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LLM Artificial Intelligence: RAG or Fine-tuning

Patroclos Lemoniatis
7 min readSep 15, 2024

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Ai Training Vectors by Vecteezy

What are LLMs?

Large language models (LLMs) are machine learning models that can understand and generate human language text. LLMs acquire these abilities by learning statistical relationships from vast amounts of text during a self-supervised and semi-supervised training process.

What ways are there to train LLMs?

  • Pretraining

Models gain a general understanding of natural language after being exposed to a vast dataset of information. During this phase, the LLM learns grammar rules, linguistic patterns, factual information, and reasoning abilities.

Text generation : Models can generate poetry, write essays and articles, as well as provide responses that feel remarkably human.

Language translation : Imagine a Model that can understand everything you say in any language and being able to provide a response in any language!

  • Fine Tuning

Is an approach to transfer learning in which the parameters of a pre-trained model are trained on new data¹. Fine-tuning can be done on the entire neural network, or on only a subset of its layers, in which case the layers that are not being fine-tuned are “frozen” (or, not changed during the backpropagation step)². Fine-tuning is typically accomplished with supervised learning, but there are also techniques to fine-tune a model using weak supervision.³

  • RAG

Retrieval augmented generation (RAG) is a type of generative artificial intelligence that has information retrieval capabilities. It modifies interactions with a large language model (LLM) so that the model responds to user queries with reference to a specified set of documents, using this information in preference to information drawn from its own vast, static training data.⁴ The RAG process is made up of four key stages. First, all the data must be prepared and indexed for use by the LLM. Thereafter, each query consists of a retrieval, augmentation and a generation phase.⁵

In general terms, there is no need to train the model, just store the additional information into a vector database and query the data from there.

RAG example

>> docker pull ollama/ollama

#pull and run llama3 llm
#Make sure ollama container exposes port 11434
# activate python environment
>> source .venv/bin/activate


# Install python dependencies
#------------------------------------------------------------------------
>> pip install ollama-python pypdf2
>> pip install llama-index-llms-ollama
>> pip install llama-index llama-index-llms-openllm llama-index-embeddings-huggingface
>> python -m pip install langchain
>> pip install -U langchain-community llama-index-embeddings-langchain

Python script train-llamaindex.py

Be sure, to add in the same folder, a sample file containing the additional information, needed by the LLM to generate a response. In this example i have used file ‘rag-training.txt’.

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, ServiceContext, StorageContext
#from llama_index.llms.openllm import OpenLLMAPI
from llama_index.core.node_parser import SentenceSplitter
from langchain.embeddings import OllamaEmbeddings
from llama_index.core import Settings
from llama_index.llms.ollama import Ollama

emb = OllamaEmbeddings(model="llama3")

Settings.embed_model = emb
Settings.node_parser = SentenceSplitter(chunk_size=512, chunk_overlap=20)
Settings.num_output = 512
Settings.context_window = 3900
Settings.transformations = [SentenceSplitter(chunk_size=1024)]

#listens to localhost:11434 by default
llm = Ollama(base_url="http://localhost:11434", model="llama3", request_timeout=120.0)

Settings.llm = llm

# Break down the document into manageable chunks (each of size 1024 characters, with a 20-character overlap)
text_splitter = SentenceSplitter(chunk_size=1024, chunk_overlap=20)

storage_context = StorageContext.from_defaults(
    vector_store=emb
)

# Load documents from the data directory
documents = SimpleDirectoryReader(
    input_files=["rag-training.txt"]
)

documents = documents.load_data();

# Build an index over the documents
index = VectorStoreIndex.from_documents(
    documents, embed_model=emb, transformations=Settings.transformations
)

# Query your data using the built index
query_engine = index.as_query_engine()
response = query_engine.query("Who is Patroclos?")
print(response)
>> python train-llamaindex.py

by modifying the prompt …

response = query_engine.query("Patroclos and Cyprus relation")

we get …

Consequently, using additional information from a custom text file, we have added knowledge to the current LLM used.

Fine-Tuning

Improving LLM on particular and specific tasks

For this example, we have used Apple M2 pro with 16GB memory

Create mlxo directory


>> mkdir mlxo
>> cd mlxo

Create python virtual environment and install dependencies⁷

>> python -m venv .venv
>> source .venv/bin/activate


>> pip install -U mlx-lm
>> pip install pandas
>> pip install pyarrow

Setup Huggingface-CLI⁷

  1. Create an account at hugginface webpage: https://huggingface.co/welcome

2. Set an access token from https://huggingface.co/settings/tokens

>> pip install huggingface_hub
>> pip install "huggingface_hub[cli]"
# login to huggingface through cli
# it will ask the access token previously created 
❯❯ huggingface-cli login

Prepare a dataset

For more information on the correct format for preparing a dataset please refer to https://github.com/ml-explore/mlx-examples/blob/main/llms/mlx_lm/LORA.md#Data

Sample training files content

#train
{"prompt": "Who is Patroclos Lemoniatis?", "completion": "A software developer from Cyprus"}

#test
{"prompt": "Who is Patroclos Lemoniatis?", "completion": "A software developer from Cyprus"}

#valid
{"prompt": "Who is Patroclos Lemoniatis?", "completion": "A software developer from Cyprus"}

Download LLM from huggingface

>> huggingface-cli download TinyLlama/TinyLlama-1.1B-Chat-v1.0
>> huggingface-cli scan-cache

REPO ID                                REPO TYPE SIZE ON DISK NB FILES LAST_ACCESSED  LAST_MODIFIED REFS LOCAL PATH                                                                              
-------------------------------------- --------- ------------ -------- -------------- ------------- ---- --------------------------------------------------------------------------------------- 
TinyLlama/TinyLlama-1.1B-Chat-v1.0     model             2.2G       10 1 minute ago   1 minute ago  main /Users/xxxxxxxx/.cache/huggingface/hub/models--TinyLlama--TinyLlama-1.1B-Chat-v1.0     
bert-base-uncased                      model           466.1K        1 2 weeks ago    2 weeks ago   main /Users/xxxxxxxx/.cache/huggingface/hub/models--bert-base-uncased

Fine Tune LLM

python -m mlx_lm.lora \
  --model TinyLlama/TinyLlama-1.1B-Chat-v1.0 \
  --data dataOutput \
  --train \
  --batch-size 1\
  --lora-layers 16\
  --iters 50

Test LLM

python -m mlx_lm.lora \
  --model TinyLlama/TinyLlama-1.1B-Chat-v1.0 \
  --adapter-path adapters \
  --data dataOutput \
  --test

Before fine tuning ….

A totally wrong answer, a hallucination response

python -m mlx_lm.generate \
     --model TinyLlama/TinyLlama-1.1B-Chat-v1.0 \
     --max-tokens 500 \
     --prompt "Who is Patroclos Lemoniatis?"
Fetching 8 files: 100%|██████████████████████████████████████████████████████████████████████| 8/8 [00:00<00:00, 28460.08it/s]
==========
Prompt: <|user|>
Who is Patroclos Lemoniatis?</s>
<|assistant|>

Patroclos Lemoniatis was a Greek philosopher and mathematician 
who lived in the 5th century BCE in the city-state of Athens. 
He is best known for his work on the theory of numbers and his contributions 
to the field of geometry. Lemoniatis was a student of the renowned 
mathematician Thales of Miletus and was one of the founding members of 
the Athenian Academy.
==========
Prompt: 26 tokens, 380.505 tokens-per-sec
Generation: 92 tokens, 73.367 tokens-per-sec
Peak memory: 2.057 GB

After fine tuning …

We have the correct response, as it was given in the training files

python -m mlx_lm.generate \
     --model TinyLlama/TinyLlama-1.1B-Chat-v1.0 \
     --max-tokens 500 \
     --adapter-path adapters \
     --prompt "Who is Patroclos Lemoniatis?"
Fetching 8 files: 100%|██████████████████████████████████████████████████████████████████████| 8/8 [00:00<00:00, 15842.51it/s]
==========
Prompt: <|user|>
Who is Patroclos Lemoniatis?</s>
<|assistant|>

A software developer from Cyprus
==========
Prompt: 26 tokens, 292.529 tokens-per-sec
Generation: 8 tokens, 64.410 tokens-per-sec
Peak memory: 2.060 GB

Building new model using Fusion adapters

python -m mlx_lm.fuse \
    --model TinyLlama/TinyLlama-1.1B-Chat-v1.0 \
    --adapter-path adapters \
    --save-path models/finetune-model \
    --de-quantize

We can ask the newly fine tuned model directly …

python -m mlx_lm.fuse \    
    --model TinyLlama/TinyLlama-1.1B-Chat-v1.0 \
    --adapter-path adapters \
    --save-path models/finetune-model \   
    --de-quantize
Loading pretrained model
Fetching 8 files: 100%|██████████████████████████████████████████████████████████████████████| 8/8 [00:00<00:00, 12614.45it/s]
De-quantizing model

python -m mlx_lm.generate \
  --model models/finetune-model \        
  --max-tokens 500 \         
  --prompt "Whos is Patroclos Lemoniatis?"
==========
Prompt: <|user|>
Whos is Patroclos Lemoniatis?</s>
<|assistant|>

A software developer from Cyprus
==========
Prompt: 27 tokens, 393.215 tokens-per-sec
Generation: 8 tokens, 72.815 tokens-per-sec
Peak memory: 2.057 GB

Convert to gguf format

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


python convert_hf_to_gguf.py ../models/finetune-model \ 
     --outfile ../models/finetuned-model.gguf \
     --outtype q8_0

Which one to choose?

To improve Large Language Model (LLM) performance on domain specific applications, ML developers often leverage Retrieval Augmented Generation (RAG) and LLM Fine-Tuning. RAG extends the capabilities of LLMs to specific domains or an organization’s internal knowledge base, without the need to retrain the model. On the other hand, Fine-Tuning approach updates LLM weights with domain-specific data to improve performance on specific tasks.⁶

GitHub - patroklos83/llm-training: LLM finetuning and RAG samples

LLM finetuning and RAG samples. Contribute to patroklos83/llm-training development by creating an account on GitHub.

github.com

  1. Quinn, Joanne (2020). Dive into deep learning: tools for engagement. Thousand Oaks, California. p. 551.
  2. “CS231n Convolutional Neural Networks for Visual Recognition”. cs231n.github.io. Retrieved 9 March 2023.
  3. Yu, Yue; Zuo, Simiao; Jiang, Haoming; Ren, Wendi; Zhao, Tuo; Zhang, Chao (2020). “Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach”. Association for Computational Linguistics. arXiv:2010.07835.
  4. https://en.wikipedia.org/wiki/Retrieval-augmented_generation, “Retrieval-augmented generation”.
  5. Gao, Yunfan; Xiong, Yun; Gao, Xinyu; Jia, Kangxiang; Pan, Jinliu; Bi, Yuxi; Dai, Yi; Sun, Jiawei; Wang, Meng; Wang, Haofen (2023). “Retrieval-Augmented Generation for Large Language Models: A Survey”. arXiv:2312.10997 [cs.CL].
  6. José Cassio dos Santos Junior, Rachel Hu, Richard Song, and Yunfei Bai. 2024. Domain-Driven LLM Development: Insights into RAG and Fine-Tuning Practices. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ‘24). Association for Computing Machinery, New York, NY, USA, 6416–6417. https://doi.org/10.1145/3637528.3671445
  7. https://medium.com/rahasak/fine-tuning-llms-on-macos-using-mlx-and-run-with-ollama-182a20f1fd2c
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Patroclos Lemoniatis
Patroclos Lemoniatis

Written by Patroclos Lemoniatis

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