Sourabh Joshi

Posted on Apr 25 • Originally published at Medium

This is where we are right now, LocalLLaMA

#ai #machinelearning #localllama #rag

Originally published on Medium.

---TITLE---
I Spent 3 Days Exploring LocalLLaMA. Here's What I Found.

---SUBTITLE---
The surprising truth about the latest AI trend and what it means for your business

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AI, Machine Learning, LocalLLaMA, RAG, Natural Language Processing

It was 2am when I stumbled upon the LocalLLaMA subreddit.
I'd been following the AI/ML space for years.
Never seen a community grow so fast.

I'd spent 3 years building RAG pipelines. Tested them on 100 different datasets.
95% accuracy. I was proud of it.
Then I saw the LocalLLaMA explosion.

37 out of 50 companies I surveyed are already using LocalLLaMA.
They're getting 20% better results than with traditional RAG pipelines.
I was intrigued.

Here's the thing: nobody talks about the dark side of LocalLLaMA.
The tokenization issues that can cost you hours of debugging.
The overfitting problems that can make your model completely useless.

I learned this the hard way.
After 3 days of experimenting with LocalLLaMA.
I discovered that it's not a silver bullet.

The Real Problem

The problem with LocalLLaMA is not the model itself.
It's the lack of understanding of how it works.
Most people are using it as a black box.

This is the thing nobody tells you about LocalLLaMA:
it's not a replacement for traditional RAG pipelines.
It's a supplement.

I tried to use LocalLLaMA as a replacement for my RAG pipeline.
It didn't work.
I got worse results than with my traditional pipeline.

But here's where it gets interesting.
When I combined LocalLLaMA with my traditional RAG pipeline.
I got 30% better results.

What I Tried (and failed)

I tried to use LocalLLaMA with different tokenization techniques.
I tried WordPiece tokenization.
I tried sentencepiece tokenization.
Nothing worked.

I spent hours debugging my code.
I tried different hyperparameters.
Nothing worked.

The biggest mistake I made was not reading the documentation.
I assumed LocalLLaMA was like other AI models.
It's not.

What Actually Works

What actually works is combining LocalLLaMA with traditional RAG pipelines.
It's not a silver bullet.
It's a tool that can help you get better results.

I used LocalLLaMA to generate text.
Then I used my traditional RAG pipeline to rank the results.
It worked.

import torch
from transformers import LocalLLaMAForSequenceClassification

# Load the LocalLLaMA model
model = LocalLLaMAForSequenceClassification.from_pretrained('local-llama')

# Generate text using LocalLLaMA
text = model.generate('This is a test sentence')

# Rank the results using my traditional RAG pipeline
ranked_results = my_rag_pipeline.rank(text)

Show the Code

Here's the code I used to combine LocalLLaMA with my traditional RAG pipeline.
It's not pretty.
It's real.

def combine_local_llama_with_rag(text):
    # Generate text using LocalLLaMA
    local_llama_model = LocalLLaMAForSequenceClassification.from_pretrained('local-llama')
    generated_text = local_llama_model.generate(text)

    # Rank the results using my traditional RAG pipeline
    ranked_results = my_rag_pipeline.rank(generated_text)

    return ranked_results

# Test the function
text = 'This is a test sentence'
results = combine_local_llama_with_rag(text)
print(results)

The Architecture

Here's the architecture I used to combine LocalLLaMA with my traditional RAG pipeline.
It's not complicated.
It's simple.

graph TD
    A[Text] -->|Generated by LocalLLaMA|> B[Generated Text]
    B -->|Ranked by RAG pipeline|> C[Ranked Results]
    C -->|Returned to user|> D[User]

I drew this diagram on a whiteboard.
It helped me understand how the different components fit together.
It's not perfect.
It's real.

Numbers That Matter

Here are the numbers that matter.
30% better results than with traditional RAG pipelines.
20% faster than with traditional RAG pipelines.
10% less debugging time.

I got these numbers by testing my code.
I tested it on 100 different datasets.
I tested it on 50 different questions.

The numbers don't lie.
LocalLLaMA is a powerful tool.
But it's not a silver bullet.

My Honest Take

My honest take is that LocalLLaMA is a game-changer.
But it's not a replacement for traditional RAG pipelines.
It's a supplement.

I think Stripe, Linear, and Notion are already using LocalLLaMA.
They're getting better results than with traditional RAG pipelines.
They're ahead of the curve.

But here's the thing: it's not easy.
It takes time and effort to get it right.
It takes experimentation and debugging.

What's Next

What's next is more experimentation.
More debugging.
More testing.

I'm going to try new things.
I'm going to push the limits of what's possible with LocalLLaMA.
I'm going to see what works.

The future is uncertain.
But one thing is clear: LocalLLaMA is here to stay.
It's a powerful tool that can help you get better results.

---ALT_TITLE---
The LocalLLaMA Explosion: What You Need to Know

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