Ask HN: People who switched from GPT to their own models. How was it?

• ggml

  69 9,920 9.8 C

  Tensor library for machine learning

If you don't care about the details of how those model servers work, then something that abstracts out the whole process like LM Studio or Ollama is all you need.

However, if you want to get into the weeds of how this actually works, I recommend you look up model quantization and some libraries like ggml[1] that actually do that for you.

[1] https://github.com/ggerganov/ggml

• llm-course

  6 30,525 8.1 Jupyter Notebook

  Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.

This is a very nice resource: https://github.com/mlabonne/llm-course

• InfluxDB

  www.influxdata.com featured

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

• text-generation-webui

  876 37,224 9.9 Python

  A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.

The other answers are recommending paths which give you #1. less control and #2. projects with smaller eco-systems.

If you want a truly general purpose front-end for LLMs, the only good solution right now is oobabooga: https://github.com/oobabooga/text-generation-webui

All other alternatives have only small fractions of the features that oobabooga supports. All other alternatives only support a fraction of the LLM backends that oobabooga supports, etc.

Suggest a related project