LLMs

Announcing Together Inference Engine – the fastest inference available

November 13, 2023・By Together

The Together Inference Engine is multiple times faster than any other inference service, with 117 tokens per second on Llama-2-70B-Chat and 171 tokens per second on Llama-2-13B-Chat

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Today we are announcing Together Inference Engine, the world’s... See more

Giskard is a Python library that automatically detects vulnerabilities of AI models, from tabular models to LLM, including: performance biases, data leakage, spurious correlation, hallucination, toxicity, security issues and many more.

It's a powerful tool that helps data scientists save time and effort drilling down on model issues, and produce... See more

• Right now, GPTs are the easiest way of sharing structured prompts, which are programs, written in plain English (or another language), that can get the AI to do useful things. I discussed creating structured prompts last week, and all the same techniques apply, but the GPT system makes structured prompts more powerful and much easier to create,

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I’ve been giving talks and speaking with engineers and non-technical audiences about interpretability since 2022, and I still struggle to explain exactly what a “feature” is. I often use words like “concept” or “style”, or establish metaphors to debugging programs or making fMRI scans of brains. Both metaphors help people outside of the subfield... See more

Some tasks are easier to improve via system design. While LLMs appear to follow remarkable scaling laws that predictably yield better results with more compute, in many applications, scaling offers lower returns-vs-cost than building a compound system. For example, suppose that the current best LLM can solve coding contest problems 30% of the

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Setting up the necessary machine learning infrastructure to run these big models is another challenge. We need a dedicated model server for running model inference (using frameworks like Triton oder vLLM), powerful GPUs to run everything robustly, and configurability in our servers to make sure they're high throughput and low latency. Tuning the... See more

For the deployment side of things, we found that the performance of our training process was quite slow, especially when it gets into these large language models and when you train from scratch. MosaicML offers what's called programmatic optimization, which is not so much on the hardware side of things, but rather on the algorithmic side. Can you... See more

We consider these aspects of our problem:

• Latency : How fast does the system need to respond to user input?


• Task Complexity : What level of understanding is required from the LLM? Is the input context and prompt super domain-specific?


• Prompt Length : How much context needs to be provided for the LLM to do its task?


• Quality : What is the acceptable

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Memory Considerations

Since co-occurrence matrices are square, they grow exponential with the number of entities being embedded. For 50k entities and a 32-bit data format, a dense matrix will already be at 10GB. 100k entities puts it at 40GB.

If you are trying to embed even more entities than that or have limited RAM available, you may need to use a... See more