A detailed glossary of essential terminology for understanding large language models and their applications.
Glossary of Key Terms
Foundation Model: A large language model (LLM) pre-trained on a massive dataset, capable of understanding and generating human-like text across a wide range of tasks.
Fine-Tuning: The process of taking a pre-trained LLM and further training it on a smaller, task-specific dataset to adapt its weights and improve its performance on that particular task or domain.
RAG (Retrieval-Augmented Generation): A framework that enhances LLM responses by retrieving relevant information from an external Knowledge Base and incorporating it into the generation process.
Knowledge Base (KB): A collection of documents or data from which relevant information is retrieved in a RAG system.
Vector Database: A specialized database that stores vector representations (embeddings) of data, optimized for efficient similarity searches used in RAG for retrieving relevant information.
Prompting: The act of providing carefully crafted input text to an LLM to guide its output generation and elicit desired responses.
Zero-Shot Learning: The ability of an LLM to perform a task based solely on the task instructions, without any prior examples.
Few-Shot Learning: The ability of an LLM to learn and perform a task given only a very small number of examples in the prompt.
Instruction Tuning: A fine-tuning technique where the training data includes specific instructions paired with desired outputs, improving the LLM’s ability to follow instructions effectively.
Hallucination: The tendency of LLMs to generate incorrect, nonsensical, or factually inconsistent information that is not supported by the input context or their training data.
Context Length: The maximum number of input tokens or words that an LLM can process and consider when generating an output.
Transformer: A popular neural network architecture widely used in LLMs, known for its attention mechanism that allows it to weigh the importance of different parts of the input sequence and its parallel processing capabilities.
In-Context Learning: The ability of an LLM to learn a new task by being provided with examples directly within the prompt, without requiring explicit fine-tuning.
Quantization: A technique used to reduce the computational resources and memory footprint of an LLM by decreasing the precision of its parameters.
Freeze Tuning: A fine-tuning method where most of the LLM’s parameters are kept frozen, and only a small subset of layers or parameters are updated during training.
Contrastive Learning: A fine-tuning approach that trains LLMs to understand the similarity and differences between data points, often used for improving the quality of embeddings.
RLHF (Reinforcement Learning from Human Feedback): A technique used to align LLM behavior with human preferences by using human feedback as a reward signal to train the model.
Reward Modeling: A component of RLHF where a separate model is trained to predict human preference scores for different LLM outputs, serving as the reward signal for reinforcement learning.
Pruning: A technique used to reduce the size and computational cost of LLMs by removing redundant or less important connections or parameters.
LoRA (Low-Rank Adaption): A Parameter-Efficient Fine-Tuning (PEFT) method that inserts a smaller set of new weight matrices into the LLM and trains only these new parameters, significantly reducing the number of trainable parameters.
SFT (Supervised Fine-Tuning): The process of updating a pre-trained LLM with labeled data (input-output pairs) to make it perform a specific task.
Transfer Learning: A machine learning technique where knowledge gained from training on a large dataset is applied to improve the performance on a smaller, related task.
PEFT (Parameter-Efficient Fine-Tuning): Techniques that update only a small fraction of an LLM’s parameters during fine-tuning, making the process more computationally efficient and cost-effective.
Agent Planning: A module in LLM applications that breaks down complex tasks into smaller, manageable steps to fulfill user requests.
LLM Agent: An application that combines the capabilities of an LLM with other modules like planning, memory, and tool use to execute complex tasks.
Agent Memory: A module that allows an LLM agent to store and recall past interactions and experiences, enabling more coherent and context-aware behavior.
Function Calling: The ability of LLM agents to interact with external tools and APIs to gather information or perform actions required to complete a task.
Vector Search: The process of finding the most relevant vector representations in a vector database based on similarity to a query vector.
Indexing: The process of organizing and structuring data in a Knowledge Base (KB) to enable efficient retrieval. In the context of RAG, it often involves converting KB chunks into vector embeddings and storing them in a vector database.
Embedding Model: An LLM or a specialized model that converts text or other data into numerical vector representations (embeddings).
AGIRetrieval: An approach used to rank and fetch Knowledge Base (KB) chunks from the vector search results, which will then be used as additional context for the LLM in RAG.
Chunking: The process of dividing large documents or the Knowledge Base into smaller, more manageable pieces (chunks) for efficient storage and retrieval in RAG.
Artificial General Intelligence (AGI): The theoretical ability of a machine to perform any intellectual task that a human being can, across a wide range of domains.
LLM Bias: Systematic and unfair prejudices present in an LLM’s predictions, often originating from biases in the training data.
Responsible AI: An overarching framework encompassing principles and practices aimed at ensuring the ethical, fair, and transparent development and deployment of AI systems.
GDPR Compliance: Ensuring that the development and deployment of AI systems adhere to the regulations outlined in the General Data Protection Regulation, which protects individuals’ privacy rights in the European Union.
AI Governance: The set of rules, policies, and frameworks that regulate the development and deployment of AI systems.
XAI (Explainable AI): Techniques and methods used to make the outputs and decision-making processes of AI models understandable and transparent to humans.
LLMOps: A set of practices and tools for managing and optimizing the entire lifecycle of LLM deployment, including development, training, deployment, monitoring, and maintenance.
Alignment: The process of ensuring that the behavior and outputs of an LLM are consistent with human values, intentions, and ethical principles.
Model Ethics: Principles and guidelines that promote ethical behavior (transparency, fairness, accountability, etc.) when deploying AI models, especially those that are publicly facing.
PII (Personally Identifiable Information): Any information that can be used to identify an individual. Handling PII requires careful processes and user consent.
Privacy-preserving AI: Techniques and methods used to train and utilize LLMs while safeguarding the privacy of sensitive data.
Adversarial Defense: Methods and techniques designed to protect LLMs against malicious attempts to manipulate their behavior or exploit vulnerabilities.
Prompt Injection: A type of adversarial attack where carefully crafted inputs are used to trick an LLM into deviating from its intended purpose or revealing sensitive information.
Adversarial Attacks: Deliberate attempts to manipulate LLMs through crafted inputs, causing them to produce incorrect, unexpected, or harmful outputs.
Jailbreaking: A type of adversarial attack that attempts to bypass the safety measures and constraints of an LLM to make it generate unsafe or prohibited content.
Red-Teaming: A security assessment process involving simulated adversarial attacks to identify vulnerabilities and weaknesses in LLM systems.
Prompt Leaking: An adversarial technique that tricks an LLM into revealing parts of its original prompt or internal workings.
Robustness: The ability of an LLM to maintain its performance and accuracy even when encountering noisy, unexpected, or adversarial inputs.
Black-Box Attacks: Adversarial attacks where the attacker has no knowledge of the LLM’s internal architecture or parameters and can only interact with it through its input and output.
White-Box Attacks: Adversarial attacks where the attacker has full knowledge of the LLM’s internal architecture, parameters, and training data.
Vulnerability: A weakness or flaw in an LLM system that can be exploited for malicious purposes, such as adversarial attacks or data breaches.
Deep-fakes: Synthetic media (images, videos, audio) generated by AI models, often used to create realistic but fake content.
Watermarking: Embedding hidden, detectable markers into LLM-generated content to identify its origin and potentially combat the spread of misinformation.
Unsupervised Learning: A machine learning paradigm where models learn patterns and structures from unlabeled data without explicit guidance or correct answers.
Supervised Learning: A machine learning paradigm where models learn from labeled data, associating inputs with their corresponding correct outputs.
Reinforcement Learning: A machine learning paradigm where an agent learns through trial and error by interacting with an environment and receiving rewards or penalties based on its actions.
Federated Learning: A decentralized machine learning approach where models are trained across multiple devices or organizations without sharing the raw data.
Online Learning: A learning paradigm where a model continuously learns from a stream of incoming data, updating its knowledge in real-time.
Continual Learning: A learning paradigm focused on enabling models to learn from a sequence of tasks or data without forgetting previously learned knowledge.
Multi-task Learning: A learning approach where a single model is trained to perform multiple different tasks, often leveraging shared knowledge between related tasks to improve performance.
Adversarial Learning: A learning paradigm that involves training models against adversarial examples or competing models to improve their robustness and ability to generalize.
Active Learning: A learning approach where the model strategically selects the most informative data points for human labeling to improve learning efficiency.
Meta-Learning: Also known as “learning to learn,” this paradigm focuses on training models to acquire general knowledge and learning skills that can be quickly applied to new, unseen tasks with minimal data.
Quiz and Answer Key
Explain the core functionality of a Foundation Model and provide a key characteristic that distinguishes this type of LLM.
A Foundation Model is an LLM designed to generate and understand human-like text across a wide range of use-cases. A key characteristic is its broad pre-training on massive datasets, enabling it to perform diverse tasks with minimal or no task-specific fine-tuning.
Describe the process of Fine-Tuning an LLM. What is the primary goal of this process?
Fine-tuning is the process of adapting a pre-trained LLM to a specific task or domain by further training it on task-specific data. The primary goal is to improve the LLM’s performance and accuracy on the targeted application.
What is Retrieval-Augmented Generation (RAG)? Briefly outline the roles of the Knowledge Base and Vector Database in this process.
Retrieval-Augmented Generation (RAG) is a framework that enhances LLM responses by retrieving relevant information from an external Knowledge Base and appending it to the prompt. The Knowledge Base is a collection of documents, while the Vector Database stores vector representations of this KB to enable efficient similarity-based retrieval.
Differentiate between Zero-Shot Learning and Few-Shot Learning in the context of prompting LLMs for specific tasks.
In Zero-Shot Learning, an LLM is given only task instructions and must rely solely on its pre-existing knowledge to perform the task. In contrast, Few-Shot Learning provides the LLM with a very small number of examples alongside the task instructions to guide its output generation.
Define Instruction Tuning and explain how it aims to improve the behavior of an LLM.
Instruction Tuning involves adjusting an LLM’s behavior during fine-tuning by providing specific instructions along with the training data. This process aims to improve the LLM’s ability to follow instructions and generate more accurate and relevant responses based on those instructions.
What is Hallucination in the context of LLMs? Provide a brief example of what this might look like.
Hallucination in LLMs refers to the tendency of these models to sometimes generate incorrect, nonsensical, or factually inconsistent information that is not grounded in the provided context or their training data. An example could be an LLM generating a fictitious historical event or attributing a quote to the wrong person.
Explain the concept of Context Length and why it is a significant factor in LLM performance.
Context Length is the maximum number of input words or tokens that an LLM can consider when generating an output. It is significant because it limits the amount of information the LLM can process at once, impacting its ability to understand long documents or maintain context over extended conversations.
Describe In-Context Learning and how it differs from traditional fine-tuning methods.
In-Context Learning involves integrating task examples directly into the prompts provided to an LLM, enabling it to understand and handle new tasks without requiring explicit fine-tuning of its weights. This approach leverages the LLM’s pre-existing knowledge and its ability to learn from the provided examples within the prompt itself.
What is Reinforcement Learning from Human Feedback (RLHF)? Briefly explain the role of Reward Modeling in this process.
Reinforcement Learning from Human Feedback (RLHF) is a technique that uses human feedback as a reward or penalty signal to further train an LLM and align its behavior with human preferences. Reward Modeling is a key component where a separate model is trained to predict the human preference score for different LLM outputs, which then serves as the reward signal during reinforcement learning.
Explain the concept of Prompt Injection and why it is considered a security vulnerability for LLMs.
Prompt Injection refers to deliberate attempts to trick LLMs with carefully crafted inputs that manipulate the model’s original instructions and cause it to perform unintended or malicious tasks. This is a vulnerability because it can be exploited to bypass safety measures, extract sensitive information, or generate harmful content.
With the growing number of LLMs like GPT-4o, LLaMA, and Claude, along with many more emerging rapidly, businesses’ key question is how to choose the best one for their needs. This guide will provide a straightforward framework for selecting the most suitable LLM for your business requirements.
Overview
The article introduces a framework to help businesses select the right LLM (Large Language Model) by evaluating cost, accuracy, scalability, and technical compatibility.
When choosing an LLM, it emphasizes that businesses should identify their specific needs—such as customer support, technical problem-solving, or data analysis.
The framework includes detailed comparisons of LLMs based on factors like fine-tuning capabilities, cost structure, latency, and security features tailored to different use cases.
Real-world case studies, such as educational tools and customer support automation, illustrate how different LLMs can be applied effectively.
The conclusion advises businesses to experiment and test LLMs with real-world data, noting there is no “one-size-fits-all” model, but the framework helps make informed decisions.
Why LLMs Matter for Your Business?
Businesses in many different industries are already gaining from Large Language Model capabilities. They can save time and money by producing content, automating customer service, and analyzing data. Also, users don’t need to learn any specialist technological skills; they just need to be proficient in natural language.
But what can LLM do?
LLMs can assist staff members in retrieving data from a database without coding or domain expertise. Thus, LLMs successfully close the skills gap by giving users access to technical knowledge, facilitating the smoothest possible integration of business and technology.
A Simple Framework for Choosing an LLM
Picking the right LLM isn’t one-size-fits-all. It depends on your specific goals and the problems you must solve. Here’s a step-by-step framework to guide you:
1. What Can It Do? (Capability)
Start by determining what your business needs the LLM for. For example, are you using it to help with customer support, answer technical questions, or do something else? Here are more questions:
Can the LLM be fine-tuned to fit your specific needs?
Can it work with your existing data?
Does it have enough “memory” to handle long inputs?
Capability Comparison
LLM
Can Be Fine-Tuned
Works with Custom Data
Memory (Context Length)
LLM 1
Yes
Yes
2048 tokens
LLM 2
No
Yes
4096 tokens
LLM 3
Yes
No
1024 tokens
For instance, Here, we could choose LLM 2 if we don’t care about fine-tuning and focus more on having a larger context window.
2. How Accurate Is It?
Accuracy is key. If you want an LLM that can give you reliable answers, test it with some real-world data to see how well it performs. Here are some questions:
Can the LLM be improved with tuning?
Does it consistently perform well?
Accuracy Comparison
LLM
General Accuracy
Accuracy with Custom Data
LLM 1
90%
85%
LLM 2
85%
80%
LLM 3
88%
86%
Here, we could choose LLM 3 if we prioritize accuracy with custom data, even if its general accuracy is slightly lower than LLM 1.
3. What Does It Cost?
LLMs can get expensive, especially when they’re in production. Some charge per use (like ChatGPT), while others have upfront costs for setup. Here are some questions:
Is the cost a one-time fee or ongoing (like a subscription)?
Is the cost worth the business benefits?
Cost Comparison
LLM
Cost
Pricing Model
LLM 1
High
Pay per API call (tokens)
LLM 2
Low
One-time hardware cost
LLM 3
Medium
Subscription-based
If minimizing ongoing costs is a priority, LLM 2 could be the best choice with its one-time hardware cost, even though LLM 1 may offer more flexibility with pay-per-use pricing.
4. Is It Compatible with Your Tech?
Make sure the LLM fits with your current tech setup. Most LLMs use Python, but your business might use something different, like Java or Node.js. Here are some questions:
Does it work with your existing technology stack?
5. Is It Easy to Maintain?
Maintenance is often overlooked, but it’s an important aspect. Some LLMs need more updates or come with limited documentation, which could make things harder in the long run. Here are some questions:
Does the LLM have good support and clear documentation?
Maintenance Comparison
LLM
Maintenance Level
Documentation Quality
LLM 1
Low (Easy)
Excellent
LLM 2
Medium (Moderate)
Limited
LLM 3
High (Difficult)
Inadequate
For instance: If ease of maintenance is a priority, LLM 1 would be the best choice, given its low maintenance needs and excellent documentation, even if other models may offer more features.
6. How Fast Is It? (Latency)
Latency is the time it takes an LLM to respond. Speed is important for some applications (like customer service), while for others, it might not be a big deal. Here are some questions:
How quickly does the LLM respond?
Latency Comparison
LLM
Response Time
Can It Be Optimized?
LLM 1
100ms
Yes (80ms)
LLM 2
300ms
Yes (250ms)
LLM 3
200ms
Yes (150ms)
For instance, If response speed is critical, such as for customer service applications, LLM 1 would be the best option with its low latency and potential for further optimization.
7. Can It Scale?
If your business is small, scaling might not be an issue. But if you’re expecting a lot of users, the LLM needs to handle multiple people or lots of data simultaneously. Here are some questions:
Can it scale up to handle more users or data?
Scalability Comparison
LLM
Max Users
Scalability Level
LLM 1
1000
High
LLM 2
500
Medium
LLM 3
1000
High
If scalability is a key factor and you anticipate a high number of users, both LLM 1 and LLM 3 would be suitable choices. Both offer high scalability to support up to 1000 users.
8. Infrastructure Needs
Different LLMs have varying infrastructure needs—some are optimized for the cloud, while others require powerful hardware like GPUs. Consider whether your business has the right setup for both development and production. Here are some questions:
Does it run efficiently on single or multiple GPUs/CPUs?
Does it support quantization for deployment on lower resources?
Can it be deployed on-premise or only in the cloud?
For instance, If your business lacks high-end hardware, a cloud-optimized LLM might be the best choice, whereas an on-premise solution would suit companies with existing GPU infrastructure.
9. Is It Secure?
Security is important, especially if you’re handling sensitive information. Make sure the LLM is secure and follows data protection laws.
Does it have secure data storage?
Is it compliant with regulations like GDPR?
Security Comparison
LLM
Security Features
GDPR Compliant
LLM 1
High
Yes
LLM 2
Medium
No
LLM 3
Low
Yes
For instance, If security and regulatory compliance are top priorities, LLM 1 would be the best option, as it offers high security and is GDPR compliant, unlike LLM 2.
10. What Kind of Support Is Available?
Good support can make or break your LLM experience, especially when encountering problems. Here are some questions:
Do the creators of the LLM provide support or help?
Is it easy to connect if any help is required to implement the LLM?
What is the availability of the support being provided?
Consider the LLM that has a good community or commercial support available.
Real-World Examples (Case Studies)
Here are some real-world examples:
Example 1: Education
Problem: Solving IIT-JEE exam questions
Key Considerations:
Needs fine-tuning for specific datasets
Accuracy is critical
Should scale to handle thousands of users
Example 2: Customer Support Automation
Problem: Automating customer queries
Key Considerations:
Security is vital (no data leaks)
Privacy matters (customers’ data must be protected)
Comparing LLM 1, 2, and 3
Criteria
LLM 1
LLM 2
LLM 3
Capability
Supports fine-tuning, custom data
Limited fine-tuning, large context
Fine-tuning supported
Accuracy
High (90%)
Medium (85%)
Medium (88%)
Cost
High (API pricing)
Low (One-time cost)
Medium (Subscription)
Tech Compatibility
Python-based
Python-based
Python-based
Maintenance
Low (Easy)
Medium (Moderate)
High (Frequent updates)
Latency
Fast (100ms)
Slow (300ms)
Moderate (200ms)
Scalability
High (1000 users)
Medium (500 users)
High (1000 users)
Security
High
Medium
Low
Support
Strong community
Limited support
Open-source community
Privacy Compliance
Yes (GDPR compliant)
No
Yes
Applying this to the cases:
Case Study 1: Education (Solving IIT-JEE Exam Questions)LLM 1 would be the ideal choice due to its strong fine-tuning capabilities for specific datasets, high accuracy, and ability to scale for thousands of users, making it perfect for handling large-scale educational applications.
Case Study 2: Customer Support AutomationLLM 1 is also the best fit here, thanks to its high security features and GDPR compliance. These features ensure that customer data is protected, which is critical for automating sensitive customer queries.
Conclusion
In summary, picking the right LLM for your business depends on several factors like cost, accuracy, scalability, and how it fits into your tech setup. This framework may help you find the right LLM and make sure to test the LLM with real-world data before committing. Remember, there’s no “perfect” LLM, but you can find the one that fits your business best by exploring, testing, and evaluating your options.
The LLM Evaluation Framework is designed for a local environment, facilitating the comprehensive evaluation and integration of large language models (LLMs). The framework comprises several key modules:
One-Pass Compilation Module: This module is a core component of the framework, integrating the Art2Dec All-in-One compiler to support multiple programming languages such as Go, Java, C++, and Python for testing. It includes also CMD and Go compilers with a string array API for languages like C, C++, Go, Java, and Python, enabling efficient compilation and execution of code. Additionally, it houses the Prompts Repo, Evaluator, Analyzer, and API module, which manages the storage and retrieval of prompts, evaluates LLM outputs, and analyzes performance data. This integration ensures a seamless workflow, allowing developers to compile, evaluate, and analyze their LLM-related tasks in a streamlined environment.
Data Ingestion Module: Capable of handling diverse data sources, including plain and binary files, databases, and programming channels, this module is responsible for the structured ingestion and preprocessing of data, feeding it into the system for analysis and evaluation.
Ollama Module: Ollama acts as a central hub for managing LLM interactions. It connects with the LLM’s repository and coordinates with various APIs, ensuring smooth communication and model deployment.
LLM Repository: A structured storage system that houses different versions and types of LLMs. This repository allows for easy access, retrieval, and management of models, facilitating rapid testing and deployment.
Chat and CMD Chat Modules: These modules provide interactive interfaces for users. The Chat module handles standard interactions with LLMs, while the CMD Chat module extends capabilities with command-line-based string array manipulations, allowing for detailed session history management.
APIs and Integrations module: The framework integrates various APIs, including those for prompts, evaluation, analysis, and the Ollama API, ensuring that all components can communicate effectively within the environment as well like make an adaptation of llm’s output to different compilers.
This framework is designed to streamline the evaluation process, providing a robust and scalable solution for working with LLMs in a controlled local environment.
IEA is a cutting-edge personal finance application that leverages the advanced capabilities of the Llama 3.1 LLM model to provide tailored financial insights and advice. Whether you’re budgeting, tracking expenses, or planning for long-term goals, IEA offers personalized guidance by understanding your unique financial situation. The app simplifies complex financial data, suggests spending strategies, and helps you make informed decisions, ensuring that your financial health is always on track.
ChatGPT is a popular chatbot and virtual assistant developed by OpenAI and has been on the market since November 30, 2022. This chart model allows you to fine-tune and steer a conversation toward the ideal duration, structure, tone, degree of detail, and language.
Fortunately, with the continuous advancements in AI, open-source ChartGPT Alternatives have emerged as powerful tools that provide the same conversational skills and additional benefits of customization and transparency.
In addition, the open-source nature of these ChartGPT alternatives empowers developers to tailor the models to their specific needs, unleashing their full potential in various software and fostering collaboration.
In this post, we’ve compiled the best open-source ChartGPT Alternatives, highlighting their cutting-edge features and benefits.
1. GPT4All
GPT4All is a free, state-of-the-art chatbot that executes locally and respects user privacy. GPU or an internet connection is not necessarily needed for the functionality of this tool.
GPT4All comes with a variety of features users can explore, including creating poems, responding to inquiries, and presenting customized writing assistance.
Its additional features include building Python code, comprehending documents, and even training your GPT4All models. On top of that, GPT4All is an open-source environment that lets you set up and execute large, customized language models locally on consumer-grade CPUs.
Whether you want an instruction-based model for more in-depth interactions or a chat-based model for quicker responses, this tool has you covered.
2. OpenChatKit
OpenChatKit is a fantastic ChatGPT alternative, offering individuals similar natural language processing (NLP) capabilities while allowing more flexibility and control.
The tool allows users to train and fine-tune their models to fit specific use cases, as it is built on EleutherAI’s GPT-NeoX framework.
OpenChatKit‘s comprehensive features also give developers the capacity to create both general-purpose and specialized chatbot tools using a full toolkit that is easily accessed under the Apache 2.0 license.
In addition to features like the use of trained models and a retrieval system, OpenChatKit lets chatbots execute a variety of tasks, including arithmetic problem-solving, narrative and code writing, and document summarization.
OpenChatKit
3. HuggingChat
HuggingChat is a comprehensive platform that features an extensive selection of cutting-edge open large language models (LLMs).
To guarantee anonymity by design, Hugging Face (HF) accounts are used for user authentication, as the conversations remain private and aren’t shared with anyone, including model authors.
For consistency in providing a broad selection of state-of-the-art LLMs, HuggingChat periodically changes these models, including Llama 2 70B, CodeLlama 35B, and Mistral 7B.
In addition, this tool offers a platform for users to engage in public discussions, offering insightful feedback and helping to shape its future.
HuggingChat
4. Koala
Koala is a sophisticated chatbot, trained using discussion data from the internet to enhance Meta’s LLaMA. With its performance being compared to ChatGPT and Stanford’s Alpaca, this special model has undergone thorough dataset curation and training in extensive user research.
In over half of the situations, the outcomes show how good Koala is at answering a variety of customer inquiries, as it matches ChatGPT and frequently outshines Alpaca.
When trained on correctly obtained data, locally run chatbots can outperform their larger equivalents by using smaller public models like Koala.
Koala Chatbot
5. Alpaca-LoRA
Alpaca-LoRA is an innovative project that uses low-rank adaptation (LoRA) to replicate Stanford Alpaca outcomes. To research consumer hardware, like Raspberry Pi this project offers a text-davinci-003-quality Instruct model that is to be used.
The code offers flexibility and scalability and can be readily extended to 13b, 30b, and 65b models. In addition to the generated LoRA weights, the project also provides a script for downloading and inferring the foundation model and LoRA.
Without the need for hyperparameter adjustment, the LoRA model exhibits similar results to the Stanford Alpaca model, demonstrating its efficacy and opening up possibilities for extra improvement through user testing and feedback.
6. ColossalChat
ColossalChat is at the forefront of open-source large AI model solutions, featuring a full RLHF pipeline that includes supervised data gathering, fine-tuning reward model training, and reinforcement learning based on the LLaMA pre-trained model. It shares a useful open-source project that closely resembles the original ChatGPT technological solution.
With its cutting-edge features, this platform offers an open-source 104K bilingual dataset in both Chinese and English, an interactive demo for online exploration without registration, and open-source RLHF training code for 7B and 13B models.
In addition, ColossalChat provides 4-bit quantized inference, for 7 billion-parameter models, making it accessible with low GPU memory needs.
Thanks to its RLHF fine-tuning feature, ColossalChat is bilingual in both English and Chinese, enabling a variety of features like general knowledge tests, email writing, algorithm development, and ChatGPT cloning methods.
To provide a high-performance, user-friendly conversational AI experience, this tool guarantees adaptability, effectiveness, and smooth integration by utilizing PyTorch.
7. Baize
Baize is an open-source chat model that was trained with LoRA and optimized for performance using 100k self-generated dialogs from ChatGPT and Alpaca’s data. The project has produced models 7B, 13B, and 30B as it aims to offer a complete chat model solution.
To strictly prohibit commercial use and only allow intended reasons purely for research, the model weights and code are made available under the GPL-3.0 license, as An important tool for the AI field, Baize provides a workable open-source project that consists of an entire RLHF method for emulating ChatGPT-like models.
For CLI and API usage, users may connect with Baize using Fastchat, offering a smooth experience for utilizing the model’s features. The project also provides an intuitive Gradio chat interface, CLI and API support, and a bilingual dataset.
8. Dolly v2
Dolly v2 is a significant language model developed by Databricks, Inc. and trained via the Databricks machine learning platform to adhere to instructions. The instruction-following model can be purchased in multiple sizes (12B, 7B, and 3B) and has a license for commercial use.
In addition, this tool can be optimized on a ~15K record instruction corpus created by Databricks personnel, and it’s also based on EleutherAI’s Pythia-12b. The aim of the model is to be used with the transformers library on GPU-equipped computers because of its excellent instruction-following ability.
With its detailed features, this tool is useful for language processing jobs, and since it is still a work-in-progress model, its performance and drawbacks are continuously being evaluated and enhanced.
9. Vicuna
Vicuna-13B is an open-source chatbot, refined using user-shared talks gathered from ShareGPT.
By exceeding other models such as LLaMA and Stanford Alpaca in over 90% of situations and surpassing OpenAI ChatGPT and Google Bard in quality, this tool has proven to be very competitive in performance.
The model provides an online demo for individuals to experience its first-hand capabilities, which are freely accessible to the public for non-commercial use.
One of Vicuna-13 B’s amazing features and strengths is its capacity to provide comprehensive and structured responses, especially when fine-tuned using 70K user-shared ChatGPT discussions.
10. ChatRWKV
ChatRWKV, an inventive chatbot powered by the RWKV (100% RNN) language model, provides an alternative to transformer-based models like ChatGPT.
Jumping right into its features, RWKV-6, the most recent version, is renowned for its quality and scaling, matching transformers, and using less VRAM while operating faster. The model can be used for non-commercial purposes with Stability EleutherAI as its sponsor.
ChatRWKV’s tailored feature is its capacity to produce excellent responses, especially true with the RWKV-6 version, which has proven to be a highly effective tool.
On top of that, the presence of RWKV Discord, a community with over 7,000 members, suggests that this technology has a healthy following, because of its open-source nature and cutting-edge resources available, making it a fantastic choice for developers and researchers interested in experiencing RNN-based language models for chatbots.
11. Cerebras-GPT
The Cerebras-GPT is a family of large language models (LLMs) developed by Cerebras Systems to aid in studying LLM scaling laws using open architectures and datasets.
Having been trained using Chinchilla scaling principles and including parameters ranging from 111M to 13B, these models are compute-optimal. On top of that, these models may be found on Hugging Face, and EleutherAI’s Pile dataset as used in their training.
Even though chatbot functionality isn’t addressed specifically, Cerebras-GPT models are meant to show off how easy and scalable it is to train LLMs using the Cerebras hardware and software stack. This implies that research and development should take precedence over using chatbots in the real world.
12. Open Assistant
OpenAssistant is a finished project that aims to make a high-quality chat-based large language model accessible. The project aims to elevate language itself, revolutionizing language innovation and making the world a better place.
This tool’s inclusive features include a chat frontend for real-time communication and a data gathering frontend for enhancing the assistant’s functionality, which OpenAssistant provides.
The release of the OpenAssistant Conversations (OASST1) corpus, highlights the democratization of large-scale alignment research. Moreover, HuggingFace hosts the final published oasst2 dataset at OpenAssistant/oasst2.
OpenAssistant’s models and code are available under the Apache 2.0 license as an open-source project, which permits a variety of usage, including for profit. The initiative is designed and managed by LAION, which includes a team of volunteers across the globe.
The project offers chances for participation in data collection and development for any individuals interested in contributing.
Conclusion
With the help of these cutting-edge resources, small businesses, researchers, and developers can take advantage of language-based technology and take on the biggest names in the market.
Even though they may not outperform GPT-4, it is clear that they have room to grow and improve with the help of the community. These models are particularly perfect substitutes for GPT-4.
Everyone’s talking aboutLarge Language Models, or LLMs, and how amazing they are. But there’s also something exciting happening with Small Language Models (SLMs) that are starting to get more attention. Big advancements in the field of NLP come from powerful or “Large” models like GPT-4 and Gemini, which are experts in handling tasks such as translating languages, summarizing text, and having conversations. These models are great because they process language much like humans do.
But, there’s a catch with these big models: they need a lot of compute power and storage, which can be expensive and hard to manage, especially in places where there’s not a lot of advanced technology.
To fix this problem, experts have come up with Small Language Models or SLMs. These smaller models don’t use as much compute and are easier to handle, making them perfect for places with less tech resources. Even though they’re smaller, they’re still powerful and can do many of the same jobs as the bigger models. So, they’re small in size but big in what they can do.
Table of contents
What are Small Language Models?
What is “Small” in Small Language Models?
Examples of Small Language Models
How do Small Language Models Work?
Differences Between Small Language Models (SLMs) and Large Language Models (LLMs).
Pros and Cons of SLMs.
What are Small Language Models?
Small language models are simple and efficient types of neural networks made for handling language tasks. They work almost as well as bigger models but use far fewer resources and need less computing power.
Imagine a language model as a student learning a new language. A small language model is like a student with a smaller notebook to write down vocabulary and grammar rules. They can still learn and use the language, but they might not be able to remember as many complex concepts or nuances as a student with a larger notebook (a larger language model).
The advantage of SLMs is that they are faster and require less computing power than their larger counterparts. This makes them more practical to use in applications where resources are limited, such as on mobile devices or in real-time systems.
However, the trade-off is that SMLs may not perform as well as larger models on more complex language tasks, such as understanding context, answering complicated questions, or generating highly coherent and nuanced text.
What is “Small” in Small Language Models?
The term “small” in small language models refers to the reduced number of parameters and the overall size of the model compared to large language models. While LLMs can have billions or even trillions of parameters, SLMs typically have a few million to a few hundred million parameters(in a few cases up to a couple of billions as well).
The number of parameters in a language model determines its capacity to learn and store information during training. More parameters generally allow a model to capture more complex patterns and nuances in the training data, leading to better performance on natural language tasks.
However, the exact definition of “small” can vary depending on the context and the current state of the art in language modeling. As model sizes have grown exponentially in recent years, what was once considered a large model might now be regarded as small.
Examples of Small Language Models
Some examples of small language models include:
GPT-2 Small: OpenAI’s GPT-2 Small model has 117 million parameters, which is considered small compared to its larger counterparts, such as GPT-2 Medium (345 million parameters) and GPT-2 Large (774 million parameters). Click here.
DistilBERT: This is a distilled version of BERT (Bidirectional Encoder Representations from Transformers) that retains 95% of BERT’s performance while being 40% smaller and 60% faster. DistilBERT has around 66 million parameters. Click here.
TinyBERT: Another compressed version of BERT, TinyBERT is even smaller than DistilBERT, with around 15 million parameters. Click here.
While SLMs typically have a few hundred million parameters, some larger models with 1-3 billion parameters can also be classified as SLMs because they can still be run on standard GPU hardware. Here are some of the examples of such models:
Phi3 Mini: Phi-3-mini is a compact language model with 3.8 billion parameters, trained on a vast dataset of 3.3 trillion tokens. Despite its smaller size, it competes with larger models like Mixtral 8x7B and GPT-3.5, achieving notable scores of 69% on MMLU and 8.38 on MT-bench. Click here.
Google Gemma 2B: Google Gemma 2B is a part of the Gemma family, lightweight open models designed for various text generation tasks. With a context length of 8192 tokens, Gemma models are suitable for deployment in resource-limited environments like laptops, desktops, or cloud infrastructures. Click here.
Databricks Dolly 3B: Databricks’ dolly-v2-3b is a commercial-grade instruction-following large language model trained on the Databricks platform. Derived from pythia-2.8b, it’s trained on around 15k instruction/response pairs covering various domains. While not state-of-the-art, it exhibits surprisingly high-quality instruction-following behavior. Click here.
How do Small Language Models Work?
Small language models use the same basic ideas as large language models, like self-attention mechanisms and transformer structures. However, they use different methods to make the model smaller and require less computing power:
Model Compression: SLMs use methods like pruning, quantization, and low-rank factorization to cut down the number of parameters. This means they simplify the model without losing much performance.
Knowledge Distillation: In this technique, a smaller model learns to act like a larger, already trained model. The student model tries to produce results similar to the teacher, effectively squeezing the essential knowledge from the big model into a smaller one.
Efficient Architectures: SLMs often use specially designed structures that focus on being efficient, such as Transformer-XL and Linformer. These designs modify the usual transformer structure to be less complex and use less memory.
Differences Between Small Language Models (SLMs) and Large Language Models (LLMs):
Challenging to deploy due to high resource requirements
Training and Inference Speed
Faster, more efficient
Slower, more computationally intensive
Performance
Competitive, but may not match state-of-the-art results on certain tasks
State-of-the-art performance on various NLP tasks
Model Size
Significantly smaller, typically 40% to 60% smaller than LLMs
Large, requiring substantial storage space
Real-world Applications
Suitable for applications with limited computational resources
Primarily used in resource-rich environments, such as cloud services and high-performance computing systems
Pros and Cons of SLMs:
Here are some pros and cons of Small Language Models:
Pros:
Computationally efficient, requiring fewer resources for training and inference
Easier to deploy on resource-constrained devices like mobile phones and edge devices
Faster training and inference times compared to LLMs
Smaller model size, making them more storage-friendly
Enable wider adoption of NLP technologies in real-world applications
Cons:
May not achieve the same level of performance as LLMs on certain complex NLP tasks
Require additional techniques like model compression and knowledge distillation, which can add complexity to the development process
May have limitations in capturing long-range dependencies and handling highly context-dependent tasks
The trade-off between model size and performance needs to be carefully considered for each specific use case
May require more extensive fine-tuning or domain adaptation compared to LLMs to achieve optimal performance on specific tasks
Despite these limitations, SMLs offer a promising approach to making NLP more accessible and efficient, enabling a wider range of applications and use cases in resource-constrained environments.
Conclusion
Small Language Models are a good alternative and adding to Large Language Models because they are efficient, less expensive, and easier to manage. They can do many different language tasks and are becoming more popular in artificial intelligence and machine learning.
Before you decide to use a Large Language Model for your project, take a moment to think about whether a Small Language Model could work just as well. This is like in the past when people used to pick complex Deep Learning models, even though simpler machine learning models could have done the job too—and that’s still something to consider today.
Streamlining Operations and Interaction with Ollama
Context Awareness in Ollama CLI
Verifying Contextual Understanding and History
Integrating a Custom Model from Hugging Face into Ollama
Downloading the Medicine Model from Hugging Face and Preparing Ollama Model Configuration File
Creating the Model and Listing in Ollama
Running the Model
Ollama Python Library: Bridging Python and Ollama with an API-like Interface
Installation
Usage
Streaming Responses
Comprehensive API Methods with Examples
Customizing the Client
Ollama with LangChain
What Is LangChain?
How to Implement Ollama with LangChain?
Important Considerations
Bonus: Ollama with a Web UI Using Docker
Key Features of Ollama’s Web UI
Setting Up Open Web UI on Your Local Machine
Prerequisites
Running the Open Web UI
Accessing Open Web UI
Login Screen
Model Selection Screen
Chat Screen with PDF Explanation Screen
Summary
Inside Look: Exploring Ollama for On-Device AI
In this tutorial, you will learn about Ollama, a renowned local LLM framework known for its simplicity, efficiency, and speed. We will explore interacting with state-of-the-art LLMs (e.g., Meta Llama 3 using CLI and APIs) and integrating them with frameworks like LangChain. Let’s dive in!
Introduction to Ollama
This recap sets the stage for today’s focus: diving into Ollama, one of the popular frameworks highlighted previously. We will explore how to set up and interact with Ollama, enhancing its functionality with custom configurations and integrating it with advanced tools like LangChain for developing robust applications.
Overview of Ollama
Ollama stands out as a highly acclaimed open-source framework specifically designed for running large language models (LLMs) locally on-premise devices. This framework supports a wide array of operating systems (e.g., macOS, Linux, and Windows), ensuring broad accessibility and ease of use. The installation process is notably straightforward, allowing users from various technical backgrounds to set up Ollama efficiently.
Once installed, Ollama offers flexible interaction modes: users can engage with it through a Command Line Interface (CLI), utilize it as an SDK (Software Development Kit), or connect via an API, catering to different preferences and requirements. Additionally, Ollama’s compatibility with advanced frameworks like LangChain enhances its functionality, making it a versatile tool for developers looking to leverage conversational AI in robust applications.
OLAMA supports an extensive range of models including the latest versions like Phi-3, Llama 3, Mistral, Mixtral, Llama2, Multimodal Llava, and CodeLama, among others. This diverse model support, coupled with various quantization options provided by GGUF, allows for significant customization and optimization to suit specific project needs.
In this tutorial, we will primarily focus on setting up Ollam on a macOS environment, reflecting our development setting for this and future posts in the series. Next, to tap into the capabilities of local LLMs with Ollama, we’ll delve into the installation process on a Mac machine.
Installing Ollama on a MacOS
Installing Ollama on a macOS is a straightforward process that allows you to quickly set up and start utilizing this powerful local LLM framework. Here’s how you can do it:
Select macOS as your operating system. This action is illustrated in the diagram below, guiding you through the selection process.
Launch the Installer
Once you have downloaded the file, you will receive a ZIP archive. Extract this archive to find the Ollama.app.
Drag and drop the Ollama.app into your Applications folder. This simple step ensures that OLAMA is integrated into your macOS system.
Start the Application
Open your Applications folder and double-click on Ollama.app to launch it.
Ollama will automatically begin running in the background and is accessible via http://localhost:11434. This means OLAMA is now serving locally on your Mac without the need for additional configuration.
Verify the Installation
Open your browser.
Type http://localhost:11434 and press Enter. If Ollama is running correctly, you should see a confirmation that it is running.
This installation not only sets Ollama as a local LLM server but also paves the way for its use as a backend to any LLM framework, enhancing your development capabilities. In future discussions, we’ll explore how to connect Ollama with AnythingLLM and other frameworks, utilizing its full potential in the local LLM ecosystem.
In the next section, we will review how to engage with OLAMA’s model registry and begin interacting with various LLMs through this dynamic platform.
OLAMA’s Model Registry: A Treasure Trove of LLMs
Ollama’s model registry, accessible at https://ollama.com/library, stands as a testament to the platform’s commitment to ease of access and user experience. It maintains its own curated list of over 100 large language models, including both text and multimodal varieties. This registry, while reflecting a similar diversity as the Hugging Face Hub, provides a streamlined mechanism for users to pull models directly into their Ollama setup.
Highlighted within the registry is Llama 3, an LLM recently released by Meta has become a crowd favorite with over 500,000 pulls, indicative of its widespread popularity and application.
Phi-3, with its 3.8 billion parameters, is another feather in Ollama’s cap, offering users Microsoft’s lightweight yet sophisticated technology for state-of-the-art performance.
Ollama’s registry is not just a repository; it’s a user-centric platform designed for efficiency. It syncs seamlessly with Ollama’s system, allowing for straightforward integration of models like the versatile Llava for multimodal tasks. This synchronization with Hugging Face models ensures that users have access to a broad and diverse range of LLMs, all while enjoying the convenience and user-friendly environment that Ollama provides.
For developers who frequent the Hugging Face Hub, Ollama’s model registry represents a familiar yet distinct experience. It encapsulates the essence of what makes Ollama unique: a focus on a seamless user journey from model discovery to local deployment.
Moving on to the Llama 3 model in the Ollama library, you’re met with a variety of options showcased through 67 tags, indicating different model configurations, including various quantization levels (e.g., 2-bit, 4-bit, 5-bit, and 8-bit). These also encompass instruction-tuned versions specifically optimized for chat and dialogue, indicating Llama 3’s versatility. OLAMA sets a default tag that, when the command ollama run llama3 is executed in the terminal, pulls the 8-billion-parameter Llama 3 model with 4-bit quantization.
The various versions of Llama 3 available in the Ollama model library cater to a range of needs, offering both nimble models for quick computations and more substantial versions for intricate tasks. This variety demonstrates Llama 3’s successful adaptation to different quantization levels post-release, ensuring users can select the ideal model specification for their requirements.
The tags allow users to fine-tune their Llama 3 experience, whether they are engaging via CLI or API. They include pre-trained and instruction-tuned models for text and dialogue.
For more detailed instructions and examples on how to utilize these models, please refer to Ollama’s official documentation and CLI commands. These provide straightforward guidance for users to run and interact effectively with Llama 3.
Ollama as a Command Line Interface Tool
In this section, we explore how to effectively use Ollama as a command line interface (CLI) tool. This tool offers a variety of functionalities for managing and interacting with local Large Language Models (LLMs). Ollama’s CLI is designed to be intuitive, drawing parallels with familiar tools like Docker, making it straightforward for users to handle AI models directly from their command line. Below, we walk through several key commands and their uses within the OLAMA framework.
History and Contextual Awareness
One of the highlights of using OLAMA is its ability to keep track of the conversation history. This allows the model to understand and relate to past interactions within the same session. For example, if you inquire, “Did I ask about cricket till now?” Ollama accurately responds by referencing the specific focus of your conversation on football, demonstrating its capability to contextualize and recall previous discussions accurately.
This section of our guide illustrates how Ollama as a CLI can be a powerful tool for managing and interacting with LLMs efficiently and effectively, enhancing productivity for developers and researchers working with AI models.
Getting Started with Ollama
When you type ollama into the command line, the system displays the usage information and a list of available commands (e.g., serve, create, show, list, pull, push, run, copy, and remove). Each command serves a specific purpose:
serve: Launches the ollama service.
create: Generates a new model file using a pre-existing model, allowing customization such as setting temperature or adding specific instructions.
show: Displays configurations for a specified model.
list: Provides a list of all models currently managed within the local environment.
pull/push: Manages the import and export of models to and from the ollama registry.
run: Executes a specified model.
Copy (cp) and Remove (rm): Manages model files by copying or deleting them.
Managing Models with Ollama
Using ollama list, you can view all models you have pulled into your local registry. For example, the list might include:
Code Llama: 13 billion parameter model
Llama 2
Llama 3: 70 billion parameter instruction fine-tuned with Q2_K quantization
Llama 3: 8 billion parameter model
The latest Phi-3 model by Microsoft
If a desired model isn’t available locally, you can retrieve it using
ollama pull. For example, executing ollama pull phi3 retrieves the phi3 model, handling all necessary files and configurations seamlessly, similar to pulling images in Docker.
Streamlining Operations and Interaction with Ollama
Ollama’s run command not only simplifies model management but also seamlessly integrates the initiation of interactive chat sessions, similar to how Docker handles container deployment and execution. Here’s how the ollama run phi3 command enhances user experience through a series of automated and interactive steps:
Check Local Availability: Ollama first checks if the model phi3 is available locally.
Automatic Download: If the model is not found locally, Ollama automatically downloads it from the registry. This process involves fetching the model along with any necessary configurations and dependencies.
Initiate Model Execution: Once the model is available locally, Ollama starts running it.
Start Chat Session: Alongside running the model, Ollama immediately initiates a chat session. This allows you to interact with the model directly through the command line. You can begin asking questions or making requests right away, and the model will respond based on its training and capabilities.
This dual-functionality of the run command — managing both the deployment and interactive engagement of models — greatly simplifies operations. It mirrors the convenience observed in Docker operations, where the docker run command fetches an image if not present locally and then launches it, ready for use. In a similar vein, ollama run ensures that the model is not only operational but also interactive as soon as it is launched, significantly enhancing productivity and user engagement by combining deployment and direct interaction in a single step.
Context Awareness in Ollama CLI
Ollama’s strength in contextual recall becomes apparent in how it manages the conversation flow. If you follow up with a request for “another fun fact?” without specifying the topic, Ollama understands from the context that the discussion still revolves around football. It then provides additional information related to the original topic, maintaining a coherent and relevant dialogue without needing repeated clarifications.
This capability is crucial for creating a natural and engaging user experience, where the model not only answers questions but also remembers the context of the interaction. It eliminates the need for users to repetitively specify the topic, making the dialogue flow more naturally and efficiently.
Verifying Contextual Understanding and History
Ollama’s contextual understanding is further highlighted when you query whether a particular topic (e.g., cricket) has been discussed. The model accurately recounts the focus of the conversation on football and confirms that cricket has not been mentioned. This demonstrates Ollama’s ability to track conversation history and understand the sequence of topics discussed.
Additionally, when prompted about past discussions, Ollama can succinctly summarize the topics covered, such as aspects of soccer culture, its terminology variations worldwide, and its socio-economic impacts. This not only shows the model’s recall capabilities but also its understanding of the discussion’s scope and details.
Integrating a Custom Model from Hugging Face into Ollama
In the realm of on-device AI, Ollama not only serves as a robust model hub or registry for state-of-the-art models like Phi-3, Llama 3, and multimodal models like Llava, but it also extends its functionality by supporting the integration of custom models. This flexibility is invaluable for users who wish to incorporate models fine-tuned on specific datasets or those sourced from popular repositories like Hugging Face.
While Ollama’s repository is extensive, it might not always house every conceivable model, especially given the vast landscape of foundation models. A noteworthy example is the work done by “Bloke,” a contributor who has extensively quantized various models for easier deployment. For this walkthrough, we’ll focus on integrating a quantized model from Bloke’s collection, specifically tailored for medical chats.
Downloading the Medicine Model from Hugging Face and Preparing Ollama Model Configuration File
Downloading the Model:
Visit this link to Bloke’s repository on Hugging Face for the medicine chat GGUF model.
Navigate to the ‘Files and Versions’ section to download a quantized GGUF model, available in various bit configurations (2-bit to 8-bit).
Preparing the Configuration File:
Once downloaded, place the model file in the same directory as your configuration file.
Create a configuration file. This file should reference the model file (e.g., medicine_chat_q4_0.GGUF), and can include parameters (e.g., temperature) to adjust the model’s response creativity.
Creating the Model and Listing in Ollama
Once you’ve configured your model settings in the med-chat-model-cfg file, the next step is to integrate this model into Ollama. This process involves creating the model directly within Ollama, which compiles it from the configuration you’ve set, preparing it for deployment much like building a Docker image.
Using the appropriate command in Ollama (refer to the provided image for the exact command), you can initiate the creation of your custom model. This procedure constructs the necessary layers and settings specified in your configuration file, effectively building the model ready for use.
After the model creation, it’s essential to confirm that the model is correctly integrated and ready for use. By executing the listing command in Ollama (ollama list), you can view all available models. This list will include your newly created medicine-chat:latest model, indicating it is successfully integrated and available in Ollama’s local model registry alongside other pre-existing models.
These steps ensure that your custom model is not only integrated but also prepared and verified for deployment, facilitating immediate use in various applications, particularly those requiring specific configurations like medical chat assistance.
Running the Model
Once your model is integrated and listed in Ollama, the next step is to deploy and test its functionality to ensure it operates as expected. Initiate the model using ollama run followed by the model name, that is, medicine-chat:latest (as shown in the image below) to start an interactive session.
Then, next we provide a sample prompt to test its functionality, such as asking about monosomy. Although the initial response might be incorrect (e.g., suggesting 46,XX), the model provides a detailed explanation, correcting itself and confirming the right answer as 45,X.
This example illustrates how Ollama’s flexibility not only supports running pre-existing models from its registry but also seamlessly integrates and executes custom models sourced externally. This capability empowers users to leverage specialized AI directly on their devices, enhancing Ollama’s utility across diverse applications.
Ollama Python Library: Bridging Python and Ollama with an API-Like Interface
The Ollama Python library provides a seamless bridge between Python programming and the Ollama platform, extending the functionality of Ollama’s CLI into the Python environment. This library enables Python developers to interact with an Ollama server running in the background, much like they would with a REST API, making it straightforward to integrate Ollama’s capabilities into Python-based applications.
Installation
Getting started with the Ollama Python library is straightforward. It can be installed via pip, Python’s package installer, which simplifies the setup process:
pip install ollama
This command installs the Ollama library, setting up your Python environment to interact directly with Ollama services.
Usage
The Ollama Python library is designed to be intuitive for those familiar with Python. Here’s how you can begin interacting with Ollama immediately after installation:
import ollama
response = ollama.chat(model=’llama2′, messages=[
{
‘role’: ‘user’,
‘content’: ‘Why is the sky blue?’,
},
])
print(response[‘message’][‘content’])
This simple example sends a message to the Ollama service and prints the response, demonstrating how easily the library can facilitate conversational AI models.
Streaming Responses
For applications requiring real-time interactions, the library supports response streaming. This feature is enabled by setting
stream=True, which allows the function calls to return a Python generator. Each part of the response is streamed back as soon as it’s available:
import ollama
stream = ollama.chat(
model=’llama2′,
messages=[{‘role’: ‘user’, ‘content’: ‘Why is the sky blue?’}],
The Ollama Python library mirrors the functionality of the Ollama REST API, providing comprehensive control over interactions with models. Here’s how you can utilize these methods in your Python projects:
a) Chat: Initiate a conversation with a specified model.
response = ollama.chat(model=’llama2′, messages=[{‘role’: ‘user’, ‘content’: ‘Why is the sky blue?’}])
print(response[‘message’][‘content’])
b) Generate: Request text generation based on a prompt.
generated_text = ollama.generate(model=’llama2′, prompt=’Tell me a story about space.’)
print(generated_text)
c) List: Retrieve a list of available models.
models = ollama.list()
print(models)
d) Create: Create a new model with custom configurations.
f) Embeddings: Generate embeddings for a given prompt.
embeddings = ollama.embeddings(model=’llama2′, prompt=’The sky is blue because of Rayleigh scattering.’)
print(embeddings)
These code snippets provide practical examples of how to implement each function provided by the Ollama Python library, enabling developers to effectively manage and interact with AI models directly from their Python applications.
Customizing the Client
For more advanced usage, developers can customize the client configuration to suit specific needs:
from ollama import Client
client = Client(host=’http://localhost:11434′)
response = client.chat(model=’llama2′, messages=[
{
‘role’: ‘user’,
‘content’: ‘Why is the sky blue?’,
},
])
This customization allows for adjustments to the host settings and timeouts, providing flexibility depending on the deployment environment or specific application requirements.
Overall, the Ollama Python library acts as a robust conduit between Python applications and the Ollama platform. It offers developers an API-like interface to harness the full potential of Ollama’s model management and interaction capabilities directly within their Python projects. For additional resources and more advanced usage examples, refer to the Ollama Python GitHub repository, which served as a key reference for the comprehensive API section discussed here.
Ollama with LangChain
Ollama can be seamlessly integrated with LangChain through the LangChain Community Python library. This library offers third-party integrations that adhere to the base interfaces of LangChain Core, making them plug-and-play components for any LangChain application.
What Is LangChain?
LangChain is a versatile framework for embedding Large Language Models (LLMs) into various applications. It supports a diverse array of chat models, including Ollama, and allows for sophisticated operation chaining through its expressive language capabilities.
The framework enhances the entire lifecycle of LLM applications, simplifying:
Development: Utilize LangChain’s open-source components and third-party integrations to build robust applications rapidly.
Productionization: Employ tools like LangSmith to monitor, inspect, and refine your models, ensuring efficient optimization and reliable deployment.
Deployment: Easily convert any model sequence into an API with LangServe, facilitating straightforward integration into existing systems.
How to Implement Ollama with LangChain?
To integrate Ollama with LangChain, begin by installing the necessary community package:
pip install langchain-community
After installation, import the Ollama module from the
langchain_community.llms class:
from langchain_community.llms import Ollama
Next, initialize an instance of the Ollama model, ensuring that the model is already available in your local Ollama model registry, which means it should have been previously pulled to your system:
llm = Ollama(model=”phi3″)
You can now utilize this instance to generate responses. For example:
response = llm.invoke(“Tell me a joke”)
print(response)
Here’s a sample output from the model:
“Why don’t scientists trust atoms? Because they make up everything!\n\nRemember, jokes are about sharing laughter and not intended to offend. Always keep the atmosphere light-hearted and inclusive when telling humor.”
Important Considerations
Before running this integration:
Ensure that the Ollama application is active on your system.
The desired model must be pre-downloaded via Ollama’s CLI or Python API. If not, you may encounter an OllamaEndpointNotFoundError, prompting you to download the model.
This integration exemplifies how Ollama and LangChain can work together to enhance the utility and accessibility of LLMs in application development.
Bonus: Ollama with a Web UI Using Docker
This section is featured as a bonus because it highlights a substantial enhancement in Ollama’s capabilities. When we began preparing this tutorial, we hadn’t planned to cover a Web UI, nor did we expect that Ollama would include a Chat UI, setting it apart from other Local LLM frameworks like LMStudio and GPT4All.
Ollama is supported by Open WebUI(formerly known as Ollama Web UI). Open Web UI is a versatile, feature-packed, and user-friendly self-hosted Web UI designed for offline operation. It accommodates a range of LLM runners, including Ollama and APIs compatible with OpenAI.
The GIF below offers a visual demonstration of Ollama’s Web User Interface (Web UI), showcasing its intuitive design and seamless integration with the Ollama model repository. This interface simplifies the process of model management, making it accessible even to those with minimal technical expertise.
Here’s an overview of the key functionalities displayed in the GIF:
Access and Interface: The GIF begins by showing how users can easily access the Web UI. This centralized platform is where all interactions with Ollama’s capabilities begin.
Model Selection and Management: Users proceed to the settings, where they can select from a variety of models. The GIF illustrates the straightforward process of navigating to the model section and choosing the desired model.
Model Integration: After selecting a model (e.g., “Llava”), it is downloaded directly from Ollama’s repository. This process mirrors what would traditionally be handled via command line interfaces or APIs but is visualized here in a more user-friendly manner.
Operational Use: With the model downloaded, the GIF shows how users can then easily upload images and utilize the model’s capabilities, such as generating descriptive bullet points about an image’s contents.
Enhanced Usability: The Web UI not only makes it easier to manage and interact with advanced models but also enhances the overall user experience by providing a graphical interface that simplifies complex operations.
The integration of Ollama’s Web UI represents a significant step forward in making advanced modeling tools more accessible and manageable. This is ideal for users across various domains who require a straightforward solution to leverage powerful models without delving into more complex technical procedures.
Key Features of Ollama’s Web UI
The GIF included in this post offers a glimpse into the innovative features of Ollama’s Web User Interface (Web UI), demonstrating its intuitive design and seamless integration with the Ollama model repository. While the GIF showcases some of these features, the actual list of capabilities extends far beyond, making it a powerful tool for both novices and seasoned tech enthusiasts. Here’s a detailed list of some standout features that enhance the user experience:
🖥️ Intuitive Interface: Inspired by user-friendly designs like ChatGPT, the interface ensures a smooth user experience.
📱 Responsive Design: The Web UI adapts flawlessly to both desktop and mobile devices, ensuring accessibility anywhere.
⚡ Swift Responsiveness: Experience rapid performance that keeps pace with your workflow.
🚀 Effortless Setup: Quick installation options with Docker or Kubernetes enhance user convenience.
🌈 Theme Customization: Personalized interface with a selection of themes to fit your style.
💻 Code Syntax Highlighting: Code more efficiently with enhanced readability features.
✒️🔢 Full Markdown and LaTeX Support: Take your content creation to the next level with extensive formatting options.
📚 Local & Remote RAG Integration: Integrate and manage documents directly in your chats for a comprehensive chat experience.
🔍 RAG Embedding Support: Tailor your document processing by selecting different RAG embedding models.
🌐 Web Browsing Capability: Directly incorporate web content into your interactions for enriched conversations.
This extensive toolkit provided by Ollama’s Web UI not only elevates the user interface but also deeply enhances the functionality, making complex tasks simpler and more accessible. Whether you’re managing data, customizing your workspace, or integrating diverse media, Ollama’s Web UI is equipped to handle an array of challenges, paving the way for a future where technology is more interactive and user-centric.
Certainly! Here’s a structured section for your blog that introduces the installation process of the Open Web UI, incorporating all the details you’ve provided:
Setting Up Open Web UI on Your Local Machine
As we delve into setting up the Open Web UI, it’s crucial to ensure a smooth and efficient installation. This user-friendly interface sits on top of the Ollama application, enhancing your interaction capabilities. Let’s walk through the steps to get the Web UI running locally, which builds upon the foundational Ollama application required for both CLI and API interactions.
Prerequisites
Before we begin the installation of the Open Web UI, there are a couple of essential prerequisites:
Ollama Application Running: Ensure that the Ollama application is operational in the background. The Open Web UI leverages this application, requiring it to be active on your local server, typically on port 11434. This setup is crucial as the Web UI acts as a wrapper, utilizing the core functionalities provided by Ollama.
Docker Installation: Your system must have Docker installed to run the Web UI.
Running the Open Web UI
With the prerequisites in place, you can proceed to launch the Open Web UI:
docker run -d \
-p 3000:8080 \
–add-host=host.docker.internal:host-gateway \
-v open-webui:/app/backend/data \
–name open-webui \
–restart always \
ghcr.io/open-webui/open-webui:main
Let’s try to understand the above command in detail:
docker run -d: This command runs a new container in detached mode, meaning the container runs in the background and does not block the terminal or command prompt.
-p 3000:8080: This option maps port 8080 inside the container to port 3000 on the host. This means that the application inside the container that listens on port 8080 is accessible using port 3000 on the host machine.
–add-host=host.docker.internal:host-gateway: This option adds an entry to the container’s /etc/hosts file. host.docker.internal is a special DNS name used to refer to the host’s internal IP address from within the container. host-gateway allows the container to access network services running on the host.
-v open-webui:/app/backend/data: This mounts the volume named open-webui at the path /app/backend/data within the container. This is useful for persistent data storage and ensuring that data generated by or used by the application is not lost when the container is stopped or restarted.
–name open-webui: Assigns the name open-webui to the new container. This is useful for identifying and managing the container using Docker commands.
–restart always: Ensures that the container restarts automatically if it stops. If the Docker daemon restarts, the container will restart unless it is manually stopped.
ghcr.io/open-webui/open-webui:main: Specifies the Docker image to use. This image is pulled from GitHub Container Registry (ghcr.io) from the repository open-webui, using the main tag.
Accessing Open Web UI
Once the Docker container is up and running, you can access the Open Web UI by navigating to
http://localhost:3000 in your web browser. This port forwards to port 8080 inside the Docker container, where the Web UI is hosted.
On your first visit, you’ll be prompted to create a user ID and password. This step is crucial for securing your access and customizing your experience. Once set up, you can start exploring the powerful features of the Open Web UI. If you’ve previously downloaded any LLMs using Ollama’s CLI, they will also appear in the UI. Isn’t that incredibly convenient?
Installing and setting up the Open Web UI is straightforward — ensure your Ollama application is running, install Docker if you haven’t, and execute a single Docker command. This setup provides a robust platform for enhancing your interactions with Ollama’s capabilities right from your local machine. Enjoy the seamless integration and expanded functionality that the Open Web UI brings to your workflow!
Login Screen
The login screen is the primary gateway for accessing the Open Web UI. It features fields for entering an email and password, with options for users to sign in or sign up. Crucially, due to the Docker container’s volume mount setup, the user data, including login credentials, are stored persistently. This means that when the container is restarted, it retains the user data from the Open WebUI and the Platform as a Service (PaaS) app backend within the specified directory, ensuring continuity and security of access.
Model Selection Screen
This screen showcases the integration with local Ollama configurations, displaying models such as
CodeLlama, Llama2, Llama3:70b, Llama3:8b, and MedicineChat,
which were previously downloaded via Ollama’s CLI from model registries like Hugging Face. These models appear in the dropdown menu due to their configurations being established locally through Ollama’s CLI. The flexibility of the Web UI extends to managing these models directly from the interface, enhancing usability by allowing users to pull or interact with models from the registry without needing to revert to the CLI.
Chat Screen with PDF Explanation Screen
This feature is particularly innovative, allowing users to upload and analyze PDF documents directly through the Web UI. In the demonstrated case, the
Llama3:8b model is selected to interpret a document about Vision Transformers (ViT). The ability to select a model for specific content analysis and to receive a detailed summary directly on the platform exemplifies the practical utility of the Web UI. It not only simplifies complex document analysis but also makes advanced AI insights accessible through an intuitive interface. These features are available in the paid version of ChatGPT-4, but in Open Web UI, they are offered for free. Of course, the performance and accuracy may vary.
Overall, these features highlight the Open Web UI’s robust functionality, designed to facilitate seamless interaction with advanced machine learning models, ensuring both efficiency and ease of use in handling AI-driven tasks.
Summary
This comprehensive tutorial explores the expansive world of Ollama, a platform designed for managing and interacting with Large Language Models (LLMs). The guide starts with an “Introduction to Ollama,” offering insights into the platform’s capabilities, particularly its role in on-device AI applications.
The post delves into practical aspects, with sections on installing Ollama on MacOS and managing models through its command line interface. It highlights the rich repository available in Ollama’s Model Registry and outlines the process of integrating custom models from external sources like Hugging Face.
Further, the tutorial discusses the Ollama Python Library in detail, which bridges Python programming with Ollama through an API-like interface, making it easier for developers to streamline their interactions with LLMs.
Next, we delve into integrating Ollama with LangChain using the LangChain Community Python library. This allows developers to incorporate Ollama’s LLM capabilities within LangChain’s framework to build robust AI applications. We cover the installation, setting up an Ollama instance, and invoking it for enhanced functionality.
A significant portion is dedicated to setting up the Ollama Web UI using Docker, which includes detailed steps from installation to accessing the Web UI. This part of the guide enhances user interaction by explaining specific UI screens like the login screen, model selection, and PDF explanation features.
The tutorial concludes with a section on the additional benefits of using the Web UI. It ensures that readers are well-equipped to utilize Ollama’s full suite of features, making advanced LLM technology accessible to a broad audience.
This guide is an essential resource for anyone interested in leveraging the power of LLMs through the Ollama platform. It guides users from basic setup to advanced model management and interaction.
