Basic Knowledge
February 15, 2024

All you need to know to Develop using Large Language Models

LLMs leverage deep learning to mimic human language, evolving AI applications across fields, underpinned by ethics, and paving the way for innovative uses in content generation, translation, and more.

In the fast-evolving realm of artificial intelligence (AI), Large Language Models (LLMs) have emerged as key players. These sophisticated AI systems harness deep learning methods and extensive data pools to understand, summarize, create, and forecast content. Whether you're dipping your toes into LLMs or thinking about harnessing their potential, this guide is designed to provide you with a comprehensive insight into LLMs and how they're applied across different fields.

What is AI, ML, and LLM

Artificial Intelligence (AI), Machine Learning (ML), and Large Language Models (LLM) are all interrelated concepts within the field of computer science, but they operate at different levels of complexity and specialization. Here's how they relate and differ:

  • Artificial Intelligence (AI): This is the broadest category among the three. AI refers to machines or software that can perform tasks which typically require human intelligence. These tasks include reasoning, learning, perception, problem-solving, and language understanding. AI systems can range from simple rule-based systems to complex neural networks.
  • Machine Learning (ML): ML is a subset of AI focused on the idea that machines can learn from data, identify patterns, and make decisions with minimal human intervention. It uses algorithms to parse data, learn from it, and make informed decisions based on what it has learned. ML includes various techniques such as supervised learning, unsupervised learning, and reinforcement learning.
  • Large Language Models (LLM): LLMs are a specific application within ML, typically utilizing deep learning techniques. They are designed to understand, generate, and translate human language. These models are "large" because they are trained on vast datasets and have millions or even billions of parameters that help them understand the nuances and complexities of language. OpenAI's GPT-3 is a well-known example of an LLM.

In summary, LLMs are specialized ML models that fall under the overarching umbrella of AI. They are specifically engineered to handle tasks related to natural language processing and are a testament to the advanced capabilities of ML in the domain of AI. Each serves its purpose and contributes to the advancement of intelligent systems, with LLMs representing some of the most sophisticated applications in the field today.

Understanding Large Language Models (LLMs)

A Large Language Model (LLM) is a deep learning algorithm that leverages extensive datasets to comprehend and generate content. Often known as llm fire in the AI realm, these models are a subset of generative AI, specifically tailored for text generation.

The core of LLMs lies in their architecture - they are modeled after the human brain, mimicking neural networks. These networks utilize nodes layered like neurons in the brain. However, LLMs are far from emulating the full capacity of the human brain. They are merely modeled upon our understanding of neural functions.

A distinctive feature of LLMs is their reliance on transformer models which are ideally suited for natural language processing tasks. These models are capable of transforming tokenized input into meaningful output by identifying relationships between tokens.

Working Mechanism of LLMs

The working of LLMs is anchored in the utilization of transformer models. These models comprise two basic components - an encoder and a decoder. The encoder processes the input text into a tokenized form. The decoder then interprets these tokens using mathematical computations to discern relationships among them.

A distinct characteristic of transformer models is the self-attention mechanism. This mechanism allows the LLM to understand the context by considering different parts of the input sequence while generating predictions. The self-attention mechanism bestows upon the LLM the ability to identify patterns that could evade the human eye.

LLMs operate through a two-step process - pre-training and fine-tuning. In the pre-training phase, they learn to predict a word in a sentence given the other words. This phase typically involves training on large, unlabeled datasets. The fine-tuning phase involves additional training on a smaller, task-specific dataset, enabling the LLM to adapt to specific tasks.

Use Cases of Large Language Models

The potential applications of LLMs are vast. They can power AI assistants, facilitate translation, generate code, summarize content, enhance search capabilities, and much more.

AI Assistants

AI assistants have leveraged the capabilities of LLMs to automate tasks, answer queries, and provide personalized recommendations.


LLMs can be employed to build chatbots tailored to specific tasks, such as answering customer queries. These bots can mimic human conversation patterns, making them ideal for customer service applications. Moreover, LLM-powered chatbots can provide 24/7 customer support, enhancing the user experience. Example is a chatbot built using LLAMA 2 and Falcon, as described in this DataCamp blog post.

Text Generation

One of the primary applications of LLMs is text generation. They can create stories, generate marketing content, or even produce code. LLMs excel in understanding what comes next in a text, making them valuable for a plethora of writing tasks.

Many models such as  the Qwen, LLaMA, and Nous series, are large-scale, multimodal, or general-purpose models that likely have the capability to perform a wide range of tasks, including translation and code generation, due to their extensive training on diverse datasets:

Qwen 1.5 Chat and Qwen 1.5

Indicate a range of capabilities, potentially including translation and code generation, with "Chat" versions possibly being more conversational.

LLaMA-2 series: Includes various sizes and specializations, some of which are instruct versions, indicating enhanced capabilities for following instructions, which could encompass code generation.

Experimental Models

These models, which appear to be newer versions or experimental iterations, could offer unique advantages in content generation.

When using these models for poetry and literature generation, the key is to provide detailed and creative prompts that guide the model towards the desired output. The more context and direction you can provide, the more likely the model will generate content that aligns with your creative goals. Additionally, experimenting with different models and settings can help you find the best match for your specific creative writing project.Their capacity to understand nuances in language makes them excellent choices for generating poetry, stories, and other literary forms.

Translation and Code Generation

LLMs are adept at language translation. They can handle complex tasks like translating text into code. For instance, if a user instruction requires code generation in a specific programming language, an LLM can generate the required code.

Models like "Nous Hermes 2 - Mistral DPO (7B)" and others in the Hermes, Mistral, and Mixtral series might be capable of handling translation tasks due to their general-purpose design and large-scale training data.
Several models in the list are specifically tailored for code generation or are capable of handling such tasks:

Gemma Instruct

Likely designed for following instructions, which could include code generation tasks.

Code Llama Instruct

Explicitly designed for code generation with a focus on understanding and generating programming code.

Code Llama Python

Tailored for Python code generation, indicating a specialization in generating Python programming language code.

Deepseek Coder Instruct

Suggests a focus on code generation or instruction-based coding tasks.

WizardCoder Python v1.0

Specifically designed for generating Python code.

Phind Code LLaMA v2

While not explicitly stated as a code generation model, the inclusion of "Code" suggests potential capabilities in this area.

Nous Hermes-2 Yi, 01-ai Yi:

Models with names suggesting a focus on linguistic capabilities might also be good candidates for translation tasks, assuming they've been exposed to multilingual training data.


LLMs can summarize long documents, research papers, meeting notes, and articles. This function can make information more accessible and digestible, aiding in faster decision-making processes.

Models that are fine-tuned to follow instructions can be particularly effective for summarization tasks. By providing clear instructions on what you need summarized and how (e.g., length, style), these models can generate concise and relevant summaries.

Platypus2 Instruct:

If this model is designed with a focus on language understanding and generation, it could be well-suited for summarization, especially if it has been trained or fine-tuned on summarization-specific datasets.

Mistral (7B) Instruct,

Mixtral-8x7B Instruct

Models with specific instruct capabilities and newer versions might offer improvements in generating accurate and coherent summaries, especially if summarization was a focus of their training or fine-tuning.

Enhanced Search

LLMs can enhance search capabilities by understanding natural language queries. Unlike basic keyword-based searches, LLM-powered search engines can provide context-aware and relevant results, revolutionizing real-time search capabilities.




These models are explicitly designed for information retrieval tasks. The "BERT-Retrieval" part of their names suggests they leverage BERT (Bidirectional Encoder Representations from Transformers) for understanding the context of both the search queries and the documents or data they are searching through. The numbers (32k, 8k, 2k) likely indicate different sizes or configurations, which could impact their speed, accuracy, or the scale of data they are optimized to handle. These models would be particularly good at understanding the nuances of natural language queries and finding the most relevant information within large datasets or document collections.

Vision and Image

In the domain of vision and image models, the core technology shifts from transformers to convolutional neural networks (CNNs) and more recently, to vision transformers (ViTs), which are adept at processing visual data. These models excel in recognizing patterns, textures, and shapes within images, enabling sophisticated applications ranging from image classification and object detection to complex image generation and enhancement tasks.

  • Stable Diffusion, Realistic Vision: These models are focused on generating and manipulating images, useful for art creation, photo editing, and more.
  • Analog Diffusion: Likely another variant in the realm of image generation and manipulation, with specific features or optimizations.

Advantages of Large Language Models

LLMs offer several advantages, making them an attractive choice for various applications.

Extensibility and Adaptability

LLMs serve as a foundation for customized use cases. Additional training can create a finely tuned model for an organization's specific needs.


One LLM can be used for many different tasks and deployments across organizations, users, and applications.


Modern LLMs are typically high-performing, with the ability to generate rapid, low-latency responses.


As the number of parameters and the volume of trained data grow in an LLM, the transformer model can deliver increasing levels of accuracy.

Ease of Training

Many LLMs are trained on unlabeled data, which helps to accelerate the training process.

Challenges and Limitations of Large Language Models

While LLMs offer many benefits, they also come with their own set of challenges and limitations.

Development Costs

LLMs generally require large quantities of expensive graphics processing unit hardware and massive data sets to run, increasing the development costs.

Operational Costs

Following the training and development phase, the cost of operating an LLM for the host organization can be substantial.


A risk with any AI trained on unlabeled data is bias, as it's not always clear that known bias has been removed.


The ability to explain how an LLM was able to generate a specific result is not easy or obvious for users.


AI hallucination occurs when an LLM provides an inaccurate response that is not based on trained data.


With billions of parameters, modern LLMs are exceptionally complicated technologies that can be particularly complex to troubleshoot.

Glitch Tokens

Glitch tokens are maliciously designed prompts that cause an LLM to malfunction, leading to an emerging trend since 2022.

Different Types of Large Language Models

There are several types of LLMs, each designed to meet specific needs.

Zero-shot Model

A zero-shot model is a large, generalized model trained on a generic corpus of data that can give a fairly accurate result for general use cases, without the need for additional training.

Fine-tuned or Domain-specific Models

Additional training on top of a zero-shot model can lead to a fine-tuned, domain-specific model. One example is OpenAI Codex, a domain-specific LLM for programming based on GPT-3.

Language Representation Model

One example of a language representation model is Bidirectional Encoder Representations from Transformers (BERT), which makes use of deep learning and transformers well suited for NLP.

Multimodal Model

Multimodal models handle both text and images. GPT-4 is an example of this type of model.

When to Fine-tune LLMs?

Fine-tuning is a more advanced technique, often requiring a substantial level of expertise in LLMs. It should be considered when the model requires specific styles, patterns, specialized skills, or internal model improvement. Fine-tuning involves updating model parameters through training on selected data, enhancing the model from within, while prompt engineering enhances the model externally.

Parameter-efficient Fine-tuning (PEFT)

PEFT reuses a pre-trained model to reduce computational and resource requirements. PEFT techniques include prompt tuning and low-rank adaptation (LoRA).

Reinforcement Learning with Human Feedback (RLHF)

In RLHF, a pre-trained model is fine-tuned via a combination of supervised and reinforcement learning. Human feedback collected by ranking or rating various model outputs creates a reward signal, which trains a reward model, guiding the LLM’s adaptation to human preferences.

Optimization Techniques for Fine-tuning

To enhance fine-tuning efficiency, consider employing quantization and zero-redundancy optimization.


Quantization is the process of reducing the precision of numerical data so it consumes less memory and increases processing speed. However, lower precision leads to lower accuracy because less information is being stored within each layer.

Zero-redundancy Optimization

Zero-redundancy optimization is a technique that reduces redundancy in model parameters. This technique can significantly reduce the memory footprint of the model, making it more efficient to train.

What are Prompts?

A prompt is the input given by a user, and the model responds based on that input. A prompt can be a question, a command, or any kind of input, depending on what's needed for a particular use case. There are several types of prompts, including zero-shot prompts, few-shot prompts, chain of thought (CoT) prompting, and more.

Zero-shot Prompts

A prompt that doesn't provide specific examples for how the model should respond is called a “zero-shot prompt.”

Few-shot Prompts

Few-shot prompts include examples within the prompt, enabling in-context learning. In this approach, the model learns from both the instructions and the provided examples to understand its task.

Chain of Thought (CoT) Prompts

CoT prompting encourages the model to provide explanations for its reasoning. Combining it with few-shot prompting can yield improved results for more intricate tasks that demand prior reasoning before generating responses.

In-context Learning (ICL)

In-context learning (ICL) entails providing context within prompts, which can be in the form of examples (few-shot prompting) or additional information. This approach empowers pre-trained LLMs to assimilate new knowledge.

What is Retrieval Augmented Generation (RAG)?

The Retrieval Augmented Generation (RAG) retrieves current context-specific information from an external database. This updated information is then fed into the LLM to generate accurate responses. RAG also enables the LLM to cite resources while generating responses.

LLM Parameters

When provided with a prompt, an LLM can generate a long list of potential responses. It operates like a prediction engine. However, in practice, LLMs typically provide a single output that represents the most likely response according to the model.


The performance of a pre-trained LLM relies on its size: Larger models tend to produce higher-quality responses. However, bigger models increase costs and require more computational resources.


Temperature influences the model's creativity. Lower temperatures yield consistent and predictable results, while higher temperatures introduce randomness, resulting in more creative outputs.

Setting the temperature below 1.0 narrows the model's choices, making it opt for the most probable words and thus producing more consistent and less varied responses. This setting is ideal for instances where reliability and caution are prioritized over novelty, although it might lead to outputs that seem less inspired or more mechanical in nature.

Conversely, increasing the temperature above 1.0 introduces a higher degree of unpredictability in the text generation process. The model ventures beyond the most likely options, incorporating less common selections that can enhance creativity and diversity in the output. This, however, carries the risk of generating content that may be more error-prone or illogical, as it steers away from the typical patterns seen in the training data.

A temperature setting of 1.0 seeks a middle ground, maintaining a balance between predictability and novelty. With this setting, the model endeavors to produce text that is coherent and reflective of its training, yet not overly constrained, offering a mix of reliability and inventiveness.

Top-p and Top-k

Top-p and Top-k selection involve choosing tokens from the highest-probability options. The sum of their probabilities determine the selection.

Number of Tokens

Tokens serve as the fundamental units of text in LLMs. A token doesn't always represent a single word; it can also encompass a group of characters.

Stop Sequences

Stop sequences can be employed to instruct the model to stop its token generation at a specific point, such as the end of a sentence or a list. It proves useful when you intend to stop token generation immediately upon encountering a stop sequence.

Repetition Penalty

Repetition penalty discourages the repetition of tokens that have appeared recently in the generated text. It encourages the model to produce more diverse tokens by reducing the likelihood of selecting tokens with higher scores.


Large Language Models present a revolutionary leap in the realm of AI, offering a wide array of applications and benefits. Despite certain limitations and challenges, the potential of these models is tremendous. As we continue to refine and fine-tune these models, we edge closer to a future where AI will significantly augment human productivity, making LLMs a valuable asset in the AI toolkit.

Our AI/ML API offers a state-of-the-art gateway to over 100 advanced large language models (LLMs). This diverse set of AI models is carefully designed to meet a wide range of industry requirements, ensuring that no matter how complex or specific your project, our technology can be the foundation for realizing your innovative ideas. Whether you're looking to build sophisticated chatbots, advanced text analytics tools, or any other AI-based application, our API provides the power and versatility you need to bring your ideas to life with efficiency and scale.

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