring_function` to score text using the judge LLM.\n",
"\n",
"Feed in a few probe sentences to get a vibe check on the judge LLM."
]
@@ -1068,11 +909,7 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "D_rvQDrvBvnJ",
- "outputId": "5d460cfb-4237-4a5b-e5d2-4974ea984805"
+ "id": "D_rvQDrvBvnJ"
},
"outputs": [],
"source": [
@@ -1092,7 +929,9 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "id": "iBjWAXfTfny0"
+ },
"source": [
"We will evaluate how well our fine-tuned model is doing by scoring the outputs of the model, as well as our base-style text (negative control) and the training-set text in Yoda-speak style (positive control).\n",
"\n",
@@ -1125,7 +964,9 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "id": "7Kz6t364fny0"
+ },
"source": [
"Let's also collect some base-style text (`base_samples`) and the training-set text in Yoda-speak style (`style_samples`). For these, we won't need to generate text, since we already have the text in the dataset."
]
@@ -1134,11 +975,7 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "ZEpUWV2EBvnK",
- "outputId": "ff1192d3-ca28-4429-d110-47736fbaf90c"
+ "id": "ZEpUWV2EBvnK"
},
"outputs": [],
"source": [
@@ -1148,7 +985,9 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "id": "drqXTryEfny0"
+ },
"source": [
"Now that we have our samples, we can score them using the judge LLM. We will use a multiprocessed scoring function to score the samples in parallel, because each sample is independent and we can submit them all as simultaneous requests to the judge LLM."
]
@@ -1184,9 +1023,11 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "id": "jQ5PKB4jfny0"
+ },
"source": [
- "Look at the average scores for each of the three types of text -- what do you observe? \n",
+ "Look at the average scores for each of the three types of text -- what do you observe?\n",
"\n",
"We can also plot the distribution of scores for each of the three types of text.\n"
]
@@ -1195,12 +1036,7 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 472
- },
- "id": "V4-g0Z3_BvnK",
- "outputId": "5497bdad-7878-4df5-b2b0-015b896ea072"
+ "id": "V4-g0Z3_BvnK"
},
"outputs": [],
"source": [
@@ -1222,16 +1058,130 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "id": "qtKce8CYfny0"
+ },
"source": [
"Use these observations to improve your model. Remember that the judge LLM is not perfect, and you can try to improve the judge LLM to better evaluate the model's outputs. A better judge LLM will give you a better evaluation of how well your Yoda model is doing, and that better evaluation will help you improve your Yoda model."
]
},
{
"cell_type": "markdown",
- "metadata": {},
"source": [
- "## 2.5: Conclusion\n",
+ "## 2.5: Monitoring with evals\n",
+ "\n",
+ "Just as we used Opik for evaluation metrics during fine-tuning and testing, we can also use Opik to monitor our LLM once it is live in deployment. This makes it easy to track the same metrics consistently across both development and deployment.\n",
+ "\n",
+ "In prior labs, we used Comet for experiment tracking, so you should have an account and API key. If not, you can sign up for a Comet account [here](https://www.comet.com/signup?from=llm&utm_source=mit_dl&utm_medium=notebook&utm_campaign=opik) if you have not done so already. We will configure Opik by setting the API key and naming our Opik project."
+ ],
+ "metadata": {
+ "id": "BeMLjanimqDr"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "os.environ[\"OPIK_API_KEY\"] = \"\" # TODO: add your OPIK or Comet API key here\n",
+ "assert OPIK_API_KEY != \"\", \"You must set your OPIK or Comet API key before running this cell!\"\n",
+ "\n",
+ "# Set the project name for Opik\n",
+ "os.environ[\"OPIK_PROJECT_NAME\"] = \"6S191_Lab3\"\n",
+ "\n",
+ "opik.configure()"
+ ],
+ "metadata": {
+ "id": "uA2nuV8n6STH"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "[Tracing](https://www.comet.com/docs/opik/tracing/concepts) helps you understand the end-to-end flow of your LLM application and pinpoint specific steps that may be causing issues.\n",
+ "\n",
+ "In the example below, we make a sample call to the chatbot and use Opik’s `@track` decorator to log data to the Opik UI, creating a record of live calls to the application. You can add the `@track` decorator to any function to trace not only LLM calls, but also other steps in your application pipeline."
+ ],
+ "metadata": {
+ "id": "VcwD9NcM6SnJ"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "@opik.track\n",
+ "def inference_chat(question, max_new_tokens=32, temperature=0.7, only_answer=False):\n",
+ "\n",
+ " # 1. Construct the prompt using the template\n",
+ " prompt = template_without_answer.format(question=question)\n",
+ "\n",
+ " # 2. Tokenize the text\n",
+ " input_ids = tokenizer(prompt, return_tensors=\"pt\").to(model.device)\n",
+ "\n",
+ " # 3. Feed through the model to predict the next token probabilities\n",
+ " with torch.no_grad():\n",
+ " outputs = model.generate(**input_ids, do_sample=True, max_new_tokens=max_new_tokens, temperature=temperature)\n",
+ "\n",
+ " # 4. Only return the answer if only_answer is True\n",
+ " output_tokens = outputs[0]\n",
+ " if only_answer:\n",
+ " output_tokens = output_tokens[input_ids['input_ids'].shape[1]:]\n",
+ "\n",
+ " # 5. Decode the tokens\n",
+ " result = tokenizer.decode(output_tokens, skip_special_tokens=True)\n",
+ "\n",
+ " # Update the current trace with evaluation scores\n",
+ " opik_context.update_current_trace(\n",
+ " feedback_scores=[\n",
+ " {\n",
+ " \"name\": \"Yoda style eval\",\n",
+ " \"value\": scoring_function(result)\n",
+ " }\n",
+ " ]\n",
+ " )\n",
+ "\n",
+ " return result"
+ ],
+ "metadata": {
+ "id": "VqQdGAKfm-8H"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Now you can make an example call to your model to see the trace logged to Opik. Once you run the cell below you should see a link to your Opik UI where your traces are logged to your project. Follow that link to see your traces in the Opik platform."
+ ],
+ "metadata": {
+ "id": "uYXA6wOPnAhb"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Let's try chatting with the model now to see the traces produced with the score\n",
+ "answer = inference_chat(\n",
+ " \"Who was the only non-Jedi to wield a lightsaber in the original Star Wars trilogy?\",\n",
+ " only_answer=True,\n",
+ " max_new_tokens=32,\n",
+ ")\n",
+ "\n",
+ "print(answer)"
+ ],
+ "metadata": {
+ "id": "uLYflR9DnCfM"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "pRhbKTy2fny0"
+ },
+ "source": [
+ "## 2.6: Conclusion\n",
"\n",
"Experiment with both your chat model and your judge LLM to try to improve the quality of the Yoda-speak. The competition for this lab will be based on the following criteria:\n",
"* **Likelihood of true Yoda-speak under your chat model**: the better your chat model does at understanding Yoda-speak, it will estimate a lower cross entropy loss for language that is true Yoda-speak. At the end of this lab, you will evaluate the likelihood of a held-out test-sample of true Yoda-speak under your chat model. Include this likelihood in your report. This gives us a quantitative measure to compare different chat models (which may have interacted with different judge LLMs).\n",
@@ -1244,11 +1194,7 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "MqnrG24FBvnK",
- "outputId": "b93c17db-6968-40f3-a012-b3b202185bb6"
+ "id": "MqnrG24FBvnK"
},
"outputs": [],
"source": [
@@ -1271,11 +1217,13 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "id": "XmlmIGJyfny0"
+ },
"source": [
"# Submission information\n",
"\n",
- "To enter the competition, please upload the following to the lab [submission site for the Large Language Models Lab](https://www.dropbox.com/request/vrDrNCkj4yDxgsi2O5Sw)):\n",
+ "To enter the competition, please upload the following to the lab [submission site for the Large Language Models Lab](https://www.dropbox.com/request/l2JH7UlrayUl1Ps5ZVZm):\n",
"\n",
"* Jupyter notebook with the code you used to generate your results;\n",
"* copy of the bar plot showing the judge LLM's scores of text in base style, generated text, and text in true Yoda-speak style;\n",
@@ -1289,11 +1237,6 @@
"\n",
""
]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": []
}
],
"metadata": {
@@ -1317,4114 +1260,6 @@
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
- },
- "widgets": {
- "application/vnd.jupyter.widget-state+json": {
- "00dffcff57a14ad28d665cd2c2a11960": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "DescriptionStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "DescriptionStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "description_width": ""
- }
- },
- "01b7fbea9de54e338e3862e09d7e353d": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "DescriptionStyleModel",
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- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "DescriptionStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "description_width": ""
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- "01bc169362704eeebd69a87d641d269e": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "HBoxModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "HBoxModel",
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- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HBoxView",
- "box_style": "",
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- "IPY_MODEL_09b97b2a1f734e38b2a9908cf59edd8d",
- "IPY_MODEL_74dc454addc64783bbf1b3897a817147"
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- }
}
},
"nbformat": 4,
|
![](\"https://i.ibb.co/Jr88sn2/mit.png\")
\n",
" Visit MIT Deep Learning![](\"https://i.ibb.co/2P3SLwK/colab.png\")
Run in Google Colab![](\"https://i.ibb.co/xfJbPmL/github.png\")
View Source on GitHub![\"Drawing\"/](\"https://raw.githubusercontent.com/MITDeepLearning/introtodeeplearning/2019/lab1/img/lstm_unrolled-01-01.png\")
\n",
+ "![\"Drawing\"/](\"https://raw.githubusercontent.com/MITDeepLearning/introtodeeplearning/master/lab1/img/lstm_unrolled-01-01.png\")
\n",
"\n",
"\n",
"\n",
@@ -415,7 +415,7 @@
"* [`tf.keras.layers.Dense`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dense): The output layer, with `vocab_size` outputs.\n",
"\n",
"\n",
- "![](\"https://i.ibb.co/BjLSRMM/ezgif-2-253dfd3f9097.gif\")
"
]
}
- ]
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "collapsed_sections": [
+ "Ag_e7xtTzT1W",
+ "NDj7KBaW8Asz"
+ ],
+ "name": "TF_Part2_Debiasing.ipynb",
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "name": "python3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
}
diff --git a/lab2/solutions/PT_Part1_MNIST_Solution.ipynb b/lab2/solutions/PT_Part1_MNIST_Solution.ipynb
index 01dc5bfa..efb23831 100644
--- a/lab2/solutions/PT_Part1_MNIST_Solution.ipynb
+++ b/lab2/solutions/PT_Part1_MNIST_Solution.ipynb
@@ -27,7 +27,7 @@
},
"outputs": [],
"source": [
- "# Copyright 2025 MIT Introduction to Deep Learning. All Rights Reserved.\n",
+ "# Copyright 2026 MIT Introduction to Deep Learning. All Rights Reserved.\n",
"#\n",
"# Licensed under the MIT License. You may not use this file except in compliance\n",
"# with the License. Use and/or modification of this code outside of MIT Introduction\n",
@@ -1026,4 +1026,4 @@
},
"nbformat": 4,
"nbformat_minor": 0
-}
\ No newline at end of file
+}
diff --git a/lab2/solutions/PT_Part2_Debiasing_Solution.ipynb b/lab2/solutions/PT_Part2_Debiasing_Solution.ipynb
index b258530b..02b4351e 100644
--- a/lab2/solutions/PT_Part2_Debiasing_Solution.ipynb
+++ b/lab2/solutions/PT_Part2_Debiasing_Solution.ipynb
@@ -27,7 +27,7 @@
},
"outputs": [],
"source": [
- "# Copyright 2025 MIT 6.S191 Introduction to Deep Learning. All Rights Reserved.\n",
+ "# Copyright 2026 MIT 6.S191 Introduction to Deep Learning. All Rights Reserved.\n",
"#\n",
"# Licensed under the MIT License. You may not use this file except in compliance\n",
"# with the License. Use and/or modification of this code outside of 6.S191 must\n",
@@ -1320,7 +1320,7 @@
"* Do you think it should be necessary for companies to demonstrate that their models, particularly in the context of tasks like facial detection, are not biased? If so, do you have thoughts on how this could be standardized and implemented?\n",
"* Do you have ideas for other ways to address issues of bias, particularly in terms of the training data?\n",
"\n",
- "**The debiased model may or may not perform well based on the initial hyperparameters. This lab competition will be focused on your answers to the questions above, experiments you tried, and your interpretation and analysis of the results. To enter the competition, please upload the following to the lab submission site for the Debiasing Faces Lab ([submission upload link](https://www.dropbox.com/request/dJZUEoqGLB43JEKzzqIc)).**\n",
+ "**The debiased model may or may not perform well based on the initial hyperparameters. This lab competition will be focused on your answers to the questions above, experiments you tried, and your interpretation and analysis of the results. To enter the competition, please upload the following to the lab submission site for the Debiasing Faces Lab ([submission upload link](https://www.dropbox.com/request/SG6AnjrtIljNPrbEOMfP)).**\n",
"\n",
"* Jupyter notebook with the code you used to generate your results;\n",
"* copy of the bar plot from section 2.6 showing the performance of your model;\n",
diff --git a/lab2/solutions/TF_Part1_MNIST_Solution.ipynb b/lab2/solutions/TF_Part1_MNIST_Solution.ipynb
index c2ae8377..90db5d6e 100644
--- a/lab2/solutions/TF_Part1_MNIST_Solution.ipynb
+++ b/lab2/solutions/TF_Part1_MNIST_Solution.ipynb
@@ -27,7 +27,7 @@
},
"outputs": [],
"source": [
- "# Copyright 2025 MIT Introduction to Deep Learning. All Rights Reserved.\n",
+ "# Copyright 2026 MIT Introduction to Deep Learning. All Rights Reserved.\n",
"#\n",
"# Licensed under the MIT License. You may not use this file except in compliance\n",
"# with the License. Use and/or modification of this code outside of MIT Introduction\n",
diff --git a/lab2/solutions/TF_Part2_Debiasing_Solution.ipynb b/lab2/solutions/TF_Part2_Debiasing_Solution.ipynb
index d6ccba60..71ce318d 100644
--- a/lab2/solutions/TF_Part2_Debiasing_Solution.ipynb
+++ b/lab2/solutions/TF_Part2_Debiasing_Solution.ipynb
@@ -1,21 +1,4 @@
{
- "nbformat": 4,
- "nbformat_minor": 0,
- "metadata": {
- "colab": {
- "name": "TF_Part2_Debiasing_Solution.ipynb",
- "provenance": [],
- "collapsed_sections": [
- "Ag_e7xtTzT1W",
- "NDj7KBaW8Asz"
- ]
- },
- "kernelspec": {
- "name": "python3",
- "display_name": "Python 3"
- },
- "accelerator": "GPU"
- },
"cells": [
{
"cell_type": "markdown",
@@ -38,11 +21,13 @@
},
{
"cell_type": "code",
+ "execution_count": null,
"metadata": {
"id": "rNbf1pRlSDby"
},
+ "outputs": [],
"source": [
- "# Copyright 2025 MIT 6.S191 Introduction to Deep Learning. All Rights Reserved.\n",
+ "# Copyright 2026 MIT 6.S191 Introduction to Deep Learning. All Rights Reserved.\n",
"#\n",
"# Licensed under the MIT License. You may not use this file except in compliance\n",
"# with the License. Use and/or modification of this code outside of 6.S191 must\n",
@@ -51,9 +36,7 @@
"# © MIT 6.S191: Introduction to Deep Learning\n",
"# http://introtodeeplearning.com\n",
"#"
- ],
- "execution_count": null,
- "outputs": []
+ ]
},
{
"cell_type": "markdown",
@@ -77,15 +60,15 @@
},
{
"cell_type": "code",
+ "execution_count": null,
"metadata": {
"id": "XQh5HZfbupFF"
},
+ "outputs": [],
"source": [
"import IPython\n",
"IPython.display.YouTubeVideo('59bMh59JQDo')"
- ],
- "execution_count": null,
- "outputs": []
+ ]
},
{
"cell_type": "markdown",
@@ -104,9 +87,11 @@
},
{
"cell_type": "code",
+ "execution_count": null,
"metadata": {
"id": "E46sWVKK6LP9"
},
+ "outputs": [],
"source": [
"!pip install comet_ml --quiet\n",
"import comet_ml\n",
@@ -130,9 +115,7 @@
"# using Runtime > Change Runtime Type > GPU\n",
"assert len(tf.config.list_physical_devices('GPU')) > 0\n",
"assert COMET_API_KEY != \"\", \"Please insert your Comet API Key\""
- ],
- "execution_count": null,
- "outputs": []
+ ]
},
{
"cell_type": "markdown",
@@ -153,17 +136,17 @@
},
{
"cell_type": "code",
+ "execution_count": null,
"metadata": {
"id": "RWXaaIWy6jVw"
},
+ "outputs": [],
"source": [
"# Get the training data: both images from CelebA and ImageNet\n",
"path_to_training_data = tf.keras.utils.get_file('train_face.h5', 'https://www.dropbox.com/s/hlz8atheyozp1yx/train_face.h5?dl=1')\n",
"# Instantiate a TrainingDatasetLoader using the downloaded dataset\n",
"loader = mdl.lab2.TrainingDatasetLoader(path_to_training_data)"
- ],
- "execution_count": null,
- "outputs": []
+ ]
},
{
"cell_type": "markdown",
@@ -176,15 +159,15 @@
},
{
"cell_type": "code",
+ "execution_count": null,
"metadata": {
"id": "DjPSjZZ_bGqe"
},
+ "outputs": [],
"source": [
"number_of_training_examples = loader.get_train_size()\n",
"(images, labels) = loader.get_batch(100)"
- ],
- "execution_count": null,
- "outputs": []
+ ]
},
{
"cell_type": "markdown",
@@ -197,9 +180,11 @@
},
{
"cell_type": "code",
+ "execution_count": null,
"metadata": {
"id": "Jg17jzwtbxDA"
},
+ "outputs": [],
"source": [
"### Examining the CelebA training dataset ###\n",
"\n",
@@ -219,9 +204,7 @@
"plt.subplot(1, 2, 2)\n",
"plt.imshow(not_face_images[idx_not_face])\n",
"plt.title(\"Not Face\"); plt.grid(False)"
- ],
- "execution_count": null,
- "outputs": []
+ ]
},
{
"cell_type": "markdown",
@@ -253,9 +236,11 @@
},
{
"cell_type": "code",
+ "execution_count": null,
"metadata": {
"id": "82EVTAAW7B_X"
},
+ "outputs": [],
"source": [
"### Define the CNN model ###\n",
"\n",
@@ -288,9 +273,7 @@
" return model\n",
"\n",
"standard_classifier = make_standard_classifier()"
- ],
- "execution_count": null,
- "outputs": []
+ ]
},
{
"cell_type": "markdown",
@@ -303,6 +286,11 @@
},
{
"cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "mi-04SAfK6lm"
+ },
+ "outputs": [],
"source": [
"### Create a Comet experiment to track our training run ###\n",
"def create_experiment(project_name, params):\n",
@@ -320,18 +308,15 @@
" experiment.flush()\n",
"\n",
" return experiment\n"
- ],
- "metadata": {
- "id": "mi-04SAfK6lm"
- },
- "execution_count": null,
- "outputs": []
+ ]
},
{
"cell_type": "code",
+ "execution_count": null,
"metadata": {
"id": "eJlDGh1o31G1"
},
+ "outputs": [],
"source": [
"### Train the standard CNN ###\n",
"\n",
@@ -376,9 +361,7 @@
"\n",
" experiment.log_metric(\"loss\", loss.numpy().mean(), step=step)\n",
" step += 1"
- ],
- "execution_count": null,
- "outputs": []
+ ]
},
{
"cell_type": "markdown",
@@ -393,9 +376,11 @@
},
{
"cell_type": "code",
+ "execution_count": null,
"metadata": {
"id": "35-PDgjdWk6_"
},
+ "outputs": [],
"source": [
"### Evaluation of standard CNN ###\n",
"\n",
@@ -406,9 +391,7 @@
"acc_standard = tf.reduce_mean(tf.cast(tf.equal(batch_y, y_pred_standard), tf.float32))\n",
"\n",
"print(\"Standard CNN accuracy on (potentially biased) training set: {:.4f}\".format(acc_standard.numpy()))"
- ],
- "execution_count": null,
- "outputs": []
+ ]
},
{
"cell_type": "markdown",
@@ -423,9 +406,11 @@
},
{
"cell_type": "code",
+ "execution_count": null,
"metadata": {
"id": "vfDD8ztGWk6x"
},
+ "outputs": [],
"source": [
"### Load test dataset and plot examples ###\n",
"\n",
@@ -435,9 +420,7 @@
" plt.figure(figsize=(5,5))\n",
" plt.imshow(np.hstack(group))\n",
" plt.title(key, fontsize=15)"
- ],
- "execution_count": null,
- "outputs": []
+ ]
},
{
"cell_type": "markdown",
@@ -450,9 +433,11 @@
},
{
"cell_type": "code",
+ "execution_count": null,
"metadata": {
"id": "GI4O0Y1GAot9"
},
+ "outputs": [],
"source": [
"### Evaluate the standard CNN on the test data ###\n",
"\n",
@@ -464,11 +449,9 @@
"yy = standard_classifier_probs.numpy().mean(1)\n",
"plt.bar(xx, yy)\n",
"plt.xticks(xx, keys)\n",
- "plt.ylim(max(0,yy.min()-yy.ptp()/2.), yy.max()+yy.ptp()/2.)\n",
+ "plt.ylim(max(0,yy.min()-np.ptp(yy)/2.), yy.max()+np.ptp(yy)/2.)\n",
"plt.title(\"Standard classifier predictions\");"
- ],
- "execution_count": null,
- "outputs": []
+ ]
},
{
"cell_type": "markdown",
@@ -552,9 +535,11 @@
},
{
"cell_type": "code",
+ "execution_count": null,
"metadata": {
"id": "S00ASo1ImSuh"
},
+ "outputs": [],
"source": [
"### Defining the VAE loss function ###\n",
"\n",
@@ -586,9 +571,7 @@
" # vae_loss = # TODO\n",
"\n",
" return vae_loss"
- ],
- "execution_count": null,
- "outputs": []
+ ]
},
{
"cell_type": "markdown",
@@ -618,9 +601,11 @@
},
{
"cell_type": "code",
+ "execution_count": null,
"metadata": {
"id": "cT6PGdNajl3K"
},
+ "outputs": [],
"source": [
"### VAE Reparameterization ###\n",
"\n",
@@ -640,9 +625,7 @@
" z = z_mean + tf.math.exp(0.5 * z_logsigma) * epsilon\n",
" # z = # TODO\n",
" return z"
- ],
- "execution_count": null,
- "outputs": []
+ ]
},
{
"cell_type": "markdown",
@@ -701,9 +684,11 @@
},
{
"cell_type": "code",
+ "execution_count": null,
"metadata": {
"id": "VjieDs8Ovcqs"
},
+ "outputs": [],
"source": [
"### Loss function for DB-VAE ###\n",
"\n",
@@ -743,9 +728,7 @@
" # total_loss = # TODO\n",
"\n",
" return total_loss, classification_loss"
- ],
- "execution_count": null,
- "outputs": []
+ ]
},
{
"cell_type": "markdown",
@@ -762,9 +745,11 @@
},
{
"cell_type": "code",
+ "execution_count": null,
"metadata": {
"id": "JfWPHGrmyE7R"
},
+ "outputs": [],
"source": [
"### Define the decoder portion of the DB-VAE ###\n",
"\n",
@@ -793,9 +778,7 @@
" ])\n",
"\n",
" return decoder"
- ],
- "execution_count": null,
- "outputs": []
+ ]
},
{
"cell_type": "markdown",
@@ -808,9 +791,11 @@
},
{
"cell_type": "code",
+ "execution_count": null,
"metadata": {
"id": "dSFDcFBL13c3"
},
+ "outputs": [],
"source": [
"### Defining and creating the DB-VAE ###\n",
"\n",
@@ -875,9 +860,7 @@
" return y_logit\n",
"\n",
"dbvae = DB_VAE(latent_dim)"
- ],
- "execution_count": null,
- "outputs": []
+ ]
},
{
"cell_type": "markdown",
@@ -915,9 +898,11 @@
},
{
"cell_type": "code",
+ "execution_count": null,
"metadata": {
"id": "ewWbf7TE7wVc"
},
+ "outputs": [],
"source": [
"# Function to return the means for an input image batch\n",
"def get_latent_mu(images, dbvae, batch_size=1024):\n",
@@ -929,9 +914,7 @@
" _, batch_mu, _ = dbvae.encode(batch)\n",
" mu[start_ind:end_ind] = batch_mu\n",
" return mu"
- ],
- "execution_count": null,
- "outputs": []
+ ]
},
{
"cell_type": "markdown",
@@ -944,9 +927,11 @@
},
{
"cell_type": "code",
+ "execution_count": null,
"metadata": {
"id": "HiX9pmmC7_wn"
},
+ "outputs": [],
"source": [
"### Resampling algorithm for DB-VAE ###\n",
"\n",
@@ -999,9 +984,7 @@
" training_sample_p /= np.sum(training_sample_p)\n",
"\n",
" return training_sample_p"
- ],
- "execution_count": null,
- "outputs": []
+ ]
},
{
"cell_type": "markdown",
@@ -1014,9 +997,11 @@
},
{
"cell_type": "code",
+ "execution_count": null,
"metadata": {
"id": "xwQs-Gu5bKEK"
},
+ "outputs": [],
"source": [
"### Training the DB-VAE ###\n",
"\n",
@@ -1089,9 +1074,7 @@
" step += 1\n",
"\n",
"experiment.end()"
- ],
- "execution_count": null,
- "outputs": []
+ ]
},
{
"cell_type": "markdown",
@@ -1115,9 +1098,11 @@
},
{
"cell_type": "code",
+ "execution_count": null,
"metadata": {
"id": "bgK77aB9oDtX"
},
+ "outputs": [],
"source": [
"dbvae_logits = [dbvae.predict(np.array(x, dtype=np.float32)) for x in test_faces]\n",
"dbvae_probs = tf.squeeze(tf.sigmoid(dbvae_logits))\n",
@@ -1128,9 +1113,7 @@
"plt.xticks(xx, keys);\n",
"plt.title(\"Network predictions on test dataset\")\n",
"plt.ylabel(\"Probability\"); plt.legend(bbox_to_anchor=(1.04,1), loc=\"upper left\");\n"
- ],
- "execution_count": null,
- "outputs": []
+ ]
},
{
"cell_type": "markdown",
@@ -1148,7 +1131,7 @@
"* Do you think it should be necessary for companies to demonstrate that their models, particularly in the context of tasks like facial detection, are not biased? If so, do you have thoughts on how this could be standardized and implemented?\n",
"* Do you have ideas for other ways to address issues of bias, particularly in terms of the training data?\n",
"\n",
- "**Try to optimize your model to achieve improved performance. To enter the competition, please upload the following to the lab submission site for the Debiasing Faces Lab ([submission upload link](https://www.dropbox.com/request/dJZUEoqGLB43JEKzzqIc)).**\n",
+ "**Try to optimize your model to achieve improved performance. To enter the competition, please upload the following to the lab submission site for the Debiasing Faces Lab ([submission upload link](https://www.dropbox.com/request/SG6AnjrtIljNPrbEOMfP)).**\n",
"\n",
"* Jupyter notebook with the code you used to generate your results;\n",
"* copy of the bar plot from section 2.6 showing the performance of your model;\n",
@@ -1162,5 +1145,22 @@
"