How to Use LLM for Product Innovation & 6 Best Models to Consider

Large Language Models are revolutionizing how we approach numerous tasks, enhancing productivity and performance across diverse sectors. For instance, imagine you're a developer integrating a chatbot into a web application to improve customer support. Along the way, you discover that the chatbot's performance can be greatly enhanced by multimodal LLM, which could reduce the number of queries that need to be handled by humans. But, as you explore how to use LLMs, you realize that integrating these cutting-edge AI models into your application could be complex. This blog will show you how to use LLMs to improve your application while minimizing the complexities associated with their integration.

Simply put, LLMs can improve your chatbot's performance, and this blog will show you how to use LLMs to strengthen your application while minimizing the complexities commonly associated with their integration. One valuable way to do this is by using Lamatic's generative AI tech stack. This tool can help you integrate a cutting-edge large language model into your product, enhancing its functionality and user experience while minimizing complexity and development overhead.

7 Steps to Mastering Large Language Models (LLMs)

1. Learn Large Language Models

Large Language Models, or LLMs, are a subset of deep learning models trained on massive corpus of text data. They’re large—with tens of billions of parameters—and perform exceptionally well on various natural language tasks.

Why are LLMs so Popular?

LLMs can understand and generate coherent, contextually relevant, and grammatically accurate text. Reasons for their popularity and widespread adoption include:

• Exceptional performance on a wide range of language tasks
• Accessibility and availability of pre-trained LLMs, democratizing AI-powered natural language understanding and generation.

How are LLMs Different from Other Deep Learning Models?

LLMs stand out from other deep learning models due to their size and architecture, which include self-attention mechanisms. Key differentiators include:The Transformer architecture revolutionized natural language processing and underpinned LLMs. It can capture long-range dependencies in text, enabling better contextual understanding. It can handle various language tasks, from text generation to translation, summarization, and question-answering.

What are the Common Use Cases of LLMs?

LLMs have found applications across language tasks, including:

• Natural Language Understanding: LLMs excel at tasks like sentiment analysis, named entity recognition and question answering.
• Text Generation: They can generate human-like text for chatbots and other content generation tasks.
• Machine Translation: LLMs have significantly improved machine translation quality.
• Content Summarization: LLMs can generate concise summaries of lengthy documents. 

Ever tried summarizing YouTube video transcripts?

2. Exploring LLM Architectures

Now that you know what LLMs are, let’s move on to learning the transformer architecture that underpins these powerful LLMs. So, in this step of your LLM journey, Transformers need all your attention (no pun intended).The original Transformer architecture, introduced in the paper "Attention Is All You Need," revolutionized natural language processing.

Key Features

• Self-attention layers
• Multi-head attention
• Feed-forward neural networks
• Encoder-decoder architecture

Use Cases

Transformers are the basis for notable LLMs like BERT and GPT. The original Transformer architecture uses an encoder-decoder architecture, but encoder-only and decoder-only variants exist. 

Here’s a comprehensive overview of these, along with their features, notable LLMs, and use cases:

3. Pre-training LLMs

Now that you’re familiar with the fundamentals of Large Language Models (LLMs) and the transformer architecture, you can learn about pre-training LLMs. Pre-training forms the foundation of LLMs by exposing them to a massive corpus of text data, enabling them to understand the aspects and nuances of the language.Here’s an overview of concepts you should know:

Objectives of Pre-training LLMs

Exposing LLMs to massive text corpora to learn:

• Language patterns
• Grammar
• Context

Learn about the specific pre-training tasks, such as:

• Masked language modeling
• Next sentence prediction

Text Corpus for LLM Pre-Training

LLMs are trained on massive and diverse text corpora, including:

• Web articles
• Books
• Other sources

These are large datasets, with billions to trillions of text tokens. Common datasets include:

• C4
• BookCorpus
• Pile
• OpenWebText
• And more

Training Procedure

Understand the technical aspects of pre-training, including:

• Optimization algorithms
• Batch sizes
• Training epochs

Learn about challenges such as mitigating biases in data. If you’re interested in learning further, refer to the module on LLM training from CS324: Large Language Models.Such pre-trained LLMs serve as a starting point for fine-tuning specific tasks. Yes, fine-tuning LLMs is our next step!

4. Fine-Tuning LLMs

After pre-training LLMs on massive text corpora, the next step is fine-tuning them for specific natural language processing tasks. Fine-tuning allows you to adapt pre-trained models to perform specific tasks like:

• Sentiment analysis
• Question answering
• Translation with higher accuracy and efficiency

Why Fine-Tune LLMs

Pre-trained LLMs have gained general language understanding but require fine-tuning to perform well on specific tasks. Fine-tuning is necessary for several reasons:

• It helps the model learn the nuances of the target task.
• It reduces the amount of data and computation needed compared to training a model from scratch.
• It leverages the pre-trained model's understanding; the fine-tuning dataset can be much smaller than the pre-training dataset.

How to Fine-Tune LLMs

• Choose the Pre-trained LLM: Choose the pre-trained LLM that matches your task. For example, select a pre-trained model with the architecture that facilitates natural language understanding if you're working on a question-answering task.
• Data Preparation: Prepare a dataset for the task you want the LLM to perform. Ensure it includes labeled examples and is formatted appropriately.
• Fine-Tuning: After you’ve chosen the base LLM and prepared the dataset, it’s time to fine-tune the model. 

But how? Are there parameter-efficient techniques? Remember, LLMs have 10s of billions of parameters. And the weight matrix is huge!

Fine-Tuning LLMs Without Access to Weights

What if you don’t have access to the weights? How do you fine-tune an LLM when you don't have access to the model’s weights and accessing the model through an API? Large language models can be used in context learning without an explicit fine-tuning step. You can leverage their ability to learn from analogy by providing input and sample output examples of the task.

Prompt tuning can be hard or soft. Hard prompt tuning involves modifying the prompts to get more helpful outputs. Soft prompt tuning involves adjusting the input tokens in the prompt directly so they don’t update the model's weights. Soft prompt tuning concatenates the input embedding with a learnable tensor. A related idea is prefix tuning, where learnable tensors are used with each Transformer block instead of only the input embeddings.As mentioned, large language models have tens of billions of parameters. Fine-tuning the weights in all the layers is a resource-intensive task. 

Recently, Parameter-Efficient Fine-Tuning Techniques (PEFT) like LoRA and QLoRA have become popular. With QLoRA, you can fine-tune a 4-bit quantized LLM on a single consumer GPU without any performance drop.These techniques introduce a small set of learnable parameters (adapters) tuned instead of the entire weight matrix.

5. Alignment and Post-Training in LLMs

Large Language models can generate content that may be harmful, biased, or misaligned with what users want or expect. Alignment refers to aligning an LLM's behavior with human preferences and ethical principles. It aims to mitigate risks associated with model behavior, including:

• Biases
• Controversial responses
• Harmful content generation

Consider leveraging the following techniques:

Reinforcement Learning from Human Feedback (RLHF)

• Utilizes human preference annotations on LLM outputs to train a reward model.
• Guides the model in aligning better with desired behaviors based on these preferences.

Contrastive Post-Training

• Applies contrastive techniques to automate the creation of preference pairs.
• Aims to refine model outputs by contrasting high-quality and low-quality responses effectively.

6. Evaluation and Continuous Learning in LLMs

Once you've fine-tuned an LLM for a specific task, it's essential to evaluate its performance and consider strategies for continuous learning and adaptation. This step ensures that your LLM remains practical and up-to-date.

Evaluation of LLMs

Evaluate the performance to assess their effectiveness and identify areas for improvement. Here are key aspects of LLM evaluation:

• Task-Specific Metrics: Choose appropriate metrics for your task. For example, you may use conventional evaluation metrics like:
  • Accuracy
  • Precision
  • Recall
  • F1 score in text classification

For language generation tasks, metrics like perplexity and BLEU scores are common.

• Human Evaluation: Have experts or crowdsourced annotators assess the quality of generated content or the model's responses in real-world scenarios.
• Bias and Fairness: Evaluate LLMs for biases and fairness concerns, particularly when deploying them in real-world applications. Analyze how models perform across different demographic groups and address any disparities.
• Robustness and Adversarial Testing: Test the LLM's robustness by subjecting it to adversarial attacks or challenging inputs. This helps uncover vulnerabilities and enhances model security.

Continuous Learning and Adaptation

To keep LLMs updated with new data and tasks, consider the following strategies:

• Data Augmentation: Continuously augment your data store to avoid performance degradation due to a lack of up-to-date info.
• Retraining: Periodically retrain the LLM with new data and fine-tune it for evolving tasks. Fine-tuning on recent data helps the model stay current.
• Active Learning: Implement active learning techniques to identify instances where the model is uncertain or likely to make errors. Collect annotations for these instances to refine the model. Another common pitfall with LLMs is hallucinations. Be sure to explore techniques like Retrieval augmentation to mitigate hallucinations.

7. Building and Deploying LLM Apps

After developing and fine-tuning an LLM for specific tasks, start building and deploying applications that leverage the LLM's capabilities. In essence, LLMs can be used to build useful real-world solutions. 

Building LLM Applications

Here are some considerations:

• Task-Specific Application Development: Develop applications tailored to your specific use cases. This may involve creating:
  • Web-based interfaces
  • Mobile apps
  • Chatbots
  • Integrations into existing software systems
• User Experience (UX) Design: Focus on user-centered design to ensure your LLM application is intuitive and user-friendly.
• API Integration: If your LLM serves as a language model backend, create RESTful APIs or GraphQL endpoints to allow other software components to interact seamlessly with the model.
• Scalability and Performance: Design applications to handle different levels of traffic and demand. Optimize for performance and scalability to ensure smooth user experiences. 

Deploying LLM Applications

You’ve developed your LLM app and are ready to deploy it to production. Here’s what you should consider:

• Cloud Deployment: For scalability and easy management, consider deploying your LLM applications on cloud platforms like:
  • AWS
  • Google Cloud
  • Azure
• Containerization: Use containerization technologies like Docker and Kubernetes to package your applications and ensure consistent deployment across different environments. Monitoring: Implement monitoring to track the performance of your deployed LLM applications and detect and address issues in real-time.

Compliance and Regulations

Data privacy and ethical considerations are undercurrents:

• Data Privacy: When handling user data and personally identifiable information (PII), ensure compliance with data privacy regulations.
• Ethical Considerations: Adhere to ethical guidelines when deploying LLM applications to mitigate:
  • Potential biases
  • Misinformation
  • Harmful content generation

You can also use frameworks like LlamaIndex and LangChain to help you build end-to-end LLM applications. 

Related Reading

• LLM Security Risks
• LLM Model Comparison
• AI-Powered Personalization
• What is an LLM Agent
• AI in Retail
• LLM Deployment
• How to Run LLM Locally
• How to Train Your Own LLM

How to Use LLM for Product Innovation

Many industry folks are still building their mental model for LLMs. This leads to reasoning errors about what LLMs can do and how to use them. Two unhelpful mental models many people have regarding LLMs are: 

• LLMs are magic: Anything that a human can do, an LLM can do roughly as well and vastly faster.
• LLMs are the same as reinforcement learning: Small datasets cause Current issues with hallucinations and accuracy. Accuracy problems will be solved with more extensive training sets, and we can rely on confidence scores to reduce the impact of inaccuracies.

These are both wrong in different but essential ways. To avoid falling into those mental models’ fallacies, I’d suggest these pillars for a useful mental model around LLMs: 

• LLMs can predict reasonable responses to any prompt: An LLM will confidently provide a reaction to any textual prompt you write and will increasingly provide a response to text plus other forms of media like images or videos.
• You cannot know whether a given response is accurate: LLMs generate unexpected results, called hallucinations, and you cannot concretely understand when they are wrong. There are no confidence scores generated that help you reason about a specific answer from an LLM.
• You can estimate accuracy for a model and a given set of prompts using evals: You can use evals – running an LLM against a known set of prompts, recording the responses, and evaluating those responses – to evaluate the likelihood that an LLM will perform well in a given scenario.
• You can generally increase accuracy by using a larger model, but it will cost more and have higher latency: For example, GPT 4 is a larger model than GPT 3.5 and generally provides higher-quality responses. Nevertheless, it’s meaningfully more expensive (~20x more expensive) and meaningfully slower (2-5x slower). Nevertheless, the quality, cost and latency are improving at every price point. You should expect the year-over-year performance at a given cost, latency or quality point to meaningfully improve over the next five years (e.g. you should expect to get GPT 4 quality at the price and latency of GPT 3.5 in 12-24 months).
• Models generally get more accurate as the corpus it’s built from grows in size: The accuracy of reinforcement learning tends to grow predictably as the dataset grows. That remains generally true for LLMs but is less predictable. Small models typically underperform large models. Large models generally outperform small models with higher-quality data. 

Supplementing large general models with specific data is called “fine-tuning,” it’s currently ambiguous whether fine-tuning a smaller model will outperform a larger model. You can run evaluations based on the available models and fine-tune datasets for your specific use case.

• Even the fastest LLMs are slower: A fast LLM might take 10+ seconds to provide a reasonably sized response. It might take a minute or two to complete if you need to perform multiple iterations to refine the initial response or to use a larger model. These will get faster, but they aren’t fast today.
• Even the most expensive LLMs are not that expensive for B2B usage: The cheapest LLM is costly for Consumer usage. Because pricing is driven by usage volume, this technology is straightforward to justify for B2B businesses with more minor, paying usage. Conversely, it’s very challenging to figure out how you will pay for significant LLM usage in a Consumer business without the risk of significantly shrinking your margin.

These aren’t perfect, but hopefully, they provide a good foundation for reasoning about what will or won’t work when applying LLMs to your product. With this foundation, it’s time to dig into more specific subtopics.

Rethink Your Workflows for LLM Integration

The workflows in most modern software are not designed to maximize the benefits of LLMs. This is hardly surprising–they were built before LLMs became common–but it does require some rethinking about workflow design. To illustrate this point, let’s think of software for a mortgage provider: 

• User creates an account.
• The product asks the user to fill in a bunch of data to understand the sort of mortgage the user wants and the user’s eligibility for such a mortgage.
• The product asks the user to provide paperwork to support the data the user just provided, perhaps some recent paychecks, bank account balances, etc.
• The internal team validates the user’s data against the user’s paperwork.

In that workflow, LLMs can still provide significant value to the business, as you could increase the efficiency of validating the paperwork matching the user-supplied information. Nevertheless, the users won’t see much benefit other than faster application validation. You can adjust the workflows to make them more valuable: 

• User creates an account.
• Product asks the user to provide paperwork.
• Product uses LLM to extract values from paperwork.
• User validates the extracted data is correct, providing some adjustments.
• Internal team reviews the user’s adjustments and any high-risk issues a rule engine raises.

Reimagining User Experiences with LLMs

Although these two products are functionally equivalent in technical complexity, their user experiences are radically different. The internal team experience is also improved. Many existing products will find that rethinking their workflows can only significantly benefit their user experiences from LLMs.

Retrieve Information to Improve LLM Responses

Models have a maximum token window of text they’ll consider in a given prompt. The 

maximum size of token windows is expanding rapidly. Still, larger token windows are slower to evaluate and cost more to evaluate, so even expanding token windows doesn’t solve the entire problem. 

Leveraging RAG for Complex Queries

One solution to navigate large datasets within a fixed token window is Retrieval Augmented Generation (RAG). To create a concrete example, you might want to create a dating app that matches individuals based on their free-form answer to the question, “What is your relationship with books, TV shows, movies and music, and how has it changed over time?” 

No token window is large enough to include every user’s response from the dating app’s database in the LLM prompt. Still, you could find twenty plausible matching users by filtering on location and then include those twenty users’ free-form answers and match amongst them.This makes a lot of sense, and the two-phase combination of an unsophisticated algorithm to get plausible components of a response and an LLM to filter through and package the plausible responses into an actual response works pretty well. 

The Importance of Effective Retrieval in RAG

I see folks get into trouble by treating RAG as a solution to a search problem rather than recognizing that RAG requires useful search as part of its implementation. A practical approach to RAG depends on a high-quality retrieval and filtering mechanism that works well on a non-trivial scale. 

For example, with a high-level view of RAG, some folks might think they can replace their search technology (e.g. Elasticsearch) with RAG, but that’s only true if your dataset is minimal. You can tolerate much higher response latencies. 

The Pitfalls of Oversimplified RAG Approaches

The challenge, from my perspective, is that most corner-cutting solutions look like they’re working on small datasets while letting you pretend that things like search relevance don’t matter, while in reality relevance significantly impacts the quality of responses when you move beyond prototyping (whether they’re search relevance or are better tuned SQL queries to retrieve more appropriate rows). 

This creates a false expectation of how the prototype will translate into a production capability, with all the predictable consequences: underestimating timelines, poor production behavior/performance, etc.

Expect Improvements Over Time 

Model performance, essentially the quality of response for a given budget in dollars or milliseconds, will continue to improve. But, it will not continue improving at this rate absent significant technological breakthroughs in creating or processing LLMs. 

I’d expect those breakthroughs to happen less frequently after the first several years and slowly from there. It’s hard to determine where we are in that cycle because there’s still an extraordinary amount of capital flowing into this space. 

The Limits to LLM Scale

In addition to technical breakthroughs, the other aspect driving innovation is building increasingly large models. It’s unclear if today’s limiting factor for model size is the availability of Nvidia GPUs, larger datasets to train models upon that are plausibly legal, capital to train new models, or financial models suggesting that the discounted future cashflow from training larger models doesn’t meet a reasonable payback period. 

The Geopolitical Implications of LLM Development

All of these have or will be the limiting constraint on LLM innovation over time, and various competitors will be best suited to make progress depending on which constraint is most relevant. (Lots of fascinating albeit fringe scenarios to contemplate here, e.g. imagine a scenario where the US government disbands copyright laws to allow training on larger datasets because it fears losing the LLM training race to countries that don’t respect US copyright laws.) 

The Future of LLM Performance

It’s safe to assume model performance will continue to improve. It’s likely true that performance will significantly improve over the next several years. I find it relatively unlikely that we’ll see a Moore’s Law scenario where LLMs continue to improve radically for several decades, but many things could easily prove me wrong. 

For example, nuclear fusion will eventually become mainstream and radically change how we think about energy utilization, truly rewriting the world’s structure. LLM training costs could be one part of that. 

Keep Humans in the Loop 

Because you cannot rely on LLMs to provide correct responses, and you cannot generate a confidence score for any given response, you have to accept potential inaccuracies either (which makes sense in many cases; humans are wrong sometimes too) or keep a Human-in-the-Loop (HITL) to validate the response. As discussed in the workflow section, many companies already have humans performing validation work who can now move into supervising LLM responses rather than generating them themselves. In other scenarios, adjusting your product’s workflows to rely on external users to serve as the HITL instead is possible. I suspect most products will depend on techniques and heuristics to determine when internal review is necessary. 

Hallucinations and Legal Liability

As mentioned before, LLMs often generate confidently wrong responses. HITL is the design principle that prevents acting on confidently wrong responses. This is because it shifts responsibility (specifically, legal liability) away from the LLM and to the specific human. For example, if you use Github Copilot to generate some code that causes a security breach, you are responsible for that security breach, not Github Copilot. 

Today, every large-scale adoption of LLMs is done in a mode that shifts responsibility for the responses to a participating human. Many early-stage entrepreneurs dream of a world with a very different loop where LLMs are relied upon without a HITL. Still, I think that will only be true for scenarios where it’s possible to shift legal liability (e.g. GitHub Copilot example), or there’s no legal liability (e.g. generating a funny poem based on their profile picture). 

Zero to One” Versus “One to N

There’s a strong desire for a world where LLMs replace software engineers, or software engineers move into a supervisory role rather than writing software. For example, an entrepreneur wants to build a copy of Reddit and uses an LLM to implement that. There’s enough evidence to assume it’s possible today to go from zero to one on a new product idea in a few weeks with an LLM and some debugging skills. Most entrepreneurs need a more profound intuition for operating and evolving software with a meaningful number of users. Some examples:

• Keeping users engaged after changing the UI requires active, deliberate work.
• Ensuring user data is secure and meets various privacy compliance obligations.
• Providing controls to meet SOC2 and providing auditable evidence of maintaining those controls.
• Migrating a database schema with customer data to support a new set of columns.
• Ratcheting query patterns to a specific set of allowed patterns that perform effectively at a larger scale.

The Limits of LLM-Based Automation

All of these are straightforward, essential components of scaling a product (e.g. going from “one to N”) that an LLM is simply not going to perform effectively at, and where I am skeptical that we’ll ever see an exceptionally reliable LLM-based replacement for skilled, human intelligence. It will be interesting to watch, though, as we see how far folks try to push the boundaries of what LLM-based automation can do to delay the onset of projects needing to hire expertise.

Copyright Law

Copyright implications are very unclear today and will remain unclear for the foreseeable future. All work done today using LLMs has to account for divergent legal outcomes. My best guess is that we will see an era of legal balkanization regarding whether LLM-generated content is copyright-able. In the longer term, LLMs will be viewed the same as any other essential technical component. For example, running a spell checker doesn’t revoke your copyright on the spell-checked document. 

You can make all sorts of good arguments as to why this perspective isn’t fair to copyright holders whose data was trained on, but I don’t think any other interpretation is workable in the long-term. 

Data Processing Agreements

One small but fascinating reality of working with LLMs today is that many customers are sensitive to the LLM providers (OpenAI, Anthropic, etc.) because these providers are relatively new companies building relatively new things with little legal precedent to derisk them. 

Adding them to your Data Processing Agreement (DPA) can create friction. The most obvious way around that friction is to rely on LLM functionality served via your existing cloud vendor (AWS, Azure, GCP, etc.). 

Provider Availability

This was very important. Still, LLM hosting is equivalent to other cloud services (e.g., you can get Anthropic via AWS or OpenAI via Azure), and very few companies will benefit from spending too much time worrying about LLM availability. I think getting direct access to LLMs via cloud providers–companies that are well-versed in scalability–is likely the winning pick here, too.

Related Reading

• How to Fine Tune LLM
• How to Build Your Own LLM
• LLM Function Calling
• LLM Prompting
• What LLM Does Copilot Use
• LLM Evaluation Metrics
• LLM Use Cases
• LLM Sentiment Analysis
• LLM Evaluation Framework
• LLM Benchmarks
• Best LLM for Coding

Top 6 Large Language Models and How to Use Them Effectively

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• Managed GenAI Middleware
• Custom GenAI API (GraphQL)
• Low Code Agent Builder
• Automated GenAI Workflow (CI/CD)
• GenOps (DevOps for GenAI)
• Edge deployment via Cloudflare workers
• Integrated Vector Database (Weaviate)

Lamatic empowers teams to rapidly implement GenAI solutions without accruing tech debt. Their platform automates workflows and ensures production-grade deployment on the edge, enabling fast, efficient GenAI integration for products needing swift AI capabilities. 

2.  GPT-4: The Most Advanced LLM Available

GPT-4 is one of the most advanced LLM models available today. OpenAI has built an impressive product around it, with an effective ecosystem that allows you to create plugins and execute code and functions.

GPT-4 is particularly good at text generation and summarization. “If you look at GPT-4,” Madhukar Kumar, CMO of SingleStore, a relational database company, said, “it is a little bit more conservative but it is far more accurate than 3.5 was, particularly around code generation.”

3. Claude 2: An LLM With an Amazing Context Window

Claude 2 from Anthropic was released in July 2023. It can be accessed via an API and a new public-facing beta website, claude.ai. Claude's main advantage is the size of the context window, which was recently expanded from 9K to 100K tokens, considerably more than the maximum 32k tokens supported by GPT-4 at the time of this writing. 

This corresponds to around 75,000 words, which allows a business to submit hundreds of pages of material for Claude to digest. 

4. Llama 2: The First Open Source LLM on Our List

Llama 2, just released from Meta, is the first open-source model on our list, though some industry observers dispute Meta’s characterization of it as “open source.” It is free for both research and commercial use, but the license has some oddly specific restrictions. 

For example, if the technology is used in an application or service with more than 700 million monthly users, a special license is required from Meta. The community agreement also forbids using Llama 2 to train other language models. While there are advantages to open source, particularly for research, the high cost of training and fine-tuning models means that, at least at the moment, commercial LLMs will generally perform better.

As the Llama 2 whitepaper described, “[C]losed product LLMs are heavily fine-tuned to align with human preferences, which greatly enhances their usability and safety. This step can require significant costs in compute and human annotation, and is often not transparent or easily reproducible, limiting progress within the community to advance AI alignment research.” 

In February, Meta released the precursor of Llama 2, LLaMA, as source-available with a non-commercial license. It soon leaked and spawned several fine-tuned models built on top of it, including Alpaca from Stanford University and Vicuna, developed by a team from the:

• University of California
• Berkeley
• Carnegie Mellon University
• Stanford
• UC San Diego 

The Promise and Limitations of Open-Source LLMs

Both of these models used a unique approach to training with synthetic instructions, but while they show promise, the Llama 2 paper again suggested: “they fall short of the bar set by their closed-source counterparts.” That said, you don’t have to pay to use an open-source model, so while deciding whether this technology is useful in your particular use case, Llama 2 could be an excellent place to start. 

5. Orca: A smaller Open Source Model With Big Ideas

Orca, from Microsoft Research, is the most experimental model we’ve selected. It is particularly interesting because it is a smaller open-source model that uses a different technique called progressive learning to train itself from the large foundation models. 

This means that Orca can learn from models like GPT-4 through imitation, improving its own reasoning capabilities. This may indicate a way that open-source models can better compete with their closed-sourced counterparts in the future, and as such, Orca is an interesting model to keep an eye on. 

6. Cohere: An LLM for Businesses

Cohere is another commercial offering. The company behind it was co-founded by Aidan Gomez, who was co-author of the seminal transformer research paper “Attention Is All You Need.” Cohere is being positioned as a cloud-neutral vendor and is targeting enterprises, as indicated by the company’s recently announced partnership with McKinsey. 

Picking an LLM

Once you’ve drawn up a shortlist of LLMs, and have identified one or two low-risk use cases to experiment with, you have the option of running multiple tests using different models to see which one works best for you, as you might do if you were evaluating an observability tool or similar. It’s also worth considering whether you can use multiple LLMs in concert. 

“I think that the future is not just picking one but an ensemble of LLMs that are good at different things,” Kumar told us.

Of course, this is only useful if you have timely access to data. During our conversation, Kumar suggested this was where contextual databases like SingleStore come in. “To truly use the power of LLMs,” he said, “you need the ability to do both lexical and semantic search, manage structured and unstructured data, handle both metadata and the vectorized data, and handle all of that in milliseconds, as you are now sitting between the end user and the LLM’s response.”

Related Reading

• Best LLM for Data Analysis
• Rag vs LLM
• AI Application Development
• Gemini Alternatives
• AI Development Platforms
• Best AI App Builder
• LLM Distillation
• AI Development Cost
• Flowise AI
• LLM vs SLM
• SageMaker Alternatives
• LangChain Alternatives
• LLM Quantization

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