What is TRLX? TRLX is a framework that uses Hugging Face transformers pipeline object to fine tune a model using RLHF. Transformer Reinforcement Learning X (TRLX) is a type of artificial intelligence (AI) that combines the capabilities of the Transformer… Read More »RLHF (Reinforcement Learning with Human Feedback) Python tutorial using TRLX
Importance of using images and graphics in product marketing content and the benefits they can provide. Images and graphics are an important element of product marketing content because they can help to capture the attention of the audience, convey information… Read More »How to use AI to save time creating images and graphics for product marketing content (blog posts, presentations, sales pitch, and videos)
Step 1: The Interpreter Find or write an interpreter for the code that you want your model to generate. This is not just limited to code. It can be any kind of an interpreter. There are many different kinds of… Read More »How to use RLHF to train a model to generate code that compiles (Tutorial)
PPO (Proximal Policy Optimization) is a type of reinforcement learning algorithm. In reinforcement learning, an agent learns to interact with its environment by taking actions and receiving rewards in order to maximize a cumulative reward. PPO is a model-free algorithm,… Read More »PPO (Proximal Policy Optimization) Explained with Code Examples in PyTorch and Tensorflow
PPO loss function is mainly comprised of two losses Story of the two losses What PPO does is make the language model generate responses that are highly rated (value loss), while forcing it not change the generated responses too much… Read More »Anatomy of a PPO loss function
What is CLIP? The architecture of CLIP is based on a transformer, a type of deep neural network that has been successful in natural language processing tasks. CLIP was trained to predict text given an image, and image given text.… Read More »Anatomy of CLIP Contrastive Language-Image Pre-training with Code
Optimize the learning rate: Choosing an appropriate learning rate can significantly impact training speed and model performance. You can use techniques such as learning rate decay or the 1cycle learning rate schedule to find an optimal learning rate. Learning Rate… Read More »Practical Ways to speed up training a PyTorch model
Model loss is a measure of how well the model is able to make correct predictions on a given dataset. It is calculated as the average of the loss values across all samples in the dataset. Lower loss values indicate… Read More »How to understand model loss and model accuracy
Here are five techniques or algorithms for data augmentation on text data: Synonym Replacement Here is some sample code to demonstrate synonym replacement import torchimport transformers# Load the pre-trained modelmodel = transformers.BertForMaskedLM.from_pretrained(‘bert-base-cased’)# Define the device and set the model to… Read More »How to deal with low training data for text data sets
To visualize the features of a fine-tuned language model in PyTorch, you can use a technique called “gradient-weighted class activation mapping” (Grad-CAM). This technique allows you to visualize which parts of the input text are most important for the model’s… Read More »How to visualize features in a fine tuned LLM using PyTorch