This paper from Meta proposes a method to not have the model reason in token space but directly model its reasoning using its hidden state. The authors also do a lot of cool interpretability work in this paper.
Aesthetically, I like it alot and its simple to implement
This paper from Meta proposes a method to not have the model reason in token space but directly model its reasoning using its hidden state. The authors also do a lot of cool interpretability work in this paper.
Aesthetically, I like it alot and its simple to implement https://t.co/QafTwY542x
Coming back to an area I used to track closely -
What are the best services / approaches for complex tables and AI?
I'm focused particularly on data ingestion and normalization but would love to hear of services / approaches relevant to downstream tasks (e.g. reasoning).
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1300+ tok/s on a MacBook
implemented batched KV caching in MLX for fast parallel LLM inference on Apple devices. can get over 20x tok/s throughput gains depending on model/precision/RAM/batch size. screenshot is for Gemma-2B in float16 + 325 prompts in the batch
code:…
been learning a lot about LLMs etc over the past year, organized some of my favorite explainers into a “textbook-shaped” resource guide
wish i’d had this at the start, maybe it can useful to others on a similar journey
genai-handbook.github.io
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