Llm

If you are a command-line fan and want to experiment with large language models (LLM), you will love AiChat. There are many popular graphical front ends for working with LLMs, such as OpenAI’s ChatGPT, and Anthropic’s Claude, but get ready for this little powerhouse for CLI lovers as it has many advanced and useful features. One such feature is an easy-to-use, out-of-the-box RAG feature (Retrieval Augmented Generation) useful for searching existing content. I’ve put together a small demo here that shows how easy it can be to use in a pinch. There are many use cases where such an approach is just the right size. ...

Large foundation models like GPT can classify text according to a well-crafted prompt instruction, and it’s remarkable how well they can do this, considering there has been no explicit training with labeled datasets. This has traditionally been done using machine learning models and logistic regression techniques. However, with generative model classification, we lose the ‘confidence level’ or the probability score of the prediction available in logistic regression. Traditional models like logistic regression provide a probability score for each class, indicating the model’s confidence level in its predictions. This confidence score is not just valuable; it’s essential for decision-making, as it helps users gauge how confident the model is about its classifications. While generative model responses may align well with the intended classification, we don’t directly get an explicit probability for each class. This can be a limitation, particularly in high-stakes applications where knowing the model’s confidence level is crucial. ...

You might want to test an expected response from a prompt sent to a large language model, but string comparisons will not help you. The inherent variability in large language model (LLM) responses will require you to find new ways to compare generated prompt results. There are a few reasons why a generated prompt result will not exactly match a prior result: the prompt itself may have changed, the model parameters may have changed, or the model’s inherent variability may inject a small amount of change in the results. ...

As I stood outside and looked at the neighborhood wasteland that post-July 4th left behind, the whiff of gunpowder still hanging in the air, I felt a burst of good neighbor energy flow through me, so I grabbed a broom. Sweeping up the street gave me time to think about the other chores I had for the day, including the writing of a new blog post, and I began to wonder how I could use ChatGPT to help me speed some things up. ...

Since transformer input is processed in parallel rather than serially, it is necessary to encode the relative positions of the input sequence of tokens in some way. The positional encoding in the transformer model uses sinusoidal functions to create a unique encoding for each position. In working through the article on Transformers, as described in the original paper “Attention is All You Need” by Vaswani et al., the following formulas are used to encode the PE tensor values: ...

Since Llama3 was released, the PyTorch llama3 documentation has a few glitches pointing at configurations in torchtune that are still referencing Llama2. The meta website is a little more up-to-date, but the documentation is a little light on details. So, I wrote this article to bring everything together. Prerequisites You’ll want to use Python 3.11 until Torch compile supports Python 3.12 , and I recommend setting up a virtual environment for this using venv or pipenv. Install torchtune pip install torchtune Install EleutherAI’s Evaluation Harness pip install lm_eval==0.4.* Download Llama3-8B model You will need to get access to Llama3 via instructions on the official Meta Llama3 page. You will also need your Hugging Face token setup from here. ...