Stories with Tag efficient AI

• SeedLM: Compressing LLM Weights into Seeds of Pseudo-Random Generators

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  Posted: 2025-04-06 08:53:41

  Verbose tl;dr

  Apple researchers introduce SeedLM, a novel approach to drastically compress large language model (LLM) weights. Instead of storing massive parameter sets, SeedLM generates them from a much smaller "seed" using a pseudo-random number generator (PRNG). This seed, along with the PRNG algorithm, effectively encodes the entire model, enabling significant storage savings. While SeedLM models trained from scratch achieve comparable performance to standard models of similar size, adapting pre-trained LLMs to this seed-based framework remains a challenge, resulting in performance degradation when compressing existing models. This research explores the potential for extreme LLM compression, offering a promising direction for more efficient deployment and accessibility of powerful language models.

  Apple researchers introduce a novel approach to drastically reduce the storage requirements of Large Language Models (LLMs), termed "SeedLM." This method leverages the concept of pseudo-random number generators (PRNGs) to reconstruct the vast weight matrices of LLMs from a significantly smaller "seed." Instead of storing the entire weight matrix, which can be billions of parameters, SeedLM stores only the seed used to initialize the PRNG. This seed, combined with the specific PRNG algorithm, can then be used to regenerate the weights on demand.

  The fundamental principle behind SeedLM is that the intricate patterns and structures within LLM weight matrices, while seemingly complex, might exhibit underlying regularities exploitable by PRNGs. By carefully selecting a PRNG and optimizing its parameters, the researchers demonstrate that a relatively small seed can effectively capture the essential information embedded within these weights, allowing for a substantial compression ratio.

  SeedLM's implementation involves a training process where the PRNG parameters and the seed itself are learned. This learning process aims to minimize the difference between the weights generated by the PRNG and the original, fully trained LLM weights. This optimization is performed alongside the standard LLM training, allowing the model to adapt to the weight generation process imposed by the PRNG. The researchers experiment with various PRNG architectures, including Xorshift, PCG, and SFC, finding that specific choices can significantly impact the performance of the resulting compressed model.

  The results presented demonstrate a substantial reduction in storage requirements, with compression ratios reaching several orders of magnitude depending on the specific model and PRNG configuration. While the compressed models using SeedLM do exhibit some performance degradation compared to their fully-weighted counterparts, the trade-off between storage savings and performance loss offers a compelling advantage, particularly for deploying LLMs on resource-constrained devices. Furthermore, the researchers explore different strategies to mitigate this performance degradation, including fine-tuning the compressed model after weight generation and employing higher-precision arithmetic during the PRNG weight generation process.

  The researchers highlight that SeedLM is not merely a compression technique but also offers potential benefits in terms of model personalization and efficient exploration of the model parameter space. By modifying the seed, one could potentially generate variations of the base LLM, enabling customization without retraining the entire model. This could be particularly useful for adapting LLMs to specific tasks or domains. Additionally, the compact representation provided by the seed facilitates efficient exploration of different model configurations, which could accelerate the process of finding optimal LLM architectures.

  While acknowledging that SeedLM is still in its early stages of development, the authors suggest that this approach represents a promising direction for addressing the growing storage demands of ever-larger LLMs, paving the way for their wider deployment across a range of devices and applications. Future research directions include exploring more sophisticated PRNG architectures, optimizing the training process for SeedLM, and investigating the impact of SeedLM on different LLM architectures and tasks.

  • Large Language Models
  • LLMs
  • model compression
  • compression
  • Pseudo-Random Generators
  • PRNGs
  • SeedLM
  • Weights
  • deep learning
  • machine learning
  • AI
  • artificial intelligence
  • neural networks
  • Apple
  • Apple Machine Learning Research
  • efficient AI
  • memory efficiency
  • Storage Efficiency

  Summary of Comments ( 17 )
  https://news.ycombinator.com/item?id=43599967

  Verbose tl;dr

  HN commenters discuss Apple's SeedLM, focusing on its novelty and potential impact. Some express skepticism about the claimed compression ratios, questioning the practicality and performance trade-offs. Others highlight the intriguing possibility of evolving or optimizing these "seeds," potentially enabling faster model adaptation and personalized LLMs. Several commenters draw parallels to older techniques like PCA and word embeddings, while others speculate about the implications for model security and intellectual property. The limited training data used is also a point of discussion, with some wondering how SeedLM would perform with a larger, more diverse dataset. A few users express excitement about the potential for smaller, more efficient models running on personal devices.

  The Hacker News thread for "SeedLM: Compressing LLM Weights into Seeds of Pseudo-Random Generators" contains several interesting comments discussing the feasibility, implications, and potential flaws of the proposed approach.

  Several commenters express skepticism about the practical applicability of SeedLM. One points out that the claim of compressing a 7B parameter model into a 100KB seed is misleading, as training requires an enormous amount of compute, negating the storage savings. They argue this makes it less of a compression technique and more of a novel training method. Another user expands on this by questioning the efficiency of the pseudo-random generator (PRG) computation itself. If the PRG is computationally expensive, retrieving the weights could become a bottleneck, outweighing the benefits of the reduced storage size.

  A related thread of discussion revolves around the nature of the PRG and the seed. Commenters debate whether the seed truly encapsulates all the information of the model or if it relies on implicit biases within the PRG's algorithm. One comment suggests the PRG itself might be encoding a significant portion of the model's "knowledge," making the seed more of a pointer than a compressed representation. This leads to speculation about the possibility of reverse-engineering the PRG to understand the learned information.

  Some users delve into the potential consequences for model security and intellectual property. They suggest that if SeedLM becomes practical, it could simplify the process of stealing or copying models, as only the small seed would need to be exfiltrated. This raises concerns about protecting proprietary models and controlling their distribution.

  Another commenter brings up the potential connection to biological systems, wondering if something akin to SeedLM might be happening in the human brain, where a relatively small amount of genetic information gives rise to complex neural structures.

  Finally, a few comments address the experimental setup and results. One commenter questions the choice of tasks used to evaluate SeedLM, suggesting they might be too simple to adequately assess the capabilities of the compressed model. Another points out the lack of comparison with existing compression techniques, making it difficult to judge the relative effectiveness of SeedLM.

  Overall, the comments reflect a mixture of intrigue and skepticism about the proposed SeedLM approach. While acknowledging the novelty of the idea, many users raise critical questions about its practical viability, computational cost, and potential security implications. The discussion highlights the need for further research to fully understand the potential and limitations of compressing large language models into pseudo-random generator seeds.

• Ask HN: Is anyone doing anything cool with tiny language models?

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  Posted: 2025-01-21 19:39:39

  Verbose tl;dr

  The Hacker News post asks if anyone is working on interesting projects using small language models (LLMs). The author is curious about applications beyond the typical large language model use cases, specifically focusing on smaller, more resource-efficient models that could run on personal devices. They are interested in exploring the potential of these compact LLMs for tasks like personal assistants, offline use, and embedded systems, highlighting the benefits of reduced latency, increased privacy, and lower operational costs.

  The Hacker News post titled "Ask HN: Is anyone doing anything cool with tiny language models?" inquires about innovative applications utilizing small-scale language models. The author expresses curiosity about the potential of these compact models, which, while not possessing the vast capabilities of their larger counterparts, offer advantages in terms of reduced computational demands and easier deployment. They are specifically interested in learning about practical and intriguing projects that leverage the strengths of these smaller language models, perhaps in niche areas or for specialized tasks where their limitations are less of a hindrance. The post essentially poses an open-ended question to the Hacker News community, seeking examples and insights into how others are exploring and exploiting the unique characteristics of these more resource-efficient language models. The author is implicitly acknowledging the growing interest in and accessibility of these smaller models, and is seeking to understand the landscape of current applications and potentially discover inspiring use cases. They are not asking for theoretical discussions on the potential of tiny language models, but rather concrete examples of projects where they are being actively employed.

  • natural language processing
  • NLP
  • small language models
  • TinyML
  • Language Models
  • On-device AI
  • Edge Computing
  • machine learning
  • AI
  • HN
  • Hacker News
  • Ask HN
  • model compression
  • efficient AI

  Summary of Comments ( 40 )
  https://news.ycombinator.com/item?id=42784365

  Verbose tl;dr

  HN users discuss various applications of small language models (SLMs). Several highlight the benefits of SLMs for on-device processing, citing improved privacy, reduced latency, and offline functionality. Specific use cases mentioned include grammar and style checking, code generation within specialized domains, personalized chatbots, and information retrieval from personal documents. Some users point to quantized models and efficient architectures like llama.cpp as enabling technologies. Others caution that while promising, SLMs still face limitations in performance compared to larger models, particularly in tasks requiring complex reasoning or broad knowledge. There's a general sense of optimism about the potential of SLMs, with several users expressing interest in exploring and contributing to this field.

  The Hacker News post "Ask HN: Is anyone doing anything cool with tiny language models?" generated a fair number of comments discussing various applications and perspectives on smaller language models.

  Several commenters highlighted the benefits of tiny language models, particularly their efficiency and lower computational demands. One user pointed out their usefulness for on-device applications, especially in situations with limited internet connectivity or where privacy is paramount. Another commenter echoed this sentiment, emphasizing the potential for personalized models trained on user data without needing to share sensitive information with external servers.

  There was a discussion about specific use cases, such as grammar and style checking, text summarization, and code generation. A commenter mentioned using a small language model for creating more engaging commit messages, while another suggested their potential for generating creative writing prompts or even entire short stories.

  Some comments delved into the technical aspects. One user discussed quantizing models to reduce their size without significant performance loss. Another pointed to specific libraries and tools designed for working with smaller language models, enabling easier experimentation and deployment. There was also mention of using smaller models as a starting point for fine-tuning on specific tasks, offering a more resource-efficient approach than training large models from scratch.

  A few commenters expressed skepticism about the capabilities of tiny language models compared to their larger counterparts, suggesting they might be too limited for complex tasks requiring deeper understanding or nuanced reasoning. However, others countered that the definition of "tiny" is relative and that even smaller models can achieve surprisingly good results for specific, well-defined tasks.

  Finally, some comments focused on the broader implications of smaller models. One user discussed the potential for democratizing access to AI technology by making it more affordable and accessible to individuals and smaller organizations. Another commenter raised the issue of potential misuse, noting that smaller models could be easier to weaponize for generating misinformation or spam.

  Overall, the comments reflect a general interest in the potential of tiny language models. While acknowledging their limitations, many commenters see them as a valuable tool for various applications, especially where efficiency, privacy, and accessibility are key considerations. The discussion also touched upon important technical considerations and the broader societal implications of this evolving technology.