Mistral LLM: A New Era in Language Models

Introduction

Mistral LLM, or Large Language Model, is a groundbreaking development in artificial intelligence. It is a generative text model with 7 billion parameters[1]. This model is the first from Mistral AI and has been named Mistral-7B-v0.1[2].

Architectural Choices

Mistral-7B-v0.1 is a decoder-based language model that employs several innovative architectural choices[2]. These include:

1. Sliding Window Attention: This feature allows the model to be trained with an 8k context length and a fixed cache size, providing a theoretical attention span of 128K tokens[2].
2. Grouped Query Attention (GQA): GQA enables faster inference and a smaller cache size[2].
3. Byte-fallback BPE tokenizer?ensures that characters are never mapped to out-of-vocabulary tokens[2].

Performance

Mistral-7B-v0.1 has demonstrated superior performance compared to other models in its category. It outperforms the 13B Llama 2 and 34B Llama 1 in specific tasks[1,3]. This impressive performance makes Mistral the best 7B large language model[3].

How does it compare to GPT models?

Mistral LLM and GPT models are both significant players in the field of large language models. Here's a comparison based on various aspects:

Performance

Mistral claims to outperform Meta's much larger LLaMA 2 70B (70 billion parameter) large language model and matches or exceeds OpenAI's GPT-3.5 on specific benchmarks[4].

Cost

Mistral AI is significantly cheaper than GPT models. It is approximately 187 times cheaper than GPT-4 and about nine times cheaper than the GPT-3.5 model[5].

Size

While GPT-4 is not strictly a language-only model and can take inputs such as images and text, Mistral offers a compelling alternative that balances cost, accessibility, and robust AI capabilities[6].

Accessibility

GPT-4 is not open source and requires API access[7]. On the other hand, Mistral models can be found on the Hugging Face Hub[7].

In conclusion, while GPT models remain a powerhouse for complex, resource-heavy applications, Mistral offers a compelling alternative that balances cost, accessibility, and robust AI capabilities[6].

Variants and Usage

In addition to the base model, there is a variant named Mistral-7B-Instruct-v0.1, which is fine-tuned to follow instructions and has demonstrated superiority over the Llama 2 13B chat model[6]. Both models can be found on the Hugging Face Hub and used via the Hugging Face Hub[8].

Applications of Mistral LLM

Mistral LLM is a versatile and powerful generative text model that can be used for various applications[9]. Here are some of them:

1. Content Creation: Mistral 7B LLM can generate high-quality text content for different domains and purposes, such as blogs, articles, reviews, summaries, captions, headlines, slogans, and more[9].
2. Education: Mistral 7B LLM can generate educational content for different levels and subjects, such as lessons, exercises, quizzes, feedback, explanations, and more[9].
3. Natural Language Processing Projects: You can use and fine-tune the Mistral 7B model to enhance your natural language processing projects[10]. This includes loading the model in Kaggle, running inference, quantizing, fine-tuning, merging, and pushing it to the Hugging Face Hub[10].

These applications make Mistral LLM a valuable tool in artificial intelligence[11-12].

Difference between Mixtral_8x7B and Mistral 7B

Mixtral 8x7B and Mistral 7B are both large language models developed by Mistral AI, but they have some key differences:

1. Architecture: Mixtral 8x7B has a similar architecture to Mistral 7B, but each layer in Mixtral 8x7B comprises eight feedforward blocks[13].
2. Performance: Mixtral 8x7B outperforms many larger models on most standard benchmarks, including Llama 2 70B and GPT-3.5 [14]. It also surpasses Mistral 7B in terms of performance[15].
3. Size: Mixtral 8x7B uses only 13B active parameters for each token, which is five times less than Llama 2 70B, making it much more efficient[16].
4. Resource Requirements: Mixtral 8x7B requires more resources than Mistral 7B. While Mistral 7B works well on a 24GB RAM 1 GPU instance, Mixtral requires 64GB of RAM and 2 GPUs[14].

In conclusion, Mixtral 8x7B is a more advanced and efficient model compared to Mistral 7B, but it requires more resources to run.

Business case

I developed two notebooks that cover how to use both?Mistral 7B[17] and?Mixtral_8x7B[18]?LLM in Google Colab?

Conclusion

Mistral LLM represents a significant advancement in the field of artificial intelligence. As we continue to explore and develop these models, we can expect to see even more impressive capabilities and applications in the future.

Mistral AI's new model: The web page introduces Mixtral 8x7B, a?sparse mixture of expert models?with?open weights?that outperforms Llama 2 70B and GPT-3.5 on most benchmarks while offering a?6x faster inference rate.

Mistral AI's funding round: The web page also announces that Mistral AI has secured?€400 million?in its Series A funding round, led by Andreessen Horowitz, and has reached a valuation of?$2 billion[19].

Mistral AI's developer platform: The web page mentions that Mistral AI has opened its developer platform, allowing other companies to integrate its models via?APIs.

Mistral AI's model architecture and performance: The web page explains how Mixtral 8x7B uses a?sparse mixture of expert?architecture to achieve high performance and efficiency. It compares it with Llama 2 and GPT-3.5 on various?benchmarks. It also shows the?multilingual?capabilities of Mixtral 8x7.

References

1.-?mistralai/Mistral-7B-v0.1 · Hugging Face

2.-?Mistral ( huggingface.co )

3.-?Mistral LLM: All Versions & Hardware Requirements – Hardware Corner ( hardware-corner.net )

4.-?Everybody's talking about Mistral, an upstart French challenger to OpenAI | Ars Technica

5.-?Mistral 7B is 187x cheaper compared to GPT-4 | by Mastering LLM (Large Language Model) | Medium

6.-?Mistral vs. GPT-4: A Comparative Analysis in Size, Cost, and MMLU Performance | by Alex Periphanos | Jan, 2024 | Medium

7.-?Top Large Language Models (LLMs): GPT-4, LLaMA 2, Mistral 7B, ChatGPT, and More - Vectara

8.-?????? LLM Comparison/Test: Brand new models for 2024 (Dolphin 2.6/2.7 Mistral/Mixtral/Phi-2, Sonya, TinyLlama) : r/LocalLLaMA ( reddit.com )

9.-?Mistral AI Launches Mistral 7B LLM Outperforming Llama 2 13B - Cloudbooklet AI

10.-?Mistral 7B Tutorial: A Step-by-Step Guide to Using and Fine-Tuning Mistral 7B | DataCamp

11.-?Mistral ( huggingface.co )

12.-?Meet Mistral 7B, Mistral's First LLM That Beats Llama 2 - Dataconomy

13.-?Deploy Mixtral-8x7B-Instruct Model in Azure Machine Learning | by Manoranjan Rajguru | Medium

14.-?Mixtral 8x7B: What you need to know about Mistral AI's latest model - Neon

15.-?????? LLM Comparison/Test: Mixtral-8x7B, Mistral, DeciLM, Synthia-MoE : r/LocalLLaMA ( reddit.com )

16.-?Mixtral 8x7B: A game-changing AI model by Mistral AI | SuperAnnotate

17.-?MLxDL/Mistral-7B-Instruct.ipynb at main · frank-morales2020/MLxDL · GitHub

18.-?MLxDL/Mixtral_8x7B.ipynb at main · frank-morales2020/MLxDL · GitHub

19.- Andreessen Horowitz's $415M Mistral investment rounds out AI strategy - PitchBook