Train with different models (Gemma-7b-it, Llama2-7b-chat, Mistral7b-Instruct; quantized due to memory constraints), using various hyperparameters Colab/Kaggle notebook training: fine-tune-with-gemma-7.ipynb Remote server multi-gpu training using Deepspeed: lora_train_gem7_ds.py vastai_ds...
Reasoning models require high-quality data and training. For LLMs or multimodal AI, it’s difficult to catch up with a closed source model from scratch. The architecture of pure reasoning models hasn’t changed much, so it’s easier to catch up in reasoning. One reason R1 caught up quickl...
LLMs undergo unsupervised or self-supervised training, which, in contrast to supervised learning, uses unlabeled data to train the model. For example, a visual object classifier for dogs needs labeled data consisting of images of dogs with the label “dog” and images of other objects with an...
After synthesis, precise particle characterization is necessary, because the physicochemical properties of a particle could have a significant impact on their biological properties. In order to address the safety issue to use the full potential of any nano material in the purpose of human welfare, ...