Why Llama 3.1 405B Is So Much Better Than GPT-4o And Claude 3.5 Sonnet Here The Result
Source: tai_blog

the AI news in the past 7 days has been insane  with so much happening in the world of AI   in this video  were diving into some of the latest AI developments from major players like Llama 3.1 405B  GPT-4o and Claude 3.5 Sonnet   Llama 3.1 405B is the first open-source model that performs on par with leading proprietary AI models in general knowledge  steerability  math  tool use  and multilingual translation  among other capabilities.   Meta announced the launch of Llama 3.1  which is the largest open-source AI model to date  and has surpassed OpenAIs GPT-4o and Anthropics Claude 3.5 Sonnet in multiple benchmark tests!   In this step-by-step guide  we will cover what Llama 3.1 405B is  how to use Llama 3.1 405B locally  and why Llama 3.1 405B is so much better than GPT-4o and Claude 3.5 Sonnet.   I highly recommend you watch this video to the end is a game changer in your chatbot that will realize the power of Llama 3.1 405B!   If you like this topic and you want to support me:   Clap my article 50 times; that will really help me out.U+1F44FFollow me on Medium and subscribe to get my latest articleU+1FAF6Follow me on my YouTube channelMore info on my discordLlama 3.1 405B is Metas largest model  trained with over 15 trillion tokens. For this  Meta optimized the entire training stack and trained it on more than 16 000 H100 GPUs  making it the first Llama model trained at this scale.   According to Meta  this version of the original model (Llama 1 and Llama 2) has 128K context length  improved reasoning and coding capabilities. Meta has also upgraded both multilingual 8B and 70B models.   Key Features of Llama 3.1 40 5B:Llama 3.1 comes with a host of features and capabilities that appeal to The users  such as:   RAG & tool use  Meta states that you can use Llama system components to extend the model using zero-shot tool use and build agentic behaviors with RAG.   Multi-lingual  Llama 3 naturally supports multilingual processing. The pre-training data includes about 50% multilingual tokens and can process and understand multiple languages.   Programming and Reasoning  Llama 3 has powerful programming capabilities  generating high-quality code with a strong understanding of syntax and logic. It can create complex code structures and perform well in various programming tasks. Llama 3 excels in logical reasoning  problem-solving  analysis  and inference. It handles complex logical tasks and solves intricate problems effectively.   Multimodal Models  Multimodal models have been developed that support image recognition  video recognition  and speech understanding capabilities  but these models are still under development and have not yet been widely released.   Benchmark ResultsMeta compared the Llama 3.1 405B model with models such as GPT-4  GPT-4o  and Claude 3.5 sonnet. The results showed that Llama 3.1 performed better than GPT-4o and Claude 3.5 sonnet on test data sets such as mathematical reasoning  complex reasoning  and Multilingual support and its long text processing ability is also excellent  receiving 95.2 points in zero scrolls quality.   it falls short compared to Claude 3.5 sonnet in tool utilization ability (BFCL  Nexus).   Although the performance on test data sets such as Multi-task Language Understanding  Human Eval  and MATH is slightly inferior to the closed-source model  the score difference is not large.   In addition  manual evaluation results show that the output performance of the Llama 3.1 405B model is comparable to GPT-4 and Claude 3.5 Sonnet  and slightly inferior to GPT-4o.   Just looking at this benchmark score  it seems to be quite promising.The benchmark results show that llama 3.1 405B is an excellent language model with strong language modeling capabilities  mathematical reasoning capabilities  complex reasoning and long text processing capabilities. however  there is still room for improvement in tool utilization capabilities and multilingual support.   Now that Ive introduced the benchmark scores  Im going to try using them and see how they perform.   How to Use Llama 3.1 40 5B Locally?Ollama is the fastest way to get up and running With local language models We recommend trying Llama 3.1 8b  which is impressive for its size and will perform well on most hardware.   Download Ollama here (it should walk you through the rest of these steps)Open a terminal and run ollama run llama3.1-8bGroq is now hosting the Llama 3.1 models  including the 70B and 8B models. Earlier  they offered the largest 405B model  but it has been temporarily removed due to high traffic and server issues. Meanwhile  the Llama 3.1 70B and 8B models are still available and can generate responses at an impressive speed of 250 tokens per second.   head to groq.com and sign in with a free account. Next  choose the llama 3.1 model from the drop-down menu.   Llama 3.1 405B vs Claude 3.5 Sonnet vslets compare Claude 3.5 Sonnet and GPT-4o and verify how much performance Llama 3.1 405B has.   This time  we will verify the following items.   Coding   I went to the LeetCode platform and selected a medium-level problem related to sorting algorithms. The problem  titled Top K Frequent Elements problem involves finding the K most frequent elements in a given dataset to test out.   Given an integer array nums and an integer k  return the k most frequent elements. You may return the answer in any order. Example 1 Input: nums = [1 1 1 2 2 3]  k = 2 Output: [1 2] or [2 1] Example 2 Input: nums = [1]  k = 1 Output: [1] Constraints 1 <= nums.length <= 105 -104 <= nums[i] <= 104Llama 3.1 405B   ```python import heapq from collections import Counter def topKFrequent(nums  k):  Returns the k most frequent elements in the given array. Args: nums (list): The input array of integers. k (int): The number of most frequent elements to return. Returns: list: The k most frequent elements in any order.  # Count the frequency of each element count = Counter(nums) # Use a heap to find the k most frequent elements return heapq.nlargest(k  count.keys()  key=count.get) ''' Here's an explanation of the code: 1. We use the `Counter` class from the `collections` module to count the frequency of each element in the input array `nums`. This gives us a dictionary `count` where the keys are the unique elements in `nums` and the values are their frequencies. 2. We use the `heapq.nlargest` function to find the k most frequent elements. We pass `k` as the first argument  `count.keys()` as the second argument (which gives us an iterable of the unique elements in `nums`)  and `key=count.get` as the third argument (which tells `heapq.nlargest` to use the frequency of each element as the key for comparison). 3. The `heapq.nlargest` function returns a list of the k most frequent elements in any order. Example use cases: ```python print(topKFrequent([1 1 1 2 2 3]  2)) # [1  2] or [2  1] print(topKFrequent([1]  1)) # [1] ``` Note that this solution has a time complexity of O(n log k) where n is the length of the input array  because we use a heap to find the k most frequent elements. This is efficient for large inputs because the heap operations are logarithmic in the size of the heap.GPT-4o   To solve the problem of finding the k most frequent elements in an integer array  you can use a combination of a dictionary to count the occurrences and a heap to keep track of the top k elements. Heres a step-by-step approach: Count the Frequencies: Use a dictionary to count the frequency of each element in the array. Use a Heap for Top k Elements: Use a min-heap to keep track of the top k elements based on their frequencies. Pythons heapq library is useful for this purpose. Extract the Elements from the Heap: After processing the array  the heap will contain the k most frequent elements. Heres the implementation of the above logic: import heapq from collections import Counter def topKFrequent(nums  k): # Step 1: Count the frequency of each element count = Counter(nums) # Step 2: Use a heap to keep track of the top k elements heap = [] for num  freq in count.items(): heapq.heappush(heap  (freq  num)) if len(heap) > k: heapq.heappop(heap) # Step 3: Extract the elements from the heap return [num for freq  num in heap] # Example 1 nums1 = [1  1  1  2  2  3] k1 = 2 print(topKFrequent(nums1  k1)) # Output: [1  2] or [2  1] # Example 2 nums2 = [1] k2 = 1 print(topKFrequent(nums2  k2)) # Output: [1]Claude 3.5 Sonnet   from collections import Counter import heapq def topKFrequent(nums  k): # Count the frequency of each number count = Counter(nums) # Use a min heap to keep track of the k most frequent elements heap = [] for num  freq in count.items(): if len(heap) < k: heapq.heappush(heap  (freq  num)) elif freq > heap[0][0]: heapq.heappop(heap) heapq.heappush(heap  (freq  num)) # Extract the numbers from the heap return [num for freq  num in heap] # Test the function print(topKFrequent([1 1 1 2 2 3]  2)) # Expected output: [1  2] print(topKFrequent([1]  1)) # Expected output: [1]All three solutions were correct when I ran the code on Visual Studio code. However  Llama3s solution was the most concise because it directly used heapq.nlargest.   In contrast  GPT-4 and Claude 3.5 Sonnet managed the heap manually. Llama3's solution might be slightly more efficient and straightforward due to its direct use of heapq.nlargest.   Overall  each solution solves the problem correctly  but Llama3's approach stands out for its simplicity and direct use of Python's heap functions   MathematicsThen I compare and verify mathematical abilities  we will have participants answer probability questions  a key concept in data science.   Alice has 2 kids and one of them is a girl. What is the probability that the other child is also a girl? You can assume that there are an equal number of males and females in the world. A) 0.5 B) 0.25 C) 0.333 D) 0.75 Llama 3.1 405B   This is a classic problem! At first glance  it might seem like the probability is 50% (1/2)  since the other child could be either a boy or a girl. However  this is actually a subtle mistake. When we say one of the children is a girl  we're not specifying which child is the girl. This means that the girl could be either the first child or the second child. Let's analyze the possible scenarios: 1. The first child is a girl  and the second child is a boy (GB). 2. The first child is a girl  and the second child is a girl (GG). 3. The first child is a boy  and the second child is a girl (BG). Notice that we've already accounted for the fact that one of the children is a girl. Now  we want to find the probability that the other child is also a girl. Out of the three possible scenarios  only one (GG) has both children as girls. However  we need to consider that the girl could be either the first or the second child  so we have two possible ways to get the GG scenario: the first child is a girl and the second child is a girl  or the first child is a girl (but we don't know that yet) and the second child is a girl (which we do know). So  when we say one of the children is a girl  we're essentially combining the GB and BG scenarios  as well as the GG scenario. This means we have a total of 3 possible scenarios  and 1 out of those 3 has both children as girls (GG). However  since we have already established that one of the children is a girl  the sample space is reduced to the 3 scenarios mentioned earlier (GB  GG  and BG)  with each scenario being equally likely. And out of those 3 scenarios  1/3 of them have the other child also being a girl. Therefore  the probability that the other child is also a girl is 1/3  or approximately 33.33%. It's worth noting that this problem is often counterintuitive  and many people initially think the probability is 50%. However  by carefully analyzing the possible scenarios and considering the reduced sample space  we can see that the correct probability is indeed 1/3.GPT-4o   to solve this problem  we need to analyze the possible scenarios and apply basic probability principles. Given that Alice has two children and we know one of them is a girl  we want to determine the probability that the other child is also a girl. List all possible combinations of two children: Girl  Girl (GG) Girl  Boy (GB) Boy  Girl (BG) Boy  Boy (BB) Exclude the combinations that do not meet the given condition (one of the children is a girl): The combination Boy  Boy (BB) does not meet the condition because neither child is a girl. So  we are left with: Girl  Girl (GG) Girl  Boy (GB) Boy  Girl (BG) Determine the probability: We now have three equally likely combinations: GG  GB  and BG. In two of these combinations (GB and BG)  the other child is a boy. In one of these combinations (GG)  the other child is a girl. Thus  the probability that the other child is also a girl is: Probability= Number of favorable outcomes /Total possible outcomes = 1/3  Therefore  the probability that the other child is also a girl is 1/3 or approximately 33.33%.Claude 3.5 Sonnet   Let's approach this step-by-step: First  let's consider all possible combinations of two children: Boy-Boy (BB) Boy-Girl (BG) Girl-Boy (GB) Girl-Girl (GG) We're told that Alice has at least one girl. This eliminates the BB possibility. So we're left with three equally likely possibilities: Boy-Girl (BG) Girl-Boy (GB) Girl-Girl (GG) Out of these three possibilities  only one (GG) satisfies the condition that the other child is also a girl. Therefore  the probability is: 1 (favorable outcome) / 3 (total possible outcomes) = 1/3 Thus  the probability that Alice's other child is also a girl is 1/3 or about 33.33%.All three models correctly identify that the probability is 1/3. Llama 3.1 gives the most detailed and thorough explanation  which is helpful for readers who might not know the problem well.   GPT-4 offers a clear and easy-to-understand explanation but lacks some details that could help readers new to the concept.   Claude 3.5 Sonnet strikes a balance between clarity and detail  giving a straightforward answer   Conclusion:The release of Llama 3.1  especially the strong performance of the 405B parameter model and the introduction of knowledge distillation  has greatly improved open-source language models. For the first time in recent years  the performance of open-source language models is very close to that of closed-source business models. This shows that in the future  Metas Llama series models will likely remain the top choice for developing open-source language models   U+1F9D9U+2642 I am AI application expert! If you want to collaborate on a project  drop an inquiry here or Book a 1-On-1 Consulting Call With M.   U+1F4DAFeel free to check out my other articles:   Why OpenHermes-2.5 Is So Much Better Than GPT-4 And LLama2 13B  Here The Resultthe AI news in the past 7 days has been insane with so much happening in the world of AIpub.towardsai.net   Why OPENCHAT Model is So Much Better Than ChATGPT?Hugging Face recently announced their new open-source Large language model  OpenChat  which is a fine-tuned version ofai.plainenglish.io   Why Command R+ is Much Better Than Mistral Large and Offers the Same Level of Performance asWhen I look at my Twitter timeline these past few days  I see quite a few tweets about  Command R+  its a Largepub.towardsai.net