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Building on HF

Ujjwal Tyagi

Ujjwal-Tyagi

·

AI & ML interests

Chief Scientist at Shirova AI, focused on advancing open-source AI, Experienced in LLM fine-tuning, model architecture, and research, with a strong interest in building scalable and efficient models

Recent Activity

liked a model 2 days ago

XiaomiMiMo/MiMo-V2.5-Pro

liked a dataset 3 days ago

nvidia/Nemotron-Image-Training-v3

posted an update 3 days ago

6 Open-Source Libraries to FineTune LLMs 1. Unsloth GitHub: https://github.com/unslothai/unsloth → Fastest way to fine-tune LLMs locally → Optimized for low VRAM (even laptops) → Plug-and-play with Hugging Face models 2. Axolotl GitHub: https://github.com/OpenAccess-AI-Collective/axolotl → Flexible LLM fine-tuning configs → Supports LoRA, QLoRA, multi-GPU → Great for custom training pipelines 3. TRL (Transformer Reinforcement Learning) GitHub: https://github.com/huggingface/trl → RLHF, DPO, PPO for LLM alignment → Built on Hugging Face ecosystem → Essential for post-training optimization 4. DeepSpeed GitHub: https://github.com/microsoft/DeepSpeed → Train massive models efficiently → Memory + speed optimization → Industry standard for scaling 5. LLaMA-Factory GitHub: https://github.com/hiyouga/LLaMA-Factory → All-in-one fine-tuning UI + CLI → Supports multiple models (LLaMA, Qwen, etc.) → Beginner-friendly + powerful 6. PEFT GitHub: https://github.com/huggingface/peft → Fine-tune with minimal compute → LoRA, adapters, prefix tuning → Best for cost-efficient training

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