This has been the problem with a lot of long context use cases. It's not just the model's support but also sufficient compute and inference time. This is exactly why I was excited for Mamba and now possibly Lightning attention.
Even though the new DCA based on which these models provide long context could be an interesting area to watch;
For those who don't know, He is the gg of `gguf`. Thank you for all your contributions! Literally the core of Ollama, LMStudio, Jan and multiple other apps!
They collaborate together! Her name is Justine Tunney - she took her “execute everywhere” work with Cosmopolitan to make Llamafile using the llama.cpp work that Giorgi has done.
SWE-Bench (+ Verified) is the benchmark (of resolving Github Issues) that companies into Coding are chasing - Devin, Claude, OpenAI - all these!
A new leader #1 - CodeStory Midwit Agent + swe-search - has been crowed with a score of 62% on SWE-bench verified (without even using any reasoning models like OpenAI o1 or o3)
This is a very impressive result. OpenAI was able to achieve 72% with o3, but that's at a very high compute cost at inference-time.
I'd be interested for Aide to release more metrics on token counts, total expenditure, etc. to better understand exactly how much test-time compute is involved here. They allude to it being a lot, but it would be nice to compare with OpenAI's o3.
ngl the total expenditure was around $10k, in terms of test-time compute we ran upto 20X agents on the same problem to first understand if the bitter lesson paradigm of "scale is the answer" really holds true.
The final submission which we did ran 5X agents and the decider was based on mean average score of the rewards, per problem the cost was around $20
We are going to push this scaling paradigm a bit more, my honest gut feeling is that swe-bench as a benchmark is prime for saturation real soon
1. These problem statements are in the training data for the LLMs
2. Brute-forcing the answer the way we are doing works and we just proved it, so someone is going to take a better stab at it real soon
Even though the new DCA based on which these models provide long context could be an interesting area to watch;
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