You will get a fine-tuned and optimized LLM with improved accuracy, latency, and cost.
Project details
If you’re using a generic LLM and, even after standard tweaks, the answers still feel sloppy, inconsistent, slow, or expensive, I help you fix that. You’ll get a fine-tuned, optimized model for your domain that performs better on your data and costs less to run.
Whether you’ve hit limits with closed APIs or want to improve an open-source model you use, I deliver an end-to-end workflow covering model selection, data prep, fine-tuning, evaluation, and deployment handoff.
Using parameter-efficient methods (LoRA/QLoRA) and modern inference runtimes (vLLM, TensorRT-LLM, Triton), I optimize your model for measurable gains in accuracy, latency, and cost. GPU compute is included unless noted.
Each project includes progress updates, benchmark reports, and deployment guidance so you can see how your model improves and how to apply it effectively. Clients handle hosting and license compliance.
The typical scope includes one use case, one model family, and about 50k cleaned tokens (30–50 pages of text). Sensitive data may only be used under NDA or in secure environments. Anything beyond that can be quoted separately.
Whether you’ve hit limits with closed APIs or want to improve an open-source model you use, I deliver an end-to-end workflow covering model selection, data prep, fine-tuning, evaluation, and deployment handoff.
Using parameter-efficient methods (LoRA/QLoRA) and modern inference runtimes (vLLM, TensorRT-LLM, Triton), I optimize your model for measurable gains in accuracy, latency, and cost. GPU compute is included unless noted.
Each project includes progress updates, benchmark reports, and deployment guidance so you can see how your model improves and how to apply it effectively. Clients handle hosting and license compliance.
The typical scope includes one use case, one model family, and about 50k cleaned tokens (30–50 pages of text). Sensitive data may only be used under NDA or in secure environments. Anything beyond that can be quoted separately.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
Conversational AI, Natural Language Generation, Natural Language Understanding, Neural Machine Translation, Sentiment AnalysisAI Development Language
PythonAI Tools
Azure OpenAI, Hugging Face, NVIDIA AI Platform, PyTorch, TensorFlowAI Models
BERT, BLOOM, ChatGPT, Dolly, GPT-3, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$400
|
Standard
$1,000
|
Advanced
$2,200
|
|---|---|---|---|
| Delivery Time | 4 days | 10 days | 18 days |
Number of Revisions | 1 | 2 | 2 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | - | - | - |
Detailed Code Comments | |||
Image Upscaling | - | - | - |
MLOps | - | - | - |
Model Deployment | - | - | |
Model Documentation | |||
Model Monitoring | - | - | - |
Model Testing & Optimization | |||
Model Tuning | - | ||
Natural Language Processing | |||
NLP Tokenization | |||
Pre-Training | - | - | - |
Prompt Engineering | - | ||
Setup File | - | ||
Source Code |
About Philip
Foundational Model Engineer | AI/ML Systems & Optimization Engineer
Surrey, Canada - 8:08 pm local time
I specialize in diagnosing failure modes in LLM and deep learning systems, reducing hallucinations, improving inference performance, and strengthening evaluation and output reliability. The goal is measurable improvements in accuracy, stability, and cost from prototype to large-scale deployment.
What I deliver
• Inference cost reductions via quantization, batching, caching, and model compression
• Faster and more consistent responses through runtime and throughput optimization
• Lower hallucination rates and improved behavioral consistency using fine-tuning, retrieval, and structured evaluation
• Stable structured outputs (JSON, XML) aligned with downstream requirements
• Improved accuracy on domain-specific and long-tail tasks
Technical areas
• LLM systems, RAG pipelines, and evaluation design
• Fine-tuning and adaptation (LoRA, adapters, distillation)
• Deep learning architectures across text, vision, structured data, and multimodal inputs
• Failure-mode and drift analysis for robustness and reliability
• Model training workflows, diagnostics, and performance audits
How I work
• Hands-on implementation and optimization
• Architecture and strategy advisory
• System diagnosis and stabilization
• Technical enablement and mentorship for internal teams
If you need AI features that are accurate, reliable, efficient, and ready for real-world conditions, I can help you design, improve, and scale them with confidence.
Steps for completing your project
After purchasing the project, send requirements so Philip can start the project.
Delivery time starts when Philip receives requirements from you.
Philip works on your project following the steps below.
Revisions may occur after the delivery date.
Model and Data Assessment
I review your current model (or recommend one), analyze your domain data, and define an improvement plan focused on accuracy, latency, and cost.
Fine-Tuning and Optimization
I apply parameter-efficient fine-tuning (LoRA / QLoRA) and, if needed, optimize for speed and cost through quantization and compression. All tuning is performed in controlled GPU environments.