Tagged “blog”
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Choosing the Right LLM for Your Needs - Key Considerations
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Consider the key factors when selecting a Large Language Model (LLM) for your project.
TL;DR ⏱️
- Benchmark Performance
- License
- Model Size
- Alignment
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Stop adding Languages to LLMs! The Potential Drawbacks of Training Multilingual Large Language Models (LLMs) for Performance and Sustainability!
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Exploring the downsides of creating multilingual LLMs and their impact on performance and resource utilization.
TL;DR ⏱️
- Challenges of building multilingual LLMs
- Inefficiencies in token usage and context length
- Increased hardware costs and reduced token training
- Weighing multilingual models against language-specific models
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Where Science Meets Innovation - My personal Highlights & Insights into the PG 2024! Do you have answers to the open Questions?
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Highlights and open questions from the Petersberger Gespräche (PG) 2024, covering AI, energy transition, chip technologies, and more.
TL;DR ⏱️
- AI and consciousness discussions
- Energy transition and regulatory challenges
- Distributed chip technologies in Europe
- Generative AI in media
- Metaverse applications beyond gaming
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Today's Research Proposal - How to achieve "real" thinking and reasoning in GenAI, rather than just relying on a silent Chain of Thought, as seen in ReflectionAI or possibly GPT-o1?
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Exploring the potential for achieving true reasoning and thinking in Generative AI models beyond the current Chain of Thought methodologies.
TL;DR ⏱️
- Current state of reasoning in models
- Possibilities for transformers to learn to think
- Customization ideas for achieving true reasoning
- Open questions and discussion points
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For more sustainability transparency in GenAI! Share your knowledge and reduce energy waste!
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Emphasizing the importance of transparency and shared knowledge to enhance sustainability in GenAI.
TL;DR ⏱️
- GenAI involves very large models and significant training efforts
- Transparency can help share emissions and reduce energy waste
- Open source models can optimize future development
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Sustainable Air-Gapped On-Prem LLM Solution! How can we make GenAI available on almost any hardware, and how is it also available as a portable demo on our Alan Notebook
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Exploring the development of a full-stack GenAI LLM solution that can run on a variety of hardware configurations, including a portable demo setup.
TL;DR ⏱️
- Developing Alan, a full-stack GenAI LLM solution
- Hosted on German hyperscaler infrastructure
- Offers a smaller version, Alan-S-LLM
- Portable demo available on Alan Notebook
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Combining the Hugging Face Model Platform and Knowledge Graph trend analysis over time could improve GenAI research and reduce waste of energy!
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Exploring the potential of leveraging knowledge graphs to analyze trends in evolving models for better GenAI research and efficiency.
TL;DR ⏱️
- Leveraging knowledge graphs for GenAI trends
- Identifying high-performing models and best practices
- Potential for a crowd-sourced GenAI cookbook
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What is the perfect approach to adjust an LLM to your GenAI use case?
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Exploring various methods to customize LLMs for specific GenAI use cases, ranging from simple to complex approaches.
TL;DR ⏱️
- Various ways to customize LLMs for specific use cases
- Approaches vary in difficulty and complexity
- Pros and cons of different methods
- More dimensions to improve GenAI use cases
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These results give me hope for sustainable AI 🌱
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I'm impressed by some of the recent advances in the field of "small" open-weight Language Models (LLMs).
TL;DR ⏱️
- Increased documentation supports reproducibility
- Data quality improves model performance
- Model distillation reduces hardware needs
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LLMs - Big vs Small. Bigger is Better!? OR Let's not waste energy!?
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The AI community is abuzz with debates over the efficacy of large versus small language models. Both have their own merits and limitations.
TL;DR ⏱️
- AI community debates model sizes
- Massive models vs. smaller, efficient models
- Insights and future predictions
- Links to further reading
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GenAI, what is plagiarism? Its Impact on Science. How should it be handled? What is your perspective?
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Discussing the implications of GenAI on scientific work and the thin line between acceptable use and plagiarism.
TL;DR ⏱️
- Use of GenAI in scientific work
- Acceptable vs. debatable vs. critical usage
- Questions and concerns about plagiarism
- The pressure on researchers and students
- Opportunities for better research
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Adjust GenAI responses towards more ethical behavior possible through system prompts!? Do you trust in such LLM-chat prepended pamphlets?
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Exploring the potential and challenges of using system prompts to guide LLM behavior towards ethical outputs.
TL;DR ⏱️
- GenAI chat interactions often include system prompts
- System prompts aim to guide ethical LLM behavior
- Challenges exist in ensuring compliance and formulation
- Questions on designing and revealing system prompts
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Alternative to GenAI creativity? Watch and try out these fun Evolutionary Algorithms. Problem-solving without GenAI and SGD-based approaches explained!
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Exploring Evolutionary Algorithms as an alternative to GenAI for problem-solving, using a fun 2D vehicle race example.
TL;DR ⏱️
- There is hype around GenAI and LLMs
- Evolutionary Algorithms (EAs) offer an alternative
- A fun example of EAs using a 2D vehicle race
- Steps involved in EAs explained
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Do we need another GenAI solution? How & why we developed a full-stack GenAI LLM+RAG tool called Alan. A sneak peek at what I am currently working on.
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An overview of the motivations and technical aspects behind developing our own GenAI solution, Alan, at Comma Soft AG.
TL;DR ⏱️
- Diverse GenAI solutions exist
- Unique motivations for developing our own tool
- Technical advantages of our solution
- Questions on custom development vs. wrappers
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Will we reach AGI, and if so, by transformers-based architectures? Share your perspective!
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Exploring the potential of transformers-based architectures in achieving Artificial General Intelligence (AGI) and the ongoing debate surrounding it.
TL;DR ⏱️
- GenAI's impact on AGI discussions
- Technical challenges with transformer-based architectures
- Optimistic yet cautious approach at Comma Soft AG
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Do you differentiate AI Ethics principles between AI/ML fields like GenAI/LLMs or Knowledge Graph-based ML? How do we deal with so-called facts on the internet as training data?
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Exploring the nuances of AI ethics across different AI/ML fields and handling internet-based training data responsibly.
TL;DR ⏱️
- AI ethics principles across different AI/ML fields
- Personal background and perspective on AI ethics
- Recommendations and further reading on AI ethics
- Questions to ponder on AI ethics practices
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What expectations do you have regarding the values and norms of your GenAI chat assistants? Highly sensitive topic in the LLM space! My take...
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Exploring the ethical considerations and expectations surrounding the values and norms embedded in GenAI chat assistants.
TL;DR ⏱️
- LLMs generate text based on training
- Alignment and finetuning influence behavior
- Ethical considerations in different languages
- Need for a holistic view on model behavior
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Be careful when you speak of Open (Source) GenAI. Why OpenAI and Meta (shouldn't) use the word Open within their GenAI efforts?
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Examining the implications of using the term "Open" in the context of GenAI by organizations like OpenAI and Meta.
TL;DR 🚅
- Open Source is a huge and important field in computer science and AI
- The word "Open" is used widely within the GenAI field: OpenAI, Open Source LLMs
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Thanks to the Open Source Community for all their efforts! Greetings from PyCon 2024
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Expressing gratitude to the open-source community and sharing experiences from PyConDE & PyData Berlin 2024.
TL;DR ⏱️
- Trip to PyCon with colleagues
- Attended insightful talks in various AI fields
- Appreciation for open-source community
- Gratitude to all contributors and supporters
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Who will take care of truly low-resource languages? A good step towards more fair GenAI LLM pricing at OpenAI for Japanese-using people!
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Exploring the challenges and recent developments in addressing low-resource languages within the GenAI landscape, with a focus on OpenAI's efforts for the Japanese language.
TL;DR ⏱️
- Issues with LLMs for low-resource languages
- Major challenges with different character languages
- OpenAI's new dedicated model for Japanese
- Concerns about AI ethics and inequality
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What is your preferred LLM family? And do you start with an already finetuned LLM? Why you have chosen this LLM? I love to hear your perspective!
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Understanding the preferences and choices behind selecting specific LLM families and their finetuned variants.
TL;DR ⏱️
- GenAI for text implemented by LLMs
- Many open-source models available
- Continuous influx of new models
- Key foundation model families
- LLM-based GenAI pipelines at Comma Soft AG
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NVIDIA Benchmark might be WRONG cause it states - You lose money AND LLM inference speed if you add more NVIDIA A100. This NVIDIA Benchmark is NOT reliable.
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Analyzing the reliability of NVIDIA's benchmark results and the implications for LLM inference speed and hardware investment.
TL;DR ⏱️
- Terms and background on LLMs and inference
- Strange findings in NVIDIA's benchmark results
- Concerns about the reliability of these benchmarks
- Questions and further reading on the topic
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Too many LLMs?! How to keep track with all the Open Source Models? Identify the finetuned-masked LLMs and its position within the GenAI landscape!
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Navigating the complex landscape of GenAI models can be challenging, but it's crucial to understand the foundational and finetuned models to make informed decisions.
TL;DR ⏱️
- The GenAI landscape is crowded with many models
- Keeping track of innovations and true effects is hard
- Transparency issues with many so-called "open-source" models
- Recommendations for navigating this landscape
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Be careful when you are using LLAMA-2! Legal risks & Sustainability Implications due to LLAMA-2 is (NOT) Open Source.
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Important considerations regarding LLAMA-2's legal and sustainability implications.
TL;DR ⏱️
- LLAMA-2's legal and sustainability challenges
- Not truly open-source according to OSD
- Technical implications of its license
- Meta's restrictions and their broader impact
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The major players in GenAI are facing challenges with their Generative AIs. GenAI capabilities and security issues related to LLMs Tools • 37C3 Presentation
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Challenges and security issues in GenAI and LLMs, highlighted at 37C3.
TL;DR ⏱️
- GenAI has immense capabilities
- Ethical and secure GenAI pipelines are crucial
- 37C3 presentation on security issues and exploitations
- Categories of threats and challenges in GenAI
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Not prompting in English?... You have Problems!! LLM Language Barriers • Democratizing GenAI and fair pricing
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Exploring the challenges of using Generative AI with languages other than English and the implications for cost and performance.
TL;DR ⏱️
- Tokenizers and their role in LLMs
- Challenges of non-English prompts
- Efficiency and fairness in GenAI
- Recommendations for LLM pipelines
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Ever wondered about open source LLLM sizes - 7B, 13B, 70B?
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Where do those model sizes come from?... My findings!
TL;DR ⏱️
- Open-source LLM alternatives to AIaaS
- Hugging Face as a source for open-source models
- Many models are finetuned variants
- Bigger models imply slower inference & higher costs
- Different use cases require different model capabilities
- Questioning the parameter step sizes of models
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DALLE has surprising guardrails. Your image is not filtered based on your prompt. "Dead cookies" may be generated ...sometimes
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Interesting findings on DALLE's content filtering mechanisms.
TL;DR ⏱️
- DALLE-3 filters your content AFTER image creation
- With prompt “dead cookies” you can reproduce inconsistent filtering over OpenAI API
- 40% of cases with same “dead cookies” prompt stop through content filter and 60% reach us over API
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Evil LLMs available! Break GenAI Alignment through finetuning!
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Need for LLM Alignment transparency?
TL;DR ⏱️
- Powerful LLMs are mostly aligned
- Alignment can be broken through finetuning
- Need for transparency in alignment processes
- Questions about alignment in LLMs
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LLAMA2 13B is faster than LLAMA2 7B, according to NVIDIA benchmark!
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Interesting findings on NVIDIA's LLAMA 2 benchmark results.
TL;DR ⏱️
- NVIDIA LLAMA 2 Benchmark results
- LLAMA 13B reported faster than LLAMA 7B
- Questions about the accuracy of these findings
- Seeking community insights
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