SubgoalXL tackles two major challenges in LLM theorem proving: Scarcity of specialized theorem-proving data Complexity in multi-step reasoning for formal proofs
Our solution: Optimizing data efficiency and structuring proofs into manageable subgoals.
LLMS
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SubgoalXL Improves LLM Theorem Proving Efficiency
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DFIntelligence: Struggle with complex files, but better than Claude alone
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Edit: after 4h of use, it's a struggle to use for quite complex tasks across multiple files, and it quickly forgets important contexts. BUT it's still better than just Claude in chat. (I find myself having to copy-paste files onto
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Training 124M Parameter LLMs on MacBook M3 in Real-Time
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It is remarkable that anyone can now train a 124M parameter LLM in about real-time on a MacBook M3. So easy to experiment. This would have been the stuff of dreams when I was in school. I training neural nets, but I really admire the people who build the hardware.
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Claude: finds files, understands queries, executes and modifies
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It finds files itself, it has a much better understanding of queries, it can run everything for you, and modify everything in multiple files at the same time. And above all, Claude.
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SambaNova’s Llama 3.1 405B API Four Times Faster Performance
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Have you tried Meta's Llama 3.1 405B yet? Compared to other providers, SambaNova’s API is 4X quicker, making it the fastest and most efficient option out there. 🎯
— SambaNova (@SambaNovaAI) 3 septembre 2024
Sign up for early access now ⤵️ https://t.co/2dZqYnYimA
Feel the speed yourself at https://t.co/b3eoOhy93x ⚡️… pic.twitter.com/RYwMCl8w1bHave you tried Meta's Llama 3.1 405B yet? Compared to other providers, SambaNova’s API is 4X quicker, making it the fastest and most efficient option out there. Sign up for early access now https://
lnkd.in/g9W_Bnjv Feel the speed yourself at http://
sambanova.ai -
AI Inference Efficiency: 3000x Faster, Cheaper, Better
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pod: Efficiency is Coming: 3000x Faster, Cheaper, Better AI Inference with @nyla_worker of @nvidia
, @convaitech
, @googleai
! The commoditization of intelligence takes on a few dimensions: Time to Open Model Equivalent: 15 months between GPT-4 and Llama 3.1 405B (h/t -
Gemini Pro 1.5 vs Sonnet 3.5: LLM Performance Across Programming Languages
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After benchmarking more than 80 LLMs, @zimmskal & team found that the best model isn't always a great match for your programming language. Google's Gemini Pro 1.5 worked well for Go, but not so much for Java & Ruby, for example. The best overall LLM was Anthropic’s Sonnet 3.5.
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Quantization Technique Reduces LLM Size and Memory Requirements
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While SOTA LLMs are too large to run on laptops, quantization is a technique that reduces LLMs’ computational and memory requirements. Quantization reduces a model’s size and speeds up processing by converting its parameters from 32-bit to lower-precision formats like 16-bit or
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Cohere paper questions human feedback as gold standard for AI
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Human Feedback for AI training might not be the golden goose we thought. I’ve just read a great paper where Cohere researchers raises significant questions about using Human feedback evaluate AI language models. Human feedback is often regarded as the gold standard for judging
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Distillation Emerges as Superior Model Improvement Technique Over DPO
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Because model merging and abliteration are destructive, DPO has been essential to healing networks. These days are now over: distillation is the new powerhouse that not only heals models but also improves them.