Vincent Granville

BondingAI

GenAI platform based on xLLM, the game-changing technology that started the LLM 2.0 revolution. Boosting productivity, automating tasks, delivering ROI at scale to Enterprise customers. Without GPU, hallucinations, security issues, or latency. With zero weight, real-time fine tuning, exhaustive but concise results, customizable relevancy scores, and a modern UI - not just a prompt box.

Chief AI Architect, Co-Founder & Investor

NVIDIA

Advocate for products and services pertaining to AI, generative AI and LLMs. Featuring curated Nvidia content in my GenAI newsletter (200k subscribers) and other media, presenting at selected Nvidia events. Deep understanding of the AI landscape and technology, building ground-breaking architecture known as LLM 2.0 / xLLM, with our startup BondingAI.io participating in the Nvidia Inception program.

AI Advocate & Startup Inception Program

GenAItechLab.com

Now part of BondingAI.io. Building better, faster, and less expensive GenAI products with explainable AI, such as NoGAN (data synthesizer). Key product:  xLLM, a fast in-memory agentic multi-LLM system built from scratch, with zero weight, no training, no latency, no hallucination, fully contextual from the ground up, with real-time fine-tuning. For scientific or enterprise search, as well as clustering and predictions.  Also, builder of the best model evaluation metric on the market.

Founder, Lead AI Architect

AB InBev

Helping Fortune 100 company InBev implement my xLLM technology as an alternative to OpenAI and LangChain to boost relevancy (hallucination-free), reduce latency, and increase speed by several orders of magnitude, while increasing security via sub-LLMs accessible to authorized users only. Offering a better solution for search and cataloging, to help users navigate their corporate repository. With a focus on concise but exhaustive results (via augmentation) and specialized sub-LLMs. Local implementation without external API calls.

GenAI & LLM Consultant

MLtechniques.com

AI optimization research lab, developing state-of-the-art technology with a focus on high quality and fast algorithms, thus cost effective. Our GenAI Web APIs are accessible on GenAItechLab.com. Covering synthetic tabular data, artificial DNA sequences, time series & geospatial data generation, agent-based modeling, synthetic graphs, and customized LLMs.

Chief AI Scientist

Data Science Central

The company was acquired in 2020.  I am still a contributing author.

Executive Data Scientist, Co-Founder, Managing Partner

Microsoft

Developed a strategy to collect the right data and detect minuscule change points in time series associated with KPI's (when the changes occur, their amplitudes and directions). Goal: assessing the impact of events influencing core metrics. Example: changing the definition of a website user resulting in shift in user count, or changing some features on Bing.com resulting in increase / decrease in pageviews. These were blind tests: I did not know when the website changes were made. But when I detected a change, it was matched against known events that took place at the time flagged by my algorithm as "change point". I designed and then helped automate this algorithm.

Machine Learning Consultant

LookSmart

Optimizing advertising campaigns, Ad arbitrage. Projects and technology include:

• Created list of top commercial keywords accounting for 85% of all pay-per-click revenue
• Leveraged Google API's to automatically find millions of high yield keywords 
• Automated taxonomy creation (NLP)  
• Click fraud detection (click jacking and Botnet detection)
• Internet traffic quality scoring using hidden decision trees 
• Automation of keyword bidding 
• Reverse engineering Google keyword pricing algorithms, for ad arbitrage

Chief Scientist

eBay

Keyword pricing optimization. Improving keyword bid pricing algorithm to update bids on millions of keywords purchased on Google AdWords. Automated daily updates. Setting bids for large tail of keywords with no conversion history, based on aggregation methods (NLP). Detection of new keywords.

Data Science Consultant

Adometry (acquired by Google)

Built scoring system to measure traffic quality associated to keywords and publishers, for keyword ad networks, search engines, or advertisers (the customers). Scores were used for fraud detection, removal of bad publishers and keyword pricing. Detected major Botnets. Developed proofs of concept, attracting Looksmart and eBay as customers.

Chief AI Scientist, GenAItechLab.com

Full-stack entrepreneur with multimillion-dollar exit. Co-Founder at BondingAI.io. Author (Wiley, Elsevier). Creator of open-source Python libraries / Web APIs such as xLLM and GenAI-Evaluation. Builder of high performance in-memory agentic multi-LLMs for professional users and enterprise, with real-time finetuning, self-tuning, no weight, no training, no latency, no hallucinations, no GPU. Made from scratch, leading to replicable results, leveraging explainable AI, adopted by Fortune 100. With a focus on delivering concise, exhaustive, relevant, and in-depth search results, references, and links. 

Vincent is a leading expert, mathematician, patent owner, former post-doc at Cambridge University, former VC-funded executive, with 20+ years of corporate experience including CNET, NBC, Visa, Wells Fargo, Microsoft, eBay.  Vincent also founded and co-founded a few startups, including one with a successful exit (Data Science Central acquired by Tech Target). He is also a top influencer for brands such as Nvidia, SingleStore, and The Linux Foundation.

Privately funded GenAI and ML Optimization research lab, creating and disseminating open source intellectual property. Developing explainable and generative AI, synthetic data, computer vision, NLP and generic ML technologies. Check out our certifications, Python libraries, eBooks, articles, courses and podcasts, including our best seller "Synthetic Data and Generative  AI" (Elsevier). Visit our company website at MLtechniques.com.

I stay away as much as possible from Python libraries, especially those that are very Blackbox. Building from scratch, avoiding a bunch of glitches inherent to any externally built library, having no hallucination, more robust technology, and Blackbox-free. On top of our RAG/LLM, we offer high-quality, homemade agents (callable from our xLLM) for:

- anomaly detection
- predictive analytics
- compression and insights for electrocardiogram data
- NoGAN tabular data synthesizer + best evaluation metric

All with better algorithms than what's on the market. I'll share more when I finished my new 200-page book, "No-Blackbox, Secure, Efficient AI & LLM Solutions" later next week.

GenAItechLab Fellow

Certified Explainable AI Professional

Certified Generative AI Professional

Hierarchical Bayesian Models

Markov Chain Monte Carlo

Markov Processes

Stochastic Geometry

Stochastic Point Processes

Time Series Modeling

University of Cambridge

Université de Namur

English

French

Chief AI Architect & Co-Founder, BondingAI.io

Method and system for scoring quality of traffic to network sites

Preservation of Scores of the Quality of Traffic to Network Sites across Clients and over Time

Scoring quality of traffic to network sites

This book features new advances in game-changing AI and LLM technologies built by GenAItechLab.com. Written in simple English, it is best suited for engineers, developers, data scientists, analysts, consultants and anyone with an analytic background interested in starting a career in AI. The emphasis is on scalable enterprise solutions, easy to implement, yet outperforming vendors both in term of speed and quality, by several orders of magnitude.

Each topic comes with GitHub links, full Python code, datasets, illustrations, and real-life case studies, including from Fortune 100 company. Some of the material is presented as enterprise projects with solution, to help you build robust applications and boost your career. You don’t need expensive GPU and cloud bandwidth to implement them: a standard laptop works.

➡️ Part 1: Hallucination-Free LLM with Real-Time Fine-Tuning

➡️ Part 2: Outperforming Neural Nets and Classic AI

➡️ Part 3: Innovations in Statistical AI

About the author

Vincent Granville is a pioneering GenAI scientist and machine learning expert, co-founder of Data Science Central (acquired by a publicly traded company in 2020), Chief AI Scientist at ML Techniques and GenAI Techlab, former VC-funded executive, author (Elsevier) and patent owner — one related to LLM. Vincent’s past corporate experience includes Visa, Wells Fargo, eBay, NBC, Microsoft, and CNET.

➡️ See content and get your copy, at https://mltblog.com/3Ua7QsF

New Book: Building Disruptive AI & LLM Technology from Scratch

With 23 top projects, 96 subprojects, and 6000 lines of Python code, this vendor-neutral coursebook is a goldmine for any analytic professional or AI/ML engineer interested in developing superior GenAI or LLM enterprise apps using ground-breaking technology.

This is not another book discussing the same topics that you learn in bootcamps, college classes, Coursera, or at work. Instead, the focus is on implementing solutions that address and fix the main problems encountered in current applications. Using foundational redesign rather than patches such as prompt engineering to fix backend design flaws.

You will learn how to quickly implement from scratch applications actually used by Fortune 100 companies, outperforming OpenAI and the likes by several order of magnitudes, in terms of quality, speed, memory requirements, costs, interpretability (explainable AI), security, latency, and training complexity.

New Book: State of the Art in GenAI & LLMs — Creative Projects, with Solutions

The GenAItechLab Fellowship program allows participants to work on state-of-the-art, enterprise-grade projects, entirely for free, at their own pace, at home or in their workplace. The goal is to help you test, enhance, and further implement applications that outperform solutions offered by AI startups or organizations such as Google or OpenAI

GenAI Fellowship

This 156 pages eBook covers the foundations of machine learning, with modern approaches to solving complex problems. Emphasis is on scalability, automation, testing, optimizing, and interpretability (explainable AI). For instance, regression techniques — including logistic and Lasso — are presented as a single method, without using advanced linear algebra. There is no need to learn 50 versions when one does it all and more. Confidence regions and prediction intervals are built using parametric bootstrap, without statistical models or probability distributions. Models (including generative models and mixtures) are mostly used to create rich synthetic data to test and benchmark various methods.

Book: Intuitive Machine Learning and Explainable AI

This off-the-beaten-path machine learning tutorial is designed for busy professionals, researchers and students eager to learn and apply methods ranging from simple to advanced, in a minimum amount of time. Offered with data sets, source code, videos, spreadsheets and solved exercises. See full description below.

Book: Stochastic Processes and Simulations: A Machine Learning Perspective

This tutorial provides a solid introduction to the Generalized Riemann Hypothesis and related functions, including Dirichlet series, Euler products, non-integer primes (Beurling primes), Dirichlet characters and Rademacher random multiplicative functions. The topic is usually explained in obscure jargon or inane generalities. To the contrary, this article will intrigue you with the beauty and power of this theory. The summary style is very compact, covering much more than traditionally taught in a first graduate course in analytic number theory. The choice of the topics is a little biased, with an emphasis on probabilistic models. My approach, discussing the “hole of the orbit” — called the eye of the Riemann zeta function in a previous article — is particularly intuitive.

Solving the Riemann Hypothesis

VP, Chief Data Scientist at Enverus, Happy here, not hunting

Collected public tweets related to topics such as: Data Science, Big Data, Business Analytics, Hadoop, Machine Learning or R programming , allowing us to track what people are talking about around the world and here we present the world cloud for each of them.
#NPL #Python #R #AWS

Word Clouds of Big Data, Data Science and Other Buzz Words

Analyzed Twitter data and produced genuine insights. Tracked twitter data from top influencer Data Scientists and evaluate impact over time employing data mining tools.


Social Media Analytics

AI/ML Engineer | Ex-Google | Ex-MIT

Stop building AI agents that ignore your instructions.

This Python framework forces LLM agents to follow your rules. Every single time.

It guarantees compliance in production environments.
No more agents that drift from specifications.

The framework is 100% open source.

Key features:
• Enforces instruction adherence at runtime
• Prevents model hallucination and rule violations  
• Works with any LLM provider
• Built for production systems

You can now deploy agents that actually listen.

Chief AI Architect, Investor & Co-Founder

For an alternative to classic AI, see my new book, just published yesterday: https://lnkd.in/gsNyzb6E

Head of AIOps @ IBM || Speaker | Lecturer | Advisor

𝗧𝗵𝗶𝘀 𝗶𝘀 𝗵𝗮𝗻𝗱𝘀-𝗱𝗼𝘄𝗻 𝘁𝗵𝗲 𝗕𝗘𝗦𝗧 𝗮𝗻𝗱 𝗦𝗜𝗠𝗣𝗟𝗘𝗦𝗧 𝗶𝗹𝗹𝘂𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗔𝗜 𝘆𝗼𝘂'𝗹𝗹 𝗲𝘃𝗲𝗿 𝘀𝗲𝗲! ⬇️ 

A lot of companies picture AI as a straightforward process: Feed in data, sprinkle some AI, and BOOM → value appears! If only reality were that simple... 😅

𝗛𝗲𝗿𝗲’𝘀 𝘁𝗵𝗲 𝗥𝗲𝗮𝗹 𝗦𝘁𝗼𝗿𝘆 𝗼𝗳 𝗔𝗜:

What actually happens behind the scenes is a complex journey that involves:

1️⃣ Data Sourcing, Cleaning, and Feature Engineering
2️⃣ Data Engineering and Modeling 
3️⃣ Training, Evaluating, and Tuning Models 
4️⃣ Operationalizing AI to ensure it delivers real-world value 

And let’s not forget the challenges: 𝗟𝗲𝗴𝗮𝗹, 𝗘𝘁𝗵𝗶𝗰𝗮𝗹, 𝗕𝗶𝗮𝘀, 𝗮𝗻𝗱 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗰𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁𝘀 that need to be managed every step of the way! AI isn't a magic wand; it's a marathon of meticulous steps!

Yes, there's tremendous value in AI.

No, it's not as simple as using ChatGPT.

↓

𝗜𝗳 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝘁𝗼 𝘀𝘁𝗮𝘆 𝗮𝗵𝗲𝗮𝗱 𝗮𝘀 𝗔𝗜 𝗿𝗲𝘀𝗵𝗮𝗽𝗲𝘀 𝘄𝗼𝗿𝗸 𝗮𝗻𝗱 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀, 𝘆𝗼𝘂 𝘄𝗶𝗹𝗹 𝗴𝗲𝘁 𝗮 𝗹𝗼𝘁 𝗼𝗳 𝘃𝗮𝗹𝘂𝗲 𝗳𝗿𝗼𝗺 𝗺𝘆 𝗳𝗿𝗲𝗲 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿: https://lnkd.in/dbf74Y9E

Also look at how I approach deep neural networks with a radically different framework. No more activation functions, no more Blackbox, still multiple layers and even sub-layers, and a universal function that has its origins in number theory: https://lnkd.in/g9pwr2Ej

🤖 Generative AI Lead @ AWS ☁️ (200k+) | Startup Advisor | Public Speaker | AI Outsider | Founder Thinkfluencer AI

𝗧𝗛𝗘 𝗪𝗛𝗔𝗟𝗘 𝗜𝗦 𝗕𝗔𝗖𝗞 🐋 

Yesterday, DeepSeek AI dropped a paper that most people will only realize matters months from now.

At first glance, it looks like “just another architecture paper.” It’s not.

This work fundamentally rethinks how information flows inside deep neural networks. Instead of treating residual connections as a messy necessity, DeepSeek introduces a manifold-constrained approach that controls and structures information propagation.

Why this is a big deal 👇

🔹 It tackles one of the most under-discussed problems in deep learning: representation drift and instability at scale
🔹 It improves training efficiency and convergence without brute-force compute
🔹 It opens a path to deeper, more stable models with fewer hacks

And yes, it matters a lot for frontier models, not just academic benchmarks

This is the kind of paper that doesn’t scream “product launch”…
…but silently becomes a building block for the next generation of models.

If 2025 was about scaling,
2026 is about controlling complexity.

And DeepSeek is clearly playing that game.

Read it.
You’ll see echoes of this idea everywhere soon. 

Link to the paper in the comments 💬 

#ai #research #deepseek

Another LLM story that may not end up well...

It is no secret that I build my own LLM with my own Blackbox-free DNN based on a new type of universal function originating from number theory, instead of the standard combo of linear weighted sums with multiple layers of activation functions and transformer / attention models. More on this in my new book to be released next week, "No-Blackbox, Secure, Efficient AI & LLM Solutions", including how I predict next token with 96% accuracy on Enterprise corpus.

That said, I use other LLMs a lot, for coding, solving math problems and to find references. Regarding the latter, I asked Perplexity "from which book is the chapter "discrete-time signals and systems (chapter 2)" which came to my attention while doing a search with Gemini. To my surprise, I got the full PDF version of that book entirely for free. 

Here is my Perplexity query: 
https://lnkd.in/gxAWDtyf. 

See results in screenshot. It links to 
https://lnkd.in/gGU4gDPD 

(click on that link at your own risk, it may not be safe). Anyway, Perplexity and other LLMs feed on StudyLib.net, a book repository that violates all copyrights.

Now books like that are very expensive, sometimes publishers ask $80 just to buy a chapter. You can blame publishers on the sad state of affairs. Most people these days look for "show me PDF version of book xyz" instead of buying it. Sometimes you get the PDF from the author's website (who does not care about making money from actual sales -- as an author I am in that category as well) and sometimes a pirated version. 

The dilemma, if you build proprietary LLMs and want to stay competitive, should you also steal everything you can, crawling websites like StudyLib? Is this going to end up in yet another lawsuit? Thankfully, my xLLM is for Enterprise only, so I don't face this dilemma.

For the story, xLLM stands for Extreme LLM, by analogy to extreme rock climbing that inspired me: a guy who climbed Everest solo in 3 days with no sherpas, no oxygen, little money, no team. That's the goal with xLLM, doing better while using a tiny fraction of the resources that everyone else consume (big budgets, big teams, lots of time, and GPU farms).

Book Review: Probabilistic Machine Learning

mltechniques.com

Mohamed Zahran

New Book: No-Blackbox, Secure, Efficient AI and LLM Solutions https://lnkd.in/gsNyzb6E

Large language models and modern AI is often presented as technology that needs deep neural networks (DNNs) with billions of Blackbox parameters, expensive and time consuming training, along with GPU farms, yet prone to hallucinations. This book presents alternatives that rely on explainable AI, featuring new algorithms based on radically different technology with trustworthy, auditable, fast, accurate, secure, replicable Enterprise AI. Most of the material is proprietary and made from scratch, showcasing the culmination of decades of research away from standard models to establish a new framework in machine learning and AI technology.

I discuss an efficient DNN architecture based on a new type of universal functions in chapter 4, with DNN distillation and protection via watermarking in chapter 5. Then, in chapter 6, I discuss non-DNN alternatives that yield exact interpolation on the training set yet benefit from benign overfitting in any dimension. Accurate predictions are obtained with a simple closed-form expression, without gradient descent or other iterative optimization technique, essentially without training.

Case studies include 96% correct predictions for the next token on a Nvidia PDF repository, automated heart beat clustering and unusually high data compression rates (big data), anomaly detection and fraud litigation linked to large-scale cybsersecurity breach (large Excel repository, automated SQL, time series and geospatial data) as well as predicting next sequence on real-world genome data with home-made LLM technology. Some datasets with 1000 dimensions are generated with the best and fastest tabular data synthesizer on the market, described in details in chapter 2 along with the best model evaluation metric. These cases correspond to different agents linked to the xLLM technology (extreme LLM) developed by the author.

I barely use Python libraries other than Numpy, staying away from TensorFlow, PyTorch or Keras. It gives you full control over the code. Also, I avoid mathematical and probabilistic models when not beneficial, making the content accessible to a larger audience not versed in statistical, probabilistic or mathematical jargon. While classic books on the subject include an introduction to calculus, algebra, probability and matrix theory, here it is replaced by outside the-box problems with solutions. It includes quantum systems, quantum approximation, non-causal signal processing, convolution, automated curve fitting without iterative algorithm, and deep dive into one of the universal functions central to my DNN, a sister of the famous Riemann zeta function in number theory.

See table of content, author background, and get your copy at https://lnkd.in/gsNyzb6E

There's definitely a feeling that companies like OpenAI were created by people solving a problem outside an enterprise context. Feels like a 25-year-old student project that eventually became much bigger but still lacks in terms of being designed for and by decision makers. 

That is our approach, building AI with that perspective in mind, yet with the deepest, most advanced and innovative tech know-how on the planet, well ahead of guys like OpenAI or Stanford CS PhDs while being very close to decision makers. By having the two co-founders being very strong on both sides: corporate execution and tech execution. See details at https://lnkd.in/g6KPESC6

Partner @ Foundation Capital

Before LLMs, Palantir was competing with Snowflake and Databricks.

Post-LLMs, they do not believe they have any competitors. Why? Snowflake/Databricks optimized for SQL and query throughput: get raw data into tables, run fast analytical reads, ship dashboards and models on top. Palantir made a different bet: an ontology, a world model where data is represented the way humans actually reason about it (objects, relationships, properties; nouns/verbs/adjectives). Back then, that was built for government analysts trying to make sense of messy, interdependent systems.

Then LLMs arrived and the ontology suddenly looked like the perfect interface because models don’t want a trillion rows. They want a structured, language-shaped substrate: named entities, typed relationships, constraints, and “what interacts with what”, something you can linearize into a coherent prompt, traverse, and act on. 

The bigger implication for decision traces is that the “context graph” problem we wrote about has multiple architectural solutions:

Platform-first (example: Palantir): prescribe the unified world model upfront. Pay the integration + ontology + embedded-team tax (months of use case discovery / workflow decomposition / “process mining”), and in return you get a substrate that can connect data to decisions because everything now lives inside the same model for an extremely absurd price.

Workflow-first (decision traces): don’t start by rebuilding the world. Instrument the moments where the world changes. Capture decision receipts at commit surfaces: inputs referenced, policy/constraints, exception path, approvals, action taken, outcome. Over time (not day 1), that write-time provenance becomes its own world model, learned from trajectories rather than imposed upfront (there will be many different methods here)

And importantly: this is still an ontology approach,  just a different kind. Palantir prescribes the ontology first. Our take is that startups can learn it bottom-up from traces. You start by capturing what people actually do at the decision surface: what evidence is referenced, which approvals happen, what exceptions recur, what actions are taken, what outcomes follow and over time, infer the minimal set of entities + relations that explain those trajectories. 

The missing piece is decision traces: without them, you have state, but not the legible “why”!!

cc Animesh Koratana

Python Code and Material from the Book "Stochastic Processes and Simulations"  -  GitHub Repository	https://lnkd.in/gPPrfz3n

10 Must-Read Articles and Books About Next-Gen AI in 2025	https://lnkd.in/dHe7AQ3Y

It's easy to outperform S&P 500 6 months in a row. Doing it 30 years in a row is another story. For the latter, see also https://lnkd.in/gwc6xzjk

ChatGPT just outperformed the S&P 500 by 7x as a stock portfolio manager.

An experiment let the AI manage a real portfolio for two months.
ChatGPT delivered +29.22% returns while the S&P 500 gained +4.11%.

The setup lets you track how AI makes investment decisions:

• Receives daily market data including prices and volumes
• Uses weekends to research and rebalance holdings
• Targets U.S. micro-cap stocks under $300M market cap
• Executes trades through manual brokerage account orders

All trading data and performance reports are public on GitHub.
You can see exactly how a language model operates as a fund manager.

The ChatGPT Micro-Cap Experiment shows complete transparency in AI investing.

Cybersecurity Use Case: AI Agent for Anomaly Detection	https://lnkd.in/gb5dt6Zt

See highlights of our anomaly detection agent, part of xLLM to automatically detect abnormal geospatial and temporal patterns with Python code, SQL queries within Python and other techniques to process a repository of Excel spreadsheets. 

In this context, the client is an advertiser on Google and Bing. One of its competitors is suspected to generate fraudulent clicks to gain an unfair and illegal competitive advantage. The data clearly exhibits confirmed click fraud: see the discussion about the hourly spikes in this report. In addition, there is considerable, highly suspicious activity consistent with illegitimate traffic hidden under VPNs, as well as via other mechanisms, with too much of multiples IPs per device in 2019 and 25\% concentration in 3 unusual zip-codes (data center related) in 2022. The shift in patterns in 2022 may indicate enhanced tactics to avoid detection, compared to 2019.  

Read full article and get Python code, at https://lnkd.in/gb5dt6Zt

The Art of Visualizing High Dimensional Data	https://lnkd.in/gC-6v7AA

A Synthetic Stock Exchange Played with Real Money. Includes Python code dealing with gigantic numbers using exact arithmetic.

New Trends in LLM: Overview with Focus on xLLM

xLLM: New Generation of Large Language Models for Enterprise	https://lnkd.in/gkh9e5TC

Universal Dataset to Test, Enhance and Benchmark AI Algorithms	https://lnkd.in/gfvURU2p

Book: Understanding Deep Learning	https://lnkd.in/gy_P-6fh

In my most recent model, there is no training as predictions on training set are always 100% correct by design. While not a DNN, it benefits from "benign overfitting" (google the term) achieving 96% correct prediction rate for next token, outside the training set, on Nvidia corporate corpus (PDFs).  Chapter 6 in my new book published yesterday is entirely devoted to it, see https://lnkd.in/gsNyzb6E

You can fine-tune 100+ open-source models without writing code.

LLaMA-Factory gives you a unified interface for training LLMs and VLMs.
It supports LLaMA, Mistral, Qwen, DeepSeek, Gemma, Phi, Yi, and 90+ others.

Key features:

• Full fine-tuning, LoRA, QLoRA, PPO, and DPO support
• Multimodal training for text, image, audio, and video
• Built-in experiment tracking via LlamaBoard, W&B, MLflow, SwanLab
• OpenAI-style API for deployment

You can run it locally, in Colab, or with Docker.
No setup required. No code edits.

Teams at Amazon, NVIDIA, and Aliyun use it daily.

The project has 60K+ GitHub stars under Apache 2.0 license.

Military-grade Fast Random Number Generator Based on Quadratic Irrationals

Number Theory: Longest Runs of Zeros in Binary Digits of Square Root of 2	https://lnkd.in/gEygjh8i

taplio

Vincent Granville's Linkedin Analytics