Published on Oct 23rd, 2023

Mistral 7B vs. Llama 2

In the world of language models, the competition is fierce. Two popular open-source LLM are Mistral 7B and Llama. Both come equipped with remarkable abilities and unique strengths, making them standout choices in the AI landscape. This article is your guide to understanding the differences and similarities between these models, with a focus on their performance and architecture.

The Rise of Mistral 7B

Mistral 7B, boasting 7.3 billion parameters, has rapidly gained recognition for its impressive performance across a diverse range of benchmarks. Notably, it outshines Llama 2 13B in all benchmark tests, and in many cases, even surpasses the larger models. Mistral 7B is no slouch in the code domain either, approaching the performance of CodeLlama 7B while maintaining proficiency in English tasks.

Fine-tuning Mistral 7B for specific tasks is possible too, as demonstrated by the developers who have fine-tuned it for various applications, achieving impressive results, even outperforming Llama 2 13B in chat applications.

Mistral 7B leverages innovative techniques to ensure faster inference and efficient handling of longer sequences with minimal computational overhead. Grouped-Query Attention (GQA) accelerates model inference by batch-processing queries, reducing computational intensity. Sliding Window Attention (SWA) enables extended sequence handling without a substantial increase in computational requirements. These techniques make Mistral more performant than other similar-sized models.

Meta's Llama 2

The Llama 2 family is a collection of pre-trained and fine-tuned generative text models ranging from 7 billion to 70 billion parameters. Llama 2 models are designed for dialogue use cases. Fine-tuned Llama 2 models, known as Llama-2-Chat, consistently outperform open-source chat models in various benchmarks and are on par with popular closed-source models such as ChatGPT and PaLM in terms of helpfulness and safety.

Llama 2 models are available in different parameter sizes, including 7B, 13B, and 70B. These models employ an auto-regressive language model and are based on an optimized transformer architecture. Fine-tuned versions of Llama 2 undergo supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align closely with human preferences for helpfulness and safety.

Choosing Between Mistral and Llama

Llama 2 70B excels in complex language tasks, benefitting from its extensive parameter count. However, for tasks that are less complex and require a balanced approach to performance and cost-effectiveness, Mistral 7B emerges as the clear winner. With its 7.3 billion parameters, Mistral 7B offers remarkable performance mostly surpassing Llama 2 13B, while its resource efficiency makes it cost-effective to run on smaller hardware. Furthermore, Mistral 7B seamlessly integrates with major cloud platforms and offers straightforward local deployment, ensuring accessibility and ease of use. Mistral 7B stands out as the more cost-effective and resource-efficient solution for a wide range of AI applications, while Llama 2 70B remains a consideration for very complex tasks.

Update: Fine-Tuned Mistral

Image Source: Zephyr AI

Zephyr AI unveiled a promising AI model with its Zephyr-7B-α model. Stemming from Mistral 7B, it's refined using Direct Preference Optimization (DPO), enhancing its performance notably on MT Bench. This open-source model, under MIT license, surpasses its predecessor Mistral, showing remarkable benchmarks and is even comparable to Llama 2 70B in many cases.