Unsloth Speeds Up Embedding Fine-Tuning 3x
Unsloth accelerates embedding model fine-tuning by three times through optimized training techniques, enabling faster development of custom text embeddings for
Someone found that Unsloth now supports embedding model fine-tuning and it’s surprisingly fast - runs 1.8-3.3x faster than standard setups while using 20% less VRAM.
Most models need only 3GB VRAM for 4bit QLoRA training, which means fine-tuning embeddings for RAG systems is now actually doable on budget GPUs. The setup is pretty straightforward:
model = FastSentenceTransformer.from_pretrained(
model_name = "unsloth/embeddinggemma-300m",
max_seq_length = 1024,
full_finetuning = False,
)Works with ModernBERT, Qwen Embedding, BGE, and most other embedding models. After training, models export to transformers, LangChain, Ollama, or llama.cpp.
There’s a free Colab notebook to try it on a T4 GPU. Update with pip install --upgrade unsloth unsloth_zoo to
Related Tips
Nvidia's DMS Cuts LLM Memory Usage by 8x
Nvidia introduces Dynamic Memory Scheduling that reduces large language model memory consumption by eight times, enabling more efficient AI inference and
Unsloth Kernels: 12x Faster MoE Training, 12GB VRAM
Unsloth Kernels achieves 12x faster Mixture of Experts model training while using only 12GB of VRAM through optimized kernel implementations and memory
Unsloth Kernels: Fine-Tune 30B MoE on Consumer GPUs
Unsloth Kernels enables efficient fine-tuning of 30 billion parameter Mixture of Experts models on consumer-grade GPUs through optimized memory management and