In our previous blog Introduction to Mistral 7B, we explored the revolutionary advancements of Mistral AI, showcasing its innovation ability. You can refer to our blog Introduction to Mistral 7B. Building upon that knowledge, join us on a journey into Mistral 7B’s advanced abilities and smart methods. We’ll compare it to the demanding LLaMA 2 model, uncovering what sets Mistral AI apart.

Comparison with LLaMA 2

Mistral AI team stated that Mistral AI 7b beats LLaMA 2 13B on all benchmarks.

Despite its smaller parameter count, Mistral 7B showcases superior performance when compared to LLaMA 2 13B. It exhibits remarkable efficiency, standing toe-to-toe with the larger LLaMA 13B model across various evaluation metrics.

Notably, in the Massive Multitask Language Understanding (MMLU) test encompassing 57 diverse subjects, Mistral 7B achieves an impressive 60.1% accuracy. In contrast, LLaMA 2 7B and 13B trail behind, recording accuracies around 44% and 55% respectively.

The efficiency of Mistral 7B extends beyond performance and resides in its innovative methodologies. Techniques such as grouped-query attention (GQA) and Sliding Window Attention (SWA) enable swifter inference, enhancing the model’s speed and cost-effectiveness in handling extensive sequences. These innovations significantly reduce overall hardware requirements while preserving output quality, marking Mistral 7B as a distinctive player in the realm of AI models.

In terms of size, Mistral 7B boasts 7.3 billion parameters, while LLaMA 2 13B escalates to 13 billion parameters, indicating a significant parameter discrepancy between the two models. Regarding hardware requirements and speed, Mistral 7B runs faster on less powerful hardware, making it more cost-effective. In contrast, LLaMA 2 13B, despite slower inference speed, demands higher resources, limiting its accessibility due to these elevated hardware requirements. While testing both models, we felt that Mistral 7B model is taking less time (average time 13 to 20 seconds) to respond than the LLaMA 2 13B (average time 33 to 35 seconds)

Here’s a comparison of the resource requirements of LLaMA 2 13B and Mistral 7B:

Resource Requirements

ModelVRAM MinimumGPU
Mistral 7B16 GBT4/ V100 (16-GB VRAM)
LLaMA 2 7B30 GBA100 (40-GB VRAM)
LLaMA 2 13B50 GBA100 (40-GB VRAM)

Result Comparison

Test 1
Q:1 What are the health benefits and potential downsides of a vegan diet?

LLaMA-2-13b-chat response: A well-planned vegan diet can provide numerous health benefits, such as reducing the risk of heart disease, type 2 diabetes, and certain types of cancer. It can also promote weight loss, improve digestion, and boost immune function. However, a vegan diet that is high in processed foods or lacks essential nutrients may lead to deficiencies in vitamin B12, iron, zinc, and omega-3 fatty acids. Proper planning and consultation with a registered dietitian can help mitigate these risks and ensure.

Mistral-7b-instruct-v0.1 response: A vegan diet has several health benefits, including lower risk of heart disease, type 2 diabetes, and certain types of cancer. It can also aid in weight loss and improve digestion. However, it’s important to note that vegan diets can be deficient in certain nutrients, such as vitamin B12, iron, calcium, and omega-3 fatty acids, so careful planning is necessary to ensure adequate intake. Additionally, some people may experience nutritional deficiencies, muscle loss, or social isolation on a vegan diet.

Observation: Mistral 7B provided a more productive answer compared to Llama 2 13B

Test 2
Q2. I’m thinking about adopting a puppy. What should I know beforehand?

LLaMA-2-13b-chat response: Before adopting a puppy, it’s essential to consider your lifestyle, living situation, and financial stability. Puppies require regular training, exercise, and veterinary care, which can be time-consuming and costly. Ensure you have enough space for a puppy, and that your family is comfortable with the idea of having a new furry member. Research different breeds and their needs, and prepare yourself for the responsibilities that come with owning a pet.

Mistral-7b-instruct-v0.1 response: Adopting a puppy can be a rewarding experience, but it also comes with responsibilities. Before adopting, make sure you have the time and resources to provide a loving home and proper care for your new furry friend. Research breeds and their specific needs, and be prepared for the commitment of training and socialization. Consider the costs of food, toys, veterinary care, and grooming. Also, make sure you are allowed to have a pet in your living situation.

Observation: Mistral 7B offered a somewhat better response than LLaMa 2 13B.

Test 3
Q3: How to kill a Linux process?

LLaMA-2-13b-chat response: There are several ways to kill a Linux process, here are a few methods:

  1. Using the kill command:
kill <PID>

Replace <PID> with the process ID of the process you want to kill. For example:

kill 1234

This will send a signal to the process with the specified PID, asking it to terminate. If the process does not respond, you may need to use a more forceful method, such as kill -9.

  1. Using the pkill command:
pkill <PID>

This command is similar to kill, but it will also

Mistral-7b-instruct-v0.1 response:
To kill a process on Linux, you can use the kill command. Here are the steps to do it:

  1. Find the Process ID (PID) of the process you want to kill. You can use the following commands to list the running processes and their PIDs:
  • ps aux | grep <process_name>
  • top or htop (this will show you a live list of running processes with their PIDs)
  1. Once you have identified the PID of the process you want to kill, use the kill command followed by the PID to send the signals that will terminate the process:
  • To simply send a SIGTERM signal (equivalent to clicking the “X” button on a window), type: kill <PID>
  • To send a more forceful SIGKILL signal, type: kill -9 <PID>

For example, if you want to kill a process named my_process with the PID of 12345, you can use the following commands:

# Display the list of running processes and find the PID of my_process
$ ps aux | grep my_process

# Once you have identified the PID, send a SIGTERM signal to terminate the process
$ kill 12345

# If the SIGTERM signal doesn't work, you can try sending a SIGKILL signal instead
$ kill -9 12345

Note that it’s generally a good idea to try sending a SIGTERM signal first, as it’s less likely to cause data loss. Also, be aware that killing a process can disrupt the running system if the process is essential or has open files or locks that other processes depend on.

Observation: Mistral 7B displayed a superior response compared to Llama 2 13B.

Test 4
Q4. How to check if the number is Krishnamurti or not using Python.

LLaMA-2-13b-chat response:
To check if a number is a Krishnamurti number or not, you can use the following approach:

  1. Check if the number is a perfect square. A perfect square is a number that can be expressed as the square of an integer, such as 4, 9, 16, 25, etc. You can use the is_perfect_square() function from the math.gcd module to check if a number is a perfect square.
  2. If the number is not a perfect square, check if it is a sum of two perfect squares. You can use the sum_of_squares() function from the math.gcd module to check if a number is a sum of two perfect squares.
  3. If the number is not a sum of two perfect squares, it is not a Krishnamurti number.

Here is some sample Python code that demonstrates this approach:

from math import is_perfect_square, sum_of_squares

def is_krishnamurti(n):
if is_perfect_square(n):
return True
elif sum_of_squares(n) == n:
return True
else:
return False

# Test the function with some examples
print(is_krishnamurti(4)) # True
print(is_krishnamurti(9)) # True
print(is_krishnamurti(16)) # True
print(is_krishnamurti(25)) # True
print(is_krishnamurti(36)) # False
print(is_krishnamurti(49)) # False

Mistral-7b-instruct-v0.1 response:
I am assuming you mean checking if a number follows the rules of the Krishna Murthy number sequence, also known as the Kautsa Yajneswariyam number sequence. This sequence is generated by starting with 0, 1, and applying the following recursive rule: the next number is the sum of the previous two numbers multiplied by 2 and then adding 1.

Here is a Python function to check if a number is part of the Krishna Murthy sequence:

def is_krishna_murthy(n):
    # First few numbers are guaranteed to be in the sequence
    if n < 4:
        return True

    # Check the sequence starting from the third number
    a, b = 0, 1
    while len(str(a)) < len(str(n)) or len(str(b)) < len(str(n)):
        a, b = b, a * 2 + 1

    return a == n

If this function returns True, the given number is a part of the Krishna Murthy sequence. Otherwise, it returns False.

Observation: Both models gave incorrect answers to this question.

Test 5
Q5: What came before the Big Bang?

LLaMA-2-13b-chat response:
The question of what came before the Big Bang is a topic of ongoing debate and research among scientists and cosmologists. The Big Bang theory suggests that the universe began as an infinitely hot and dense point around 13.8 billion years ago, and has been expanding and cooling ever since. However, the theory does not provide an explanation for what caused the Big Bang or what existed before the universe began.

There are several theories and hypotheses that attempt to explain what might have come before the Big Bang, but none of them have been proven conclusively. Some of these theories include:

  1. The multiverse hypothesis: This theory suggests that our universe is just one of many universes that exist in a larger multidimensional space. The Big Bang could have been the result of a collision or interaction between our universe and another universe in the multidimensional space.
  2. The eternal inflation theory: This theory suggests that our universe is just one small part of an endlessly expanding and contracting multiverse. The Big Bang could have been the result of a previous contraction of the multiverse, which eventually led to the formation of our universe.
  3. The cyclic model: This theory suggests that the universe undergoes cycles of expansion and contraction, with the Big Bang being the result of a previous contraction.
  4. The quantum fluctuation theory: This theory suggests that the Big Bang was the result of a quantum fluctuation in the vacuum energy of the universe.

Overall, while there are many theories and hypotheses that attempt to explain what came before the Big Bang, none of them have been proven conclusively. The origins of the universe remain one of the greatest mysteries in modern science.

Mistral-7b-instruct-v0.1 response:
According to current scientific understanding, the Big Bang theory is the best explanation for the origin and evolution of the universe. It describes how the universe began as a hot, dense point nearly 13.8 billion years ago. This singularity was not a place or a thing in the way we understand those terms, but rather a state of extreme density and temperature. There was no time or space before the Big Bang, as those concepts only applied to the universe after it began expanding.

Some alternative theories propose the existence of a multi-verse or a cyclic universe, but these theories are not widely accepted in the scientific community and there is currently no empirical evidence to support them. The origin and nature of the universe is still an area of active research and debate in cosmology.

Observation: Llama 2 13B outperformed Mistral 7B

Test 6
Q6. When will the next solar storm happen?

LLaMA-2-13b-chat response:
Solar storms, also known as solar flares and coronal mass ejections (CMEs), are complex and unpredictable events that can impact Earth’s magnetic field and potentially disrupt our technological infrastructure. While we can’t predict exactly when the next solar storm will occur, we can provide some information about the typical frequency and intensity of these events.

Solar storms are caused by the release of magnetic energy from the sun’s corona, which can lead to the formation of CMEs and solar flares. These events can occur at any time, but they are more common during periods of high solar activity, such as during solar maxima.

The current solar cycle, known as Solar Cycle 25, is expected to peak in the mid-2020s, and it is likely that we will see an increase in the frequency and intensity of solar storms during this time. However, it’s important to note that solar storms can occur at any time, even during periods of low solar activity.

To give you an idea of the typical frequency and intensity of solar storms, here are some statistics:

  • CMEs: On average, there are about 100 CMEs per year, but only a small fraction of these are strong enough to cause significant disruptions on Earth.
  • Solar flares: On average, there are about 200 solar flares per year, with the majority of them being minor and not causing any significant disruptions.
  • Solar storms: On average, there are about 5-10 solar storms per year, with the majority of them being mild and not causing any significant disruptions.

It’s important to note that the frequency and intensity of solar storms can vary significantly from one solar cycle to the next, and even within a single solar.

Mistral-7b-instruct-v0.1 response:
Predicting the exact date and time of the next significant solar storm can be challenging, as it depends on various solar activity conditions that can change rapidly. Solar storms, also known as solar flares or coronal mass ejections (CMEs), are caused by explosive events in the Sun’s magnetic field.

The Sun experiences an 11-year solar cycle, during which solar activity generally increases and then decreases. The most recent solar maximum, which is associated with increased solar activity, occurred around 2014. Solar activity is currently decreasing towards solar minimum.

However, even though solar activity is generally decreasing, solar storms can still occur at any time, even during solar minimum. According to NASA, the last major solar storm, a CME, occurred on September 10, 2017.

To monitor solar activity and predict potential solar storms, scientists use various observatories, such as the Solar Dynamics Observatory (SDO), the Solar Terrestrial Relations Observatory (STEREO), and the Helioseismic and Magnetic Imager (HMI). These observatories constantly monitor the Sun for signs of solar activity and can provide early warnings of potential solar storms.

While it’s not possible to predict the exact date and time of the next solar storm, scientists can provide estimates of the likelihood and potential impact of solar activity based on current observations and historical data. The National Oceanic and Atmospheric Administration (NOAA) and the European Space Agency (ESA) both maintain space weather forecasts that provide information on solar activity and potential solar storms.

If you’re interested in receiving alerts for potential solar storms, you can sign up for space weather alerts from NOAA or ESA. These alerts provide advance warning of potential solar activity that could impact Earth’s magnetic field and power grids.

Observation: However, Mistral 7B provided the correct answer to the question, whereas Llama 2 13B somehow indicated the expected time as 2020, which is not entirely accurate.

Conclusion

In a comparative evaluation across various questions, Mistral 7B consistently showcased superior performance over Llama 2 13B in the majority of queries, displaying more accuracy and proficiency in questions. Mistral 7B seems to be a more proficient performer in this assessment.

If you want to test the LLama 2 13B model, then you can check out our blog How To Run LLaMA-13B Or OpenChat-8192 On A Single GPU.

Categories: Natural Language Processing NLP

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