Introduction to Mixtral 8x7B
Mixtral 8x7B, developed by Mistral AI, presents a significant advancement in the field of AI language models. Its architecture and performance have placed it in the spotlight, especially in comparison with established models like GPT-3.5 and Llama 2. Mixtral employs a unique Sparse Mixture-of-Experts (SMoE) architecture, a decoder-only model that selects from a set of 8 distinct groups of parameters, termed ‘experts’. This innovative approach allows the model to process inputs efficiently, using only a fraction of its total parameters per token. With 46.7 billion total parameters, it manages to operate as efficiently as a model with only 12.9 billion parameters, balancing speed and computational cost effectively.
In terms of performance, Mixtral has shown impressive results in various benchmarks, surpassing Llama 2 70B and equating or outperforming GPT-3.5 in most standard tests. The model excels in handling a large token context of up to 32k tokens and demonstrates proficiency in multiple languages, including English, French, Italian, German, and Spanish. Furthermore, it is particularly strong in code generation and can be fine-tuned for instruction-following applications.
One of the most notable features of Mixtral 8x7B is its efficiency and the ability to run on hardware with lower capabilities. This includes machines without dedicated GPUs, such as the latest Apple Mac computers, making it more accessible for a wider range of users and applications. This accessibility is a step towards democratizing advanced AI technology, expanding its potential uses beyond high-end servers to more modest computing environments.
Mixtral 8x7B's open-source nature, being released under an Apache 2.0 license, stands in contrast to other major AI models that are often closed-source. This approach aligns with Mistral’s commitment to an open, responsible, and decentralized approach to technology, offering more transparency and flexibility for developers and researchers.
However, the model's openness and advanced capabilities come with their own set of concerns, particularly regarding ethical considerations. The absence of built-in safety guardrails in Mixtral 8x7B raises concerns about the potential generation of unsafe content, a challenge that needs careful attention, especially in applications where content moderation is crucial.
In summary, Mixtral 8x7B is a powerful and innovative AI language model that combines technical sophistication with practical design. Its performance, efficiency, and open-source availability make it a notable addition to the AI landscape. However, the lack of safety measures necessitates a cautious approach in its application, especially in scenarios requiring stringent content moderation.
A Note on Mixtral’s SMoE Architecture
The Sparse Mixture-of-Experts (SMoE) architecture used by Mixtral is an advanced AI model design that represents a significant shift from traditional neural network structures. To understand this, let's break down the key components and principles behind this architecture:
1. Mixture-of-Experts (MoE) Concept: The MoE approach involves a collection of 'expert' networks, each specialized in different tasks or types of data. In traditional neural networks, all inputs pass through the same layers and neurons, regardless of their nature. However, in an MoE system, different inputs are processed by different 'experts', depending on their relevance to the input.
2. Sparsity in SMoE: The term 'sparse' in this context refers to the fact that not all experts are engaged for every input. At any given time, only a subset of experts is activated to process a specific input. This sparsity is crucial for efficiency, as it reduces the computational load compared to a situation where all experts are active for every input.
3. Decoder-Only Model: Mixtral, being a decoder-only model, means that it focuses on generating outputs based on the input it receives, unlike encoder-decoder models which first encode an input into a representation and then decode it to an output. This structure is particularly suited for tasks like language generation, where the model produces text based on the input context.
4. Efficient Parameter Usage: Mixtral has a total of 46.7 billion parameters, but due to its sparse nature, it only uses around 12.9 billion parameters per token. This means that for any given input token, the model dynamically selects which subset of parameters (or experts) to use. This selective engagement of parameters allows Mixtral to operate with the efficiency of a much smaller model, while still retaining the capability of a large model.
5. Balancing Speed and Computational Cost: By employing a sparse architecture, Mixtral is able to balance speed and computational cost effectively. The model can process inputs quickly because it doesn't need to engage its entire parameter set for every token, thereby reducing the computational load and improving efficiency.
In summary, the Sparse Mixture-of-Experts architecture in Mixtral represents a sophisticated approach to AI model design, enabling high efficiency and effectiveness by selectively using parts of its vast parameter set as needed. This architecture is particularly beneficial for large-scale models, allowing them to maintain high performance without incurring the full computational cost of their size.
In this article, we’ll delve into Mixtral’s capabilities by building a simple RAG pipeline to query the latest cricket news articles. If you want to understand what RAG pipelines are, you can read this article.
Launching a GPU Node
Head over to https://myaccount.e2enetworks.com/ to check out the cloud GPUs you might need for implementing the code in this article.
We will be selecting NVIDIA’s V100 GPU as the choice for our node.
Setting Up the Environment
Loading a Quantized Mixtral 8x7B Model
We’ll use Hugging Face to load our model.
Adjusting the Model Quantization Parameters to Improve Speed
Let’s load the model onto our GPU.
Let’s check how much our Mixtral model knows about cricket before moving forward to give it context about the latest news.
Creating a RAG Using LangChain and FAISS
Langchain is a comprehensive framework for designing RAG applications. You can read up more on the Langchain API here.
FAISS or Facebook AI Similarity Search is a vector database developed by Facebook specifically designed for efficiency and accuracy in similarity search and clustering in high-dimensional spaces. It's particularly useful for a variety of tasks, including image retrieval, recommendation systems, and natural language processing.
In this article, we’ll implement a RAG pipeline using LangChain and FAISS. First let's chunk our documents and convert them into vector embeddings.
In Python, the asyncio module provides support for writing asynchronous code using the async/await syntax. However, there are situations where nested calls to asyncio.run() might result in an error. This is because the event loop is already running, and calling run() again can lead to conflicts.
nest_asyncio is a workaround for this issue. The apply() function patches the asyncio module to allow nested calls to asyncio.run() without raising an error.
Now let’s check our vector database and see if it can retrieve similar chunks of content.
As you can see, similarity search gave us a relevant chunk of data.
Building an LLM Chain for Question-Answering
Let’s first try to work with our LLM Chain without giving it any context.
We get a generic answer about Shreyas Iyer without any information about the recent news articles, because there was no context provided.
Creating a RAG Chain
Now let’s create a RAG chain with our LLM so it has context available for the questions we ask.
Now we get some information about Shreyas Iyer in context to recent news.
Let’s take a look at some examples:
Our project is complete. Hope you enjoyed this tutorial.