This colab notebook uses 2 OSS LLMs: - Zephyr 7B - SDXL

And it produces 4 outputs: - Blog content - Blog title - Meta description - Header image(s)

Zephyr is really powerful and cost effective in terms of producing high quality output while constraining itself to the provided topic. It follows system prompts very well.

SDXL on the other hand is great for image generation in OSS domain. This API also supports styles, so they can be applied as per the blog theme requirements.

At the core, the LLM APIs are powered by monsterapi.ai

Finetuning Mistral on No Robots dataset leads to higher performance compared to Finetune Zephyr 7B OSS model.

Just speak and use natural language to ask for an image and this notebook will generate high fidelity images using a pipeline of 3 LLMs hosted on MonsterAPI.

This integration results in access of LLMs like Llama 2, Falcon and MPT easily and at low cost for RAG question answering on PDFs and docs.

This guide walks you through using a no-code llm finetuner for finetuning Llama 2 7B and 13B models without any complex setup of infrastructure to run it.

The only way to get rid of centralized choke points is to actually go decentralized. At Q Blocks, we're working on making this solution a reality for a lot of the ML devs constrained by the computing costs on cloud.

Similar to this approach, at qblocks.cloud we bring under-utilized GPU servers from crypto miners and data centers to use for AI training and deployments at 50-80% low cost than traditional clouds. On-demand and at scale.

In a way, it is similar to our approach at Q Blocks. Decentralized GPU computing for ML.