Typo in Prompt_Engineering_with_Llama_3.ipynb (#806)

3e699520 · Igor Kasianenko · GitHub · a346e19d · 4efe4390 · 3e699520
Unverified Commit 3e699520 authored 3 months ago by Igor Kasianenko Committed by GitHub 3 months ago
--- a/recipes/quickstart/Prompt_Engineering_with_Llama_3.ipynb
+++ b/recipes/quickstart/Prompt_Engineering_with_Llama_3.ipynb
@@ -152,7 +152,7 @@
   "source": [
    "## Notebook Setup\n",
    "\n",
-    "The following APIs will be used to call LLMs throughout the guide. As an example, we'll call Llama 3.1 chat using [Grok](https://console.groq.com/playground?model=llama3-70b-8192).\n",
+    "The following APIs will be used to call LLMs throughout the guide. As an example, we'll call Llama 3.1 chat using [Groq](https://console.groq.com/playground?model=llama3-70b-8192).\n",
    "\n",
    "To install prerequisites run:"
   ]

 %% Cell type:markdown id: tags:
 <a href="https://colab.research.google.com/github/meta-llama/llama-recipes/blob/main/recipes/quickstart/Prompt_Engineering_with_Llama_3.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
 # Prompt Engineering with Llama 3.1
 Prompt engineering is using natural language to produce a desired response from a large language model (LLM).
 This interactive guide covers prompt engineering & best practices with Llama 3.1.
 %% Cell type:markdown id: tags:
 ## Introduction
 %% Cell type:markdown id: tags:
 ### Why now?
 [Vaswani et al. (2017)](https://arxiv.org/abs/1706.03762) introduced the world to transformer neural networks (originally for machine translation). Transformers ushered an era of generative AI with diffusion models for image creation and large language models (`LLMs`) as **programmable deep learning networks**.
 Programming foundational LLMs is done with natural language – it doesn't require training/tuning like ML models of the past. This has opened the door to a massive amount of innovation and a paradigm shift in how technology can be deployed. The science/art of using natural language to program language models to accomplish a task is referred to as **Prompt Engineering**.
 %% Cell type:markdown id: tags:
 ### Llama Models
 In 2023, Meta introduced the [Llama language models](https://ai.meta.com/llama/) (Llama Chat, Code Llama, Llama Guard). These are general purpose, state-of-the-art LLMs.
 Llama models come in varying parameter sizes. The smaller models are cheaper to deploy and run; the larger models are more capable.
 #### Llama 3.1
 1. `llama-3.1-8b` - base pretrained 8 billion parameter model
 1. `llama-3.1-70b` - base pretrained 70 billion parameter model
 1. `llama-3.1-405b` - base pretrained 405 billion parameter model
 1. `llama-3.1-8b-instruct` - instruction fine-tuned 8 billion parameter model
 1. `llama-3.1-70b-instruct` - instruction fine-tuned 70 billion parameter model
 1. `llama-3.1-405b-instruct` - instruction fine-tuned 405 billion parameter model (flagship)
 #### Llama 3
 1. `llama-3-8b` - base pretrained 8 billion parameter model
 1. `llama-3-70b` - base pretrained 70 billion parameter model
 1. `llama-3-8b-instruct` - instruction fine-tuned 8 billion parameter model
 1. `llama-3-70b-instruct` - instruction fine-tuned 70 billion parameter model (flagship)
 #### Llama 2
 1. `llama-2-7b` - base pretrained 7 billion parameter model
 1. `llama-2-13b` - base pretrained 13 billion parameter model
 1. `llama-2-70b` - base pretrained 70 billion parameter model
 1. `llama-2-7b-chat` - chat fine-tuned 7 billion parameter model
 1. `llama-2-13b-chat` - chat fine-tuned 13 billion parameter model
 1. `llama-2-70b-chat` - chat fine-tuned 70 billion parameter model (flagship)
 %% Cell type:markdown id: tags:
 Code Llama is a code-focused LLM built on top of Llama 2 also available in various sizes and finetunes:
 %% Cell type:markdown id: tags:
 #### Code Llama
 1. `codellama-7b` - code fine-tuned 7 billion parameter model
 1. `codellama-13b` - code fine-tuned 13 billion parameter model
 1. `codellama-34b` - code fine-tuned 34 billion parameter model
 1. `codellama-70b` - code fine-tuned 70 billion parameter model
 1. `codellama-7b-instruct` - code & instruct fine-tuned 7 billion parameter model
 2. `codellama-13b-instruct` - code & instruct fine-tuned 13 billion parameter model
 3. `codellama-34b-instruct` - code & instruct fine-tuned 34 billion parameter model
 3. `codellama-70b-instruct` - code & instruct fine-tuned 70 billion parameter model
 1. `codellama-7b-python` - Python fine-tuned 7 billion parameter model
 2. `codellama-13b-python` - Python fine-tuned 13 billion parameter model
 3. `codellama-34b-python` - Python fine-tuned 34 billion parameter model
 3. `codellama-70b-python` - Python fine-tuned 70 billion parameter model
 %% Cell type:markdown id: tags:
 ## Getting an LLM
 Large language models are deployed and accessed in a variety of ways, including:
 1. **Self-hosting**: Using local hardware to run inference. Ex. running Llama on your Macbook Pro using [llama.cpp](https://github.com/ggerganov/llama.cpp).
    * Best for privacy/security or if you already have a GPU.
 1. **Cloud hosting**: Using a cloud provider to deploy an instance that hosts a specific model. Ex. running Llama on cloud providers like AWS, Azure, GCP, and others.
    * Best for customizing models and their runtime (ex. fine-tuning a model for your use case).
 1. **Hosted API**: Call LLMs directly via an API. There are many companies that provide Llama inference APIs including AWS Bedrock, Replicate, Anyscale, Together and others.
    * Easiest option overall.
 %% Cell type:markdown id: tags:
 ### Hosted APIs
 Hosted APIs are the easiest way to get started. We'll use them here. There are usually two main endpoints:
 1. **`completion`**: generate a response to a given prompt (a string).
 1. **`chat_completion`**: generate the next message in a list of messages, enabling more explicit instruction and context for use cases like chatbots.
 %% Cell type:markdown id: tags:
 ## Tokens
 LLMs process inputs and outputs in chunks called *tokens*. Think of these, roughly, as words – each model will have its own tokenization scheme. For example, this sentence...
 > Our destiny is written in the stars.
 ...is tokenized into `["Our", " destiny", " is", " written", " in", " the", " stars", "."]` for Llama 3. See [this](https://tiktokenizer.vercel.app/?model=meta-llama%2FMeta-Llama-3-8B) for an interactive tokenizer tool.
 Tokens matter most when you consider API pricing and internal behavior (ex. hyperparameters).
 Each model has a maximum context length that your prompt cannot exceed. That's 128k tokens for Llama 3.1, 4K for Llama 2, and 100K for Code Llama.
 %% Cell type:markdown id: tags:
 ## Notebook Setup
-The following APIs will be used to call LLMs throughout the guide. As an example, we'll call Llama 3.1 chat using [Grok](https://console.groq.com/playground?model=llama3-70b-8192).
+The following APIs will be used to call LLMs throughout the guide. As an example, we'll call Llama 3.1 chat using [Groq](https://console.groq.com/playground?model=llama3-70b-8192).
 To install prerequisites run:
 %% Cell type:code id: tags:
 ``` python
 import sys
 !{sys.executable} -m pip install groq
 ```
 %% Cell type:code id: tags:
 ``` python
 import os
 from typing import Dict, List
 from groq import Groq
 # Get a free API key from https://console.groq.com/keys
 os.environ["GROQ_API_KEY"] = "YOUR_GROQ_API_KEY"
 LLAMA3_405B_INSTRUCT = "llama-3.1-405b-reasoning" # Note: Groq currently only gives access here to paying customers for 405B model
 LLAMA3_70B_INSTRUCT = "llama-3.1-70b-versatile"
 LLAMA3_8B_INSTRUCT = "llama3.1-8b-instant"
 DEFAULT_MODEL = LLAMA3_70B_INSTRUCT
 client = Groq()
 def assistant(content: str):
    return { "role": "assistant", "content": content }
 def user(content: str):
    return { "role": "user", "content": content }
 def chat_completion(
    messages: List[Dict],
    model = DEFAULT_MODEL,
    temperature: float = 0.6,
    top_p: float = 0.9,
 ) -> str:
    response = client.chat.completions.create(
        messages=messages,
        model=model,
        temperature=temperature,
        top_p=top_p,
    )
    return response.choices[0].message.content
 def completion(
    prompt: str,
    model: str = DEFAULT_MODEL,
    temperature: float = 0.6,
    top_p: float = 0.9,
 ) -> str:
    return chat_completion(
        [user(prompt)],
        model=model,
        temperature=temperature,
        top_p=top_p,
    )
 def complete_and_print(prompt: str, model: str = DEFAULT_MODEL):
    print(f'==============\n{prompt}\n==============')
    response = completion(prompt, model)
    print(response, end='\n\n')
 ```
 %% Cell type:markdown id: tags:
 ### Completion APIs
 Let's try Llama 3.1!
 %% Cell type:code id: tags:
 ``` python
 complete_and_print("The typical color of the sky is: ")
 ```
 %% Cell type:code id: tags:
 ``` python
 complete_and_print("which model version are you?")
 ```
 %% Cell type:markdown id: tags:
 ### Chat Completion APIs
 Chat completion models provide additional structure to interacting with an LLM. An array of structured message objects is sent to the LLM instead of a single piece of text. This message list provides the LLM with some "context" or "history" from which to continue.
 Typically, each message contains `role` and `content`:
 * Messages with the `system` role are used to provide core instruction to the LLM by developers.
 * Messages with the `user` role are typically human-provided messages.
 * Messages with the `assistant` role are typically generated by the LLM.
 %% Cell type:code id: tags:
 ``` python
 response = chat_completion(messages=[
    user("My favorite color is blue."),
    assistant("That's great to hear!"),
    user("What is my favorite color?"),
 ])
 print(response)
 # "Sure, I can help you with that! Your favorite color is blue."
 ```
 %% Cell type:markdown id: tags:
 ### LLM Hyperparameters
 #### `temperature` & `top_p`
 These APIs also take parameters which influence the creativity and determinism of your output.
 At each step, LLMs generate a list of most likely tokens and their respective probabilities. The least likely tokens are "cut" from the list (based on `top_p`), and then a token is randomly selected from the remaining candidates (`temperature`).
 In other words: `top_p` controls the breadth of vocabulary in a generation and `temperature` controls the randomness within that vocabulary. A temperature of ~0 produces *almost* deterministic results.
 [Read more about temperature setting here](https://community.openai.com/t/cheat-sheet-mastering-temperature-and-top-p-in-chatgpt-api-a-few-tips-and-tricks-on-controlling-the-creativity-deterministic-output-of-prompt-responses/172683).
 Let's try it out:
 %% Cell type:code id: tags:
 ``` python
 def print_tuned_completion(temperature: float, top_p: float):
    response = completion("Write a haiku about llamas", temperature=temperature, top_p=top_p)
    print(f'[temperature: {temperature} | top_p: {top_p}]\n{response.strip()}\n')
 print_tuned_completion(0.01, 0.01)
 print_tuned_completion(0.01, 0.01)
 # These two generations are highly likely to be the same
 print_tuned_completion(1.0, 1.0)
 print_tuned_completion(1.0, 1.0)
 # These two generations are highly likely to be different
 ```
 %% Cell type:markdown id: tags:
 ## Prompting Techniques
 %% Cell type:markdown id: tags:
 ### Explicit Instructions
 Detailed, explicit instructions produce better results than open-ended prompts:
 %% Cell type:code id: tags:
 ``` python
 complete_and_print(prompt="Describe quantum physics in one short sentence of no more than 12 words")
 # Returns a succinct explanation of quantum physics that mentions particles and states existing simultaneously.
 ```
 %% Cell type:markdown id: tags:
 You can think about giving explicit instructions as using rules and restrictions to how Llama 3 responds to your prompt.
 - Stylization
    - `Explain this to me like a topic on a children's educational network show teaching elementary students.`
    - `I'm a software engineer using large language models for summarization. Summarize the following text in under 250 words:`
    - `Give your answer like an old timey private investigator hunting down a case step by step.`
 - Formatting
    - `Use bullet points.`
    - `Return as a JSON object.`
    - `Use less technical terms and help me apply it in my work in communications.`
 - Restrictions
    - `Only use academic papers.`
    - `Never give sources older than 2020.`
    - `If you don't know the answer, say that you don't know.`
 Here's an example of giving explicit instructions to give more specific results by limiting the responses to recently created sources.
 %% Cell type:code id: tags:
 ``` python
 complete_and_print("Explain the latest advances in large language models to me.")
 # More likely to cite sources from 2017
 complete_and_print("Explain the latest advances in large language models to me. Always cite your sources. Never cite sources older than 2020.")
 # Gives more specific advances and only cites sources from 2020
 ```
 %% Cell type:markdown id: tags:
 ### Example Prompting using Zero- and Few-Shot Learning
 A shot is an example or demonstration of what type of prompt and response you expect from a large language model. This term originates from training computer vision models on photographs, where one shot was one example or instance that the model used to classify an image ([Fei-Fei et al. (2006)](http://vision.stanford.edu/documents/Fei-FeiFergusPerona2006.pdf)).
 #### Zero-Shot Prompting
 Large language models like Llama 3 are unique because they are capable of following instructions and producing responses without having previously seen an example of a task. Prompting without examples is called "zero-shot prompting".
 Let's try using Llama 3 as a sentiment detector. You may notice that output format varies - we can improve this with better prompting.
 %% Cell type:code id: tags:
 ``` python
 complete_and_print("Text: This was the best movie I've ever seen! \n The sentiment of the text is: ")
 # Returns positive sentiment
 complete_and_print("Text: The director was trying too hard. \n The sentiment of the text is: ")
 # Returns negative sentiment
 ```
 %% Cell type:markdown id: tags:
 #### Few-Shot Prompting
 Adding specific examples of your desired output generally results in more accurate, consistent output. This technique is called "few-shot prompting".
 In this example, the generated response follows our desired format that offers a more nuanced sentiment classifer that gives a positive, neutral, and negative response confidence percentage.
 See also: [Zhao et al. (2021)](https://arxiv.org/abs/2102.09690), [Liu et al. (2021)](https://arxiv.org/abs/2101.06804), [Su et al. (2022)](https://arxiv.org/abs/2209.01975), [Rubin et al. (2022)](https://arxiv.org/abs/2112.08633).
 %% Cell type:code id: tags:
 ``` python
 def sentiment(text):
    response = chat_completion(messages=[
        user("You are a sentiment classifier. For each message, give the percentage of positive/netural/negative."),
        user("I liked it"),
        assistant("70% positive 30% neutral 0% negative"),
        user("It could be better"),
        assistant("0% positive 50% neutral 50% negative"),
        user("It's fine"),
        assistant("25% positive 50% neutral 25% negative"),
        user(text),
    ])
    return response
 def print_sentiment(text):
    print(f'INPUT: {text}')
    print(sentiment(text))
 print_sentiment("I thought it was okay")
 # More likely to return a balanced mix of positive, neutral, and negative
 print_sentiment("I loved it!")
 # More likely to return 100% positive
 print_sentiment("Terrible service 0/10")
 # More likely to return 100% negative
 ```
 %% Cell type:markdown id: tags:
 ### Role Prompting
 Llama will often give more consistent responses when given a role ([Kong et al. (2023)](https://browse.arxiv.org/pdf/2308.07702.pdf)). Roles give context to the LLM on what type of answers are desired.
 Let's use Llama 3 to create a more focused, technical response for a question around the pros and cons of using PyTorch.
 %% Cell type:code id: tags:
 ``` python
 complete_and_print("Explain the pros and cons of using PyTorch.")
 # More likely to explain the pros and cons of PyTorch covers general areas like documentation, the PyTorch community, and mentions a steep learning curve
 complete_and_print("Your role is a machine learning expert who gives highly technical advice to senior engineers who work with complicated datasets. Explain the pros and cons of using PyTorch.")
 # Often results in more technical benefits and drawbacks that provide more technical details on how model layers
 ```
 %% Cell type:markdown id: tags:
 ### Chain-of-Thought
 Simply adding a phrase encouraging step-by-step thinking "significantly improves the ability of large language models to perform complex reasoning" ([Wei et al. (2022)](https://arxiv.org/abs/2201.11903)). This technique is called "CoT" or "Chain-of-Thought" prompting.
 Llama 3.1 now reasons step-by-step naturally without the addition of the phrase. This section remains for completeness.
 %% Cell type:code id: tags:
 ``` python
 prompt = "Who lived longer, Mozart or Elvis?"
 complete_and_print(prompt)
 # Llama 2 would often give the incorrect answer of "Mozart"
 complete_and_print(f"{prompt} Let's think through this carefully, step by step.")
 # Gives the correct answer "Elvis"
 ```
 %% Cell type:markdown id: tags:
 ### Self-Consistency
 LLMs are probablistic, so even with Chain-of-Thought, a single generation might produce incorrect results. Self-Consistency ([Wang et al. (2022)](https://arxiv.org/abs/2203.11171)) introduces enhanced accuracy by selecting the most frequent answer from multiple generations (at the cost of higher compute):
 %% Cell type:code id: tags:
 ``` python
 import re
 from statistics import mode
 def gen_answer():
    response = completion(
        "John found that the average of 15 numbers is 40."
        "If 10 is added to each number then the mean of the numbers is?"
        "Report the answer surrounded by backticks (example: `123`)",
    )
    match = re.search(r'`(\d+)`', response)
    if match is None:
        return None
    return match.group(1)
 answers = [gen_answer() for i in range(5)]
 print(
    f"Answers: {answers}\n",
    f"Final answer: {mode(answers)}",
    )
 # Sample runs of Llama-3-70B (all correct):
 # ['60', '50', '50', '50', '50'] -> 50
 # ['50', '50', '50', '60', '50'] -> 50
 # ['50', '50', '60', '50', '50'] -> 50
 ```
 %% Cell type:markdown id: tags:
 ### Retrieval-Augmented Generation
 You'll probably want to use factual knowledge in your application. You can extract common facts from today's large models out-of-the-box (i.e. using just the model weights):
 %% Cell type:code id: tags:
 ``` python
 complete_and_print("What is the capital of the California?")
 # Gives the correct answer "Sacramento"
 ```
 %% Cell type:markdown id: tags:
 However, more specific facts, or private information, cannot be reliably retrieved. The model will either declare it does not know or hallucinate an incorrect answer:
 %% Cell type:code id: tags:
 ``` python
 complete_and_print("What was the temperature in Menlo Park on December 12th, 2023?")
 # "I'm just an AI, I don't have access to real-time weather data or historical weather records."
 complete_and_print("What time is my dinner reservation on Saturday and what should I wear?")
 # "I'm not able to access your personal information [..] I can provide some general guidance"
 ```
 %% Cell type:markdown id: tags:
 Retrieval-Augmented Generation, or RAG, describes the practice of including information in the prompt you've retrived from an external database ([Lewis et al. (2020)](https://arxiv.org/abs/2005.11401v4)). It's an effective way to incorporate facts into your LLM application and is more affordable than fine-tuning which may be costly and negatively impact the foundational model's capabilities.
 This could be as simple as a lookup table or as sophisticated as a [vector database]([FAISS](https://github.com/facebookresearch/faiss)) containing all of your company's knowledge:
 %% Cell type:code id: tags:
 ``` python
 MENLO_PARK_TEMPS = {
    "2023-12-11": "52 degrees Fahrenheit",
    "2023-12-12": "51 degrees Fahrenheit",
    "2023-12-13": "51 degrees Fahrenheit",
 }
 def prompt_with_rag(retrived_info, question):
    complete_and_print(
        f"Given the following information: '{retrived_info}', respond to: '{question}'"
    )
 def ask_for_temperature(day):
    temp_on_day = MENLO_PARK_TEMPS.get(day) or "unknown temperature"
    prompt_with_rag(
        f"The temperature in Menlo Park was {temp_on_day} on {day}'",  # Retrieved fact
        f"What is the temperature in Menlo Park on {day}?",  # User question
    )
 ask_for_temperature("2023-12-12")
 # "Sure! The temperature in Menlo Park on 2023-12-12 was 51 degrees Fahrenheit."
 ask_for_temperature("2023-07-18")
 # "I'm not able to provide the temperature in Menlo Park on 2023-07-18 as the information provided states that the temperature was unknown."
 ```
 %% Cell type:markdown id: tags:
 ### Program-Aided Language Models
 LLMs, by nature, aren't great at performing calculations. Let's try:
 $$
 ((-5 + 93 * 4 - 0) * (4^4 + -7 + 0 * 5))
 $$
 (The correct answer is 91383.)
 %% Cell type:code id: tags:
 ``` python
 complete_and_print("""
 Calculate the answer to the following math problem:
 ((-5 + 93 * 4 - 0) * (4^4 + -7 + 0 * 5))
 """)
 # Gives incorrect answers like 92448, 92648, 95463
 ```
 %% Cell type:markdown id: tags:
 [Gao et al. (2022)](https://arxiv.org/abs/2211.10435) introduced the concept of "Program-aided Language Models" (PAL). While LLMs are bad at arithmetic, they're great for code generation. PAL leverages this fact by instructing the LLM to write code to solve calculation tasks.
 %% Cell type:code id: tags:
 ``` python
 complete_and_print(
    """
    # Python code to calculate: ((-5 + 93 * 4 - 0) * (4^4 + -7 + 0 * 5))
    """,
 )
 ```
 %% Cell type:code id: tags:
 ``` python
 # The following code was generated by Llama 3 70B:
 result = ((-5 + 93 * 4 - 0) * (4**4 - 7 + 0 * 5))
 print(result)
 ```
 %% Cell type:markdown id: tags:
 ### Limiting Extraneous Tokens
 A common struggle with Llama 2 is getting output without extraneous tokens (ex. "Sure! Here's more information on..."), even if explicit instructions are given to Llama 2 to be concise and no preamble. Llama 3.x can better follow instructions.
 Check out this improvement that combines a role, rules and restrictions, explicit instructions, and an example:
 %% Cell type:code id: tags:
 ``` python
 complete_and_print(
    "Give me the zip code for Menlo Park in JSON format with the field 'zip_code'",
 )
 # Likely returns the JSON and also "Sure! Here's the JSON..."
 complete_and_print(
    """
    You are a robot that only outputs JSON.
    You reply in JSON format with the field 'zip_code'.
    Example question: What is the zip code of the Empire State Building? Example answer: {'zip_code': 10118}
    Now here is my question: What is the zip code of Menlo Park?
    """,
 )
 # "{'zip_code': 94025}"
 ```
 %% Cell type:markdown id: tags:
 ## Additional References
 - [PromptingGuide.ai](https://www.promptingguide.ai/)
 - [LearnPrompting.org](https://learnprompting.org/)
 - [Lil'Log Prompt Engineering Guide](https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/)
 %% Cell type:markdown id: tags:
 ## Author & Contact
 Edited by [Dalton Flanagan](https://www.linkedin.com/in/daltonflanagan/) (dalton@meta.com) with contributions from Mohsen Agsen, Bryce Bortree, Ricardo Juan Palma Duran, Kaolin Fire, Thomas Scialom.