AirLLM v4.0.0 — train Qwen3.8-Flash-Next under 6GB

Streamed LoRA: frozen weights stay on disk, adapters stay on GPU. Qwen3.8-Flash-Next trains under 6GB (RTX 3060 Ti); Qwen3.8-27B trains in ~2GB at seq 512.

pip install -U airllm
pip install git+https://github.com/huggingface/transformers.git

Point --data at a JSONL file ({"text": "..."} or {"prompt": "...", "completion": "..."}):

python air_llm/examples/train_qwen38_flash_next_lora.py   --data my_data.jsonl   --seq-len 512   --epochs 1   --save-adapter qwen38-flash-next-lora.pt

Flash-Next still needs in-tree qwen4_exp (GitHub transformers main today).