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I will fine tune your llm with lora qlora on custom data
AI Engineer and LLM Fine Tuning, RAG, AI Chatbots and Agents
I’m an AI Engineer specializing in LLM solutions that solve real business problems. I provide AI model fine-tuning for more accurate, task-specific results, RAG systems that securely connect AI to pri...
About this Gig
Need to fine-tune an LLM for your business, product, or specific task?
I will fine-tune your LLM on your custom dataset using LoRA, QLoRA, PEFT, and supervised fine-tuning (SFT) to improve task-specific behavior, responses, formatting, and instruction following.
I work with popular open-source models including Llama, Mistral, Qwen, Gemma, Phi, and other compatible Hugging Face models.
What I can provide:
- LLM fine-tuning with LoRA / QLoRA
- Custom dataset preparation and formatting
- Instruction and supervised fine-tuning
- Domain-specific model customization
- Model evaluation and testing
- Fine-tuned model or LoRA adapter
- Training configuration and inference setup
- Source code and training files
Your model can be customized for customer support, classification, structured responses, instruction following, professional writing, and other specialized tasks.
If you're unsure whether fine-tuning or RAG is right for your project, I can help you choose the appropriate approach.
Send me your model, dataset, and requirements before ordering so I can review the project and recommend the right solution.
Programming Language:
Python
Data Type:
Text
AI Engine:
Llama
•
Other
My Portfolio
FAQ
Which LLMs can you fine-tune?
I can fine-tune compatible models such as Llama, Mistral, Qwen, Gemma, Phi, and other open-source LLMs.
Can you prepare my dataset?
Yes. I can clean, format, validate, and prepare your dataset for fine-tuning.
Do you support LoRA and QLoRA?
Yes. I specialize in parameter-efficient fine-tuning using LoRA and QLoRA.
Can you fine-tune 3B, 7B, or larger models?
Yes, depending on the model, dataset, training method, and available GPU resources.
Will fine-tuning give my model new knowledge?
Fine-tuning mainly teaches behavior, patterns, style, and task-specific responses. RAG is better for changing knowledge.
Can you evaluate the fine-tuned model?
Yes. Evaluation can be included to compare model performance before and after fine-tuning.
Can you deploy the fine-tuned model?
Yes. Deployment or inference setup can be included depending on the package and infrastructure.
Should I use fine-tuning or RAG?
It depends on your goal. I can review your project and recommend the more suitable approach.
