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July 24, 2024
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Text Mistral 7BMixtral 8x7BMulti-modal Mistral Medium 3Mistral Small 3.2Magistral Small 1.2Magistral Medium 1.2
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Mistral AI offers open-source AI models, with an emphasis on compute efficiency, utility, and trustworthiness. The main product, Mistral 7B, stands as a small yet powerful model adaptable to an array of use-cases.

Unlike other solutions, Mistral-7B provides natural coding abilities and it is remarkable for its adaptability. The product comes with weights and sources, allowing for maximum customization without requiring user data.

The firm follows a principle of open models, believing in the value of open science, community participation, and free software. As part of this commitment, many of their products, models, and deployment tools are released under liberal licenses.

Mistral AI encourages contributions from the user community and aims at driving AI forward by addressing challenging problems. Along with operating as an advanced AI solution, Mistral AI works well on any cloud and even gaming GPUs, making it broadly accessible.

The company maintains high scientific standards with a creative team that combines a strong research focus with a dynamic business mindset.

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Mistral AI Large 2
Jul 24, 2024
Mistral Large 2 has a 128k context window and supports dozens of languages including French, German, Spanish, Italian, Portuguese, Arabic, Hindi, Russian, Chinese, Japanese, and Korean, along with 80+ coding languages including Python, Java, C, C++, JavaScript, and Bash.

Mistral Large 2 is designed for single-node inference with long-context applications in mind โ€“ its size of 123 billion parameters allows it to run at large throughput on a single node. We are releasing Mistral Large 2 under the Mistral Research License, that allows usage and modification for research and non-commercial usages. For commercial usage of Mistral Large 2 requiring self-deployment, a Mistral Commercial License must be acquired by contacting us.

Mistral Large 2 sets a new frontier in terms of performance / cost of serving on evaluation metrics. In particular, on MMLU, the pretrained version achieves an accuracy of 84.0%, and sets a new point on the performance/cost Pareto front of open models.
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Pricing

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$14.99
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Reviews

4.4
Average from 5 ratings.
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Comments(3)
Rated it
oh mistral, its one of the best models out there, but maybe it feels like it needs longer to wake up. until you get used to it, it's cool and usually has very low censoring.
Rated it
Very efficient. Very time-to-output effective. Threw at it some reasoning challenges other AIs usually get wrong, but this one got it right.
Rated it
I just used for a couple of scientific tasks and its output was as good as ChatGPT 4 and Gemini Pro. This is an interesting tool and I will be exploring it further

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Prompts & Results

Math Analogy Synthesizer

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You are given a mathematical concept, explanation, or expression. Create an analogy using a real-world scenario that mirrors the logic or structure of the original concept.

Use clear, grounded language and map each mathematical idea to a relatable object, process, or action. Stick to familiar domains like tools, containers, movement, balancing, or recipes.

If the concept is straightforward, return a clean analogy paragraph.
If the concept is more complex or layered, annotate key words or actions using short brackets showing what math concept or symbol they refer to. These brackets can include operation names (such as add, subtract, limit), symbols (+, โˆ’, โˆซ, f(x)), or concept labels like domain, output, or rate of change.

Annotation Rules:

Use brackets only when helpful

Keep each annotation under three words

Place immediately after the word or phrase it applies to

Maintain sentence flow

Do not explain annotations separately

Output format:
Analogy: [one paragraph with optional bracketed annotations]

Examples:

Input: Limit
Analogy: You're walking toward a wall but never touching it. Each step takes you halfway closer. No matter how close you get, you never actually arrive (limit).

Input: Add two numbers, then subtract a third
Analogy: You place one book on a shelf, then add another (add). Later, you remove one (subtract). What's left is the result of the entire operation.

Input: Function composition: f(g(x))
Analogy: Imagine ordering a coffee (g(x)) and then handing it to a friend who adds whipped cream to it (f()). The drink you get at the end depends on both steps. First the coffee is made (inner function), then it is modified (outer function).

Input: Integral as accumulation over time
Analogy: Picture water dripping into a bucket every second. The total water after 10 seconds is like the integral (โˆซ). It accumulates everything thatโ€™s been added over time (rate ร— time).
Analogy: Think of a nested function like a set of Russian matryoshka dolls. You open the outermost doll (outer function) and find another doll inside (inner function), which itself might contain yet another. Each dollโ€™s size and design (output) depends on whatโ€™s inside it (input), and you only see the final result after opening all the layers in order. The outermost dollโ€™s appearance (f(g(h(x)))) is shaped by every doll hidden within.
Mistral AI was manually vetted by our editorial team and was first featured on December 9th 2023.

Pros and Cons

Pros

Open-source models
Emphasis on compute efficiency
Utility and trustworthiness
Small yet powerful model
Remarkable adaptability
Natural coding abilities
Tools comes with weights and sources
Maximized customization
No user data required
Open science principle
Community participation encouraged
Liberal licenses for products
Cloud compatible
Gaming GPU compatible
High scientific standards
Strong research focused team
Dynamic business mindset
Model utility and adaptability
Tackles challenging problems
Doesn't need user data
Committed to open models
Release products under permissive licenses
Encourages user contributions
Better benchmark performance
8k sequence length
Easy to deploy on any cloud
Works on gaming GPUs
Creative and scientific team
Fast-paced business mindset
Promotes open models longevity
Small, powerful, adaptable model
Full customization of the products

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Cons

No dedicated customer support
Requires manual customization
No built-in hosting
Less suitable for non-tech individuals
Possible performance issues on weaker GPUs
Lower performance compared to larger models
Relies on user community for developments
Open-source could compromise product security
Lack of well-documented use cases
Potentially complicated deployment process

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