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A.I Artificial Intelligence NLP

An introduction to vector embeddings

               Have you ever wondered how machines truly understand the meaning of words? While they lack the nuanced comprehension we humans possess, they’ve learned to represent words as numerical vectors, opening a world of possibilities for tasks like search, translation, and even creative writing.

Cross Post – https://www.linkedin.com/pulse/introduction-vector-embeddings-kanti-kalyan-arumilli-wqg8c

Imagine each word in the English language as a point in a multi-dimensional space. Words with similar meanings cluster together, while those with contrasting meanings reside farther apart. This is the essence of vector embeddings: representing words as numerical vectors, capturing semantic relationships between them.

These vectors aren’t arbitrary; they’re learned through sophisticated machine learning algorithms trained on massive text datasets.

Vector embeddings revolutionize how machines process language because:

Semantic Similarity:  Words with similar meanings have vectors that are close together in the “semantic space.” This allows machines to identify synonyms, antonyms, and even subtle relationships between words.

Contextual Understanding: Capturing the nuanced meaning of a word based on its surrounding words.

Improved Performance: Embedding vectors as input to machine learning models often leads to significant performance gains in tasks like text classification, sentiment analysis, machine translation and neural search.

There are various types such as Dense, Sparse and Late Interaction. In each type there are several models trained on various datasets, fine-tuned on different datasets. The computational expenses and requirements are significantly different. Some models need high cpu, memory yet underperform and some models need lesser cpu and memory and yet perform well. However, based on the dataset and number of tokens used for generation, models trained on same datasets and higher number of tokens usually outperform models trained on same datasets and lower number of tokens.

Here is a very interesting link – https://huggingface.co/spaces/mteb/leaderboard

The above page lists several models, memory requirements, scores for various tasks, size of embeddings generated etc… Most of these models are free under MIT license and some are commercial.

In the past I have written a blog post about https://www.alightservices.com/2024/04/27/how-to-get-text-embeddings-from-meta-llama-using-c-net/ converting llama 2 / 3 into gguf and how to interact using C#.

Most of the free models mentioned in the above leaderboard have gguf and can be directly used from C# or via free HTTP local server for getting embeddings such as ollama, llama.cpp. But some models don’t have gguf, probably some can be converted or some might not. Some models have onnx format available. Some might need python code for generating embeddings. I have tried IronPython. But not suggesting IronPython or any 3rd party wrappers because of less reliability. Here is a blog post mentioning about Python integration from .Net https://www.alightservices.com/2024/04/09/c-net-python-and-nlp-natural-language-processing/

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Proud partner of Microsoft for Startups

Mr. Kanti Kalyan Arumilli

Arumilli Kanti Kalyan, Founder & CEO
Arumilli Kanti Kalyan, Founder & CEO

B.Tech, M.B.A

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kantikalyan@gmail.com, kantikalyan@outlook.com, admin@alightservices.com, kantikalyan.arumilli@alightservices.com, KArumilli2020@student.hult.edu, KantiKArumilli@outlook.com and 3 more rarely used email addresses – hardly once or twice a year.

Categories
A.I Artificial Intelligence Llama LLM

Have you tried Ollama – ChatGPT on your local machine, great software!

Most of you know, from around 2016, I had interest in DataScience/Machine Learning/Artificial Intelligence and even did some courses as a hobby! I am primarily .Net full-stack web developer, but A.I has been fascinating and I have been hobbyist!

In 2021 I started my own startup, 2023 prototyped a concept for a SaaS product known as WebVeta! 2024 – launched an mvp and now is the time to dive into A.I. Over the past 2 weeks, I was experimenting with several different things in A.I both from a development perspective, features perspective!

Over the past 2 days I am playing around with a nice software that allows working with several LLMs from local machine! I would say at least 16GB of RAM, possibly slightly higher.

https://ollama.com

https://github.com/ollama/ollama

The setup instructions are straightforward!

On the Github page, under “Community Integrations” -> “Web & Desktop” there are several web and desktop clients for UI, choose one of those based on your operating system and you can play around with a large set of A.I models. The list of models can be found at: https://ollama.com/library

Try llama3, phi3 if you have enough CPU and RAM! Or try the smaller models – tinydolphin, tinyllama! There are several coding related LLMs i.e GitHub co-pilot’ish and there are some Visual Studio Code extensions that can communicate with locally version of Ollama and help with code!

Remember the LLMs need to be downloaded, the exact syntax is provided on the LLMs pages, but the general syntax is:

ollama pull <LLM_NAME>

I have used https://github.com/ollama-ui/ollama-ui on Linux, https://github.com/tgraupmann/WinForm_Ollama_Copilot for the client UI!

The client UI’s query and get the available local LLMs and allow specifying / selecting which particular LLM to interact with.

If anyone interested let me know via any of my social media profiles, I might consider doing a small demo for any enthusiasts!

Ollama is a great tool and great effort by the team of developers who developed Ollama! Thank you!

WebVeta – Advanced, unified, consistent search for your website(s), from content of your website(s), blogs(s). First 50 customers, who sign-up prior to 15/05/2024 get unlimited access to existing features, newer features for at least 1 year. Sign up now! https://webveta.alightservices.com/

Mr. Kanti Kalyan Arumilli

Arumilli Kanti Kalyan, Founder & CEO
Arumilli Kanti Kalyan, Founder & CEO

B.Tech, M.B.A

Facebook

LinkedIn

Threads

Instagram

Youtube

Founder & CEO, Lead Full-Stack .Net developer

ALight Technology And Services Limited

ALight Technologies USA Inc

Youtube

Facebook

LinkedIn

Phone / SMS / WhatsApp on the following 3 numbers:

+91-789-362-6688, +1-480-347-6849, +44-07718-273-964

+44-33-3303-1284 (Preferred number if calling from U.K, No WhatsApp)

kantikalyan@gmail.com, kantikalyan@outlook.com, admin@alightservices.com, kantikalyan.arumilli@alightservices.com, KArumilli2020@student.hult.edu, KantiKArumilli@outlook.com and 3 more rarely used email addresses – hardly once or twice a year.

Categories
.Net A.I Artificial Intelligence C# Llama LLM NLP

How to get text embeddings from Meta Llama using C# .Net

This post is about getting text embeddings i.e vector representation of text using C# .Net and using Meta’s Llama 2!

Meta’s Llama

Meta (Facebook) has released few different LLM’s, the latest Llama3, but this blog post about Llama2. Using Llama3 might be similar, but I have not tried yet! There are few more things that can be tried, but those are out of scope and this is an end to end blog post for using Llama2 using C#.

https://llama.meta.com/

From the above link provide click “Download Models”, provide information. Then links to some github, some keys are provided. Make note of the keys. The keys are valid for 24 hours and each model can be downloaded 5 times.

llama.cpp

We use llama.cpp for certain activities:

https://github.com/ggerganov/llama.cpp

LLamaSharp

This is the wrapper for interacting from C# .Net with Llama models.

I have introduced the tools and software that are going to be used. Now, let’s look at the different steps:

  1. Download Llama model (Meta’s Llama has Llama 2 and Llama 3, each has smaller and larger models, this discusses the smallest model from Llama 2)
  2. Prepare and convert Llama model into gguf format.
  3. Use in C# code

Download Llama model:

Once you submit your information and receive the keys from Meta Facebook, clone the repo:

https://github.com/meta-llama/llama for Llama2,

https://github.com/meta-llama/llama3 for Llama3

git clone https://github.com/meta-llama/llama

Navigate into llama folder, then run download.sh

cd llama
sudo ./download.sh

You would be prompted for the download key, enter the key.

Now 12.5 GB file gets downloaded into a folder “llama-2-7b”

Prepare and convert Llama model into gguf format:

We are going to convert the Llama model into gguf format. For this we need Python3 and Python3-Pip, if these are not installed, install using the following command

sudo apt install python3 python3-pip

Clone the llama.cpp repo into a different directory.

git clone https://github.com/ggerganov/llama.cpp

Navigate into llama.cpp and compile

cd llama.cpp
make -j

Install the requirement for python:

python3 -m pip install -r requirements.txt

Now copy the entire “llama-2-7b” into llama.cpp/models.

Listing models directory should show “llama–2-7b”

ls ./models
python3 convert.py models/llama-2-7b/

This generates a 2.17 GB file ggml-model-f32.gguf

Now run the following command:

./quantize ./models/llama-2-7b/ggml-model-f32.gguf ./models/llama-2-7b/ggml-model-Q4_K_M.gguf Q4_K_M

This should generate a 3.79 GB file.

Optional (I have NOT tried this yet)

The following extra params can be passed for the python3 convert.py models/llama-2-7b/

python convert.py models/llama-2-7b/ --vocab-type bpe

C# code

Create a new or in an existing project add the following Nuget packages:

LLamaSharp

LLamaSharp.Backend.Cpu or LLamaSharp.Backend.Cuda11 or 
LLamaSharp.Backend.Cuda12 or LLamaSharp.Backend.OpenCL

// I used LLamaSharp.Backend.Cpu

Use the following using statements:

using LLama;
using LLama.Common;

The following code is adapted from the samples of LlamaSharp – https://github.com/SciSharp/LLamaSharp/blob/master/LLama.Examples/Examples/GetEmbeddings.cs

string modelPath = PATH_TO_GGUF_FILE

var @params = new ModelParams(modelPath) {EmbeddingMode = true };
using var weights = LLamaWeights.LoadFromFile(@params);
var embedder = new LLamaEmbedder(weights, @params);

Use the path for your .gguf from quantize step file’s path.

Here is code for getting embeddings:

float[] embeddings = embedder.GetEmbeddings("Hello, this is sample text for embeddings").Result;

Hope this helps some people, I am .Net developer (primarily C#), A.I enthusiast.

Mr. Kanti Kalyan Arumilli

Arumilli Kanti Kalyan, Founder & CEO
Arumilli Kanti Kalyan, Founder & CEO

B.Tech, M.B.A

Facebook

LinkedIn

Threads

Instagram

Youtube

Founder & CEO, Lead Full-Stack .Net developer

ALight Technology And Services Limited

ALight Technologies USA Inc

Youtube

Facebook

LinkedIn

Phone / SMS / WhatsApp on the following 3 numbers:

+91-789-362-6688, +1-480-347-6849, +44-07718-273-964

+44-33-3303-1284 (Preferred number if calling from U.K, No WhatsApp)

kantikalyan@gmail.com, kantikalyan@outlook.com, admin@alightservices.com, kantikalyan.arumilli@alightservices.com, KArumilli2020@student.hult.edu, KantiKArumilli@outlook.com and 3 more rarely used email addresses – hardly once or twice a year.