anshuman @athleticKoder
maximizing shareholder value; mostly ml here; heyyanshuman.com Joined February 2020-
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@sundarpichai a technical report plis plis plis, on pretraining and post training techniques.
@oneill_c good read x.com/athleticKoder/…
pretty much what MLA does. but that's an architectural choice to be taken before pre-training. borrowed from MLA, but applied to any model out there to compress the effective KV Cache. I wonder if there is an open recipe about it. and vLLM should figure out a way to support it.
pretty much what MLA does. but that's an architectural choice to be taken before pre-training. borrowed from MLA, but applied to any model out there to compress the effective KV Cache. I wonder if there is an open recipe about it. and vLLM should figure out a way to support it. (in my knowledge it doesn't, yet)
1/ You can shrink a language model's KV cache by 200×, in a single forward pass, and it still answers correctly. At 256k context that's 36 GiB of cache down to ~360 MiB, with no change to the base model. Here's how we did it 👇
@osanseviero Gemma4 is genuinely very very strong base model. been working on something huge at work and can't stop appreciating how good pretraining of gemma4 is.
@MilksandMatcha @tyler_fong_ Congratsss Sarah!
We have released the biggest protein data collection on Hugging Face, guys! We have been working on this for more than 3 weeks now, starting from curating the raw data, doing a lot of filtering, splitting the datasets, sharding them, and doing a lot of analysis. Everything is summarized in our recent blog post.
LLMs got FineWeb, The Pile, RedPajama, Dolma. Protein ML got per-paper supplementary tables and FTP mirrors scattered across a dozen institutions. Today we're releasing AminoWeb on @huggingface : 29 cleaned, ML-ready protein datasets, ~7.5 TB total. Sequence, structure,
@1littlecoder @nitprashant @emergentlabs hehe thanks for the tag. but not looking for a carrier pivot from core ML to growth hacking.
@dexhorthy @darin_gordon you might be smart enough if i just post raw ideas. but not everyone on this app/planet is smart enough :D
@dexhorthy @thdxr yup, these model feel so good at times and then slop at times. if the taste in writing is so bad, imagine code quality in a vibe coder's codebase. one model to rule them (tasks) all is essentially a flawed approach if we talk about taste
@thdxr @dexhorthy :/
@paulabartabajo_ @dexhorthy i do agree with you there. i just have a lot of running thoughts and so much information to share but time has always been the bottleneck. gpt 5.5 has bad taste for writing. especially those recurring templates and style that pisses reader off at times. funnily enough when i
i do agree with you there. i just have a lot of running thoughts and so much information to share but time has always been the bottleneck. gpt 5.5 has bad taste for writing. especially those recurring templates and style that pisses reader off at times. funnily enough when i first asked gpt 5.5 to put together a draft it created five thousand single lines as article. it was so so bad.
@TheIshanGoswami didn't you guys raised series B like few months ago?
@1littlecoder @nebiustf @waqasmakhdum congratulationssss
@swyx what no wayyy. let's goo. let me know if I can help
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