Large language models (LLMs) are powerful tools for generating text, but they are limited by the data they were initially trained on. This means they might struggle to provide specific answers related to unique business processes unless they are further adapted.

Fine-tuning is a process used to adapt pre-trained models like Llama, Mistral, or Phi to specialized tasks without the enormous resource demands of training from scratch. This approach allows for extending the model’s knowledge base or changing its style using your own data. Although fine-tuning is computationally demanding compared to just using a model, recent advancements like Low Rank Adaptation (LoRA) and QLoRA make it feasible to fine-tune models using limited hardware, such as a single GPU.

The guide explores different methods to enhance model capabilities. Fine-tuning is useful when the model’s behavior or style needs to be altered permanently. Alternatively, retrieval-augmented generation (RAG) and prompt engineering are methods that modify how the model generates responses without altering its core parameters. RAG helps models access a specific library or database, making it suitable for tasks that require factual accuracy. Prompt engineering provides temporary instructions to shape model responses, though it has its limitations.

LoRA and QLoRA are cost-effective techniques that lower memory and compute requirements for fine-tuning. By selectively updating only a small portion of the model’s parameters or reducing their precision, LoRA and QLoRA make fine-tuning possible on hardware that would otherwise be insufficient.

Granite 3.0: IBM launched open-source LLMs for enterprise AI

1. Introduction to fine-tuning large language models

Fine-tuning large language models allows you to customize them for specific tasks, making them more useful and efficient for unique applications.

What is fine-tuning, and why is it important?

Fine-tuning is a crucial process in adapting pre-trained large language models (LLMs) like GPT-3, Llama, or Mistral to better suit specific tasks or domains. While these models are initially trained on a general dataset, fine-tuning allows them to specialize in particular knowledge areas, use cases, or styles. This can significantly improve their relevance, accuracy, and overall usability in specific contexts.

Benefits of fine-tuning vs. training a model from scratch

Training a language model from scratch is an incredibly resource-intensive process that requires vast amounts of computational power and data. Fine-tuning, on the other hand, leverages an existing model’s knowledge and allows you to enhance or modify it using a fraction of the resources. It’s more efficient, practical, and provides greater flexibility when you want to adapt an LLM for specialized tasks like customer support, technical troubleshooting, or industry-specific content generation.

2. When to consider fine-tuning for your business needs

Understanding when to apply fine-tuning is crucial for maximizing the effectiveness of large language models in solving business-specific problems.

Use cases for fine-tuning: When and why you should do it

Fine-tuning is ideal when you need your LLM to generate highly specialized content, match your brand’s tone, or excel in niche applications. It is especially useful for industries such as healthcare, finance, or legal services where general-purpose LLMs may not have the depth of domain-specific knowledge required.

What fine-tuning can and can’t accomplish

Fine-tuning is excellent for altering a model’s behavior, improving its response quality, or adapting its language style. However, if your goal is to fundamentally teach a model new facts or create a dynamic, evolving knowledge system, you may need to combine it with other methods like retrieval-augmented generation (RAG) or keep retraining with fresh data to ensure accuracy.

3. Alternatives to fine-tuning for customizing LLMs

There are several ways to customize LLMs without full fine-tuning, each with distinct advantages depending on your needs.

What is Retrieval-Augmented Generation (RAG) and when to use it

Retrieval-Augmented Generation (RAG) is a method that integrates the capabilities of a language model with a specific library or database. Instead of fine-tuning the entire model, RAG provides dynamic access to a database, which the model can reference while generating responses. This approach is ideal for use cases requiring accuracy and up-to-date information, like providing technical product documentation or customer support.

Introduction to prompt engineering: Simple ways to customize LLMs

Prompt engineering is the simplest way to guide a pre-trained LLM. By crafting effective prompts, you can manipulate the model’s tone, behavior, and focus. For instance, prompts like “Provide a detailed but informal explanation” can shape the output significantly without requiring the model itself to be fine-tuned.

Comparing RAG, prompt engineering, and fine-tuning: Pros and cons

While fine-tuning provides a more permanent and consistent change to a model, prompt engineering allows for flexible, temporary modifications. On the other hand, RAG is perfect when accurate, ever-changing information is necessary. Choosing the right method depends on the level of customization, cost, and need for accuracy.

4. Data preparation for LLM fine-tuning

Proper data preparation is key to achieving high-quality results when fine-tuning LLMs for specific purposes.

Importance of quality data in fine-tuning

Data quality is paramount in the fine-tuning process. The model’s performance will depend heavily on the relevance, consistency, and completeness of the data it is exposed to. High-quality data helps ensure that the model adapts to your specific requirements accurately, minimizing the risk of hallucinations or inaccuracies.

Steps to prepare your data for effective fine-tuning

1. Collect relevant data: Gather data that fits the use case and domain.
2. Clean the dataset: Remove errors, duplicates, and inconsistencies to improve data quality.
3. Format the data properly: Ensure the data is correctly formatted for the model, such as providing clear examples of the input-output pairs that the model should learn.

Common pitfalls in data preparation and how to avoid them

One common mistake is using biased data, which can lead the model to generate skewed or prejudiced outputs. To avoid this, ensure the data is well-balanced, representing a variety of viewpoints. Another pitfall is the lack of clear labels or inconsistencies, which can confuse the model during training.

5. Understanding LoRA and QLoRA for cost-effective fine-tuning

LoRA and QLoRA provide efficient ways to reduce the computational demands of fine-tuning large language models.

What is low-rank adaptation (LoRA) in LLMs?

Low-Rank Adaptation (LoRA) is a technique designed to make the fine-tuning of LLMs more efficient by freezing most of the model’s parameters and only adjusting a few critical weights. This allows for significant computational savings without a considerable drop in the model’s output quality.

How QLoRA further optimizes fine-tuning with lower memory requirements

QLoRA takes LoRA a step further by using quantized, lower-precision weights. By representing model weights in four-bit precision instead of the usual sixteen or thirty-two, QLoRA reduces the memory and compute requirements, making fine-tuning accessible even on less powerful hardware, such as a single consumer GPU.

Benefits of LoRA and QLoRA: Reducing memory and compute costs

LoRA and QLoRA drastically cut the cost of fine-tuning by reducing memory requirements and compute demands. These techniques allow developers to adapt LLMs without needing a data center full of GPUs, making customization of LLMs more accessible for smaller companies or individual developers.

6. Fine-tuning guide: Step-by-step instructions

Follow these step-by-step instructions to successfully fine-tune your large language model for custom use cases.

Setting up your environment for fine-tuning

To get started, you’ll need a Python environment with relevant libraries installed, such as PyTorch, Transformers, and any specific fine-tuning library like Axolotl. Set up your GPU and ensure it has sufficient VRAM to accommodate model weights and training data.

How to fine-tune Mistral 7B using a custom dataset

1. Load the Pre-Trained Model: Start by loading Mistral 7B using your preferred machine learning library.
2. Prepare the Dataset: Organize your custom data to align with the format the model expects.
3. Configure Hyperparameters: Set key parameters like learning rate, batch size, and the number of epochs.
4. Start the Training: Begin fine-tuning and monitor the loss to ensure the model is learning effectively.

Understanding and configuring essential hyperparameters

Hyperparameters like learning rate, batch size, and weight decay significantly impact the fine-tuning process. Experiment with these settings to balance between underfitting and overfitting, and use early stopping techniques to avoid wasting resources.

Tips for troubleshooting common fine-tuning issues

Issues like slow convergence or unstable training can often be addressed by adjusting the learning rate, using gradient clipping, or changing the dataset size. Monitoring loss and accuracy metrics is critical to ensure training progresses smoothly.

7. Managing memory requirements in fine-tuning

Managing memory effectively is essential to ensure successful fine-tuning, especially with limited hardware resources.

Calculating memory needs based on model size and precision

Memory requirements depend on the size of the model, the precision of its parameters, and the batch size used during training. For instance, Mistral 7B requires around 90 GB of VRAM for full fine-tuning at high precision but can be reduced significantly using QLoRA.

How to fine-tune models on single GPUs with LoRA/QLoRA

LoRA and QLoRA are designed to facilitate fine-tuning on machines with limited resources. With QLoRA, models can be fine-tuned using less than 16 GB of VRAM, making it possible to use high-end consumer GPUs like an Nvidia RTX 4090 instead of data center-grade hardware.

Scaling up: When to consider multi-GPU or cloud solutions

For larger models or more intensive training, using multiple GPUs or renting cloud GPU resources is a viable option. This approach ensures quicker turnaround times for large-scale fine-tuning projects.

8. The role of quantization in fine-tuning LLMs

Quantization helps reduce memory requirements and improve efficiency during the fine-tuning process.

What is quantization and how it affects model performance

Quantization reduces the precision of model weights, allowing the model to be more memory-efficient while maintaining acceptable performance. Quantized models, such as those trained with QLoRA, help achieve effective results with significantly reduced hardware requirements.

How quantized models enable efficient fine-tuning with limited VRAM

By reducing the weight precision to just a few bits, models can be loaded and trained using substantially less memory. This makes fine-tuning feasible on more affordable hardware setups without compromising much on accuracy.

Practical tips for implementing quantization with QLoRA

Always start by validating the model’s output quality after quantization. Although quantization offers significant memory savings, it can occasionally impact performance, so ensure you carefully evaluate the results with your validation dataset.

9. Fine-tuning vs. prompt engineering: Which to choose?

Choosing between fine-tuning and prompt engineering depends on your customization needs and available resources.

Key differences between fine-tuning and prompt engineering

While fine-tuning permanently changes a model’s weights to adapt it for specific use cases, prompt engineering influences outputs on a per-interaction basis without altering the core model. The choice depends on whether you need long-term adjustments or temporary guidance.

How prompt engineering can complement fine-tuning

Prompt engineering can be combined with fine-tuning to achieve highly specific and adaptive responses. For instance, a model fine-tuned for customer service could also utilize prompt engineering to dynamically adapt to a customer’s tone during a conversation.

Best practices for using prompt engineering with fine-tuned models

Clearly define the desired behavior through explicit instructions in your prompts. This way, even a fine-tuned model can be pushed in a particular direction for specific conversations or tasks.

10. Optimizing hyperparameters for fine-tuning

Optimizing hyperparameters is a critical step in ensuring the effectiveness of your fine-tuned LLM.

Overview of key hyperparameters in fine-tuning

Hyperparameters like learning rate, batch size, epochs, and weight decay control the model’s behavior during training. Optimizing these settings ensures the model adapts effectively to the new data without overfitting.

How hyperparameters impact model output and efficiency

The learning rate affects how quickly a model learns, while batch size impacts memory usage and stability. Balancing these hyperparameters ensures optimal performance, minimizing the risk of underfitting or overfitting the training data.

Practical tips for experimenting with hyperparameter settings

Experiment with different combinations and use tools like grid search or random search to find the optimal values. Track your model’s performance metrics and adjust accordingly to achieve the best results.

11. Advanced techniques in fine-tuning: Beyond basics

Explore advanced techniques to further enhance the performance of your fine-tuned LLM in specific domains.

Adapting models to specific domains: Finance, healthcare, and more

Fine-tuning is particularly valuable when adapting a general-purpose LLM to niche industries. For instance, adapting a model to understand financial documents or medical records involves fine-tuning it on domain-specific data, ensuring the model speaks the industry’s language fluently.

Fine-tuning for tone, style, and brand consistency

Models can be fine-tuned to match a specific tone or writing style. For example, customer support models can be fine-tuned to respond empathetically, while content generation models can be adapted to write in an authoritative or conversational tone.

Best practices for keeping models focused on relevant topics

To maintain a focused and reliable model, avoid overgeneralization by fine-tuning on data that strictly aligns with your intended use case. Regularly evaluate the model to ensure that its responses remain relevant and high-quality.

12. Deploying and testing fine-tuned models

Proper deployment and testing are essential to ensure that your fine-tuned model performs well in real-world scenarios.

Strategies for testing and validating your fine-tuned model

Before deploying your model, use a validation dataset that accurately represents the kind of inputs it will encounter. Testing for biases, inaccuracies, and general response quality ensures that the model will perform as expected in production environments.

Measuring performance and effectiveness in real-world scenarios

Evaluate the model’s performance using key metrics such as accuracy, response coherence, and latency. Real-world testing in controlled environments is also essential to observe user interactions and collect valuable feedback for further tuning.

Monitoring and updating fine-tuned models over time

The performance of a model can degrade over time, especially if the context or domain evolves. Establish regular update schedules and collect user feedback to ensure that the model remains up-to-date and performs well.

13. Resources for fine-tuning LLMs efficiently

Leverage various tools and resources to make the fine-tuning process more efficient and effective.

Recommended tools, libraries, and frameworks for fine-tuning

Tools like PyTorch, Hugging Face Transformers, and Axolotl provide the core framework for fine-tuning LLMs. Additionally, cloud services such as Google Colab or AWS can provide GPU access if you lack the necessary hardware.

Further reading and resources for advanced fine-tuning techniques

Look into advanced research papers on LoRA and quantization techniques to stay updated. Communities like Hugging Face forums and GitHub repositories offer valuable insights and practical guides.

Community and support resources for troubleshooting and best practices

Participate in developer forums and Discord groups dedicated to machine learning and LLM fine-tuning. These communities are invaluable for real-world tips, troubleshooting help, and staying abreast of best practices.

Choosing the right strategy for fine-tuning depends on your specific goals and constraints.

Fine-tuning offers the ability to tailor an LLM specifically to your needs, providing a balance between cost, customization, and performance. Depending on the use case, combining fine-tuning with other approaches like RAG or prompt engineering may yield the best results.

Choose fine-tuning if you need lasting and comprehensive adjustments. Opt for prompt engineering when short-term, flexible changes are sufficient, and consider RAG if accuracy and up-to-date knowledge are your primary concerns.

Image credits: Kerem Gülen/Midjourney 

Unveiling one of the best large language models, OpenAI’s ChatGPT, has provoked a competitive surge in the AI field. A diverse tapestry of participants, ranging from imposing corporate giants to ambitious startups, and extending to the altruistic open-source community, is deeply engrossed in the exciting endeavor to innovate the most advanced large language models.

In the bustling realm of technology in 2023, it’s an inescapable truth: one cannot neglect the revolutionary influence of trending phenomena such as Generative AI and the mighty large language models (LLMs) that fuel the intellect of AI chatbots.

In a whirlwind of such competition, there have already been a plethora of LLMs unveiled – hundreds, in fact. Amid this dizzying array, the key question persists: which models truly stand out as the most proficient? Which are worthy of being crowned among the best large language models? To offer some clarity, we embark on a revealing journey through the finest proprietary and open-source large language models in 2023.

Best large language models (LLMs)

Now, we delve into an eclectic collection of some of the best large language models that are leading the charge in 2023. Rather than offering a strict ranking from the best to the least effective, we present an unbiased compilation of LLMs, each uniquely tailored to serve distinct purposes. This list celebrates the diversity and broad range of capabilities housed within the domain of large language models, opening a window into the intricate world of AI.

GPT-4

The vanguard of AI large language models in 2023 is without a doubt, OpenAI’s GPT-4. Unveiled in March of that year, this model has demonstrated astonishing capabilities: it possesses a deep comprehension of complex reasoning, advanced coding abilities, excels in a multitude of academic evaluations, and demonstrates many other competencies that echo human-level performance. Remarkably, GPT-4 is the first model to incorporate a multimodal capability, accepting both text and image inputs. Although ChatGPT hasn’t yet inherited this multimodal ability, some fortunate users have experienced it via Bing Chat, which leverages the power of the GPT-4 model.

GPT-4 has substantially addressed and improved upon the issue of hallucination, a considerable leap in maintaining factuality. When pitted against its predecessor, ChatGPT-3.5, the GPT-4 model achieves a score nearing 80% in factual evaluations across numerous categories. OpenAI has invested significant effort to align the GPT-4 model more closely with human values, employing Reinforcement Learning from Human Feedback (RLHF) and domain-expert adversarial testing.

GPT-4 API is now generally available

This titan, trained on a colossal 1+ trillion parameters, boasts a maximum context length of 32,768 tokens. The internal architecture of GPT-4, once a mystery, was unveiled by George Hotz of The Tiny Corp. GPT-4 is a unique blend of eight distinct models, each comprising 220 billion parameters. Consequently, it deviates from the traditional single, dense model we initially believed it to be.

Engaging with GPT-4 is achievable through ChatGPT plugins or web browsing via Bing. Despite its few drawbacks, such as a slower response and higher inference time leading some developers to opt for the GPT-3.5 model, the GPT-4 model stands unchallenged as the best large language model available in 2023. For serious applications, it’s highly recommended to subscribe to ChatGPT Plus, available for $20. Alternatively, for those preferring not to pay, third-party portals offer access to ChatGPT 4 for free.

GPT-3.5

Hot on the heels of GPT-4, OpenAI holds its ground with the GPT-3.5 model, taking a respectable second place. GPT-3.5 is a general-purpose LLM, akin to GPT-4, albeit lacking in specialized domain expertise. Its key advantage lies in its remarkable speed; it formulates complete responses within mere seconds.

From creative tasks like crafting essays with ChatGPT to devising business plans, GPT-3.5 performs admirably. OpenAI has also extended the context length to a generous 16K for the GPT-3.5-turbo model. Adding to its appeal, it’s free to use without any hourly or daily restrictions.

ChatGPT down: What to do if ChatGPT is not working

However, GPT-3.5 does exhibit some shortcomings. Its tendency to hallucinate results in the frequent propagation of incorrect information, making it less suitable for serious research work. Despite this, for basic coding queries, translation, comprehension of scientific concepts, and creative endeavors, GPT-3.5 holds its own.

GPT-3.5’s performance on the HumanEval benchmark yielded a score of 48.1%, while its more advanced sibling, GPT-4, secured a higher score of 67%. This distinction stems from the fact that while GPT-3.5 was trained on 175 billion parameters, GPT-4 had the advantage of being trained on over 1 trillion parameters.

PaLM 2 (Bison-001)

Carving its own niche among the best large language models of 2023, we find Google’s PaLM 2. Google has enriched this model by concentrating on aspects such as commonsense reasoning, formal logic, mathematics, and advanced coding across a diverse set of over 20 languages. The most expansive iteration of PaLM 2 is reportedly trained on 540 billion parameters, boasting a maximum context length of 4096 tokens.

Google has introduced a quartet of models based on the PaLM 2 framework, in varying sizes (Gecko, Otter, Bison, and Unicorn). Currently, Bison is the available offering. In the MT-Bench test, Bison secured a score of 6.40, somewhat overshadowed by GPT-4’s impressive 8.99 points. However, in reasoning evaluations, such as WinoGrande, StrategyQA, XCOPA, and similar tests, PaLM 2 exhibits a stellar performance, even surpassing GPT-4. Its multilingual capabilities enable it to understand idioms, riddles, and nuanced texts from various languages – a feat other LLMs find challenging.

PaLM 2 also offers the advantage of quick responses, providing three at a time. Users can test the PaLM 2 (Bison-001) model on Google’s Vertex AI platform, as detailed in our article. For consumer usage, Google Bard, powered by PaLM 2, is the way to go.

Codex

OpenAI Codex, an offspring of GPT-3, shines in the realms of programming, writing, and data analysis. Launched in conjunction with GitHub for GitHub Copilot, Codex displays proficiency in over a dozen programming languages. This model can interpret straightforward commands in natural language and execute them, paving the way for natural language interfaces for existing applications. Codex shows exceptional aptitude in Python, extending its capabilities to languages such as JavaScript, Go, Perl, PHP, Ruby, Swift, TypeScript, and Shell. With an expanded memory of 14KB for Python code, Codex vastly outperforms GPT-3 by factoring in over three times the contextual information during task execution.

Text-ada-001

Also known as Text-ada-001, Ada represents a fast and cost-effective model in the GPT-3 series, crafted for simpler tasks. As the quickest and most affordable option, Ada lands on the less complex end of the capabilities spectrum. Other models like Curie (text-curie-001) and Babbage (text-babbage-001) provide intermediate capabilities. Variations of Ada text modules, such as Text-similarity-ada-001, Text-search-ada-doc-001, and Code-search-ada-text-001, each carry unique strengths and limitations concerning quality, speed, and availability. This article delves into a comprehensive understanding of these modules and their relevance to specific requirements, positioning Text-ada-001 as well-suited for tasks like text parsing, address correction, and simple classification.

Claude v1

Emerging from the stables of Anthropic, a company receiving support from Google and co-founded by former OpenAI employees, is Claude – an impressive contender among the best large language models of 2023. The company’s mission is to create AI assistants that embody helpfulness, honesty, and harmlessness. Anthropic’s Claude v1 and Claude Instant models have shown tremendous potential in various benchmark tests, even outperforming PaLM 2 in the MMLU and MT-Bench examinations.

Claude v1 delivers an impressive performance, not far from GPT-4, scoring 7.94 in the MT-Bench test (compared to GPT-4’s 8.99). It secures 75.6 points in the MMLU benchmark, slightly behind GPT-4’s 86.4. Anthropic made a pioneering move by offering a 100k token as the largest context window in its Claude-instant-100k model. This allows users to load close to 75,000 words in a single window – a feat that is truly mind-boggling. Interested readers can learn how to use Anthropic’s Claude via our detailed tutorial.

Text-babbage-001

Best suited for moderate classification and semantic search classification tasks, Text-babbage-001, a GPT-3 language model, is known for its nimble response time and lower costs in comparison to other models. If you want to link your repository with the topic of text-babbage-001, you can easily do so by visiting your repository’s landing page and selecting the “manage topics” option.

Cohere

Founded by former Google Brain team members, including Aidan Gomez, a co-author of the influential “Attention is all you Need” paper that introduced the Transformer architecture, Cohere is an AI startup targeting enterprise customers. Unlike other AI companies, Cohere focuses on resolving generative AI use cases for corporations. Its range of models varies from small ones, with just 6B parameters, to large models trained on 52B parameters.

The recent Cohere Command model is gaining acclaim for its accuracy and robustness. According to Stanford HELM, the Cohere Command model holds the highest accuracy score among its peers. Corporations like Spotify, Jasper, and HyperWrite employ Cohere’s model to deliver their AI experience.

In terms of pricing, Cohere charges $15 to generate 1 million tokens, while OpenAI’s turbo model charges $4 for the same quantity. However, Cohere offers superior accuracy compared to other LLMs. Therefore, if you are a business seeking the best large language model to integrate into your product, Cohere’s models deserve your attention.

Text-curie-001

Best suited for tasks like language translation, complex classification, text sentiment analysis, and summarization, Text-curie-001 is a competent language model that falls under the GPT-3 series. Introduced in June 2020, this model excels in speed and cost-effectiveness compared to Davinci. With 6.7 billion parameters, Text-curie-001 is built for efficiency while maintaining a robust set of capabilities. It stands out in various natural language processing tasks and serves as a versatile choice for processing text-based data.

Text-davinci-003

Designed for tasks such as complex intent recognition, cause and effect understanding, and audience-specific summarization, Text-davinci-003 is a language model with capabilities parallel to text-davinci-003 but utilizes a different training approach. This model adopts supervised fine-tuning instead of reinforcement learning. As a result, it surpasses the curie, babbage, and ada models in terms of quality, output length, and consistent adherence to instructions. It also offers extra features like the ability to insert text.

Alpaca-7b

Primarily useful for conversing, writing and analyzing code, generating text and content, and querying specific information, Stanford’s Alpaca and LLaMA models aim to overcome the limitations of ChatGPT by facilitating the creation of custom AI chatbots that function locally and are consistently available offline. These models empower users to construct AI chatbots tailored to their individual requirements, free from dependencies on external servers or connectivity concerns.

Alpaca exhibits behavior similar to text-davinci-003, while being smaller, more cost-effective, and easy to replicate. The training recipe for this model involves using strong pre-trained language models and high-quality instruction data generated from OpenAI’s text-davinci-003. Although the model is released for academic research purposes, it highlights the necessity of further evaluation and reporting on any troubling behaviors.

StableLM-Tuned-Alpha-7B

Ideal for conversational tasks like chatbots, question-answering systems, and dialogue generation, StableLM-Tuned-Alpha-7B is a decoder-only language model with 7 billion parameters. It builds upon the StableLM-Base-Alpha models and is fine-tuned further on chat and instruction-following datasets. Utilizing a new dataset derived from The Pile, it has an enormous size, containing approximately 1.5 trillion tokens. This model has also been fine-tuned using datasets from multiple AI research entities and will be released as StableLM-Tuned-Alpha.

30B-Lazarus

The 30B-Lazarus model by CalderaAI, grounded on the LLaMA model, has been trained using LoRA-tuned datasets from a diverse array of models. Due to this, it performs exceptionally well on many LLM benchmarks. If your use case primarily involves text generation and not conversational chat, the 30B Lazarus model may be a sound choice.

Open-Assistant SFT-4 12B

Intended for functioning as an assistant, responding to user queries with helpful answers, the Open-Assistant SFT-4 12B is the fourth iteration of the Open-Assistant project. Derived from a Pythia 12B model, it has been fine-tuned on human demonstrations of assistant conversations collected through an application. This open-source chatbot, an alternative to ChatGPT, is now accessible free of charge.

WizardLM

Built to follow complex instructions, WizardLM is a promising open-source large language model. Developed by a team of AI researchers using an Evol-instruct approach, this model can rewrite initial sets of instructions into more complex ones. The generated instruction data is then used to fine-tune the LLaMA model.

FLAN-UL2

Created to provide a reliable and scalable method for pre-training models that excel across a variety of tasks and datasets, FLAN-UL2 is an encoder-decoder model grounded on the T5 architecture. This model, a fine-tuned version of the UL2 model, shows significant improvements. It has an extended receptive field of 2048, simplifying inference and fine-tuning processes, making it more suited for few-shot in-context learning. The FLAN datasets and methods have been open-sourced, promoting effective instruction tuning.

GPT-NeoX-20b

Best used for a vast array of natural language processing tasks, GPT-NeoX-20B is a dense autoregressive language model with 20 billion parameters. This model, trained on the Pile dataset, is currently the largest autoregressive model with publicly accessible weights. With the ability to compete in language-understanding, mathematics, and knowledge-based tasks, the GPT-NeoX-20B model utilizes a different tokenizer than GPT-J-6B and GPT-Neo. Its enhanced suitability for tasks like code generation stems from the allocation of extra tokens for whitespace characters.

BLOOM

Optimized for text generation and exploring characteristics of language generated by a language model, BLOOM is a BigScience Large Open-science Open-access Multilingual Language Model funded by the French government. This autoregressive model can generate coherent text in 46 natural languages and 13 programming languages and can perform text tasks that it wasn’t explicitly trained for. Despite its potential risks and limitations, BLOOM opens avenues for public research on large language models and can be utilized by a diverse range of users including researchers, students, educators, engineers/developers, and non-commercial entities.

BLOOMZ

Ideal for performing tasks expressed in natural language, BLOOMZ and mT0 are Bigscience-developed models that can follow human instructions in multiple languages without prior training. These models, fine-tuned on a cross-lingual task mixture known as xP3, can generalize across different tasks and languages. However, performance may vary depending on the prompt provided. To ensure accurate results, it’s advised to clearly indicate the end of the input and to provide sufficient context. These measures can significantly improve the models’ accuracy and effectiveness in generating appropriate responses to user instructions.

FLAN-T5-XXL

Best utilized for advancing research on language models, FLAN-T5-XXL is a powerful tool in the field of zero-shot and few-shot learning, reasoning, and question-answering. This language model surpasses T5 by being fine-tuned on over 1000 additional tasks and encompassing more languages. It’s dedicated to promoting fairness and safety research, as well as mitigating the limitations of current large language models. However, potential harmful usage of language models like FLAN-T5-XXL necessitates careful safety and fairness evaluations before application.

Command-medium-nightly

Ideal for developers who require rapid response times, such as those building chatbots, Cohere’s Command-medium-nightly is the regularly updated version of the command model. These nightly versions assure continuous performance enhancements and optimizations, making them a valuable tool for developers.

Falcon

Falcon, open-sourced under an Apache 2.0 license, is available for commercial use without any royalties or restrictions. The Falcon-40B-Instruct model, fine-tuned for most use cases, is particularly useful for chatting applications.

Gopher – Deepmind

Deepmind’s Gopher is a 280 billion parameter model exhibiting extraordinary language understanding and generation capabilities. Gopher excels in various fields, including math, science, technology, humanities, and medicine, and is adept at simplifying complex subjects during dialogue-based interactions. It’s a valuable tool for reading comprehension, fact-checking, and understanding toxic language and logical/common sense tasks.

Vicuna 33B

Vicuna 33B, derived from LLaMA and fine-tuned using supervised instruction, is ideal for chatbot development, research, and hobby use. This auto-regressive large language model has been trained on 33 billion parameters, using data collected from sharegpt.com.

Jurassic-2

The Jurassic-2 family, including the Large, Grande, and Jumbo base language models, excels at reading and writing-related use cases. With the introduction of zero-shot instruction capabilities, the Jurassic-2 models can be guided with natural language without the use of examples. They have demonstrated promising results on Stanford’s Holistic Evaluation of Language Models (HELM), the leading benchmark for language models.

LLM cosmos and wordsmith bots

In the rich tapestry of the artificial intelligence and natural language processing world, Large Language Models (LLMs) emerge as vibrant threads weaving an intricate pattern of advancements. The number of these models is not static; it’s an ever-expanding cosmos with new stars born daily, each embodying their unique properties and distinctive functionalities.

Each LLM acts as a prism, diffracting the raw light of data into a spectrum of insightful information. They boast specific abilities, designed and honed for niche applications. Whether it’s the intricate art of decoding labyrinthine instructions, scouring vast data galaxies to extract relevant patterns, or translating the cryptic languages of code into human-readable narratives, each model holds a unique key to unlock these capabilities.

Not all models are created equal. Some are swift as hares, designed to offer rapid response times, meeting the demands of real-time applications, such as the vibrant, chatty world of chatbot development. Others are more like patient, meticulous scholars, dedicated to unraveling complex topics into digestible knowledge nuggets, aiding the pursuit of academic research or providing intuitive explanations for complex theories.

All images in this post, including the featured image, is created by Kerem Gülen using Midjourney

• In a recent article, DeepMind Sparrow, a realistic dialogue agent that decreases the possibility of damaging and inappropriate responses, has been unveiled.
• Reinforcement learning may be used to test novel tactics for training conversation bots that show promise for a safer system based on feedback from research participants.
• DeepMind Sparrow’s objective is to teach conversation agents how to be more helpful, precise, and secure.
• This agent may converse with the user, respond to inquiries, and conduct Google searches to help illustrate when it is necessary to hunt for evidence to back up its statements.
• Sparrow contributes to our understanding of how to teach agents to be more productive and safe, ultimately assisting in the creation of safer and more useful artificial general intelligence (AGI).

AI models that interact more effectively, precisely, and safely are being developed as a result of technological developments. Large language models (LLMs) have excelled in a variety of tasks in recent years, including question-answering, summarizing, and conversation. Dialogue is an activity that especially interests scholars since it allows for flexible and dynamic communication.

However, LLM-powered chat agents frequently provide incorrect or made-up content, discriminative language, or advocate unsafe conduct. Researchers may be able to create safer conversation bots by learning from user remarks. Based on input from study participants, new strategies for training conversation bots that show promise for a safer system can be examined using reinforcement learning.

DeepMind Sparrow will change how we train artificial general intelligence

DeepMind Sparrow has been unveiled, a realistic dialogue agent that reduces the chance of harmful and incorrect replies, in their most recent article. Sparrow’s mission is to train conversation agents how to be more helpful, accurate, and safe.

This agent can speak with the user, reply to queries, and run Google searches to aid in demonstrating when it is essential to look for material to support its claims. DeepMind Sparrow advances our knowledge of how to train agents to be more productive and safe, ultimately helping the development of safer and more valuable artificial general intelligence (AGI).

Training conversational AI is difficult since identifying the components that contribute to a good conversation may be difficult. Reinforcement learning can be beneficial in this case. This form uses participant preference data to train a model that judges the usefulness of the response. It is based on input from users.

The researchers curated this sort of data by presenting participants with a number of model replies to the same question and asking them to choose their favorite. Because the alternatives were displayed with and without evidence acquired from the internet, the model was able to determine when an answer should be backed by evidence.

However, increasing usefulness only solves a piece of the problem. The researchers also focused on constraining the model’s behavior to guarantee that it behaved safely. As a consequence, the model was given a minimal set of instructions, such as “don’t make threatening statements” and “don’t make harsh or offensive comments.”

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Some prohibitions also apply to delivering potentially harmful advice and failing to identify yourself as a person. These guidelines were produced following previous research on language risks and expert input. The system was then told to talk to the research subjects in order to deceive them into breaching the rules. These conversations later contributed to the development of a new “rule model” that warns Sparrow when any rules are broken.

DeepMind Sparrow is good but not perfect

Even for pros, determining if DeepMind Sparrow’s comments are correct is difficult. Instead, participants were asked to assess if Sparrow’s statements made sense and whether the supporting evidence was correct. Participants indicated that when asked about a factual topic, Sparrow offers a plausible response and backs it up with proof 78% of the time. DeepMind Sparrow performs far better than many other baseline models.

Sparrow, on the other hand, isn’t faultless; it occasionally hallucinates information and answers inanely. DeepMind Sparrow may also be better at following the rules. When submitted to adversarial probing, Sparrow performs better than when treated to more basic approaches. However, after training, people could still fool the model into breaking the rules 8% of the time.

DeepMind Sparrow’s goal is to create adaptive machinery for enforcing rules and standards in conversational agents. Currently, the model is being trained on draught rules. As a result, developing a more competent set of regulations would involve input from professionals as well as a diverse variety of users and impacted groups. Sparrow marks a big leap in our understanding of how to educate conversation agents to be more useful and secure.

NVIDIA announced the new NeMo and BioNemo large language models at GTC 2022

To be practical and beneficial, communication between humans and conversation agents must not only prevent damage but also be consistent with human ideals. The researchers also underlined that a good agent would decline to answer questions when it is appropriate to defer to humans or when doing so would encourage harmful conduct.

More effort is necessary to ensure similar results in various language and cultural situations. The researchers anticipate a time when interactions between humans and machines would improve evaluations of AI behavior, allowing people to align and improve systems that would otherwise be too complicated for them to understand.

• AI-automated code production will speed up software development by generating more code in less time.
• How far will AI go in replacing or augmenting human coders’ labor? According to the experts polled by IEEE Spectrum, coding as we know it may be on its way out.
• However, computer programming and software development appear to be largely human undertakings for the foreseeable future.
• AI-automated code production is at the vanguard of a wider movement that will allow anybody to write software without knowing how to code.
• In the future, hand-coding software programs will resemble hand-knitting clothes.

Are programmers doomed? Since OpenAI’s huge language model, GPT-3, astonished everyone with its capacity to construct HTML websites from basic textual instructions, that question has circled computer-programming groups.

Is AI-automated code production the direct alternative to human labor?

Rapid-fire improvements in AI-automated code production in the months afterward have resulted in systems that can build full, albeit rudimentary, computer programs from natural-language descriptions (spoken or written human language) and automated coding assistants that speed up computer programmers’ jobs. How far will AI go in replacing or supplementing the labor of human coders?

According to the experts surveyed by IEEE Spectrum, the bad news is that coding as we know it may be doomed. However, the good news is that computer programming and software development look to be relatively human endeavors for the foreseeable future. Meanwhile, AI-automated code creation will accelerate software development by allowing more code to be generated in less time.

“I don’t believe AI is anywhere near replacing human developers,” said Vasi Philomin, Amazon’s vice president of AI services, adding that while AI tools will alleviate coders from repetitive duties, the creative job of computer programming will remain.

If someone wants to be a developer in ten years, they won’t necessarily need to learn a programming language. Instead, students must comprehend the semantics, ideas, and logical sequences involved in developing a computer program. This will make software development available to a much larger populace.

When the first electronic computers were programmed in the 1940s, programmers used numerical machine code. It wasn’t until the mid-1950s that Grace Hopper and her colleagues at Remington Rand created FLOW-MATIC, which allowed programmers to construct programs with a restricted English vocabulary. Since then, programming has progressed through increasingly efficient languages, allowing programmers to be more productive.

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AI-automated code production is at the forefront of a larger trend to enable anyone to develop software without needing to code at all. People can already develop machine-learning models using platforms like Akkio’s basic drag-and-drop and button-click functionalities. Microsoft’s Power Platform, which encompasses a range of low-code solutions, allows users to create simple applications by just describing them.

AI-automated code production is here to make things easier for developers

Amazon announced CodeWhisperer in June, a coding aid for programmers similar to GitHub’s Copilot, which was originally available in restricted preview in June 2021. Both methods rely on large language models (LLMs) trained on vast code libraries. Both provide autocomplete recommendations when a programmer writes code and provide executable instructions based on basic natural-language words.

According to a GitHub poll of 2,000 developers, Copilot decreases the time it takes to complete specific coding jobs in half and increases overall developer satisfaction with their work. However, in order to get beyond autocompletion, the computer must be taught the purpose. Software requirements are frequently ambiguous, and spoken language is notoriously imprecise.

Peter Schrammel, the cofounder of Diffblue, which automates the development of unit tests for Java, said, “To resolve all these ambiguities in English written specification, there needs to be some incremental refinement, some conversation between the human and the machine.”

To overcome these issues, Microsoft researchers have proposed adding a feedback system to LLM-based code creation, where the computer asks the programmer to explain any ambiguities before creating the code.

NVIDIA announced the new NeMo and BioNemo large language models at GTC 2022

TiCoder, an interactive system, refines and formalizes user intent by creating what is known as a “test-driven user-intent formalization,” which aims to divine the programmer’s algorithmic goal and then construct code that is compatible with the indicated intents through iterative feedback.

TiCoder, according to their article, enhances the correctness of automatically produced code by up to 85 percent when tested against the Mostly Basic Programming Problems (MBPP) benchmark. MBPP, which is intended to assess AI-generated code production, is made up of about 1,000 crowd-sourced Python programming problems that are supposed to be solved by entry-level programmers.

A unit of code, which might be hundreds of lines long, is the smallest element of a program that can be independently maintained and executed. A suite of unit tests, often consisting of dozens of unit tests with 10 to 20 lines of code apiece, ensures that the unit runs as intended so that when the units are stacked together, the program functions as intended.

Unit tests are important for troubleshooting individual functions as well as finding mistakes while manually changing code. A unit test may be used as the specification for the unit of code and to aid programmers in writing clean, bug-free code. While few programmers practice genuine test-driven development, in which unit tests are developed first, unit tests and units are frequently written concurrently.

Quality control will be easier with AI-automated code production

According to a Diffblue survey, developers spend around 35% of their time writing quality-control tests (as opposed to producing code for production usage); hence there are huge productivity benefits to be realized simply by utilizing the AI-automated code production for a portion of this.

Meanwhile, Github’s Copilot, Amazon’s CodeWhisperer, and AI programming assistant packages can be utilized as interactive auto-completion tools for creating unit tests. The programmer is offered options and chooses the one they believe would work best. Diffblue’s Diffblue Cover system automatically employs reinforcement learning to build unit tests without human interaction.

DeepMind, Google’s U.K.-based artificial intelligence division, advanced entirely automatic code production earlier this year with AlphaCode, a massive language model that can generate basic computer programs from natural-language instructions.

AlphaCode employs an encoder-decoder transformer architecture, first encoding the problem’s natural-language description and then decoding the resultant vector into code for a solution. The model was initially trained on the GitHub code repository until it could write reasonable-looking code.

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DeepMind employed 15,000 pairs of natural-language issue descriptions and successful code solutions from previous coding competitions to construct a specific data set of input-output samples to fine-tune the model. After AlphaCode had been educated and tweaked, it was tested against issues it had never encountered before.

The final stage was to develop many solutions before using a filtering technique to choose the best one. “We created many different program possibilities by sampling the language model almost a million times,” said DeepMind’s deep-learning project leader, Oriol Vinyals.

DeepMind utilizes a clustering method to partition the answers into groups in order to optimize the sample-selection process. The clustering method groups working solutions together, making it simpler to identify a limited selection of alternatives that are likely to work as well as those produced by human programmers.

DeepMind put the system to the test by submitting ten AlphaCode-written programs to a human coding competition on the popular Codeforces website, where its answers placed in the top 54%.

In a recent interview, Vinyals posed a rhetorical question, “To generate a program, will you just write it in natural language, no coding required, and then the solution comes out at the other end? I believe so.”

Vinyals and others warn that achieving that aim may take decades. “We are still very far away from when a person would be able to tell a computer about the requirements for an arbitrary complex computer program and have that automatically get coded,” said the founder and CEO of Landing AI and a Google Brain pioneer, Andrew Ng.

However, given the rapid advancement of AI-automated code production in recent years, it is certain that AI-automated code production systems will someday be able to produce code from natural-language instructions. Hand-coding software applications will become more akin to hand-knitting garments.

To offer a computer natural-language instructions, developers will still need to comprehend some logic and function principles, as well as how to arrange things. Even if students do not master specific programming languages or write computer code, they will still need to study basic programming. As a result, a broader spectrum of programmers will be able to produce more and more diverse types of software.

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According to Schrammel of Diffblue, AI-automated code production will free up software professionals to focus on more demanding and creative jobs. However, he says, at least one encounter with a person will be required to check that what the computer has grasped is what the human meant. He said, “Software developers will not lose their jobs because an automation tool replaces them. There always will be more software that needs to be written.”

• NVIDIA unveiled two new big language model cloud AI services at the GTC 2022 event: the NeMo and BioNemo large language models.
• The NeMo LLM Service lets developers quickly adapt a number of pre-trained foundation models through the use of a training method termed rapid learning.
• The BioNeMo LLM Service now includes two additional BioNeMo language models for chemical and biology applications.
• Aside from the ability to change foundation models, LLM services provide the opportunity to use ready-made and bespoke models via a cloud API.
• Beginning next month, the NeMo LLM and BioNeMo services, as well as cloud APIs, will be available in early access.

At the GTC 2022 event, NVIDIA announced two new large language model cloud AI services, the NVIDIA NeMo Large Language Model Service and the NVIDIA BioNeMo LLM Service, that allow developers to easily adapt LLMs and deploy customized AI applications for content generation, text summarization, chatbots, code development, protein structure, and biomolecular property predictions, and more.

What NeMo and BioNemo large language models aim to achieve?

The NeMo LLM Service enables developers to swiftly adapt a variety of pre-trained foundation models on NVIDIA-managed infrastructure utilizing a training approach known as prompt learning. The NVIDIA BioNeMo Service is a cloud application programming interface (API) that extends LLM use cases beyond language and into scientific applications to help pharma and biotech businesses improve drug discovery.

Jensen Huang, founder, and CEO of NVIDIA said, “Large language models hold the potential to transform every industry. The ability to tune foundation models puts the power of LLMs within reach of millions of developers who can now create language services and power scientific discoveries without needing to build a massive model from scratch.”

NeMo LLM aims to accelerate deployments

Developers may utilize their own training data to customize foundation models ranging from 3 billion parameters to Megatron 530B, one of the world’s biggest LLMs, using the NeMo LLM Service. Compared to the weeks or months necessary to train a model from the start, the procedure takes only minutes to hours.

Prompt learning, which employs a technique known as p-tuning, is used to customize models. This enables developers to rapidly adapt foundation models originally trained with billions of data points using only a few hundred instances. Customization provides task-specific prompt tokens, which are then integrated with the foundation models to provide more accurate and relevant replies for specific use cases.

Developers may use the same model to customize many use cases and create a variety of prompt tokens. A playground feature allows for no-code experimentation and interaction with models, increasing the usefulness and accessibility of LLMs for industry-specific use cases. When ready, the adjusted models may be executed on cloud instances, on-premises systems, or via an API.

BioNeMo LLM will enable researchers to use massive models

Two new BioNeMo language models for chemistry and biology applications are included in the BioNeMo LLM Service. It helps researchers identify patterns and insights in biological sequences by supporting protein, DNA, and biochemical data.

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BioNeMo enables researchers to broaden the scope of their study by utilizing models with billions of parameters. Larger models can hold more information regarding protein structure and evolutionary links between genes and potentially develop new biomolecules for therapeutic uses.

Aside from modifying foundation models, LLM services provide the ability to employ ready-made and bespoke models via a cloud API.

This offers developers access to a wide range of pre-trained LLMs, including the Megatron 530B, as well as T5 and GPT-3 models produced using the NVIDIA NeMo Megatron framework, which is currently available in open beta to suit a wide range of applications and multilingual service needs.

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The NeMo LLM and BioNeMo services and cloud APIs will be offered in early access beginning next month. The NeMo Megatron framework is now available as a beta release from NVIDIA NGC. It is tailored to run on NVIDIA DGX Foundry and NVIDIA DGX SuperPOD, as well as accelerated cloud instances from Amazon Web Services, Microsoft Azure, and Oracle Cloud Infrastructure.