Crypto Payments Firm RedotPay Enlists Circle Payment Network in Brazil

Hong Kong-based RedotPay, a crypto payment fintech serving over four million users, has partnered with stablecoin issuer Circle’s new payment network (CPN), to take the headache out of Brazil’s cross-border transactions. The CPN collaboration means RedotPay users can now send cryptocurrency directly to Brazilian bank accounts, with funds automatically converted into Brazilian Real (BRL) upon … Read more

UK-Listed Investments Platform IG Offers Spot Crypto Trading to Retail Customers

Investment platform IG (IGG) said it has begun offering crypto trading to retail investors, becoming the first publicly-listed firm in the U.K. to do so. This marks IG’s first offering of crypto exposure through spot trading of bitcoin BTC, ether ETH and a range of smaller tokens. The company’s crypto service has previously been confined … Read more

Crypto Soared in May as Institutions, States, and Regulators Embrace Bitcoin: Ikigai’s Kling

Travis Kling of Ikigai Asset Management shared his May highlights for bitcoin BTC and broader crypto ecosystem, underscoring the remarkable scale of institutional adoption throughout the month. May was a blockbuster month for the crypto sector, headlined by unprecedented institutional investment, key legal developments, and heightened adoption by both private and public entities. Bitcoin BTC … Read more

Teaching Old Preconditioners New Tricks: How GNNs Supercharge Linear Solvers

:::info Authors: (1) Vladislav Trifonov, Skoltech (vladislav.trifonov@skoltech.ru); (2) Alexander Rudikov, AIRI, Skoltech; (3) Oleg Iliev, Fraunhofer ITWM; (4) Ivan Oseledets, AIRI, Skoltech; (5) Ekaterina Muravleva, Skoltech. ::: Table of Links Abstract and 1 Introduction 2 Neural design of preconditioner 3 Learn correction for ILU and 3.1 Graph neural network with preserving sparsity pattern 3.2 PreCorrector … Read more

From Prototype to Promise: MaRDIFlow Charts the Future of Math Computing

:::info Authors: (1) Pavan L. Veluvali, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, 39106 Magdeburg; (2) Jan Heiland, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, 39106 Magdeburg; (3) Peter Benner, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, 39106 Magdeburg. ::: Table of Links … Read more

Bringing Big AI Models to Small Devices

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 Run-time environment 3.2 Choice of LLMs 3.3 Choice of benchmarks 3.4 Evaluation procedure 3.5 Model parameters and 3.6 Source code and data Evaluation 4.1 Pass@1 … Read more

Why 4-Bit Quantization Is the Sweet Spot for Code LLMs

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 Run-time environment 3.2 Choice of LLMs 3.3 Choice of benchmarks 3.4 Evaluation procedure 3.5 Model parameters and 3.6 Source code and data Evaluation 4.1 Pass@1 … Read more

Do Smaller, Full-Precision Models Outperform Quantized 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 Run-time environment 3.2 Choice of LLMs 3.3 Choice of benchmarks 3.4 Evaluation procedure 3.5 Model parameters and 3.6 Source code and data Evaluation 4.1 Pass@1 … Read more

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 Run-time environment 3.2 Choice of LLMs 3.3 Choice of benchmarks 3.4 Evaluation procedure 3.5 Model parameters and 3.6 Source code and data Evaluation 4.1 Pass@1 … Read more

What Makes Code LLMs Accurate?

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 Run-time environment 3.2 Choice of LLMs 3.3 Choice of benchmarks 3.4 Evaluation procedure 3.5 Model parameters and 3.6 Source code and data Evaluation 4.1 Pass@1 … Read more

Inside the Evaluation Pipeline for Code LLMs With LuaUnit

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 Run-time environment 3.2 Choice of LLMs 3.3 Choice of benchmarks 3.4 Evaluation procedure 3.5 Model parameters and 3.6 Source code and data Evaluation 4.1 Pass@1 … Read more

Why Lua Is the Ideal Benchmark for Testing Quantized 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 Run-time environment 3.2 Choice of LLMs 3.3 Choice of benchmarks 3.4 Evaluation procedure 3.5 Model parameters and 3.6 Source code and data Evaluation 4.1 Pass@1 … Read more

Running Quantized Code Models on a Laptop Without a GPU

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 Run-time environment 3.2 Choice of LLMs 3.3 Choice of benchmarks 3.4 Evaluation procedure 3.5 Model parameters and 3.6 Source code and data Evaluation 4.1 Pass@1 … Read more

Evaluation Benchmarks for Code LLMs

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 Run-time environment 3.2 Choice of LLMs 3.3 Choice of benchmarks 3.4 Evaluation procedure 3.5 Model parameters and 3.6 Source code and data Evaluation 4.1 Pass@1 … Read more

A Review of Top Open-Source Code LLMs and Quantization Techniques

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 Run-time environment 3.2 Choice of LLMs 3.3 Choice of benchmarks 3.4 Evaluation procedure 3.5 Model parameters and 3.6 Source code and data Evaluation 4.1 Pass@1 … Read more

Can LLMs Run on Your Laptop? A Study on Quantized Code Models

:::info Author: (1) Enkhbold Nyamsuren, School of Computer Science and IT University College Cork Cork, Ireland, T12 XF62 (enyamsuren@ucc.ie). ::: 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 Run-time environment 3.2 Choice of … Read more

Case Studies in MaRDIFlow: Methanization and Cahn-Hilliard Equation Implementations

:::info Authors: (1) Pavan L. Veluvali, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, 39106 Magdeburg; (2) Jan Heiland, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, 39106 Magdeburg; (3) Peter Benner, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, 39106 Magdeburg. ::: Table of Links … Read more

Technical Implementation of MaRDIFlow: Metadata-Driven Workflow Abstraction

:::info Authors: (1) Pavan L. Veluvali, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, 39106 Magdeburg; (2) Jan Heiland, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, 39106 Magdeburg; (3) Peter Benner, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, 39106 Magdeburg. ::: Table of Links … Read more