Cheng Liu

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🚀 Fine-tuning Flux for Precision Image Generation! 🖼️ Recent work with Flux, the cutting-edge image generation model from Black Forest Labs, has revealed its immense potential. Flux represents a significant leap in AI-powered image creation, offering unparalleled quality and control. Key features of Flux: • Built on advanced flow-matching technology • Produces high-fidelity images with remarkable consistency • Offers faster inference times compared to traditional diffusion models 🔧 Fine-tuning Flux: Recent experiments with fine-tuning Flux using LoRA (Low-Rank Adaptation) have shown promising results. This technique allows for efficient customization of the model for specific styles or domains without the need for extensive computational resources. I've been fortunate to work on fine-tuning Flux for specialised applications and it is truly impressive. Have you worked with Flux or other AI image generation models? Share your thoughts on the future of AI in visual content creation! #AI #MachineLearning #ComputerVision #Flux #ImageGeneration

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Learn how an image-text multi-modality model can perform image classification, image retrieval, and image captioning #llms #llmops #image #opencv #chatgpt4 #gemini #aiml https://lnkd.in/gJFh9Ruf

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The BayLearn 2024 abstract submission deadline has been extended to Aug 5, 2024. If you are working on cutting-edge ML research, please consider submitting an abstract. BayLearn 2024 will be an in-person event, held on Oct 10, 2024: https://baylearn.org This is a great opportunity for student researchers to showcase their latest research work and get good feedback. Submissions are non-archival and the review process is quite informal. #machinelearning #research

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📊 The Power of High-Quality, Diverse, Real-World Data Fine-Tuning LLMs 🌍 When it comes to training Large Language Models (LLMs), the mantra isn’t just “more data”—it’s better data. While having more data can be beneficial, it’s the quality, diversity, and real-world relevance of the data that truly drive impactful results. Here’s why: 1️⃣ Higher Quality: clean, well-curated data improves model accuracy and reduces noise, making the model more reliable and stable. Poor-quality data often leads to inconsistent responses and increased hallucinations. Unbiased labeling consistency is key. 2️⃣ Diversity: it ensures the model can generalize across different contexts and user scenarios. This makes the model more robust, safe and inclusive, which is especially important in NLP for avoiding bias and ensuring representation. 3️⃣ Real-World Datasets: training on real datasets helps models perform better in real-world applications, as it learns from actual patterns and usage scenarios, making the model more practical and effective. For LLMs, there’s a general playbook to prepare data effectively: (1) Collect Instruction-Response Pairs: gather examples of how tasks should be answered, covering various domains and complexity levels. (2) Concatenate Pairs: organize the data into structured sequences, allowing the model to learn from end-to-end interactions. (3) Tokenize: process the data into tokens, using padding and/ or truncation as needed to maintain uniform length. I particularly love 🤗 transformers library's AutoTokenizer to identify which tokenizer to use on every model. (4) Split into Train/Dev/Test Sets: partition your data into training, validation, and testing sets to ensure proper model evaluation and tuning. Ultimately, data quality prevails over quantity, specially as we live in the LLMs era when models have been pre-trained with almost all the public data available in the 🌎 . A well-prepared dataset tailored to your application will make all the difference in the performance and reliability of your model. #AI #MachineLearning #DataQuality #LLM #NLP #ModelTraining #DataPreparation

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I'm excited to share two papers from the Retrieval team at Walmart that were presented at CIKM last week. They're both about improving the relevance of Embedding-Based Retrieval. 𝗥𝗲𝗹𝗲𝘃𝗮𝗻𝗰𝗲 𝗙𝗶𝗹𝘁𝗲𝗿𝗶𝗻𝗴 𝗳𝗼𝗿 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴-𝗯𝗮𝘀𝗲𝗱 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹. https://lnkd.in/gNQe7QhA The first is about how to intelligently choose a query-dependent cutoff on the cosine score to remove irrelevant items from the retrieved set. Since cosine scores aren't comparable across queries, we introduce a "Cosine Adapter" to map the scores to interpretable scores. 𝗘𝗻𝗵𝗮𝗻𝗰𝗶𝗻𝗴 𝗥𝗲𝗹𝗲𝘃𝗮𝗻𝗰𝗲 𝗼𝗳 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴-𝗯𝗮𝘀𝗲𝗱 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗮𝘁 𝗪𝗮𝗹𝗺𝗮𝗿𝘁. https://lnkd.in/gdqkc2hR The second is about model training techniques we learned to improve the relevance of embedding models. In particular, we describe how to use a teacher model to provide relevance labels for training Juexin (Joy) Lin Nicholas Rossi, PhD Feng LIU Zhen Yang Sachin Yadav Praveen Reddy Suram Satya Chembolu Prijith Chandran Hrushikesh Mohapatra Alessandro Magnani Ciya Liao

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#Llama3 is released now. Some insights: 1. 15T tokens processed 2. 8B and 70B instruct and fine-tuned variants 3. 400B variant under training (multimodal) 4. 24,576 NVIDIA H100 Tensor Core GPUs linked with an NVIDIA Quantum-2 InfiniBand network. model-card: https://lnkd.in/dJzjzdmW #AGI #Llama3 #GenAI #opensource

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Two new papers this week with my colleagues at Visa: 1. Measuring machine learning model robustness in production using properties of harmonic functions (https://lnkd.in/gnhbseY6) 2. Measuring LLM hallucination based on same, unsupervised and automated! (https://lnkd.in/geMGX7HV)

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📬 In collaboration with Vu Trinh, we present a deep dive into MapReduce. MapReduce may be old and not really needed today, but I would still encourage folks to understand the technology as it's one of the game changing and core data processing tech which inspired a lot of innovation that we see today. 👉https://lnkd.in/evWP2uh8 #dataengineering

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In this article we are providing the major architectural building blocks and blueprint for building end-to-end GenAI-based applications that are ready for production. I’d like to call out several key considerations as you go about implementing and designing these llm-based applications. #llms #llmops #chatgpt4 #aiml #gemini #mlops #data https://lnkd.in/dMev5ncq

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Just Arrived in Vienna for #ICML2024, I will be presenting our work on improving GNNs' generalization ability with ensemble learning (https://lnkd.in/gimXe5fS) at Oral 5A (Thu 25 Jul 10:45 a.m. — 11 a.m.) and Poster 2407 (Thu 25 Jul 11:30 a.m. — 1 p.m.). Paper title: From Coarse to Fine: Enable Comprehensive Graph Self-supervised Learning with Multi-granular Semantic Ensemble. A heartfelt thank you to all my collaborators for their incredible support; this work would not have been possible without them. I am also looking forward to reconnecting with old friends and making new ones. If you are attending ICML or are interested in my research, please join me during my presentation session. Additionally, please feel free to reach out to exchange ideas if you are interested in topics such as (1) improving graph model generalization across different datasets, and (2) foundational models for time series forecasting. See you in Vienna! 🇦🇹 #ICML2024 #MachineLearning #ArtificialIntelligence

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Positional encoding is a crucial aspect of language models, enabling them to differentiate between the first and last tokens in a sequence. For example, without positional encoding, a model would treat "the cat sat on mat" the same as "the mat sat on cat," failing to capture the correct meaning. In my recent blog post, I dive deeper into Rotary Positional Embeddings (RoPE), a powerful approach used by many LLMs to overcome limitations in traditional methods, especially when dealing with increasingly longer and complex sequences. https://lnkd.in/gH52FZGN

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Intuitive Example, Principles, Code Explanation and Insights about CRAG Take your LLM performance to the next level. #llm #llmops #chatgpt4 #mlops #ai #datascience #ml #gemini tps://https://lnkd.in/dDkZiZ5m

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Crafting effective prompts is an art itself, but getting it right can mean the difference between a great output and total garbage! Today, we’ll explore the 5 prompt engineering techniques that every AI engineer should know to get the most out of LLMs. #llms #llmops #chatgpt4 #rag #gemini #ai https://lnkd.in/d3an85KE

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Shoutout to Shrestha Basu Mallick and her team at Google for launching Data Science Agent: https://lnkd.in/eBER2f3T about a week ago. This experiment enables one to generate a Colab notebook that handles data cleaning, data exploration, plotting, Q&A on data, and predictive modeling. I know many of us in #learningsciences #learninganalytics #datascience #educationaldatamining #learningengineering #quantitativeethnography will find this agent helpful!

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