I hope to switch llama2-7b-chat and llama3-8b models. But it cost a lot of memory size if I load both. How to clear one if I am going to load the second model? #model_name = 'meta-llama/Llama-2-7b-chat-hf' model_name = 'meta-llama/Meta-Llama-3-8B-Instruct' #tokenizer_...
Dealing with LLMs can come at a cost given the expertise and resources required to build and train your models.NVIDIA NeMooffers pretrained language models that can be flexibly adapted to solve almost any language processing task while we can focus entirely on the art of getting the best output...
Build different models and compare different algorithms (e.g., SVM vs. logistic regression vs. Random Forests, etc.). Here, we’d want to use nested cross-validation. In nested cross-validation, we have an outer k-fold cross-validation loop to split the data into training and test folds,...
As LLMs are language models, the best way to test them is with language: yes, asking LLM questions is a good way of evaluating its performance. But not just any questions: you need to evaluate LLMs from different angles by asking them specific questions and giving them specific tasks. ...
However, as the adoption of generative AI accelerates, companies will need to fine-tune their Large Language Models (LLM) using their own data sets to maximize the value of the technology and address their unique needs. There is an opportunity for organizations to leverage their Content Knowledge...
Deploy a vLLM model as shown below. Unclear - what model args (ie. --engine-use-ray) are required? What env. vars? What about k8s settings resources.limits.nvidia.com/gpu: 1 and env vars like CUDA_VISIBLE_DEVICES? Our whole goal here is to run larger models than a single instance ...
We defined a test in test_hallucinations.py so we can find out if our application is generating quizzes that aren’t in our test bank. This is a basic example of a model-graded evaluation, where we use one LLM to review the results of AI-generated output from another LLM. In our pr...
Assess LLM quality with precision using Dataiku. Explore metrics and methods to help data teams eliminate guesswork and ensure scalable AI solutions.
“Extensive auto-regressive pre-training enables LLMs to acquire good text representations, and only minimal fine-tuning is required to transform them into effective embedding models,” they write. Their findings also suggest that LLMs should be able to generate suitable training data to fine-tune...
Large language models (LLMs) are the underlying technology that has powered the meteoric rise of generative AI chatbots. Tools like ChatGPT, Google Bard, and Bing Chat all rely on LLMs to generate human-like responses to your prompts and questions. But just what are LLMs, and how do the...