gbickford

8 ms·

1.
by gbickford 2y ago
Small models don't "know" as much so they hallucinate more. They are better suited for generations that are based in a ground truth, like in a RAG setup. A better comparison might be Flash 2.0 vs 4o-mini. Even then, the model
2.
by gbickford 2y ago
It's for visualizing datasets where fine-grained cluster details and broader relationships matter. There are example renderings in the paper. From the README: > PaCMAP (Pairwise Controlled Manifold Approximation) is a dimensionality
3.
by gbickford 2y ago
> Create Hello World application: > sudo mgrg -i -u $(whoami) helloworld Why does it need sudo to compile?
4.
by gbickford 2y ago
If you look in the `config.json`[1] it shows `Zamba2ForCausalLM`. You can use a version of the transformers library to do inference that supports that. The model card states that you have to use their fork of transformers.[2] 1. https:&#x2
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by gbickford 2y ago
This article is from 2017
6.
by gbickford 2y ago
This is true. Devs are looking for frameworks. See CrewAI who refuses to allow users to disable some pretty aggressive telemetry, yet they have a huge number of GH stars. The abstractions are handy if you have no idea what you are doing but
7.
by gbickford 2y ago
It's always disappointing when people publish things to GitHub without the intention of collaborating or sharing.
8.
by gbickford 2y ago
I couldn't find any training code in the MXL examples.
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by gbickford 2y ago
> Relationship with CVNets > CoreNet evolved from CVNets, to encompass a broader range of applications beyond computer vision. Its expansion facilitated the training of foundational models, including LLMs. We can expect it to have gro
10.
by gbickford 3y ago
It's a thing in Chrome Dev Tools now: https://developer.chrome.com/docs/devtools/coverage/
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by gbickford 3y ago
There's gotta be somewhere in the middle. Vercel's movements feel a lot like the "Embrace, extend, and extinguish" playbook. Maybe there is a class of developer out there that doesn't get spooked by that but it defi
12.
by gbickford 3y ago
Llama.cpp is an inference engine. The author of llama.cpp designed gguf. Funcionary is a model that does function calling. You can download functionary weights in the gguf format and then run it using llama.cpp on low-end machines using CPU
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by gbickford 3y ago
Have you tried generating two sets of qapairs, one with bad answers, and using DPO?
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by gbickford 3y ago
The authors don't seem to care about the principle of least privilege: https://github.com/ollama/ollama/issues/851#issuecomment-177... It makes me wonder what other security issues they might now care ab
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by gbickford 3y ago
This paper is well written. The results are pretty wild. They observed some amazing reduction in training resources required to achieve similar benchmarks to models trained on conventional data: > We observe that even at the first checkp