2025-06-02
Scott Wu is the Co-founder and CEO of Cognition
#ai #technology #growth
How to get salt in Fantasy Life i: The Girl Who Steals Time
One of life's most essential seasonings.
PreCorrector Takes the Lead: How It Stacks Up Against Other Neural Preconditioning Methods
PreCorrector outperforms neural operators and classical methods by learning IC corrections. Future: theoretical loss analysis and sparse matrix generalization.
PreCorrector Proves Its Worth: Classical Preconditioners Meet Their Neural Match
PreCorrector outperforms classical IC by 2-3x on complex systems, reduces eigenvalue gaps, generalizes across grids/datasets with <10% loss.
How to unlock multiplayer in Fantasy Life i: The Girl Who Steals Time
Everything is better with friends.
How to change your appearance in Fantasy Life i: The Girl Who Steals Time
Sometimes you've got to shake it up a little bit.
Teaching Old Preconditioners New Tricks: How GNNs Supercharge Linear Solvers
GNNs enhance classical preconditioners (ILU/IC) for iterative linear solvers, outperforming neural and classical methods with sparse patterns.
From Prototype to Promise: MaRDIFlow Charts the Future of Math Computing
MaRDIFlow delivers FAIR workflow automation for mathematical sciences through abstract I/O objects, multi-layered descriptions, and ELN integration.
Bringing Big AI Models to Small Devices
4-bit quantized code LLMs with 7B parameters run well on average laptops, enabling AI democratization by making powerful coding models accessible beyond large servers.
Why 4-Bit Quantization Is the Sweet Spot for Code LLMs
4-bit quantization offers the best trade-off in code LLMs, enabling near-competitive performance on laptops, though accuracy issues and dataset opacity persist.
Do Smaller, Full-Precision Models Outperform Quantized Code Models?
Quantization level doesn’t affect lines of code much, but higher precision increases inference time. Low-param FP16 models match 2-bit models in quality but not 4-bit ones.
The V-Shaped Mystery of Inference Time in Low-Bit Code Models
Table of Links Abstract and Introduction Related Works 2.1 Code LLMs 2.2 Quantization 2.3 Evaluation benchmarks for code LLMs and 2.4 Evaluation metrics 2.5 Low- and high-resource languages Methodology 3.1...
What Makes Code LLMs Accurate?
This section details the evaluation setup for code LLMs using LuaUnit-based unit tests, measuring metrics like pass@1, inference time, LOC, and error types to understand how quantization affects model accuracy...
Inside the Evaluation Pipeline for Code LLMs With LuaUnit
This section details the evaluation setup for code LLMs using LuaUnit-based unit tests, measuring metrics like pass@1, inference time, LOC, and error types to understand how quantization affects model accuracy...
Why Lua Is the Ideal Benchmark for Testing Quantized Code Models
Lua, as a low-resource language with unique features, is ideal for benchmarking quantized code models using multilingual test sets like HumanEval, MBPP, and MCEVAL.
Running Quantized Code Models on a Laptop Without a GPU
This section outlines the Python-based setup and hardware used to run 7B code LLMs via llama-cpp-python, and explains the rationale for model selection.
Evaluation Benchmarks for Code LLMs
Popular benchmarks like HumanEval, MBPP, and MCEVAL test how well code LLMs generate and understand code across languages. Lua is a strong candidate for evaluating low-resource performance due to its...
A Review of Top Open-Source Code LLMs and Quantization Techniques
This section reviews top multilingual code LLMs and explores post-training quantization methods that reduce model size and computational needs with minimal performance loss.
Can LLMs Run on Your Laptop? A Study on Quantized Code Models
This study benchmarks quantized 7B code LLMs for Lua on CPU-only laptops, finding 4-bit quantization offers the best balance between size and performance—though still underperforms compared to top foundational models.
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