Script
Challenges
Where i fit in
Passionate
Strenghts
Engagement
Why SynergySol
strategic location
AI Potential
Experience
I’ve also been an advisor in KaggleX for about
four years, guiding smart mentees on projects like GenAI agents and llm-based recommendation
systems using GCP.
and several healthcare-focused ML and GenAI projects at Keylead
AI-driven marketing for Cinere
I’ve led major projects in banking (Saman, Mellat)
Introducing
with over 10 years of hands-on experience in applied machine learning and generative AI.
I have degrees in Math, Computer Science, and AI
Hi Synergy Sol Trading Team!
Thank you for this opportunity
Hugging Face
APIs
Model Clases
Trainers
...
SFT
DPO
Transformers
(Main)
. . .
HQQ
AWQ
GPTQ
GGUF
Training
Distributed Traing
Parallelism methods
Distributed CPUs
DeepSpeed
Accelrate
Training API
Optimizerz
Fine-tuning
Trainer
Inference
Agents
LLMs
Prompt engineering
Optimizing inference
Caching
Serving
Pipeline API
Pipeline
Base Classes
Preprocessors
Feature extractors
Tokenizers
Models
Loading models
Text Representation
NN BASED EMBEDDINGS
Continius Space
Fine-tuning Based
embeddings
Language-Specific Embeddings
Multi Modal Embeddings
Domain-Specific Embeddings
SentiBERT
PatentBERT
ClinicalBERT
QA.SciBERT
BioBERT
Knowledge-Enriched Embeddings
Cross-Lingual Embeddings
Feature Based
Embedding
Dynamic
Embeddings
UNIfied pre-trained
Language Model (UNILM)
BERT
GPT
ELMo
ULMFiT
CoVe
C2V
Static
Embeddings
FastText
Glove
Word2vec
SkipGRAM
CBOW
Statistical Encoding
Conceptual Embedding
Explicit Semantic
Analysis (ESA)
Dimentionally
Reduction
Feature Transformation
ICA
LDA
Feature Selection
Discrete Space
TF-IDF
Term Frequency
Bag of Words
CountVectorizer
Phase III: Opt Inference & Serving
Projects
Prompting and Gaurdrails
Phase IV: Chatbots and AI Agents
Practice
Anjanava Book
AI Agents in Action Book
Liu et al Paper
Phase I: LLMOps
RYAN DOAN Book: Essential Guide to LLMOps
Chip Huyen Book: AI Engineering
Phase II:
RAG Fundamentals
Projects And Articles
Research Assistant
Andrei Gheorghiu Book
Ben Auffarth Book
Theory
Phase I:
LLMs Fundamentals
Farameworks
Deepspeed
Axolotl
Project & Articles
Fine-Tuning On Medicare Data
Llama 3.2
Deep Seek
Gemma 3
The Ultimate Guide to Fine-Tuning Paper
To article with Code
Hugging Face Cources
Working With HF
TRL
Transformers
LLM Course
Jay Alammar
Part III
Part II: Chapter 4 (text Classification) ,
6 (prompt Engineering)
Part I
KaggleX, GenAI Agents, LLM-Based recommendation
Business Sectors
Technology and Software Development
Manufacturing
Knowledge Management
Supply Chain Management
Human Resources and Talent Management
Research and Development
Healthcare
Education and Training
Regulatory Compliance
Customer Relationship Management (CRM)
Sales and Marketing
Finance and Banking
e-Business
Basic LLMs Tasks
Content Generation and Correction
Information Extraction
Text-to-Text Transformation
Semantic Search
Sentiment Analysis
Content Personalization
Ethical and Bias Evaluation
Paraphrasing
Language Translation
Text Summarization
Conversational AI
Question Answering
Banking
AI_Driven Marketing
Most research on word embedding implementations usually
focuses on general-domain text generation. However, as the
authors in [108] demonstrate, such general-domain applica-
tions do not work optimally when used in the domain-specific
analysis of very large corpora, for example, in the biomedical
domain
Feature-based techniques can either generate static or
dynamic embeddings. Static embeddings are non-contextual,
as the embeddings remain the same or are static regardless
of the context. To learn such word embeddings, shallow
networks are used. Whereas, in dynamic embeddings, the
embeddings of the same word changes based on the context,
hence addressing the polysemy aspect of the words
healthcare-focused
In statistical methods, words are represented using vectors
of numbers, and the corpus is represented as a collection
of such vectors, forming a matrix. Such statistical methods
reduce documents of arbitrary length to fixed-length lists of
numbers. These vector representations were helpful since
they enabled researchers to use linear algebra operations
to manipulate the vectors and compute distances and sim-
ilarities.
LLMs Learning Path
Efficient LLMs
Efficient Inference
System-Level Inference
Efficiency Optimization
vLLM ,
DeepSpeed-Inference
Algorithm-Level Inference
Efficiency Optimization
Effitient Architecture
Long Context LLMs
MOE
Efficient Attention
Hardware-Assisted Attention
FlashAttention, vAttention
Learnable Pattern Strategies
HyperAttention
Fixed Pattern Strategies
Sparse Transformer , Longformer , Lightning Attention-2
Model Compression
Knowledge Distillation
Low-Rank Approximation
Parameter Pruning
Quantization
Quantization-Aware Training
Post-Training Quantization
Weight-Activation Co-Quantization
RPTQ, QLLM
Weight-Only Quantization
GPTQ, AWQ, SpQR
Efficient Fine-Tuning
Frameworks
Unsloth
MEFT (Q-LoRA, QA-LoRA, ...)
PEFT
Prompt Tuning
Prefix Tuning
Adapter-based Tuning
Low-Rank Adaptation (LoRA, DoRA)
AI Apps
Frameworks
Local
Gradio
Jan
Deployment
Anyscale
Hugging Face Inference Endpoints.
Serving
vLLM
BentoML
Integration
LlamaIndex
Scaled Multi-Agents
Local AI Agents
Local Chatbots
LLM-Based Recommendation Chatbot
Medical QA ChatBot
Research Assistant(Talking Papers)
Generative AI Agents
Frameworks and Practices
RASA
Langchain / LangGraph
CrewAI
SmolAgents
Courses
HF Agent Cource
Books
A DVANCES AND C HALLENGES IN F OUNDATION AGENTS, Liu
AI Agents in Action, MICHEAL LANHAM
https://arxiv.org/pdf/2401.03428
https://www.arxiv.org/pdf/2504.01990
LLMs Adaptation
Output ConfiguRATION
Gaurdrails
Prompt Eng
RAG
20 Types of RAG Arch
Langchain
Indexes
weaviate
Faiss
Pinecone
Qdrant
Chroma
Prompt
Agent
Chains
Memory
Fine-Tuning ( Transfer learning, Strategies )
- Instruction-tuning
- Alignment-tuning
- Transfer Learning
Fundamentals
HF Transformers, pyTorch
HF Course:
https://huggingface.co/docs/transformers/en/quicktour
Language Modeling and llms
https://arxiv.org/pdf/2302.08575
ChatgptDiscussion:
https://chatgpt.com/share/6819003f-e674-8005-af8b-11a7ef37bd70
https://arxiv.org/pdf/2303.18223
https://arxiv.org/pdf/2402.06853
https://arxiv.org/pdf/2303.05759
https://wandb.ai/madhana/Language-Models/reports/Language-Modeling-A-Beginner-s-Guide---VmlldzozMzk3NjI3
- Full Language Modeling
- Prefix Language Modeling
- Masked Language Modeling
- Unified Language Modeling
From Seq-to-Seq and RNN
to Attention and Transformers
Papers
Lewis- Wolf
Vasilev
