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Fiverr Pro
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$
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I will fine tune llm on your data, gpt, llama
DATA SCIENTIST
Unlock the power of data with me! I'm a Machine Learning and Data Science expert, passionate about extracting insights from complex datasets. With a strong background in Python, I deliver accurate and...
Niveau 2
Répond à des critères de performance élevés et a fait ses preuves en matière de satisfaction clients.
Très réactif
Connu(e) pour ses réponses exceptionnellement rapides
À propos de ce service
Why choose me
- 160+ completed Fiverr orders + 5 years on Fiverr
- Strong expertise in Machine Learning, Data Science, NLP, Computer Vision, Generative AI, Deep Learning, and RAG Based Systems
- Hands-on with Time Series Forecasting, AI Agents, and Chatbot Development
I provide AI model fine tuning / LLM fine tuning to match your data, style, and output format. I fine tune and align models and deliver a reproducible training + inference package.
Fine tuning stack (not limited to):
- LangChain (integration-ready workflows)
- Unsloth + Unsloth Docker image
- LoRA / QLoRA
- Lamini
- LlamaFactory (LLaMA-Factory)
- Stable Diffusion fine-tuning (LoRA/DreamBooth-style workflows)
- Run & fine-tune DeepSeek-R1-0528
- Train your own Reasoning Model
- Train and Run Mistral 3
Alignment methods
- SFT, DPO, GRPO, PPO (RLHF)
- Quant Aware Training (QAT)
Deliverables (depending on package):
- Dataset formatting/prep
- Training code/notebook + configs
- Fine-tuned adapters/model checkpoints
- Evaluation summary + inference script
- API + Production Setup Guide
If you want reliable outputs, not just a model that runs, message me your objective and sample data, and I'll handle training, evaluation, and a clean handover.
Mon portfolio
FAQ
I don’t have the hardware to run a large model. What can we do?
No problem. In the Enterprise package, I can deploy your model as an API (FastAPI) on a GPU hosting provider or cloud instance. You’ll get a working endpoint + setup guide. Hosting/GPU costs are paid by you directly to the provider (so you keep ownership and billing control).
Fine-tuning vs RAG - which one should I choose?
Fine-tuning improves behavior (format, tone, reasoning style). RAG improves knowledge accuracy from documents and stays up to date. Many projects work best as hybrid (RAG + light fine-tuning).
How do you measure improvement?
I run a baseline vs fine-tuned comparison using your test examples (and/or custom checks like format accuracy, refusal behavior, and response consistency) and share a short evaluation summary.
Will you use my API keys / cloud account?
If hosting or OpenAI training is needed, you provide keys/access via Fiverr requirements.
