apaz @apaz_cli
https://t.co/EYtS07MR7w Making GPUs go brrr Hiding in your wifi Joined July 2019-
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Terrible license. I didn't expect such onerous terms for a 300M param model, but here we are. Also, curious wording. They do not compare to Qwen3-Embedding-0.6B, because they do not beat it. With that said it's probably a good model.
Terrible license. I didn't expect such onerous terms for a 300M param model, but here we are. Also, curious wording. They do not compare to Qwen3-Embedding-0.6B, because they do not beat it. With that said it's probably a good model.
The number of LLM tools that seize up when they encounter <|endoftext|> in a string literal astonishes me. I thought everyone knew to be careful about this.
I'm implementing a tokenizer for a project, and it astonishes me that tiktoken is generally considered "fast". Agony.
The SF health cult has convinced me, I'm getting into supplements. Starting out with the basics, plus a multivitamin. The D,L-Phenylalanine is a personal headcannon. But how does anybody swallow these things? They're huge. Can't split them, they're full of foul tasting liquid.
What confuses me is that if you look at the scaling curves, it's clearly better. If you're not doing MoE + quantized training you're a schmuck, especially if you're doing RL, which you should be doing for human preference posttraining anyway, even if you don't believe in…
What confuses me is that if you look at the scaling curves, it's clearly better. If you're not doing MoE + quantized training you're a schmuck, especially if you're doing RL, which you should be doing for human preference posttraining anyway, even if you don't believe in…
God this must be so embarrassing
Please for the love of all that is holy tell me they were already doing this
Jinja has import statements, I'm 'boutta crash out. I'm not sure why everyone is standardizing on it for tool call prompting.
Tonight I was at a hypnosis lecture, and someone asked: "When you imagine yourself on a beach, how do you experience it? Do you see it? Hear it? Feel it?" The thing is, I don't. None of the above. Or I imagine in third person. I think somewhere along the way my brain got fried…
Same form factor as V3/R1, hoping they revisited the pretraining data with the intent of making it better to do RL on. The more stuff that approximates logical reasoning in the pretraining data, the better.
Same form factor as V3/R1, hoping they revisited the pretraining data with the intent of making it better to do RL on. The more stuff that approximates logical reasoning in the pretraining data, the better.
I am faced with the sobering reality that writing an efficient mxfp4 kernel for gpt-oss is not possible in llama.cpp because of the memory layout. Blocks of quantized elements are not stored contiguously, so you cannot issue vector loads across mfxp4 blocks. Sadge.

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