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Omni Lingual Models

Recent Activity

liangyuch authored a paper 6 days ago

UniT: Unified Multimodal Chain-of-Thought Test-time Scaling

liangyuch submitted a paper 6 days ago

UniT: Unified Multimodal Chain-of-Thought Test-time Scaling

Taishi-N324 authored a paper 12 days ago

On the Optimal Reasoning Length for RL-Trained Language Models

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ajibawa-2023

posted an update about 14 hours ago

Post

PHP-Code-Large

Dataset: ajibawa-2023/PHP-Code-Large

PHP-Code-Large is a large-scale corpus of PHP source code comprising more than 12 million lines of PHP code. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, software engineering automation, and static program analysis for the PHP ecosystem.

By providing a high-volume, language-specific corpus, PHP-Code-Large enables systematic experimentation in PHP-focused model training, domain adaptation, and downstream code understanding tasks.

PHP-Code-Large addresses the need for a dedicated PHP-only dataset at substantial scale, enabling focused research across backend systems, CMS platforms, APIs, and full-stack PHP environments.

ajibawa-2023

posted an update 5 days ago

Post

3186

JavaScript-Code-Large
ajibawa-2023/JavaScript-Code-Large

JavaScript-Code-Large is a large-scale corpus of JavaScript source code comprising around 5 million JavaScript files. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, software engineering automation, and program analysis for the JavaScript ecosystem.

By providing a high-volume, language-specific corpus, JavaScript-Code-Large enables systematic experimentation in JavaScript-focused model training, domain adaptation, and downstream code understanding tasks.

JavaScript-Code-Large addresses the need for a dedicated JavaScript-only dataset at substantial scale, enabling focused research across frontend, backend, and full-stack JavaScript environments. .

liangyuch

authored a paper 6 days ago

UniT: Unified Multimodal Chain-of-Thought Test-time Scaling

Paper • 2602.12279 • Published 11 days ago • 19

liangyuch

submitted a paper to Daily Papers 6 days ago

UniT: Unified Multimodal Chain-of-Thought Test-time Scaling

Paper • 2602.12279 • Published 11 days ago • 19

ajibawa-2023

posted an update 7 days ago

Post

3113

Java-Code-Large ( ajibawa-2023/Java-Code-Large)

Java-Code-Large is a large-scale corpus of publicly available Java source code comprising more than 15 million java codes. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, software engineering automation, and program analysis.

By providing a high-volume, language-specific corpus, Java-Code-Large enables systematic experimentation in Java-focused model training, domain adaptation, and downstream code understanding tasks.

Taishi-N324

authored a paper 12 days ago

On the Optimal Reasoning Length for RL-Trained Language Models

Paper • 2602.09591 • Published 14 days ago • 5

felfri

authored 3 papers about 1 month ago

Who Evaluates AI's Social Impacts? Mapping Coverage and Gaps in First and Third Party Evaluations

Paper • 2511.05613 • Published Nov 6, 2025

ActivationReasoning: Logical Reasoning in Latent Activation Spaces

Paper • 2510.18184 • Published Oct 21, 2025

Inference-time Physics Alignment of Video Generative Models with Latent World Models

Paper • 2601.10553 • Published Jan 15 • 12

Ziyang

authored a paper about 2 months ago

DiffCoT: Diffusion-styled Chain-of-Thought Reasoning in LLMs

Paper • 2601.03559 • Published Jan 7 • 14

terryyz

authored a paper 3 months ago

From Code Foundation Models to Agents and Applications: A Practical Guide to Code Intelligence

Paper • 2511.18538 • Published Nov 23, 2025 • 298

Locutusque

posted an update 4 months ago

Post

2767

🚀 AutoXLA - Accelerating Large Models on TPU
AutoXLA is an experimental library that automates the distribution, optimization, and quantization of large language models for TPUs using PyTorch/XLA. It extends the Hugging Face Transformers interface with TPU-aware features such as automatic sharding, custom attention kernels, and quantization-aware loading, making large-scale deployment and training both simpler and faster.
With quantization and Splash Attention kernels, AutoXLA achieves up to 4× speedups over standard Flash Attention implementations, significantly improving throughput for both inference and training workloads.
Whether you’re experimenting with distributed setups (FSDP, 2D, or 3D sharding) or optimizing memory via LanguageModelQuantizer, AutoXLA is built to make scaling LLMs on TPU seamless.
⚠️ Note: This is an experimental repository. Expect rough edges! Please report bugs or unexpected behavior through GitHub issues.
🔗 GitHub Repository: https://github.com/Locutusque/AutoXLA