- Train and fine-tune LLMs using supervised fine-tuning (SFT).
- Work with open-source models such as LLaMA, Mistral, Qwen, and similar architectures.
- Build LoRA / Q-LoRA pipelines for efficient fine-tuning.
- Implement and optimize data preprocessing workflows, including tokenization and long-context handling.
- Use and extend Hugging Face Transformers & Datasets for training and inference.
- Parse and process structured and semi-structured data, including XML/XSD files.
- Implement document parsing solutions for Office formats (python-docx, OpenXML).
- Deploy, run, and maintain models fully offline and in air-gapped environments.
- Perform model optimization and quantization (GGUF, GPTQ, AWQ, bitsandbytes).
- Build and maintain inference systems using frameworks like vLLM, TGI, and Ollama.
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