I will fine tune custom llm llama 3 qwen model using qlora

Y
yongpan_liang
Y
yongpan_liang
yongpan l
fine tune custom llm llama 3 qwen model using qlora
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fine tune custom llm llama 3 qwen model using qlora

About this gig

Looking to adapt open-weight LLMs like Llama 3, Qwen 2.5, or Mistral to your exact brand voice, domain knowledge, or coding workflows? I provide fast, local, high-precision QLoRA fine-tuning services.

What I Offer:

  • Custom Model Fine-Tuning: Tailoring open-source models using efficient QLoRA/Unsloth architecture.
  • Dataset Cleaning & Formatting: Structuring raw text, CSVs, or unorganized data into optimized JSONL prompt templates.
  • Flexible Delivery: Exporting lightweight LoRA adapter weights (.safetensors) or fully merged GGUF models ready for local deployment in Ollama or LM Studio.
  • 100% Privacy & Security: Your data is processed in isolated environment pipelines with zero cloud data retention.

Supported Models:

  • Meta Llama 3 / 3.1 (8B, 70B)
  • Qwen 2.5 series
  • Mistral / Mixtral
  • DeepSeek open weights

Note: Please send a message with your dataset format or project requirements before placing an order to ensure the best package match!

  • AI engine

    • Mistral AI
    • DeepSeek
    • Meta Llama
    • Hugging Face
    • Ollama

  • Tools & frameworks

    • LangChain
    • LlamaIndex
    • DSPy
    • Other

  • AI solution type

    • AI chatbot
    • AI assistant
    • Internal AI tool
    • Other

  • Purpose

    • Content creation
    • Knowledge management
    • Workflow automation
    • Document processing
    • Data analysis

Get to know yongpan l

yongpan l

LLM Fine Tuning and Local AI Specialist

  • FromAustralia
  • Member sinceAug 2026
  • Avg. response time1 hour

  • Languages

    English, Chinese
I specialize in custom LLM fine-tuning, dataset preparation, and local AI model deployment. Using efficient QLoRA methods and Unsloth optimization, I train open-weight models (Llama 3, Qwen 2.5, Mistral) tailored to your specific needs. What I offer: • Fine-tuning open-source LLMs on custom datasets (.jsonl, .csv, plain text) • Model optimization, LoRA adapter extraction, and GGUF conversions for local running (Ollama / LM Studio) • Dataset cleaning, formatting, and prompt formatting • High data privacy: Secure local training pipelines with zero external cloud retention

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