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.
This paper introduces a novel, parameter-free method for compressing key-value (KV) caches in large language models (LLMs), aiming to reduce memory footprint and enable longer context windows. The approach, called KV-Cache Decay, leverages the inherent decay in the relevance of past tokens to the current prediction. It dynamically prunes less important KV entries based on their age and a learned, context-specific decay rate, which is estimated directly from the attention scores without requiring any additional trainable parameters. Experiments demonstrate that KV-Cache Decay achieves significant memory reductions while maintaining or even improving performance compared to baselines, facilitating longer context lengths and more efficient inference. This method provides a simple yet effective way to manage the memory demands of growing context windows in LLMs.
Hacker News users discuss the potential impact of the parameter-free KV cache compression technique on reducing the memory footprint of large language models (LLMs). Some express excitement about the possibility of running powerful LLMs on consumer hardware, while others are more cautious, questioning the trade-off between compression and performance. Several commenters delve into the technical details, discussing the implications for different hardware architectures and the potential benefits for specific applications like personalized chatbots. The practicality of applying the technique to existing models is also debated, with some suggesting it might require significant re-engineering. Several users highlight the importance of open-sourcing the implementation for proper evaluation and broader adoption. A few also speculate about the potential competitive advantages for companies like Google, given their existing infrastructure and expertise in this area.
Succinct data structures represent data in space close to the information-theoretic lower bound, while still allowing efficient queries. The blog post explores several examples, starting with representing a bit vector using only one extra bit beyond the raw data, while still supporting constant-time rank and select operations. It then extends this to compressed bit vectors using Elias-Fano encoding and explains how to represent arbitrary sets and sparse arrays succinctly. Finally, it touches on representing trees succinctly, demonstrating how to support various navigation operations efficiently despite the compact representation. Overall, the post emphasizes the power of succinct data structures to achieve substantial space savings without significant performance degradation.
Hacker News users discussed the practicality and performance trade-offs of succinct data structures. Some questioned the real-world benefits given the complexity and potential performance hits compared to simpler, less space-efficient solutions, especially with the abundance of cheap memory. Others highlighted the value in specific niches like bioinformatics and embedded systems where memory is constrained. The discussion also touched on the difficulty of implementing and debugging these structures and the lack of mature libraries in common languages. A compelling comment highlighted the use case of storing large language models efficiently, where succinct data structures can significantly reduce storage requirements and memory access times, potentially enabling new applications on resource-constrained devices. Others noted the theoretical elegance of the approach, even if practical applications remain somewhat niche.
This paper introduces Visual Key-Value (KV) Cache Quantization, a technique for compressing the visual features stored in the key-value cache of multimodal large language models (MLLMs). By aggressively quantizing these 16-bit features down to 1-bit representations, the memory footprint of the visual cache is significantly reduced, enabling efficient storage and faster retrieval of visual information. This quantization method employs a learned codebook specifically designed for visual features and incorporates techniques to mitigate the information loss associated with extreme compression. Experiments demonstrate that this approach maintains competitive performance on various multimodal tasks while drastically reducing memory requirements, paving the way for more efficient and scalable deployment of MLLMs.
HN users discuss the tradeoffs of quantizing key/value caches in multimodal LLMs. Several express skepticism about the claimed performance gains, questioning the methodology and the applicability to real-world scenarios. Some point out the inherent limitations of 1-bit quantization, particularly regarding accuracy and retrieval quality. Others find the approach interesting, but highlight the need for further investigation into the impact on different model architectures and tasks. The discussion also touches upon alternative quantization techniques and the importance of considering memory bandwidth alongside storage capacity. A few users share relevant resources and personal experiences with quantization in similar contexts.
The F8 is a new 8-bit computer architecture designed for efficiency in both code size and memory usage, especially when programming in C. It aims to achieve performance comparable to 16-bit systems while maintaining the simplicity and resource efficiency of 8-bit designs. This is accomplished through features like a hybrid stack/register-based architecture, variable-width instructions, and dedicated instructions for common C operations like pointer manipulation and function calls. The F8 also emphasizes practical applications with features like a built-in bootloader and support for direct connection to peripherals.
Hacker News users discussed the F8 architecture's unusual design choices. Several commenters questioned the practical applications given the performance tradeoffs for memory efficiency, particularly with modern memory availability. Some debated the value of 8-bit architectures in niche applications like microcontrollers, while others pointed out existing alternatives like AVR. The unusual register structure and lack of hardware stack were also discussed, with some suggesting it might hinder C compiler optimization. A few expressed interest in the unique approach, though skepticism about real-world viability was prevalent. Overall, the comments reflected a cautious curiosity towards F8 but with reservations about its usefulness compared to established architectures.
Summary of Comments ( 17 )
https://news.ycombinator.com/item?id=43599967
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.