#3710 Slicing Large General LLMs into Smaller Specialize LLMs

-

#3710 Slicing Large General LLMs into Smaller Specialize LLMs

AI scaling laws: Universal guide estimates how LLMs will perform based on smaller models in same family

https://techxplore.com/news/2025-09-ai-scaling-laws-universal-llms.html

#3710 How might K-Nearest-Neighbors and Classification help "cut up" an existing general trillion-parameter LLM into smaller specialized LLMs?

Large language models (LLMs) with trillions of parameters, while powerful, can be computationally expensive and inefficient for specific tasks. "Cutting up" these massive models into smaller, specialized versions can lead to more efficient and accurate performance in niche domains. K-Nearest-Neighbors (KNN) and classification can play a crucial role in this process by identifying and grouping similar data to train smaller, expert models. This approach, often complemented by techniques like knowledge distillation and Mixture of Experts (MoE), offers a pathway to more manageable and specialized AI systems.

The Role of K-Nearest-Neighbors and Classification

The core idea is to leverage the vast knowledge of a large, generalist LLM to create a dataset that can be used to train smaller, more focused models. This is where KNN and classification come into play.

Imagine a massive dataset of prompts and their corresponding responses generated by the trillion-parameter LLM. This dataset represents the model's broad understanding of language and various topics. To create specialized models, this data needs to be segmented into coherent domains.

First, the text of the prompts and responses can be converted into numerical representations called embeddings. These embeddings capture the semantic meaning of the text, allowing for mathematical comparison.

Next, a clustering algorithm, for which K-Nearest-Neighbors can be a foundational element, is applied to these embeddings. The goal is to group similar prompts and responses. For instance, all queries related to medical diagnoses would cluster together, separate from clusters about coding or creative writing. Each of these clusters represents a potential domain for a specialized LLM.

Once these clusters are defined, a classification model can be trained. This classifier learns to take a new, unseen prompt and assign it to one of the predefined clusters. For example, when a user asks, "What are the symptoms of diabetes?", the classifier would direct this prompt to the "medical" cluster.

From Classification to Specialized Models

After the data is clustered and a classifier is in place, the process of creating the smaller, specialized LLMs can begin. There are a couple of primary methods to achieve this:

  • Fine-tuning: For each cluster of data, a smaller, pre-trained LLM (much smaller than the original trillion-parameter model) can be fine-tuned on that specific dataset. This process adjusts the model's parameters to become an expert in that particular domain. The result is a collection of smaller models, each highly proficient in its designated area.

  • Knowledge Distillation: This technique involves using the large "teacher" LLM to train a smaller "student" LLM. The student model learns to mimic the output of the teacher model on a specific subset of data (one of the clusters). This is a powerful way to transfer the nuanced knowledge of the large model to a more compact and efficient one.

The "Mixture of Experts" Analogy

The resulting system functions similarly to a Mixture of Experts (MoE) model. In an MoE architecture, there are multiple "expert" networks, each specializing in a different type of data. A "gating network" or router decides which expert is best suited to handle a given input.

In our hypothetical system, the classification model acts as the gating network, directing incoming prompts to the appropriate specialized LLM (the "expert"). This approach allows the system to handle a wide range of tasks efficiently without needing to activate the entire massive model for every query.

By combining the organizational power of K-Nearest-Neighbors and classification with the model optimization techniques of fine-tuning and knowledge distillation, it's possible to deconstruct a general-purpose, trillion-parameter LLM into a more agile and effective collection of specialized models. This not only makes the technology more accessible but also paves the way for more accurate and context-aware AI applications.


From <https://gemini.google.com/app/>  Google Gemini 2.5 Pro

Comments

Post a Comment

Popular posts from this blog

#2892 Example of ChatGPT o1 Prompt

-
#2892 Example of ChatGPT o1 Prompt #2892 Consider this prompt example from a tutorial:  """The Anatomy of an o1 Prompt Goal: I want a list of the best medium-length hikes within two hours of San Francisco. Each hike should provide an unique adventure, and be lesser know. Return Format: For each hike, return the name of the hike as I'd find it on AllTrails, then provide the starting address of the hike, the ending address of the hike, distance, drive time, hike duration, and what makes it a cool and unique adventure. Warnings: Be careful to make sure the name of the trail is correct, that it actually exists, and that the time is correct! Context Dump: For context: my girlfriend and I hike a ton! We've don pretty much all of the local SF hikes, whether that's Presidio or Golden Gate Park. we definitely want to get out of town -- we did mount tam pretty recently, the whole thing from the beginning of the stairs to Stinson -- it was really long and we are def...
Read more
-
#65 Compose a song about mankind traveling to the stars to find life, riches and happiness Verse 1 : We set out on a journey to the stars Searching for a new world, free from scars A place where we can find our destiny And build a life that's meant to be Chorus : We are the explorers, the brave and true On a mission to find what's waiting for us In the vast expanse of the universe We'll go where no man has gone before Verse 2 : We'll cross the galaxy, we'll go far and wide To find a home where we can thrive We'll brave the dangers that come our way And find the riches that lay hidden in the stars Chorus : We are the explorers, the brave and true On a mission to find what's waiting for us In the vast expanse of the universe We'll go where no man has gone before Bridge : We'll meet new friends and see new sights And learn about the mysteries of the night We'll find new worlds and new life And see what the future holds in our sights Chorus : We are ...
Read more

#3105 Federal and State Spending Idiocy

-
#3105 Federal and State Spending Idiocy A historic highway overpass for animals cost nearly $100 million and is almost done https://www.yahoo.com/news/historic-highway-overpass-animals-cost-100115264.html #3105 Considering that a state is building a 100-foot wide grass covered overpass across 6 lanes of interstate and shoulders, describe all budget items and the total budget for this task. Reasoned about overpass construction budgeting for a few seconds I'm mapping out a budget for a 100-foot wide grass-covered overpass. Categories include design, permitting, site preparation, structural construction, landscaping, signage, safety features, environmental mitigation, traffic management, and contingency, totaling an approximate $20-30 million. Below is a sample, high-level breakdown of typical budget items for constructing a 100-foot-wide, grass-covered overpass across six interstate lanes (plus shoulders). Actual costs vary significantly depending on location, terrain, materials, lab...
Read more