[AMAZON US] LEGO Harry Potter Buckbeak The Hippogriff Collectible Toy, Magical Creature Figure, Fun Harry Potter Toy for Kids, Harry Potter Themed Birthday Gift for 9 Year Old Boys, Girls or Any Fan, 76427
2025.01.20 07:41 Cute-Dog-3053 [AMAZON US] LEGO Harry Potter Buckbeak The Hippogriff Collectible Toy, Magical Creature Figure, Fun Harry Potter Toy for Kids, Harry Potter Themed Birthday Gift for 9 Year Old Boys, Girls or Any Fan, 76427
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2025.01.20 07:41 Clear_Performance237 [7]Shiny Giratina after 3787 encounters
![[7]Shiny Giratina after 3787 encounters](https://b.thumbs.redditmedia.com/58ecvsL7AAiVT6BqTMyyXpCh3tI4-EP4QBADCqunVSI.jpg "[7]Shiny Giratina after 3787 encounters") | Bidoof fighting for its life submitted by Clear_Performance237 to ShinyPokemon [link] [comments] |
2025.01.20 07:41 Difficult-Race-1188 Don't do RAG, it's time for CAG
What Does CAG Promise? Retrieval-Free Long-Context Paradigm: Introduced a novel approach leveraging long-context LLMs with preloaded documents and precomputed KV caches, eliminating retrieval latency, errors, and system complexity.
Performance Comparison: Experiments showing scenarios where long-context LLMs outperform traditional RAG systems, especially with manageable knowledge bases.
Practical Insights: Actionable insights into optimizing knowledge-intensive workflows, demonstrating the viability of retrieval-free methods for specific applications.
CAG offers several significant advantages over traditional RAG systems:
- Reduced Inference Time: By eliminating the need for real-time retrieval, the inference process becomes faster and more efficient, enabling quicker responses to user queries.
- Unified Context: Preloading the entire knowledge collection into the LLM provides a holistic and coherent understanding of the documents, resulting in improved response quality and consistency across a wide range of tasks.
- Simplified Architecture: By removing the need to integrate retrievers and generators, the system becomes more streamlined, reducing complexity, improving maintainability, and lowering development overhead.
Check out AIGuys for more such articles: https://medium.com/aiguysOther Improvements For knowledge-intensive tasks, the increased compute is often allocated to incorporate more external knowledge. However, without effectively utilizing such knowledge, solely expanding context does not always enhance performance.
Two inference scaling strategies: In-context learning and iterative prompting.
These strategies provide additional flexibility to scale test-time computation (e.g., by increasing retrieved documents or generation steps), thereby enhancing LLMs’ ability to effectively acquire and utilize contextual information.
Two key questions that we need to answer:
(1) How does RAG performance benefit from the scaling of inference computation when optimally configured?
(2) Can we predict the optimal test-time compute allocation for a given budget by modeling the relationship between RAG performance and inference parameters?
RAG performance improves almost linearly with the increasing order of magnitude of the test-time compute under optimal inference parameters. Based on our observations, we derive inference scaling laws for RAG and the corresponding computation allocation model, designed to predict RAG performance on varying hyperparameters.
Read more here: https://arxiv.org/pdf/2410.04343
Another work, that focused more on the design from a hardware (optimization) point of view:
They designed the Intelligent Knowledge Store (IKS), a type-2 CXL device that implements a scale-out near-memory acceleration architecture with a novel cache-coherent interface between the host CPU and near-memory accelerators.
IKS offers 13.4–27.9× faster exact nearest neighbor search over a 512GB vector database compared with executing the search on Intel Sapphire Rapids CPUs. This higher search performance translates to 1.7–26.3× lower end-to-end inference time for representative RAG applications. IKS is inherently a memory expander; its internal DRAM can be disaggregated and used for other applications running on the server to prevent DRAM — which is the most expensive component in today’s servers — from being stranded.
Read more here: https://arxiv.org/pdf/2412.15246
Another paper presents a comprehensive study of the impact of increased context length on RAG performance across 20 popular open-source and commercial LLMs. We ran RAG workflows while varying the total context length from 2,000 to 128,000 tokens (and 2 million tokens when possible) on three domain-specific datasets, and reported key insights on the benefits and limitations of long context in RAG applications.
Their findings reveal that while retrieving more documents can improve performance, only a handful of the most recent state-of-the-art LLMs can maintain consistent accuracy at long context above 64k tokens. They also identify distinct failure modes in long context scenarios, suggesting areas for future research.
Read more here: https://arxiv.org/pdf/2411.03538
Understanding CAG Framework CAG (Context-Aware Generation) framework leverages the extended context capabilities of long-context LLMs to eliminate the need for real-time retrieval. By preloading external knowledge sources (e.g., a document collection D={d1,d2,… }) and precomputing the key-value (KV) cache (C_KV), it overcomes the inefficiencies of traditional RAG systems. The framework operates in three main phases:
1. External Knowledge Preloading
- A curated collection of documents D is preprocessed to fit within the model’s extended context window.
- The LLM processes these documents, transforming them into a precomputed key-value (KV) cache, which encapsulates the inference state of the LLM. The LLM (M) encodes D into a precomputed KV cache:
- This precomputed cache is stored for reuse, ensuring the computational cost of processing D is incurred only once, regardless of subsequent queries.
- During inference, the KV cache (C_KV) is loaded with the user query Q.
- The LLM utilizes this cached context to generate responses, eliminating retrieval latency and reducing the risks of errors or omissions that arise from dynamic retrieval. The LLM generates a response by leveraging the cached context:
- This approach eliminates retrieval latency and minimizes the risks of retrieval errors. The combined prompt P=Concat(D,Q) ensures a unified understanding of the external knowledge and query.
- To maintain performance, the KV cache is efficiently reset. As new tokens (t1,t2,…,tk) are appended during inference, the reset process truncates these tokens:
- As the KV cache grows with new tokens sequentially appended, resetting involves truncating these new tokens, allowing for rapid reinitialization without reloading the entire cache from the disk. This avoids reloading the entire cache from the disk, ensuring quick reinitialization and sustained responsiveness.
2025.01.20 07:41 RadiantBootyGal This meme is something I miss.: Financial Advisor Dog cuts the budget.
 | submitted by RadiantBootyGal to dogmemes [link] [comments] |
2025.01.20 07:41 Babathegreat7 28[M4F] US/Anywhere - Looking to make friends. Any taker
Hey there! I'm in the US. I’m an entrepreneur who’s long days don’t allow for personal time, and I’d like to change that. I’m looking for someone to chat with and get to know. Someone to have a nice conversation with. I want to hear about your day and how your doing. I’m shy at first but loosen up fast. I’m a good listener. I like to always have fun at whatever I’m doing. I'm non judgmental and don’t get offended. Looks aren’t important to me, if that’s a concern of yours. Hope to chat and get to know you soon. DM to start off a great friendship
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2025.01.20 07:41 Quirky-Literature-16 Why is Belliard tunnel closed this morning??
Crazy how the tunnel is closed without any announcement, or does anyone know anything?
submitted by Quirky-Literature-16 to brussels [link] [comments]
2025.01.20 07:41 Signal-Education4019 2 tickets for Coldplay 26th show at MRP
 | Hey there!! I’ve got 2 tickets for Coldplay 26th show. Block A Bay 4 YY 91 92- seated tickets. After taxes and stuff the tickets cost 11k each. So, 22k is the price I bought it for. Let me know if you’re interested submitted by Signal-Education4019 to Coldplaytickets [link] [comments] |
2025.01.20 07:41 AfigureGeek The original and best XENA Funko Pop!
 | submitted by AfigureGeek to ActionFigureGeek [link] [comments] |
2025.01.20 07:41 Low-Background-3051 .
 | submitted by Low-Background-3051 to sgcelebrity [link] [comments] |
2025.01.20 07:41 toolkit65 Intense pain in teeth
 | I’m 4 almost 5 days post op. Pain has severely increased since last night I woke up at 4:30 to throbbing sharp aches in my bottom teeth more on the right side. Since then (20 hours) it’s been off and on but mostly on. I took Tylenol and ibuprofen an hour ago because it came back on suddenly and significantly worse. I’m laying here awake in tears at almost midnight bc I can’t fall asleep with the pain. It’s not really my extraction sites that hurt but my canine and the few teeth surrounding it. It shoots up towards my temple as well. Pics of the left and right lower extraction sites submitted by toolkit65 to WisdomToothRecovery [link] [comments] |
2025.01.20 07:41 qualia-assurance Registration opens for Ulverston event after 2024 success
 | submitted by qualia-assurance to WholesomeCumbria [link] [comments] |
2025.01.20 07:41 ph_cd_amelia hello new week...monday's outfit
submitted by ph_cd_amelia to crossdressing [link] [comments]
2025.01.20 07:41 Rafiq_Hasanov BUSD代付代购代买老字号[信halchiou]visa代付代充代转
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submitted by Rafiq_Hasanov to FsPosterSubreddit [link] [comments]
2025.01.20 07:41 lombrike Can streamers becomes in any way cringier?
 | That's because of people like him that you guys lost btw submitted by lombrike to Destiny [link] [comments] |
2025.01.20 07:41 MyMansInComatose I told my boyfriend if he kept smoking cigs he finds on the ground he'll eventually catch something.
Guess who just contracted mono? 😊 (I'm gonna fucking kill something- I mean make him aware)
Anyways, he'll probably infect half the town by sharing cigs/vapes/etc before I get the chance to let him know that we both definitely have it since I got it from him, or he's so tired he isn't coming out of his house at all.
submitted by MyMansInComatose to teenagers [link] [comments]
2025.01.20 07:41 DreamPirates Prarthana Behere Marathi Actress #PrarthanaBehere
 | submitted by DreamPirates to dreampirates [link] [comments] |
2025.01.20 07:41 Datasealer What will happen to coffer keys, catalyst charges, etc... when season 2 starts?
Anyone knows what will happen to those resources once season 2 starts? Do they get taken away or something. Right now I have 50+ keys on my main and it seems unfair to keep them once season 2 begins.
Also on my main I have 5 catalyst charges and that translates to full set on week one. Do these reset in the new season? If they do, I should use them to create some transmog for myself.
Again on the topic of keys - key shards and radiant echoes. Will those be taken away? If not, but keys are taken away, that is a sneaky way to get a key boost at the start of season 2.
submitted by Datasealer to wow [link] [comments]
2025.01.20 07:41 Rafiq_Hasanov TRX代购靠谱网站老店[信halchiou]visa代付
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2025.01.20 07:41 Rafiq_Hasanov Bybit10年店信halchiou专业代付 支付宝代扫码商家
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2025.01.20 07:41 Decent_Army8265 Powers I'd like to see in either Gen V or the Boys
 | submitted by Decent_Army8265 to GenV [link] [comments] |
2025.01.20 07:41 Rafiq_Hasanov T老店[薇信:halchiou]虚拟币代付 RX购买
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2025.01.20 07:41 Rafiq_Hasanov usd最新《微信:halchiou》贝宝Paypal代付 t代买通过微信支付购买
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submitted by Rafiq_Hasanov to FsPosterSubreddit [link] [comments]
2025.01.20 07:41 smoothiecat Gemma’s Eternal Sunshine theory
another quick theory to add to the pot! I think the MDR work is very akin to the Lacuna company from Eternal Sunshine of the Spotless Mind. Meaning that the work Mark is doing for Lumon, finding the scary numbers that produce bad feelings (as well as good ones like Mark mentioned Helle could work on instead after her suicide attempt) are actually erasing peoples’ memories.
But unlike Eternal Sunshine, the friends & family of the patients don’t know that this is happening in Severance. So I suspect Gemma wanted out of her marriage with Mark for some reason (perhaps her pain in not being able to conceive), went to Lumon to be severed & discovered this sort of “permanent severance” she could do instead. So her car crash death was faked so her outie families would never look into & discover this new type of severance the company was testing.
Since Ms. Casey was out of testing in season one I believe MDR was testing this on other people, but now in season 2 Mark is specifically erasing Gemma’s memories as punishment by Lumon.
And if you want to be Taylor Swift fan theory about it, Ms. Casey (K-C) is Clementine Kruczynski’s initials backwards. lol
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