-by E2E Networks Limited


  • Text embedding is a key part of Natural Language Processing (NLP). It’s a method that changes words or phrases into numerical vectors, which helps us understand their meaning. This is really helpful for many NLP tasks. But, the traditional ways of doing text embedding can be slow and use up a lot of computer power.

To address the challenges posed by traditional text embedding methods, several open-source models have been developed. These models, including Gemma, Mistral AI, and Llama 3, are designed with a focus on speed and efficiency. However, detailed information about their inner workings and their specific applications in text embedding is not widely available. For those interested in exploring these models, they are available on the TIR AI platform. This platform could potentially provide more insights into the models and their use in text embedding.

There’s also something called JSONL (JSON Lines). It’s a handy way to store structured data in a text format, with one JSON object per line. This makes it easier to process the data bit by bit or all at once.

Meta Llama3:

  • Meta AI introduced a new family of large language models (LLMs) called Llama in February 2023. Llama was released in four sizes, with the 13B model outperforming OpenAI’s GPT-3, which has 135B parameters. Parameters are a measure of an AI model’s size and complexity, with more parameters generally meaning more power. In July 2023, Meta released Llama 2, an upgraded version of the first model, in 7B, 13B, and 70B sizes. Llama 2 was trained on 40% more data than its predecessor.

Google Gemma:

  • Gemma is a family of lightweight, open-source models from Google, based on the same technology used to create the Gemini models. These text-to-text, decoder-only language models are available in English and come in pre-trained and instruction-tuned variants. Gemma models are ideal for text generation tasks such as question answering, summarization, and reasoning. Their small size allows for deployment in environments with limited resources, such as laptops, desktops, or personal cloud infrastructure, making state-of-the-art AI models accessible to everyone and fostering innovation.

Mistral AI:

  • Mistral 7B is a powerful language model created by Mistral AI, boasting 7 billion parameters. It’s designed to be both efficient and high-performing, making it a great choice for real-world applications that need quick responses. When it was first released, Mistral 7B outperformed Llama 2, the best open-source 13B model, across all benchmarks.

To improve its performance, Mistral 7B uses a technique called Sliding Window Attention. This method helps to reduce the number of operations needed, which otherwise would increase significantly with the length of the sequence. It also helps to manage memory usage, which tends to increase with the number of tokens.

Here’s how it works: each token can pay attention to a maximum of ‘W’ tokens from the previous layer. In this case, ‘W’ is set to 3. This means that even tokens outside the sliding window can still influence the prediction of the next word. This results in faster processing times and better utilization of resources.


In this blog post, we explored a range of open-source models offering faster and more cost-effective solutions for text embedding, a crucial task in natural language processing (NLP). Meta AI’s Llama models deliver high performance with fewer parameters than traditional models like GPT-3, making them efficient for embedding tasks. Google’s lightweight Gemma models, optimized for text-to-text tasks, can be deployed in resource-limited environments, broadening access to advanced text embeddings. The Mistral 7B model stands out for its efficiency and high performance, ideal for real-time applications requiring quick responses.

Sentence Transformers provide state-of-the-art text and image embeddings, making them versatile for various tasks. Universal Sentence Encoder (USE) offers efficient and accurate embeddings, balancing model complexity and resource consumption. StarSpace, a general-purpose neural embedding model, excels in generating high-quality text embeddings for a wide range of tasks.

These models highlight significant advancements in text embedding technology and demonstrate the potential of open-source solutions to democratize powerful NLP tools. Leveraging these models enables faster, more efficient, and cost-effective text embeddings, driving innovation in the field of natural language processing.