IQ4_XS 5.17 GB, Q4_K_S 5.39 GB, IQ4_NL 5.37 GB, Q4_0 5.38 GB, Q4_1 5.84 GB, Q4_K_M 5.68 GB, UD-Q4_K_XL 5.97 GB

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.

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when have you been built?

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.

====

But when supposedly running it from https://huggingface.co/chat/models/openai/gpt-oss-20b:

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when have you been built?

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.

====

And that makes me thinking that although https://huggingface.co/chat claims to be using the models available to public at https://huggingface.co , it doesn't seems to be true and I raised this question here https://huggingface.co/ggml-org/gpt-oss-20b-GGUF/discussions... , https://github.com/huggingface/inference-playground/issues/1... and https://github.com/ggml-org/llama.cpp/discussions/15396#disc... .

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).

You can see the scrapped text for the whole seession here https://github.com/ggml-org/llama.cpp/discussions/17600

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.

There is also https://mingodad.github.io/plgh/json2ebnf.html to generate EBNF for railroad diagram generation form tree-sitter grammars.

Any feedback, contribution is welcome !

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.

- https://github.com/mingodad/CorsixTH-ljs - https://github.com/mingodad/ZeroBraneStudioLJS

- https://github.com/mingodad/ljs

- https://github.com/mingodad/ljs-5.4

- https://github.com/mingodad/ljs-5.1

- https://github.com/mingodad/ljsjit

- https://github.com/mingodad/raptorjit-ljs

- https://github.com/mingodad/CorsixTH-ljs

- https://github.com/mingodad/snabb-ljs

- https://github.com/mingodad/ZeroBraneStudioLJS

gdb -batch -ex "info variables" -ex quit --args binary-to-examine