Nixtla’s TimeGPT is a generative pre-trained forecasting model for time series data. TimeGPT can produce accurate forecasts for new time series without training, using only historical values as inputs. TimeGPT can be used across a plethora of tasks including demand forecasting, anomaly detection, financial forecasting, and more.

The TimeGPT model “reads” time series data much like the way humans read a sentence – from left to right. It looks at windows of past data, which we can think of as “tokens”, and predicts what comes next. This prediction is based on patterns the model identifies in past data and extrapolates into the future.

The API provides an interface to TimeGPT, allowing users to leverage its forecasting capabilities to predict future events. TimeGPT can also be used for other time series-related tasks, such as what-if scenarios, anomaly detection, and more.

https://nixtla.github.io/nixtla/docs/getting-started/getting_started_short.html

https://youtube.com/watch?v=POBcYr0sbcg&si=VD5odGEisjt2fzUi

DSPy is the framework for solving advanced tasks with language models (LMs) and retrieval models (RMs). DSPy unifies techniques for prompting and fine-tuning LMs — and approaches for reasoning, self-improvement, and augmentation with retrieval and tools. All of these are expressed through modules that compose and learn.

To make this possible:

• DSPy provides composable and declarative modules for instructing LMs in a familiar Pythonic syntax. It upgrades « prompting techniques » like chain-of-thought and self-reflection from hand-adapted string manipulation tricks into truly modular generalized operations that learn to adapt to your task.
• DSPy introduces an automatic compiler that teaches LMs how to conduct the declarative steps in your program. Specifically, the DSPy compiler will internally trace your program and then craft high-quality prompts for large LMs (or train automatic finetunes for small LMs) to teach them the steps of your task.

The DSPy compiler bootstraps prompts and finetunes from minimal data without needing manual labels for the intermediate steps in your program. Instead of brittle « prompt engineering » with hacky string manipulation, you can explore a systematic space of modular and trainable pieces.

For complex tasks, DSPy can routinely teach powerful models like GPT-3.5 and local models like T5-base or Llama2-13b to be much more reliable at tasks. DSPy will compile the same programinto different few-shot prompts and/or finetunes for each LM.

If you want to see DSPy in action, open our intro tutorial notebook.

Graph RAG is an Knowledge-enabled RAG approach to retrieve information from Knowledge Graph on given task. Typically, this is to build context based on entities’ SubGraph related to the task.

GraphStore backed RAG vs VectorStore RAG

As we compared how Graph RAG helps in some use cases in this tutorial, it’s shown Knowledge Graph as the unique format of information could mitigate several issues caused by the nature of the “split and embedding” RAG approach.

Graph RAG

https://www.philschmid.de/gptq-llama

• Hugging Face Optimum team collaborated with AutoGPTQ library for a simple API to apply GPTQ quantization on language models.
• GPTQ quantization allows open LLMs to 8, 4, 3, or 2 bits, enabling them to run on smaller hardware with minimal performance loss.
• The blog covers:

1. Setting up the development environment.
2. Preparing the quantization dataset.
3. Loading and quantizing the model.
4. Testing performance and inference speed.
5. Bonus: Running inference with text generation.

• GPTQ’s purpose is explained before diving into the tutorial.

• Large Language Models (LLMs) and prompt-based heuristics are being used for off-the-shelf solutions to various NLP problems.
• LLM-based few-shot methods have shown promise but lag in Named Entity Recognition (NER) compared to other methods.
• « PromptNER » is introduced as a new algorithm for few-shot and cross-domain NER.
• PromptNER needs entity definitions and few-shot examples for a new NER task.
• PromptNER uses LLM to generate potential entities and explanations for their compatibility with entity type definitions.
• PromptNER achieves state-of-the-art performance in few-shot NER on ConLL, GENIA, and FewNERD datasets.
• It also outperforms previous methods in Cross Domain NER, setting new records on 3 out of 5 CrossNER domains with an average F1 gain of 3%.

https://arxiv.org/pdf/2305.15444.pdf

https://github.com/promptslab/Promptify

https://cameronrwolfe.substack.com/p/the-history-of-open-source-llms-better

• Value of Open-source LLM Research: Aims to democratize influential technology; despite initial struggles and criticism, open-source LLMs gained popularity and significance.
• Early Challenges: Initial open-source LLMs performed poorly and faced criticism, posing difficulties for advancement.
• Transformative Research Line: Focuses on enhancing open-source LLMs, leading to high-performing pre-trained models accessible to all.
• Significance of High-Performing Models: Creation of powerful, cost-effective pre-trained LLMs revolutionized research accessibility.
• Series Overview: Part two of a three-part series on open-source LLM history. The first part explored initial open-source LLM attempts.
• Study Focus: This overview delves into the most popular open-source base models, emphasizing pre-trained models not yet fine-tuned or aligned.
• Future Exploration: Subsequent installment will discuss fine-tuning and alignment of models for diverse practical applications.

https://cameronrwolfe.substack.com/p/practical-prompt-engineering-part

• Prompt engineering: An empirical science focused on optimizing LLM (Large Language Model) performance through various prompting strategies.
• Aims to understand prompting mechanics and employs techniques to enhance LLM capabilities.
• Zero/few-shot learning: A fundamental technique where LLMs perform tasks with minimal or no training examples, showcasing their remarkable adaptability.
• Instruction prompting: Another vital technique involving explicit instructions in prompts to guide LLM behavior.
• Overview intends to impart practical insights and strategies for effective prompt engineering and LLM utilization.
• Provides actionable tricks and takeaways for prompt engineers and LLM practitioners to enhance their effectiveness.

https://cameronrwolfe.substack.com/p/the-history-of-open-source-llms-early

• Language modeling research traces back to models like GPT, GPT-2, and pre-transformer methods such as ULMFit.
• GPT-3’s proposal marked the initial rise in popularity by showcasing impressive few-shot learning through self-supervised pre-training and in-context learning.
• The recognition of GPT-3 led to the creation of various large language models (LLMs), including InstructGPT and ChatGPT, sparking widespread interest in generative AI.
• Early LLMs often remained closed source, limiting researchers’ understanding and improvement of their workings.
• Open-source variants of popular language models began to emerge gradually, although they initially lagged behind proprietary models in performance.
• These early open-source models laid the groundwork for increased transparency in LLM research and inspired the development of more potent subsequent models like Falcon and LLaMA-21.
• The overview is part of a three-part series that delves into the history of open-source language models, exploring their beginnings, recent developments, and the application of imitation and alignment techniques to enhance their performance.

cleanlab automatically detects problems in a ML dataset. This data-centric AI package facilitates machine learning with messy, real-world data by providing clean labels for robust training and flagging errors in your data

Paper: https://arxiv.org/pdf/1911.00068.pdf

Code : Code

Feature Visualization

• Rank Features: pairwise ranking of features to detect relationships
• Parallel Coordinates: horizontal visualization of instances
• Radial Visualization: separation of instances around a circular plot
• PCA Projection: projection of instances based on principal components
• Manifold Visualization: high dimensional visualization with manifold learning
• Joint Plots: direct data visualization with feature selection

Classification Visualization

• Class Prediction Error: shows error and support in classification
• Classification Report: visual representation of precision, recall, and F1
• ROC/AUC Curves: receiver operator characteristics and area under the curve
• Precision-Recall Curves: precision vs recall for different probability thresholds
• Confusion Matrices: visual description of class decision making
• Discrimination Threshold: find a threshold that best separates binary classes

Regression Visualization

• Prediction Error Plot: find model breakdowns along the domain of the target
• Residuals Plot: show the difference in residuals of training and test data
• Alpha Selection: show how the choice of alpha influences regularization
• Cook’s Distance: show the influence of instances on linear regression

Clustering Visualization

• K-Elbow Plot: select k using the elbow method and various metrics
• Silhouette Plot: select k by visualizing silhouette coefficient values
• Intercluster Distance Maps: show relative distance and size/importance of clusters

Model Selection Visualization

• Validation Curve: tune a model with respect to a single hyperparameter
• Learning Curve: show if a model might benefit from more data or less complexity
• Feature Importances: rank features by importance or linear coefficients for a specific model
• Recursive Feature Elimination: find the best subset of features based on importance

Target Visualization

• Balanced Binning Reference: generate a histogram with vertical lines showing the recommended value point to bin the data into evenly distributed bins
• Class Balance: see how the distribution of classes affects the model
• Feature Correlation: display the correlation between features and dependent variables

Text Visualization

• Term Frequency: visualize the frequency distribution of terms in the corpus
• t-SNE Corpus Visualization: use stochastic neighbor embedding to project documents
• Dispersion Plot: visualize how key terms are dispersed throughout a corpus
• UMAP Corpus Visualization: plot similar documents closer together to discover clusters
• PosTag Visualization: plot the counts of different parts-of-speech throughout a tagged corpus