You will get Custom Optimized AI Model: Efficient Transformer for Short Sequences
Project details
I build custom, optimized AI models based on the Efficient Transformer architecture, specifically designed for short biological and chemical sequences — including DNA, RNA, proteins, SMILES strings, or custom symbolic sequences (e.g., k-mers, motifs, or domain-specific tokens).
Unlike generic language models, my approach tailors the tokenizer, attention mechanism, and positional encoding to your sequence type and length (typically < 512 tokens), ensuring high accuracy with minimal compute. The result is a lightweight, fast, and interpretable model that fits real-world bio/cheminformatics constraints.
You provide:
Your labeled dataset (FASTA, CSV, JSON, etc.)
Task definition (classification, regression, etc.)
Target metric (e.g., F1, AUC, MSE)
I deliver (based on selected tier):
✅ A validated proof of concept
✅ A fully optimized, reusable model with custom architecture
✅ A Hugging Face–deployable model with documentation, tokenizer, and inference code
Ideal for researchers, biotech startups, or computational chemists who need a purpose-built model — not an overkill LLM.
Flexible tiers: from quick feasibility testing ($300) to production-ready HF deployment ($800).
Unlike generic language models, my approach tailors the tokenizer, attention mechanism, and positional encoding to your sequence type and length (typically < 512 tokens), ensuring high accuracy with minimal compute. The result is a lightweight, fast, and interpretable model that fits real-world bio/cheminformatics constraints.
You provide:
Your labeled dataset (FASTA, CSV, JSON, etc.)
Task definition (classification, regression, etc.)
Target metric (e.g., F1, AUC, MSE)
I deliver (based on selected tier):
✅ A validated proof of concept
✅ A fully optimized, reusable model with custom architecture
✅ A Hugging Face–deployable model with documentation, tokenizer, and inference code
Ideal for researchers, biotech startups, or computational chemists who need a purpose-built model — not an overkill LLM.
Flexible tiers: from quick feasibility testing ($300) to production-ready HF deployment ($800).
AI Development Type
Deep Learning, Knowledge Representation, Model TuningAI Tools
PyTorchAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$350
|
Standard
$650
|
Advanced
$800
|
|---|---|---|---|
| Delivery Time | 14 days | 28 days | 50 days |
Number of Revisions | 1 | 3 | 5 |
AI Model Integration | - | - | |
Detailed Code Comments | - | ||
Knowledge Graph | - | - | |
Model Documentation | |||
Ontology | - | - | - |
Source Code | - | ||
Taxonomy | - | - | - |
Optional add-ons
You can add these on the next page.
Additional Revision
+$75About Genta
Data Analyst
Lubuk Sikaping, Indonesia - 6:30 am local time
I design and implement data pipelines and machine learning solutions that turn complex problems into actionable results—whether in science, finance, or language technology.
Recent work includes:
Developing efficient NLP systems (e.g., a fast Indonesian lemmatizer using embeddings + FAISS)
Building custom tokenizers and lightweight transformers (RoPE, GQA, sliding-window attention)
Creating end-to-end ML workflows for large-scale data: from cleaning millions of records to training, evaluation, and fast similarity search
Publishing datasets and models for scientific and financial applications (molecular representations, crypto time series)
Core strengths: Python • PyTorch / Hugging Face • ETL & Data Engineering • Model Optimization • Vector Search (FAISS) • Technical Communication • Rapid Prototyping
I focus on practical, maintainable solutions—with clear documentation, regular updates, and performance in mind. My background in interdisciplinary research helps me connect ideas across fields and adapt quickly to new domains.
I can help you with:
✔️ Data cleaning, transformation, and pipeline automation
✔️ Machine learning prototyping or deployment (NLP, forecasting, classification)
✔️ Custom model development or fine-tuning (especially efficient/compact architectures)
✔️ Technical documentation, proofreading, or scientific writing
✔️ Exploratory analysis and turning raw data into insights
Open to short-term tasks and long-term collaborations—let’s build something useful together.
Steps for completing your project
After purchasing the project, send requirements so Genta can start the project.
Delivery time starts when Genta receives requirements from you.
Genta works on your project following the steps below.
Revisions may occur after the delivery date.
Phase 0: Kickoff & Data Validation (All Tiers)
Confirm task type (classification, regression, etc.) Review & validate client’s dataset (format, labels, sequence length) Define evaluation metric(s) and success criteria Agree on deliverables & timeline
Phase 1: Preprocessing & Tokenization
[T1] Basic preprocessing (cleaning, train/val/test split) [T2/T3] Custom tokenizer design (e.g., k-mer for DNA, byte-level for SMILES, amino acid vocab for proteins) [T2/T3] Tokenizer training + serialization