69 points by rhgraysonii 3 days ago | 17 comments
- This is really impressive. Can you say a bit about the underlying process? I'm guessing this is post-training qantization? Isn't PTQ also resource-intensive? (Ie might not work on any machine)
- This is interesting. I wonder how it could work with something like https://github.com/JustVugg/colibri.
- Reminds me of https://github.com/AlexsJones/llmfit
- LLMFit tells you what can run on something. I built something quite similar to their search into Shoehorn now.
- The project name is perfect!
- I gotta laugh at some of the models it suggests, for example:
> AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-GGUF
you’re telling me you managed to fit Fable 5 into just 4B?
- I gotta laugh at your thought process: knowing Fable 5 is a large frontier model, you're telling me that the first thing that came to your mind on seeing that model name is that it's a quantized version of Fable? As opposed to a distillation/fine-tuning on Fable responses?
- Well to be fair here... the title of this post doesn't mention fine tuning, it mentions quantization.
- Don't make fun of people you think are ignorant, it's a pretty shitty look
- Well, then don't get all snarky and dismissive of things you might not be knowledgeable about ("you" here referring to OP).
- does this work similar to airllm? i am wondering how it would handle something like quantizing kimi k3 on a budget of 8 gbs, or is that something you are not attempting to solve yet?
- Yes that is exactly what this does.
- Could you explain what happens when you try to shoehorn a 2.4T parameter model into a 24gb m4 mac?
- extreme divergence would be my guess
- Wondering the same thing but for 48gb M5 Max.
- tried it out but based on the model sizing result i got i got an insufficient memory error when the server started running
- If you could post an issue if you still have the error around that would be awesome.
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