mistral 7b

7B refers to a specific model size for large language models (LLMs) consisting of seven billion parameters. With the growing importance of LLMs, there are several options in the market. Each option has a particular model size, providing a wide range of choices to users.

However, in this blog we will explore two LLMs of 7B – Mistral 7B and Llama-2 7B, navigating the differences and similarities between the two options. Before we dig deeper into the showdown of the two 7B LLMs, let’s do a quick recap of the language models.

Understanding Mistral 7B and Llama-2 7B

Mistral 7B is an LLM powerhouse created by Mistral AI. The model focuses on providing enhanced performance and increased efficiency with reduced computing resource utilization. Thus, it is a useful option for conditions where computational power is limited.

Moreover, the Mistral LLM is a versatile language model, excelling at tasks like reasoning, comprehension, tackling STEM problems, and even coding.

Read more and gain deeper insight into Mistral 7B

On the other hand, Llama-2 7B is produced by Meta AI to specifically target the art of conversation. The researchers have fine-tuned the model, making it a master of dialog applications, and empowering it to generate interactive responses while understanding the basics of human language.

The Llama model is available on platforms like Hugging Face, allowing you to experiment with it as you navigate the conversational abilities of the LLM. Hence, these are the two LLMs with the same model size that we can now compare across multiple aspects.

Battle of the 7Bs: Mistral vs Llama

Now, we can take a closer look at comparing the two language models to understand the aspects of their differences.

Performance

When it comes to performance, Mistral AI’s model excels in its ability to handle different tasks. It has successfully reached the benchmark scores with every standardized test for various challenges in reasoning, comprehension, problem-solving, and much more.

On the contrary, Meta AI’s production takes on a specialized approach. In this case, the art of conversation. While it will not score outstanding results and produce benchmark scores for a variety of tasks, its strength lies in its ability to understand and respond fluently within a dialogue.

Efficiency

Mistral 7B operates with remarkable efficiency due to the adoption of a technique called Group-Query Attention (GQA). It allows the language model to group similar queries for faster inference and results.

GQA is the middle ground between the quality of Multi-Head Attention (MHA) and the speed of Multi-Query Attention (MQA) approaches. Hence, allowing the model to strike a balance between performance and efficiency.

However, scarce knowledge of the training data of Llama-2 7B limits the understanding of its efficiency. We can still say that a broader and more diverse dataset can enhance the model’s efficiency in producing more contextually relevant responses.

Accessibility

When it comes to accessibility of the two models, both are open-source resources that are open for use and experimentation. It can be noted though, that the Llama-2 model offers easier access through platforms like Hugging Face.

Meanwhile, the Mistral language model requires some deeper navigation and understanding of the resources provided by Mistral AI. It demands some research, unlike its competitor for information access.

Hence, these are some notable differences between the two language models. While these aspects might determine the usability and access of the models, each one has the potential to contribute to the development of LLM applications significantly.

Choosing the right model

Since we understand the basic differences, the debate comes down to selecting the right model for use. Based on the highlighted factors of comparison here, we can say that Mistral is an appropriate choice for applications that require overall efficiency and high performance in a diverse range of tasks.

Meanwhile, Llama-2 is more suited for applications that are designed to attain conversational prowess and dialog expertise. While this distinction of use makes it easier to pick the right model, some key factors to consider also include:

• Future Development – Since both models are new, you must stay in touch with their ongoing research and updates. These advancements can bring new information to light, impacting your model selection.
• Community Support – It is a crucial factor for any open-source tool. Investigate communities for both models to get a better understanding of the models’ power. A more active and thriving community will provide you with valuable insights and assistance, making your choice easier.

Future prospects for the language models

As the digital world continues to evolve, it is accurate to expect the language models to update into more powerful resources in the future. Among some potential routes for Mistral 7B is the improvement of GQA for better efficiency and the ability to run on even less powerful devices.

Moreover, Mistral AI can make the model more readily available by providing access to it through different platforms like Hugging Face. It will also allow a diverse developer community to form around it, opening doors for more experimentation with the model.

As for Llama-2 7B, future prospects can include advancements in dialog modeling. Researchers can work to empower the model to understand and process emotions in a conversation. It can also target for multimodal data handling, going beyond textual inputs to handle audio or visual inputs as well.

Thus, we can speculate several trajectories for the development of these two language models. In this discussion, it can be said that no matter in what direction, an advancement of the models is guaranteed in the future. It will continue to open doors for improved research avenues and LLM applications.

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.