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I will build private local ai with rag and fine tune llms
Chanaka
Level 2
About this gig
Need AI that runs on your own machine or server instead of sending private data to third-party APIs?
I build private, local AI systems using open-source LLMs, RAG, Ollama and custom model tuning. Your solution can run fully offline after deployment when the selected model and workflow support it.
I can help with:
- Local LLM setup with Ollama, llama.cpp or vLLM
- RAG chatbots for PDFs, DOCX, TXT, CSV and knowledge bases
- Source citations and document-grounded answers
- LoRA / QLoRA / PEFT fine-tuning for suitable use cases
- Model quantization and hardware optimization
- Vector databases such as FAISS, Chroma or Qdrant
- API, web UI, Docker and self-hosted deployment
- Evaluation of accuracy, retrieval quality and hallucinations
Not sure whether you need RAG or fine-tuning? I will recommend the simplest approach that fits your goal instead of training a model unnecessarily.
You receive clean source code, setup instructions and a solution matched to your hardware and privacy requirements.
For fine-tuning, large datasets or existing-system integrations, please message me before ordering so we can confirm scope, data and compute requirements.
Get to know Chanaka
Chanaka
Level 2
- FromSri Lanka
- Member sinceDec 2022
- Avg. response time1 hour
- Last delivery2 days
Languages
English
I'm a professional Pinescript coder with over 3 years of experience. As a Computer Science engineer, I have a strong background in coding and a passion for various fields. I offer Pinescript to Python conversion services and I can convert any Pinescript code logic into Python.
In addition, I also offer Pinescript coding services. I can code any strategy or indicator in Pinescript, whether it's a simple moving average or a complex system.
FAQ
What is the difference between RAG and fine-tuning?
RAG lets the AI retrieve information from your documents or knowledge base when answering questions. Fine-tuning changes model behavior by training it on examples. For frequently changing business knowledge, RAG is often the better solution. I can evaluate your use case and recommend the appropriate
Can you use OpenAI, Claude or Gemini instead of a local model?
Yes, if your project benefits from cloud models. However, this Gig primarily specializes in private, self-hosted and local AI solutions.
Can the AI run completely offline?
Yes, when the selected model and workflow support local inference. I can build solutions using Ollama, llama.cpp, vLLM and other self-hosted tools without requiring OpenAI or Claude APIs after deployment.
Will my documents be sent to an external AI provider?
Not in a fully local deployment. The final system can process documents and generate responses locally. If your data is highly confidential, please provide anonymized or sample data during development or arrange the solution so sensitive data remains in your environment.
Which AI models can you work with?
I can work with suitable open-source models including Llama, Qwen, Mistral, Gemma, DeepSeek models and other Hugging Face/GGUF-compatible models depending on your hardware and use case.
Can you fine-tune my own model?
Yes. I can work with LoRA, QLoRA and PEFT-based fine-tuning where appropriate. Fine-tuning requirements depend heavily on the model, dataset, GPU resources and desired outcome, so please contact me before ordering a training project.
What computer hardware do I need?
That depends on the model size and required performance. Send me your operating system, RAM, CPU, GPU and GPU VRAM if available. I can recommend an appropriate model and quantization level for your hardware.
Can the AI answer questions from PDFs and company documents?
Yes. I can build RAG systems for PDFs, DOCX, TXT, CSV and other structured or unstructured knowledge sources. Scanned PDFs requiring OCR may need additional work.
Can you add this AI to my existing application?
Yes. I can expose the AI through a REST API or integrate it with an existing application. Please contact me first for integrations involving an existing codebase.
Do I receive the source code?
Yes, source code is included according to the selected package.
