I will fine tune and train custom llm ai model on your data

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haseebb_asif
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haseebb_asif
Haseeb A

About this gig

An LLM out of the box doesn't know anything about your business. It pads, it hedges, and it answers in a generic voice that isn't yours. Fine-tuning is how you fix that I train an open-source model on your own data so it actually learns your domain.

What you get back: a fine tuned model (Llama, Mistral, Qwen, or whatever you want to use), the LoRA adapter plus inference code so you can run it yourself, and a before/after eval so you're not just trusting me that it improved. I'll help you prep your dataset too. Honestly that part getting the data clean and formatted right is where most fine tuning projects succeed or fail.

I work with LoRA, QLoRA and PEFT, so you're paying for efficient training, not wasted GPU hours. Good for chatbots, Q&A, classification, structured output, brand voice, domain specific knowledge.

On me: I'm not someone who just chains API calls together. I've rebuilt deep learning papers from scratch and trained models myself, which mostly matters to you for one reason I'll tell you straight when fine tuning won't actually solve your problem, rather than just taking the money.

Before ordering, message me. Send your goal, a sample of your data, and the model you're

Get to know Haseeb A

Haseeb A

Machine Learning Engineer

  • FromPakistan
  • Member sinceMay 2026
  • Languages

    Urdu, English
I build end to end ML solutions in PyTorch, TensorFlow, and JAX, specializing in generative modeling, computer vision, and agentic AI. From custom neural networks (CNNs, GANs, StyleGAN2, Pix2Pix, NeRF) to RAG and multi-agent LLM workflows with LangChain, CrewAI, and vector databases. My work includes 95.2% accuracy medical imaging models and an award-winning geospatial AI platform (1st Place, EnviroCast 2026). Backed by research experience at Stevens Institute of Technology, I deliver clean, reproducible, production ready code. Message me before ordering so I can tailor the right solution.

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