I will fine tune open source llms with lora full tuning and rl

I can help you design and implement advanced LLM training and fine-tuning workflows for domain-specific assistants, reasoning models, chatbots, instruction-following models, and task-optimized language systems.

Data collection and dataset preparation

 * Web and document-based data collection

 * Instruction dataset creation

 * Prompt-response pair generation

 * Conversation and domain dataset curation

 * Data cleaning, deduplication, filtering, and formatting

 * Preference data preparation for reward modeling or RL

Supervised Fine-Tuning (SFT)

 * LoRA / QLoRA fine-tuning

 * Freeze fine-tuning

 * Full fine-tuning

 * Instruction tuning

 * Chat model tuning

 * Domain adaptation for finance, crypto, legal, support, technical, and private datasets

Reinforcement Learning methods

 * RLHF-style pipeline design

 * Reward modeling

 * Preference optimization

 * DPO / ORPO / PPO-style training workflows

 * Alignment tuning for response quality, format, and task behavior

Training framework setup

 * Hugging Face Transformers

 * TRL

 * PEFT

 * DeepSpeed

 * Accelerate

 * PyTorch

 * bitsandbytes

 * vLLM inference integration

 * Multi-GPU and distributed training setup