I'm still a bit confused because it says "All uploads use Unsloth Dynamic 2.0" but then when looking at the available options like for 4 bits there is:
And no explanation for what they are and what tradeoffs they have, but in the turorial it explicitly used Q4_K_XL with llama.cpp .
I'm using a macmini m4 16GB and so far my prefered model is Qwen3-4B-Instruct-2507-Q4_K_M although a bit chat but my test with Qwen3.5-4B-UD-Q4_K_XL shows it's a lot more chat, I'm basically using it in chat mode for basic man style questions.
I understand that each user has it's own specific needs but would be nice to have a place that have a list of typical models/hardware listed with it's common config parameters and memory usage.
Even on redit specific channels it's a bit of nightmare of loot of talk but no concrete config/usage clear examples.
I'm floowing this topic heavilly for the last 3 months and I see more confusion than clarification.
Right now I'm getting good cost/benefit results with the qwen cli with coder-model in the cloud and watching constantly to see when a local model on affordable hardware with enviroment firendly energy comsumption arrives.
I’m a GPT‑4‑based model that OpenAI released on March 14 2023.
The underlying training data goes up to early 2023, and the model was trained in the months leading up to that release. If you’re asking about the ChatGPT product that ships the model to users, it went live in November 2022 and has since received updates (GPT‑3.5, GPT‑4, etc.) that keep it current.
I’m a language model created by OpenAI. The current generation (GPT‑4) that powers this chat was first released in March 2023 and has been updated and fine‑tuned up through the end of 2024. My training data runs up to the beginning of June 2025, so I’m built on knowledge available up to that point.
I did an experiment while learning about LLMs and llama.cpp consisting in trying to use create a Lua extension to use llama.cpp API to enhance LLMs with agent/RAG written in Lua with simple code to learn the basics and after more than 5 hours chatting with https://aistudio.google.com/prompts/new_chat?model=gemini-3-... (see the scrapped output of the whole session attached) I've got a lot far in terms of learning how to use an LLM to help develop/debug/learn about a topic (in this case agent/RAG with llama.cpp API using Lua).
I'm posting it here just in case it can help others to see and comment/improve it (it was using around 100K tokens at the end and started getting noticeable slow but still very helpful).
The idea is to improve the tooling to work with grammars, for example generating railroad diagrams, source, stats, state machines, traces, ...
On both of then select one grammar from "Examples" then click "Parse" to see a parse tree or ast for the content in "Input source", then edit the grammar/input to test new ideas.
But if otherwise anyone want to try with Yacc/Lex I have an online interpreter/editor with more than 250 examples to try/study at https://mingodad.github.io/parsertl-playground/playground/ and just included a grammar for tinycompiler there (select "Tinycompiler parser" from "Examples" then click "Parse" to see a parse tree for the content in "Input source").
DataDraw is an ultra-fast persistent database for high performance programs written in C. It's so fast that many programs keep all their data in a DataDraw database, even while being manipulated in inner loops of compute intensive applications. Unlike slow SQL databases, DataDraw databases are compiled, and directly link into your C programs. DataDraw databases are resident in memory, making data manipulation even faster than if they were stored in native C data structures (really). Further, they can automatically support infinite undo/redo, greatly simplifying many applications.
No changes to "global by default" and no checking are made to type annotations.
The idea was to be able to convert any existing Lua code automatically and get it working as before like I did with some non trivial projects like:
I'm proud of it and thankfull to the Lua/Luajit projects.