Viewing profile — omneity

I thought the actual 30 papers have never been disclosed. Do you have a source tying the recommendations back to Ilya, or did you come up with this list?

Glad it helped! A little credit on the post would go a long way :)

The Web MIDI API[0] used by the author has a built-in precise scheduler, that has higher precision and works better than the unreliable setTimeout approach used by OP when coupled …

I do think it might improve but only marginally. You are however likely to observe better results in smaller models since they're usually more strapped for "cognitive capacity", so…

It’s not as simple. I trained an LLM before on exactly this, to scratch the itch of this question. The task was simple, using the MS-MARCO[0] dataset which contains queries, search…

The Open in OpenRouter is the same as in OpenSea, as it's the same founder. Make of that what you will.

You can increase the context window beyond its max trained context using RoPE scaling[0] which will require more VRAM. But you can increase your context window for the same VRAM by…

Funny, I’ve been cracking[0] at this exact problem with a purpose-built model[1]: 0: https://huggingface.co/posts/omarkamali/593639295164067 1: https://omneitylabs.com/models/sawto…

Strong vibes from the novel Manna. https://marshallbrain.com/manna1

I'm pretty sure it should be possible to distill HS-TasNet into a version approximate and fast enough for the purpose of animating LEDs. At the end it's "just" chunking streamed au…

Excellent, thank you mandeepj! Curious about the language coverage of your agent and if / how you plan to eval your agent, if you're willing to share more.

Hey, this is super cool! I’ve been working on a similar problem, focusing on low-resource and underserved languages including the Mayan family, and have published some research and…

Or your willingness to put up with power banks.

This is a great project. FYI all you need is the size of an LLM and the memory amount & bandwidth to know if it fits and the tok/s It’s a simple formula: llm_size = number of param…

It’s a trivial calculation to make (+/- 10%). Number of params == “variables” in memory VRAM footprint ~= number of params * size of a param A 4B model at 8 bits will result in 4GB…

Attention is calculated during the forward pass of the model, which happens in both inference (forward only) and training (forward & backward).

The model being 32B could run in There's a few pre-quantized options[0] or you can quantize it yourself with llama.cpp[1]. You can run the resulting gguf with llama.cpp `llama-cli`…

RDMA over Thunderbolt is a thing now.

I just went through an eerily similar situation where the coding agent was able to muster some pretty advanced math (information geometry) to solve my problem at hand. But while I …

Very cool insights, thanks for sharing! Do you have benchmarks for the SGLang vs vLLM latency and throughput question? Not to challenge your point, but I’d like to reproduce these …

Except this is GLM 4.7 Flash which has 32B total params, 3B active. It should fit with a decent context window of 40k or so in 20GB of ram at 4b weights quantization and you can sa…