Supercharge LangChain apps with an LLM Cache

Supercharge LangChain apps with an LLM Cache

In this blog post Supercharge LangChain apps with an LLM cache for speed and cost we will show how to make LangChain applications faster, cheaper, and more reliable by caching LLM outputs. Supercharge LangChain apps with an LLM cache for speed and cost is about one...
The Autonomy of Tensors

The Autonomy of Tensors

In this blog post The Autonomy of Tensors for Smarter we will explore what gives tensors their “autonomy,” why it matters for both engineers and leaders, and how to put it to work in production. Tensors are often described as multi‑dimensional arrays. That’s true, but...
Alpaca vs Phi-3 for Fine-Tuning

Alpaca vs Phi-3 for Fine-Tuning

In this blog post Alpaca vs Phi-3 for Instruction Fine-Tuning in Practice we will unpack the trade-offs between these two popular paths to instruction-tuned models, show practical steps to fine-tune them, and help you choose the right option for your team. Instruction...
Preparing Input Text for Training LLMs

Preparing Input Text for Training LLMs

In this blog post Preparing Input Text for Training LLMs that Perform in Production we will walk through the decisions and steps that make training data truly useful. Whether you’re pretraining from scratch or fine-tuning an existing model, disciplined data prep is...
Loading and Saving PyTorch Weights

Loading and Saving PyTorch Weights

In this blog post Best Practices for Loading and Saving PyTorch Weights in Production we will map out the practical ways to persist and restore your models without surprises. Whether you build models or manage teams shipping them, understanding how PyTorch saves...
Practical ways to fine-tune LLMs

Practical ways to fine-tune LLMs

In this blog post Practical ways to fine-tune LLMs and choosing the right method we will walk through what fine-tuning is, when you should do it, the most useful types of fine-tuning, and a practical path to ship results. Large language models are astonishingly...