mistral ai

In the dynamic world of artificial intelligence, strides in innovation are commonplace. At the forefront of these developments is Mistral AI, a European company emerging as a strong contender in the Large Language Models (LLM) arena with its latest offering: Mistral Large. With capabilities meant to rival industry giants, Mistral AI is poised to leave a significant imprint on the tech landscape.

 

Features of Mistral AI’s large model

 

Mistral AI’s new flagship model, codenamed Mistral Large, isn’t just a mere ripple in the AI pond; it’s a technological tidal wave. As we take a look at what sets it apart, let’s compare the main features and capabilities of Mistral AI’s Large model, as detailed in the sources, with those commonly attributed to GPT-4.

 

 

Language support

Mistral Large: Natively fluent in English, French, Spanish, German, and Italian.
GPT-4: is known for supporting multiple languages, but the exact list isn’t specified in the sources.

 

Scalability

Mistral Large: Offers different versions, including Mistral Small for lower latency and cost optimization.
GPT-4: Provides various scales of models, but specific details on versions aren’t provided in the sources.

 

Training and cost

Mistral Large: Charges $8 per million input tokens and $24 per million output tokens.
GPT-4: Mistral Large is noted to be 20% cheaper than GPT-4 Turbo, which suggests GPT-4 would be more expensive.

 

Performance on benchmarks

Mistral Large: Claims to rank second after GPT-4 on commonly used benchmarks and only marginally outperforms offerings from Google and Meta under the MMLU benchmark.

GPT-4

It is known to be one of the leading models in terms of benchmark performance, but no specific details on benchmark scores are provided in the sources.

Cost to train

Mistral Large: The model reportedly cost less than $22 million to train.
GPT-4: cost over $100 million to develop, according to claims.

Multilingual Abilities

Le Chat supports a variety of languages including English, French, Spanish, German, and Italian 1.

Different Versions

Users can choose between three different models, namely Mistral Small, Mistral Large, and Mistral Next, the latter of which is designed to be brief and concise.

Web Access

Currently, Le Chat does not have the capability to access the internet 1.

Free Beta Access

Le Chat is available in a beta version that is free for users, requiring just a sign-up to use 2.

Planned Enterprise Version

Mistral AI plans to offer a paid version for enterprise clients with features like central billing and the ability to define moderation mechanisms

Please note that this comparison is based on the information provided within the sources, which may not include all features and capabilities of GPT-4 or Mistral Large.

 

Mistral AI vs. GPT-4: A comparative look

 

 

Against the backdrop of OpenAI’s GPT-4 stands Mistral Large, challenging the status quo with outstanding features. While GPT-4 shines with its multi-language support and high benchmark performance, Mistral Large offers a competitive edge through:

 

Affordability: It’s 20% cheaper than GPT-4 Turbo, negotiating cost-savings for AI-powered projects.

 

Benchmark Performance: Mistral Large competes closely with GPT-4, ranking just behind it while surpassing other tech behemoths in several benchmarks.

 

Multilingual Prowess: Exceptionally fluent across English, French, Spanish, German, and Italian, Mistral Large breaks language barriers with ease.

 

Efficiency in Development: Crafted with capital efficiency in mind, Mistral AI invested less than $22 million in training its model, a fraction of the cost incurred by its counterparts.

 

Commercially Savvy: The model offers a paid API with usage-based pricing, balancing accessibility with a monetized business strategy, presenting a cost-effective solution for developers and businesses.

 

 

Practical applications of Mistral AI’s Large and GPT-4

 

The applications of both Mistral AI’s Large and GPT-4 sprawl across various industries and use cases, such as:

 

Natural Language Understanding: Both models demonstrate excellence in understanding and generating human-like text, pushing the boundaries of conversational AI.

 

Multilingual Support: Business expansion and global communication are facilitated through the multilingual capabilities of both LLMs.

 

Code Generation: Their ability to understand and generate code makes them invaluable tools for software developers and engineers.

 

Recommendations for use

 

As businesses and individuals navigate through the options in large language models, here’s why you might consider each tool:

 

Choose Mistral AI’s Large: If you’re looking for a cost-effective solution with efficient multilingual support and the flexibility of scalable versions to suit different needs 2.

 

Opt for GPT-4: Should your project require the prestige and robustness associated with OpenAI’s cutting-edge research and model performance, GPT-4 remains an industry benchmark 3.

 

 

Final note

 

In conclusion, while both Mistral AI’s Large and GPT-4 stand as pioneers in their own right, the choice ultimately aligns with your specific requirements and constraints. With Mistral AI nipping at the heels of OpenAI, the world of AI remains an exciting space to watch.

 

The march of AI is relentless, and as Mistral AI parallels the giants in the tech world, make sure to keep abreast of their developments, for the choice you make today could redefine your technological trajectory tomorrow.

The race of big tech and startups to create the top language model has us eager to see how things change.

Different companies are training new models to achieve better accuracy, enhanced understanding of context, and more nuanced generation capabilities, pushing the boundaries of what AI can achieve in terms of natural language understanding and generation.

A standout approach in this field is employed by Mistral AI through its development of the Mixtral model.

Distinctive for its use of the Sparse Mixture of Experts (SMoE) technique, Mixtral amalgamates the expertise of various specialized models. Each of these models excels in different areas of data processing, enabling Mixtral to navigate the complexities of language with notable precision.

This article aims to provide an in-depth examination of Mixtral, including its operational framework, unique attributes, and performance metrics. We will explore how Mixtral differentiates itself from other models in the market and the advantages it offers.

How does Mixtral work; What is so unique in its framework?

The Mixtral 8x7B model is a smart tool that’s built to be really good at a bunch of different tasks. It does this by not using all its tools at once, but just a few at a time for each piece of information it looks at.

Think of it like a toolbox where, out of 8 tools, it picks the best 2 for the job at hand. Each layer of Mixtral has these 8 special tools or “experts,” and it chooses which ones to use based on what it’s working on. This way, it can be really efficient and do its job well without needing to use everything it has all at once.

The process from the input through the router to the expert and the resulting output works as follows:

Input: A given input vector, representing a token from a sequence, enters the model. Each token is processed individually by going through the layers of the model. The input is part of a larger context, which can be a span of up to 32k tokens. Read how embeddings work here.

Router: After the initial input, the router within the Mixture of Experts layer determines which experts to engage for processing the token. Specifically, the router selects 2 out of the 8 available experts based on the token’s characteristics. This selection is done using a gating network that assigns weights to the experts, guiding which experts are to be used.

Experts: Once the experts are selected by the router, the input token is processed by these experts. Each expert consists of a standard feedforward block as found in a transformer architecture. The outputs of the two chosen experts are then combined through a weighted sum, where the weights are determined by the gating network’s output.

Output: The final output for the token is the combined result from the two experts it was routed to. Essentially, the output of the MoE layer is the weighted sum of the outputs of the expert networks.

This process is repeated for each token within the sequence, allowing the Mixtral model to effectively process and generate the response or continuation based on the input it receives.

Unique Attributes of Mixtral’s Approach

1. High Temporal Locality

The interesting part is that Mixtral tends to pick the same expert or group of experts for words that are close together or related in some way i.e. the model possesses “high temporal locality”.

It’s like noticing that a certain part of your game has a lot of jumping, so you stick with the character who’s best at jumping for that whole section.

The implications of such high temporal locality are substantial for both training and inference efficiency. It suggests that expert assignments can be somewhat predicted over time, providing opportunities to optimize the model’s training and runtime performance.

For instance, the predictability in expert utilization can lead to more efficient caching strategies, wherein the outputs of frequently used experts are temporarily stored, thus speeding up computations for consecutive tokens that are routed to the same experts.

2. Computational Efficiency via Dual Expert Strategy

Mixtral uses only two out of eight experts to handle each piece of data it processes. This selective engagement is key for its computational efficiency, allowing it to work as fast as a model with 12 billion parameters, even though it has four times as many parameters in total.

Performance of Mixtral

Mixtral 8x7B is compared directly with Llama 2 70B and GPT-3.5 and is found to perform similarly or above these models in benchmarks. Specifically, it scores higher on MMLU and does exceptionally well on MT-Bench.

 

Hallucinations and Bias

In comparison with Llama 2, Mixtral exhibits reduced bias in the BBQ benchmark. Furthermore, it tends to show a more favorable outlook than Llama 2 in the BOLD benchmark, while maintaining comparable variations across different aspects.

 Multilingualism

Mixtral vastly outperforms Llama 2 70B on multilingual benchmarks, demonstrating its strength in understanding and generating text across different languages

Charting the Future: Mixtral’s Revolutionary Path in AI Efficiency and Multilinguality

Mistral AI’s Mixtral model has carved out a niche for itself, showcasing the power and precision of the Sparse Mixture of Experts approach. As we’ve navigated through the intricacies of Mixtral, from its unique architecture to its standout performances on various benchmarks, it’s clear that this model is not just another entrant in the race to AI supremacy. It’s a harbinger of a nuanced, efficient future in large language models.

By strategically deploying only two of its eight available experts for each input token, Mixtral achieves a balance between computational efficiency and deep, nuanced understanding that few models can claim. This approach not only enhances processing speed but also reduces bias and improves performance across languages, setting a new standard for what AI can achieve.

As we conclude our exploration of the Genius of Mixtral of Experts by Mistral AI, it’s evident that this model represents a significant leap forward. Through its adept handling of complex language tasks, Mixtral stands as a testament to the potential of combining specialized expertise with smart, scalable architecture. The future of AI looks brighter with Mixtral paving the way, promising models that are not only more efficient and versatile but also more understanding of the vast tapestry of human language.

Mistral AI, a startup co-founded by individuals with experience at Google’s DeepMind and Meta, made a significant entrance into the world of LLMs with Mistral 7B.

This model can be easily accessed and downloaded from GitHub or via a 13.4-gigabyte torrent, emphasizing accessibility. Mistral 7b, a 7.3 billion parameter model with the sheer size of some of its competitors, Mistral 7b punches well above its weight in terms of capability and efficiency. 

What makes Mistral 7b a great competitor? 

One of the key strengths of Mistral 7b lies in its architecture. Unlike many LLMs relying solely on transformer networks, Mistral 7b incorporates a hybrid approach, leveraging transformers and recurrent neural networks (RNNs). This unique blend allows Mistral 7b to excel at tasks that require both long-term memory and context awareness, such as question answering and code generation. 

Furthermore, Mistral 7b utilizes innovative attention mechanisms like group query attention and sliding window attention. These techniques enable the model to focus on relevant parts of the input data more effectively, improving performance and efficiency. 

 

Learn in detail about llm evaluation method

 

Mistral 7b architecture 

Mistral 7B is an architecture based on transformer architecture and introduces several innovative features and parameters. Here’s a gist of the architectural details: 

 

1. Sliding window attention: 

Mistral 7B addresses the quadratic complexity of vanilla attention by implementing Sliding Window Attention (SWA). 

SWA allows each token to attend to a maximum of W tokens from the previous layer (here, W = 3). 

Tokens outside the sliding window still influence next-word prediction. 

Information can propagate forward by up to k × W tokens after k attention layers. 

Parameters include dim = 4096, n_layers = 32, head_dim = 128, hidden_dim = 14336, n_heads = 32, n_kv_heads = 8, window_size = 4096, context_len = 8192, and vocab_size = 32000. 

 

 

Source:E2Enetwork 

 

 

2. Rolling Buffer Cache: 

This fixed-size cache serves as the “memory” for the sliding window attention. It efficiently stores key-value pairs for recent timesteps, eliminating the need for recomputing that information. A set attention span stays constant, managed by a rolling buffer cache limiting its size. 

Within the cache, each time step’s keys and values are stored at a specific location, determined by i mod W, where W is the fixed cache size. When the position i exceeds W, previous values in the cache get replaced. 

This method slashes cache memory usage by 8 times while maintaining the model’s effectiveness. 

 

 

Source:E2Enetwork 

 

 

3. Pre-fill and chunking: 

During sequence generation, the cache is pre-filled with the provided prompt to enhance context. For long prompts, chunking divides them into smaller segments, each treated with both cache and current chunk attention, further optimizing the process.

When creating a sequence, tokens are guessed step by step, with each token relying on the ones that came before it. The starting information, known as the prompt, lets us fill the (key, value) cache beforehand with this prompt.

The chunk size can determine the window size, and the attention mask is used across both the cache and the chunk. This ensures the model gets the necessary information while staying efficient. 

 

Source:E2Enetwork 

 

 

Comparison of performance: Mistral 7B vs Llama2-13B  

The true test of any LLM lies in its performance on real-world tasks. Mistral 7b has been benchmarked against several established models, including Llama 2 (13B parameters) and Llama 1 (34B parameters).

The results are impressive, with Mistral 7b outperforming both models on all tasks tested. It even approaches the performance of CodeLlama 7B (also 7B parameters) on code-related tasks while maintaining strong performance on general language tasks. Performance comparisons were conducted across a wide range of benchmarks, encompassing various aspects.

 

 

1. Performance comparison 

Mistral 7B surpasses Llama2-13B across various benchmarks, excelling in commonsense reasoning, world knowledge, reading comprehension, and mathematical tasks. Its dominance isn’t marginal; it’s a robust demonstration of its capabilities. 

 

2. Equivalent Model Capacity 

In reasoning, comprehension, and STEM tasks, Mistral 7B functions akin to a Llama2 model over three times its size. This not only highlights its efficiency in memory usage but also its enhanced processing speed. Essentially, it offers immense power within an elegantly streamlined design. 

 

3. Knowledge-based assessments 

Mistral 7B demonstrates superiority in most assessments and competes equally with Llama2-13B in knowledge-based benchmarks. This parallel performance in knowledge tasks is especially intriguing, given Mistral 7B’s comparatively restrained parameter count. 

 

 

Source:MistralAI 

 

Beyond benchmarks: Practical applications 

The capabilities of Mistral 7b extend far beyond benchmark scores Mistral 7B isn’t limited to a single skill. It performs exceptionally well across various tasks, spanning code-related fields and English language tasks. Remarkably, it matches CodeLlama-7B’s performance in coding tasks, highlighting its adaptability and wide-ranging abilities.  Some of the common works in each field are mentioned below: 

• Natural Language Processing (NLP): Machine translation, text summarization, question answering, and sentiment analysis. 
• Code Generation and Analysis: Generate code snippets, translate natural language to code, and analyze existing code for potential issues. 
• Creative Writing: Compose poems, scripts, musical pieces, and other creative text formats. 
• Education and Research: Assist with research tasks, generate educational materials, and personalize learning experiences. 

 

 

 

Source:E2Enetwork 

 

Source:MistralAI 

 

A cost-effective Solution 

One of the most compelling aspects of Mistral 7b is its cost-effectiveness. Compared to models of similar size, Mistral 7b requires significantly less computational resources to run. This makes it a more accessible option for individuals and organizations with limited budgets. Additionally, Mistral AI offers flexible deployment options, allowing users to run the model on their own infrastructure or through the cloud. 

 

Versatile deployment 

Mistral 7B stands out due to its Apache 2.0 license, granting broad accessibility for diverse users, including individuals, major corporations, and governmental bodies.

This open-source license not only ensures inclusivity but also permits customization and adaptation to suit specific needs. It empowers users to modify, share, and utilize Mistral 7B for a wide array of applications, fostering innovation and collaboration in the community. 

 

The decentralization issue vs transparency 

Mistral AI prioritizes transparency and open access, yet safety concerns arise due to the fully decentralized ‘Mistral-7B-v0.1’ model, capable of unmoderated response generation.

Unlike models such as GPT and Llama, it lacks mechanisms to discern appropriate responses, posing potential exploitation risks. However, despite safety concerns, decentralized Language Model Models (LLMs) offer advantages, democratizing AI access and enabling positive applications. 

 

Are large language models the zero shot reasoners? Read more here

 

Conclusion 

Mistral 7b is a testament to the power of innovation in the LLM domain. Despite its relatively small size, it has established itself as a force to be reckoned with, delivering impressive performance across a wide range of tasks. With its focus on efficiency and cost-effectiveness, Mistral 7b is poised to democratize access to cutting-edge language technology and shape the future of how we interact with machines.