[CL]《How to Train Data-Efficient LLMs》N Sachdeva, B Coleman, W Kang, J Ni, L Hong, E H. Chi, J Caverlee, J McAuley, D Z Cheng [Google DeepMind] (2024) http://t.cn/A6Y6plVH #机器学习##人工智能##论...
These are a few reasons you might want to run your own LLM. Or maybe you don’t want the whole world to see what you’re doing with the LLM. It’s risky to send confidential or IP-protected information to a cloud service. If they’re ever hacked, you might be exposed. In this a...
this is the first attempt to use a growth strategy to train an LLM with 100B+ parameters from scratch. Simultaneously, it is probably the lowest-cost model with 100B+ parameters, costing only 100,000 US dollars. Second, we address several instability issues via promising approaches...
Learning techniques: These algorithms are focused on training the model to make fewer prediction errors, require less data, and converge faster. It also gives scope for trimming the parameters for obtaining a smaller footprint or a more efficient model. One example of this is distillation that al...
The training compute budget is often calculated in advance. Since it is feasible to train theselarge modelsnot more than once, it becomes very critical to accurately estimate the best model hyperparameters for a given compute budget. In the past, it has been proved that there exists a power-...
A promising approach to balancing these trade-offs is the “distilling step-by-step” method. This method involves extracting informative natural language rationales from a large LLM and using these rationales to train smaller, task-specific models. Here’s how it works: ...
To train a family of LLMs, first train the largest one, then prune and distill iteratively to obtain smaller LLMs. If the largest model is trained using a multi-phase training strategy, it is best to prune and retrain the model obtained from the final stage of training. Prune an availabl...
Alpaca-LoRA is a smaller version ofStanford Alpacathat consumes less power and can able to run on low-end devices like Raspberry Pie. Alpaca-LoRA usesLow-Rank Adaptation(LoRA)to accelerate the training of large models while consuming less memory. ...
Hi, thank you very much for open source. I want to use my own Image and caption, and QA data to fine-tune the BLIP2 data. Should my process be to prepare the same data set for okvaq, and then run the /run_scripts/blip2/eval/eval_okvqa_ze...
Use an appropriate regularization techniques to avoid overfitting, Trying out the learning rate from smaller and gradually become bigger, Use fewer epoch as the training as LLM usually learn the new data quite fast, Don’t ignore the computational cost, as it would become higher with bigger data...