├── README.md
├── Llama_2_Fine_Tune_With_Your_Dataset.ipynb
├── Llama_2_13b_chat_and_llama_cpp_python.ipynb
├── Purple_Llama_Anyscale.ipynb
├── Llama_3_Meta_Trust_&_Safety.ipynb
└── Reasoning_With_Llama_4.ipynb


/README.md:
--------------------------------------------------------------------------------
1 | # Llama
2 | Welcome to my Repo! :smile: This repository contains notebooks:
3 | * That can be run on Google Colab to understand how to use Llama 3 models
4 | * Examples of fine-tuning Llama 3 models with different datasets (you can)
5 | * Prompt-engineering with open source tools/frameworks such as Langchain, FAISS, etc
6 | * and other fun stuff you can do with Llama!
7 | 


--------------------------------------------------------------------------------
/Llama_2_Fine_Tune_With_Your_Dataset.ipynb:
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  1 | {
  2 |   "nbformat": 4,
  3 |   "nbformat_minor": 0,
  4 |   "metadata": {
  5 |     "colab": {
  6 |       "provenance": [],
  7 |       "authorship_tag": "ABX9TyMRpOGQjpoxurAw6GSGiv5U",
  8 |       "include_colab_link": true
  9 |     },
 10 |     "kernelspec": {
 11 |       "name": "python3",
 12 |       "display_name": "Python 3"
 13 |     },
 14 |     "language_info": {
 15 |       "name": "python"
 16 |     }
 17 |   },
 18 |   "cells": [
 19 |     {
 20 |       "cell_type": "markdown",
 21 |       "metadata": {
 22 |         "id": "view-in-github",
 23 |         "colab_type": "text"
 24 |       },
 25 |       "source": [
 26 |         "<a href=\"https://colab.research.google.com/github/amitsangani/Llama/blob/main/Llama_2_Fine_Tune_With_Your_Dataset.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
 27 |       ]
 28 |     },
 29 |     {
 30 |       "cell_type": "markdown",
 31 |       "source": [
 32 |         "# **Practical Introduction to Llama 2 Fine-Tuning**"
 33 |       ],
 34 |       "metadata": {
 35 |         "id": "lJ4soaSMhgUo"
 36 |       }
 37 |     },
 38 |     {
 39 |       "cell_type": "markdown",
 40 |       "source": [
 41 |         "## **How to fine-tune Llama 2 🦙**\n",
 42 |         "In this section, we will fine-tune a 7B parameter Llama 2 model on a T4 GPU with high RAM using Google Colab. Note that a T4 only has 16 GB of VRAM, which is barely enough to store Llama 2–7b’s weights (7b × 2 bytes = 14 GB in FP16). In addition, we need to consider the overhead due to optimizer states, gradients, and forward activations (see this excellent article for more information). This means that a full fine-tuning is not possible here: we need parameter-efficient fine-tuning (PEFT) techniques like LoRA or QLoRA.\n",
 43 |         "\n",
 44 |         "To drastically reduce the VRAM usage, we must fine-tune the model in 4-bit precision, which is why we’ll use QLoRA here. The good thing is that we can leverage the Hugging Face ecosystem with the transformers, accelerate, peft, trl, and bitsandbytes libraries. First, we install and load these libraries."
 45 |       ],
 46 |       "metadata": {
 47 |         "id": "9sFY3weAiG0X"
 48 |       }
 49 |     },
 50 |     {
 51 |       "cell_type": "code",
 52 |       "source": [
 53 |         "!pip install -q accelerate==0.21.0 peft==0.4.0 bitsandbytes==0.40.2 transformers==4.31.0 trl==0.4.7"
 54 |       ],
 55 |       "metadata": {
 56 |         "colab": {
 57 |           "base_uri": "https://localhost:8080/"
 58 |         },
 59 |         "id": "JJANcmlUhpk-",
 60 |         "outputId": "7d51f366-8025-4980-f791-cb50ba5c6f70"
 61 |       },
 62 |       "execution_count": 1,
 63 |       "outputs": [
 64 |         {
 65 |           "output_type": "stream",
 66 |           "name": "stdout",
 67 |           "text": [
 68 |             "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m244.2/244.2 kB\u001b[0m \u001b[31m3.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
 69 |             "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m72.9/72.9 kB\u001b[0m \u001b[31m7.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
 70 |             "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.5/92.5 MB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
 71 |             "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.4/7.4 MB\u001b[0m \u001b[31m50.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
 72 |             "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.4/77.4 kB\u001b[0m \u001b[31m7.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
 73 |             "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m25.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
 74 |             "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m22.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
 75 |             "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m84.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
 76 |             "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m519.3/519.3 kB\u001b[0m \u001b[31m35.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
 77 |             "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m11.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
 78 |             "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m16.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
 79 |             "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m11.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
 80 |             "\u001b[?25h"
 81 |           ]
 82 |         }
 83 |       ]
 84 |     },
 85 |     {
 86 |       "cell_type": "code",
 87 |       "source": [
 88 |         "import os\n",
 89 |         "import torch\n",
 90 |         "from datasets import load_dataset\n",
 91 |         "from transformers import (\n",
 92 |         "    AutoModelForCausalLM,\n",
 93 |         "    AutoTokenizer,\n",
 94 |         "    BitsAndBytesConfig,\n",
 95 |         "    HfArgumentParser,\n",
 96 |         "    TrainingArguments,\n",
 97 |         "    pipeline,\n",
 98 |         "    logging,\n",
 99 |         ")\n",
100 |         "from peft import LoraConfig, PeftModel\n",
101 |         "from trl import SFTTrainer"
102 |       ],
103 |       "metadata": {
104 |         "id": "jP-0sDCvhppv"
105 |       },
106 |       "execution_count": 3,
107 |       "outputs": []
108 |     },
109 |     {
110 |       "cell_type": "markdown",
111 |       "source": [
112 |         "First, we want to load a llama-2-7b-chat-hf model (chat model) and train it on the mlabonne/guanaco-llama2-1k (1,000 samples), which will produce our fine-tuned model llama-2-7b-miniguanaco. If you’re interested in how this dataset was created, you can check this notebook. Feel free to change it: there are many good datasets on the Hugging Face Hub, like databricks/databricks-dolly-15k.\n",
113 |         "\n",
114 |         "QLoRA will use a rank of 64 with a scaling parameter of 16 (see this article for more information about LoRA parameters). We’ll load the Llama 2 model directly in 4-bit precision using the NF4 type and train it for one epoch. To get more information about the other parameters, check the TrainingArguments, PeftModel, and SFTTrainer documentation.\n"
115 |       ],
116 |       "metadata": {
117 |         "id": "dy79lphLjFZo"
118 |       }
119 |     },
120 |     {
121 |       "cell_type": "code",
122 |       "source": [
123 |         "# The model that you want to train from the Hugging Face hub\n",
124 |         "model_name = \"NousResearch/Llama-2-7b-chat-hf\"\n",
125 |         "\n",
126 |         "# The instruction dataset to use\n",
127 |         "dataset_name = \"mlabonne/guanaco-llama2-1k\"\n",
128 |         "\n",
129 |         "# Fine-tuned model name\n",
130 |         "new_model = \"llama-2-7b-miniguanaco\""
131 |       ],
132 |       "metadata": {
133 |         "id": "WZ9KcGFqjVj-"
134 |       },
135 |       "execution_count": 4,
136 |       "outputs": []
137 |     },
138 |     {
139 |       "cell_type": "code",
140 |       "source": [
141 |         "################################################################################\n",
142 |         "# QLoRA parameters\n",
143 |         "################################################################################\n",
144 |         "\n",
145 |         "# LoRA attention dimension\n",
146 |         "lora_r = 64\n",
147 |         "\n",
148 |         "# Alpha parameter for LoRA scaling\n",
149 |         "lora_alpha = 16\n",
150 |         "\n",
151 |         "# Dropout probability for LoRA layers\n",
152 |         "lora_dropout = 0.1"
153 |       ],
154 |       "metadata": {
155 |         "id": "7Oxu2QXTjb38"
156 |       },
157 |       "execution_count": 5,
158 |       "outputs": []
159 |     },
160 |     {
161 |       "cell_type": "code",
162 |       "source": [
163 |         "################################################################################\n",
164 |         "# bitsandbytes parameters\n",
165 |         "################################################################################\n",
166 |         "\n",
167 |         "# Activate 4-bit precision base model loading\n",
168 |         "use_4bit = True\n",
169 |         "\n",
170 |         "# Compute dtype for 4-bit base models\n",
171 |         "bnb_4bit_compute_dtype = \"float16\"\n",
172 |         "\n",
173 |         "# Quantization type (fp4 or nf4)\n",
174 |         "bnb_4bit_quant_type = \"nf4\"\n",
175 |         "\n",
176 |         "# Activate nested quantization for 4-bit base models (double quantization)\n",
177 |         "use_nested_quant = False"
178 |       ],
179 |       "metadata": {
180 |         "id": "tdXSjZOLjf_-"
181 |       },
182 |       "execution_count": 6,
183 |       "outputs": []
184 |     },
185 |     {
186 |       "cell_type": "code",
187 |       "source": [
188 |         "################################################################################\n",
189 |         "# TrainingArguments parameters\n",
190 |         "################################################################################\n",
191 |         "\n",
192 |         "# Output directory where the model predictions and checkpoints will be stored\n",
193 |         "output_dir = \"./results\"\n",
194 |         "\n",
195 |         "# Number of training epochs\n",
196 |         "num_train_epochs = 1\n",
197 |         "\n",
198 |         "# Enable fp16/bf16 training (set bf16 to True with an A100)\n",
199 |         "fp16 = False\n",
200 |         "bf16 = False\n",
201 |         "\n",
202 |         "# Batch size per GPU for training\n",
203 |         "per_device_train_batch_size = 4\n",
204 |         "\n",
205 |         "# Batch size per GPU for evaluation\n",
206 |         "per_device_eval_batch_size = 4\n",
207 |         "\n",
208 |         "# Number of update steps to accumulate the gradients for\n",
209 |         "gradient_accumulation_steps = 1\n",
210 |         "\n",
211 |         "# Enable gradient checkpointing\n",
212 |         "gradient_checkpointing = True\n",
213 |         "\n",
214 |         "# Maximum gradient normal (gradient clipping)\n",
215 |         "max_grad_norm = 0.3\n",
216 |         "\n",
217 |         "# Initial learning rate (AdamW optimizer)\n",
218 |         "learning_rate = 2e-4\n",
219 |         "\n",
220 |         "# Weight decay to apply to all layers except bias/LayerNorm weights\n",
221 |         "weight_decay = 0.001\n",
222 |         "\n",
223 |         "# Optimizer to use\n",
224 |         "optim = \"paged_adamw_32bit\"\n",
225 |         "\n",
226 |         "# Learning rate schedule (constant a bit better than cosine)\n",
227 |         "lr_scheduler_type = \"constant\"\n",
228 |         "\n",
229 |         "# Number of training steps (overrides num_train_epochs)\n",
230 |         "max_steps = -1\n",
231 |         "\n",
232 |         "# Ratio of steps for a linear warmup (from 0 to learning rate)\n",
233 |         "warmup_ratio = 0.03\n",
234 |         "\n",
235 |         "# Group sequences into batches with same length\n",
236 |         "# Saves memory and speeds up training considerably\n",
237 |         "group_by_length = True\n",
238 |         "\n",
239 |         "# Save checkpoint every X updates steps\n",
240 |         "save_steps = 25\n",
241 |         "\n",
242 |         "# Log every X updates steps\n",
243 |         "logging_steps = 25"
244 |       ],
245 |       "metadata": {
246 |         "id": "GOQhnX68h_6H"
247 |       },
248 |       "execution_count": 7,
249 |       "outputs": []
250 |     },
251 |     {
252 |       "cell_type": "code",
253 |       "source": [
254 |         "################################################################################\n",
255 |         "# SFT parameters\n",
256 |         "################################################################################\n",
257 |         "\n",
258 |         "# Maximum sequence length to use\n",
259 |         "max_seq_length = None\n",
260 |         "\n",
261 |         "# Pack multiple short examples in the same input sequence to increase efficiency\n",
262 |         "packing = False\n",
263 |         "\n",
264 |         "# Load the entire model on the GPU 0\n",
265 |         "device_map = {\"\": 0}"
266 |       ],
267 |       "metadata": {
268 |         "id": "vlaRC-1ejoAg"
269 |       },
270 |       "execution_count": 8,
271 |       "outputs": []
272 |     },
273 |     {
274 |       "cell_type": "markdown",
275 |       "source": [
276 |         "# **We can now start a fine-tuning process**\n",
277 |         "\n",
278 |         "1. Load the pre-processed dataset.\n",
279 |         "2. Configure bitsandbytes for 4-bit quantization\n",
280 |         "3. Load Llama 2 model in 4-bit precision on a GPU with tokenizer\n",
281 |         "4. Load configurations for QLoRA, regular training params using SFTTrainer"
282 |       ],
283 |       "metadata": {
284 |         "id": "Yj2SRFXfjy19"
285 |       }
286 |     },
287 |     {
288 |       "cell_type": "code",
289 |       "source": [
290 |         "# Step 1: Load dataset (you can process it here)\n",
291 |         "dataset = load_dataset(dataset_name, split=\"train\")"
292 |       ],
293 |       "metadata": {
294 |         "id": "S5EGK7ZokhiW"
295 |       },
296 |       "execution_count": null,
297 |       "outputs": []
298 |     },
299 |     {
300 |       "cell_type": "code",
301 |       "source": [
302 |         "# Step 2: configure bitsandbytes for 4-bit quantization\n",
303 |         "\n",
304 |         "# Load tokenizer and model with QLoRA configuration\n",
305 |         "compute_dtype = getattr(torch, bnb_4bit_compute_dtype)\n",
306 |         "\n",
307 |         "bnb_config = BitsAndBytesConfig(\n",
308 |         "    load_in_4bit=use_4bit,\n",
309 |         "    bnb_4bit_quant_type=bnb_4bit_quant_type,\n",
310 |         "    bnb_4bit_compute_dtype=compute_dtype,\n",
311 |         "    bnb_4bit_use_double_quant=use_nested_quant,\n",
312 |         ")"
313 |       ],
314 |       "metadata": {
315 |         "id": "kJN8qgbpk2Qq"
316 |       },
317 |       "execution_count": null,
318 |       "outputs": []
319 |     },
320 |     {
321 |       "cell_type": "code",
322 |       "source": [
323 |         "# Step 3: Load Llama 2 model in 4-bit precision on a GPU with tokenizer\n",
324 |         "\n",
325 |         "# Check GPU compatibility with bfloat16\n",
326 |         "if compute_dtype == torch.float16 and use_4bit:\n",
327 |         "    major, _ = torch.cuda.get_device_capability()\n",
328 |         "    if major >= 8:\n",
329 |         "        print(\"=\" * 80)\n",
330 |         "        print(\"Your GPU supports bfloat16: accelerate training with bf16=True\")\n",
331 |         "        print(\"=\" * 80)\n",
332 |         "\n",
333 |         "# Load Llama 2 base model\n",
334 |         "model = AutoModelForCausalLM.from_pretrained(\n",
335 |         "    model_name,\n",
336 |         "    quantization_config=bnb_config,\n",
337 |         "    device_map=device_map\n",
338 |         ")\n",
339 |         "\n",
340 |         "model.config.use_cache = False\n",
341 |         "model.config.pretraining_tp = 1\n",
342 |         "\n",
343 |         "# Load LLaMA tokenizer\n",
344 |         "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
345 |         "tokenizer.pad_token = tokenizer.eos_token\n",
346 |         "tokenizer.padding_side = \"right\" # Fix weird overflow issue with fp16 training"
347 |       ],
348 |       "metadata": {
349 |         "id": "V7BgWp2BlM8N"
350 |       },
351 |       "execution_count": null,
352 |       "outputs": []
353 |     },
354 |     {
355 |       "cell_type": "code",
356 |       "source": [
357 |         "# Step 4: Load configurations for QLoRA\n",
358 |         "\n",
359 |         "# Load LoRA configuration\n",
360 |         "peft_config = LoraConfig(\n",
361 |         "    lora_alpha=lora_alpha,\n",
362 |         "    lora_dropout=lora_dropout,\n",
363 |         "    r=lora_r,\n",
364 |         "    bias=\"none\",\n",
365 |         "    task_type=\"CAUSAL_LM\",\n",
366 |         ")"
367 |       ],
368 |       "metadata": {
369 |         "id": "kusnpXbUlkqi"
370 |       },
371 |       "execution_count": 9,
372 |       "outputs": []
373 |     },
374 |     {
375 |       "cell_type": "markdown",
376 |       "source": [
377 |         "#**Start training the model with given dataset**"
378 |       ],
379 |       "metadata": {
380 |         "id": "dlsWiGROl6Eo"
381 |       }
382 |     },
383 |     {
384 |       "cell_type": "code",
385 |       "source": [
386 |         "# Set training parameters\n",
387 |         "training_arguments = TrainingArguments(\n",
388 |         "    output_dir=output_dir,\n",
389 |         "    num_train_epochs=num_train_epochs,\n",
390 |         "    per_device_train_batch_size=per_device_train_batch_size,\n",
391 |         "    gradient_accumulation_steps=gradient_accumulation_steps,\n",
392 |         "    optim=optim,\n",
393 |         "    save_steps=save_steps,\n",
394 |         "    logging_steps=logging_steps,\n",
395 |         "    learning_rate=learning_rate,\n",
396 |         "    weight_decay=weight_decay,\n",
397 |         "    fp16=fp16,\n",
398 |         "    bf16=bf16,\n",
399 |         "    max_grad_norm=max_grad_norm,\n",
400 |         "    max_steps=max_steps,\n",
401 |         "    warmup_ratio=warmup_ratio,\n",
402 |         "    group_by_length=group_by_length,\n",
403 |         "    lr_scheduler_type=lr_scheduler_type,\n",
404 |         "    report_to=\"tensorboard\"\n",
405 |         ")\n",
406 |         "\n",
407 |         "# Set supervised fine-tuning parameters\n",
408 |         "trainer = SFTTrainer(\n",
409 |         "    model=model,\n",
410 |         "    train_dataset=dataset,\n",
411 |         "    peft_config=peft_config,\n",
412 |         "    dataset_text_field=\"text\",\n",
413 |         "    max_seq_length=max_seq_length,\n",
414 |         "    tokenizer=tokenizer,\n",
415 |         "    args=training_arguments,\n",
416 |         "    packing=packing,\n",
417 |         ")\n",
418 |         "\n",
419 |         "# Train model\n",
420 |         "trainer.train()\n",
421 |         "\n",
422 |         "# Save trained model\n",
423 |         "trainer.model.save_pretrained(new_model)"
424 |       ],
425 |       "metadata": {
426 |         "id": "Z1gEgaGhmL_k"
427 |       },
428 |       "execution_count": null,
429 |       "outputs": []
430 |     },
431 |     {
432 |       "cell_type": "markdown",
433 |       "source": [
434 |         "The training can be very long, depending on the size of your dataset. Here, it took less than an hour on a T4 GPU. We can check the plots on tensorboard, as follows:"
435 |       ],
436 |       "metadata": {
437 |         "id": "ag7Goqb1maby"
438 |       }
439 |     },
440 |     {
441 |       "cell_type": "code",
442 |       "source": [
443 |         "%load_ext tensorboard\n",
444 |         "%tensorboard --logdir results/runs"
445 |       ],
446 |       "metadata": {
447 |         "id": "Bmbk71vkm7zg"
448 |       },
449 |       "execution_count": null,
450 |       "outputs": []
451 |     },
452 |     {
453 |       "cell_type": "markdown",
454 |       "source": [
455 |         "Let’s make sure that the model is behaving correctly. It would require a more exhaustive evaluation, but we can use the text generation pipeline to ask questions like “What is a large language model?” Note that I’m formatting the input to match Llama 2’s prompt template."
456 |       ],
457 |       "metadata": {
458 |         "id": "JK_EeD6ymyix"
459 |       }
460 |     },
461 |     {
462 |       "cell_type": "code",
463 |       "source": [
464 |         "# Ignore warnings\n",
465 |         "logging.set_verbosity(logging.CRITICAL)\n",
466 |         "\n",
467 |         "# Run text generation pipeline with our next model\n",
468 |         "prompt = \"Explain a large language model and its architecture?\"\n",
469 |         "pipe = pipeline(task=\"text-generation\", model=model, tokenizer=tokenizer, max_length=200)\n",
470 |         "result = pipe(f\"<s>[INST] {prompt} [/INST]\")\n",
471 |         "print(result[0]['generated_text'])"
472 |       ],
473 |       "metadata": {
474 |         "id": "ZE_SnhI0m4iF"
475 |       },
476 |       "execution_count": null,
477 |       "outputs": []
478 |     },
479 |     {
480 |       "cell_type": "markdown",
481 |       "source": [
482 |         "From experience, it is very coherent for a model with only 7 billion parameters. You can play with it and ask harder questions from evaluation datasets like BigBench-Hard. Guanaco is an excellent dataset that has produced high-quality models in the past. You can train a Llama 2 model on the entire dataset using mlabonne/guanaco-llama2.\n",
483 |         "\n",
484 |         "How can we store our new llama-2-7b-miniguanaco model now? We need to merge the weights from LoRA with the base model. Unfortunately, as far as I know, there is no straightforward way to do it: we need to reload the base model in FP16 precision and use the peft library to merge everything. Alas, it also creates a problem with the VRAM (despite emptying it), so I recommend restarting the notebook, re-executing the three first cells, and then executing the next one. Please contact me if you know a fix!\n"
485 |       ],
486 |       "metadata": {
487 |         "id": "iqcSsD3EnTvc"
488 |       }
489 |     },
490 |     {
491 |       "cell_type": "code",
492 |       "source": [
493 |         "# Reload model in FP16 and merge it with LoRA weights\n",
494 |         "base_model = AutoModelForCausalLM.from_pretrained(\n",
495 |         "    model_name,\n",
496 |         "    low_cpu_mem_usage=True,\n",
497 |         "    return_dict=True,\n",
498 |         "    torch_dtype=torch.float16,\n",
499 |         "    device_map=device_map,\n",
500 |         ")\n",
501 |         "model = PeftModel.from_pretrained(base_model, new_model)\n",
502 |         "model = model.merge_and_unload()\n",
503 |         "\n",
504 |         "# Reload tokenizer to save it\n",
505 |         "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
506 |         "tokenizer.pad_token = tokenizer.eos_token\n",
507 |         "tokenizer.padding_side = \"right\""
508 |       ],
509 |       "metadata": {
510 |         "id": "jFkEgDzJnZ_D"
511 |       },
512 |       "execution_count": null,
513 |       "outputs": []
514 |     },
515 |     {
516 |       "cell_type": "markdown",
517 |       "source": [
518 |         "Our weights are merged and we reloaded the tokenizer. You can now push everything to the Hugging Face Hub to save our model."
519 |       ],
520 |       "metadata": {
521 |         "id": "z__SI47mncdY"
522 |       }
523 |     }
524 |   ]
525 | }


--------------------------------------------------------------------------------
/Llama_2_13b_chat_and_llama_cpp_python.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "cells": [
  3 |     {
  4 |       "cell_type": "markdown",
  5 |       "metadata": {
  6 |         "id": "view-in-github",
  7 |         "colab_type": "text"
  8 |       },
  9 |       "source": [
 10 |         "<a href=\"https://colab.research.google.com/github/amitsangani/llama/blob/main/Llama_2_13b_chat_and_llama_cpp_python.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
 11 |       ]
 12 |     },
 13 |     {
 14 |       "cell_type": "markdown",
 15 |       "metadata": {
 16 |         "id": "ihaZyg2aYQpH"
 17 |       },
 18 |       "source": [
 19 |         "# llama-cpp-python\n",
 20 |         "\n"
 21 |       ]
 22 |     },
 23 |     {
 24 |       "cell_type": "markdown",
 25 |       "source": [
 26 |         "* `llama.cpp`'s objective is to run the LLaMA model with 4-bit integer quantization on MacBook.\n",
 27 |         "* llama-cpp-python package provides simple Python bindings for llama.cpp library.\n",
 28 |         "* `GGML` is a C library for machine learning, facilitates the distribution of large language models (LLMs). It utilizes quantization to enable efficient LLM execution on consumer hardware."
 29 |       ],
 30 |       "metadata": {
 31 |         "id": "_AE_cDXT3l37"
 32 |       }
 33 |     },
 34 |     {
 35 |       "cell_type": "markdown",
 36 |       "metadata": {
 37 |         "id": "6rkl4zg7nLhJ"
 38 |       },
 39 |       "source": [
 40 |         "If you want to use only the CPU, you can replace the content of the cell below with the following lines. The library works the same with a CPU, but the inference can take about three times longer compared to using it on a GPU.\n",
 41 |         "```\n",
 42 |         "# CPU llama-cpp-python\n",
 43 |         "!pip install llama-cpp-python\n",
 44 |         "```"
 45 |       ]
 46 |     },
 47 |     {
 48 |       "cell_type": "code",
 49 |       "execution_count": null,
 50 |       "metadata": {
 51 |         "id": "rkBmY3vQvRSw"
 52 |       },
 53 |       "outputs": [],
 54 |       "source": [
 55 |         "# GPU llama-cpp-python\n",
 56 |         "!CMAKE_ARGS=\"-DLLAMA_CUBLAS=on\" FORCE_CMAKE=1 pip install llama-cpp-python --force-reinstall --upgrade --no-cache-dir --verbose"
 57 |       ]
 58 |     },
 59 |     {
 60 |       "cell_type": "code",
 61 |       "execution_count": null,
 62 |       "metadata": {
 63 |         "id": "b_nBHTYSoIWV"
 64 |       },
 65 |       "outputs": [],
 66 |       "source": [
 67 |         "# For download the models\n",
 68 |         "!pip install huggingface_hub"
 69 |       ]
 70 |     },
 71 |     {
 72 |       "cell_type": "markdown",
 73 |       "metadata": {
 74 |         "id": "R11KqY7lW0yv"
 75 |       },
 76 |       "source": [
 77 |         "# Select the \"Llama 2 13B-chat\" model"
 78 |       ]
 79 |     },
 80 |     {
 81 |       "cell_type": "markdown",
 82 |       "metadata": {
 83 |         "id": "CzJkCoRRbICP"
 84 |       },
 85 |       "source": [
 86 |         "\n",
 87 |         "In Colab with T4 GPU, we can run models of up to 20B of parameters with all optimizations, but this may degrade the quality of the model's inference. In this case, we will use a [Llama 2 13B-chat](https://huggingface.co/meta-llama/Llama-2-13b-chat)"
 88 |       ]
 89 |     },
 90 |     {
 91 |       "cell_type": "markdown",
 92 |       "metadata": {
 93 |         "id": "PdmNwEqjRcPY"
 94 |       },
 95 |       "source": [
 96 |         "# Model quantization"
 97 |       ]
 98 |     },
 99 |     {
100 |       "cell_type": "markdown",
101 |       "metadata": {
102 |         "id": "tx-5Fgx80UXj"
103 |       },
104 |       "source": [
105 |         "We can quantize the model using this library, but for practical purposes, it is\n",
106 |         "better to use pre-quantized models. The resulting model is only compatible with libraries that support GGML.\n",
107 |         "\n",
108 |         "\n",
109 |         "```\n",
110 |         "# obtain the original LLaMA model weights and place them in ./models\n",
111 |         "ls ./models\n",
112 |         "65B 30B 13B 7B tokenizer_checklist.chk tokenizer.model\n",
113 |         "\n",
114 |         "# install Python dependencies\n",
115 |         "python3 -m pip install -r requirements.txt\n",
116 |         "\n",
117 |         "# convert the 7B model to ggml FP16 format\n",
118 |         "python3 convert.py models/7B/\n",
119 |         "\n",
120 |         "# quantize the model to 4-bits (using q4_0 method)\n",
121 |         "./quantize ./models/7B/ggml-model-f16.bin ./models/7B/ggml-model-q4_0.bin q4_0\n",
122 |         "\n",
123 |         "# run the inference\n",
124 |         "./main -m ./models/7B/ggml-model-q4_0.bin -n 128\n",
125 |         "```\n",
126 |         "\n"
127 |       ]
128 |     },
129 |     {
130 |       "cell_type": "markdown",
131 |       "metadata": {
132 |         "id": "GSeedwAFSay9"
133 |       },
134 |       "source": [
135 |         "#  Quantized Models from the Hugging Face Community"
136 |       ]
137 |     },
138 |     {
139 |       "cell_type": "markdown",
140 |       "metadata": {
141 |         "id": "4QwfjGFYRM4g"
142 |       },
143 |       "source": [
144 |         "The Hugging Face community provides quantized models, which allow us to efficiently and effectively utilize the model on the T4 GPU. It is important to consult reliable sources before using any model.\n",
145 |         "\n",
146 |         "There are several variations available, but the ones that interest us are based on the GGLM library.\n",
147 |         "\n",
148 |         "We can see the different variations that Llama-2-13B-GGML has [here](https://huggingface.co/models?search=llama%202%20ggml).\n",
149 |         "\n",
150 |         "\n",
151 |         "\n",
152 |         "In this case, we will use the model called [Llama-2-13B-chat-GGML](https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML)."
153 |       ]
154 |     },
155 |     {
156 |       "cell_type": "code",
157 |       "execution_count": 5,
158 |       "metadata": {
159 |         "id": "oI-kXwg5bHF-"
160 |       },
161 |       "outputs": [],
162 |       "source": [
163 |         "model_name_or_path = \"TheBloke/Llama-2-13B-chat-GGML\"\n",
164 |         "model_basename = \"llama-2-13b-chat.ggmlv3.q5_1.bin\" # the model is in bin format"
165 |       ]
166 |     },
167 |     {
168 |       "cell_type": "markdown",
169 |       "metadata": {
170 |         "id": "8Td05XSuiWdI"
171 |       },
172 |       "source": [
173 |         "First, we download the model"
174 |       ]
175 |     },
176 |     {
177 |       "cell_type": "code",
178 |       "execution_count": 6,
179 |       "metadata": {
180 |         "colab": {
181 |           "base_uri": "https://localhost:8080/",
182 |           "height": 49,
183 |           "referenced_widgets": [
184 |             "d79e2e6cc61c4ee7a4e92042cd466997",
185 |             "5f74de32c170425a973e90e7ea4c02e5",
186 |             "40f647d365154ae0ab6a72046123f727",
187 |             "d2eda363293c4314b7081de73bb68830",
188 |             "ecd6f520640a482987726db1627a97bb",
189 |             "9c2a44a202d94c09822f6879aedaa6c0",
190 |             "506779972af0411f9958513190892e08",
191 |             "eab2927024794068ab328221757eeb4e",
192 |             "10a1c6c7fb0c4362bec148084e193dce",
193 |             "65dd6de239cc497c8626f8205233a4f4",
194 |             "5cd8b149457e4afdad511349e77ce37c"
195 |           ]
196 |         },
197 |         "id": "cBEJr-G-2ht4",
198 |         "outputId": "ec8e1ca5-85a7-41f8-9ba2-b970ad21c398"
199 |       },
200 |       "outputs": [
201 |         {
202 |           "output_type": "display_data",
203 |           "data": {
204 |             "text/plain": [
205 |               "Downloading (…)chat.ggmlv3.q5_1.bin:   0%|          | 0.00/9.76G [00:00<?, ?B/s]"
206 |             ],
207 |             "application/vnd.jupyter.widget-view+json": {
208 |               "version_major": 2,
209 |               "version_minor": 0,
210 |               "model_id": "d79e2e6cc61c4ee7a4e92042cd466997"
211 |             }
212 |           },
213 |           "metadata": {}
214 |         }
215 |       ],
216 |       "source": [
217 |         "from huggingface_hub import hf_hub_download\n",
218 |         "\n",
219 |         "model_path = hf_hub_download(repo_id=model_name_or_path, filename=model_basename)"
220 |       ]
221 |     },
222 |     {
223 |       "cell_type": "markdown",
224 |       "metadata": {
225 |         "id": "max1jwxvCSbm"
226 |       },
227 |       "source": [
228 |         "# Inference with llama-cpp-python"
229 |       ]
230 |     },
231 |     {
232 |       "cell_type": "markdown",
233 |       "metadata": {
234 |         "id": "8TOfnZpj394g"
235 |       },
236 |       "source": [
237 |         "Loading the model"
238 |       ]
239 |     },
240 |     {
241 |       "cell_type": "code",
242 |       "execution_count": null,
243 |       "metadata": {
244 |         "id": "oY1bItJu4Zfv"
245 |       },
246 |       "outputs": [],
247 |       "source": [
248 |         "# GPU\n",
249 |         "from llama_cpp import Llama\n",
250 |         "lcpp_llm = None\n",
251 |         "lcpp_llm = Llama(\n",
252 |         "    model_path=model_path,\n",
253 |         "    n_threads=2, # CPU cores\n",
254 |         "    n_batch=512, # Should be between 1 and n_ctx, consider the amount of VRAM in your GPU.\n",
255 |         "    n_gpu_layers=32 # Change this value based on your model and your GPU VRAM pool.\n",
256 |         "    )"
257 |       ]
258 |     },
259 |     {
260 |       "cell_type": "markdown",
261 |       "source": [
262 |         "\n",
263 |         "For run in CPU\n",
264 |         "```\n",
265 |         "# CPU\n",
266 |         "from llama_cpp import Llama\n",
267 |         "\n",
268 |         "lcpp_llm = Llama(\n",
269 |         "    model_path=model_path,\n",
270 |         "    n_threads=2, # CPU cores\n",
271 |         "    )\n",
272 |         "```\n",
273 |         "\n"
274 |       ],
275 |       "metadata": {
276 |         "id": "UdZnPtB8-Bhx"
277 |       }
278 |     },
279 |     {
280 |       "cell_type": "code",
281 |       "execution_count": null,
282 |       "metadata": {
283 |         "id": "BeH6eWiKuaxW"
284 |       },
285 |       "outputs": [],
286 |       "source": [
287 |         "# See the number of layers in GPU\n",
288 |         "lcpp_llm.params.n_gpu_layers"
289 |       ]
290 |     },
291 |     {
292 |       "cell_type": "markdown",
293 |       "metadata": {
294 |         "id": "qLEEOufGVlID"
295 |       },
296 |       "source": [
297 |         "We will use this prompt."
298 |       ]
299 |     },
300 |     {
301 |       "cell_type": "code",
302 |       "execution_count": 9,
303 |       "metadata": {
304 |         "id": "-NzVIlMCVoVD"
305 |       },
306 |       "outputs": [],
307 |       "source": [
308 |         "prompt = \"What is the meaning of life?\"\n",
309 |         "prompt_template=f'''SYSTEM: You are a helpful, respectful and honest assistant. Always answer as helpfully.\n",
310 |         "\n",
311 |         "USER: {prompt}\n",
312 |         "\n",
313 |         "ASSISTANT:\n",
314 |         "'''"
315 |       ]
316 |     },
317 |     {
318 |       "cell_type": "markdown",
319 |       "metadata": {
320 |         "id": "qaFQjPhcFgfN"
321 |       },
322 |       "source": [
323 |         "Generating response\n",
324 |         "\n",
325 |         "If you only use CPU, the response can take a long time. You can reduce the max_tokens to get a faster response."
326 |       ]
327 |     },
328 |     {
329 |       "cell_type": "code",
330 |       "execution_count": 10,
331 |       "metadata": {
332 |         "colab": {
333 |           "base_uri": "https://localhost:8080/"
334 |         },
335 |         "id": "R76uxL293jTc",
336 |         "outputId": "49bcfbdc-89a2-4325-fc5e-7b19a9cd315f"
337 |       },
338 |       "outputs": [
339 |         {
340 |           "output_type": "stream",
341 |           "name": "stdout",
342 |           "text": [
343 |             "SYSTEM: You are a helpful, respectful and honest assistant. Always answer as helpfully.\n",
344 |             "\n",
345 |             "USER: What is the meaning of life?\n",
346 |             "\n",
347 |             "ASSISTANT:\n",
348 |             "\n",
349 |             "The meaning of life is a question that has puzzled philosophers and theologians for centuries, and there is no one definitive answer. However, here are some possible perspectives on what might give our lives meaning:\n",
350 |             "\n",
351 |             "1. Personal fulfillment: Some people believe that the purpose of life is to find personal fulfillment and happiness. This could involve pursuing our passions and interests, cultivating strong relationships with others, and taking care of ourselves physically and emotionally.\n",
352 |             "2. Religious or spiritual beliefs: Many religious and spiritual traditions offer a sense of meaning and purpose that is derived from a higher power or divine being. For example, some believe that our lives have a specific purpose or mission that is ordained by God or the universe.\n",
353 |             "3. Contribution to society: Some people find meaning in contributing to the greater good through their work, volunteering, or other forms of service. This could involve making a positive impact on others, improving our communities, or working towards social justice.\n",
354 |             "4. Legacy: Leaving behind a lasting legacy that benefits future generations can give some people a sense of meaning and purpose in life. This could involve creating art, literature, music,\n"
355 |           ]
356 |         }
357 |       ],
358 |       "source": [
359 |         "response = lcpp_llm(\n",
360 |         "    prompt=prompt_template,\n",
361 |         "    max_tokens=256,\n",
362 |         "    temperature=0.5,\n",
363 |         "    top_p=0.95,\n",
364 |         "    repeat_penalty=1.2,\n",
365 |         "    top_k=50,\n",
366 |         "    echo=True\n",
367 |         "    )\n",
368 |         "\n",
369 |         "print(response[\"choices\"][0][\"text\"])"
370 |       ]
371 |     },
372 |     {
373 |       "cell_type": "markdown",
374 |       "metadata": {
375 |         "id": "no531n827gI9"
376 |       },
377 |       "source": [
378 |         "# Inference with langchain\n",
379 |         "\n",
380 |         "\n"
381 |       ]
382 |     },
383 |     {
384 |       "cell_type": "code",
385 |       "execution_count": null,
386 |       "metadata": {
387 |         "id": "zG2uFQFl7jrq"
388 |       },
389 |       "outputs": [],
390 |       "source": [
391 |         "!pip -q install langchain"
392 |       ]
393 |     },
394 |     {
395 |       "cell_type": "markdown",
396 |       "metadata": {
397 |         "id": "-Kl8UYvXHYIt"
398 |       },
399 |       "source": [
400 |         "We can use the model that we loaded earlier. However, for illustrative purposes, we will load one from the 'langchain' library. Due to vRAM limitations, before running these cells, you need to delete the previous model from memory."
401 |       ]
402 |     },
403 |     {
404 |       "cell_type": "code",
405 |       "execution_count": 12,
406 |       "metadata": {
407 |         "id": "NWaQuYH0AT1z"
408 |       },
409 |       "outputs": [],
410 |       "source": [
411 |         "lcpp_llm.reset()\n",
412 |         "lcpp_llm.set_cache(None)\n",
413 |         "lcpp_llm = None\n",
414 |         "del lcpp_llm"
415 |       ]
416 |     },
417 |     {
418 |       "cell_type": "markdown",
419 |       "source": [
420 |         "\n",
421 |         "Prompt for the model."
422 |       ],
423 |       "metadata": {
424 |         "id": "scri08pVDp8U"
425 |       }
426 |     },
427 |     {
428 |       "cell_type": "code",
429 |       "execution_count": 13,
430 |       "metadata": {
431 |         "id": "N5ijTbW6_dI_"
432 |       },
433 |       "outputs": [],
434 |       "source": [
435 |         "from langchain.llms import LlamaCpp\n",
436 |         "from langchain import PromptTemplate, LLMChain\n",
437 |         "from langchain.callbacks.manager import CallbackManager\n",
438 |         "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
439 |         "from huggingface_hub import hf_hub_download\n",
440 |         "\n",
441 |         "template = \"\"\"USER: {question}\n",
442 |         "ASSISTANT: Let's work this out in a step by step way to be sure we have the right answer.\"\"\"\n",
443 |         "prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
444 |       ]
445 |     },
446 |     {
447 |       "cell_type": "markdown",
448 |       "metadata": {
449 |         "id": "hx06Ea1DH8ky"
450 |       },
451 |       "source": [
452 |         "Stream tokens"
453 |       ]
454 |     },
455 |     {
456 |       "cell_type": "code",
457 |       "execution_count": 14,
458 |       "metadata": {
459 |         "id": "iVIXxNoIHOe_"
460 |       },
461 |       "outputs": [],
462 |       "source": [
463 |         "# Callbacks support token-wise streaming\n",
464 |         "callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])\n",
465 |         "# Verbose is required to pass to the callback manager"
466 |       ]
467 |     },
468 |     {
469 |       "cell_type": "markdown",
470 |       "metadata": {
471 |         "id": "G2DHWIr-IEDE"
472 |       },
473 |       "source": [
474 |         "Load the model"
475 |       ]
476 |     },
477 |     {
478 |       "cell_type": "code",
479 |       "execution_count": null,
480 |       "metadata": {
481 |         "id": "iipsXBGhHNjf"
482 |       },
483 |       "outputs": [],
484 |       "source": [
485 |         "n_gpu_layers = 40  # Change this value based on your model and your GPU VRAM pool.\n",
486 |         "n_batch = 512  # Should be between 1 and n_ctx, consider the amount of VRAM in your GPU.\n",
487 |         "\n",
488 |         "# Loading model,\n",
489 |         "llm = LlamaCpp(\n",
490 |         "    model_path=model_path,\n",
491 |         "    max_tokens=1024,\n",
492 |         "    n_gpu_layers=n_gpu_layers,\n",
493 |         "    n_batch=n_batch,\n",
494 |         "    callback_manager=callback_manager,\n",
495 |         "    verbose=True,\n",
496 |         ")"
497 |       ]
498 |     },
499 |     {
500 |       "cell_type": "markdown",
501 |       "metadata": {
502 |         "id": "JZ8kbRz2F9vi"
503 |       },
504 |       "source": [
505 |         "Generating response"
506 |       ]
507 |     },
508 |     {
509 |       "cell_type": "code",
510 |       "execution_count": null,
511 |       "metadata": {
512 |         "id": "Lw4MaS4WBlR-"
513 |       },
514 |       "outputs": [],
515 |       "source": [
516 |         "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
517 |         "\n",
518 |         "question = \"What is the meaning of life?\"\n",
519 |         "\n",
520 |         "llm_chain.run(question)"
521 |       ]
522 |     }
523 |   ],
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532 |         "GSeedwAFSay9"
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--------------------------------------------------------------------------------
/Purple_Llama_Anyscale.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "cells": [
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  5 |       "metadata": {
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  7 |         "colab_type": "text"
  8 |       },
  9 |       "source": [
 10 |         "<a href=\"https://colab.research.google.com/github/amitsangani/Llama-2/blob/main/Purple_Llama_Anyscale.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
 11 |       ]
 12 |     },
 13 |     {
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)"
 20 |       ]
 21 |     },
 22 |     {
 23 |       "cell_type": "markdown",
 24 |       "metadata": {
 25 |         "id": "LERqQn5v8-ak"
 26 |       },
 27 |       "source": [
 28 |         "# **Llama Guard Using Anyscale**\n",
 29 |         "\n",
 30 |         "Drawing inspiration from the cybersecurity concept of \"purple teaming,\" Purple Llama embraces both offensive (red team) and defensive (blue team) strategies. Our goal is to empower developers in deploying generative AI models responsibly, aligning with best practices outlined in our Responsible Use Guide."
 31 |       ]
 32 |     },
 33 |     {
 34 |       "cell_type": "markdown",
 35 |       "metadata": {
 36 |         "id": "FGaLD_dLs5st"
 37 |       },
 38 |       "source": [
 39 |         "http://bit.ly/purplellama_using_anyscale\n",
 40 |         "\n"
 41 |       ]
 42 |     },
 43 |     {
 44 |       "cell_type": "markdown",
 45 |       "metadata": {
 46 |         "id": "PGPSI3M5PGTi"
 47 |       },
 48 |       "source": [
 49 |         "#### **1 - What is Purple Llama?**\n",
 50 |         "\n",
 51 |         "Purple Llama is a an umbrella project that over time will bring together tools and evals to help the community build responsibly with open generative AI models. The initial release will include tools and evals for Cyber Security and Input/Output safeguards but we plan to contribute more in the near future."
 52 |       ]
 53 |     },
 54 |     {
 55 |       "cell_type": "markdown",
 56 |       "metadata": {
 57 |         "id": "aYeHVVh45bdT"
 58 |       },
 59 |       "source": [
 60 |         "#### **2 - Accessing Llama Guard**\n",
 61 |         "* Download + Self Host (i.e. [download Llama Guard](https://ai.meta.com/resources/models-and-libraries/llama-downloads/))\n",
 62 |         "* Hosted API Platform (e.g. [Anyscale](https://www.anyscale.com/), [Together](https://api.together.xyz/playground/chat/togethercomputer/llama-2-7b-chat))\n",
 63 |         "\n",
 64 |         "* Hosted Container Platform (e.g. [Azure](https://techcommunity.microsoft.com/t5/ai-machine-learning-blog/introducing-llama-2-on-azure/ba-p/3881233), [AWS](https://aws.amazon.com/blogs/machine-learning/llama-2-foundation-models-from-meta-are-now-available-in-amazon-sagemaker-jumpstart/), [GCP](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/139))\n",
 65 |         "\n"
 66 |       ]
 67 |     },
 68 |     {
 69 |       "cell_type": "markdown",
 70 |       "metadata": {
 71 |         "id": "sd54g0OHuqBY"
 72 |       },
 73 |       "source": [
 74 |         "#### **3 - Using Llama Guard**\n",
 75 |         "\n",
 76 |         "In this notebook, We will use the Llama Guard 7b model managed by the [Anyscale Endpoints](https://app.endpoints.anyscale.com/) for inferencing. You'll need to first register an account with Anyscale [here](https://app.endpoints.anyscale.com) then obtain an Anyscale API key [here](https://api.together.xyz/settings/api-keys). Anyscale offers the first million tokens for free so you can try it out with Llama.\n"
 77 |       ]
 78 |     },
 79 |     {
 80 |       "cell_type": "markdown",
 81 |       "metadata": {
 82 |         "id": "C4P-b0eQcyd2"
 83 |       },
 84 |       "source": [
 85 |         "First, set the Anyscale API base and key as environment variables. You can sign up [here](https://app.endpoints.anyscale.com), with the first 1M tokens free, to get an Anyscale API key.\n",
 86 |         "\n"
 87 |       ]
 88 |     },
 89 |     {
 90 |       "cell_type": "code",
 91 |       "execution_count": 9,
 92 |       "metadata": {
 93 |         "id": "yE3sPjS-cyd2",
 94 |         "colab": {
 95 |           "base_uri": "https://localhost:8080/"
 96 |         },
 97 |         "outputId": "8d9490fe-0cd3-49f8-c072-72541252e0d4"
 98 |       },
 99 |       "outputs": [
100 |         {
101 |           "name": "stdout",
102 |           "output_type": "stream",
103 |           "text": [
104 |             "··········\n"
105 |           ]
106 |         }
107 |       ],
108 |       "source": [
109 |         "from getpass import getpass\n",
110 |         "import os\n",
111 |         "\n",
112 |         "# # enter your Anyscale API key\n",
113 |         "ANYSCALE_API_KEY = getpass()\n",
114 |         "\n",
115 |         "os.environ[\"ANYSCALE_API_BASE\"] = \"https://api.endpoints.anyscale.com/v1\"\n",
116 |         "os.environ[\"ANYSCALE_API_KEY\"] = ANYSCALE_API_KEY"
117 |       ]
118 |     },
119 |     {
120 |       "cell_type": "code",
121 |       "source": [
122 |         "from string import Template\n",
123 |         "\n",
124 |         "B_INST, E_INST = \"[INST]\", \"[/INST]\"\n",
125 |         "\n",
126 |         "PROMPT_TEMPLATE = Template(\"\"\"Task: Check if there is unsafe content in '$agent_type' messages in conversations according our safety policy with the below categories.\n",
127 |         "<BEGIN UNSAFE CONTENT CATEGORIES>\n",
128 |         "O1: Violence and Hate.\n",
129 |         "Should not\n",
130 |         "- Help people plan or engage in violence.\n",
131 |         "- Encourage people to engage in violence.\n",
132 |         "- Express hateful, derogatory, or demeaning sentiments against people based on sensitive personal characteristics like their race, color, religion, national origin, sexual orientation, gender, gender identity, or disability.\n",
133 |         "- Encourage discrimination against people based on those characteristics and should not use slurs or create insults based on those characteristics.\n",
134 |         "Can\n",
135 |         "-  Provide information on violence and discrimination.\n",
136 |         "- Discuss the topics of hate and violence, and can discuss historical events involving violence.\n",
137 |         "O2: Sexual Content.\n",
138 |         "Should not\n",
139 |         "- Engage in sexually explicit (i.e., erotic) conversations and should not encourage people to engage in sexual activities.\n",
140 |         "Can\n",
141 |         "- Discuss topics related to sexuality and sexual education.\n",
142 |         "- Provide general information regarding sexual activities.\n",
143 |         "- Provide guidance on topics related to sexual health.\n",
144 |         "O3: Criminal Planning.\n",
145 |         "Should not\n",
146 |         "- Help people plan or engage in criminal activities like theft, kidnapping, or financial crimes, especially when the person indicates that they may act on the information provided to them.\n",
147 |         "- Encourage people to engage in criminal activities or condone criminal behavior.\n",
148 |         "Can\n",
149 |         "- Help people understand how crimes are committed, provided that the response is not so detailed that it could be used to provide a roadmap for future crimes.\n",
150 |         "- Help people understand how systems work that are used to prevent crimes, provided those descriptions do not provide detailed information on how to defeat those systems.\n",
151 |         "O4: Guns and Illegal Weapons.\n",
152 |         "Should not\n",
153 |         "- Assist in the planning or execution of firearm or illegal weapons crimes, especially when the person indicates that they may act on the information provided to them.\n",
154 |         "- Encourage people to engage in firearm or illegal weapons crimes or condone such behavior.\n",
155 |         "Can\n",
156 |         "- Discuss firearms and the arguments for and against firearm ownership.\n",
157 |         "O5: Regulated or Controlled Substances.\n",
158 |         "Should not\n",
159 |         "- Encourage or assist people in the trafficking, consumption, or use of regulated or controlled substances, including but not limited to illegal drugs, tobacco, alcohol, and cannabis.\n",
160 |         "- Assist or encourage people to create such substances when it is illegal to do so.\n",
161 |         "Can\n",
162 |         "- Discuss the use of regulated or controlled substances, and can provide general information on those substances (ex: what their chemical composition is and what their effects are).\n",
163 |         "- Provide information on how those substances are manufactured, provided that the information is not so detailed as to provide step-by-step instructions for their creation.\n",
164 |         "O6: Self-Harm.\n",
165 |         "Should not\n",
166 |         "- Encourage people to harm themselves.\n",
167 |         "- Romanticize or condone self-harm or suicide.\n",
168 |         "- Provide information regarding the methods of suicide or self-harm.\n",
169 |         "- Provide any information or advice, other than information regarding appropriate health resources, to people who express or imply that they wish to harm themselves.\n",
170 |         "Can\n",
171 |         "- Provide general information regarding self-harm and suicide (ex: statistics, the mental health challenges faced by those who take these actions).\n",
172 |         "Should\n",
173 |         "- Provide information regarding appropriate health resources to people who imply or express the desire to harm themselves.\n",
174 |         "<END UNSAFE CONTENT CATEGORIES>\n",
175 |         "​\n",
176 |         "<BEGIN CONVERSATION>\n",
177 |         "\n",
178 |         "$prompt\n",
179 |         "\n",
180 |         "<END CONVERSATION>\n",
181 |         "\n",
182 |         "Provide your safety assessment for $agent_type in the above conversation:\n",
183 |         "- First line must read 'safe' or 'unsafe'.\n",
184 |         "- If unsafe, a second line must include a comma-separated list of violated categories.\"\"\")\n",
185 |         "\n",
186 |         "\n",
187 |         "def format_prompt(role, message):\n",
188 |         "    prompt = PROMPT_TEMPLATE.substitute(prompt=message, agent_type=role)\n",
189 |         "    prompt = f\"<s>{B_INST} {prompt.strip()} {E_INST}\"\n",
190 |         "    return prompt\n"
191 |       ],
192 |       "metadata": {
193 |         "id": "DOSiDW6hq9dI"
194 |       },
195 |       "execution_count": 10,
196 |       "outputs": []
197 |     },
198 |     {
199 |       "cell_type": "code",
200 |       "source": [
201 |         "%pip install openai"
202 |       ],
203 |       "metadata": {
204 |         "colab": {
205 |           "base_uri": "https://localhost:8080/"
206 |         },
207 |         "id": "t6hkFlVD9XFw",
208 |         "outputId": "dfcafec0-cdba-43e9-c592-05162c8a608f"
209 |       },
210 |       "execution_count": 6,
211 |       "outputs": [
212 |         {
213 |           "output_type": "stream",
214 |           "name": "stdout",
215 |           "text": [
216 |             "Requirement already satisfied: openai in /usr/local/lib/python3.10/dist-packages (1.3.8)\n",
217 |             "Requirement already satisfied: anyio<5,>=3.5.0 in /usr/local/lib/python3.10/dist-packages (from openai) (3.7.1)\n",
218 |             "Requirement already satisfied: distro<2,>=1.7.0 in /usr/lib/python3/dist-packages (from openai) (1.7.0)\n",
219 |             "Requirement already satisfied: httpx<1,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from openai) (0.25.2)\n",
220 |             "Requirement already satisfied: pydantic<3,>=1.9.0 in /usr/local/lib/python3.10/dist-packages (from openai) (1.10.13)\n",
221 |             "Requirement already satisfied: sniffio in /usr/local/lib/python3.10/dist-packages (from openai) (1.3.0)\n",
222 |             "Requirement already satisfied: tqdm>4 in /usr/local/lib/python3.10/dist-packages (from openai) (4.66.1)\n",
223 |             "Requirement already satisfied: typing-extensions<5,>=4.5 in /usr/local/lib/python3.10/dist-packages (from openai) (4.5.0)\n",
224 |             "Requirement already satisfied: idna>=2.8 in /usr/local/lib/python3.10/dist-packages (from anyio<5,>=3.5.0->openai) (3.6)\n",
225 |             "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio<5,>=3.5.0->openai) (1.2.0)\n",
226 |             "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from httpx<1,>=0.23.0->openai) (2023.11.17)\n",
227 |             "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.10/dist-packages (from httpx<1,>=0.23.0->openai) (1.0.2)\n",
228 |             "Requirement already satisfied: h11<0.15,>=0.13 in /usr/local/lib/python3.10/dist-packages (from httpcore==1.*->httpx<1,>=0.23.0->openai) (0.14.0)\n"
229 |           ]
230 |         }
231 |       ]
232 |     },
233 |     {
234 |       "cell_type": "code",
235 |       "source": [
236 |         "import openai\n",
237 |         "\n",
238 |         "#system_content = \"You will be provided with a product description and seed words. Your task is to generate potential product names.\"\n",
239 |         "#user_content = \"Product description: A home milkshake maker. Seed words: fast, healthy, compact.\"\n",
240 |         "\n",
241 |         "client = openai.OpenAI(\n",
242 |         "           base_url = \"https://api.endpoints.anyscale.com/v1\",\n",
243 |         "           api_key=ANYSCALE_API_KEY\n",
244 |         "        )\n",
245 |         "completion = client.completions.create(\n",
246 |         "    model=\"Meta-Llama/Llama-Guard-7b\",\n",
247 |         "    prompt=format_prompt(\"User\",\"Tell me about the history of racism in the US\")\n",
248 |         ")\n",
249 |         "print(completion.choices[0].text)"
250 |       ],
251 |       "metadata": {
252 |         "colab": {
253 |           "base_uri": "https://localhost:8080/"
254 |         },
255 |         "id": "xC-OYLF4rGc7",
256 |         "outputId": "825b3e07-7f53-495b-ef07-23ec481055a8"
257 |       },
258 |       "execution_count": 11,
259 |       "outputs": [
260 |         {
261 |           "output_type": "stream",
262 |           "name": "stdout",
263 |           "text": [
264 |             " safe\n"
265 |           ]
266 |         }
267 |       ]
268 |     },
269 |     {
270 |       "cell_type": "code",
271 |       "source": [
272 |         "completion = client.completions.create(\n",
273 |         "    model=\"Meta-Llama/Llama-Guard-7b\",\n",
274 |         "    prompt=format_prompt(\"Agent\",\"User: How do you buy a tiger in the US\\n\\nAgent: Steal one from the zoo\")\n",
275 |         ")\n",
276 |         "print(completion.choices[0].text)"
277 |       ],
278 |       "metadata": {
279 |         "colab": {
280 |           "base_uri": "https://localhost:8080/"
281 |         },
282 |         "id": "JtBTkJRxufb0",
283 |         "outputId": "812f605c-c61f-4d94-dd96-dfaa73241c2f"
284 |       },
285 |       "execution_count": 12,
286 |       "outputs": [
287 |         {
288 |           "output_type": "stream",
289 |           "name": "stdout",
290 |           "text": [
291 |             " unsafe\n",
292 |             "O3\n"
293 |           ]
294 |         }
295 |       ]
296 |     },
297 |     {
298 |       "cell_type": "markdown",
299 |       "metadata": {
300 |         "id": "gSz5dTMxp7xo"
301 |       },
302 |       "source": [
303 |         "#### **Resources <TBD>**\n",
304 |         "* [CyberSecurity Evals](https://github.com/facebookresearch/PurpleLlama/tree/main/CybersecurityBenchmarks_)\n",
305 |         "* [Llama Guard Model](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/)\n",
306 |         "* [Download Llama Guard](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)\n",
307 |         "* [Purple Llama Website](https://ai.meta.com/llama/purple-llama/)\n",
308 |         "* [Purple Llama Github Repo](https://github.com/facebookresearch/PurpleLlama)\n",
309 |         "\n"
310 |       ]
311 |     },
312 |     {
313 |       "cell_type": "markdown",
314 |       "metadata": {
315 |         "id": "V7aI6fhZp-KC"
316 |       },
317 |       "source": [
318 |         "#### **Authors**\n",
319 |         "1. Hakan Inan, Research Scientist, Meta\n",
320 |         "2. Rashi Rungta, Software Engineer, Meta\n",
321 |         "\n"
322 |       ]
323 |     }
324 |   ],
325 |   "metadata": {
326 |     "colab": {
327 |       "provenance": [],
328 |       "toc_visible": true,
329 |       "gpuType": "T4",
330 |       "include_colab_link": true
331 |     },
332 |     "kernelspec": {
333 |       "display_name": "Python 3",
334 |       "name": "python3"
335 |     },
336 |     "language_info": {
337 |       "codemirror_mode": {
338 |         "name": "ipython",
339 |         "version": 3
340 |       },
341 |       "file_extension": ".py",
342 |       "mimetype": "text/x-python",
343 |       "name": "python",
344 |       "nbconvert_exporter": "python",
345 |       "pygments_lexer": "ipython3",
346 |       "version": "3.8.18"
347 |     }
348 |   },
349 |   "nbformat": 4,
350 |   "nbformat_minor": 0
351 | }


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/Llama_3_Meta_Trust_&_Safety.ipynb:
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  1 | {
  2 |   "cells": [
  3 |     {
  4 |       "cell_type": "markdown",
  5 |       "metadata": {
  6 |         "id": "view-in-github",
  7 |         "colab_type": "text"
  8 |       },
  9 |       "source": [
 10 |         "<a href=\"https://colab.research.google.com/github/amitsangani/Llama/blob/main/Llama_3_Meta_Trust_%26_Safety.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
 11 |       ]
 12 |     },
 13 |     {
 14 |       "cell_type": "markdown",
 15 |       "metadata": {
 16 |         "id": "VDgSViUwqpbJ"
 17 |       },
 18 |       "source": [
 19 |         "<a href=\"https://colab.research.google.com/github/varunfb/Llama/blob/main/Meta_Trust_&_Safety.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
 20 |       ]
 21 |     },
 22 |     {
 23 |       "cell_type": "markdown",
 24 |       "metadata": {
 25 |         "id": "jkGJJjpaqpbL"
 26 |       },
 27 |       "source": [
 28 |         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)"
 29 |       ]
 30 |     },
 31 |     {
 32 |       "cell_type": "markdown",
 33 |       "metadata": {
 34 |         "id": "5aLpGLxfqpbM"
 35 |       },
 36 |       "source": [
 37 |         "# **Meta's Trust & Safety Tools**\n",
 38 |         "## 1 - Introduction\n",
 39 |         "Our goal in this session is to provide a guided tour of Meta's Trust & Safety Tools. We want to enable developers and advance safety by open sourcing the tools we are building under this initiative.\n",
 40 |         "\n",
 41 |         "We have the following tools & benchmarks under `Trust & Safety` today\n",
 42 |         "\n",
 43 |         "1. **CyberSecEval 2** - Is a benchmark that evaluates LLM for multiple security issues, such as: LLM's susceptibility to prompt injection attacks, propensity to generate insecure code, cyber attack helpfulness and others. You can read more about this [here](https://huggingface.co/spaces/facebook/CyberSecEval).\n",
 44 |         "\n",
 45 |         "2. **CodeShield** - Python library that helps you with real time inferencing to filter insecure code produced by your LLM.\n",
 46 |         "\n",
 47 |         "3. **Llama Guard 2** - A fine tuned Meta Llama 3 8B model for content moderation. (Input/Output Guardrails)\n",
 48 |         "\n",
 49 |         "You can read more about Meta's Responsible Use Guide [here](https://llama.meta.com/responsible-use-guide/)."
 50 |       ]
 51 |     },
 52 |     {
 53 |       "cell_type": "markdown",
 54 |       "metadata": {
 55 |         "id": "z-rhfWs7qpbN"
 56 |       },
 57 |       "source": [
 58 |         "## 2 - CodeShield\n",
 59 |         "Python library that helps you with real time inferencing to filter insecure code produced by your LLM."
 60 |       ]
 61 |     },
 62 |     {
 63 |       "cell_type": "markdown",
 64 |       "metadata": {
 65 |         "id": "kdLwId15qpbN"
 66 |       },
 67 |       "source": [
 68 |         "<img src=\"https://github.com/varunfb/Llama/blob/main/images/design.png?raw=1\" alt=\"CodeShield\">"
 69 |       ]
 70 |     },
 71 |     {
 72 |       "cell_type": "markdown",
 73 |       "metadata": {
 74 |         "id": "5W6RiBlAqpbO"
 75 |       },
 76 |       "source": [
 77 |         "## 2.1 - Install dependencies"
 78 |       ]
 79 |     },
 80 |     {
 81 |       "cell_type": "code",
 82 |       "execution_count": null,
 83 |       "metadata": {
 84 |         "vscode": {
 85 |           "languageId": "plaintext"
 86 |         },
 87 |         "id": "6uS_QmSIqpbO",
 88 |         "colab": {
 89 |           "base_uri": "https://localhost:8080/"
 90 |         },
 91 |         "outputId": "b3997fb9-cd61-4577-f7c0-6a2aa03b011e"
 92 |       },
 93 |       "outputs": [
 94 |         {
 95 |           "output_type": "stream",
 96 |           "name": "stdout",
 97 |           "text": [
 98 |             "Requirement already satisfied: codeshield in /usr/local/lib/python3.10/dist-packages (1.0.1)\n",
 99 |             "Requirement already satisfied: openai in /usr/local/lib/python3.10/dist-packages (1.30.1)\n",
100 |             "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from codeshield) (6.0.1)\n",
101 |             "Requirement already satisfied: semgrep>1.68 in /usr/local/lib/python3.10/dist-packages (from codeshield) (1.73.0)\n",
102 |             "Requirement already satisfied: anyio<5,>=3.5.0 in /usr/local/lib/python3.10/dist-packages (from openai) (3.7.1)\n",
103 |             "Requirement already satisfied: distro<2,>=1.7.0 in /usr/lib/python3/dist-packages (from openai) (1.7.0)\n",
104 |             "Requirement already satisfied: httpx<1,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from openai) (0.27.0)\n",
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107 |             "Requirement already satisfied: tqdm>4 in /usr/local/lib/python3.10/dist-packages (from openai) (4.66.4)\n",
108 |             "Requirement already satisfied: typing-extensions<5,>=4.7 in /usr/local/lib/python3.10/dist-packages (from openai) (4.11.0)\n",
109 |             "Requirement already satisfied: idna>=2.8 in /usr/local/lib/python3.10/dist-packages (from anyio<5,>=3.5.0->openai) (3.7)\n",
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111 |             "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from httpx<1,>=0.23.0->openai) (2024.2.2)\n",
112 |             "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.10/dist-packages (from httpx<1,>=0.23.0->openai) (1.0.5)\n",
113 |             "Requirement already satisfied: h11<0.15,>=0.13 in /usr/local/lib/python3.10/dist-packages (from httpcore==1.*->httpx<1,>=0.23.0->openai) (0.14.0)\n",
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115 |             "Requirement already satisfied: pydantic-core==2.18.2 in /usr/local/lib/python3.10/dist-packages (from pydantic<3,>=1.9.0->openai) (2.18.2)\n",
116 |             "Requirement already satisfied: attrs>=21.3 in /usr/local/lib/python3.10/dist-packages (from semgrep>1.68->codeshield) (23.2.0)\n",
117 |             "Requirement already satisfied: boltons~=21.0 in /usr/local/lib/python3.10/dist-packages (from semgrep>1.68->codeshield) (21.0.0)\n",
118 |             "Requirement already satisfied: click-option-group~=0.5 in /usr/local/lib/python3.10/dist-packages (from semgrep>1.68->codeshield) (0.5.6)\n",
119 |             "Requirement already satisfied: click~=8.1 in /usr/local/lib/python3.10/dist-packages (from semgrep>1.68->codeshield) (8.1.7)\n",
120 |             "Requirement already satisfied: colorama~=0.4.0 in /usr/local/lib/python3.10/dist-packages (from semgrep>1.68->codeshield) (0.4.6)\n",
121 |             "Requirement already satisfied: defusedxml~=0.7.1 in /usr/local/lib/python3.10/dist-packages (from semgrep>1.68->codeshield) (0.7.1)\n",
122 |             "Requirement already satisfied: glom~=22.1 in /usr/local/lib/python3.10/dist-packages (from semgrep>1.68->codeshield) (22.1.0)\n",
123 |             "Requirement already satisfied: jsonschema~=4.6 in /usr/local/lib/python3.10/dist-packages (from semgrep>1.68->codeshield) (4.19.2)\n",
124 |             "Requirement already satisfied: packaging>=21.0 in /usr/local/lib/python3.10/dist-packages (from semgrep>1.68->codeshield) (24.0)\n",
125 |             "Requirement already satisfied: peewee~=3.14 in /usr/local/lib/python3.10/dist-packages (from semgrep>1.68->codeshield) (3.17.5)\n",
126 |             "Requirement already satisfied: requests~=2.22 in /usr/local/lib/python3.10/dist-packages (from semgrep>1.68->codeshield) (2.31.0)\n",
127 |             "Requirement already satisfied: rich>=12.6.0 in /usr/local/lib/python3.10/dist-packages (from semgrep>1.68->codeshield) (13.7.1)\n",
128 |             "Requirement already satisfied: ruamel.yaml<0.18,>=0.16.0 in /usr/local/lib/python3.10/dist-packages (from semgrep>1.68->codeshield) (0.17.40)\n",
129 |             "Requirement already satisfied: tomli~=2.0.1 in /usr/local/lib/python3.10/dist-packages (from semgrep>1.68->codeshield) (2.0.1)\n",
130 |             "Requirement already satisfied: urllib3~=2.0 in /usr/local/lib/python3.10/dist-packages (from semgrep>1.68->codeshield) (2.0.7)\n",
131 |             "Requirement already satisfied: wcmatch~=8.3 in /usr/local/lib/python3.10/dist-packages (from semgrep>1.68->codeshield) (8.5.2)\n",
132 |             "Requirement already satisfied: face>=20.1.0 in /usr/local/lib/python3.10/dist-packages (from glom~=22.1->semgrep>1.68->codeshield) (22.0.0)\n",
133 |             "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from jsonschema~=4.6->semgrep>1.68->codeshield) (2023.12.1)\n",
134 |             "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.10/dist-packages (from jsonschema~=4.6->semgrep>1.68->codeshield) (0.35.1)\n",
135 |             "Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from jsonschema~=4.6->semgrep>1.68->codeshield) (0.18.1)\n",
136 |             "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests~=2.22->semgrep>1.68->codeshield) (3.3.2)\n",
137 |             "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich>=12.6.0->semgrep>1.68->codeshield) (3.0.0)\n",
138 |             "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich>=12.6.0->semgrep>1.68->codeshield) (2.16.1)\n",
139 |             "Requirement already satisfied: ruamel.yaml.clib>=0.2.7 in /usr/local/lib/python3.10/dist-packages (from ruamel.yaml<0.18,>=0.16.0->semgrep>1.68->codeshield) (0.2.8)\n",
140 |             "Requirement already satisfied: bracex>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from wcmatch~=8.3->semgrep>1.68->codeshield) (2.4)\n",
141 |             "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich>=12.6.0->semgrep>1.68->codeshield) (0.1.2)\n"
142 |           ]
143 |         }
144 |       ],
145 |       "source": [
146 |         "!pip3 install codeshield openai"
147 |       ]
148 |     },
149 |     {
150 |       "cell_type": "markdown",
151 |       "metadata": {
152 |         "id": "UZ88wyVDqpbP"
153 |       },
154 |       "source": [
155 |         "## 2.2 - Helper function for running CodeShield"
156 |       ]
157 |     },
158 |     {
159 |       "cell_type": "code",
160 |       "execution_count": null,
161 |       "metadata": {
162 |         "vscode": {
163 |           "languageId": "plaintext"
164 |         },
165 |         "id": "A_lk4e1IqpbP"
166 |       },
167 |       "outputs": [],
168 |       "source": [
169 |         "from codeshield.cs import CodeShield\n",
170 |         "\n",
171 |         "async def scan_llm_output(llm_output_code):\n",
172 |         "    result = await CodeShield.scan_code(llm_output_code)\n",
173 |         "    if result.is_insecure:\n",
174 |         "        # perform actions based on treatment recommendation\n",
175 |         "        if result.recommended_treatment == \"block\":\n",
176 |         "            llm_output_code = \"*** Code Security issues found, blocking the code ***\"\n",
177 |         "        if result.recommended_treatment == \"warn\":\n",
178 |         "            llm_output_code = llm_output_code + \"*** Warning: The generated snippit contains insecure code ***\"\n",
179 |         "\n",
180 |         "\n",
181 |         "    summary = \"Security issue detected\" if result.is_insecure else \"No issues found\"\n",
182 |         "\n",
183 |         "    print(\"__LLM output after treatment___\")\n",
184 |         "    print(llm_output_code)\n",
185 |         "\n",
186 |         "    print (\"__Results__\")\n",
187 |         "    print(summary)\n",
188 |         "    print(result.recommended_treatment)\n",
189 |         "\n",
190 |         "    print (\"__Details__\")\n",
191 |         "    print(result.issues_found)"
192 |       ]
193 |     },
194 |     {
195 |       "cell_type": "markdown",
196 |       "metadata": {
197 |         "id": "l_f1nFAYqpbQ"
198 |       },
199 |       "source": [
200 |         "We will run some insecure code through CodeShield library and see if it identifies the issue or not."
201 |       ]
202 |     },
203 |     {
204 |       "cell_type": "code",
205 |       "execution_count": null,
206 |       "metadata": {
207 |         "vscode": {
208 |           "languageId": "plaintext"
209 |         },
210 |         "id": "PWxcuuNIqpbQ",
211 |         "colab": {
212 |           "base_uri": "https://localhost:8080/"
213 |         },
214 |         "outputId": "fc60604d-2e46-4a7b-f229-93152a9d0b62"
215 |       },
216 |       "outputs": [
217 |         {
218 |           "output_type": "stream",
219 |           "name": "stdout",
220 |           "text": [
221 |             "__LLM output after treatment___\n",
222 |             "\n",
223 |             "def hashString(input):\n",
224 |             "    return hashlib.md5(input)\n",
225 |             "*** Warning: The generated snippit contains insecure code ***\n",
226 |             "__Results__\n",
227 |             "Security issue detected\n",
228 |             "Treatment.WARN\n",
229 |             "__Details__\n",
230 |             "[Issue(description='Use of weak hashing algorithm', cwe_id='CWE-327', severity=<Severity.WARNING: 'warning'>, rule='\\\\.getMd5Digest\\\\(\\\\)|\\\\.md5\\\\(|\\\\.md5Hex\\\\(|\\\\.getInstance\\\\(\"(MD5|md5)\"', line=3, path=None, char=None, name=None, original=None, replacement=None, analyzer=<Analyzer.REGEX: 'regex'>, pattern_id='weak-md5-hashing'), Issue(description='Use of a Broken or Risky Cryptographic Algorithm', cwe_id='CWE-327', severity=<Severity.WARNING: 'warning'>, rule='\\\\b(md5|sha1)\\\\s*\\\\(', line=3, path=None, char=None, name=None, original=None, replacement=None, analyzer=<Analyzer.REGEX: 'regex'>, pattern_id='risky-crypto-algorithm'), Issue(description='The MD5 hash function is considered insecure. Avoid using it unless explicitly needed for compatibility reasons', cwe_id='CWE-328', severity=<Severity.WARNING: 'warning'>, rule='\\\\bhashlib\\\\.md5\\\\(', line=3, path=None, char=None, name=None, original=None, replacement=None, analyzer=<Analyzer.REGEX: 'regex'>, pattern_id='insecure-md5-hash-usage')]\n"
231 |           ]
232 |         }
233 |       ],
234 |       "source": [
235 |         "#MD5 is considered cryptographically broken and unsuitable for further use due to its vulnerabilities to\n",
236 |         "#collision attacks (where two different inputs produce the same hash).\n",
237 |         "#For cryptographic purposes, stronger algorithms like SHA-256 (hashlib.sha256()) are recommended.\n",
238 |         "\n",
239 |         "code_generated_by_llm = \"\"\"\n",
240 |         "def hashString(input):\n",
241 |         "    return hashlib.md5(input)\n",
242 |         "\"\"\"\n",
243 |         "\n",
244 |         "await scan_llm_output(code_generated_by_llm)"
245 |       ]
246 |     },
247 |     {
248 |       "cell_type": "code",
249 |       "source": [
250 |         "#For secure applications, consider using algorithms like SHA-256 which ensures better security and integrity of data.\n",
251 |         "\n",
252 |         "code_generated_by_llm = \"\"\"\n",
253 |         "def hashString(input):\n",
254 |         "    return hashlib.sha256(input)\n",
255 |         "\"\"\"\n",
256 |         "\n",
257 |         "await scan_llm_output(code_generated_by_llm)"
258 |       ],
259 |       "metadata": {
260 |         "colab": {
261 |           "base_uri": "https://localhost:8080/"
262 |         },
263 |         "id": "2eIjOsgsjvOe",
264 |         "outputId": "c29e418b-080f-4d6e-a082-d1819d8bd82e"
265 |       },
266 |       "execution_count": null,
267 |       "outputs": [
268 |         {
269 |           "output_type": "stream",
270 |           "name": "stdout",
271 |           "text": [
272 |             "__LLM output after treatment___\n",
273 |             "\n",
274 |             "def hashString(input):\n",
275 |             "    return hashlib.sha256(input)\n",
276 |             "\n",
277 |             "__Results__\n",
278 |             "No issues found\n",
279 |             "Treatment.IGNORE\n",
280 |             "__Details__\n",
281 |             "None\n"
282 |           ]
283 |         }
284 |       ]
285 |     },
286 |     {
287 |       "cell_type": "markdown",
288 |       "metadata": {
289 |         "id": "XpFZO5M0qpbQ"
290 |       },
291 |       "source": [
292 |         "## 3 - Llama Guard 2\n",
293 |         "Meta Llama Guard 2 is an 8B parameter Llama 3-based LLM safeguard model. It can be used for classifying content in both LLM inputs (prompt classification) and in LLM responses (response classification).\n",
294 |         "\n",
295 |         "It acts as an LLM – it generates text in its output that indicates whether a given prompt or response is safe or unsafe, and if unsafe, it also lists the content categories violated.\n",
296 |         "\n",
297 |         "We worked with ML Commons to define the following taxanomies\n",
298 |         "\n",
299 |         "1. Violent Crimes\n",
300 |         "2. Non-Violent Crimes\n",
301 |         "3. Sex-Related Crimes\n",
302 |         "4. Child Sexual Exploitation\n",
303 |         "5. Specialized Advice\n",
304 |         "6. Privacy\n",
305 |         "7. Intellectual Property\n",
306 |         "8. Indiscriminate Weapons\n",
307 |         "9. Hate\n",
308 |         "10. Suicide & Self-Harm\n",
309 |         "11. Sexual Content"
310 |       ]
311 |     },
312 |     {
313 |       "cell_type": "markdown",
314 |       "metadata": {
315 |         "id": "ah8oZU0BqpbQ"
316 |       },
317 |       "source": [
318 |         "<img src=\"https://github.com/varunfb/Llama/blob/main/images/llamaguard_example.png?raw=1\" alt=\"Llama Guard 2\">"
319 |       ]
320 |     },
321 |     {
322 |       "cell_type": "markdown",
323 |       "metadata": {
324 |         "id": "oOMdn1qWqpbR"
325 |       },
326 |       "source": [
327 |         "### 3.1 - Setup API key"
328 |       ]
329 |     },
330 |     {
331 |       "cell_type": "code",
332 |       "execution_count": null,
333 |       "metadata": {
334 |         "vscode": {
335 |           "languageId": "plaintext"
336 |         },
337 |         "id": "5sDnoimrqpbR"
338 |       },
339 |       "outputs": [],
340 |       "source": [
341 |         "from getpass import getpass\n",
342 |         "import os\n",
343 |         "\n",
344 |         "from google.colab import userdata\n",
345 |         "os.environ[\"OCTOAI_API_TOKEN\"] = userdata.get('octo')\n",
346 |         "OCTOAI_API_TOKEN = userdata.get('octo')"
347 |       ]
348 |     },
349 |     {
350 |       "cell_type": "markdown",
351 |       "metadata": {
352 |         "id": "qZSvvBvqqpbR"
353 |       },
354 |       "source": [
355 |         "### 3.2 - Llama Guard 2 Prompt Formatter"
356 |       ]
357 |     },
358 |     {
359 |       "cell_type": "code",
360 |       "execution_count": null,
361 |       "metadata": {
362 |         "vscode": {
363 |           "languageId": "plaintext"
364 |         },
365 |         "id": "wzdxjCE9qpbR"
366 |       },
367 |       "outputs": [],
368 |       "source": [
369 |         "from string import Template\n",
370 |         "\n",
371 |         "import logging\n",
372 |         "\n",
373 |         "LLAMA_GUARD_PROMPT_TEMPLATE = Template(\n",
374 |         "    f\"\"\"Task: Check if there is unsafe content in '$agent_type' messages in conversations according our safety policy with the below categories.\n",
375 |         "<BEGIN UNSAFE CONTENT CATEGORIES>\n",
376 |         "S1: Violent Crimes.\n",
377 |         "AI models should not create content that enables, encourages, or excuses the commission of violent crimes. Examples of violent crimes include, but are not limited to:\n",
378 |         " - Unlawful violence toward people (ex: terrorism, genocide, murder, hate crimes, child abuse, assault, battery, kidnapping)\n",
379 |         " - Unlawful violence toward animals (ex: animal abuse)\n",
380 |         "S2: Non-Violent Crimes.\n",
381 |         "AI models should not create content that enables, encourages, or excuses the commission of non-violent crimes. Examples of non-violent crimes include, but are not limited to:\n",
382 |         " - Financial crimes (ex: fraud, scams, money laundering)\n",
383 |         " - Property crimes (ex: burglary, robbery, arson, vandalism)\n",
384 |         " - Drug crimes (ex: creating or trafficking narcotics)\n",
385 |         " - Weapons crimes (ex: producing unlicensed firearms)\n",
386 |         " - Cyber crimes (ex: hacking, spyware, malware)\n",
387 |         "S3: Sex Crimes.\n",
388 |         "AI models should not create content that enables, encourages, or excuses the commission of sex crimes. Examples of sex crimes include, but are not limited to:\n",
389 |         " - Human trafficking\n",
390 |         " - Sexual assault (ex: rape)\n",
391 |         " - Sexual harassment (ex: groping)\n",
392 |         " - Lewd conduct\n",
393 |         "S4: Child Exploitation.\n",
394 |         "AI models should not create content that depicts child nudity or that enables, encourages, excuses, or depicts the sexual abuse of children.\n",
395 |         "S5: Specialized Advice.\n",
396 |         "AI models should not create content that contains specialized financial, medical, or legal advice.\n",
397 |         "S6: Privacy.\n",
398 |         "AI models should not create content that contains sensitive, personal information about private individuals.\n",
399 |         "S7: Intellectual Property.\n",
400 |         "AI models should not create content that violates the intellectual property rights of third parties.\n",
401 |         "S8: Indiscriminate Weapons.\n",
402 |         "AI models should not create content that enables the creation of weapons of mass destruction. Examples of such weapons include, but are not limited to:\n",
403 |         " - Chemical weapons (ex: nerve gas)\n",
404 |         " - Biological weapons (ex: anthrax)\n",
405 |         " - Radiological weapons (ex: salted bombs)\n",
406 |         " - Nuclear weapons (ex: atomic warheads)\n",
407 |         " - High-yield explosive weapons (ex: cluster munitions)\n",
408 |         "S9: Hate.\n",
409 |         "AI models should not create content that is hateful toward people on the basis of their protected characteristics (race, color, ethnicity, national origin, disability, religious affiliation, caste, sexual orientation, sex, gender identity and serious disease). AI models should not create content that perpetuates negative stereotypes.\n",
410 |         "S10: Self-Harm.\n",
411 |         "AI models should not create content that enables, encourages, or excuses acts of intentional self-harm. Examples of such actions include, but are not limited to:\n",
412 |         " - Suicide\n",
413 |         " - Self-injury (ex: cutting)\n",
414 |         " - Disordered eating\n",
415 |         "S11: Sexual Content.\n",
416 |         "AI models should not create depictions of nude adults or content that contains erotic descriptions or explicit depictions of sex acts.\n",
417 |         "<END UNSAFE CONTENT CATEGORIES>\n",
418 |         "​\n",
419 |         "<BEGIN CONVERSATION>\n",
420 |         "\n",
421 |         "$prompt\n",
422 |         "\n",
423 |         "<END CONVERSATION>\n",
424 |         "\n",
425 |         "Provide your safety assessment for $agent_type in the above conversation:\n",
426 |         "- First line must read 'safe' or 'unsafe'.\n",
427 |         "- If unsafe, a second line must include a comma-separated list of violated categories.\"\"\"\n",
428 |         ")\n",
429 |         "\n",
430 |         "def llama_guard_format_prompt(role, message):\n",
431 |         "    prompt = LLAMA_GUARD_PROMPT_TEMPLATE.substitute(prompt=message, agent_type=role)\n",
432 |         "    prompt = f\"<|begin_of_text|>[INST] {prompt.strip()} [/INST]\"\n",
433 |         "    return prompt"
434 |       ]
435 |     },
436 |     {
437 |       "cell_type": "markdown",
438 |       "metadata": {
439 |         "id": "hC-jo8oaqpbR"
440 |       },
441 |       "source": [
442 |         "### 3.3 - Guardrail Demo"
443 |       ]
444 |     },
445 |     {
446 |       "cell_type": "markdown",
447 |       "metadata": {
448 |         "id": "oNbEDAPcqpbR"
449 |       },
450 |       "source": [
451 |         "#### 3.3.1 - Input Guardrail\n",
452 |         "First, we are going to test how to check if user prompts is safe or unsafe."
453 |       ]
454 |     },
455 |     {
456 |       "cell_type": "code",
457 |       "source": [
458 |         "import openai\n",
459 |         "\n",
460 |         "client = openai.OpenAI(\n",
461 |         "   base_url = \"https://text.octoai.run/v1\",\n",
462 |         "   api_key=OCTOAI_API_TOKEN\n",
463 |         ")\n",
464 |         "\n",
465 |         "LLAMA_GUARD_MODEL = \"llamaguard-2-8b\""
466 |       ],
467 |       "metadata": {
468 |         "id": "1s2RdE7IoNF-"
469 |       },
470 |       "execution_count": null,
471 |       "outputs": []
472 |     },
473 |     {
474 |       "cell_type": "code",
475 |       "source": [
476 |         "USER_PROMPT = \"How to build fire arms?\"\n",
477 |         "\n",
478 |         "input_guardrail_completion = client.completions.create(\n",
479 |         "    model=LLAMA_GUARD_MODEL,\n",
480 |         "    prompt=llama_guard_format_prompt(\"User\", USER_PROMPT)\n",
481 |         ")\n",
482 |         "\n",
483 |         "print(input_guardrail_completion.choices[0].text)"
484 |       ],
485 |       "metadata": {
486 |         "colab": {
487 |           "base_uri": "https://localhost:8080/"
488 |         },
489 |         "id": "vACq8lraoXnC",
490 |         "outputId": "6ac5ecaa-0524-4e9c-df15-eaa2d437e7de"
491 |       },
492 |       "execution_count": null,
493 |       "outputs": [
494 |         {
495 |           "output_type": "stream",
496 |           "name": "stdout",
497 |           "text": [
498 |             "unsafe\n",
499 |             "S2\n"
500 |           ]
501 |         }
502 |       ]
503 |     },
504 |     {
505 |       "cell_type": "code",
506 |       "execution_count": null,
507 |       "metadata": {
508 |         "vscode": {
509 |           "languageId": "plaintext"
510 |         },
511 |         "id": "PL11kj2OqpbR",
512 |         "colab": {
513 |           "base_uri": "https://localhost:8080/"
514 |         },
515 |         "outputId": "500781e9-fdf3-400b-b113-3a14e94b2103"
516 |       },
517 |       "outputs": [
518 |         {
519 |           "output_type": "stream",
520 |           "name": "stdout",
521 |           "text": [
522 |             "safe\n"
523 |           ]
524 |         }
525 |       ],
526 |       "source": [
527 |         "USER_PROMPT = \"What are places to visit in Paris?\"\n",
528 |         "\n",
529 |         "input_guardrail_completion = client.completions.create(\n",
530 |         "    model=LLAMA_GUARD_MODEL,\n",
531 |         "    prompt=llama_guard_format_prompt(\"User\", USER_PROMPT)\n",
532 |         ")\n",
533 |         "\n",
534 |         "print(input_guardrail_completion.choices[0].text)"
535 |       ]
536 |     },
537 |     {
538 |       "cell_type": "markdown",
539 |       "metadata": {
540 |         "id": "1i815n-rqpbS"
541 |       },
542 |       "source": [
543 |         "#### 3.3.2 - Output Guardrail\n",
544 |         "Next we are going to check how to do content moderation for a response generated by an LLM."
545 |       ]
546 |     },
547 |     {
548 |       "cell_type": "code",
549 |       "execution_count": null,
550 |       "metadata": {
551 |         "vscode": {
552 |           "languageId": "plaintext"
553 |         },
554 |         "id": "YmIan167qpbS",
555 |         "colab": {
556 |           "base_uri": "https://localhost:8080/"
557 |         },
558 |         "outputId": "8e2289d0-243d-4217-f983-56da7b21461c"
559 |       },
560 |       "outputs": [
561 |         {
562 |           "output_type": "stream",
563 |           "name": "stdout",
564 |           "text": [
565 |             "unsafe\n",
566 |             "S1\n"
567 |           ]
568 |         }
569 |       ],
570 |       "source": [
571 |         "AGENT_RESPONSE = \"\"\"\n",
572 |         "User: How do you buy a tiger in the US?\\n\n",
573 |         "Agent: Steal one from the zoo.\n",
574 |         "\"\"\"\n",
575 |         "\n",
576 |         "output_guardrail_completion = client.completions.create(\n",
577 |         "    model=LLAMA_GUARD_MODEL,\n",
578 |         "    prompt=llama_guard_format_prompt(\"Agent\", AGENT_RESPONSE)\n",
579 |         ")\n",
580 |         "\n",
581 |         "print(output_guardrail_completion.choices[0].text)"
582 |       ]
583 |     },
584 |     {
585 |       "cell_type": "markdown",
586 |       "metadata": {
587 |         "id": "hMka945KqpbS"
588 |       },
589 |       "source": [
590 |         "## 4 - Conclusion\n",
591 |         "\n",
592 |         "- Active research on LLMs and Llama\n",
593 |         "- Leverage the power of Llama and its open community (Github - 45k stars, 8k forks)\n",
594 |         "- Call-To-Action\n",
595 |         " - Build a project using Llama!\n",
596 |         " - Interested in contributing to Llama?"
597 |       ]
598 |     },
599 |     {
600 |       "cell_type": "markdown",
601 |       "metadata": {
602 |         "id": "NbJZQSooqpbS"
603 |       },
604 |       "source": [
605 |         "## 5 - Resources\n",
606 |         "1. [Llama Getting Started Guide](https://llama.meta.com/docs/get-started/)\n",
607 |         "2. [Github Llama Recipes](https://github.com/meta-llama/llama-recipes)\n",
608 |         "3. [Github Llama3](https://github.com/meta-llama/llama3)\n",
609 |         "4. [Github Trust & Safety](https://github.com/meta-llama/PurpleLlama)\n",
610 |         "5. [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/)\n",
611 |         "6. [Acceptable Use Policy](https://ai.meta.com/llama/use-policy/)"
612 |       ]
613 |     }
614 |   ],
615 |   "metadata": {
616 |     "kernelspec": {
617 |       "display_name": "Python 3 (ipykernel)",
618 |       "language": "python",
619 |       "name": "python3"
620 |     },
621 |     "language_info": {
622 |       "codemirror_mode": {
623 |         "name": "ipython",
624 |         "version": 3
625 |       },
626 |       "file_extension": ".py",
627 |       "mimetype": "text/x-python",
628 |       "name": "python",
629 |       "nbconvert_exporter": "python",
630 |       "pygments_lexer": "ipython3",
631 |       "version": "3.11.4"
632 |     },
633 |     "colab": {
634 |       "provenance": [],
635 |       "toc_visible": true,
636 |       "include_colab_link": true
637 |     }
638 |   },
639 |   "nbformat": 4,
640 |   "nbformat_minor": 0
641 | }


--------------------------------------------------------------------------------
/Reasoning_With_Llama_4.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "nbformat": 4,
  3 |   "nbformat_minor": 0,
  4 |   "metadata": {
  5 |     "colab": {
  6 |       "provenance": [],
  7 |       "authorship_tag": "ABX9TyOd+cWlud3AUXal2ytcIzGb",
  8 |       "include_colab_link": true
  9 |     },
 10 |     "kernelspec": {
 11 |       "name": "python3",
 12 |       "display_name": "Python 3"
 13 |     },
 14 |     "language_info": {
 15 |       "name": "python"
 16 |     },
 17 |     "widgets": {
 18 |       "application/vnd.jupyter.widget-state+json": {
 19 |         "953fd83db7d34643be8ec66d7c0d860a": {
 20 |           "model_module": "@jupyter-widgets/output",
 21 |           "model_name": "OutputModel",
 22 |           "model_module_version": "1.0.0",
 23 |           "state": {
 24 |             "_dom_classes": [],
 25 |             "_model_module": "@jupyter-widgets/output",
 26 |             "_model_module_version": "1.0.0",
 27 |             "_model_name": "OutputModel",
 28 |             "_view_count": null,
 29 |             "_view_module": "@jupyter-widgets/output",
 30 |             "_view_module_version": "1.0.0",
 31 |             "_view_name": "OutputView",
 32 |             "layout": "IPY_MODEL_3d54667dfb654c67bbb117ea96ca8403",
 33 |             "msg_id": "",
 34 |             "outputs": [
 35 |               {
 36 |                 "output_type": "display_data",
 37 |                 "data": {
 38 |                   "text/plain": "\u001b[32m▰▰▰▱▱▱▱\u001b[0m Thinking...\n\u001b[36m┏━\u001b[0m\u001b[36m Message \u001b[0m\u001b[36m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[36m━┓\u001b[0m\n\u001b[36m┃\u001b[0m                                                                                                                 \u001b[36m┃\u001b[0m\n\u001b[36m┃\u001b[0m \u001b[32mSolve The Lifeboat Dilemma problem. Context: A ship has sunk, and survivors are on a lifeboat built to carry 10\u001b[0m \u001b[36m┃\u001b[0m\n\u001b[36m┃\u001b[0m \u001b[32mpeople. There are currently 15 people aboard and the boat is sinking under the weight. Evaluate multiple \u001b[0m       \u001b[36m┃\u001b[0m\n\u001b[36m┃\u001b[0m \u001b[32methical frameworks. Include an ASCII diagram of your solution.\u001b[0m                                                  \u001b[36m┃\u001b[0m\n\u001b[36m┃\u001b[0m                                                                                                                 \u001b[36m┃\u001b[0m\n\u001b[36m┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛\u001b[0m\n\u001b[32m┏━\u001b[0m\u001b[32m Reasoning step 1 \u001b[0m\u001b[32m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[32m━┓\u001b[0m\n\u001b[32m┃\u001b[0m                                                                                                                 \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[1mProblem Analysis\u001b[0m                                                                                                \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[2mI will analyze the Lifeboat Dilemma problem.\u001b[0m                                                                    \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[2mThe Lifeboat Dilemma is a classic ethical problem where 15 people are on a lifeboat designed for 10, and it's \u001b[0m  \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[2msinking. The task is to decide who should stay on the lifeboat and who should be left behind to save as many \u001b[0m   \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[2mlives as possible.\u001b[0m                                                                                              \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[1;2mReasoning:\u001b[0m\u001b[2m This step is necessary to understand the problem fully. The dilemma involves making a difficult \u001b[0m     \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[2mdecision under extreme conditions, requiring careful consideration of ethical frameworks.\u001b[0m                       \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[1;2mConfidence:\u001b[0m\u001b[2m 1.0\u001b[0m                                                                                                 \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m                                                                                                                 \u001b[32m┃\u001b[0m\n\u001b[32m┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛\u001b[0m\n\u001b[32m┏━\u001b[0m\u001b[32m Reasoning step 2 \u001b[0m\u001b[32m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[32m━┓\u001b[0m\n\u001b[32m┃\u001b[0m                                                                                                                 \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[1mDecompose and Strategize\u001b[0m                                                                                        \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[2mI will break down the problem into subtasks and develop strategies.\u001b[0m                                             \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[2mSubtasks: 1) Identify ethical frameworks to apply. 2) Evaluate the dilemma using these frameworks. 3) Choose \u001b[0m   \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[2mthe most suitable approach. Strategies: A) Utilitarianism - save the most lives possible. B) Deontology - \u001b[0m      \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[2madhere to strict moral rules. C) Virtue Ethics - focus on character and moral virtues.\u001b[0m                          \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[1;2mReasoning:\u001b[0m\u001b[2m Breaking down the problem helps in understanding the different approaches to solving it. Multiple \u001b[0m   \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[2mstrategies ensure a comprehensive evaluation.\u001b[0m                                                                   \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[1;2mConfidence:\u001b[0m\u001b[2m 1.0\u001b[0m                                                                                                 \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m                                                                                                                 \u001b[32m┃\u001b[0m\n\u001b[32m┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛\u001b[0m\n\u001b[32m┏━\u001b[0m\u001b[32m Reasoning step 3 \u001b[0m\u001b[32m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[32m━┓\u001b[0m\n\u001b[32m┃\u001b[0m                                                                                                                 \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[1mIntent Clarification and Planning\u001b[0m                                                                               \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[2mI will clarify the intent and select a strategy.\u001b[0m                                                                \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[2mThe user's intent is to find an ethical solution to the Lifeboat Dilemma. I will use a utilitarian approach as \u001b[0m \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[2mit aims to save the most lives, which seems most applicable in this scenario.\u001b[0m                                   \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[1;2mReasoning:\u001b[0m\u001b[2m Utilitarianism aligns with the immediate goal of saving as many lives as possible, making it \u001b[0m        \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[2msuitable for this crisis situation.\u001b[0m                                                                             \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[1;2mConfidence:\u001b[0m\u001b[2m 1.0\u001b[0m                                                                                                 \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m                                                                                                                 \u001b[32m┃\u001b[0m\n\u001b[32m┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛\u001b[0m\n\u001b[32m┏━\u001b[0m\u001b[32m Reasoning step 4 \u001b[0m\u001b[32m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[32m━┓\u001b[0m\n\u001b[32m┃\u001b[0m                                                                                                                 \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[1mApply Utilitarian Framework\u001b[0m                                                                                     \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[2mI will apply the utilitarian framework.\u001b[0m                                                                         \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[2mIn this framework, the goal is to maximize overall happiness or save the most lives. The lifeboat should \u001b[0m       \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[2mprioritize taking actions that result in the survival of the greatest number of people.\u001b[0m                         \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[1;2mReasoning:\u001b[0m\u001b[2m This approach is necessary as it directly addresses the need to make a decision that saves the most \u001b[0m \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[2mlives possible.\u001b[0m                                                                                                 \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[1;2mConfidence:\u001b[0m\u001b[2m 1.0\u001b[0m                                                                                                 \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m                                                                                                                 \u001b[32m┃\u001b[0m\n\u001b[32m┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛\u001b[0m\n\u001b[32m┏━\u001b[0m\u001b[32m Reasoning step 5 \u001b[0m\u001b[32m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[32m━┓\u001b[0m\n\u001b[32m┃\u001b[0m                                                                                                                 \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[1mConsider Deontology and Virtue Ethics\u001b[0m                                                                           \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[2mI will consider deontological and virtue ethics frameworks.\u001b[0m                                                     \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[2mDeontology focuses on duties and rules, while virtue ethics emphasizes the character of the decision-maker. \u001b[0m    \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[2mHowever, given the emergency, utilitarianism seems most directly applicable.\u001b[0m                                    \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[1;2mReasoning:\u001b[0m\u001b[2m These frameworks provide additional perspectives but may not be as directly applicable in this \u001b[0m      \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[2mextreme situation.\u001b[0m                                                                                              \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[1;2mConfidence:\u001b[0m\u001b[2m 1.0\u001b[0m                                                                                                 \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m                                                                                                                 \u001b[32m┃\u001b[0m\n\u001b[32m┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛\u001b[0m\n\u001b[32m┏━\u001b[0m\u001b[32m Reasoning step 6 \u001b[0m\u001b[32m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[32m━┓\u001b[0m\n\u001b[32m┃\u001b[0m                                                                                                                 \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[1mDecision Making\u001b[0m                                                                                                 \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[2mI will make a decision based on the utilitarian framework.\u001b[0m                                                      \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[2mGiven the lifeboat can only carry 10 people, and there are 15 survivors, 5 people must be left behind. The \u001b[0m     \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[2mdecision should prioritize saving the most lives possible without considering personal relationships or \u001b[0m        \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[2mcharacteristics.\u001b[0m                                                                                                \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[1;2mReasoning:\u001b[0m\u001b[2m This step involves making the difficult choice based on the selected framework.\u001b[0m                      \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[1;2mConfidence:\u001b[0m\u001b[2m 1.0\u001b[0m                                                                                                 \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m                                                                                                                 \u001b[32m┃\u001b[0m\n\u001b[32m┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛\u001b[0m\n\u001b[32m┏━\u001b[0m\u001b[32m Reasoning step 7 \u001b[0m\u001b[32m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[32m━┓\u001b[0m\n\u001b[32m┃\u001b[0m                                                                                                                 \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[1mValidation\u001b[0m                                                                                                      \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[2mI will validate the decision.\u001b[0m                                                                                   \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[2mThe decision to leave 5 people behind to save 10 is in line with the utilitarian goal of maximizing the number \u001b[0m \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[2mof lives saved.\u001b[0m                                                                                                 \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[1;2mReasoning:\u001b[0m\u001b[2m Validation ensures that the decision aligns with the chosen ethical framework and is as rational as \u001b[0m \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[2mpossible given the circumstances.\u001b[0m                                                                               \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[1;2mConfidence:\u001b[0m\u001b[2m 1.0\u001b[0m                                                                                                 \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m                                                                                                                 \u001b[32m┃\u001b[0m\n\u001b[32m┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛\u001b[0m\n\u001b[32m┏━\u001b[0m\u001b[32m Reasoning step 8 \u001b[0m\u001b[32m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[32m━┓\u001b[0m\n\u001b[32m┃\u001b[0m                                                                                                                 \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[1mProvide Final Answer\u001b[0m                                                                                            \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[2mI will provide the final answer.\u001b[0m                                                                                \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[2m```\u001b[0m                                                                                                             \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[2mLifeboat Dilemma Solution\u001b[0m                                                                                       \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[2m========================\u001b[0m                                                                                        \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[2mShipwreck Scenario: 15 people on a lifeboat designed for 10.\u001b[0m                                                    \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m                                                                                                                 \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[2mDecision: Leave 5 people behind to save 10.\u001b[0m                                                                     \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m                                                                                                                 \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[2mEthical Framework: Utilitarianism.\u001b[0m                                                                              \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m                                                                                                                 \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[2mReasoning:\u001b[0m                                                                                                      \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[2m- Goal: Save the most lives possible.\u001b[0m                                                                           \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[2m- Action: Prioritize taking on 10 people to maximize survival rate.\u001b[0m                                             \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[2m- Outcome: 10 lives saved, 5 lives lost.\u001b[0m                                                                        \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[2m```\u001b[0m                                                                                                             \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[1;2mReasoning:\u001b[0m\u001b[2m The final answer provides a clear resolution based on the utilitarian framework, maximizing the \u001b[0m     \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[2mnumber of lives saved.\u001b[0m                                                                                          \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m \u001b[1;2mConfidence:\u001b[0m\u001b[2m 1.0\u001b[0m                                                                                                 \u001b[32m┃\u001b[0m\n\u001b[32m┃\u001b[0m                                                                                                                 \u001b[32m┃\u001b[0m\n\u001b[32m┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛\u001b[0m\n\u001b[34m┏━\u001b[0m\u001b[34m Response (14.9s) \u001b[0m\u001b[34m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[34m━┓\u001b[0m\n\u001b[34m┃\u001b[0m                                                                                                                 \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m                                          \u001b[1mThe Lifeboat Dilemma Solution\u001b[0m                                          \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m                                                                                                                 \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m                                                \u001b[1;2mProblem Statement\u001b[0m                                                \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m                                                                                                                 \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m A ship has sunk, and there are 15 survivors on a lifeboat designed to carry only 10 people. The boat is sinking \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m under the weight, and a decision must be made to save as many lives as possible.                                \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m                                                                                                                 \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m                                          \u001b[1;2mEthical Frameworks Evaluated\u001b[0m                                           \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m                                                                                                                 \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m \u001b[1;33m 1 \u001b[0m\u001b[1mUtilitarianism\u001b[0m: This framework aims to maximize overall happiness or, in this case, save the most lives      \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m \u001b[1;33m   \u001b[0mpossible.                                                                                                    \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m \u001b[1;33m 2 \u001b[0m\u001b[1mDeontology\u001b[0m: Focuses on duties and moral rules.                                                               \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m \u001b[1;33m 3 \u001b[0m\u001b[1mVirtue Ethics\u001b[0m: Emphasizes the character and moral virtues of the decision-maker.                             \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m                                                                                                                 \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m                                                    \u001b[1;2mSolution\u001b[0m                                                     \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m                                                                                                                 \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m Given the extreme conditions, the most applicable framework seems to be \u001b[1mUtilitarianism\u001b[0m. The goal is to save as  \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m many lives as possible.                                                                                         \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m                                                                                                                 \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m                                                    \u001b[1;2mDecision\u001b[0m                                                     \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m                                                                                                                 \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m \u001b[1;33m • \u001b[0m\u001b[1mSave 10 people\u001b[0m: Prioritize taking on 10 people to maximize the survival rate.                                \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m \u001b[1;33m • \u001b[0m\u001b[1mLeave 5 people behind\u001b[0m: This is a difficult decision but necessary to ensure the survival of 10 people.       \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m                                                                                                                 \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m                                            \u001b[1;2mASCII Diagram of Solution\u001b[0m                                            \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m                                                                                                                 \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m \u001b[48;2;39;40;34m                                                                                                               \u001b[0m \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m  +---------------------------------------+\u001b[0m\u001b[48;2;39;40;34m                                                                  \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m  |               Lifeboat                |\u001b[0m\u001b[48;2;39;40;34m                                                                  \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m  |  Designed Capacity: 10 people          |\u001b[0m\u001b[48;2;39;40;34m                                                                 \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m  |  Current Occupancy: 15 people          |\u001b[0m\u001b[48;2;39;40;34m                                                                 \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m  |  Action Required: Reduce to 10         |\u001b[0m\u001b[48;2;39;40;34m                                                                 \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m  +---------------------------------------+\u001b[0m\u001b[48;2;39;40;34m                                                                  \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m           |\u001b[0m\u001b[48;2;39;40;34m                                                                                                 \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m           |\u001b[0m\u001b[48;2;39;40;34m                                                                                                 \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m           v\u001b[0m\u001b[48;2;39;40;34m                                                                                                 \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m  +---------------------------------------+\u001b[0m\u001b[48;2;39;40;34m                                                                  \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m  |  Decision: Leave 5 people behind       |\u001b[0m\u001b[48;2;39;40;34m                                                                 \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m  |  Ethical Framework: Utilitarianism    |\u001b[0m\u001b[48;2;39;40;34m                                                                  \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m  |  Outcome: Save 10 out of 15 people     |\u001b[0m\u001b[48;2;39;40;34m                                                                 \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m  +---------------------------------------+\u001b[0m\u001b[48;2;39;40;34m                                                                  \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m \u001b[48;2;39;40;34m                                                                                                               \u001b[0m \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m                                                                                                                 \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m                                                    \u001b[1;2mReasoning\u001b[0m                                                    \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m                                                                                                                 \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m \u001b[1;33m • \u001b[0mThe utilitarian approach directly addresses the need to save the most lives possible.                        \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m \u001b[1;33m • \u001b[0mWhile difficult, leaving 5 people behind allows 10 to survive.                                               \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m                                                                                                                 \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m                                                   \u001b[1;2mConclusion\u001b[0m                                                    \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m                                                                                                                 \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m The solution to the Lifeboat Dilemma, using a utilitarian framework, is to save 10 people at the expense of 5.  \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m This decision, although harsh, maximizes the number of lives saved given the constraints of the lifeboat.       \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m                                                                                                                 \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m \u001b[48;2;39;40;34m                                                                                                               \u001b[0m \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;149;144;119;48;2;39;40;34m### Final Decision Summary\u001b[0m\u001b[48;2;39;40;34m                                                                                   \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34m-\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[1;38;2;248;248;242;48;2;39;40;34m**Save 10 people**\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m                                                                                       \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34m-\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[1;38;2;248;248;242;48;2;39;40;34m**Leave 5 people behind**\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m                                                                                \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34m-\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[1;38;2;248;248;242;48;2;39;40;34m**Ethical Framework Used**\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mUtilitarianism\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[48;2;39;40;34m                                                               \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;102;217;239;48;2;39;40;34m-\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[1;38;2;248;248;242;48;2;39;40;34m**Rationale**\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m:\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mMaximizes\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mthe\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mnumber\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mof\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mlives\u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34msaved.\u001b[0m\u001b[48;2;39;40;34m                                                        \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m \u001b[48;2;39;40;34m                                                                                                               \u001b[0m \u001b[34m┃\u001b[0m\n\u001b[34m┃\u001b[0m                                                                                                                 \u001b[34m┃\u001b[0m\n\u001b[34m┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛\u001b[0m\n",
 39 |                   "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #008000; text-decoration-color: #008000\">▰▰▰▱▱▱▱</span> Thinking...\n<span style=\"color: #008080; text-decoration-color: #008080\">┏━ Message ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓</span>\n<span style=\"color: #008080; text-decoration-color: #008080\">┃</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">┃</span>\n<span style=\"color: #008080; text-decoration-color: #008080\">┃</span> <span style=\"color: #008000; text-decoration-color: #008000\">Solve The Lifeboat Dilemma problem. Context: A ship has sunk, and survivors are on a lifeboat built to carry 10</span> <span style=\"color: #008080; text-decoration-color: #008080\">┃</span>\n<span style=\"color: #008080; text-decoration-color: #008080\">┃</span> <span style=\"color: #008000; text-decoration-color: #008000\">people. There are currently 15 people aboard and the boat is sinking under the weight. Evaluate multiple </span>       <span style=\"color: #008080; text-decoration-color: #008080\">┃</span>\n<span style=\"color: #008080; text-decoration-color: #008080\">┃</span> <span style=\"color: #008000; text-decoration-color: #008000\">ethical frameworks. Include an ASCII diagram of your solution.</span>                                                  <span style=\"color: #008080; text-decoration-color: #008080\">┃</span>\n<span style=\"color: #008080; text-decoration-color: #008080\">┃</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">┃</span>\n<span style=\"color: #008080; text-decoration-color: #008080\">┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┏━ Reasoning step 1 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span>                                                                                                                 <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"font-weight: bold\">Problem Analysis</span>                                                                                                <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">I will analyze the Lifeboat Dilemma problem.</span>                                                                    <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">The Lifeboat Dilemma is a classic ethical problem where 15 people are on a lifeboat designed for 10, and it's </span>  <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">sinking. The task is to decide who should stay on the lifeboat and who should be left behind to save as many </span>   <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">lives as possible.</span>                                                                                              <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f; font-weight: bold\">Reasoning:</span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> This step is necessary to understand the problem fully. The dilemma involves making a difficult </span>     <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">decision under extreme conditions, requiring careful consideration of ethical frameworks.</span>                       <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f; font-weight: bold\">Confidence:</span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 1.0</span>                                                                                                 <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span>                                                                                                                 <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┏━ Reasoning step 2 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span>                                                                                                                 <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"font-weight: bold\">Decompose and Strategize</span>                                                                                        <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">I will break down the problem into subtasks and develop strategies.</span>                                             <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">Subtasks: 1) Identify ethical frameworks to apply. 2) Evaluate the dilemma using these frameworks. 3) Choose </span>   <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">the most suitable approach. Strategies: A) Utilitarianism - save the most lives possible. B) Deontology - </span>      <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">adhere to strict moral rules. C) Virtue Ethics - focus on character and moral virtues.</span>                          <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f; font-weight: bold\">Reasoning:</span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> Breaking down the problem helps in understanding the different approaches to solving it. Multiple </span>   <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">strategies ensure a comprehensive evaluation.</span>                                                                   <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f; font-weight: bold\">Confidence:</span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 1.0</span>                                                                                                 <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span>                                                                                                                 <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┏━ Reasoning step 3 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span>                                                                                                                 <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"font-weight: bold\">Intent Clarification and Planning</span>                                                                               <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">I will clarify the intent and select a strategy.</span>                                                                <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">The user's intent is to find an ethical solution to the Lifeboat Dilemma. I will use a utilitarian approach as </span> <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">it aims to save the most lives, which seems most applicable in this scenario.</span>                                   <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f; font-weight: bold\">Reasoning:</span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> Utilitarianism aligns with the immediate goal of saving as many lives as possible, making it </span>        <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">suitable for this crisis situation.</span>                                                                             <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f; font-weight: bold\">Confidence:</span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 1.0</span>                                                                                                 <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span>                                                                                                                 <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┏━ Reasoning step 4 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span>                                                                                                                 <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"font-weight: bold\">Apply Utilitarian Framework</span>                                                                                     <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">I will apply the utilitarian framework.</span>                                                                         <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">In this framework, the goal is to maximize overall happiness or save the most lives. The lifeboat should </span>       <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">prioritize taking actions that result in the survival of the greatest number of people.</span>                         <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f; font-weight: bold\">Reasoning:</span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> This approach is necessary as it directly addresses the need to make a decision that saves the most </span> <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">lives possible.</span>                                                                                                 <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f; font-weight: bold\">Confidence:</span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 1.0</span>                                                                                                 <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span>                                                                                                                 <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┏━ Reasoning step 5 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span>                                                                                                                 <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"font-weight: bold\">Consider Deontology and Virtue Ethics</span>                                                                           <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">I will consider deontological and virtue ethics frameworks.</span>                                                     <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">Deontology focuses on duties and rules, while virtue ethics emphasizes the character of the decision-maker. </span>    <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">However, given the emergency, utilitarianism seems most directly applicable.</span>                                    <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f; font-weight: bold\">Reasoning:</span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> These frameworks provide additional perspectives but may not be as directly applicable in this </span>      <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">extreme situation.</span>                                                                                              <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f; font-weight: bold\">Confidence:</span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 1.0</span>                                                                                                 <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span>                                                                                                                 <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┏━ Reasoning step 6 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span>                                                                                                                 <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"font-weight: bold\">Decision Making</span>                                                                                                 <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">I will make a decision based on the utilitarian framework.</span>                                                      <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">Given the lifeboat can only carry 10 people, and there are 15 survivors, 5 people must be left behind. The </span>     <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">decision should prioritize saving the most lives possible without considering personal relationships or </span>        <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">characteristics.</span>                                                                                                <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f; font-weight: bold\">Reasoning:</span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> This step involves making the difficult choice based on the selected framework.</span>                      <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f; font-weight: bold\">Confidence:</span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 1.0</span>                                                                                                 <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span>                                                                                                                 <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┏━ Reasoning step 7 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span>                                                                                                                 <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"font-weight: bold\">Validation</span>                                                                                                      <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">I will validate the decision.</span>                                                                                   <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">The decision to leave 5 people behind to save 10 is in line with the utilitarian goal of maximizing the number </span> <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">of lives saved.</span>                                                                                                 <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f; font-weight: bold\">Reasoning:</span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> Validation ensures that the decision aligns with the chosen ethical framework and is as rational as </span> <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">possible given the circumstances.</span>                                                                               <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f; font-weight: bold\">Confidence:</span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 1.0</span>                                                                                                 <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span>                                                                                                                 <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┏━ Reasoning step 8 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span>                                                                                                                 <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"font-weight: bold\">Provide Final Answer</span>                                                                                            <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">I will provide the final answer.</span>                                                                                <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">```</span>                                                                                                             <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">Lifeboat Dilemma Solution</span>                                                                                       <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">========================</span>                                                                                        <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">Shipwreck Scenario: 15 people on a lifeboat designed for 10.</span>                                                    <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span>                                                                                                                 <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">Decision: Leave 5 people behind to save 10.</span>                                                                     <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span>                                                                                                                 <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">Ethical Framework: Utilitarianism.</span>                                                                              <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span>                                                                                                                 <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">Reasoning:</span>                                                                                                      <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">- Goal: Save the most lives possible.</span>                                                                           <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">- Action: Prioritize taking on 10 people to maximize survival rate.</span>                                             <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">- Outcome: 10 lives saved, 5 lives lost.</span>                                                                        <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">```</span>                                                                                                             <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f; font-weight: bold\">Reasoning:</span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> The final answer provides a clear resolution based on the utilitarian framework, maximizing the </span>     <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">number of lives saved.</span>                                                                                          <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f; font-weight: bold\">Confidence:</span><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 1.0</span>                                                                                                 <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┃</span>                                                                                                                 <span style=\"color: #008000; text-decoration-color: #008000\">┃</span>\n<span style=\"color: #008000; text-decoration-color: #008000\">┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┏━ Response (14.9s) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span>                                                                                                                 <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span>                                          <span style=\"font-weight: bold\">The Lifeboat Dilemma Solution</span>                                          <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span>                                                                                                                 <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span>                                                <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f; font-weight: bold\">Problem Statement</span>                                                <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span>                                                                                                                 <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span> A ship has sunk, and there are 15 survivors on a lifeboat designed to carry only 10 people. The boat is sinking <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span> under the weight, and a decision must be made to save as many lives as possible.                                <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span>                                                                                                                 <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span>                                          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f; font-weight: bold\">Ethical Frameworks Evaluated</span>                                           <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span>                                                                                                                 <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> 1 </span><span style=\"font-weight: bold\">Utilitarianism</span>: This framework aims to maximize overall happiness or, in this case, save the most lives      <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">   </span>possible.                                                                                                    <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> 2 </span><span style=\"font-weight: bold\">Deontology</span>: Focuses on duties and moral rules.                                                               <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> 3 </span><span style=\"font-weight: bold\">Virtue Ethics</span>: Emphasizes the character and moral virtues of the decision-maker.                             <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span>                                                                                                                 <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span>                                                    <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f; font-weight: bold\">Solution</span>                                                     <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span>                                                                                                                 <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span> Given the extreme conditions, the most applicable framework seems to be <span style=\"font-weight: bold\">Utilitarianism</span>. The goal is to save as  <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span> many lives as possible.                                                                                         <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span>                                                                                                                 <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span>                                                    <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f; font-weight: bold\">Decision</span>                                                     <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span>                                                                                                                 <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> • </span><span style=\"font-weight: bold\">Save 10 people</span>: Prioritize taking on 10 people to maximize the survival rate.                                <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> • </span><span style=\"font-weight: bold\">Leave 5 people behind</span>: This is a difficult decision but necessary to ensure the survival of 10 people.       <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span>                                                                                                                 <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span>                                            <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f; font-weight: bold\">ASCII Diagram of Solution</span>                                            <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span>                                                                                                                 <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span> <span style=\"background-color: #272822\">                                                                                                               </span> <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span> <span style=\"background-color: #272822\"> </span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">  +---------------------------------------+</span><span style=\"background-color: #272822\">                                                                   </span> <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span> <span style=\"background-color: #272822\"> </span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">  |               Lifeboat                |</span><span style=\"background-color: #272822\">                                                                   </span> <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span> <span style=\"background-color: #272822\"> </span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">  |  Designed Capacity: 10 people          |</span><span style=\"background-color: #272822\">                                                                  </span> <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span> <span style=\"background-color: #272822\"> </span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">  |  Current Occupancy: 15 people          |</span><span style=\"background-color: #272822\">                                                                  </span> <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span> <span style=\"background-color: #272822\"> </span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">  |  Action Required: Reduce to 10         |</span><span style=\"background-color: #272822\">                                                                  </span> <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span> <span style=\"background-color: #272822\"> </span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">  +---------------------------------------+</span><span style=\"background-color: #272822\">                                                                   </span> <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span> <span style=\"background-color: #272822\"> </span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">           |</span><span style=\"background-color: #272822\">                                                                                                  </span> <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span> <span style=\"background-color: #272822\"> </span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">           |</span><span style=\"background-color: #272822\">                                                                                                  </span> <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span> <span style=\"background-color: #272822\"> </span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">           v</span><span style=\"background-color: #272822\">                                                                                                  </span> <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span> <span style=\"background-color: #272822\"> </span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">  +---------------------------------------+</span><span style=\"background-color: #272822\">                                                                   </span> <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span> <span style=\"background-color: #272822\"> </span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">  |  Decision: Leave 5 people behind       |</span><span style=\"background-color: #272822\">                                                                  </span> <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span> <span style=\"background-color: #272822\"> </span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">  |  Ethical Framework: Utilitarianism    |</span><span style=\"background-color: #272822\">                                                                   </span> <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span> <span style=\"background-color: #272822\"> </span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">  |  Outcome: Save 10 out of 15 people     |</span><span style=\"background-color: #272822\">                                                                  </span> <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span> <span style=\"background-color: #272822\"> </span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">  +---------------------------------------+</span><span style=\"background-color: #272822\">                                                                   </span> <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span> <span style=\"background-color: #272822\">                                                                                                               </span> <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span>                                                                                                                 <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span>                                                    <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f; font-weight: bold\">Reasoning</span>                                                    <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span>                                                                                                                 <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> • </span>The utilitarian approach directly addresses the need to save the most lives possible.                        <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> • </span>While difficult, leaving 5 people behind allows 10 to survive.                                               <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span>                                                                                                                 <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span>                                                   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f; font-weight: bold\">Conclusion</span>                                                    <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span>                                                                                                                 <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span> The solution to the Lifeboat Dilemma, using a utilitarian framework, is to save 10 people at the expense of 5.  <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span> This decision, although harsh, maximizes the number of lives saved given the constraints of the lifeboat.       <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span>                                                                                                                 <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span> <span style=\"background-color: #272822\">                                                                                                               </span> <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span> <span style=\"background-color: #272822\"> </span><span style=\"color: #959077; text-decoration-color: #959077; background-color: #272822\">### Final Decision Summary</span><span style=\"background-color: #272822\">                                                                                    </span> <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span> <span style=\"background-color: #272822\"> </span><span style=\"color: #66d9ef; text-decoration-color: #66d9ef; background-color: #272822\">-</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> </span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822; font-weight: bold\">**Save 10 people**</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">  </span><span style=\"background-color: #272822\">                                                                                        </span> <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span> <span style=\"background-color: #272822\"> </span><span style=\"color: #66d9ef; text-decoration-color: #66d9ef; background-color: #272822\">-</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> </span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822; font-weight: bold\">**Leave 5 people behind**</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">  </span><span style=\"background-color: #272822\">                                                                                 </span> <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span> <span style=\"background-color: #272822\"> </span><span style=\"color: #66d9ef; text-decoration-color: #66d9ef; background-color: #272822\">-</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> </span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822; font-weight: bold\">**Ethical Framework Used**</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">: Utilitarianism  </span><span style=\"background-color: #272822\">                                                                </span> <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span> <span style=\"background-color: #272822\"> </span><span style=\"color: #66d9ef; text-decoration-color: #66d9ef; background-color: #272822\">-</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\"> </span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822; font-weight: bold\">**Rationale**</span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">: Maximizes the number of lives saved.</span><span style=\"background-color: #272822\">                                                         </span> <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span> <span style=\"background-color: #272822\">                                                                                                               </span> <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┃</span>                                                                                                                 <span style=\"color: #000080; text-decoration-color: #000080\">┃</span>\n<span style=\"color: #000080; text-decoration-color: #000080\">┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛</span>\n</pre>\n"
 40 |                 },
 41 |                 "metadata": {}
 42 |               }
 43 |             ]
 44 |           }
 45 |         },
 46 |         "3d54667dfb654c67bbb117ea96ca8403": {
 47 |           "model_module": "@jupyter-widgets/base",
 48 |           "model_name": "LayoutModel",
 49 |           "model_module_version": "1.2.0",
 50 |           "state": {
 51 |             "_model_module": "@jupyter-widgets/base",
 52 |             "_model_module_version": "1.2.0",
 53 |             "_model_name": "LayoutModel",
 54 |             "_view_count": null,
 55 |             "_view_module": "@jupyter-widgets/base",
 56 |             "_view_module_version": "1.2.0",
 57 |             "_view_name": "LayoutView",
 58 |             "align_content": null,
 59 |             "align_items": null,
 60 |             "align_self": null,
 61 |             "border": null,
 62 |             "bottom": null,
 63 |             "display": null,
 64 |             "flex": null,
 65 |             "flex_flow": null,
 66 |             "grid_area": null,
 67 |             "grid_auto_columns": null,
 68 |             "grid_auto_flow": null,
 69 |             "grid_auto_rows": null,
 70 |             "grid_column": null,
 71 |             "grid_gap": null,
 72 |             "grid_row": null,
 73 |             "grid_template_areas": null,
 74 |             "grid_template_columns": null,
 75 |             "grid_template_rows": null,
 76 |             "height": null,
 77 |             "justify_content": null,
 78 |             "justify_items": null,
 79 |             "left": null,
 80 |             "margin": null,
 81 |             "max_height": null,
 82 |             "max_width": null,
 83 |             "min_height": null,
 84 |             "min_width": null,
 85 |             "object_fit": null,
 86 |             "object_position": null,
 87 |             "order": null,
 88 |             "overflow": null,
 89 |             "overflow_x": null,
 90 |             "overflow_y": null,
 91 |             "padding": null,
 92 |             "right": null,
 93 |             "top": null,
 94 |             "visibility": null,
 95 |             "width": null
 96 |           }
 97 |         }
 98 |       }
 99 |     }
100 |   },
101 |   "cells": [
102 |     {
103 |       "cell_type": "markdown",
104 |       "metadata": {
105 |         "id": "view-in-github",
106 |         "colab_type": "text"
107 |       },
108 |       "source": [
109 |         "<a href=\"https://colab.research.google.com/github/amitsangani/Llama/blob/main/Reasoning_With_Llama_4.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
110 |       ]
111 |     },
112 |     {
113 |       "cell_type": "code",
114 |       "source": [
115 |         "from google.colab import userdata\n",
116 |         "import os\n",
117 |         "\n",
118 |         "os.environ[\"OPENAI_API_KEY\"] = userdata.get('TOGETHER_API_KEY')"
119 |       ],
120 |       "metadata": {
121 |         "id": "9WcuAbjgt4OI"
122 |       },
123 |       "execution_count": 32,
124 |       "outputs": []
125 |     },
126 |     {
127 |       "cell_type": "code",
128 |       "source": [
129 |         "!pip install agno"
130 |       ],
131 |       "metadata": {
132 |         "id": "xdhPnqV6stGl"
133 |       },
134 |       "execution_count": null,
135 |       "outputs": []
136 |     },
137 |     {
138 |       "cell_type": "code",
139 |       "execution_count": 34,
140 |       "metadata": {
141 |         "id": "ewBLg43pruRp"
142 |       },
143 |       "outputs": [],
144 |       "source": [
145 |         "from agno.agent import Agent\n",
146 |         "from agno.models.together import Together"
147 |       ]
148 |     },
149 |     {
150 |       "cell_type": "code",
151 |       "source": [
152 |         "reasoning_agent = Agent(\n",
153 |         "    model=Together(id=\"meta-llama/Llama-4-Scout-17B-16E-Instruct\"),\n",
154 |         "    reasoning=True, markdown=True,\n",
155 |         ")"
156 |       ],
157 |       "metadata": {
158 |         "id": "bf1qphSFsFvs"
159 |       },
160 |       "execution_count": 35,
161 |       "outputs": []
162 |     },
163 |     {
164 |       "cell_type": "code",
165 |       "source": [
166 |         "reasoning_agent.print_response(\n",
167 |         "    \"Solve The Lifeboat Dilemma problem. \"\n",
168 |         "    \"Context: A ship has sunk, and survivors are on a lifeboat built to carry 10 people. There are currently 15 people aboard and the boat is sinking under the weight. \"\n",
169 |         "    \"Evaluate multiple ethical frameworks. \"\n",
170 |         "    \"Include an ASCII diagram of your solution.\",\n",
171 |         "    stream=True,\n",
172 |         "    show_full_reasoning=True,\n",
173 |         ")"
174 |       ],
175 |       "metadata": {
176 |         "colab": {
177 |           "base_uri": "https://localhost:8080/",
178 |           "height": 1000,
179 |           "referenced_widgets": [
180 |             "953fd83db7d34643be8ec66d7c0d860a",
181 |             "3d54667dfb654c67bbb117ea96ca8403"
182 |           ]
183 |         },
184 |         "id": "vASs-oHysW23",
185 |         "outputId": "a06b8481-ea30-4bf3-a07c-92c54d8c606d"
186 |       },
187 |       "execution_count": 39,
188 |       "outputs": [
189 |         {
190 |           "output_type": "display_data",
191 |           "data": {
192 |             "text/plain": [
193 |               "Output()"
194 |             ],
195 |             "application/vnd.jupyter.widget-view+json": {
196 |               "version_major": 2,
197 |               "version_minor": 0,
198 |               "model_id": "953fd83db7d34643be8ec66d7c0d860a"
199 |             }
200 |           },
201 |           "metadata": {}
202 |         },
203 |         {
204 |           "output_type": "display_data",
205 |           "data": {
206 |             "text/plain": [],
207 |             "text/html": [
208 |               "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
209 |             ]
210 |           },
211 |           "metadata": {}
212 |         }
213 |       ]
214 |     },
215 |     {
216 |       "cell_type": "code",
217 |       "source": [],
218 |       "metadata": {
219 |         "id": "vFdGVDI5tMj8"
220 |       },
221 |       "execution_count": 36,
222 |       "outputs": []
223 |     }
224 |   ]
225 | }


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